pax_global_header00006660000000000000000000000064146542644770014535gustar00rootroot0000000000000052 comment=2d11cd7fa98225e4bada7b833d8ec0072ddbfd4e ignite-0.5.1/000077500000000000000000000000001465426447700130175ustar00rootroot00000000000000ignite-0.5.1/.github/000077500000000000000000000000001465426447700143575ustar00rootroot00000000000000ignite-0.5.1/.github/FUNDING.yml000066400000000000000000000013401465426447700161720ustar00rootroot00000000000000# These are supported funding model platforms github: [vfdev-5] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] patreon: # Replace with a single Patreon username open_collective: pytorch-ignite # Replace with a single Open Collective username ko_fi: # Replace with a single Ko-fi username tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry liberapay: # Replace with a single Liberapay username issuehunt: # Replace with a single IssueHunt username otechie: # Replace with a single Otechie username custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] ignite-0.5.1/.github/ISSUE_TEMPLATE/000077500000000000000000000000001465426447700165425ustar00rootroot00000000000000ignite-0.5.1/.github/ISSUE_TEMPLATE/bug-report.md000066400000000000000000000011341465426447700211510ustar00rootroot00000000000000--- name: "\U0001F41B Bug Report" about: Submit a bug report to help us improve Ignite --- ## πŸ› Bug description ## Environment - PyTorch Version (e.g., 1.4): - Ignite Version (e.g., 0.3.0): - OS (e.g., Linux): - How you installed Ignite (`conda`, `pip`, source): - Python version: - Any other relevant information: ignite-0.5.1/.github/ISSUE_TEMPLATE/documentation.md000066400000000000000000000004701465426447700217360ustar00rootroot00000000000000--- name: "\U0001F4DA Documentation" about: Report an issue, comment or suggestion related to project's docs labels: "docs" --- ## πŸ“š Documentation ignite-0.5.1/.github/ISSUE_TEMPLATE/feature-request.md000066400000000000000000000012101465426447700221770ustar00rootroot00000000000000--- name: "\U0001F680 Feature Request" about: Submit a proposal/request for a new Ingite feature --- ## πŸš€ Feature ignite-0.5.1/.github/ISSUE_TEMPLATE/questions-help-support.md000066400000000000000000000010101465426447700235460ustar00rootroot00000000000000--- name: "❓Questions/Help/Support" about: Do you have a question? labels: "question" --- ## ❓ Questions/Help/Support ignite-0.5.1/.github/ISSUE_TEMPLATE/user-feedback.md000066400000000000000000000005101465426447700215600ustar00rootroot00000000000000--- name: "\U0001F44D User feedback" about: Say thanks or why you don't like title: "" labels: "" assignees: "" --- > This is a place to leave any feedback on this package. > If you like the work, feel free to say thanks here > If you do not like something, please, share it with us and we can see how to improve > Thank you ! ignite-0.5.1/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000003361465426447700201620ustar00rootroot00000000000000Fixes #{issue number} Description: Check list: - [ ] New tests are added (if a new feature is added) - [ ] New doc strings: description and/or example code are in RST format - [ ] Documentation is updated (if required) ignite-0.5.1/.github/failed_schedule_issue_template.md000066400000000000000000000005361465426447700231100ustar00rootroot00000000000000--- title: Scheduled workflow failed labels: - bug --- Oh no, something went wrong in the scheduled workflow **{{ env.GITHUB_WORKFLOW }} with commit {{ env.GITHUB_SHA }}**. Please look into it: {{ env.GITHUB_SERVER_URL }}/{{ env.GITHUB_REPOSITORY }}/actions/runs/{{ env.GITHUB_RUN_ID }} Feel free to close this if this was just a one-off error. ignite-0.5.1/.github/pr-labeler-config.yml000066400000000000000000000027111465426447700203730ustar00rootroot00000000000000# Add 'docker' to any changes within 'docker' folder or any subfolders docker: - changed-files: - any-glob-to-any-file: docker/** # Add 'docs' to any changes within 'docs' folder docs: - changed-files: - any-glob-to-any-file: docs/** # Add 'ci' to any changes in '.github' folder ci: - changed-files: - any-glob-to-any-file: .github/** # Add 'examples' to any changes within 'examples' folder examples: - changed-files: - any-glob-to-any-file: examples/** # Add 'base' to any changes within 'base' folder "module: base": - changed-files: - any-glob-to-any-file: ignite/base/**/* # Add 'contrib' to any changes within 'contrib' folder "module: contrib": - changed-files: - any-glob-to-any-file: ignite/contrib/**/* # Add 'distributed' to any changes within 'distributed' folder "module: distributed": - changed-files: - any-glob-to-any-file: ignite/distributed/**/* # Add 'engine' to any changes within 'engine' folder "module: engine": - changed-files: - any-glob-to-any-file: ignite/engine/**/* # Add 'handlers' to any changes within 'handlers' folder "module: handlers": - changed-files: - any-glob-to-any-file: ignite/handlers/**/* # Add 'metrics' to any changes within 'metrics' folder "module: metrics": - changed-files: - any-glob-to-any-file: ignite/metrics/**/* - # Add 'utils' to any changes within 'utils' module "module: utils": - changed-files: - any-glob-to-any-file: ignite/utils.py ignite-0.5.1/.github/workflows/000077500000000000000000000000001465426447700164145ustar00rootroot00000000000000ignite-0.5.1/.github/workflows/binaries-nightly-release.yml000066400000000000000000000036071465426447700240330ustar00rootroot00000000000000name: Nightly Releases on: # https://docs.github.com/en/free-pro-team@latest/actions/reference/workflow-syntax-for-github-actions#onschedule schedule: # Run at 00:00 UTC Every Day - cron: "0 0 * * *" jobs: build-publish: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: "3.10" - name: Setup nightly version run: | sed -i "s/__version__ = \"\(.*\)\"/__version__ = \"\1.dev$(date -u +%Y%m%d)\"/g" ignite/__init__.py cat ignite/__init__.py - name: Install dependencies shell: bash -l {0} run: | conda install -y pytorch torchvision cpuonly -c pytorch-nightly pip install -r requirements-dev.txt - name: Build and Publish Conda binaries shell: bash -l {0} env: ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} UPLOAD_USER: "pytorch-nightly" run: | chmod +x ./conda.recipe/build_and_upload.sh ./conda.recipe/build_and_upload.sh - name: Build and Publish PyPI binaries shell: bash -l {0} run: | # workaround to fix https://github.com/pytorch/ignite/issues/2373 pip uninstall -y twine pkginfo pip install --upgrade --no-cache-dir twine 'pkginfo>=1.8.2' python setup.py sdist bdist_wheel twine --version twine check dist/* TWINE_USERNAME="${{ secrets.PYPI_USER }}" TWINE_PASSWORD="${{ secrets.PYPI_TOKEN }}" twine upload --verbose dist/* - uses: JasonEtco/create-an-issue@v2 name: Create issue if nightly releases failed if: failure() env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: filename: .github/failed_schedule_issue_template.md ignite-0.5.1/.github/workflows/build_docs.sh000066400000000000000000000002101465426447700210500ustar00rootroot00000000000000sphinx-versioning --use-master-conf --use-master-templates build --greatest-tag --whitelist-branches master docs/source docs/build/html ignite-0.5.1/.github/workflows/code-style.yml000066400000000000000000000015131465426447700212070ustar00rootroot00000000000000name: Format python code on: push: paths: - "**.py" - "setup.cfg" - "requirements-dev.txt" - "pyproject.toml" - "tests/run_code_style.sh" - ".github/workflows/code-style.yml" - "!assets/**" - "!docker/**" - "!docs/**" - "!conda.recipe" jobs: code-style: runs-on: ubuntu-latest steps: - if: github.event_name == 'push' uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: "3.8" - run: | bash ./tests/run_code_style.sh install bash ./tests/run_code_style.sh fmt - name: Commit and push changes uses: stefanzweifel/git-auto-commit-action@v4 with: commit_message: "autopep8 fix" env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} ignite-0.5.1/.github/workflows/discord_issues.yml000066400000000000000000000020401465426447700221550ustar00rootroot00000000000000name: Discuss "help-wanted" issue on Discord on: issues: types: - labeled workflow_dispatch: inputs: issue_number: description: 'Issue number' required: true permissions: issues: write jobs: discord: runs-on: ubuntu-latest steps: - name: "Discuss on Discord-Issues" if: ${{ github.event.label.name == 'help wanted' }} uses: EndBug/discuss-on-discord@v1.1.0 with: discord_bot_token: ${{ secrets.DISCORD_BOT_TOKEN }} destination: ${{ secrets.DISCORD_BOT_DESTINATION }} issue_number: ${{ github.event.inputs.issue_number || github.event.issue.number }} issue_comment: Hey πŸ‘‹, I've just created a [thread]($THREAD_LINK$) for this issue on [PyTorch-Ignite Discord](https://pytorch-ignite.ai/chat) where you can quickly talk to the community on the topic. discord_message: New issue created in `${{ github.repository }}`: ignite-0.5.1/.github/workflows/discord_pull_requests.yaml000066400000000000000000000021641465426447700237210ustar00rootroot00000000000000name: Discuss "help-wanted" PR on Discord on: pull_request: types: - labeled workflow_dispatch: inputs: pull_request_number: description: 'Pull request number' required: true permissions: pull-requests: write jobs: discord: runs-on: ubuntu-latest steps: - name: "Discuss on Discord-PR (Non-maintainer only)" if: ${{ github.event.label.name == 'help wanted' }} uses: EndBug/discuss-on-discord@v1.1.0 with: discord_bot_token: ${{ secrets.DISCORD_BOT_TOKEN }} destination: ${{ secrets.DISCORD_BOT_DESTINATION }} issue_number: ${{ github.event.inputs.pull_request_number || github.event.pull_request.number }} issue_comment: Hey πŸ‘‹, I've just created a [thread]($THREAD_LINK$) for this pull request on [PyTorch-Ignite Discord](https://pytorch-ignite.ai/chat) where you can quickly talk to the community on the topic. discord_message: New PR created in `${{ github.repository }}`: ignite-0.5.1/.github/workflows/docker-build.yml000066400000000000000000000242751465426447700215150ustar00rootroot00000000000000name: Build/Publish Docker Images on: pull_request: paths: - docker/** - ".github/workflows/docker-build.yml" release: types: [published] workflow_dispatch: concurrency: # -- group: docker-build-publish-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true jobs: setup: name: Setup runs-on: ubuntu-latest outputs: modified: ${{ steps.set-modified.outputs.modified }} pth_version: ${{ steps.set-versions.outputs.pth_version }} hvd_version: ${{ steps.set-versions.outputs.hvd_version }} msdp_version: ${{ steps.set-versions.outputs.msdp_version }} steps: - uses: actions/checkout@v4 - name: Changed Files Exporter if: github.event_name == 'pull_request' id: files uses: umani/changed-files@v4.1.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} - name: Get a list of modified files if: github.event_name == 'pull_request' run: echo "modified=${{ steps.files.outputs.files_created }} ${{ steps.files.outputs.files_updated }}" >> $GITHUB_ENV - name: Set outputs id: set-modified run: echo "modified=${{ env.modified }}" >> $GITHUB_OUTPUT - name: Set versions id: set-versions working-directory: docker run: | echo "pth_version=$(python -c "import configparser; cfg=configparser.ConfigParser(); cfg.read('docker.cfg'); print(cfg.get('DEFAULT', 'build_docker_image_pytorch_version'))")" >> $GITHUB_OUTPUT echo "hvd_version=$(python -c "import configparser; cfg=configparser.ConfigParser(); cfg.read('docker.cfg'); print(cfg.get('DEFAULT', 'build_docker_image_hvd_version'))")" >> $GITHUB_OUTPUT build-hvd: name: Build all Horovod flavoured PyTorch-Ignite images needs: setup if: github.event_name != 'pull_request' || (contains(needs.setup.outputs.modified, 'hvd/') || contains(needs.setup.outputs.modified, 'docker.cfg')) # Cherry-picked from https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml env: REPOSITORY: ${{ github.repository }} PR_NUMBER: ${{ github.event.pull_request.number }} runs-on: amz2023.linux.4xlarge steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Setup Linux uses: ./test-infra/.github/actions/setup-linux - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Build docker images working-directory: ${{ github.repository }}/docker run: | export PTH_VERSION=${{ needs.setup.outputs.pth_version }} export HVD_VERSION=${{ needs.setup.outputs.hvd_version }} bash build.sh hvd hvd-base bash build.sh hvd hvd-vision bash build.sh hvd hvd-nlp - name: Publish docker images if: github.event_name == 'workflow_dispatch' || github.event_name == 'release' env: DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }} DOCKER_USER: ${{ secrets.DOCKER_USER }} working-directory: ${{ github.repository }}/docker run: | bash push_all.sh hvd-base bash push_all.sh hvd-vision bash push_all.sh hvd-nlp build-hvd-apex: name: Build all Horovod + Apex flavoured PyTorch-Ignite images needs: setup if: github.event_name != 'pull_request' || (contains(needs.setup.outputs.modified, 'hvd/') || contains(needs.setup.outputs.modified, 'docker.cfg')) # Cherry-picked from https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml env: REPOSITORY: ${{ github.repository }} PR_NUMBER: ${{ github.event.pull_request.number }} runs-on: amz2023.linux.12xlarge steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Setup Linux uses: ./test-infra/.github/actions/setup-linux - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Build docker images working-directory: ${{ github.repository }}/docker run: | export PTH_VERSION=${{ needs.setup.outputs.pth_version }} export HVD_VERSION=${{ needs.setup.outputs.hvd_version }} bash build.sh hvd hvd-apex bash build.sh hvd hvd-apex-vision bash build.sh hvd hvd-apex-nlp - name: Publish docker images if: github.event_name == 'workflow_dispatch' || github.event_name == 'release' env: DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }} DOCKER_USER: ${{ secrets.DOCKER_USER }} working-directory: ${{ github.repository }}/docker run: | bash push_all.sh hvd-apex bash push_all.sh hvd-apex-vision bash push_all.sh hvd-apex-nlp build-main: name: Build all PyTorch-Ignite images needs: setup if: github.event_name != 'pull_request' || (contains(needs.setup.outputs.modified, 'main/') || contains(needs.setup.outputs.modified, 'docker.cfg')) # Cherry-picked from https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml env: REPOSITORY: ${{ github.repository }} PR_NUMBER: ${{ github.event.pull_request.number }} runs-on: amz2023.linux.4xlarge steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Setup Linux uses: ./test-infra/.github/actions/setup-linux - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Build docker images working-directory: ${{ github.repository }}/docker run: | export PTH_VERSION=${{ needs.setup.outputs.pth_version }} export HVD_VERSION=${{ needs.setup.outputs.hvd_version }} bash build.sh main base bash build.sh main vision bash build.sh main nlp - name: Publish docker images if: github.event_name == 'workflow_dispatch' || github.event_name == 'release' env: DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }} DOCKER_USER: ${{ secrets.DOCKER_USER }} working-directory: ${{ github.repository }}/docker run: | bash push_all.sh base bash push_all.sh vision bash push_all.sh nlp build-main-apex: name: Build all PyTorch-Ignite images with Apex needs: setup if: github.event_name != 'pull_request' || (contains(needs.setup.outputs.modified, 'main/') || contains(needs.setup.outputs.modified, 'docker.cfg')) # Cherry-picked from https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml env: REPOSITORY: ${{ github.repository }} PR_NUMBER: ${{ github.event.pull_request.number }} runs-on: amz2023.linux.12xlarge steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Setup Linux uses: ./test-infra/.github/actions/setup-linux - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Build docker images working-directory: ${{ github.repository }}/docker run: | export PTH_VERSION=${{ needs.setup.outputs.pth_version }} export HVD_VERSION=${{ needs.setup.outputs.hvd_version }} bash build.sh main apex bash build.sh main apex-vision bash build.sh main apex-nlp - name: Publish docker images if: github.event_name == 'workflow_dispatch' || github.event_name == 'release' env: DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }} DOCKER_USER: ${{ secrets.DOCKER_USER }} working-directory: ${{ github.repository }}/docker run: | bash push_all.sh apex bash push_all.sh apex-vision bash push_all.sh apex-nlp ignite-0.5.1/.github/workflows/docs.yml000066400000000000000000000047231465426447700200750ustar00rootroot00000000000000name: Build docs on: push: branches: - master pull_request: paths-ignore: - "tests/**" - "docker/**" release: types: [published] workflow_dispatch: jobs: build-deploy: permissions: contents: write if: (github.ref == 'refs/heads/master' && github.event_name == 'push') || github.event_name == 'release' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: "3.10" - run: sudo npm install katex -g - uses: actions/cache@v3 with: path: ~/.cache/pip key: pip-${{ hashFiles('requirements-dev.txt') }}-${{ hashFiles('docs/requirements.txt') }} - name: Install docs deps run: bash .github/workflows/install_docs_deps.sh - name: Build docs run: bash .github/workflows/build_docs.sh - name: Deploy docs uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: docs/build/html publish_branch: gh-pages commit_message: Deploy pytorch/ignite docs force_orphan: true linkcheck: if: github.event_name == 'pull_request' || github.event_name == 'push' runs-on: ubuntu-latest timeout-minutes: 10 steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: "3.10" - uses: actions/cache@v3 with: path: ~/.cache/pip key: pip-${{ hashFiles('requirements-dev.txt') }}-${{ hashFiles('docs/requirements.txt') }} - name: Install docs deps run: bash .github/workflows/install_docs_deps.sh - name: make linkcheck working-directory: ./docs/ run: make linkcheck doctest: if: github.event_name == 'pull_request' || github.event_name == 'push' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: "3.10" - run: sudo npm install katex -g - uses: actions/cache@v3 with: path: ~/.cache/pip key: pip-${{ hashFiles('requirements-dev.txt') }}-${{ hashFiles('docs/requirements.txt') }} - name: Install docs deps run: bash .github/workflows/install_docs_deps.sh - name: make doctest working-directory: ./docs/ run: | make html make doctest make coverage ignite-0.5.1/.github/workflows/gpu-hvd-tests.yml000066400000000000000000000144671465426447700216650ustar00rootroot00000000000000name: Run HVD-specific unit tests on GPUs on: push: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_gpu_tests.sh" - "tests/run_code_style.sh" - "examples/**.py" - "requirements-dev.txt" - ".github/workflows/gpu-hvd-tests.yml" workflow_dispatch: concurrency: # -- group: gpu-hvd-tests-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true # Cherry-picked from https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml jobs: gpu-hvd-tests: strategy: matrix: pytorch-channel: [pytorch] fail-fast: false env: DOCKER_IMAGE: "pytorch/conda-builder:cuda12.1" REPOSITORY: ${{ github.repository }} PR_NUMBER: ${{ github.event.pull_request.number }} runs-on: amz2023.linux.8xlarge.nvidia.gpu timeout-minutes: 60 steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Setup Linux uses: ./test-infra/.github/actions/setup-linux - name: Pull docker image uses: ./test-infra/.github/actions/pull-docker-image with: docker-image: ${{ env.DOCKER_IMAGE }} - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Start Pytorch container working-directory: ${{ github.repository }} run: | docker run --name pthd --gpus=all --rm \ --cap-add=SYS_PTRACE \ --detach \ --ipc=host \ --security-opt seccomp=unconfined \ --shm-size=2g \ --tty \ --ulimit stack=10485760:83886080 \ -v $PWD:/work \ -w /work \ ${DOCKER_IMAGE} script=$(cat << EOF set -xe nvidia-smi ls -alh conda --version python --version EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Install PyTorch and dependencies continue-on-error: false run: | script=$(cat << EOF set -xe # Install PyTorch if [ "${{ matrix.pytorch-channel }}" == "pytorch" ]; then pip install --upgrade torch torchvision --index-url https://download.pytorch.org/whl/cu121 else pip install --upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu121 fi python -c "import torch; print(torch.__version__, ', CUDA is available: ', torch.cuda.is_available()); exit(not torch.cuda.is_available())" pip list # Install dependencies pip install -r requirements-dev.txt pip install -e . EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Install Horovod with NCCL GPU ops run: | script=$(cat << EOF set -xe # Can't build Horovod with recent pytorch due to pytorch required C++17 standard # and horovod is still using C++14 # HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch] # Using a similar hack as described here: # https://github.com/horovod/horovod/issues/3941#issuecomment-1732505345 git clone --recursive https://github.com/horovod/horovod.git /horovod cd /horovod sed -i "s/CMAKE_CXX_STANDARD 14/CMAKE_CXX_STANDARD 17/g" CMakeLists.txt sed -i "s/CMAKE_CXX_STANDARD 14/CMAKE_CXX_STANDARD 17/g" horovod/torch/CMakeLists.txt HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_WITH_PYTORCH=1 python setup.py install horovodrun --check-build pip list EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Run GPU and CPU Unit HVD Tests run: | script=$(cat << EOF set -xe bash tests/run_gpu_tests.sh 2 hvd CUDA_VISIBLE_DEVICES="" pytest --cov ignite --cov-append --cov-report term-missing --cov-report xml -vvv tests/ignite -m distributed -k hvd EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: file: ${{ github.repository }}/coverage.xml flags: gpu-2 fail_ci_if_error: false - name: Run examples in container continue-on-error: false run: | SCRIPT=$(cat << EOF set -xe # Install additional example dependencies pip install fire # Check training on CIFAR10, run with horovod backend using horovodrun # initial run CI=1 horovodrun -np 2 python -u examples/cifar10/main.py run --backend=horovod --checkpoint_every=200 --stop_iteration=500 # resume CI=1 horovodrun -np 2 python examples/cifar10/main.py run --checkpoint_every=200 --num_epochs=7 --resume-from=/tmp/output-cifar10/resnet18_backend-horovod-2_stop-on-500/training_checkpoint_400.pt # Check training on CIFAR10 using spawn # initial run CI=1 python -u examples/cifar10/main.py run --backend=horovod --nproc_per_node=2 --checkpoint_every=200 --stop_iteration=500 # resume CI=1 python -u examples/cifar10/main.py run --backend=horovod --nproc_per_node=2 --checkpoint_every=200 --num_epochs=7 --resume-from=/tmp/output-cifar10/resnet18_backend-horovod-2_stop-on-500/training_checkpoint_400.pt EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Teardown Linux if: ${{ always() }} uses: ./test-infra/.github/actions/teardown-linux ignite-0.5.1/.github/workflows/gpu-tests.yml000066400000000000000000000134071465426447700210770ustar00rootroot00000000000000name: Run unit tests on GPUs on: push: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_gpu_tests.sh" - "tests/run_code_style.sh" - "examples/**.py" - "requirements-dev.txt" - ".github/workflows/gpu-tests.yml" workflow_dispatch: concurrency: # -- group: gpu-tests-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true # Cherry-picked from https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml jobs: gpu-tests: strategy: matrix: pytorch-channel: [pytorch, pytorch-nightly] fail-fast: false env: DOCKER_IMAGE: "pytorch/conda-builder:cuda12.1" REPOSITORY: ${{ github.repository }} PR_NUMBER: ${{ github.event.pull_request.number }} runs-on: amz2023.linux.8xlarge.nvidia.gpu timeout-minutes: 85 steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Setup Linux uses: ./test-infra/.github/actions/setup-linux - name: Pull docker image uses: ./test-infra/.github/actions/pull-docker-image with: docker-image: ${{ env.DOCKER_IMAGE }} - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Start Pytorch container working-directory: ${{ github.repository }} run: | docker run --name pthd --gpus=all --rm \ --cap-add=SYS_PTRACE \ --detach \ --ipc=host \ --security-opt seccomp=unconfined \ --shm-size=2g \ --tty \ --ulimit stack=10485760:83886080 \ -v $PWD:/work \ -w /work \ ${DOCKER_IMAGE} script=$(cat << EOF set -xe nvidia-smi ls -alh conda --version python --version EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Install PyTorch and dependencies continue-on-error: false run: | script=$(cat << EOF set -xe # Install PyTorch if [ "${{ matrix.pytorch-channel }}" == "pytorch" ]; then pip install --upgrade torch torchvision --index-url https://download.pytorch.org/whl/cu121 else pip install --upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu121 fi python -c "import torch; print(torch.__version__, ', CUDA is available: ', torch.cuda.is_available()); exit(not torch.cuda.is_available())" pip list # Install dependencies pip install -r requirements-dev.txt pip install -e . EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Run GPU Unit Tests continue-on-error: false uses: nick-fields/retry@v2.9.0 with: max_attempts: 5 timeout_minutes: 25 shell: bash command: docker exec -t pthd /bin/bash -xec 'bash tests/run_gpu_tests.sh 2' new_command_on_retry: docker exec -e USE_LAST_FAILED=1 -t pthd /bin/bash -xec 'bash tests/run_gpu_tests.sh 2' - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: file: ${{ github.repository }}/coverage.xml flags: gpu-2 fail_ci_if_error: false - name: Run examples in container continue-on-error: false run: | SCRIPT=$(cat << EOF set -xe # Install additional example dependencies pip install fire # Check training on cifar10, run without backend ## initial run CI=1 python examples/cifar10/main.py run --checkpoint_every=200 --stop_iteration=500 ## resume CI=1 python examples/cifar10/main.py run --checkpoint_every=200 --num_epochs=7 --resume-from=/tmp/output-cifar10/resnet18_backend-None-1_stop-on-500/training_checkpoint_400.pt # Check training on cifar10, run with NCCL backend using torchrun ## initial run CI=1 torchrun --nproc_per_node=2 examples/cifar10/main.py run --backend=nccl --checkpoint_every=200 --stop_iteration=500 ## resume CI=1 torchrun --nproc_per_node=2 examples/cifar10/main.py run --backend=nccl --checkpoint_every=200 --num_epochs=7 --resume-from=/tmp/output-cifar10/resnet18_backend-nccl-2_stop-on-500/training_checkpoint_400.pt # Check training on cifar10, run with NCCL backend using spawn ## initial run CI=1 python -u examples/cifar10/main.py run --backend=nccl --nproc_per_node=2 --checkpoint_every=200 --stop_iteration=500 ## resume CI=1 python -u examples/cifar10/main.py run --backend=nccl --nproc_per_node=2 --checkpoint_every=200 --num_epochs=7 --resume-from=/tmp/output-cifar10/resnet18_backend-nccl-2_stop-on-500/training_checkpoint_400.pt EOF ) docker exec -t pthd /bin/bash -c "${script}" - name: Teardown Linux if: ${{ always() }} uses: ./test-infra/.github/actions/teardown-linux ignite-0.5.1/.github/workflows/hvd-tests.yml000066400000000000000000000047601465426447700210670ustar00rootroot00000000000000name: Run Horovod tests on: push: branches: - master - "*.*.*" paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - ".github/workflows/hvd-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - ".github/workflows/hvd-tests.yml" workflow_dispatch: concurrency: # -- group: hvd-tests-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true jobs: horovod-tests: runs-on: ubuntu-latest timeout-minutes: 60 strategy: matrix: python-version: [3.8] pytorch-channel: [pytorch] steps: - uses: actions/checkout@v4 - name: Get year & week number id: get-date run: echo "date=$(/bin/date "+%Y-%U")" >> $GITHUB_OUTPUT shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | python3 -m pip install -U pip echo "pip_cache=$(python3 -m pip cache dir)" >> $GITHUB_OUTPUT shell: bash -l {0} - uses: actions/cache@v3 with: path: | ~/conda_pkgs_dir ${{ steps.pip-cache.outputs.pip_cache }} key: ${{ steps.get-date.outputs.date }}-horovod-${{ hashFiles('requirements-dev.txt') }} - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - name: Install dependencies shell: bash -l {0} run: | #install other dependencies pip install torch torchvision -f https://download.pytorch.org/whl/cpu/torch_stable.html pip install -r requirements-dev.txt pip install horovod python setup.py install # Download MNIST: https://github.com/pytorch/ignite/issues/1737 # to "/tmp" for cpu tests - name: Download MNIST uses: pytorch-ignite/download-mnist-github-action@master with: target_dir: /tmp - name: Run Tests uses: nick-fields/retry@v3 with: max_attempts: 5 timeout_minutes: 15 shell: bash command: bash tests/run_cpu_tests.sh new_command_on_retry: USE_LAST_FAILED=1 bash tests/run_cpu_tests.sh - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: file: ./coverage.xml flags: hvd-cpu fail_ci_if_error: false ignite-0.5.1/.github/workflows/install_docs_deps.sh000066400000000000000000000007021465426447700224400ustar00rootroot00000000000000# remove pkg-resources as it causes failure when installing https://github.com/pytorch-ignite/sphinxcontrib-versioning pip uninstall -y pkg-resources setuptools && pip install --upgrade setuptools pip wheel pip install torch torchvision -f https://download.pytorch.org/whl/cpu/torch_stable.html -U pip install -r requirements-dev.txt pip install -r docs/requirements.txt pip install git+https://github.com/pytorch-ignite/sphinxcontrib-versioning.git ignite-0.5.1/.github/workflows/mps-tests.yml000066400000000000000000000101441465426447700210760ustar00rootroot00000000000000name: Run unit tests on M1 on: push: branches: - master - "*.*.*" paths: - "ignite/**" - "tests/ignite/**" - "tests/run_code_style.sh" - "examples/**.py" - "requirements-dev.txt" - ".github/workflows/mps-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_code_style.sh" - "examples/**.py" - "requirements-dev.txt" - ".github/workflows/mps-tests.yml" workflow_dispatch: concurrency: # -- group: mps-tests-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true # Cherry-picked from # - https://github.com/pytorch/vision/blob/main/.github/workflows/tests.yml # - https://github.com/pytorch/test-infra/blob/main/.github/workflows/macos_job.yml jobs: mps-tests: strategy: matrix: python-version: [3.8] pytorch-channel: ["pytorch"] skip-distrib-tests: [1] fail-fast: false runs-on: ["macos-m1-stable"] timeout-minutes: 60 steps: - name: Clean workspace run: | echo "::group::Cleanup debug output" sudo rm -rfv "${GITHUB_WORKSPACE}" mkdir -p "${GITHUB_WORKSPACE}" echo "::endgroup::" - name: Checkout repository (pytorch/test-infra) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: pytorch/test-infra path: test-infra - name: Checkout repository (${{ github.repository }}) uses: actions/checkout@v3 with: # Support the use case where we need to checkout someone's fork repository: ${{ github.repository }} ref: ${{ github.ref }} path: ${{ github.repository }} fetch-depth: 1 - name: Setup miniconda uses: ./test-infra/.github/actions/setup-miniconda with: python-version: ${{ matrix.python-version }} - name: Install PyTorch if: ${{ matrix.pytorch-channel == 'pytorch' }} shell: bash -l {0} run: | conda shell.bash hook conda activate $CONDA_ENV pip install torch torchvision - name: Install PyTorch (nightly) if: ${{ matrix.pytorch-channel == 'pytorch-nightly' }} shell: bash -l {0} run: | conda shell.bash hook conda activate $CONDA_ENV pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu - name: Install dependencies shell: bash -l {0} working-directory: ${{ github.repository }} run: | conda activate $CONDA_ENV # TODO: We add set -xe to explicitly fail the CI if one of the commands is failing. # Somehow the step is passing even if a subcommand failed set -xe pip install -r requirements-dev.txt echo "1 returned code: $?" pip install -e . echo "2 returned code: $?" pip list echo "3 returned code: $?" # Download MNIST: https://github.com/pytorch/ignite/issues/1737 # to "/tmp" for unit tests - name: Download MNIST uses: pytorch-ignite/download-mnist-github-action@master with: target_dir: /tmp # Copy MNIST to "." for the examples - name: Copy MNIST run: | cp -R /tmp/MNIST . - name: Run Tests shell: bash -l {0} working-directory: ${{ github.repository }} run: | conda activate $CONDA_ENV SKIP_DISTRIB_TESTS=${{ matrix.skip-distrib-tests }} bash tests/run_cpu_tests.sh - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: file: ${{ github.repository }}/coverage.xml flags: mps fail_ci_if_error: false - name: Run MNIST Examples shell: bash -l {0} working-directory: ${{ github.repository }} run: | conda activate $CONDA_ENV python examples/mnist/mnist.py --epochs=1 ignite-0.5.1/.github/workflows/pytorch-version-tests.yml000066400000000000000000000100631465426447700234520ustar00rootroot00000000000000name: PyTorch version tests on: # https://docs.github.com/en/free-pro-team@latest/actions/reference/workflow-syntax-for-github-actions#onschedule schedule: # Run at 00:00 UTC Every Day - cron: "0 0 * * *" workflow_dispatch: jobs: build: runs-on: ubuntu-latest timeout-minutes: 85 strategy: max-parallel: 5 fail-fast: false matrix: python-version: [3.8, 3.9, "3.10"] pytorch-version: [2.3.1, 2.2.2, 2.1.2, 2.0.1, 1.13.1, 1.12.1, 1.10.0, 1.8.1, 1.5.1] exclude: - pytorch-version: 1.5.1 python-version: 3.9 - pytorch-version: 1.5.1 python-version: "3.10" # disabling python 3.9 support with PyTorch 1.7.1 and 1.8.1, to stop repeated pytorch-version test fail. # https://github.com/pytorch/ignite/issues/2383 - pytorch-version: 1.8.1 python-version: 3.9 - pytorch-version: 1.8.1 python-version: "3.10" - pytorch-version: 1.10.0 python-version: "3.10" - pytorch-version: 1.11.0 python-version: "3.10" steps: - uses: actions/checkout@v4 - name: Get year & week number id: get-date run: echo "date=$(/bin/date "+%Y-%U")" >> $GITHUB_OUTPUT shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | python3 -m pip install -U pip echo "pip_cache=$(python3 -m pip cache dir)" >> $GITHUB_OUTPUT shell: bash -l {0} - uses: actions/cache@v3 with: path: | ~/conda_pkgs_dir ${{ steps.pip-cache.outputs.pip_cache }} key: ${{ steps.get-date.outputs.date }}-pytorch-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.pytorch-version }}-${{ hashFiles('requirements-dev.txt') }} restore-keys: | ${{ steps.get-date.outputs.date }}-pytorch-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.pytorch-version }}- - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: ${{ matrix.python-version }} use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! - name: Install dependencies shell: bash -l {0} run: | conda install pytorch=${{ matrix.pytorch-version }} torchvision cpuonly python=${{ matrix.python-version }} -c pytorch pip install -r requirements-dev.txt python setup.py install # pytorch>=1.9.0,<1.11.0 is using "from setuptools import distutils; distutils.version.LooseVersion" anti-pattern # which raises the error: AttributeError: module 'distutils' has no attribute 'version' for setuptools>59 bad_pth_version=$(python -c "import torch; print('.'.join(torch.__version__.split('.')[:2]) in ['1.9', '1.10'])") if [ "${bad_pth_version}" == "True" ]; then pip install --upgrade "setuptools<59" python -c "from setuptools import distutils; distutils.version.LooseVersion" fi - name: Download MNIST uses: pytorch-ignite/download-mnist-github-action@master with: target_dir: /tmp - name: Run Tests uses: nick-fields/retry@v3 with: max_attempts: 5 timeout_minutes: 15 shell: bash command: bash -l tests/run_cpu_tests.sh "not test_time_profilers" new_command_on_retry: USE_LAST_FAILED=1 bash -l tests/run_cpu_tests.sh "not test_time_profilers" create-issue: runs-on: ubuntu-latest # https://docs.github.com/en/actions/reference/context-and-expression-syntax-for-github-actions#needs-context needs: build if: always() && needs.build.result == 'failure' steps: - uses: actions/checkout@v4 - uses: JasonEtco/create-an-issue@v2 name: Create issue if pytorch version tests failed with: filename: .github/failed_schedule_issue_template.md env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} ignite-0.5.1/.github/workflows/stable-release-anaconda.yml000066400000000000000000000014461465426447700235760ustar00rootroot00000000000000name: Anaconda Stable Releases on: release: types: [published] jobs: conda-build-publish: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: "3.10" - name: Install dependencies shell: bash -l {0} run: | conda install -y pytorch torchvision cpuonly -c pytorch python setup.py install - name: Build and Publish Conda binaries shell: bash -l {0} env: ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} UPLOAD_USER: "pytorch" run: | chmod +x ./conda.recipe/build_and_upload.sh ./conda.recipe/build_and_upload.sh ignite-0.5.1/.github/workflows/stable-release-pypi.yml000066400000000000000000000017721465426447700230150ustar00rootroot00000000000000name: PyPI Stable Releases on: release: types: [published] jobs: build-publish: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: "3.10" - name: Install dependencies shell: bash -l {0} run: | conda install -y pytorch torchvision cpuonly -c pytorch pip install -r requirements-dev.txt - name: Build and Publish PyPI binaries shell: bash -l {0} run: | # workaround to fix https://github.com/pytorch/ignite/issues/2373 pip uninstall -y twine pkginfo pip install --upgrade --no-cache-dir twine 'pkginfo>=1.8.2' python setup.py sdist bdist_wheel twine --version twine check dist/* TWINE_USERNAME="${{ secrets.PYPI_USER }}" TWINE_PASSWORD="${{ secrets.PYPI_TOKEN }}" twine upload --verbose dist/* ignite-0.5.1/.github/workflows/tpu-tests.yml000066400000000000000000000070651465426447700211170ustar00rootroot00000000000000name: Run TPU tests on: push: branches: - master - "*.*.*" paths: - "ignite/**" - "tests/ignite/**" - "tests/run_tpu_tests.sh" - "tests/run_code_style.sh" - "requirements-dev.txt" - ".github/workflows/tpu-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_tpu_tests.sh" - "tests/run_code_style.sh" - "requirements-dev.txt" - ".github/workflows/tpu-tests.yml" workflow_dispatch: concurrency: # -- group: tpu-tests-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true jobs: tpu-tests: runs-on: ubuntu-latest strategy: fail-fast: false matrix: xla-version: [nightly] steps: - uses: actions/checkout@v4 - name: Set up Python 3.10 uses: actions/setup-python@v4 with: python-version: "3.10" architecture: "x64" - name: Get year & week number id: get-date run: echo "date=$(/bin/date "+%Y-%U")" >> $GITHUB_OUTPUT shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | pip3 install -U "pip<24" echo "pip_cache=$(pip cache dir)" >> $GITHUB_OUTPUT shell: bash -l {0} - uses: actions/cache@v3 with: path: | ${{ steps.pip-cache.outputs.pip_cache }} key: ${{ steps.get-date.outputs.date }}-pytorch-${{ runner.os }}-${{ matrix.xla-version }}-${{ hashFiles('requirements-dev.txt') }} restore-keys: | ${{ steps.get-date.outputs.date }}-pytorch-${{ runner.os }}-${{ matrix.xla-version }}- - name: Install Torch XLA and others run: | ## Install mkl (alternative approach to https://github.com/pytorch/xla/blob/b0ba29f98a695671972d4a4cc07441014dba2892/.kokoro/common.sh#L31-L32) sudo apt-get update && sudo apt-get install -y libopenblas-dev libomp5 pip install mkl==2021.4.0 ## Install torch & xla and torchvision pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp310-cp310-linux_x86_64.whl # Check installation python -c "import torch" ## Install test deps and Ignite pip install -r requirements-dev.txt python setup.py install # Download MNIST: https://github.com/pytorch/ignite/issues/1737 # to "/tmp" for tpu tests - name: Download MNIST uses: pytorch-ignite/download-mnist-github-action@master with: target_dir: /tmp - name: Run Tests uses: nick-fields/retry@v3 with: max_attempts: 5 timeout_minutes: 25 shell: bash command: | python -c "import torch_xla; print('torch xla version:', torch_xla.__version__)" bash tests/run_tpu_tests.sh new_command_on_retry: USE_LAST_FAILED=1 bash tests/run_tpu_tests.sh env: LD_LIBRARY_PATH: ${{ env.LD_LIBRARY_PATH }}:${{ env.Python_ROOT_DIR }}/lib XRT_DEVICE_MAP: "CPU:0;/job:localservice/replica:0/task:0/device:XLA_CPU:0" XRT_WORKERS: "localservice:0;grpc://localhost:40934" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: file: ./coverage.xml flags: tpu fail_ci_if_error: false ignite-0.5.1/.github/workflows/triage.yml000066400000000000000000000024271465426447700204170ustar00rootroot00000000000000name: Triage on: pull_request_target: # types: [opened] # issues: # types: [opened] jobs: triage: permissions: contents: read pull-requests: write runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Pull Request Labeler uses: actions/labeler@v5 with: configuration-path: .github/pr-labeler-config.yml repo-token: "${{ secrets.GITHUB_TOKEN }}" # Turned off due to unexpected behavior on issue opening+labeling? https://github.com/pytorch/ignite/issues/1836 # - name: Welcome # uses: actions/first-interaction@v1 # with: # issue-message: "**Thank you for opening your First Issue!**\n\nWe appreciate a lot user's feedback on what we are doing!\n\nIf you'd like to contribute to the project, please check out our [Contributing Guide](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md)." # pr-message: "**Thank you for opening your First Pull Request!**\nWe appreciate a lot community contributions as pull requests!\n\nIf you would like to get more details on the project development, please take a look at our [Contributing Guide](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md)." # repo-token: ${{ secrets.GITHUB_TOKEN }} ignite-0.5.1/.github/workflows/unit-tests.yml000066400000000000000000000157511465426447700212670ustar00rootroot00000000000000name: Run unit tests on: push: branches: - master - "*.*.*" paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - "tests/run_code_style.sh" - "examples/**.py" - "requirements-dev.txt" - ".github/workflows/unit-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - "tests/run_code_style.sh" - "examples/**.py" - "requirements-dev.txt" - ".github/workflows/unit-tests.yml" workflow_dispatch: concurrency: # -- group: unit-tests-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }} cancel-in-progress: true jobs: cpu-tests: runs-on: ${{ matrix.os }} timeout-minutes: 85 defaults: run: shell: bash strategy: max-parallel: 10 fail-fast: false matrix: os: [ubuntu-latest] python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] pytorch-channel: [pytorch, pytorch-nightly] include: # includes a single build on windows - os: windows-latest pytorch-channel: pytorch python-version: 3.9 skip-distrib-tests: 1 # includes a single build on macosx - os: macos-latest pytorch-channel: pytorch python-version: 3.9 skip-distrib-tests: 1 steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - name: Get year & week number id: get-date run: | echo "date=$(/bin/date "+%Y-%U")" >> $GITHUB_OUTPUT - name: Get pip cache dir id: pip-cache run: | pip install -U pip || python -m pip install -U pip echo "pip_cache=$(pip cache dir)" >> $GITHUB_OUTPUT - uses: actions/cache@v3 with: path: | ${{ steps.pip-cache.outputs.pip_cache }} key: ${{ steps.get-date.outputs.date }}-pytorch-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.pytorch-channel }}-${{ hashFiles('requirements-dev.txt') }} restore-keys: | ${{ steps.get-date.outputs.date }}-pytorch-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.pytorch-channel }}- - run: pip install pip wheel setuptools -Uqq - name: Install PyTorch if: ${{ matrix.pytorch-channel == 'pytorch' }} run: pip install torch torchvision -f https://download.pytorch.org/whl/cpu/torch_stable.html - name: Install PyTorch (nightly) if: ${{ matrix.pytorch-channel == 'pytorch-nightly' }} run: pip install torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html --pre - name: Install dependencies run: | pip install -r requirements-dev.txt python setup.py install pip list - name: Check code formatting run: | bash ./tests/run_code_style.sh install bash ./tests/run_code_style.sh lint - name: Run Mypy # https://github.com/pytorch/ignite/pull/2780 # if: ${{ matrix.os == 'ubuntu-latest' && matrix.pytorch-channel == 'pytorch-nightly'}} run: | bash ./tests/run_code_style.sh mypy # Download MNIST: https://github.com/pytorch/ignite/issues/1737 # to "/tmp" for unit tests - name: Download MNIST uses: pytorch-ignite/download-mnist-github-action@master with: target_dir: /tmp # Copy MNIST to "." for the examples - name: Copy MNIST run: | cp -R /tmp/MNIST . - name: Run Tests uses: nick-fields/retry@v3 with: max_attempts: 5 timeout_minutes: 15 shell: bash command: SKIP_DISTRIB_TESTS=${{ matrix.skip-distrib-tests }} bash tests/run_cpu_tests.sh new_command_on_retry: USE_LAST_FAILED=1 SKIP_DISTRIB_TESTS=${{ matrix.skip-distrib-tests }} bash tests/run_cpu_tests.sh - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: file: ./coverage.xml flags: cpu fail_ci_if_error: false - name: Run MNIST Examples run: | # MNIST # 1) mnist.py python examples/mnist/mnist.py --epochs=1 - name: Run MNIST with loggers Examples if: ${{ matrix.os == 'ubuntu-latest' }} run: | # 2) mnist_with_visdom.py python -c "from visdom.server.build import download_scripts; download_scripts()" # download scripts : https://github.com/facebookresearch/visdom/blob/master/py/server.py#L929 python -m visdom.server & sleep 10 python examples/mnist/mnist_with_visdom.py --epochs=1 kill %1 # 3.1) mnist_with_tensorboard.py with tbX python examples/mnist/mnist_with_tensorboard.py --epochs=1 # 3.2) mnist_with_tensorboard.py with native torch tb pip uninstall -y tensorboardX python examples/mnist/mnist_with_tensorboard.py --epochs=1 - name: Run MNIST Example With Crash if: ${{ matrix.os == 'ubuntu-latest' }} continue-on-error: true run: | # 4) mnist_save_resume_engine.py python examples/mnist/mnist_save_resume_engine.py --epochs=2 --crash_iteration 1100 - name: Resume MNIST from previous crash if: ${{ matrix.os == 'ubuntu-latest' }} run: | python examples/mnist/mnist_save_resume_engine.py --epochs=2 --resume_from=/tmp/mnist_save_resume/checkpoint_1.pt - name: Run GAN example if: ${{ matrix.os == 'ubuntu-latest' }} run: | # DCGAN python examples/gan/dcgan.py --dataset fake --dataroot /tmp/fakedata --output-dir /tmp/outputs-dcgan --batch-size 2 --epochs 2 --workers 0 - name: Run RL Examples if: ${{ matrix.os == 'ubuntu-latest' }} run: | # RL # 1) Actor-Critic python examples/reinforcement_learning/actor_critic.py --max-episodes=2 # 2) Reinforce python examples/reinforcement_learning/reinforce.py --max-episodes=2 - name: Run Neural Style Example if: ${{ matrix.os == 'ubuntu-latest' }} run: | #fast-neural-style #train mkdir -p ~/.cache/torch/checkpoints/ && wget "https://download.pytorch.org/models/vgg16-397923af.pth" -O ~/.cache/torch/checkpoints/vgg16-397923af.pth python examples/fast_neural_style/neural_style.py train --epochs 1 --cuda 0 --dataset test --dataroot . --image_size 32 --style_image examples/fast_neural_style/images/style_images/mosaic.jpg --style_size 32 - name: Run SR Example if: ${{ matrix.os == 'ubuntu-latest' }} run: | # Super-Resolution python examples/super_resolution/main.py --upscale_factor 3 --crop_size 180 --batch_size 4 --test_batch_size 100 --n_epochs 1 --lr 0.001 --threads 2 --debug ignite-0.5.1/.gitignore000066400000000000000000000007661465426447700150200ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files *.pyc __pycache__/ *.py[cod] *$py.class # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST **/.DS_Store # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Documentation /docs/src/ /docs/build/ /docs/source/generated/ # Virtualenv .venv/ .python-version ignite-0.5.1/.pre-commit-config.yaml000066400000000000000000000013021465426447700172740ustar00rootroot00000000000000exclude: "^conda.recipe" repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v3.4.0 hooks: - id: check-toml - id: check-yaml - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/pre-commit/mirrors-prettier rev: v2.2.1 hooks: - id: prettier exclude_types: ["python", "jupyter", "shell", "gitignore"] - repo: https://github.com/omnilib/ufmt rev: v2.5.1 hooks: - id: ufmt additional_dependencies: - black == 24.3.0 - usort == 1.0.8.post1 - repo: https://github.com/pycqa/flake8 rev: 7.0.0 hooks: - id: flake8 args: ["--config", "setup.cfg"] ignite-0.5.1/CITATION000066400000000000000000000004771465426447700141640ustar00rootroot00000000000000@misc{pytorch-ignite, author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani}, title = {High-level library to help with training neural networks in PyTorch}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/pytorch/ignite}}, }ignite-0.5.1/CODE_OF_CONDUCT.md000066400000000000000000000064331465426447700156240ustar00rootroot00000000000000# Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: - Using welcoming and inclusive language - Being respectful of differing viewpoints and experiences - Gracefully accepting constructive criticism - Focusing on what is best for the community - Showing empathy towards other community members Examples of unacceptable behavior by participants include: - The use of sexualized language or imagery and unwelcome sexual attention or advances - Trolling, insulting/derogatory comments, and personal or political attacks - Public or private harassment - Publishing others' private information, such as a physical or electronic address, without explicit permission - Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies within all project spaces, and it also applies when an individual is representing the project or its community in public spaces. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at . All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq ignite-0.5.1/CONTRIBUTING.md000066400000000000000000000345121465426447700152550ustar00rootroot00000000000000# Contributing to Ignite This project is a community effort, and everyone is welcome to contribute ! If you are interested in contributing to Ignite, there are many ways to help out. Your contributions may fall into the following categories: 1. It helps us very much if you could - Report issues you’re facing - Give a :+1: on issues that others reported and that are relevant to you - Spread a word about the project or simply :star: to say "I use it" 2. Answering queries on the issue tracker, investigating bugs and reviewing other developers’ pull requests are very valuable contributions that decrease the burden on the project maintainers. 3. You would like to improve the documentation. This is no less important than improving the library itself! If you find a typo in the documentation, do not hesitate to submit a GitHub pull request. 4. You would like propose a new feature and implement it - Post about your intended feature, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it. 5. You would like implement a feature or bug-fix for an outstanding issue - Look at the issues labelled as ["help wanted"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) - Pick an issue and comment on the task that you want to work on this feature. - If you need more context on a particular issue, please ask and we shall provide. ## Table of Contents - [Table of Contents](#table-of-contents) - [Developing Ignite](#developing-ignite) - [Quickstart guide for first-time contributors](#quickstart-guide-for-first-time-contributors) - [Installation](#installation) - [Code development](#code-development) - [Codebase structure](#codebase-structure) - [Formatting Code](#formatting-code) - [Formatting without pre-commit](#formatting-without-pre-commit) - [Formatting with pre-commit](#formatting-with-pre-commit) - [Run tests](#run-tests) - [Run distributed tests only on CPU](#run-distributed-tests-only-on-cpu) - [Run Mypy checks](#run-mypy-checks) - [Send a PR](#send-a-pr) - [Sync up with the upstream](#sync-up-with-the-upstream) - [Writing documentation](#writing-documentation) - [Local documentation building and deploying](#local-documentation-building-and-deploying) - [Install requirements](#install-requirements) - [Build](#build) - [Local deployment](#local-deployment) ## Developing Ignite ### Quickstart guide for first-time contributors
- Install [miniconda](https://docs.conda.io/en/latest/miniconda.html) for your system. - Create an isolated conda environment for pytorch-ignite: ```bash conda create -n pytorch-ignite-dev python=3.8 ``` - Activate the newly created environment: ```bash conda activate pytorch-ignite-dev ``` - When developing please take care of preserving `.gitignore` file and make use of `.git/info/exclude` to exclude custom files like: `.idea`, `.vscode` etc. - Please refer to [github first contributions guidelines](https://github.com/firstcontributions/first-contributions) and don't hesitate to ask the pytorch-ignite community in case of any doubt. - A good way to start is to tackle one of the [good first issues](https://github.com/pytorch/ignite/labels/good%20first%20issue).
### Installation 1) Make a fork of the repository on the GitHub (see [here](https://github.com/firstcontributions/first-contributions#fork-this-repository) for details). As a result, for example your username is `happy-ignite-developer`, then you should be able to see your fork on the GitHub, e.g https://github.com/happy-ignite-developer/ignite.git 2) Clone your fork locally and setup `upstream`. Assuming your username is `happy-ignite-developer`: ```bash git clone https://github.com/happy-ignite-developer/ignite.git cd ignite git remote add upstream https://github.com/pytorch/ignite.git git remote -v ``` You might see the following output: ``` origin https://github.com/happy-ignite-developer/ignite.git (fetch) origin https://github.com/happy-ignite-developer/ignite.git (push) upstream https://github.com/pytorch/ignite (fetch) upstream https://github.com/pytorch/ignite (push) ``` 3) Sync and install all necessary dependencies: ```bash git pull upstream master python setup.py develop pip install -r requirements-dev.txt bash ./tests/run_code_style.sh install ``` ### Code development #### Codebase structure - [ignite](ignite) - Core library files - [engine](ignite/engine) - Module containing core classes like Engine, Events, State. - [handlers](ignite/handlers) - Module containing out-of-the-box handlers - [metrics](ignite/metrics) - Module containing out-of-the-box metrics - [contrib](ignite/contrib) - Contrib module with other metrics, handlers classes that may require additional dependencies - [distributed](ignite/distributed) - Module with helpers for distributed computations - [tests](tests) - Python unit tests - [examples](examples) - Examples and notebook tutorials - [docs](docs) - Documentation files If you modify the code, you will most probably also need to code some tests to ensure the correct behaviour. We are using `pytest` to write our tests: - naming convention for files `test_*.py`, e.g. `test_precision.py` - naming of testing functions `def test_*`, e.g. `def test_precision_on_random_data()` - if test function should run on GPU, please **make sure to add `cuda`** in the test name, e.g. `def test_something_on_cuda()`. Additionally, we may want to decorate it with `@pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU")`. For more examples, please see https://github.com/pytorch/ignite/blob/master/tests/ignite/engine/test_create_supervised.py - if test function checks distributed configuration, we have to mark the test as `@pytest.mark.distributed` and additional conditions depending on the intended checks. For example, please see https://github.com/pytorch/ignite/blob/master/tests/ignite/metrics/test_accuracy.py New code should be compatible with Python 3.X versions. Once you finish implementing a feature or bugfix and tests, please run lint checking and tests: #### Formatting Code To ensure the codebase complies with a style guide, we use [flake8](https://flake8.pycqa.org/en/latest/) and [ufmt](https://ufmt.omnilib.dev/) ([black](https://black.readthedocs.io/en/stable/) and [usort](https://usort.readthedocs.io/en/stable/)) to format and check codebase for compliance with PEP8. ##### Formatting without pre-commit If you choose not to use pre-commit, you can take advantage of IDE extensions configured to black format or invoke black manually to format files and commit them. To install `flake8`, `ufmt` and `mypy`, please run ```bash bash ./tests/run_code_style.sh install ``` To format files and commit changes: ```bash # This should autoformat the files bash ./tests/run_code_style.sh fmt # If everything is OK, then commit git add . git commit -m "Added awesome feature" ``` ##### Formatting with pre-commit To automate the process, we have configured the repo with [pre-commit hooks](https://pre-commit.com/) to use Β΅fmt to autoformat the staged files to ensure every commit complies with a style guide. This requires some setup, which is described below: 1. Install pre-commit in your python environment. 2. Run pre-commit install that configures a virtual environment to invoke ufmt and flake8 on commits. ```bash pip install pre-commit pre-commit install ``` 3. When files are committed: - If the stages files are not compliant with black or Β΅sort, Β΅fmt will autoformat the staged files. If this were to happen, files should be staged and committed again. See example code below. - If the staged files are not compliant with flake8, errors will be raised. These errors should be fixed and the files should be committed again. See example code below. ```bash git add . git commit -m "Added awesome feature" # DONT'T WORRY IF ERRORS ARE RAISED. # YOUR CODE IS NOT COMPLIANT WITH flake8, Β΅sort or black # Fix any flake8 errors by following their suggestions # Β΅fmt will automatically format the files so they might look different, but you'll need to stage the files # again for committing # After fixing any flake8 errors git add . git commit -m "Added feature" ``` #### Run tests: To run a specific test, for example `test_terminate` from `test_engine.py`: ```bash pytest tests/ignite/engine/test_engine.py -vvv -k test_terminate ``` To run all tests with coverage (assuming installed `pytest-cov` and `pytest-xdist`): ```bash bash tests/run_cpu_tests.sh ``` On Windows, distributed tests should be skipped ```bash SKIP_DISTRIB_TESTS=1 bash tests/run_cpu_tests.sh ``` ##### Run distributed tests only on CPU To run distributed tests only (assuming installed `pytest-xdist`): ```bash export WORLD_SIZE=2 CUDA_VISIBLE_DEVICES="" pytest --dist=each --tx $WORLD_SIZE*popen//python=python tests/ -m distributed -vvv ``` #### Run Mypy checks: To run mypy to check the optional static type: ```bash bash ./tests/run_code_style.sh mypy ``` To change any config for specif folder, please see the file mypy.ini #### Send a PR If everything is OK, please send a Pull Request to https://github.com/pytorch/ignite from your fork. If you are not familiar with creating a Pull Request, here are some guides: - https://github.com/firstcontributions/first-contributions - http://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request - https://help.github.com/articles/creating-a-pull-request/ **NOTE : When sending a PR, please kindly check if the changes are required to run in the CI.** For example, typo changes in `CONTRIBUTING.md`, `README.md` are not required to run in the CI. So, please add `[skip ci]` in the PR title to save the resources. Ignite has setup several CIs. - GitHub Actions - Netlify So, please add - `[skip actions]` for the changes which are not required to run on GitHub Actions, - `[skip netlify]` for the changes which are not required to run on Netlify PR Preview build, or - `[skip ci]` for the changes which are not required to run on any CI. **NOTE : Those skip statements are case sensitive, need open bracket `[` and close bracket `]`. And, Ignite has followed a convention of starting with `skip` word.** ##### Sync up with the upstream First, make sure you have set [upstream](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/configuring-a-remote-for-a-fork) by running: ```bash git remote add upstream https://github.com/pytorch/ignite ``` Then you can see if you have set up multiple remote correctly by running `git remote -v`: ```bash origin https://github.com/{YOUR_USERNAME}/ignite.git (fetch) origin https://github.com/{YOUR_USERNAME}/ignite.git (push) upstream https://github.com/pytorch/ignite (fetch) upstream https://github.com/pytorch/ignite (push) ``` Now you can get the latest development into your forked repository with this: ```bash git fetch upstream git checkout master git merge upstream/master ``` ### Writing documentation Ignite uses [Google style](https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html#type-annotations) for formatting docstrings, specially from an example of `Google style with Python 3 type annotations` and - [`.. versionadded::`] directive for adding new classes, class methods, functions, - [`.. versionchanged::`] directive for adding new arguments, changing internal behaviours, fixing bugs and - [`.. deprecated::`] directive for deprecations. Examples: ``versionadded`` usage [link](https://github.com/pytorch/ignite/blob/52c69251dd9d97c32da1df0477ec3854e5702029/ignite/handlers/state_param_scheduler.py#L24), ``versionchanged`` usage [link](https://github.com/pytorch/ignite/blob/d2020e4e253ac1455a757c2db895c68ccfd2b958/ignite/metrics/metric.py#L281-L282) Length of line inside docstrings block must be limited to 120 characters. [`.. versionadded::`]: https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-versionadded [`.. versionchanged::`]: https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-versionchanged [`.. deprecated::`]: https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-deprecated #### Local documentation building and deploying Please, follow the instructions to build and deploy the documentation locally. ##### Install requirements ```bash cd docs pip install -r requirements.txt ``` [Katex](https://katex.org/) is also needed to build the documentation. To install katex, you need to have [nodejs](https://nodejs.org/en/) installed. Optionaly, we can install `nodejs/npm` using conda: `conda install nodejs`. Then you can install katex with [npm](https://www.npmjs.com/) or [yarn](https://yarnpkg.com/) (if installed). ```bash npm install -g katex # or if you use yarn package manager yarn global add katex ``` ##### Build ```bash cd docs make html ``` ##### Local deployment Please, use python 3.X for the command below: ```bash cd docs/build python -m http.server # python -m http.server 1234 ``` Then open the browser at `localhost:` (e.g. `localhost:1234`) and click to `html` folder. #### Examples testing (doctests) PyTorch-Ignite uses **Sphinx directives**. Every code that needs to be tested should be under `.. testcode::` and expected output should be under `.. testoutput::`. For example: ```py .. testcode:: def process_function(engine, batch): y_pred, y = batch return y_pred, y engine = Engine(process_function) metric = SSIM(data_range=1.0) metric.attach(engine, 'ssim') preds = torch.rand([4, 3, 16, 16]) target = preds * 0.75 state = engine.run([[preds, target]]) print(state.metrics['ssim']) .. testoutput:: 0.9218971... ``` If the floating point results are needed for assertion and the results can vary per operating systems and PyTorch versions, we could assert the results up to 4 or 6 decimal places and match the rest of the results with `...`. Learn more about `sphinx.ext.doctest` in [the official documentation](https://www.sphinx-doc.org/en/master/usage/extensions/doctest.html). To make writing doctests easy, there are some configuratons defined in `conf.py`. Search `doctest_global_setup` in [conf.py](docs/source/conf.py) to see which variables and functions are available. To run doctests locally: ```sh cd docs make html && make doctest ``` ignite-0.5.1/LICENSE000066400000000000000000000027671465426447700140400ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2018-present, PyTorch-Ignite team All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ignite-0.5.1/README.md000066400000000000000000000652721465426447700143120ustar00rootroot00000000000000
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## TL;DR Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
PyTorch-Ignite teaser _Click on the image to see complete code_
### Features - [Less code than pure PyTorch](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png) while ensuring maximum control and simplicity - Library approach and no program's control inversion - _Use ignite where and when you need_ - Extensible API for metrics, experiment managers, and other components # Table of Contents - [Table of Contents](#table-of-contents) - [Why Ignite?](#why-ignite) - [Simplified training and validation loop](#simplified-training-and-validation-loop) - [Power of Events & Handlers](#power-of-events--handlers) - [Execute any number of functions whenever you wish](#execute-any-number-of-functions-whenever-you-wish) - [Built-in events filtering](#built-in-events-filtering) - [Stack events to share some actions](#stack-events-to-share-some-actions) - [Custom events to go beyond standard events](#custom-events-to-go-beyond-standard-events) - [Out-of-the-box metrics](#out-of-the-box-metrics) - [Installation](#installation) - [Nightly releases](#nightly-releases) - [Docker Images](#docker-images) - [Using pre-built images](#using-pre-built-images) - [Getting Started](#getting-started) - [Documentation](#documentation) - [Additional Materials](#additional-materials) - [Examples](#examples) - [Tutorials](#tutorials) - [Reproducible Training Examples](#reproducible-training-examples) - [Communication](#communication) - [User feedback](#user-feedback) - [Contributing](#contributing) - [Projects using Ignite](#projects-using-ignite) - [Citing Ignite](#citing-ignite) - [About the team & Disclaimer](#about-the-team--disclaimer) # Why Ignite? Ignite is a **library** that provides three high-level features: - Extremely simple engine and event system - Out-of-the-box metrics to easily evaluate models - Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics ## Simplified training and validation loop No more coding `for/while` loops on epochs and iterations. Users instantiate engines and run them.
Example ```python from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.metrics import Accuracy # Setup training engine: def train_step(engine, batch): # Users can do whatever they need on a single iteration # Eg. forward/backward pass for any number of models, optimizers, etc # ... trainer = Engine(train_step) # Setup single model evaluation engine evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()}) def validation(): state = evaluator.run(validation_data_loader) # print computed metrics print(trainer.state.epoch, state.metrics) # Run model's validation at the end of each epoch trainer.add_event_handler(Events.EPOCH_COMPLETED, validation) # Start the training trainer.run(training_data_loader, max_epochs=100) ```
## Power of Events & Handlers The cool thing with handlers is that they offer unparalleled flexibility (compared to, for example, callbacks). Handlers can be any function: e.g. lambda, simple function, class method, etc. Thus, we do not require to inherit from an interface and override its abstract methods which could unnecessarily bulk up your code and its complexity. ### Execute any number of functions whenever you wish
Examples ```python trainer.add_event_handler(Events.STARTED, lambda _: print("Start training")) # attach handler with args, kwargs mydata = [1, 2, 3, 4] logger = ... def on_training_ended(data): print(f"Training is ended. mydata={data}") # User can use variables from another scope logger.info("Training is ended") trainer.add_event_handler(Events.COMPLETED, on_training_ended, mydata) # call any number of functions on a single event trainer.add_event_handler(Events.COMPLETED, lambda engine: print(engine.state.times)) @trainer.on(Events.ITERATION_COMPLETED) def log_something(engine): print(engine.state.output) ```
### Built-in events filtering
Examples ```python # run the validation every 5 epochs @trainer.on(Events.EPOCH_COMPLETED(every=5)) def run_validation(): # run validation # change some training variable once on 20th epoch @trainer.on(Events.EPOCH_STARTED(once=20)) def change_training_variable(): # ... # Trigger handler with customly defined frequency @trainer.on(Events.ITERATION_COMPLETED(event_filter=first_x_iters)) def log_gradients(): # ... ```
### Stack events to share some actions
Examples Events can be stacked together to enable multiple calls: ```python @trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10)) def run_validation(): # ... ```
### Custom events to go beyond standard events
Examples Custom events related to backward and optimizer step calls: ```python from ignite.engine import EventEnum class BackpropEvents(EventEnum): BACKWARD_STARTED = 'backward_started' BACKWARD_COMPLETED = 'backward_completed' OPTIM_STEP_COMPLETED = 'optim_step_completed' def update(engine, batch): # ... loss = criterion(y_pred, y) engine.fire_event(BackpropEvents.BACKWARD_STARTED) loss.backward() engine.fire_event(BackpropEvents.BACKWARD_COMPLETED) optimizer.step() engine.fire_event(BackpropEvents.OPTIM_STEP_COMPLETED) # ... trainer = Engine(update) trainer.register_events(*BackpropEvents) @trainer.on(BackpropEvents.BACKWARD_STARTED) def function_before_backprop(engine): # ... ``` - Complete snippet is found [here](https://pytorch.org/ignite/faq.html#creating-custom-events-based-on-forward-backward-pass). - Another use-case of custom events: [trainer for Truncated Backprop Through Time](https://pytorch.org/ignite/contrib/engines.html#ignite.contrib.engines.create_supervised_tbptt_trainer).
## Out-of-the-box metrics - [Metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics) for various tasks: Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 [regression metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics). - Users can also [compose their metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from existing ones using arithmetic operations or torch methods.
Example ```python precision = Precision(average=False) recall = Recall(average=False) F1_per_class = (precision * recall * 2 / (precision + recall)) F1_mean = F1_per_class.mean() # torch mean method F1_mean.attach(engine, "F1") ```
# Installation From [pip](https://pypi.org/project/pytorch-ignite/): ```bash pip install pytorch-ignite ``` From [conda](https://anaconda.org/pytorch/ignite): ```bash conda install ignite -c pytorch ``` From source: ```bash pip install git+https://github.com/pytorch/ignite ``` ## Nightly releases From pip: ```bash pip install --pre pytorch-ignite ``` From conda (this suggests to install [pytorch nightly release](https://anaconda.org/pytorch-nightly/pytorch) instead of stable version as dependency): ```bash conda install ignite -c pytorch-nightly ``` ## Docker Images ### Using pre-built images Pull a pre-built docker image from [our Docker Hub](https://hub.docker.com/u/pytorchignite) and run it with docker v19.03+. ```bash docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash ```
List of available pre-built images Base - `pytorchignite/base:latest` - `pytorchignite/apex:latest` - `pytorchignite/hvd-base:latest` - `pytorchignite/hvd-apex:latest` - `pytorchignite/msdp-apex:latest` Vision: - `pytorchignite/vision:latest` - `pytorchignite/hvd-vision:latest` - `pytorchignite/apex-vision:latest` - `pytorchignite/hvd-apex-vision:latest` - `pytorchignite/msdp-apex-vision:latest` NLP: - `pytorchignite/nlp:latest` - `pytorchignite/hvd-nlp:latest` - `pytorchignite/apex-nlp:latest` - `pytorchignite/hvd-apex-nlp:latest` - `pytorchignite/msdp-apex-nlp:latest`
For more details, see [here](docker). # Getting Started Few pointers to get you started: - [Quick Start Guide: Essentials of getting a project up and running](https://pytorch-ignite.ai/tutorials/beginner/01-getting-started/) - [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch-ignite.ai/concepts/) - Full-featured template examples (coming soon) # Documentation - Stable API documentation and an overview of the library: https://pytorch.org/ignite/ - Development version API documentation: https://pytorch.org/ignite/master/ - [FAQ](https://pytorch.org/ignite/faq.html), ["Questions on Github"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+) and ["Questions on Discuss.PyTorch"](https://discuss.pytorch.org/c/ignite). - [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap) ## Additional Materials - [Distributed Training Made Easy with PyTorch-Ignite](https://labs.quansight.org/blog/2021/06/distributed-made-easy-with-ignite/) - [PyTorch Ecosystem Day 2021 Breakout session presentation](https://colab.research.google.com/drive/1qhUgWQ0N2U71IVShLpocyeY4AhlDCPRd) - [Tutorial blog post about PyTorch-Ignite](https://labs.quansight.org/blog/2020/09/pytorch-ignite/) - [8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem](https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem?utm_source=reddit&utm_medium=post&utm_campaign=blog-model-training-libraries-pytorch-ecosystem) - Ignite Posters from Pytorch Developer Conferences: - [2021](https://drive.google.com/file/d/1YXrkJIepPk_KltSG1ZfWRtA5IRgPFz_U) - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg) - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5) # Examples ## Tutorials - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb) [Text Classification using Convolutional Neural Networks](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb) [Variational Auto Encoders](https://github.com/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb) [Convolutional Neural Networks for Classifying Fashion-MNIST Dataset](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) [Training Cycle-GAN on Horses to Zebras with Nvidia/Apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) - [ logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-apex) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) [Another training Cycle-GAN on Horses to Zebras with Native Torch CUDA AMP](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) - [logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-torch-amp) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb) [Finetuning EfficientNet-B0 on CIFAR100](https://github.com/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb) [Hyperparameters tuning with Ax](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb) [Basic example of LR finder on MNIST](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb) [Benchmark mixed precision training on Cifar100: torch.cuda.amp vs nvidia/apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb) [MNIST training on a single TPU](https://github.com/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) [CIFAR10 Training on multiple TPUs](https://github.com/pytorch/ignite/tree/master/examples/cifar10) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb) [Basic example of handlers time profiling on MNIST training example](https://github.com/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb) ## Reproducible Training Examples Inspired by [torchvision/references](https://github.com/pytorch/vision/tree/master/references), we provide several reproducible baselines for vision tasks: - [ImageNet](examples/references/classification/imagenet) - logs on Ignite Trains server coming soon ... - [Pascal VOC2012](examples/references/segmentation/pascal_voc2012) - logs on Ignite Trains server coming soon ... Features: - Distributed training: native or horovod and using [PyTorch native AMP](https://pytorch.org/docs/stable/notes/amp_examples.html) ## Code-Generator application The easiest way to create your training scripts with PyTorch-Ignite: - https://code-generator.pytorch-ignite.ai/ # Communication - [GitHub issues](https://github.com/pytorch/ignite/issues): questions, bug reports, feature requests, etc. - [Discuss.PyTorch](https://discuss.pytorch.org/c/ignite), category "Ignite". - [PyTorch-Ignite Discord Server](https://discord.gg/djZtm3EmKj): to chat with the community - [GitHub Discussions](https://github.com/pytorch/ignite/discussions): general library-related discussions, ideas, Q&A, etc. ## User feedback We have created a form for ["user feedback"](https://github.com/pytorch/ignite/issues/new/choose). We appreciate any type of feedback, and this is how we would like to see our community: - If you like the project and want to say thanks, this the right place. - If you do not like something, please, share it with us, and we can see how to improve it. Thank you! # Contributing Please see the [contribution guidelines](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md) for more information. As always, PRs are welcome :) # Projects using Ignite
Research papers - [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://github.com/BlackHC/BatchBALD) - [A Model to Search for Synthesizable Molecules](https://github.com/john-bradshaw/molecule-chef) - [Localised Generative Flows](https://github.com/jrmcornish/lgf) - [Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature](https://github.com/hammerlab/t-cell-relation-extraction) - [Variational Information Distillation for Knowledge Transfer](https://github.com/amzn/xfer/tree/master/var_info_distil) - [XPersona: Evaluating Multilingual Personalized Chatbot](https://github.com/HLTCHKUST/Xpersona) - [CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images](https://github.com/ucuapps/CoronaryArteryStenosisScoreClassification) - [Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog](https://github.com/ictnlp/DSTC8-AVSD) - [Adversarial Decomposition of Text Representation](https://github.com/text-machine-lab/adversarial_decomposition) - [Uncertainty Estimation Using a Single Deep Deterministic Neural Network](https://github.com/y0ast/deterministic-uncertainty-quantification) - [DeepSphere: a graph-based spherical CNN](https://github.com/deepsphere/deepsphere-pytorch) - [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://github.com/lidq92/LinearityIQA) - [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://github.com/lidq92/MDTVSFA) - [Deep Signature Transforms](https://github.com/patrick-kidger/Deep-Signature-Transforms) - [Neural CDEs for Long Time-Series via the Log-ODE Method](https://github.com/jambo6/neuralCDEs-via-logODEs) - [Volumetric Grasping Network](https://github.com/ethz-asl/vgn) - [Mood Classification using Listening Data](https://github.com/fdlm/listening-moods) - [Deterministic Uncertainty Estimation (DUE)](https://github.com/y0ast/DUE) - [PyTorch-Hebbian: facilitating local learning in a deep learning framework](https://github.com/Joxis/pytorch-hebbian) - [Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks](https://github.com/rpatrik96/lod-wmm-2019) - [Learning explanations that are hard to vary](https://github.com/gibipara92/learning-explanations-hard-to-vary) - [The role of disentanglement in generalisation](https://github.com/mmrl/disent-and-gen) - [A Probabilistic Programming Approach to Protein Structure Superposition](https://github.com/LysSanzMoreta/Theseus-PP) - [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://github.com/auriml/Rx-thorax-automatic-captioning)
Blog articles, tutorials, books - [State-of-the-Art Conversational AI with Transfer Learning](https://github.com/huggingface/transfer-learning-conv-ai) - [Tutorial on Transfer Learning in NLP held at NAACL 2019](https://github.com/huggingface/naacl_transfer_learning_tutorial) - [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition) - [Once Upon a Repository: How to Write Readable, Maintainable Code with PyTorch](https://towardsdatascience.com/once-upon-a-repository-how-to-write-readable-maintainable-code-with-pytorch-951f03f6a829) - [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/) - [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783) - [PyTorch Igniteβ€Š-β€ŠClassifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)
Toolkits - [Project MONAI - AI Toolkit for Healthcare Imaging](https://github.com/Project-MONAI/MONAI) - [DeepSeismic - Deep Learning for Seismic Imaging and Interpretation](https://github.com/microsoft/seismic-deeplearning) - [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl) - [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt) - [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)
Others - [Implementation of "Attention is All You Need" paper](https://github.com/akurniawan/pytorch-transformer) - [Implementation of DropBlock: A regularization method for convolutional networks in PyTorch](https://github.com/miguelvr/dropblock) - [Kaggle Kuzushiji Recognition: 2nd place solution](https://github.com/lopuhin/kaggle-kuzushiji-2019) - [Unsupervised Data Augmentation experiments in PyTorch](https://github.com/vfdev-5/UDA-pytorch) - [Hyperparameters tuning with Optuna](https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py) - [Logging with ChainerUI](https://chainerui.readthedocs.io/en/latest/reference/module.html#external-library-support) - [FixMatch experiments in PyTorch and Ignite (CTA dataaug policy)](https://github.com/vfdev-5/FixMatch-pytorch) - [Kaggle Birdcall Identification Competition: 1st place solution](https://github.com/ryanwongsa/kaggle-birdsong-recognition) - [Logging with Aim - An open-source experiment tracker](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite)
See other projects at ["Used by"](https://github.com/pytorch/ignite/network/dependents?package_id=UGFja2FnZS02NzI5ODEwNA%3D%3D) If your project implements a paper, represents other use-cases not covered in our official tutorials, Kaggle competition's code, or just your code presents interesting results and uses Ignite. We would like to add your project to this list, so please send a PR with brief description of the project. # Citing Ignite If you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project. ``` @misc{pytorch-ignite, author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani}, title = {High-level library to help with training neural networks in PyTorch}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/pytorch/ignite}}, } ``` # About the team & Disclaimer PyTorch-Ignite is a [NumFOCUS Affiliated Project](https://www.numfocus.org/), operated and maintained by volunteers in the PyTorch community in their capacities as individuals (and not as representatives of their employers). See the ["About us"](https://pytorch-ignite.ai/about/community/#about-us) page for a list of core contributors. For usage questions and issues, please see the various channels [here](#communication). 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+Ό©οΛφμK_ύσ28j­gލ`nQJYάςΡ†&L°#ώιδΐΝλ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°#λ0B°7†αAf`5’˜IENDB`‚ignite-0.5.1/assets/logo/ignite_logo_dark.svg000066400000000000000000000130001465426447700212740ustar00rootroot00000000000000ignite-0.5.1/assets/logo/ignite_logo_guidelines.md000066400000000000000000000037611465426447700223210ustar00rootroot00000000000000# PyTorch Ignite Logo Guidelines These guidelines are meant to help keep the PyTorch Ignite logo (as developed in [#1221](https://github.com/pytorch/ignite/issues/1221)) consistent and recognizable across all its uses. They also provide a common language for referring to the logos and their components. The primary logo is the combination of the logomark and wordmark next to each other. The logomark is the flame alone (no text) and the wordmark is only the text. It's preferable to use the primary logo whenever possible, and the logomark when a smaller version is needed. ## Color The full color options are a combonation of PyTorch's main orange (`#ee4c2c`) with yellow details (`#eaa700`). Light options are white (`#FFFFFF`) and dark options dark grey (`#2a2a2a`). The alternate "mixed" logo uses the full color logomark with a dark grey wordmark. Whenever possible, use the full color logos. One color logos (light or dark) are to be used when full color will not have enough contrast, usually when logos must be on colored backgrounds or are being reproduced somewhere that doesn't support color. Please note: The orange (`#ee4c2c`) and yellow (`#eaa700`) do not meet WCAG 2.1 color contrast recommendations for text or UI when used with white or other light colors. Make sure to use these colors primarily as decorative elements or with a dark color for text and/or UI. Accessibility should not be overlooked. ## Type The PyTorch Ignite wordmark is made from Oxygen (by Vernon Adams @vernnobile). ## Minimum Size For consistent legibility, please do not display the primary logo at less than 60px wide or the logomark at less than 15px wide. ## Logo Integrity A few other notes to keep in mind when using the logo: - Make sure to scale the logo proportionally. - Maintain a good amount of space around the logo. Don’t let it overlap with text, images, or other elements. - Do not try and recreate or modify the logo. 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έ4ή!Q*μ9>€^]Υ /…‹‰^ξΦXNhq₯„I(W2~j¦fκzηbfλbvhΉnh‰>f‹φi€k§¦λ’ŽλzΎ„¦ήk‡ŽλtukΓ&μ‡Ά„ψ°kΐ>lΉ&μŽ^ΧΕζk»nλcNiΕ¦μΎΦhΝφkzήμΗή\Ξ¦gўkΆΆlh^λΔή릆λ·ΦθFμΑvμΣΦlΛmžξl1ΦkΟi»kcξΡξkΔΞλΞFΙlαVm€~lΗFm؎nεfνιvνk†λαήνΞύάr5cэεYή‘ZNέΕΫκΕ{c―ώ¦΄3θ±&k›ζsUλמνε^εΠmbcι~λΦΆoΘ–mύΦοΖΞνεpϊΎo–mΫmοοΗοߎξΒ~νΣ¦p{vnΫmmώκΨΎpΰ†p§Φoθžpϊ.ξΟπΏπ§lξŸp―lχowp ο§φοΒnkάFξΒl·pοoθfq ―ρΧρίπ$gp oςΧξ§ζξΠύnπo«ΆH-oσf]ε{<_foXΧχζξV–o4Οqtπ4ΗqδVς%ΟpΗρό–σ6oW wσ9ίs·ζο;χs?‡sΕt<ŸsA·gωφp)oqCίσD_t5·s8WrDίν>WλgςJWσMηtIησF·σ8χτWτΰ†tL·]D7u&ΞυAGuu u&ΧtWοt σ)Ώg3χn}ΖςŠΤrθ€;ignite-0.5.1/assets/tldr/teaser.ipynb000066400000000000000000000352171465426447700176240ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## PyTorch-Ignite \n", "\n", "Setup the runtime to any of your choice:\n", "- CPU\n", "- GPU\n", "- TPU\n", "\n", "### TL;DR\n", "\n", "[PyTorch-Ignite](https://github.com/pytorch/ignite) is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Install PyTorch-Ignite\n", "!pip install -q pytorch-ignite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setup helper methods\n", "\n", "- dataflow\n", "- model, optimizer, criterion, learning rate scheduler" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "in_colab = \"COLAB_TPU_ADDR\" in os.environ\n", "with_torchrun = \"WORLD_SIZE\" in os.environ\n", "\n", "if in_colab:\n", " VERSION = !curl -s https://api.github.com/repos/pytorch/xla/releases/latest | grep -Po '\"tag_name\": \"v\\K.*?(?=\")'\n", " !pip install --upgrade -q cloud-tpu-client==0.10 torch=={VERSION[0]} torchvision https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{VERSION[0][:-2]}-cp38-cp38-linux_x86_64.whl\n", "\n", "!pip list | grep torch" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.optim.lr_scheduler import StepLR\n", "from torchvision import datasets, models\n", "from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor\n", "\n", "import ignite.distributed as idist\n", "from ignite.contrib.engines import common\n", "from ignite.handlers import ProgressBar\n", "from ignite.engine import Engine, Events, create_supervised_evaluator\n", "from ignite.metrics import Accuracy\n", "\n", "\n", "train_transform = Compose(\n", " [\n", " Pad(4),\n", " RandomCrop(32, fill=128),\n", " RandomHorizontalFlip(),\n", " ToTensor(),\n", " Normalize((0.485, 0.456, 0.406), (0.229, 0.23, 0.225)),\n", " ]\n", ")\n", "\n", "test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.23, 0.225)),])\n", "\n", "\n", "def get_train_test_datasets(path):\n", " # - Get train/test datasets\n", " if idist.get_rank() > 0:\n", " # Ensure that only rank 0 download the dataset\n", " idist.barrier()\n", "\n", " train_ds = datasets.CIFAR10(root=path, train=True, download=True, transform=train_transform)\n", " test_ds = datasets.CIFAR10(root=path, train=False, download=False, transform=test_transform)\n", "\n", " if idist.get_rank() == 0:\n", " # Ensure that only rank 0 download the dataset\n", " idist.barrier()\n", "\n", " return train_ds, test_ds\n", "\n", "\n", "def get_model(name):\n", " if name in models.__dict__:\n", " fn = models.__dict__[name]\n", " else:\n", " raise RuntimeError(f\"Unknown model name {name}\")\n", "\n", " return fn(num_classes=10)\n", "\n", "\n", "def get_dataflow(config):\n", "\n", " train_dataset, test_dataset = get_train_test_datasets(config.get(\"data_path\", \".\"))\n", "\n", " # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu\n", " train_loader = idist.auto_dataloader(\n", " train_dataset,\n", " batch_size=config.get(\"batch_size\", 512),\n", " num_workers=config.get(\"num_workers\", 8),\n", " shuffle=True,\n", " drop_last=True,\n", " )\n", " config[\"num_iters_per_epoch\"] = len(train_loader)\n", "\n", " test_loader = idist.auto_dataloader(\n", " test_dataset,\n", " batch_size=2 * config.get(\"batch_size\", 512),\n", " num_workers=config.get(\"num_workers\", 8),\n", " shuffle=False,\n", " )\n", " return train_loader, test_loader\n", "\n", "\n", "def initialize(config):\n", " model = get_model(config[\"model\"])\n", " # Adapt model for distributed settings if configured\n", " model = idist.auto_model(model)\n", "\n", " optimizer = optim.SGD(\n", " model.parameters(),\n", " lr=config.get(\"learning_rate\", 0.1),\n", " momentum=config.get(\"momentum\", 0.9),\n", " weight_decay=config.get(\"weight_decay\", 1e-5),\n", " nesterov=True,\n", " )\n", " optimizer = idist.auto_optim(optimizer)\n", " criterion = nn.CrossEntropyLoss().to(idist.device())\n", "\n", " le = config[\"num_iters_per_epoch\"]\n", " lr_scheduler = StepLR(optimizer, step_size=le, gamma=0.9)\n", "\n", " return model, optimizer, criterion, lr_scheduler\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setup your trainer\n", "\n", "Trainer is defined as Ignite Engine with user's `train_step` logic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def create_trainer(model, optimizer, criterion, lr_scheduler, config):\n", "\n", " # Define any training logic for iteration update\n", " def train_step(engine, batch):\n", " x, y = batch[0].to(idist.device()), batch[1].to(idist.device())\n", "\n", " model.train()\n", " y_pred = model(x)\n", " loss = criterion(y_pred, y)\n", "\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", " lr_scheduler.step()\n", "\n", " return loss.item()\n", "\n", " # Define trainer engine\n", " trainer = Engine(train_step)\n", "\n", " if idist.get_rank() == 0:\n", " # Add any custom handlers\n", " @trainer.on(Events.ITERATION_COMPLETED(every=200))\n", " def save_checkpoint():\n", " fp = Path(config.get(\"output_path\", \"output\")) / \"checkpoint.pt\"\n", " torch.save(model.state_dict(), fp)\n", "\n", " # Add progress bar showing batch loss value\n", " ProgressBar().attach(trainer, output_transform=lambda x: {\"batch loss\": x})\n", "\n", " return trainer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setup training and validation\n", "\n", "Assemble all parts together in the training method configured with `config` dictionary :\n", "- instantiate dataflow, model, optimizer, criterion, lr scheduler\n", "- instantiate trainer, validation engine (`evaluator`)\n", "- setup validation phase and print results \n", "- setup tensorboard logger\n", "- start the training" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def training(local_rank, config):\n", "\n", " # Setup dataflow and\n", " train_loader, val_loader = get_dataflow(config)\n", " model, optimizer, criterion, lr_scheduler = initialize(config)\n", "\n", " # Setup model trainer and evaluator\n", " trainer = create_trainer(model, optimizer, criterion, lr_scheduler, config)\n", " evaluator = create_supervised_evaluator(model, metrics={\"accuracy\": Accuracy()}, device=idist.device())\n", "\n", " # Run model evaluation every 3 epochs and show results\n", " @trainer.on(Events.EPOCH_COMPLETED(every=3))\n", " def evaluate_model():\n", " state = evaluator.run(val_loader)\n", " if idist.get_rank() == 0:\n", " print(state.metrics)\n", "\n", " # Setup tensorboard experiment tracking\n", " if idist.get_rank() == 0:\n", " tb_logger = common.setup_tb_logging(\n", " config.get(\"output_path\", \"output\"), trainer, optimizer, evaluators={\"validation\": evaluator},\n", " )\n", "\n", " trainer.run(train_loader, max_epochs=config.get(\"max_epochs\", 3))\n", "\n", " if idist.get_rank() == 0:\n", " tb_logger.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run on your infrastructure" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext tensorboard\n", "%tensorboard --logdir=output" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2020-07-24 08:44:07,245 ignite.distributed.launcher.Parallel INFO: - Run '' in 1 processes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b71e1d83d024410ea3692ffbf444abfb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Failed download. Trying https -> http instead. Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ce0c2f1d11af49ae85d5c2f657533ef9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Extracting ./cifar-10-python.tar.gz to .\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2020-07-24 08:44:17,932 ignite.distributed.auto.auto_dataloader INFO: Use data loader kwargs for dataset 'Dataset CIFAR10': \n", "\t{'batch_size': 512, 'num_workers': 8, 'shuffle': True, 'drop_last': True, 'pin_memory': True}\n", "2020-07-24 08:44:17,933 ignite.distributed.auto.auto_dataloader INFO: Use data loader kwargs for dataset 'Dataset CIFAR10': \n", "\t{'batch_size': 1024, 'num_workers': 8, 'shuffle': False, 'pin_memory': True}\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=97.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=97.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=97.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "2020-07-24 08:44:45,937 ignite.distributed.launcher.Parallel INFO: End of run\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'accuracy': 0.513}\n" ] } ], "source": [ "# --- Single computation device ---\n", "# $ python main.py\n", "#\n", "if __name__ == \"__main__\" and not (in_colab or with_torchrun):\n", "\n", " backend = None\n", " nproc_per_node = None\n", " config = {\n", " \"model\": \"resnet18\",\n", " \"dataset\": \"cifar10\",\n", " }\n", "\n", " with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel:\n", " parallel.run(training, config)\n", "\n", "\n", "# --- Multiple GPUs ---\n", "# $ torchrun --nproc_per_node=2 main.py\n", "#\n", "if __name__ == \"__main__\" and with_torchrun:\n", "\n", " backend = \"nccl\" # or \"nccl\", \"gloo\"\n", " nproc_per_node = None\n", " config = {\n", " \"model\": \"resnet18\",\n", " \"dataset\": \"cifar10\",\n", " }\n", "\n", " with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel:\n", " parallel.run(training, config)\n", "\n", "# --- Multiple TPUs ---\n", "# In Colab\n", "#\n", "if in_colab:\n", "\n", " backend = \"xla-tpu\"\n", " nproc_per_node = 8\n", " config = {\n", " \"model\": \"resnet18\",\n", " \"dataset\": \"cifar10\",\n", " }\n", "\n", " with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel:\n", " parallel.run(training, config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other links\n", "\n", "- Full featured CIFAR10 example: https://github.com/pytorch/ignite/tree/master/examples/cifar10\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.6 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "vscode": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } } }, "nbformat": 4, "nbformat_minor": 4 } ignite-0.5.1/assets/tldr/teaser.py000066400000000000000000000143571465426447700171350ustar00rootroot00000000000000import os from pathlib import Path import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import StepLR from torchvision import datasets, models from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor import ignite.distributed as idist from ignite.contrib.engines import common from ignite.handlers import ProgressBar from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.metrics import Accuracy in_colab = "COLAB_TPU_ADDR" in os.environ with_torchrun = "WORLD_SIZE" in os.environ train_transform = Compose( [ Pad(4), RandomCrop(32, fill=128), RandomHorizontalFlip(), ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.23, 0.225)), ] ) test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.23, 0.225)),]) def get_train_test_datasets(path): # - Get train/test datasets if idist.get_rank() > 0: # Ensure that only rank 0 download the dataset idist.barrier() train_ds = datasets.CIFAR10(root=path, train=True, download=True, transform=train_transform) test_ds = datasets.CIFAR10(root=path, train=False, download=False, transform=test_transform) if idist.get_rank() == 0: # Ensure that only rank 0 download the dataset idist.barrier() return train_ds, test_ds def get_model(name): if name in models.__dict__: fn = models.__dict__[name] else: raise RuntimeError(f"Unknown model name {name}") return fn(num_classes=10) def get_dataflow(config): train_dataset, test_dataset = get_train_test_datasets(config.get("data_path", ".")) # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu train_loader = idist.auto_dataloader( train_dataset, batch_size=config.get("batch_size", 512), num_workers=config.get("num_workers", 8), shuffle=True, drop_last=True, ) config["num_iters_per_epoch"] = len(train_loader) test_loader = idist.auto_dataloader( test_dataset, batch_size=2 * config.get("batch_size", 512), num_workers=config.get("num_workers", 8), shuffle=False, ) return train_loader, test_loader def initialize(config): model = get_model(config["model"]) # Adapt model for distributed settings if configured model = idist.auto_model(model) optimizer = optim.SGD( model.parameters(), lr=config.get("learning_rate", 0.1), momentum=config.get("momentum", 0.9), weight_decay=config.get("weight_decay", 1e-5), nesterov=True, ) optimizer = idist.auto_optim(optimizer) criterion = nn.CrossEntropyLoss().to(idist.device()) le = config["num_iters_per_epoch"] lr_scheduler = StepLR(optimizer, step_size=le, gamma=0.9) return model, optimizer, criterion, lr_scheduler # slide 1 #################################################################### def create_trainer(model, optimizer, criterion, lr_scheduler, config): # Define any training logic for iteration update def train_step(engine, batch): x, y = batch[0].to(idist.device()), batch[1].to(idist.device()) model.train() y_pred = model(x) loss = criterion(y_pred, y) optimizer.zero_grad() loss.backward() optimizer.step() lr_scheduler.step() return loss.item() # Define trainer engine trainer = Engine(train_step) if idist.get_rank() == 0: # Add any custom handlers @trainer.on(Events.ITERATION_COMPLETED(every=200)) def save_checkpoint(): fp = Path(config.get("output_path", "output")) / "checkpoint.pt" torch.save(model.state_dict(), fp) # Add progress bar showing batch loss value ProgressBar().attach(trainer, output_transform=lambda x: {"batch loss": x}) return trainer # slide 2 #################################################################### def training(local_rank, config): # Setup dataflow and train_loader, val_loader = get_dataflow(config) model, optimizer, criterion, lr_scheduler = initialize(config) # Setup model trainer and evaluator trainer = create_trainer(model, optimizer, criterion, lr_scheduler, config) evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()}, device=idist.device()) # Run model evaluation every 3 epochs and show results @trainer.on(Events.EPOCH_COMPLETED(every=3)) def evaluate_model(): state = evaluator.run(val_loader) if idist.get_rank() == 0: print(state.metrics) # Setup tensorboard experiment tracking if idist.get_rank() == 0: tb_logger = common.setup_tb_logging( config.get("output_path", "output"), trainer, optimizer, evaluators={"validation": evaluator}, ) trainer.run(train_loader, max_epochs=config.get("max_epochs", 3)) if idist.get_rank() == 0: tb_logger.close() # slide 3 #################################################################### # Simply run everything on your infrastructure # --- Single computation device --- # $ python main.py # if __name__ == "__main__" and not (in_colab or with_torchrun): backend = None nproc_per_node = None config = { "model": "resnet18", "dataset": "cifar10", } with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel: parallel.run(training, config) # --- Multiple GPUs --- # $ torchrun --nproc_per_node=2 main.py # if __name__ == "__main__" and with_torchrun: backend = "nccl" # or "nccl", "gloo", ... nproc_per_node = None config = { "model": "resnet18", "dataset": "cifar10", } with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel: parallel.run(training, config) # --- Multiple TPUs --- # In Colab # if in_colab: backend = "xla-tpu" nproc_per_node = 8 config = { "model": "resnet18", "dataset": "cifar10", } with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel: parallel.run(training, config) # Full featured CIFAR10 example: # https://github.com/pytorch/ignite/tree/master/examples/cifar10 ignite-0.5.1/codecov.yml000066400000000000000000000003331465426447700151630ustar00rootroot00000000000000coverage: precision: 2 round: down range: "95...100" status: patch: default: target: 90 project: default: threshold: 1% changes: false comment: false ignore: - "tests/" ignite-0.5.1/conda.recipe/000077500000000000000000000000001465426447700153515ustar00rootroot00000000000000ignite-0.5.1/conda.recipe/build_and_upload.sh000066400000000000000000000017401465426447700211740ustar00rootroot00000000000000#!/bin/bash echo "Build and upload Conda binaries" # ANACONDA_TOKEN should be provided # How to generate ANACONDA_TOKEN: https://docs.anaconda.com/anaconda-cloud/user-guide/tasks/work-with-accounts#creating-access-tokens # https://conda.io/docs/user-guide/tasks/build-packages/install-conda-build.html if [ -z $ANACONDA_TOKEN ]; then echo "Can not find ANACONDA_TOKEN env variable" echo "Please, export ANACONDA_TOKEN= before calling this script" exit 1 fi if [ -z $UPLOAD_USER ]; then echo "Can not find UPLOAD_USER env variable" echo "Please, export UPLOAD_USER= before calling this script" exit 1 fi set -xeu conda install -y conda-build conda-verify anaconda-client conda config --set anaconda_upload no conda build --no-test --output-folder conda_build conda.recipe -c pytorch # Upload to Anaconda conda config --set anaconda_upload yes ls conda_build/*/*.tar.bz2 | xargs -I {} anaconda -v -t $ANACONDA_TOKEN upload -u $UPLOAD_USER {} ignite-0.5.1/conda.recipe/meta.yaml000066400000000000000000000012621465426447700171640ustar00rootroot00000000000000{% set data = load_setup_py_data() %} package: name: ignite version: {{ data['version'] }} source: path: .. build: number: 0 noarch: python script: python setup.py install --single-version-externally-managed --record=record.txt # https://conda.io/docs/user-guide/tasks/build-packages/define-metadata.html#export-runtime-requirements requirements: build: - python>=3.6 - setuptools - pytorch>=1.3 run: - python>=3.6 - pytorch>=1.3 test: imports: - ignite - ignite.engine - ignite.handlers - ignite.metrics - ignite.contrib about: home: {{ data['url'] }} license: {{ data['license'] }} summary: {{ data['description'] }} ignite-0.5.1/docker/000077500000000000000000000000001465426447700142665ustar00rootroot00000000000000ignite-0.5.1/docker/README.md000066400000000000000000000064631465426447700155560ustar00rootroot00000000000000# Docker for users We provide Dockerfiles in order to build containerized execution environment that ease the use of Ignite for computer vision and NLP tasks. These images are also provided with the following Horovod configuration: ```bash Available Frameworks: [ ] TensorFlow [X] PyTorch [ ] MXNet Available Controllers: [ ] MPI [X] Gloo Available Tensor Operations: [X] NCCL [ ] DDL [ ] CCL [ ] MPI [X] Gloo ``` ## Installation - [main/Dockerfile.base](main/Dockerfile.base): latest stable PyTorch, Ignite with minimal dependencies - `docker pull pytorchignite/base:latest` - [main/Dockerfile.vision](main/Dockerfile.vision): base image with useful computer vision libraries - `docker pull pytorchignite/vision:latest` - [main/Dockerfile.nlp](main/Dockerfile.nlp): base image with useful NLP libraries - `docker pull pytorchignite/nlp:latest` - [main/Dockerfile.apex](main/Dockerfile.apex): multi-stage NVIDIA/apex build with latest Pytorch, Ignite image with minimal dependencies - `docker pull pytorchignite/apex:latest` - [main/Dockerfile.apex-vision](main/Dockerfile.nlp): base apex with useful computer vision libraries - `docker pull pytorchignite/apex-vision:latest` - [main/Dockerfile.apex-nlp](main/Dockerfile.nlp): base apex with useful NLP libraries - `docker pull pytorchignite/apex-nlp:latest` - [hvd/Dockerfile.hvd-base](hvd/Dockerfile.hvd-base): multi-stage Horovod build with latest stable PyTorch, Ignite with minimal dependencies - `docker pull pytorchignite/hvd-base:latest` - [hvd/Dockerfile.hvd-vision](hvd/Dockerfile.hvd-vision): base Horovod image with useful computer vision libraries - `docker pull pytorchignite/hvd-vision:latest` - [hvd/Dockerfile.hvd-nlp](hvd/Dockerfile.hvd-nlp): base Horovod image with useful NLP libraries - `docker pull pytorchignite/hvd-nlp:latest` - [hvd/Dockerfile.hvd-apex](hvd/Dockerfile.hvd-apex): multi-stage NVIDIA/apex and Horovod build with latest Pytorch, Ignite image with minimal dependencies - `docker pull pytorchignite/hvd-apex:latest` - [hvd/Dockerfile.hvd-apex-vision](hvd/Dockerfile.hvd-apex-vision): base Horovod apex with useful computer vision libraries - `docker pull pytorchignite/hvd-apex-vision:latest` - [hvd/Dockerfile.hvd-apex-nlp](hvd/Dockerfile.hvd-apex-nlp): base Horovod apex with useful NLP libraries - `docker pull pytorchignite/hvd-apex-nlp:latest` **Deprecated images** (no version updates) - [msdp/Dockerfile.msdp-apex-base](msdp/Dockerfile.msdp-apex): multi-stage MSDeepSpeed build with latest Pytorch, Ignite image with minimal dependencies - `docker pull pytorchignite/msdp-apex:latest` - [msdp/Dockerfile.msdp-apex-vision](msdp/Dockerfile.msdp-apex-vision): base MSDeepSpeed build with useful computer vision libraries - `docker pull pytorchignite/msdp-apex-vision:latest` - [msdp/Dockerfile.msdp-apex-nlp](msdp/Dockerfile.msdp-apex-nlp): base MSDeepSpeed build with useful NLP libraries - `docker pull pytorchignite/msdp-apex-nlp:latest` ## How to use ```bash docker run -it -v $PWD:/workspace/project --network=host --ipc=host pytorchignite/base:latest /bin/bash ``` ## Building the image yourself Dockerfiles are supplied to build images with dependencies that ease the use of Ignite for computer vision / NLP tasks: ```bash cd main docker build -t pytorchignite/base:latest -f Dockerfile.base . ``` ignite-0.5.1/docker/build.sh000066400000000000000000000050571465426447700157300ustar00rootroot00000000000000#!/bin/bash if [ -z "$1" ]; then echo "Folder name should be provided. Usage, for example: bash build.sh hvd hvd-apex" exit 1 fi if [ -z "$2" ]; then echo "Image name should be provided. Usage, for example: bash build.sh hvd hvd-apex" exit 1 fi folder_name=$1 image_name=$2 echo "Build ${folder_name}/Dockerfile.${image_name} PyTorch-Ignite image" # Start script from ignite docker folder if [ ! -d ${folder_name} ]; then echo "Can not find ${folder_name} folder" exit 1 fi # Check Dockerfile exists if [ ! -f ${folder_name}/Dockerfile.${image_name} ]; then echo "Can not find ${folder_name}/Dockerfile.${image_name}" exit 1 fi if [ -z "${PTH_VERSION}" ]; then echo "PTH_VERSION is not set. Please, call the script with: PTH_VERSION=... bash build.sh hvd hvd-apex" exit 1 fi if [ ${folder_name} == "hvd" ] && [ -z "${HVD_VERSION}" ]; then echo "HVD_VERSION is not set" exit 1 fi if [ ${folder_name} == "msdp" ] && [ -z "${MSDP_VERSION}" ]; then echo "MSDP_VERSION is not set" exit 1 fi retry() { cmd=$1 msg=$2 counter=0 limit=3 while [ "$counter" -lt "$limit" ]; do echo "(Re-)Try: $cmd" bash -c "$cmd" && break echo $msg counter="$(( $counter + 1 ))" done if [ $counter -eq $limit ]; then exit 1 fi } curr_dir=$PWD cd $curr_dir/${folder_name} set -eu image_tag="" pth_version=${PTH_VERSION} opt_build_args="" if [ -n "${HVD_VERSION:-}" ]; then opt_build_args="${opt_build_args} --build-arg HVD_VERSION=${HVD_VERSION}" fi if [ -n "${MSDP_VERSION:-}" ]; then opt_build_args="${opt_build_args} --build-arg MSDP_VERSION=${MSDP_VERSION}" fi echo "opt_build_args: ${opt_build_args}" retry "docker build --build-arg PTH_VERSION=${pth_version} ${opt_build_args} -t pytorchignite/${image_name}:latest -f Dockerfile.${image_name} ." "\nBuild failed: ${image_name}" if [ -z $image_tag ]; then image_tag=`docker run --rm -i pytorchignite/${image_name}:latest python -c "import torch; import ignite; print(torch.__version__ + \"-\" + ignite.__version__, end=\"\")"` fi docker tag pytorchignite/${image_name}:latest pytorchignite/${image_name}:${image_tag} cd $curr_dir # Test built image echo "Show installed packages:" docker run --rm -i pytorchignite/${image_name}:${image_tag} pip list echo "Test pytorchignite/${image_name}:${image_tag}" docker run --rm -i -v $PWD:/ws -w /ws -e HVD_VERSION=${HVD_VERSION:-} -e MSDP_VERSION=${MSDP_VERSION:-} pytorchignite/${image_name}:${image_tag} /bin/bash -c "python test_image.py pytorchignite/${image_name}:${image_tag}" echo "OK"ignite-0.5.1/docker/docker.cfg000066400000000000000000000002301465426447700162110ustar00rootroot00000000000000[DEFAULT] build_docker_image_pytorch_version = 2.4.0-cuda12.4-cudnn9 build_docker_image_hvd_version = v0.28.1 build_docker_image_msdp_version = v0.14.0 ignite-0.5.1/docker/hvd/000077500000000000000000000000001465426447700150475ustar00rootroot00000000000000ignite-0.5.1/docker/hvd/Dockerfile.hvd-apex000066400000000000000000000061001465426447700205510ustar00rootroot00000000000000# Multi-stage build # Dockerfile.hvd-apex ARG PTH_VERSION # 1/Building apex with pytorch:*-devel FROM pytorch/pytorch:${PTH_VERSION}-devel AS apex-hvd-builder ENV CUDA_HOME=/usr/local/cuda # Install git RUN apt-get update && apt-get install -y --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Build apex RUN echo "Setup NVIDIA Apex" && \ tmp_apex_path="/tmp/apex" && \ rm -rf $tmp_apex_path && \ git clone https://github.com/NVIDIA/apex $tmp_apex_path && \ cd $tmp_apex_path && \ pip install packaging && \ pip wheel -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" . ARG HVD_VERSION # Build Horovod RUN apt-get update && apt-get install -y git && \ git clone --recursive --depth 1 --branch ${HVD_VERSION} https://github.com/horovod/horovod.git /horovod && \ conda install -y cmake nccl -c conda-forge && \ cd /horovod && \ # temporary -std=c++17 fix sed -i "s/CMAKE_CXX_STANDARD 14/CMAKE_CXX_STANDARD 17/g" CMakeLists.txt && \ sed -i "s/CMAKE_CXX_STANDARD 14/CMAKE_CXX_STANDARD 17/g" horovod/torch/CMakeLists.txt && \ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_LINK=SHARED HOROVOD_WITHOUT_MPI=1 HOROVOD_WITH_PYTORCH=1 pip wheel --no-cache-dir . && \ rm -rf /var/lib/apt/lists/* # Build runtime image FROM pytorch/pytorch:${PTH_VERSION}-runtime # Apex COPY --from=apex-hvd-builder /tmp/apex/apex-*.whl /tmp/apex/ RUN pip install --no-cache-dir /tmp/apex/apex-*.whl && \ rm -fr /tmp/apex # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ apt-get -y install --no-install-recommends tzdata git && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get autoremove -y && \ apt-get clean -y && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm \ fire # Replace pillow with pillow-simd RUN apt-get update && apt-get -y install --no-install-recommends g++ && \ pip uninstall -y pillow && \ CC="cc -mavx2" pip install --upgrade --no-cache-dir --force-reinstall pillow-simd && \ apt-get remove -y g++ && \ apt-get autoremove -y && \ rm -rf /var/lib/apt/lists/* # Checkout Ignite examples only RUN mkdir -p pytorch-ignite-examples && \ cd pytorch-ignite-examples && \ git init && \ git config core.sparsecheckout true && \ echo examples >> .git/info/sparse-checkout && \ git remote add -f origin https://github.com/pytorch/ignite.git && \ git pull origin master && \ # rm very large .git folder rm -rf .git # Horovod RUN conda install -y nccl -c conda-forge ENV LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH COPY --from=apex-hvd-builder /horovod/horovod-*.whl /horovod/ RUN cd /horovod && \ pip install --no-cache-dir horovod-*.whl && \ rm -fr /horovodignite-0.5.1/docker/hvd/Dockerfile.hvd-apex-nlp000066400000000000000000000003571465426447700213500ustar00rootroot00000000000000# Dockerfile.hvd-apex-nlp FROM pytorchignite/hvd-apex:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir transformers \ spacy \ nltk ignite-0.5.1/docker/hvd/Dockerfile.hvd-apex-vision000066400000000000000000000006651465426447700220700ustar00rootroot00000000000000# Dockerfile.hvd-apex-vision FROM pytorchignite/hvd-apex:latest # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python-headless \ py_config_runner \ clearml ignite-0.5.1/docker/hvd/Dockerfile.hvd-base000066400000000000000000000045001465426447700205300ustar00rootroot00000000000000# Multi-stage build # Dockerfile.hvd-base ARG PTH_VERSION FROM pytorch/pytorch:${PTH_VERSION}-devel as builder ARG HVD_VERSION # Build Horovod RUN apt-get update && apt-get install -y git && \ git clone --recursive --depth 1 --branch ${HVD_VERSION} https://github.com/horovod/horovod.git /horovod && \ conda install -y cmake nccl -c conda-forge && \ cd /horovod && \ # temporary -std=c++17 fix sed -i "s/CMAKE_CXX_STANDARD 14/CMAKE_CXX_STANDARD 17/g" CMakeLists.txt && \ sed -i "s/CMAKE_CXX_STANDARD 14/CMAKE_CXX_STANDARD 17/g" horovod/torch/CMakeLists.txt && \ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_LINK=SHARED HOROVOD_WITHOUT_MPI=1 HOROVOD_WITH_PYTORCH=1 pip wheel --no-cache-dir . && \ rm -rf /var/lib/apt/lists/* # Build runtime image FROM pytorch/pytorch:${PTH_VERSION}-runtime # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ apt-get -y install --no-install-recommends tzdata git && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get autoremove -y && \ apt-get clean -y && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm \ fire # Replace pillow with pillow-simd RUN apt-get update && apt-get -y install --no-install-recommends g++ && \ pip uninstall -y pillow && \ CC="cc -mavx2" pip install --upgrade --no-cache-dir --force-reinstall pillow-simd && \ apt-get remove -y g++ && \ apt-get autoremove -y && \ rm -rf /var/lib/apt/lists/* # Checkout Ignite examples only RUN mkdir -p pytorch-ignite-examples && \ cd pytorch-ignite-examples && \ git init && \ git config core.sparsecheckout true && \ echo examples >> .git/info/sparse-checkout && \ git remote add -f origin https://github.com/pytorch/ignite.git && \ git pull origin master && \ # rm very large .git folder rm -rf .git # Horovod RUN conda install -y nccl -c conda-forge ENV LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH COPY --from=builder /horovod/horovod-*.whl /horovod/ RUN cd /horovod && \ pip install --no-cache-dir horovod-*.whl && \ rm -fr /horovodignite-0.5.1/docker/hvd/Dockerfile.hvd-nlp000066400000000000000000000003521465426447700204100ustar00rootroot00000000000000# Dockerfile.hvd-nlp FROM pytorchignite/hvd-base:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir transformers \ spacy \ nltk ignite-0.5.1/docker/hvd/Dockerfile.hvd-vision000066400000000000000000000006601465426447700211300ustar00rootroot00000000000000# Dockerfile.hvd-vision FROM pytorchignite/hvd-base:latest # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python-headless \ py_config_runner \ clearml ignite-0.5.1/docker/main/000077500000000000000000000000001465426447700152125ustar00rootroot00000000000000ignite-0.5.1/docker/main/Dockerfile.apex000066400000000000000000000042151465426447700201420ustar00rootroot00000000000000# Multi-stage build # Dockerfile.apex ARG PTH_VERSION # 1/Building apex with pytorch:*-devel FROM pytorch/pytorch:${PTH_VERSION}-devel AS apex-builder ENV CUDA_HOME=/usr/local/cuda # Install git RUN apt-get update && apt-get install -y --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Build apex RUN echo "Setup NVIDIA Apex" && \ tmp_apex_path="/tmp/apex" && \ rm -rf $tmp_apex_path && \ git clone https://github.com/NVIDIA/apex $tmp_apex_path && \ cd $tmp_apex_path && \ pip install packaging && \ pip wheel -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" . # 2/ Build the runtime image FROM pytorch/pytorch:${PTH_VERSION}-runtime COPY --from=apex-builder /tmp/apex/apex-*.whl /tmp/apex/ RUN pip install --no-cache-dir /tmp/apex/apex-*.whl && \ rm -fr /tmp/apex # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ apt-get -y install --no-install-recommends tzdata git && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get autoremove -y && \ apt-get clean -y && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm \ fire # replace pillow with pillow-simd RUN apt-get update && apt-get -y install --no-install-recommends g++ && \ pip uninstall -y pillow && \ CC="cc -mavx2" pip install --upgrade --no-cache-dir --force-reinstall pillow-simd && \ apt-get remove -y g++ && \ apt-get autoremove -y && \ rm -rf /var/lib/apt/lists/* # Checkout Ignite examples only RUN mkdir -p pytorch-ignite-examples && \ cd pytorch-ignite-examples && \ git init && \ git config core.sparsecheckout true && \ echo examples >> .git/info/sparse-checkout && \ git remote add -f origin https://github.com/pytorch/ignite.git && \ git pull origin master && \ # rm very large .git folder rm -rf .gitignite-0.5.1/docker/main/Dockerfile.apex-nlp000066400000000000000000000003471465426447700207330ustar00rootroot00000000000000# Dockerfile.apex-nlp FROM pytorchignite/apex:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir transformers \ spacy \ nltk ignite-0.5.1/docker/main/Dockerfile.apex-vision000066400000000000000000000006551465426447700214530ustar00rootroot00000000000000# Dockerfile.apex-vision FROM pytorchignite/apex:latest # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python-headless \ py_config_runner \ clearml ignite-0.5.1/docker/main/Dockerfile.base000066400000000000000000000024751465426447700201250ustar00rootroot00000000000000# Dockerfile.base ARG PTH_VERSION FROM pytorch/pytorch:${PTH_VERSION}-runtime # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ apt-get -y install --no-install-recommends tzdata git && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get autoremove -y && \ apt-get clean -y && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm \ fire # Replace pillow with pillow-simd RUN apt-get update && apt-get -y install --no-install-recommends g++ && \ pip uninstall -y pillow && \ CC="cc -mavx2" pip install --upgrade --no-cache-dir --force-reinstall pillow-simd && \ apt-get remove -y g++ && \ apt-get autoremove -y && \ rm -rf /var/lib/apt/lists/* # Checkout Ignite examples only RUN mkdir -p pytorch-ignite-examples && \ cd pytorch-ignite-examples && \ git init && \ git config core.sparsecheckout true && \ echo examples >> .git/info/sparse-checkout && \ git remote add -f origin https://github.com/pytorch/ignite.git && \ git pull origin master && \ # rm very large .git folder rm -rf .gitignite-0.5.1/docker/main/Dockerfile.nlp000066400000000000000000000003411465426447700177720ustar00rootroot00000000000000# Dockerfile.nlp FROM pytorchignite/base:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir transformers \ spacy \ nltkignite-0.5.1/docker/main/Dockerfile.vision000066400000000000000000000006501465426447700205130ustar00rootroot00000000000000# Dockerfile.vision FROM pytorchignite/base:latest # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python-headless \ py_config_runner \ clearml ignite-0.5.1/docker/msdp/000077500000000000000000000000001465426447700152315ustar00rootroot00000000000000ignite-0.5.1/docker/msdp/Dockerfile.msdp-apex000066400000000000000000000055401465426447700211240ustar00rootroot00000000000000# Multi-stage build # Dockerfile.msdp-apex ARG PTH_VERSION # 1/Building apex with pytorch:*-devel FROM pytorch/pytorch:${PTH_VERSION}-devel AS apex-msdp-builder ENV CUDA_HOME=/usr/local/cuda # Install git RUN apt-get update && apt-get install -y --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Build apex RUN echo "Setup NVIDIA Apex" && \ tmp_apex_path="/tmp/apex" && \ rm -rf $tmp_apex_path && \ git clone https://github.com/NVIDIA/apex $tmp_apex_path && \ cd $tmp_apex_path && \ pip install packaging && \ pip wheel -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" . # For pip --use-feature option RUN python -m pip install --upgrade pip # MSDeepSpeed ARG MSDP_VERSION RUN conda install cmake llvmdev=9.0.1 -c conda-forge RUN git clone https://github.com/microsoft/DeepSpeed.git -b ${MSDP_VERSION} /tmp/DeepSpeed && cd /tmp/DeepSpeed && \ ./install.sh --allow_sudo # Build using devel image FROM pytorch/pytorch:${PTH_VERSION}-devel # Apex COPY --from=apex-msdp-builder /tmp/apex/apex-*.whl /tmp/apex/ RUN pip install --no-cache-dir /tmp/apex/apex-*.whl && \ rm -fr /tmp/apex # MSDeepSpeed RUN conda install cmake llvmdev=9.0.1 -c conda-forge COPY --from=apex-msdp-builder /tmp/DeepSpeed/dist/deepspeed-*.whl /msdp/ RUN cd /msdp && export CUDA_HOME=/usr/local/cuda && \ pip install --no-cache-dir deepspeed-*.whl && \ rm -fr /msdp # Renew nvidia signing key # https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/ RUN apt-key del 7fa2af80 && \ rm /etc/apt/sources.list.d/nvidia-ml.list && \ apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ apt-get -y install --no-install-recommends tzdata git && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get autoremove -y && \ apt-get clean -y && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm \ fire # Replace pillow with pillow-simd RUN pip uninstall -y pillow && \ CC="cc -mavx2" pip install --upgrade --no-cache-dir --force-reinstall pillow-simd # Checkout Ignite examples only RUN mkdir -p pytorch-ignite-examples && \ cd pytorch-ignite-examples && \ git init && \ git config core.sparsecheckout true && \ echo examples >> .git/info/sparse-checkout && \ git remote add -f origin https://github.com/pytorch/ignite.git && \ git pull origin master && \ # rm very large .git folder rm -rf .gitignite-0.5.1/docker/msdp/Dockerfile.msdp-apex-nlp000066400000000000000000000004461465426447700217130ustar00rootroot00000000000000# Dockerfile.msdp-apex-nlp FROM pytorchignite/msdp-apex:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir transformers \ spacy \ nltk \ torchtext ignite-0.5.1/docker/msdp/Dockerfile.msdp-apex-vision000066400000000000000000000006671465426447700224360ustar00rootroot00000000000000# Dockerfile.msdp-apex-vision FROM pytorchignite/msdp-apex:latest # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python-headless \ py_config_runner \ clearml ignite-0.5.1/docker/push_all.sh000066400000000000000000000045741465426447700164430ustar00rootroot00000000000000#!/bin/bash echo "Push all PyTorch-Ignite docker images" if [ -z $DOCKER_USER ]; then echo "Can not find DOCKER_USER env variable" echo "Please, export DOCKER_USER= before calling this script" exit 1 fi if [ -z $DOCKER_TOKEN ]; then echo "Can not find DOCKER_TOKEN env variable" echo "Please, export DOCKER_TOKEN= before calling this script" exit 1 fi if [ -z "$1" ]; then push_selected_image="all" else push_selected_image="$1" fi set -eu echo $DOCKER_TOKEN | docker login --username=$DOCKER_USER --password-stdin set -xeu if [ ${push_selected_image} == "all" ]; then image_name="base" image_tag=`docker run --rm -i pytorchignite/${image_name}:latest python -c "import torch; import ignite; print(torch.__version__ + \"-\" + ignite.__version__, end=\"\")"` for image_name in "base" "vision" "nlp" "apex" "apex-vision" "apex-nlp" do docker push pytorchignite/${image_name}:latest docker push pytorchignite/${image_name}:${image_tag} done image_name="hvd-base" image_tag=`docker run --rm -i pytorchignite/${image_name}:latest python -c "import torch; import ignite; print(torch.__version__ + \"-\" + ignite.__version__, end=\"\")"` for image_name in "hvd-base" "hvd-vision" "hvd-nlp" "hvd-apex" "hvd-apex-vision" "hvd-apex-nlp" do docker push pytorchignite/${image_name}:latest docker push pytorchignite/${image_name}:${image_tag} done # DEPRECATED due to no activity # image_name="msdp-apex" # image_tag=`docker run --rm -i pytorchignite/${image_name}:latest python -c "import torch; import ignite; print(torch.__version__ + \"-\" + ignite.__version__, end=\"\")"` # for image_name in "msdp-apex" "msdp-apex-vision" "msdp-apex-nlp" # do # docker push pytorchignite/${image_name}:latest # docker push pytorchignite/${image_name}:${image_tag} # done else image_name=${push_selected_image} image_tag=`docker run --rm -i pytorchignite/${image_name}:latest python -c "import torch; import ignite; print(torch.__version__ + \"-\" + ignite.__version__, end=\"\")"` docker push pytorchignite/${image_name}:latest docker push pytorchignite/${image_name}:${image_tag} fi # If use locally, mind to clean dangling images # docker images | grep 'pytorchignite\|' | awk '{print $3}' | xargs docker rmi -f # or # docker image prune ignite-0.5.1/docker/test_image.py000066400000000000000000000036771465426447700167760ustar00rootroot00000000000000# # Tests : # For all images # can import torch and its version == required one # can import ignite and its version == required one # for all -vision images # can import opencv without driver issue # for all horovod images # can import horovod and its version == required one # import argparse import importlib import os def check_package(package_name, expected_version=None): mod = importlib.import_module(package_name) if expected_version is not None: assert hasattr(mod, "__version__"), f"Imported package {package_name} does not have __version__ attribute" version = mod.__version__ assert ( version == expected_version ), f"Version mismatch for package {package_name}: got {version} but expected {expected_version}" if __name__ == "__main__": parser = argparse.ArgumentParser("Check docker image script") parser.add_argument("image", type=str, help="Docker image to check") args = parser.parse_args() docker_image_name = args.image name, version = docker_image_name.split(":") assert version != "latest", version torch_version, ignite_version = version.split("-") _, image_type = name.split("/") check_package("torch", expected_version=torch_version) check_package("ignite", expected_version=ignite_version) if "hvd" in image_type: assert "HVD_VERSION" in os.environ val = os.environ["HVD_VERSION"] hvd_version = val if val[0] != "v" else val[1:] check_package("horovod", expected_version=hvd_version) if "msdp" in image_type: assert "MSDP_VERSION" in os.environ val = os.environ["MSDP_VERSION"] hvd_version = val if val[0] != "v" else val[1:] check_package("deepspeed", expected_version=hvd_version) if "vision" in image_type: check_package("cv2") if "nlp" in image_type: check_package("transformers") if "apex" in image_type: check_package("apex") ignite-0.5.1/docs/000077500000000000000000000000001465426447700137475ustar00rootroot00000000000000ignite-0.5.1/docs/Makefile000066400000000000000000000030131465426447700154040ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = -j auto -WT --keep-going --color SPHINXBUILD = sphinx-build SPHINXPROJ = ignite SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) docset: html doc2dash --name $(SPHINXPROJ) --icon $(SOURCEDIR)/_static/img/pytorch-logo-flame.png --enable-js --online-redirect-url https://pytorch.org/ignite/ --force $(BUILDDIR)/html/ # Manually fix because Zeal doesn't deal well with `icon.png`-only at 2x resolution. cp $(SPHINXPROJ).docset/icon.png $(SPHINXPROJ).docset/icon@2x.png convert $(SPHINXPROJ).docset/icon@2x.png -resize 16x16 $(SPHINXPROJ).docset/icon.png rebuild: rm -rf source/generated && make clean && make html clean: @echo "Cleaning up..." python -c "import shutil; shutil.rmtree('$(BUILDDIR)', ignore_errors=True)" python -c "import shutil; shutil.rmtree('$(SOURCEDIR)/generated', ignore_errors=True)" python -c "import os; [os.remove(f) for f in os.listdir('.') if f.endswith('.pyc')]" python -c "import shutil; import os; [shutil.rmtree(f) for f in os.listdir('.') if f == '__pycache__' and os.path.isdir(f)]" .PHONY: help Makefile docset # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ignite-0.5.1/docs/make.bat000066400000000000000000000014121465426447700153520ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build set SPHINXPROJ=ignite if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. 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Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . exclude_patterns = [] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_title = f"{project} {version} Documentation" html_theme = "pytorch_sphinx_theme" html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] html_theme_options = { "canonical_url": "https://pytorch.org/ignite/", "collapse_navigation": False, "display_version": True, "logo_only": True, "navigation_with_keys": True, } html_logo = "_templates/_static/img/ignite_logo.svg" html_favicon = "_templates/_static/img/ignite_logomark.svg" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static", "_templates/_static"] html_context = { "extra_css_files": [ # 'https://fonts.googleapis.com/css?family=Lato', # '_static/css/pytorch_theme.css' "_static/css/ignite_theme.css", "https://cdn.jsdelivr.net/npm/@docsearch/css@3", ], } html_last_updated_fmt = "%m/%d/%Y, %X" html_permalinks = True html_permalinks_icon = "#" # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = "ignitedoc" # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, "ignite.tex", "ignite Documentation", "Torch Contributors", "manual"), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "ignite", "ignite Documentation", [author], 1)] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "ignite", "ignite Documentation", author, "ignite", "One line description of project.", "Miscellaneous", ), ] # -- Extension configuration ------------------------------------------------- # -- Options for intersphinx extension --------------------------------------- # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "torch": ("https://pytorch.org/docs/stable/", None), } # -- Options for todo extension ---------------------------------------------- # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # -- Type hints configs ------------------------------------------------------ autodoc_inherit_docstrings = True autoclass_content = "both" autodoc_typehints = "description" napoleon_attr_annotations = True # -- Autosummary patch to get list of a classes, funcs automatically ---------- from importlib import import_module from inspect import getmembers, isclass, isfunction from docutils.parsers.rst import directives from docutils.statemachine import StringList from sphinx.ext.autosummary import Autosummary class AutolistAutosummary(Autosummary): """Autosummary with autolisting for modules. By default it tries to import all public names (__all__), otherwise import all classes and/or functions in a module. Options: - :autolist: option to get list of classes and functions from currentmodule. - :autolist-classes: option to get list of classes from currentmodule. - :autolist-functions: option to get list of functions from currentmodule. Example Usage: .. currentmodule:: ignite.metrics .. autosummary:: :nosignatures: :autolist: """ # Add new option _option_spec = Autosummary.option_spec.copy() _option_spec.update( { "autolist": directives.unchanged, "autolist-classes": directives.unchanged, "autolist-functions": directives.unchanged, } ) option_spec = _option_spec def run(self): for auto in ("autolist", "autolist-classes", "autolist-functions"): if auto in self.options: # Get current module name module_name = self.env.ref_context.get("py:module") # Import module module = import_module(module_name) # Get public names (if possible) try: names = getattr(module, "__all__") except AttributeError: # Get classes defined in the module cls_names = [ name[0] for name in getmembers(module, isclass) if name[-1].__module__ == module_name and not (name[0].startswith("_")) ] # Get functions defined in the module fn_names = [ name[0] for name in getmembers(module, isfunction) if (name[-1].__module__ == module_name) and not (name[0].startswith("_")) ] names = cls_names + fn_names # It may happen that module doesn't have any defined class or func if not names: names = [name[0] for name in getmembers(module)] # Filter out members w/o doc strings filtered_names = [] for name in names: try: if not name.startswith("_") and getattr(module, name).__doc__ is not None: filtered_names.append(name) except AttributeError: continue names = filtered_names if auto == "autolist": # Get list of all classes and functions inside module names = [ name for name in names if (isclass(getattr(module, name)) or isfunction(getattr(module, name))) ] else: if auto == "autolist-classes": # Get only classes check = isclass elif auto == "autolist-functions": # Get only functions check = isfunction else: raise NotImplementedError names = [name for name in names if check(getattr(module, name))] # Update content self.content = StringList(names) return super().run() # --- autosummary config ----------------------------------------------------- autosummary_generate = True # --- nitpicky config : check internal links are correct or not -------------- nitpicky = True # ignore links which can't be referenced nitpick_ignore = [ ("py:class", ".."), ("py:class", "TextIO"), ("py:class", "torch.device"), ("py:class", "_MpDeviceLoader"), ("py:class", "torch.nn.modules.module.Module"), ("py:class", "torch.optim.optimizer.Optimizer"), ("py:class", "torch.utils.data.dataset.Dataset"), ("py:class", "torch.utils.data.sampler.BatchSampler"), ("py:class", "torch.cuda.amp.grad_scaler.GradScaler"), ("py:class", "torch.optim.lr_scheduler._LRScheduler"), ("py:class", "torch.optim.lr_scheduler.LRScheduler"), ("py:class", "torch.utils.data.dataloader.DataLoader"), ] linkcheck_ignore = [ "https://github.com/fossasia/visdom#visdom-arguments-python-only", "https://github.com/pytorch/ignite/tree/master/examples/cifar10#check-resume-training", "https://github.com/pytorch/ignite/tree/master/examples/mnist#training-save--resume", "https://machinelearningmastery.com/gentle-introduction-backpropagation-time/", ] def setup(app): app.add_directive("autosummary", AutolistAutosummary, override=True) ignite-0.5.1/docs/source/contrib/000077500000000000000000000000001465426447700167075ustar00rootroot00000000000000ignite-0.5.1/docs/source/contrib/engines.rst000066400000000000000000000012051465426447700210670ustar00rootroot00000000000000ignite.contrib.engines ====================== Contribution module of engines and helper tools: ignite.contrib.engines.tbptt .. currentmodule:: ignite.contrib.engines.tbptt .. autosummary:: :nosignatures: :autolist: ignite.contrib.engines.common .. currentmodule:: ignite.contrib.engines.common .. autosummary:: :nosignatures: :autolist: Truncated Backpropagation Throught Time --------------------------------------- .. automodule:: ignite.contrib.engines.tbptt :members: Helper methods to setup trainer/evaluator ----------------------------------------- .. automodule:: ignite.contrib.engines.common :members: ignite-0.5.1/docs/source/contrib/handlers.rst000066400000000000000000000016101465426447700212370ustar00rootroot00000000000000ignite.contrib.handlers ======================= Contribution module of handlers Parameter scheduler [deprecated] -------------------------------- .. deprecated:: 0.4.4 Use :class:`~ignite.handlers.param_scheduler.ParamScheduler` instead, will be removed in version 0.6.0. Was moved to :ref:`param-scheduler-label`. LR finder [deprecated] ---------------------- .. deprecated:: 0.4.4 Use :class:`~ignite.handlers.lr_finder.FastaiLRFinder` instead, will be removed in version 0.6.0. Time profilers [deprecated] --------------------------- .. deprecated:: 0.4.6 Use :class:`~ignite.handlers.time_profilers.BasicTimeProfiler` instead, will be removed in version 0.6.0. Use :class:`~ignite.handlers.time_profilers.HandlersTimeProfiler` instead, will be removed in version 0.6.0. Loggers [deprecated] -------------------- .. deprecated:: 0.5.0 Loggers moved to :ref:`Loggers`. ignite-0.5.1/docs/source/contrib/metrics.rst000066400000000000000000000005351465426447700211120ustar00rootroot00000000000000ignite.contrib.metrics ======================= Contrib module metrics [deprecated] ----------------------------------- .. deprecated:: 0.5.0 All metrics moved to :ref:`Complete list of metrics`. Regression metrics [deprecated] -------------------------------- .. deprecated:: 0.5.0 All metrics moved to :ref:`Complete list of metrics`. ignite-0.5.1/docs/source/defaults.rst000066400000000000000000000023201465426447700176050ustar00rootroot00000000000000:orphan: .. toggle:: .. testcode:: default, 1, 2, 3, 4, 5 from collections import OrderedDict import torch from torch import nn, optim from ignite.engine import * from ignite.handlers import * from ignite.metrics import * from ignite.metrics.regression import * from ignite.utils import * # create default evaluator for doctests def eval_step(engine, batch): return batch default_evaluator = Engine(eval_step) # create default optimizer for doctests param_tensor = torch.zeros([1], requires_grad=True) default_optimizer = torch.optim.SGD([param_tensor], lr=0.1) # create default trainer for doctests # as handlers could be attached to the trainer, # each test must define his own trainer using `.. testsetup:` def get_default_trainer(): def train_step(engine, batch): return batch return Engine(train_step) # create default model for doctests default_model = nn.Sequential(OrderedDict([ ('base', nn.Linear(4, 2)), ('fc', nn.Linear(2, 1)) ])) manual_seed(666) ignite-0.5.1/docs/source/distributed.rst000066400000000000000000000077061465426447700203350ustar00rootroot00000000000000ignite.distributed ================== Helper module to use distributed settings for multiple backends: - backends from native torch distributed configuration: "nccl", "gloo", "mpi" - XLA on TPUs via `pytorch/xla `_ - using `Horovod framework `_ as a backend Distributed launcher and `auto` helpers --------------------------------------- We provide a context manager to simplify the code of distributed configuration setup for all above supported backends. In addition, methods like :meth:`~ignite.distributed.auto.auto_model`, :meth:`~ignite.distributed.auto.auto_optim` and :meth:`~ignite.distributed.auto.auto_dataloader` helps to adapt in a transparent way provided model, optimizer and data loaders to existing configuration: .. code-block:: python # main.py import ignite.distributed as idist def training(local_rank, config, **kwargs): print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) train_loader = idist.auto_dataloader(dataset, batch_size=32, num_workers=12, shuffle=True, **kwargs) # batch size, num_workers and sampler are automatically adapted to existing configuration # ... model = resnet50() model = idist.auto_model(model) # model is DDP or DP or just itself according to existing configuration # ... optimizer = optim.SGD(model.parameters(), lr=0.01) optimizer = idist.auto_optim(optimizer) # optimizer is itself, except XLA configuration and overrides `step()` method. # User can safely call `optimizer.step()` (behind `xm.optimizer_step(optimizier)` is performed) backend = "nccl" # torch native distributed configuration on multiple GPUs # backend = "xla-tpu" # XLA TPUs distributed configuration # backend = None # no distributed configuration # # dist_configs = {'nproc_per_node': 4} # Use specified distributed configuration if launch as python main.py # dist_configs["start_method"] = "fork" # Add start_method as "fork" if using Jupyter Notebook with idist.Parallel(backend=backend, **dist_configs) as parallel: parallel.run(training, config, a=1, b=2) Above code may be executed with `torch.distributed.launch`_ tool or by python and specifying distributed configuration in the code. For more details, please, see :class:`~ignite.distributed.launcher.Parallel`, :meth:`~ignite.distributed.auto.auto_model`, :meth:`~ignite.distributed.auto.auto_optim` and :meth:`~ignite.distributed.auto.auto_dataloader`. Complete example of CIFAR10 training can be found `here `_. .. _torch.distributed.launch: https://pytorch.org/docs/stable/distributed.html#launch-utility ignite.distributed.auto ----------------------- .. currentmodule:: ignite.distributed.auto .. autosummary:: :nosignatures: :toctree: generated DistributedProxySampler auto_dataloader auto_model auto_optim .. Note :: In distributed configuration, methods :meth:`~ignite.distributed.auto.auto_model`, :meth:`~ignite.distributed.auto.auto_optim` and :meth:`~ignite.distributed.auto.auto_dataloader` will have effect only when distributed group is initialized. ignite.distributed.launcher --------------------------- .. currentmodule:: ignite.distributed.launcher .. autosummary:: :nosignatures: :toctree: generated Parallel ignite.distributed.utils ------------------------ This module wraps common methods to fetch information about distributed configuration, initialize/finalize process group or spawn multiple processes. .. currentmodule:: ignite.distributed.utils .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.distributed.utils :members: .. attribute:: has_native_dist_support True if `torch.distributed` is available .. attribute:: has_xla_support True if `torch_xla` package is found ignite-0.5.1/docs/source/engine.rst000066400000000000000000000225531465426447700172550ustar00rootroot00000000000000ignite.engine ============== Main module of the library containing: ignite.engine.engine -------------------- .. currentmodule:: ignite.engine.engine .. autosummary:: :nosignatures: :toctree: generated Engine ignite.engine.events -------------------- .. currentmodule:: ignite.engine.events .. autosummary:: :nosignatures: :toctree: generated CallableEventWithFilter EventEnum Events EventsList State RemovableEventHandle ignite.engine.deterministic --------------------------- Helper methods for deterministic training .. currentmodule:: ignite.engine.deterministic .. autosummary:: :nosignatures: :toctree: generated DeterministicEngine ReproducibleBatchSampler keep_random_state update_dataloader helper methods to define supervised trainer and evaluator --------------------------------------------------------- .. currentmodule:: ignite.engine .. autosummary:: :nosignatures: :toctree: generated create_supervised_trainer create_supervised_evaluator supervised_training_step supervised_training_step_amp supervised_training_step_apex supervised_training_step_tpu supervised_evaluation_step supervised_evaluation_step_amp Resuming the training --------------------- It is possible to resume the training from a checkpoint and approximately reproduce original run's behaviour. Using Ignite, this can be easily done using :class:`~ignite.handlers.checkpoint.Checkpoint` handler. Engine provides two methods to serialize and deserialize its internal state :meth:`~ignite.engine.engine.Engine.state_dict` and :meth:`~ignite.engine.engine.Engine.load_state_dict`. In addition to serializing model, optimizer, lr scheduler, metrics, etc., user can store the trainer and then resume the training. For example: .. code-block:: python from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, DiskSaver trainer = ... model = ... optimizer = ... lr_scheduler = ... data_loader = ... metric = ... to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'metric': metric} handler = Checkpoint(to_save, DiskSaver('/tmp/training', create_dir=True)) trainer.add_event_handler(Events.EPOCH_COMPLETED, handler) trainer.run(data_loader, max_epochs=100) .. code-block:: bash ls /tmp/training > "checkpoint_50000.pt" We can then restore the training from the last checkpoint. .. code-block:: python from ignite.handlers import Checkpoint trainer = ... model = ... optimizer = ... lr_scheduler = ... data_loader = ... metric = ... to_load = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'metric': metric} checkpoint = torch.load(checkpoint_file) Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint) trainer.run(train_loader, max_epochs=100) It is also possible to store checkpoints every N iterations and continue the training from one of these checkpoints, i.e from iteration. Complete examples that resumes the training from a checkpoint can be found here: - `save/resume MNIST `_ - `save/resume Distributed CIFAR10 `_ Deterministic training ---------------------- In general, it is rather difficult task to achieve deterministic and reproducible trainings as it relies on multiple aspects, e.g. data version, code version, software environment, hardware etc. According to `PyTorch note on randomness `_: there are some steps to take in order to make computations deterministic on your specific problem on one specific platform and PyTorch release: - setup random state seed - set `cudnn to deterministic `_ if applicable By default, these two options can be enough to run and rerun experiments in a deterministic way. Ignite's engine does not impact this behaviour. In this module we provide helper methods and classes to make additional ":ref:`Dataflow synchronization`" to ensure that model sees the same data for a given epoch: - :class:`~ignite.engine.deterministic.DeterministicEngine` - :class:`~ignite.engine.deterministic.ReproducibleBatchSampler` Dataflow synchronization ------------------------ Ignite provides an option to control the dataflow by synchronizing random state on epochs. In this way, for a given iteration/epoch the dataflow can be the same for a given seed. More precisely it is roughly looks like: .. code-block:: python for e in range(num_epochs): set_seed(seed + e) do_single_epoch_iterations(dataloader) In addition, if data provider is ``torch.utils.data.DataLoader``, batch data indices can be made completely deterministic. Here is a trivial example of usage: .. code-block:: python import torch from torch.utils.data import DataLoader from ignite.engine import DeterministicEngine, Events from ignite.utils import manual_seed def random_train_data_loader(size): data = torch.arange(0, size) return DataLoader(data, batch_size=4, shuffle=True) def print_train_data(engine, batch): i = engine.state.iteration e = engine.state.epoch print("train", e, i, batch.tolist()) trainer = DeterministicEngine(print_train_data) print("Original Run") manual_seed(56) trainer.run(random_train_data_loader(40), max_epochs=2, epoch_length=5) print("Resumed Run") # Resume from 2nd epoch trainer.load_state_dict({"epoch": 1, "epoch_length": 5, "max_epochs": 2, "rng_states": None}) manual_seed(56) trainer.run(random_train_data_loader(40)) .. code-block:: text Original Run train 1 1 [31, 13, 3, 4] train 1 2 [23, 18, 6, 16] train 1 3 [10, 8, 33, 36] train 1 4 [1, 37, 19, 9] train 1 5 [20, 30, 14, 26] train 2 6 [29, 35, 38, 34] train 2 7 [7, 22, 12, 17] train 2 8 [25, 21, 24, 15] train 2 9 [39, 5, 2, 28] train 2 10 [27, 11, 32, 0] Resumed Run train 2 6 [29, 35, 38, 34] train 2 7 [7, 22, 12, 17] train 2 8 [25, 21, 24, 15] train 2 9 [39, 5, 2, 28] train 2 10 [27, 11, 32, 0] We can see that the data samples are exactly the same between original and resumed runs. Complete examples that simulates a crash on a defined iteration and resumes the training from a checkpoint can be found here: - `save/resume MNIST `_ - `save/resume Distributed CIFAR10 `_ .. Note :: In case when input data is `torch.utils.data.DataLoader`, previous batches are skipped and the first provided batch corresponds to the batch after the checkpoint iteration. Internally, while resuming, previous datapoint indices are just skipped without fetching the data. .. warning:: However, while resuming from iteration, random data augmentations are not synchronized in the middle of the epoch and thus batches remaining until the end of the epoch can be different of those from the initial run. .. warning:: However, please, keep in mind that there can be an issue with dataflow synchronization on every epoch if user's handler synchronizes the random state, for example, by calling periodically ``torch.manual_seed(seed)`` during the run. This can have an impact on the dataflow: .. code-block:: python def random_train_data_generator(): while True: yield torch.randint(0, 100, size=(1, )) trainer = DeterministicEngine(print_train_data) @trainer.on(Events.ITERATION_COMPLETED(every=3)) def user_handler(): # handler synchronizes the random state torch.manual_seed(12) a = torch.rand(1) trainer.run(random_train_data_generator(), max_epochs=3, epoch_length=5); .. code-block:: text train 1 1 [32] train 1 2 [29] train 1 3 [40] train 1 4 [3] <--- train 1 5 [22] train 2 6 [77] train 2 7 [3] <--- train 2 8 [22] train 2 9 [77] train 2 10 [3] <--- train 3 11 [22] train 3 12 [77] train 3 13 [3] <--- train 3 14 [22] train 3 15 [77] Initially, the function ``random_train_data_generator()`` generates randomly data batches using the random state set up by ``trainer``. This is intended behaviour until ``user_handler()`` is called. After ``user_handler()`` execution, random state is altered and thus ``random_train_data_generator()`` will produce random batches based on altered random state. We provide helper decorator :meth:`~ignite.engine.deterministic.keep_random_state` to save and restore random states for `torch`, `numpy` and `random`. Therefore, we can deal with described issue using this decorator: .. code-block:: python from ignite.engine.deterministic import keep_random_state @trainer.on(Events.ITERATION_COMPLETED(every=3)) @keep_random_state def user_handler(): # handler synchronizes the random state torch.manual_seed(12) a = torch.rand(1) ignite-0.5.1/docs/source/exceptions.rst000066400000000000000000000002411465426447700201570ustar00rootroot00000000000000ignite.exceptions ================= .. currentmodule:: ignite.exceptions .. autosummary:: :nosignatures: :autolist: .. autoclass:: NotComputableError ignite-0.5.1/docs/source/handlers.rst000066400000000000000000000364361465426447700176150ustar00rootroot00000000000000ignite.handlers =============== Complete list of generic handlers ---------------------------------- .. currentmodule:: ignite.handlers .. autosummary:: :nosignatures: :toctree: generated checkpoint.Checkpoint DiskSaver checkpoint.ModelCheckpoint ema_handler.EMAHandler early_stopping.EarlyStopping lr_finder.FastaiLRFinder terminate_on_nan.TerminateOnNan TimeLimit time_profilers.BasicTimeProfiler time_profilers.HandlersTimeProfiler timing.Timer global_step_from_engine stores.EpochOutputStore .. autosummary:: :nosignatures: :toctree: generated :template: classwithcall.rst checkpoint.BaseSaveHandler param_scheduler.ParamScheduler state_param_scheduler.StateParamScheduler Loggers -------- .. currentmodule:: ignite.handlers .. autosummary:: :nosignatures: :toctree: generated :recursive: base_logger clearml_logger mlflow_logger neptune_logger polyaxon_logger tensorboard_logger tqdm_logger visdom_logger wandb_logger fbresearch_logger .. seealso:: Below are a comprehensive list of examples of various loggers. * See `tensorboardX mnist example `_ and `CycleGAN and EfficientNet notebooks `_ for detailed usage. * See `visdom mnist example `_ for detailed usage. * See `neptune mnist example `_ for detailed usage. * See `tqdm mnist example `_ for detailed usage. * See `wandb mnist example `_ for detailed usage. * See `clearml mnist example `_ for detailed usage. .. _param-scheduler-label: Parameter scheduler ------------------- .. currentmodule:: ignite.handlers.param_scheduler .. autosummary:: :nosignatures: :toctree: generated BaseParamScheduler ConcatScheduler CosineAnnealingScheduler CyclicalScheduler LRScheduler LinearCyclicalScheduler ParamGroupScheduler ParamScheduler PiecewiseLinear ReduceLROnPlateauScheduler create_lr_scheduler_with_warmup State Parameter scheduler ------------------------- .. currentmodule:: ignite.handlers.state_param_scheduler .. autosummary:: :nosignatures: :toctree: generated StateParamScheduler LambdaStateScheduler PiecewiseLinearStateScheduler ExpStateScheduler StepStateScheduler MultiStepStateScheduler More on parameter scheduling ---------------------------- In this section there are visual examples of various parameter schedulings that can be achieved. Example with :class:`~ignite.handlers.param_scheduler.CosineAnnealingScheduler` ``````````````````````````````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.handlers import CosineAnnealingScheduler lr_values_1 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20)) lr_values_2 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20, cycle_mult=1.3)) lr_values_3 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20, start_value_mult=0.7)) lr_values_4 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20, end_value_mult=0.1)) plt.figure(figsize=(25, 5)) plt.subplot(141) plt.title("Cosine annealing") plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) plt.subplot(142) plt.title("Cosine annealing with cycle_mult=1.3") plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) plt.subplot(143) plt.title("Cosine annealing with start_value_mult=0.7") plt.plot(lr_values_3[:, 0], lr_values_3[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) plt.subplot(144) plt.title("Cosine annealing with end_value_mult=0.1") plt.plot(lr_values_4[:, 0], lr_values_4[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) .. image:: ./_static/img/schedulers/cosine_annealing_example.png Example with :class:`ignite.handlers.param_scheduler.LinearCyclicalScheduler` ````````````````````````````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.handlers import LinearCyclicalScheduler lr_values_1 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20)) lr_values_2 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20, cycle_mult=1.3)) lr_values_3 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20, start_value_mult=0.7)) lr_values_4 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr', start_value=1e-1, end_value=2e-2, cycle_size=20, end_value_mult=0.1)) plt.figure(figsize=(25, 5)) plt.subplot(141) plt.title("Linear cyclical scheduler") plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) plt.subplot(142) plt.title("Linear cyclical scheduler with cycle_mult=1.3") plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) plt.subplot(143) plt.title("Linear cyclical scheduler with start_value_mult=0.7") plt.plot(lr_values_3[:, 0], lr_values_3[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) plt.subplot(144) plt.title("Linear cyclical scheduler with end_value_mult=0.1") plt.plot(lr_values_4[:, 0], lr_values_4[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.ylim([0.0, 0.12]) .. image:: ./_static/img/schedulers/linear_cyclical_example.png Example with :class:`ignite.handlers.param_scheduler.ConcatScheduler` ````````````````````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.handlers import LinearCyclicalScheduler, CosineAnnealingScheduler, ConcatScheduler import torch t1 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([t1], lr=0.1) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.5, cycle_size=30) scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.5, end_value=0.01, cycle_size=50) durations = [15, ] lr_values_1 = np.array(ConcatScheduler.simulate_values(num_events=100, schedulers=[scheduler_1, scheduler_2], durations=durations)) t1 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([t1], lr=0.1) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.5, cycle_size=30) scheduler_2 = CosineAnnealingScheduler(optimizer, "momentum", start_value=0.5, end_value=0.01, cycle_size=50) durations = [15, ] lr_values_2 = np.array(ConcatScheduler.simulate_values(num_events=100, schedulers=[scheduler_1, scheduler_2], durations=durations, param_names=["lr", "momentum"])) plt.figure(figsize=(25, 5)) plt.subplot(131) plt.title("Concat scheduler of linear + cosine annealing") plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.subplot(132) plt.title("Concat scheduler of linear LR scheduler\n and cosine annealing on momentum") plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.subplot(133) plt.title("Concat scheduler of linear LR scheduler\n and cosine annealing on momentum") plt.plot(lr_values_2[:, 0], lr_values_2[:, 2], label="momentum") plt.xlabel("events") plt.ylabel("values") plt.legend() .. image:: ./_static/img/schedulers/concat_example.png Piecewise linear scheduler ^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.handlers import LinearCyclicalScheduler, ConcatScheduler scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.0, end_value=0.6, cycle_size=50) scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.6, end_value=0.0, cycle_size=150) durations = [25, ] lr_values = np.array(ConcatScheduler.simulate_values(num_events=100, schedulers=[scheduler_1, scheduler_2], durations=durations)) plt.title("Piecewise linear scheduler") plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() .. image:: ./_static/img/schedulers/piecewise_linear.png Example with :class:`ignite.handlers.param_scheduler.LRScheduler` ````````````````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.handlers import LRScheduler import torch from torch.optim.lr_scheduler import ExponentialLR, StepLR, CosineAnnealingLR tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.1) lr_scheduler_1 = StepLR(optimizer=optimizer, step_size=10, gamma=0.77) lr_scheduler_2 = ExponentialLR(optimizer=optimizer, gamma=0.98) lr_scheduler_3 = CosineAnnealingLR(optimizer=optimizer, T_max=10, eta_min=0.01) lr_values_1 = np.array(LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler_1)) lr_values_2 = np.array(LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler_2)) lr_values_3 = np.array(LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler_3)) plt.figure(figsize=(25, 5)) plt.subplot(131) plt.title("Torch LR scheduler wrapping StepLR") plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.subplot(132) plt.title("Torch LR scheduler wrapping ExponentialLR") plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.subplot(133) plt.title("Torch LR scheduler wrapping CosineAnnealingLR") plt.plot(lr_values_3[:, 0], lr_values_3[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() .. image:: ./_static/img/schedulers/lr_scheduler.png Concatenate with torch schedulers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.handlers import LRScheduler, ConcatScheduler import torch from torch.optim.lr_scheduler import ExponentialLR, StepLR t1 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([t1], lr=0.1) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.001, end_value=0.1, cycle_size=30) lr_scheduler = ExponentialLR(optimizer=optimizer, gamma=0.7) scheduler_2 = LRScheduler(lr_scheduler=lr_scheduler) durations = [15, ] lr_values_1 = np.array(ConcatScheduler.simulate_values(num_events=30, schedulers=[scheduler_1, scheduler_2], durations=durations)) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.001, end_value=0.1, cycle_size=30) lr_scheduler = StepLR(optimizer=optimizer, step_size=10, gamma=0.7) scheduler_2 = LRScheduler(lr_scheduler=lr_scheduler) durations = [15, ] lr_values_2 = np.array(ConcatScheduler.simulate_values(num_events=75, schedulers=[scheduler_1, scheduler_2], durations=durations)) plt.figure(figsize=(15, 5)) plt.subplot(121) plt.title("Concat scheduler of linear + ExponentialLR") plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() plt.subplot(122) plt.title("Concat scheduler of linear + StepLR") plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() .. image:: ./_static/img/schedulers/concat_linear_exp_step_lr.png Example with :class:`ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler` ````````````````````````````````````````````````````````````````````````````````````` .. code-block:: python import matplotlib.pyplot as plt import numpy as np from ignite.handlers import ReduceLROnPlateauScheduler metric_values = [0.7, 0.78, 0.81, 0.82, 0.82, 0.83, 0.80, 0.81, 0.84, 0.78] num_events = 10 init_lr = 0.1 lr_values = np.array(ReduceLROnPlateauScheduler.simulate_values( num_events, metric_values, init_lr, factor=0.5, patience=1, mode='max', threshold=0.01, threshold_mode='abs' ) ) plt.figure(figsize=(15, 5)) plt.suptitle("ReduceLROnPlateauScheduler") plt.subplot(121) plt.plot(lr_values[:, 1], label="learning rate") plt.xticks(lr_values[:, 0]) plt.xlabel("events") plt.ylabel("values") plt.legend() plt.subplot(122) plt.plot(metric_values, label="metric") plt.xticks(lr_values[:, 0]) plt.xlabel("events") plt.ylabel("values") plt.legend() .. image:: ./_static/img/schedulers/reduce_lr_on_plateau_example.png ignite-0.5.1/docs/source/index.rst000066400000000000000000000013631465426447700171130ustar00rootroot00000000000000Ignite Your Networks! ===================== PyTorch-Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. .. image:: https://raw.githubusercontent.com/pytorch/ignite/master/assets/logo/ignite_logo_mixed.png :target: https://pytorch-ignite.ai All our documentation moved to `pytorch-ignite.ai `_ .. automodule:: ignite .. toctree:: :maxdepth: 2 :caption: Package Reference engine handlers metrics distributed exceptions utils .. automodule:: ignite.contrib .. toctree:: :maxdepth: 2 :caption: Contrib Package Reference contrib/engines contrib/metrics contrib/handlers ignite-0.5.1/docs/source/metrics.rst000066400000000000000000000360711465426447700174560ustar00rootroot00000000000000ignite.metrics ============== Metrics provide a way to compute various quantities of interest in an online fashion without having to store the entire output history of a model. .. _attach-engine: Attach Engine API ------------------ The metrics as stated above are computed in a online fashion, which means that the metric instance accumulates some internal counters on each iteration and metric value is computed once the epoch is ended. Internal counters are reset after every epoch. In practice, this is done with the help of three methods: :meth:`~ignite.metrics.metric.Metric.reset()`, :meth:`~ignite.metrics.metric.Metric.update()` and :meth:`~ignite.metrics.metric.Metric.compute()`. Therefore, a user needs to attach the metric instance to the engine so that the above three methods can be triggered on execution of certain :class:`~ignite.engine.events.Events`. The :meth:`~ignite.metrics.metric.Metric.reset()` method is triggered on ``EPOCH_STARTED`` event and it is responsible to reset the metric to its initial state. The :meth:`~ignite.metrics.metric.Metric.update()` method is triggered on ``ITERATION_COMPLETED`` event as it updates the state of the metric using the passed batch output. And :meth:`~ignite.metrics.metric.Metric.compute()` is triggered on ``EPOCH_COMPLETED`` event. It computes the metric based on its accumulated states. The metric value is computed using the output of the engine's ``process_function``: .. code-block:: python from ignite.engine import Engine from ignite.metrics import Accuracy def process_function(engine, batch): # ... return y_pred, y engine = Engine(process_function) metric = Accuracy() metric.attach(engine, "accuracy") # ... state = engine.run(data) print(f"Accuracy: {state.metrics['accuracy']}") If the engine's output is not in the format ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``, the user can use the ``output_transform`` argument to transform it: .. code-block:: python from ignite.engine import Engine from ignite.metrics import Accuracy def process_function(engine, batch): # ... return {'y_pred': y_pred, 'y_true': y, ...} engine = Engine(process_function) def output_transform(output): # `output` variable is returned by above `process_function` y_pred = output['y_pred'] y = output['y_true'] return y_pred, y # output format is according to `Accuracy` docs metric = Accuracy(output_transform=output_transform) metric.attach(engine, "accuracy") # ... state = engine.run(data) print(f"Accuracy: {state.metrics['accuracy']}") .. warning:: Please, be careful when using ``lambda`` functions to setup multiple ``output_transform`` for multiple metrics .. code-block:: python # Wrong # metrics_group = [Accuracy(output_transform=lambda output: output[name]) for name in names] # As lambda can not store `name` and all `output_transform` will use the last `name` # A correct way. For example, using functools.partial from functools import partial def ot_func(output, name): return output[name] metrics_group = [Accuracy(output_transform=partial(ot_func, name=name)) for name in names] For more details, see `here `_ .. Note :: Most of implemented metrics are adapted to distributed computations and reduce their internal states across supported devices before computing metric value. This can be helpful to run the evaluation on multiple nodes/GPU instances/TPUs with a distributed data sampler. Following code snippet shows in detail how to use metrics: .. code-block:: python device = f"cuda:{local_rank}" model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank, ], output_device=local_rank) test_sampler = DistributedSampler(test_dataset) test_loader = DataLoader( test_dataset, batch_size=batch_size, sampler=test_sampler, num_workers=num_workers, pin_memory=True ) evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy()}, device=device) .. Note :: Metrics cannot be serialized using `pickle` module because the implementation is based on lambda functions. Therefore, use the third party library `dill` to overcome the limitation of `pickle`. Reset, Update, Compute API -------------------------- User can also call directly the following methods on the metric: - :meth:`~ignite.metrics.metric.Metric.reset()` : resets internal variables and accumulators - :meth:`~ignite.metrics.metric.Metric.update()` : updates internal variables and accumulators with provided batch output ``(y_pred, y)`` - :meth:`~ignite.metrics.metric.Metric.compute()` : computes custom metric and return the result This API gives a more fine-grained/custom usage on how to compute a metric. For example: .. code-block:: python from ignite.metrics import Precision # Define the metric precision = Precision() # Start accumulation: for x, y in data: y_pred = model(x) precision.update((y_pred, y)) # Compute the result print("Precision: ", precision.compute()) # Reset metric precision.reset() # Start new accumulation: for x, y in data: y_pred = model(x) precision.update((y_pred, y)) # Compute new result print("Precision: ", precision.compute()) Metric arithmetics ------------------ Metrics could be combined together to form new metrics. This could be done through arithmetics, such as ``metric1 + metric2``, use PyTorch operators, such as ``(metric1 + metric2).pow(2).mean()``, or use a lambda function, such as ``MetricsLambda(lambda a, b: torch.mean(a + b), metric1, metric2)``. For example: .. code-block:: python from ignite.metrics import Precision, Recall precision = Precision(average=False) recall = Recall(average=False) F1 = (precision * recall * 2 / (precision + recall)).mean() .. note:: This example computes the mean of F1 across classes. To combine precision and recall to get F1 or other F metrics, we have to be careful that ``average=False``, i.e. to use the unaveraged precision and recall, otherwise we will not be computing F-beta metrics. Metrics also support indexing operation (if metric's result is a vector/matrix/tensor). For example, this can be useful to compute mean metric (e.g. precision, recall or IoU) ignoring the background: .. code-block:: python from ignite.metrics import ConfusionMatrix cm = ConfusionMatrix(num_classes=10) iou_metric = IoU(cm) iou_no_bg_metric = iou_metric[:9] # We assume that the background index is 9 mean_iou_no_bg_metric = iou_no_bg_metric.mean() # mean_iou_no_bg_metric.compute() -> tensor(0.12345) How to create a custom metric ----------------------------- To create a custom metric one needs to create a new class inheriting from :class:`~ignite.metrics.metric.Metric` and override three methods : - :meth:`~ignite.metrics.metric.Metric.reset()` : resets internal variables and accumulators - :meth:`~ignite.metrics.metric.Metric.update()` : updates internal variables and accumulators with provided batch output ``(y_pred, y)`` - :meth:`~ignite.metrics.metric.Metric.compute()` : computes custom metric and return the result For example, we would like to implement for illustration purposes a multi-class accuracy metric with some specific condition (e.g. ignore user-defined classes): .. code-block:: python from ignite.metrics import Metric from ignite.exceptions import NotComputableError # These decorators helps with distributed settings from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced class CustomAccuracy(Metric): def __init__(self, ignored_class, output_transform=lambda x: x, device="cpu"): self.ignored_class = ignored_class self._num_correct = None self._num_examples = None super(CustomAccuracy, self).__init__(output_transform=output_transform, device=device) @reinit__is_reduced def reset(self): self._num_correct = torch.tensor(0, device=self._device) self._num_examples = 0 super(CustomAccuracy, self).reset() @reinit__is_reduced def update(self, output): y_pred, y = output[0].detach(), output[1].detach() indices = torch.argmax(y_pred, dim=1) mask = (y != self.ignored_class) mask &= (indices != self.ignored_class) y = y[mask] indices = indices[mask] correct = torch.eq(indices, y).view(-1) self._num_correct += torch.sum(correct).to(self._device) self._num_examples += correct.shape[0] @sync_all_reduce("_num_examples", "_num_correct:SUM") def compute(self): if self._num_examples == 0: raise NotComputableError('CustomAccuracy must have at least one example before it can be computed.') return self._num_correct.item() / self._num_examples We imported necessary classes as :class:`~ignite.metrics.metric.Metric`, :class:`~ignite.exceptions.NotComputableError` and decorators to adapt the metric for distributed setting. In ``reset`` method, we reset internal variables ``_num_correct`` and ``_num_examples`` which are used to compute the custom metric. In ``updated`` method we define how to update the internal variables. And finally in ``compute`` method, we compute metric value. Notice that ``_num_correct`` is a tensor, since in ``update`` we accumulate tensor values. ``_num_examples`` is a python scalar since we accumulate normal integers. For differentiable metrics, you must detach the accumulated values before adding them to the internal variables. We can check this implementation in a simple case: .. code-block:: python import torch torch.manual_seed(8) m = CustomAccuracy(ignored_class=3) batch_size = 4 num_classes = 5 y_pred = torch.rand(batch_size, num_classes) y = torch.randint(0, num_classes, size=(batch_size, )) m.update((y_pred, y)) res = m.compute() print(y, torch.argmax(y_pred, dim=1)) # Out: tensor([2, 2, 2, 3]) tensor([2, 1, 0, 0]) print(m._num_correct, m._num_examples, res) # Out: 1 3 0.3333333333333333 Metrics and its usages ---------------------- By default, `Metrics` are epoch-wise, it means - :meth:`~ignite.metrics.metric.Metric.reset()` is triggered every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.update()` is triggered every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.compute()` is triggered every ``EPOCH_COMPLETED``. Usages can be user defined by creating a class inheriting for :class:`~ignite.metrics.metric.MetricUsage`. See the list below of usages. Complete list of usages ~~~~~~~~~~~~~~~~~~~~~~~ - :class:`~ignite.metrics.metric.MetricUsage` - :class:`~ignite.metrics.metric.EpochWise` - :class:`~ignite.metrics.metric.RunningEpochWise` - :class:`~ignite.metrics.metric.BatchWise` - :class:`~ignite.metrics.metric.RunningBatchWise` - :class:`~ignite.metrics.metric.SingleEpochRunningBatchWise` - :class:`~ignite.metrics.metric.BatchFiltered` Metrics and distributed computations ------------------------------------ In the above example, ``CustomAccuracy`` has ``reset``, ``update``, ``compute`` methods decorated with :meth:`~ignite.metrics.metric.reinit__is_reduced`, :meth:`~ignite.metrics.metric.sync_all_reduce`. The purpose of these features is to adapt metrics in distributed computations on supported backend and devices (see :doc:`distributed` for more details). More precisely, in the above example we added ``@sync_all_reduce("_num_examples", "_num_correct:SUM")`` over ``compute`` method. This means that when ``compute`` method is called, metric's interal variables ``self._num_examples`` and ``self._num_correct:SUM`` are summed up over all participating devices. We specify the reduction operation ``self._num_correct:SUM`` or we keep the default ``self._num_examples`` as the default is ``SUM``. We currently support four reduction operations (SUM, MAX, MIN, PRODUCT). Therefore, once collected, these internal variables can be used to compute the final metric value. Complete list of metrics ------------------------ .. currentmodule:: ignite.metrics .. autosummary:: :nosignatures: :toctree: generated Average GeometricAverage VariableAccumulation Accuracy confusion_matrix.ConfusionMatrix ClassificationReport DiceCoefficient JaccardIndex IoU mIoU EpochMetric Fbeta Frequency Loss MeanAbsoluteError MeanPairwiseDistance MeanSquaredError metric.Metric metric_group.MetricGroup metrics_lambda.MetricsLambda MultiLabelConfusionMatrix MutualInformation precision.Precision PSNR recall.Recall RootMeanSquaredError RunningAverage SSIM TopKCategoricalAccuracy Bleu Rouge RougeL RougeN InceptionScore FID CosineSimilarity Entropy KLDivergence JSDivergence MaximumMeanDiscrepancy AveragePrecision CohenKappa GpuInfo PrecisionRecallCurve RocCurve ROC_AUC regression.CanberraMetric regression.FractionalAbsoluteError regression.FractionalBias regression.GeometricMeanAbsoluteError regression.GeometricMeanRelativeAbsoluteError regression.ManhattanDistance regression.MaximumAbsoluteError regression.MeanAbsoluteRelativeError regression.MeanError regression.MeanNormalizedBias regression.MedianAbsoluteError regression.MedianAbsolutePercentageError regression.MedianRelativeAbsoluteError regression.PearsonCorrelation regression.R2Score regression.WaveHedgesDistance .. note:: Module ignite.metrics.regression provides implementations of metrics useful for regression tasks. Definitions of metrics are based on `Botchkarev 2018`_, page 30 "Appendix 2. Metrics mathematical definitions". Helpers for customizing metrics ------------------------------- MetricUsage ~~~~~~~~~~~ .. autoclass:: ignite.metrics.metric.MetricUsage EpochWise ~~~~~~~~~ .. autoclass:: ignite.metrics.metric.EpochWise RunningEpochWise ~~~~~~~~~~~~~~~~ .. autoclass:: ignite.metrics.metric.RunningEpochWise BatchWise ~~~~~~~~~ .. autoclass:: ignite.metrics.metric.BatchWise RunningBatchWise ~~~~~~~~~~~~~~~~ .. autoclass:: ignite.metrics.metric.RunningBatchWise SingleEpochRunningBatchWise ~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ignite.metrics.metric.SingleEpochRunningBatchWise BatchFiltered ~~~~~~~~~~~~~ .. autoclass:: ignite.metrics.metric.BatchFiltered .. currentmodule:: ignite.metrics.metric reinit__is_reduced ~~~~~~~~~~~~~~~~~~ .. autofunction:: reinit__is_reduced sync_all_reduce ~~~~~~~~~~~~~~~ .. autofunction:: sync_all_reduce .. _`Botchkarev 2018`: https://arxiv.org/abs/1809.03006 ignite-0.5.1/docs/source/utils.rst000066400000000000000000000002671465426447700171460ustar00rootroot00000000000000ignite.utils ============ Module with helper methods .. currentmodule:: ignite.utils .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.utils :members: ignite-0.5.1/examples/000077500000000000000000000000001465426447700146355ustar00rootroot00000000000000ignite-0.5.1/examples/cifar10/000077500000000000000000000000001465426447700160625ustar00rootroot00000000000000ignite-0.5.1/examples/cifar10/.gitignore000066400000000000000000000000331465426447700200460ustar00rootroot00000000000000output cifar10 raw_pytorch ignite-0.5.1/examples/cifar10/README.md000066400000000000000000000100521465426447700173370ustar00rootroot00000000000000# CIFAR10 Example with Ignite In this example, we show how to use _Ignite_ to train a neural network: - on 1 or more GPUs or TPUs - compute training/validation metrics - log learning rate, metrics etc - save the best model weights Configurations: - [x] single GPU - [x] multi GPUs on a single node - [x] multi GPUs on multiple nodes - [x] TPUs on Colab ## Requirements: - pytorch-ignite: `pip install pytorch-ignite` - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` - [tensorboardx](https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` - [python-fire](https://github.com/google/python-fire): `pip install fire` - Optional: [clearml](https://github.com/allegroai/clearml): `pip install clearml` Alternatively, install the all requirements using `pip install -r requirements.txt`. ## Usage: Run the example on a single GPU: ```bash python main.py run ``` For more details on accepted arguments: ```bash python main.py run -- --help ``` If user would like to provide already downloaded dataset, the path can be setup in parameters as ```bash --data_path="/path/to/cifar10/" ``` ### Distributed training #### Single node, multiple GPUs Let's start training on a single node with 2 gpus: ```bash # using torchrun torchrun --nproc_per_node=2 main.py run --backend="nccl" ``` or ```bash # using function spawn inside the code python -u main.py run --backend="nccl" --nproc_per_node=2 ``` ##### Using [Horovod](https://horovod.readthedocs.io/en/latest/index.html) as distributed backend Please, make sure to have Horovod installed before running. Let's start training on a single node with 2 gpus: ```bash # horovodrun horovodrun -np=2 python -u main.py run --backend="horovod" ``` or ```bash # using function spawn inside the code python -u main.py run --backend="horovod" --nproc_per_node=2 ``` #### Colab, on 8 TPUs Same code can be run on TPUs: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) #### Multiple nodes, multiple GPUs Let's start training on two nodes with 2 gpus each. We assuming that master node can be connected as `master`, e.g. `ping master`. 1. Execute on master node ```bash torchrun \ --nnodes=2 \ --nproc_per_node=2 \ --node_rank=0 \ --master_addr=master --master_port=2222 \ main.py run --backend="nccl" ``` 2. Execute on worker node ```bash torchrun \ --nnodes=2 \ --nproc_per_node=2 \ --node_rank=1 \ --master_addr=master --master_port=2222 \ main.py run --backend="nccl" ``` ### Check resume training #### Single GPU Initial training with a stop on 1000 iteration (~11 epochs) ```bash python main.py run --stop_iteration=1000 ``` Resume from the latest checkpoint ```bash python main.py run --resume-from=/tmp/output-cifar10/resnet18_backend-None-1_stop-on-1000/training_checkpoint_1000.pt ``` ### Distributed training #### Single node, multiple GPUs Initial training on a single node with 2 gpus with a stop on 1000 iteration (~11 epochs): ```bash # using torchrun torchrun --nproc_per_node=2 main.py run --backend="nccl" --stop_iteration=1000 ``` Resume from the latest checkpoint ```bash torchrun --nproc_per_node=2 main.py run --backend="nccl" \ --resume-from=/tmp/output-cifar10/resnet18_backend-nccl-2_stop-on-1000/training_checkpoint_1000.pt ``` Similar commands can be adapted for other cases. ## ClearML fileserver If `ClearML` server is used (i.e. `--with_clearml` argument), the configuration to upload artifact must be done by modifying the `ClearML` configuration file `~/clearml.conf` generated by `clearml-init`. According to the [documentation](https://allegro.ai/clearml/docs/docs/examples/reporting/artifacts.html), the `output_uri` argument can be configured in `sdk.development.default_output_uri` to fileserver uri. If server is self-hosted, `ClearML` fileserver uri is `http://localhost:8081`. For more details, see https://allegro.ai/clearml/docs/docs/examples/reporting/artifacts.html ignite-0.5.1/examples/cifar10/main.py000066400000000000000000000334121465426447700173630ustar00rootroot00000000000000from datetime import datetime from pathlib import Path from typing import Any, Optional import fire import torch import torch.nn as nn import torch.optim as optim import utils from torch.cuda.amp import autocast, GradScaler import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine, PiecewiseLinear from ignite.metrics import Accuracy, Loss from ignite.utils import manual_seed, setup_logger def training(local_rank, config): rank = idist.get_rank() manual_seed(config["seed"] + rank) device = idist.device() logger = setup_logger(name="CIFAR10-Training") log_basic_info(logger, config) output_path = config["output_path"] if rank == 0: if config["stop_iteration"] is None: now = datetime.now().strftime("%Y%m%d-%H%M%S") else: now = f"stop-on-{config['stop_iteration']}" folder_name = f"{config['model']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}" output_path = Path(output_path) / folder_name if not output_path.exists(): output_path.mkdir(parents=True) config["output_path"] = output_path.as_posix() logger.info(f"Output path: {config['output_path']}") if "cuda" in device.type: config["cuda device name"] = torch.cuda.get_device_name(local_rank) if config["with_clearml"]: from clearml import Task task = Task.init("CIFAR10-Training", task_name=output_path.stem) task.connect_configuration(config) # Log hyper parameters hyper_params = [ "model", "batch_size", "momentum", "weight_decay", "num_epochs", "learning_rate", "num_warmup_epochs", ] task.connect({k: config[k] for k in hyper_params}) # Setup dataflow, model, optimizer, criterion train_loader, test_loader = get_dataflow(config) config["num_iters_per_epoch"] = len(train_loader) model, optimizer, criterion, lr_scheduler = initialize(config) # Create trainer for current task trainer = create_trainer(model, optimizer, criterion, lr_scheduler, train_loader.sampler, config, logger) # Let's now setup evaluator engine to perform model's validation and compute metrics metrics = { "Accuracy": Accuracy(), "Loss": Loss(criterion), } # We define two evaluators as they wont have exactly similar roles: # - `evaluator` will save the best model based on validation score evaluator = create_evaluator(model, metrics=metrics, config=config) train_evaluator = create_evaluator(model, metrics=metrics, config=config) def run_validation(engine): epoch = trainer.state.epoch state = train_evaluator.run(train_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(test_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) trainer.add_event_handler(Events.EPOCH_COMPLETED(every=config["validate_every"]) | Events.COMPLETED, run_validation) if rank == 0: # Setup TensorBoard logging on trainer and evaluators. Logged values are: # - Training metrics, e.g. running average loss values # - Learning rate # - Evaluation train/test metrics evaluators = {"training": train_evaluator, "test": evaluator} tb_logger = common.setup_tb_logging(output_path, trainer, optimizer, evaluators=evaluators) # Store 2 best models by validation accuracy starting from num_epochs / 2: best_model_handler = Checkpoint( {"model": model}, get_save_handler(config), filename_prefix="best", n_saved=2, global_step_transform=global_step_from_engine(trainer), score_name="test_accuracy", score_function=Checkpoint.get_default_score_fn("Accuracy"), ) evaluator.add_event_handler( Events.COMPLETED(lambda *_: trainer.state.epoch > config["num_epochs"] // 2), best_model_handler ) # In order to check training resuming we can stop training on a given iteration if config["stop_iteration"] is not None: @trainer.on(Events.ITERATION_STARTED(once=config["stop_iteration"])) def _(): logger.info(f"Stop training on {trainer.state.iteration} iteration") trainer.terminate() try: trainer.run(train_loader, max_epochs=config["num_epochs"]) except Exception as e: logger.exception("") raise e if rank == 0: tb_logger.close() def run( seed: int = 543, data_path: str = "/tmp/cifar10", output_path: str = "/tmp/output-cifar10/", model: str = "resnet18", batch_size: int = 512, momentum: float = 0.9, weight_decay: float = 1e-4, num_workers: int = 12, num_epochs: int = 24, learning_rate: float = 0.4, num_warmup_epochs: int = 4, validate_every: int = 3, checkpoint_every: int = 1000, backend: Optional[str] = None, resume_from: Optional[str] = None, log_every_iters: int = 15, nproc_per_node: Optional[int] = None, stop_iteration: Optional[int] = None, with_clearml: bool = False, with_amp: bool = False, **spawn_kwargs: Any, ): """Main entry to train an model on CIFAR10 dataset. Args: seed (int): random state seed to set. Default, 543. data_path (str): input dataset path. Default, "/tmp/cifar10". output_path (str): output path. Default, "/tmp/output-cifar10". model (str): model name (from torchvision) to setup model to train. Default, "resnet18". batch_size (int): total batch size. Default, 512. momentum (float): optimizer's momentum. Default, 0.9. weight_decay (float): weight decay. Default, 1e-4. num_workers (int): number of workers in the data loader. Default, 12. num_epochs (int): number of epochs to train the model. Default, 24. learning_rate (float): peak of piecewise linear learning rate scheduler. Default, 0.4. num_warmup_epochs (int): number of warm-up epochs before learning rate decay. Default, 4. validate_every (int): run model's validation every ``validate_every`` epochs. Default, 3. checkpoint_every (int): store training checkpoint every ``checkpoint_every`` iterations. Default, 1000. backend (str, optional): backend to use for distributed configuration. Possible values: None, "nccl", "xla-tpu", "gloo" etc. Default, None. nproc_per_node (int, optional): optional argument to setup number of processes per node. It is useful, when main python process is spawning training as child processes. resume_from (str, optional): path to checkpoint to use to resume the training from. Default, None. log_every_iters (int): argument to log batch loss every ``log_every_iters`` iterations. It can be 0 to disable it. Default, 15. stop_iteration (int, optional): iteration to stop the training. Can be used to check resume from checkpoint. with_clearml (bool): if True, experiment ClearML logger is setup. Default, False. with_amp (bool): if True, enables native automatic mixed precision. Default, False. **spawn_kwargs: Other kwargs to spawn run in child processes: master_addr, master_port, node_rank, nnodes """ # check to see if the num_epochs is greater than or equal to num_warmup_epochs if num_warmup_epochs >= num_epochs: raise ValueError( "num_epochs cannot be less than or equal to num_warmup_epochs, please increase num_epochs or decrease " "num_warmup_epochs" ) # catch all local parameters config = locals() config.update(config["spawn_kwargs"]) del config["spawn_kwargs"] spawn_kwargs["nproc_per_node"] = nproc_per_node if backend == "xla-tpu" and with_amp: raise RuntimeError("The value of with_amp should be False if backend is xla") with idist.Parallel(backend=backend, **spawn_kwargs) as parallel: parallel.run(training, config) def get_dataflow(config): # - Get train/test datasets with idist.one_rank_first(local=True): train_dataset, test_dataset = utils.get_train_test_datasets(config["data_path"]) # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu train_loader = idist.auto_dataloader( train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True ) test_loader = idist.auto_dataloader( test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False ) return train_loader, test_loader def initialize(config): model = utils.get_model(config["model"]) # Adapt model for distributed settings if configured model = idist.auto_model(model) optimizer = optim.SGD( model.parameters(), lr=config["learning_rate"], momentum=config["momentum"], weight_decay=config["weight_decay"], nesterov=True, ) optimizer = idist.auto_optim(optimizer) criterion = nn.CrossEntropyLoss().to(idist.device()) le = config["num_iters_per_epoch"] milestones_values = [ (0, 0.0), (le * config["num_warmup_epochs"], config["learning_rate"]), (le * config["num_epochs"], 0.0), ] lr_scheduler = PiecewiseLinear(optimizer, param_name="lr", milestones_values=milestones_values) return model, optimizer, criterion, lr_scheduler def log_metrics(logger, epoch, elapsed, tag, metrics): metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) logger.info(f"Epoch[{epoch}] - Evaluation time (seconds): {elapsed:.3f}\n - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): logger.info(f"Train {config['model']} on CIFAR10") logger.info(f"- PyTorch version: {torch.__version__}") logger.info(f"- Ignite version: {ignite.__version__}") if torch.cuda.is_available(): # explicitly import cudnn as # torch.backends.cudnn can not be pickled with hvd spawning procs from torch.backends import cudnn logger.info(f"- GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}") logger.info(f"- CUDA version: {torch.version.cuda}") logger.info(f"- CUDNN version: {cudnn.version()}") logger.info("\n") logger.info("Configuration:") for key, value in config.items(): logger.info(f"\t{key}: {value}") logger.info("\n") if idist.get_world_size() > 1: logger.info("\nDistributed setting:") logger.info(f"\tbackend: {idist.backend()}") logger.info(f"\tworld size: {idist.get_world_size()}") logger.info("\n") def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, config, logger): device = idist.device() # Setup Ignite trainer: # - let's define training step # - add other common handlers: # - TerminateOnNan, # - handler to setup learning rate scheduling, # - ModelCheckpoint # - RunningAverage` on `train_step` output # - Two progress bars on epochs and optionally on iterations with_amp = config["with_amp"] scaler = GradScaler(enabled=with_amp) def train_step(engine, batch): x, y = batch[0], batch[1] if x.device != device: x = x.to(device, non_blocking=True) y = y.to(device, non_blocking=True) model.train() with autocast(enabled=with_amp): y_pred = model(x) loss = criterion(y_pred, y) optimizer.zero_grad() scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() return { "batch loss": loss.item(), } trainer = Engine(train_step) trainer.logger = logger to_save = {"trainer": trainer, "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler} metric_names = [ "batch loss", ] common.setup_common_training_handlers( trainer=trainer, train_sampler=train_sampler, to_save=to_save, save_every_iters=config["checkpoint_every"], save_handler=get_save_handler(config), lr_scheduler=lr_scheduler, output_names=metric_names if config["log_every_iters"] > 0 else None, with_pbars=False, clear_cuda_cache=False, ) resume_from = config["resume_from"] if resume_from is not None: checkpoint_fp = Path(resume_from) assert checkpoint_fp.exists(), f"Checkpoint '{checkpoint_fp.as_posix()}' is not found" logger.info(f"Resume from a checkpoint: {checkpoint_fp.as_posix()}") checkpoint = torch.load(checkpoint_fp.as_posix(), map_location="cpu") Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint) return trainer def create_evaluator(model, metrics, config, tag="val"): with_amp = config["with_amp"] device = idist.device() @torch.no_grad() def evaluate_step(engine: Engine, batch): model.eval() x, y = batch[0], batch[1] if x.device != device: x = x.to(device, non_blocking=True) y = y.to(device, non_blocking=True) with autocast(enabled=with_amp): output = model(x) return output, y evaluator = Engine(evaluate_step) for name, metric in metrics.items(): metric.attach(evaluator, name) return evaluator def get_save_handler(config): if config["with_clearml"]: from ignite.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=config["output_path"]) return DiskSaver(config["output_path"], require_empty=False) if __name__ == "__main__": fire.Fire({"run": run}) ignite-0.5.1/examples/cifar10/requirements.txt000066400000000000000000000000721465426447700213450ustar00rootroot00000000000000pytorch-ignite torchvision tqdm tensorboardX fire clearml ignite-0.5.1/examples/cifar10/utils.py000066400000000000000000000021371465426447700175770ustar00rootroot00000000000000import os from pathlib import Path from torchvision import datasets, models from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor train_transform = Compose( [ Pad(4), RandomCrop(32, fill=128), RandomHorizontalFlip(), ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ] ) test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]) def get_train_test_datasets(path): path = Path(path) if not path.exists(): path.mkdir(parents=True) download = True else: download = True if len(os.listdir(path)) < 1 else False train_ds = datasets.CIFAR10(root=path, train=True, download=download, transform=train_transform) test_ds = datasets.CIFAR10(root=path, train=False, download=False, transform=test_transform) return train_ds, test_ds def get_model(name): if name in models.__dict__: fn = models.__dict__[name] else: raise RuntimeError(f"Unknown model name {name}") return fn(num_classes=10) ignite-0.5.1/examples/cifar100_amp_benchmark/000077500000000000000000000000001465426447700210115ustar00rootroot00000000000000ignite-0.5.1/examples/cifar100_amp_benchmark/benchmark_fp32.py000066400000000000000000000043021465426447700241460ustar00rootroot00000000000000import fire import torch from torch.nn import CrossEntropyLoss from torch.optim import SGD from torchvision.models import wide_resnet50_2 from utils import get_train_eval_loaders from ignite.engine import convert_tensor, create_supervised_evaluator, Engine, Events from ignite.handlers import ProgressBar, Timer from ignite.metrics import Accuracy, Loss def main(dataset_path, batch_size=256, max_epochs=10): assert torch.cuda.is_available() assert torch.backends.cudnn.enabled, "NVIDIA/Apex:Amp requires cudnn backend to be enabled." torch.backends.cudnn.benchmark = True device = "cuda" train_loader, test_loader, eval_train_loader = get_train_eval_loaders(dataset_path, batch_size=batch_size) model = wide_resnet50_2(num_classes=100).to(device) optimizer = SGD(model.parameters(), lr=0.01) criterion = CrossEntropyLoss().to(device) def train_step(engine, batch): x = convert_tensor(batch[0], device, non_blocking=True) y = convert_tensor(batch[1], device, non_blocking=True) optimizer.zero_grad() y_pred = model(x) loss = criterion(y_pred, y) loss.backward() optimizer.step() return loss.item() trainer = Engine(train_step) timer = Timer(average=True) timer.attach(trainer, step=Events.EPOCH_COMPLETED) ProgressBar(persist=True).attach(trainer, output_transform=lambda out: {"batch loss": out}) metrics = {"Accuracy": Accuracy(), "Loss": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics=metrics, device=device, non_blocking=True) def log_metrics(engine, title): for name in metrics: print(f"\t{title} {name}: {engine.state.metrics[name]:.2f}") @trainer.on(Events.COMPLETED) def run_validation(_): print(f"- Mean elapsed time for 1 epoch: {timer.value()}") print("- Metrics:") with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "Train"): evaluator.run(eval_train_loader) with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "Test"): evaluator.run(test_loader) trainer.run(train_loader, max_epochs=max_epochs) if __name__ == "__main__": fire.Fire(main) ignite-0.5.1/examples/cifar100_amp_benchmark/benchmark_nvidia_apex.py000066400000000000000000000045621465426447700256730ustar00rootroot00000000000000import fire import torch from apex import amp from torch.nn import CrossEntropyLoss from torch.optim import SGD from torchvision.models import wide_resnet50_2 from utils import get_train_eval_loaders from ignite.engine import convert_tensor, create_supervised_evaluator, Engine, Events from ignite.handlers import ProgressBar, Timer from ignite.metrics import Accuracy, Loss def main(dataset_path, batch_size=256, max_epochs=10, opt="O1"): assert torch.cuda.is_available() assert torch.backends.cudnn.enabled, "NVIDIA/Apex:Amp requires cudnn backend to be enabled." torch.backends.cudnn.benchmark = True device = "cuda" train_loader, test_loader, eval_train_loader = get_train_eval_loaders(dataset_path, batch_size=batch_size) model = wide_resnet50_2(num_classes=100).to(device) optimizer = SGD(model.parameters(), lr=0.01) criterion = CrossEntropyLoss().to(device) model, optimizer = amp.initialize(model, optimizer, opt_level=opt) def train_step(engine, batch): x = convert_tensor(batch[0], device, non_blocking=True) y = convert_tensor(batch[1], device, non_blocking=True) optimizer.zero_grad() y_pred = model(x) loss = criterion(y_pred, y) with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() optimizer.step() return loss.item() trainer = Engine(train_step) timer = Timer(average=True) timer.attach(trainer, step=Events.EPOCH_COMPLETED) ProgressBar(persist=True).attach(trainer, output_transform=lambda out: {"batch loss": out}) metrics = {"Accuracy": Accuracy(), "Loss": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics=metrics, device=device, non_blocking=True) def log_metrics(engine, title): for name in metrics: print(f"\t{title} {name}: {engine.state.metrics[name]:.2f}") @trainer.on(Events.COMPLETED) def run_validation(_): print(f"- Mean elapsed time for 1 epoch: {timer.value()}") print("- Metrics:") with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "Train"): evaluator.run(eval_train_loader) with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "Test"): evaluator.run(test_loader) trainer.run(train_loader, max_epochs=max_epochs) if __name__ == "__main__": fire.Fire(main) ignite-0.5.1/examples/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py000066400000000000000000000056311465426447700263520ustar00rootroot00000000000000import fire import torch from torch.cuda.amp import autocast, GradScaler from torch.nn import CrossEntropyLoss from torch.optim import SGD from torchvision.models import wide_resnet50_2 from utils import get_train_eval_loaders from ignite.engine import convert_tensor, create_supervised_evaluator, Engine, Events from ignite.handlers import ProgressBar, Timer from ignite.metrics import Accuracy, Loss def main(dataset_path, batch_size=256, max_epochs=10): assert torch.cuda.is_available() assert torch.backends.cudnn.enabled, "NVIDIA/Apex:Amp requires cudnn backend to be enabled." torch.backends.cudnn.benchmark = True device = "cuda" train_loader, test_loader, eval_train_loader = get_train_eval_loaders(dataset_path, batch_size=batch_size) model = wide_resnet50_2(num_classes=100).to(device) optimizer = SGD(model.parameters(), lr=0.01) criterion = CrossEntropyLoss().to(device) scaler = GradScaler() def train_step(engine, batch): x = convert_tensor(batch[0], device, non_blocking=True) y = convert_tensor(batch[1], device, non_blocking=True) optimizer.zero_grad() # Runs the forward pass with autocasting. with autocast(): y_pred = model(x) loss = criterion(y_pred, y) # Scales loss. Calls backward() on scaled loss to create scaled gradients. # Backward passes under autocast are not recommended. # Backward ops run in the same precision that autocast used for corresponding forward ops. scaler.scale(loss).backward() # scaler.step() first unscales the gradients of the optimizer's assigned params. # If these gradients do not contain infs or NaNs, optimizer.step() is then called, # otherwise, optimizer.step() is skipped. scaler.step(optimizer) # Updates the scale for next iteration. scaler.update() return loss.item() trainer = Engine(train_step) timer = Timer(average=True) timer.attach(trainer, step=Events.EPOCH_COMPLETED) ProgressBar(persist=True).attach(trainer, output_transform=lambda out: {"batch loss": out}) metrics = {"Accuracy": Accuracy(), "Loss": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics=metrics, device=device, non_blocking=True) def log_metrics(engine, title): for name in metrics: print(f"\t{title} {name}: {engine.state.metrics[name]:.2f}") @trainer.on(Events.COMPLETED) def run_validation(_): print(f"- Mean elapsed time for 1 epoch: {timer.value()}") print("- Metrics:") with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "Train"): evaluator.run(eval_train_loader) with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "Test"): evaluator.run(test_loader) trainer.run(train_loader, max_epochs=max_epochs) if __name__ == "__main__": fire.Fire(main) ignite-0.5.1/examples/cifar100_amp_benchmark/utils.py000066400000000000000000000035411465426447700225260ustar00rootroot00000000000000import random from torch.utils.data import DataLoader, Subset from torchvision.datasets.cifar import CIFAR100 from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomErasing, RandomHorizontalFlip, ToTensor def get_train_eval_loaders(path, batch_size=256): """Setup the dataflow: - load CIFAR100 train and test datasets - setup train/test image transforms - horizontally flipped randomly and augmented using cutout. - each mini-batch contained 256 examples - setup train/test data loaders Returns: train_loader, test_loader, eval_train_loader """ train_transform = Compose( [ Pad(4), RandomCrop(32), RandomHorizontalFlip(), ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), RandomErasing(), ] ) test_transform = Compose([ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) train_dataset = CIFAR100(root=path, train=True, transform=train_transform, download=True) test_dataset = CIFAR100(root=path, train=False, transform=test_transform, download=False) train_eval_indices = [random.randint(0, len(train_dataset) - 1) for i in range(len(test_dataset))] train_eval_dataset = Subset(train_dataset, train_eval_indices) train_loader = DataLoader( train_dataset, batch_size=batch_size, num_workers=12, shuffle=True, drop_last=True, pin_memory=True ) test_loader = DataLoader( test_dataset, batch_size=batch_size, num_workers=12, shuffle=False, drop_last=False, pin_memory=True ) eval_train_loader = DataLoader( train_eval_dataset, batch_size=batch_size, num_workers=12, shuffle=False, drop_last=False, pin_memory=True ) return train_loader, test_loader, eval_train_loader ignite-0.5.1/examples/cifar10_qat/000077500000000000000000000000001465426447700167275ustar00rootroot00000000000000ignite-0.5.1/examples/cifar10_qat/.gitignore000066400000000000000000000000331465426447700207130ustar00rootroot00000000000000output cifar10 raw_pytorch ignite-0.5.1/examples/cifar10_qat/README.md000066400000000000000000000050111465426447700202030ustar00rootroot00000000000000# Example of Quantization Aware Training (QAT) with Ignite on CIFAR10 Model's implementation is based on https://discuss.pytorch.org/t/evaluator-returns-nan/107972/3 In this example, we show how to use _Ignite_ to train a neural network: - on 1 or more GPUs - compute training/validation metrics - log learning rate, metrics etc - save the best model weights Configurations: - [x] single GPU - [x] multi GPUs on a single node ## Requirements: - pytorch-ignite: `pip install pytorch-ignite` - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` - [tensorboardx](https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` - [python-fire](https://github.com/google/python-fire): `pip install fire` - [brevitas](https://github.com/Xilinx/brevitas): `pip install git+https://github.com/Xilinx/brevitas.git` ## Usage: We can train, for example, ResNet-18 with 8 bit weights and activations. Run the example on a single GPU: ```bash CUDA_VISIBLE_DEVICES=0 python main.py run --model="resnet18_QAT_8b" ``` Note: torch DataParallel is not working (v1.7.1) with QAT. For details on accepted arguments: ```bash python main.py run -- --help ``` If user would like to provide already downloaded dataset, the path can be setup in parameters as ```bash --data_path="/path/to/cifar10/" ``` Other available models can be found [here](utils.py): - resnet18_QAT_8b - ResNet-18 with 8 bit weights and activations - resnet18_QAT_6b - ResNet-18 with 6 bit weights and activations - resnet18_QAT_5b - ResNet-18 with 5 bit weights and activations - resnet18_QAT_4b - ResNet-18 with 4 bit weights and activations - torchvision models ### Distributed training #### Single node, multiple GPUs Let's start training on a single node with 2 gpus: ```bash # using torch.distributed.launch python -u -m torch.distributed.launch --nproc_per_node=2 --use_env main.py run --backend="nccl" --model="resnet18_QAT_8b" ``` ##### Using [Horovod](https://horovod.readthedocs.io/en/latest/index.html) as distributed backend Please, make sure to have Horovod installed before running. Let's start training on a single node with 2 gpus: ```bash # horovodrun horovodrun -np=2 python -u main.py run --backend="horovod" --model="resnet18_QAT_8b" ``` or ```bash # using function spawn inside the code python -u main.py run --backend="horovod" --nproc_per_node=2 --model="resnet18_QAT_8b" ``` ### Online logs On TensorBoard.dev: https://tensorboard.dev/experiment/Kp9Wod3XR36Sg2I1gAh1cA/ ignite-0.5.1/examples/cifar10_qat/main.py000066400000000000000000000306411465426447700202310ustar00rootroot00000000000000from datetime import datetime from pathlib import Path import fire import torch import torch.nn as nn import torch.optim as optim import utils from torch.cuda.amp import autocast, GradScaler import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import create_supervised_evaluator, Engine, Events from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine, PiecewiseLinear from ignite.metrics import Accuracy, Loss from ignite.utils import manual_seed, setup_logger def training(local_rank, config): rank = idist.get_rank() manual_seed(config["seed"] + rank) device = idist.device() logger = setup_logger(name="CIFAR10-QAT-Training", distributed_rank=local_rank) log_basic_info(logger, config) output_path = config["output_path"] if rank == 0: now = datetime.now().strftime("%Y%m%d-%H%M%S") folder_name = f"{config['model']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}" output_path = Path(output_path) / folder_name if not output_path.exists(): output_path.mkdir(parents=True) config["output_path"] = output_path.as_posix() logger.info(f"Output path: {config['output_path']}") if "cuda" in device.type: config["cuda device name"] = torch.cuda.get_device_name(local_rank) if config["with_clearml"]: from clearml import Task task = Task.init("CIFAR10-Training", task_name=output_path.stem) task.connect_configuration(config) # Log hyper parameters hyper_params = [ "model", "batch_size", "momentum", "weight_decay", "num_epochs", "learning_rate", "num_warmup_epochs", ] task.connect({k: config[k] for k in hyper_params}) # Setup dataflow, model, optimizer, criterion train_loader, test_loader = get_dataflow(config) config["num_iters_per_epoch"] = len(train_loader) model, optimizer, criterion, lr_scheduler = initialize(config) # Create trainer for current task trainer = create_trainer(model, optimizer, criterion, lr_scheduler, train_loader.sampler, config, logger) # Let's now setup evaluator engine to perform model's validation and compute metrics metrics = { "Accuracy": Accuracy(), "Loss": Loss(criterion), } # We define two evaluators as they wont have exactly similar roles: # - `evaluator` will save the best model based on validation score evaluator = create_supervised_evaluator(model, metrics=metrics, device=device, non_blocking=True) train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device, non_blocking=True) def run_validation(engine): epoch = trainer.state.epoch state = train_evaluator.run(train_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(test_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) trainer.add_event_handler(Events.EPOCH_COMPLETED(every=config["validate_every"]) | Events.COMPLETED, run_validation) if rank == 0: # Setup TensorBoard logging on trainer and evaluators. Logged values are: # - Training metrics, e.g. running average loss values # - Learning rate # - Evaluation train/test metrics evaluators = {"training": train_evaluator, "test": evaluator} tb_logger = common.setup_tb_logging(output_path, trainer, optimizer, evaluators=evaluators) # Store 2 best models by validation accuracy starting from num_epochs / 2: best_model_handler = Checkpoint( {"model": model}, get_save_handler(config), filename_prefix="best", n_saved=2, global_step_transform=global_step_from_engine(trainer), score_name="test_accuracy", score_function=Checkpoint.get_default_score_fn("Accuracy"), ) evaluator.add_event_handler( Events.COMPLETED(lambda *_: trainer.state.epoch > config["num_epochs"] // 2), best_model_handler ) try: trainer.run(train_loader, max_epochs=config["num_epochs"]) except Exception as e: logger.exception("") raise e if rank == 0: tb_logger.close() def run( seed=543, data_path="/tmp/cifar10", output_path="/tmp/output-cifar10/", model="resnet18_QAT_8b", batch_size=512, momentum=0.9, weight_decay=1e-4, num_workers=12, num_epochs=24, learning_rate=0.4, num_warmup_epochs=4, validate_every=3, checkpoint_every=1000, backend=None, resume_from=None, log_every_iters=15, nproc_per_node=None, with_clearml=False, with_amp=False, **spawn_kwargs, ): """Main entry to train an model on CIFAR10 dataset. Args: seed (int): random state seed to set. Default, 543. data_path (str): input dataset path. Default, "/tmp/cifar10". output_path (str): output path. Default, "/tmp/output-cifar10". model (str): model name (from torchvision) to setup model to train. Default, "resnet18". batch_size (int): total batch size. Default, 512. momentum (float): optimizer's momentum. Default, 0.9. weight_decay (float): weight decay. Default, 1e-4. num_workers (int): number of workers in the data loader. Default, 12. num_epochs (int): number of epochs to train the model. Default, 24. learning_rate (float): peak of piecewise linear learning rate scheduler. Default, 0.4. num_warmup_epochs (int): number of warm-up epochs before learning rate decay. Default, 4. validate_every (int): run model's validation every ``validate_every`` epochs. Default, 3. checkpoint_every (int): store training checkpoint every ``checkpoint_every`` iterations. Default, 200. backend (str, optional): backend to use for distributed configuration. Possible values: None, "nccl", "xla-tpu", "gloo" etc. Default, None. nproc_per_node (int, optional): optional argument to setup number of processes per node. It is useful, when main python process is spawning training as child processes. resume_from (str, optional): path to checkpoint to use to resume the training from. Default, None. log_every_iters (int): argument to log batch loss every ``log_every_iters`` iterations. It can be 0 to disable it. Default, 15. with_clearml (bool): if True, experiment ClearML logger is setup. Default, False. with_amp (bool): if True, enables native automatic mixed precision. Default, False. **spawn_kwargs: Other kwargs to spawn run in child processes: master_addr, master_port, node_rank, nnodes """ # check to see if the num_epochs is greater than or equal to num_warmup_epochs if num_warmup_epochs >= num_epochs: raise ValueError( "num_epochs cannot be less than or equal to num_warmup_epochs, please increase num_epochs or decrease " "num_warmup_epochs" ) # catch all local parameters config = locals() config.update(config["spawn_kwargs"]) del config["spawn_kwargs"] spawn_kwargs["nproc_per_node"] = nproc_per_node with idist.Parallel(backend=backend, **spawn_kwargs) as parallel: parallel.run(training, config) def get_dataflow(config): # - Get train/test datasets with idist.one_rank_first(local=True): train_dataset, test_dataset = utils.get_train_test_datasets(config["data_path"]) # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu train_loader = idist.auto_dataloader( train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True ) test_loader = idist.auto_dataloader( test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False ) return train_loader, test_loader def initialize(config): model = utils.get_model(config["model"]) # Adapt model for distributed settings if configured model = idist.auto_model(model, find_unused_parameters=True) optimizer = optim.SGD( model.parameters(), lr=config["learning_rate"], momentum=config["momentum"], weight_decay=config["weight_decay"], nesterov=True, ) optimizer = idist.auto_optim(optimizer) criterion = nn.CrossEntropyLoss().to(idist.device()) le = config["num_iters_per_epoch"] milestones_values = [ (0, 0.0), (le * config["num_warmup_epochs"], config["learning_rate"]), (le * config["num_epochs"], 0.0), ] lr_scheduler = PiecewiseLinear(optimizer, param_name="lr", milestones_values=milestones_values) return model, optimizer, criterion, lr_scheduler def log_metrics(logger, epoch, elapsed, tag, metrics): metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) logger.info(f"\nEpoch {epoch} - Evaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): logger.info(f"Quantization Aware Training {config['model']} on CIFAR10") logger.info(f"- PyTorch version: {torch.__version__}") logger.info(f"- Ignite version: {ignite.__version__}") if torch.cuda.is_available(): # explicitly import cudnn as # torch.backends.cudnn can not be pickled with hvd spawning procs from torch.backends import cudnn logger.info(f"- GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}") logger.info(f"- CUDA version: {torch.version.cuda}") logger.info(f"- CUDNN version: {cudnn.version()}") logger.info("\n") logger.info("Configuration:") for key, value in config.items(): logger.info(f"\t{key}: {value}") logger.info("\n") if idist.get_world_size() > 1: logger.info("\nDistributed setting:") logger.info(f"\tbackend: {idist.backend()}") logger.info(f"\tworld size: {idist.get_world_size()}") logger.info("\n") def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, config, logger): device = idist.device() # Setup Ignite trainer: # - let's define training step # - add other common handlers: # - TerminateOnNan, # - handler to setup learning rate scheduling, # - ModelCheckpoint # - RunningAverage` on `train_step` output # - Two progress bars on epochs and optionally on iterations with_amp = config["with_amp"] scaler = GradScaler(enabled=with_amp) def train_step(engine, batch): x, y = batch[0], batch[1] if x.device != device: x = x.to(device, non_blocking=True) y = y.to(device, non_blocking=True) model.train() with autocast(enabled=with_amp): y_pred = model(x) loss = criterion(y_pred, y) optimizer.zero_grad() scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() return { "batch loss": loss.item(), } trainer = Engine(train_step) trainer.logger = logger to_save = {"trainer": trainer, "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler} metric_names = [ "batch loss", ] common.setup_common_training_handlers( trainer=trainer, train_sampler=train_sampler, to_save=to_save, save_every_iters=config["checkpoint_every"], save_handler=get_save_handler(config), lr_scheduler=lr_scheduler, output_names=metric_names if config["log_every_iters"] > 0 else None, with_pbars=False, clear_cuda_cache=False, ) resume_from = config["resume_from"] if resume_from is not None: checkpoint_fp = Path(resume_from) assert checkpoint_fp.exists(), f"Checkpoint '{checkpoint_fp.as_posix()}' is not found" logger.info(f"Resume from a checkpoint: {checkpoint_fp.as_posix()}") checkpoint = torch.load(checkpoint_fp.as_posix(), map_location="cpu") Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint) return trainer def get_save_handler(config): if config["with_clearml"]: from ignite.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=config["output_path"]) return DiskSaver(config["output_path"], require_empty=False) if __name__ == "__main__": fire.Fire({"run": run}) ignite-0.5.1/examples/cifar10_qat/pact.py000066400000000000000000000014571465426447700202370ustar00rootroot00000000000000# Implementation taken from https://discuss.pytorch.org/t/evaluator-returns-nan/107972/3 # Ref: https://arxiv.org/abs/1805.06085 import torch import torch.nn as nn class PACTClip(torch.autograd.Function): @staticmethod def forward(ctx, x, alpha): ctx.save_for_backward(x, alpha) return torch.clamp(x, 0, alpha.data) @staticmethod def backward(ctx, dy): x, alpha = ctx.saved_tensors dx = dy.clone() dx[x < 0] = 0 dx[x > alpha] = 0 dalpha = dy.clone() dalpha[x <= alpha] = 0 return dx, torch.sum(dalpha) class PACTReLU(nn.Module): def __init__(self, alpha=6.0): super().__init__() self.alpha = nn.Parameter(torch.tensor(alpha)) def forward(self, x): return PACTClip.apply(x, self.alpha) ignite-0.5.1/examples/cifar10_qat/utils.py000066400000000000000000000250731465426447700204500ustar00rootroot00000000000000import os from pathlib import Path import brevitas.nn as qnn import torch import torch.nn as nn from pact import PACTReLU from torchvision import datasets, models from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor train_transform = Compose( [ Pad(4), RandomCrop(32, fill=128), RandomHorizontalFlip(), ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ] ) test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]) def get_train_test_datasets(path): path = Path(path) if not path.exists(): path.mkdir(parents=True) download = True else: download = True if len(os.listdir(path)) < 1 else False train_ds = datasets.CIFAR10(root=path, train=True, download=download, transform=train_transform) test_ds = datasets.CIFAR10(root=path, train=False, download=False, transform=test_transform) return train_ds, test_ds def get_model(name): __dict__ = globals() if name in models.__dict__: fn = models.__dict__[name] elif name in ["resnet18_QAT_8b", "resnet18_QAT_6b", "resnet18_QAT_5b", "resnet18_QAT_4b"]: fn = __dict__[name] else: raise RuntimeError("Unknown model name {}".format(name)) return fn(num_classes=10) # Below code is taken from https://discuss.pytorch.org/t/evaluator-returns-nan/107972/3 def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1, weight_bit_width=8): """3x3 convolution with padding""" return qnn.QuantConv2d( in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation, weight_bit_width=weight_bit_width, ) def conv1x1(in_planes, out_planes, stride=1, weight_bit_width=8): """1x1 convolution""" return qnn.QuantConv2d( in_planes, out_planes, kernel_size=1, stride=stride, bias=False, weight_bit_width=weight_bit_width ) def make_PACT_relu(bit_width=8): relu = qnn.QuantReLU(bit_width=bit_width) relu.act_impl = PACTReLU() return relu class BasicBlock(nn.Module): expansion = 1 def __init__( self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, bit_width=8, ): super().__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError("BasicBlock only supports groups=1 and base_width=64") if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride, weight_bit_width=bit_width) self.bn1 = norm_layer(planes) self.relu = make_PACT_relu(bit_width=bit_width) self.conv2 = conv3x3(planes, planes, weight_bit_width=bit_width) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class Bottleneck(nn.Module): # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) # while original implementation places the stride at the first 1x1 convolution(self.conv1) # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. # This variant is also known as ResNet V1.5 and improves accuracy according to # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. expansion = 4 def __init__( self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, bit_width=8, ): super().__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.0)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width, weight_bit_width=bit_width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation, weight_bit_width=bit_width) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion, weight_bit_width=bit_width) self.bn3 = norm_layer(planes * self.expansion) self.relu = make_PACT_relu(bit_width=bit_width) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class ResNet_QAT_Xb(nn.Module): def __init__( self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, bit_width=8, ): super().__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError( "replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation) ) self.groups = groups self.base_width = width_per_group self.conv1 = qnn.QuantConv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = make_PACT_relu() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0], bit_width=bit_width) self.layer2 = self._make_layer( block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], bit_width=bit_width ) self.layer3 = self._make_layer( block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], bit_width=bit_width ) self.layer4 = self._make_layer( block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], bit_width=bit_width ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): # qnn.QuantConv2d includes nn.Conv2d inside. nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False, bit_width=8): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride, weight_bit_width=bit_width), norm_layer(planes * block.expansion), ) layers = [] layers.append( block( self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer, bit_width=bit_width, ) ) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append( block( self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer, bit_width=bit_width, ) ) return nn.Sequential(*layers) def _forward_impl(self, x): # See note [TorchScript super()] x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x): return self._forward_impl(x) def _resnet_QAT_Xb(block, layers, **kwargs): model = ResNet_QAT_Xb(block, layers, **kwargs) return model def resnet18_QAT_8b(*args, **kwargs): return _resnet_QAT_Xb(BasicBlock, [2, 2, 2, 2], **kwargs) def resnet18_QAT_6b(*args, **kwargs): return _resnet_QAT_Xb(BasicBlock, [2, 2, 2, 2], bit_width=6, **kwargs) def resnet18_QAT_5b(*args, **kwargs): return _resnet_QAT_Xb(BasicBlock, [2, 2, 2, 2], bit_width=5, **kwargs) def resnet18_QAT_4b(*args, **kwargs): return _resnet_QAT_Xb(BasicBlock, [2, 2, 2, 2], bit_width=4, **kwargs) ignite-0.5.1/examples/fast_neural_style/000077500000000000000000000000001465426447700203605ustar00rootroot00000000000000ignite-0.5.1/examples/fast_neural_style/README.md000066400000000000000000000062361465426447700216460ustar00rootroot00000000000000# fast-neural-style ### Introduction This example is ported over from [pytorch-examples](https://github.com/pytorch/examples/tree/master/fast_neural_style). It uses `ignite` to implement an algorithm for artistic style transfer as described in [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155).

### Requirements - `torch` - `torchvision` - `ignite` Example for `virtualenv` setup: `virtualenv --python=/usr/bin/python3.5 env` `source env/bin/activate` `pip install torch torchvision pytorch-ignite` The code runs on CPU, but GPU allows it to run much faster. If using GPU, please ensure proper libraries are installed. ### Documentation #### Training Code can be used to train a style transfer model for any image. To run code correctly, ensure that [MSCOCO dataset](http://images.cocodataset.org/zips/train2014.zip) and a style image are downloaded. Since the code using Pytorch's Dataset functions, ensure that directory with MSCOCO dataset is formatted as shown below. The directory should be setup such that the location of the dataset is MSCOCO, which contains a single folder 0, containing all the images. ```bash β”œβ”€β”€ MSCOCO β”‚ β”œβ”€β”€ 0 β”‚ β”‚ β”œβ”€β”€ RY48TY43YT.jpg β”‚ β”‚ β”œβ”€β”€ 4324J0FNFL.jpg β”‚ β”‚ β”œβ”€β”€ Y9REWJKNFE.jpg ``` ##### Example `python neural_style.py train --epochs 2 --cuda 1 --dataset mscoco --dataroot /path/to/mscoco --style_image ./images/style_images/mosaic.jpg` ##### Flags - `--epochs`: number of training epochs, default is 2. - `--batch_size`: batch size for training, default is 8. - `--dataset`: type of dataset. - `--dataroot`: path to training dataset, the path should point to a folder containing another folder with all the training images. - `--style_image`: path to style-image. - `--checkpoint_model_dir`: path to folder where checkpoints of trained models will be saved. - `--checkpoint_interval`: number of batches after which a checkpoint of trained model will be created. - `--image_size`: size of training images, default is 256 X 256. - `--style_size`: size of style-image, default is the original size of style image. - `--cuda`: set it to 1 for running on GPU, 0 for CPU. - `--seed`: random seed for training. - `--content_weight`: weight for content-loss, default is 1e5. - `--style_weight`: weight for style-loss, default is 1e10. - `--lr`: learning rate, default is 1e-3. #### Evaluation Code can be used to stylize an image using a trained style transfer model. ##### Example `python neural_style.py eval --content_image ./images/content_images/amber.jpg --output_image test.png --cuda 1 --model /tmp/checkpoints/checkpoint_net_2.pth` #### Flags - `--content_image`: path to content image you want to stylize. - `--content_scale`: factor for scaling down the content image. - `--output_image`: path for saving the output image. - `--model`: saved model to be used for stylizing the image. - `--cuda`: set it to 1 for running on GPU, 0 for CPU. ignite-0.5.1/examples/fast_neural_style/handlers.py000066400000000000000000000022401465426447700225300ustar00rootroot00000000000000import sys class Progbar(object): def __init__(self, loader, metrics): self.num_iterations = len(loader) self.output_stream = sys.stdout self.metrics = metrics self.alpha = 0.98 def _calc_running_avg(self, engine): for k, v in engine.state.output.items(): old_v = self.metrics.get(k, v) new_v = self.alpha * old_v + (1 - self.alpha) * v self.metrics[k] = new_v def __call__(self, engine): self._calc_running_avg(engine) num_seen = engine.state.iteration - self.num_iterations * (engine.state.epoch - 1) percent_seen = 100 * float(num_seen) / self.num_iterations equal_to = int(percent_seen / 10) done = int(percent_seen) == 100 bar = "[" + "=" * equal_to + ">" * (not done) + " " * (10 - equal_to) + "]" message = f"Epoch {engine.state.epoch} | {percent_seen:.2f}% | {bar}" for key, value in self.metrics.items(): message += f" | {key}: {value:.2e}" message += "\r" 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torchvision import datasets, transforms from transformer_net import TransformerNet from vgg import Vgg16 from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint def check_paths(args): try: if args.checkpoint_model_dir is not None and not (Path(args.checkpoint_model_dir).exists()): Path(args.checkpoint_model_dir).mkdir(parents=True) except OSError as e: raise OSError(e) def check_manual_seed(args): seed = args.seed or random.randint(1, 10000) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def check_dataset(args): transform = transforms.Compose( [ transforms.Resize(args.image_size), transforms.CenterCrop(args.image_size), transforms.ToTensor(), transforms.Lambda(lambda x: x.mul(255)), ] ) if args.dataset in {"folder", "mscoco"}: train_dataset = datasets.ImageFolder(args.dataroot, transform) elif args.dataset == "test": train_dataset = datasets.FakeData( size=args.batch_size, image_size=(3, 32, 32), num_classes=1, transform=transform ) else: raise RuntimeError(f"Invalid dataset name: {args.dataset}") train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=0) return train_loader def train(args): device = torch.device("cuda" if args.cuda else "cpu") train_loader = check_dataset(args) transformer = TransformerNet().to(device) optimizer = Adam(transformer.parameters(), args.lr) mse_loss = torch.nn.MSELoss() vgg = Vgg16(requires_grad=False).to(device) style_transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(lambda x: x.mul(255))]) style = utils.load_image(args.style_image, size=args.style_size) style = style_transform(style) style = style.repeat(args.batch_size, 1, 1, 1).to(device) features_style = vgg(utils.normalize_batch(style)) gram_style = [utils.gram_matrix(y) for y in features_style] running_avgs = OrderedDict() def step(engine, batch): x, _ = batch x = x.to(device) n_batch = len(x) optimizer.zero_grad() y = transformer(x) x = utils.normalize_batch(x) y = utils.normalize_batch(y) features_x = vgg(x) features_y = vgg(y) content_loss = args.content_weight * mse_loss(features_y.relu2_2, features_x.relu2_2) style_loss = 0.0 for ft_y, gm_s in zip(features_y, gram_style): gm_y = utils.gram_matrix(ft_y) style_loss += mse_loss(gm_y, gm_s[:n_batch, :, :]) style_loss *= args.style_weight total_loss = content_loss + style_loss total_loss.backward() optimizer.step() return {"content_loss": content_loss.item(), "style_loss": style_loss.item(), "total_loss": total_loss.item()} trainer = Engine(step) checkpoint_handler = ModelCheckpoint( args.checkpoint_model_dir, "checkpoint", n_saved=10, require_empty=False, create_dir=True ) progress_bar = Progbar(loader=train_loader, metrics=running_avgs) trainer.add_event_handler( event_name=Events.EPOCH_COMPLETED(every=args.checkpoint_interval), handler=checkpoint_handler, to_save={"net": transformer}, ) trainer.add_event_handler(event_name=Events.ITERATION_COMPLETED, handler=progress_bar) trainer.run(train_loader, max_epochs=args.epochs) def stylize(args): device = torch.device("cuda" if args.cuda else "cpu") content_transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(lambda x: x.mul(255))]) content_image = utils.load_image(args.content_image, scale=args.content_scale) content_image = content_transform(content_image) content_image = content_image.unsqueeze(0).to(device) with torch.no_grad(): style_model = torch.load(args.model) style_model.to(device) output = style_model(content_image).cpu() utils.save_image(args.output_image, output[0]) def main(): main_arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style") subparsers = main_arg_parser.add_subparsers(title="subcommands", dest="subcommand") train_arg_parser = subparsers.add_parser("train", help="parser for training arguments") train_arg_parser.add_argument("--epochs", type=int, default=2, help="number of training epochs, default is 2") train_arg_parser.add_argument("--batch_size", type=int, default=8, help="batch size for training, default is 8") train_arg_parser.add_argument( "--dataset", type=str, required=True, choices={"test", "folder", "mscoco"}, help="type of dataset to be used." ) train_arg_parser.add_argument( "--dataroot", type=str, required=True, help="path to training dataset, the path should point to a folder " "containing another folder with all the training images", ) train_arg_parser.add_argument("--style_image", type=str, default="test", help="path to style-image") train_arg_parser.add_argument("--test_image", type=str, default="test", help="path to test-image") train_arg_parser.add_argument( "--checkpoint_model_dir", type=str, default="/tmp/checkpoints", help="path to folder where checkpoints of trained models will be saved", ) train_arg_parser.add_argument( "--checkpoint_interval", type=int, default=1, help="number of batches after which a checkpoint of trained model will be created", ) train_arg_parser.add_argument( "--image_size", type=int, default=256, help="size of training images, default is 256 X 256" ) train_arg_parser.add_argument( "--style_size", type=int, default=None, help="size of style-image, default is the original size of style image" ) train_arg_parser.add_argument("--cuda", type=int, default=1, help="set it to 1 for running on GPU, 0 for CPU") train_arg_parser.add_argument("--seed", type=int, default=42, help="random seed for training") train_arg_parser.add_argument( "--content_weight", type=float, default=1e5, help="weight for content-loss, default is 1e5" ) train_arg_parser.add_argument( "--style_weight", type=float, default=1e10, help="weight for style-loss, default is 1e10" ) train_arg_parser.add_argument("--lr", type=float, default=1e-3, help="learning rate, default is 1e-3") eval_arg_parser = subparsers.add_parser("eval", help="parser for evaluation/stylizing arguments") eval_arg_parser.add_argument( "--content_image", type=str, required=True, help="path to content image you want to stylize" ) eval_arg_parser.add_argument( "--content_scale", type=float, default=None, help="factor for scaling down the content image" ) eval_arg_parser.add_argument("--output_image", type=str, required=True, help="path for saving the output image") eval_arg_parser.add_argument( "--model", type=str, required=True, help="saved model to be used for stylizing the image." ) eval_arg_parser.add_argument("--cuda", type=int, required=True, help="set it to 1 for running on GPU, 0 for CPU") args = main_arg_parser.parse_args() if args.subcommand is None: raise ValueError("ERROR: specify either train or eval") if args.cuda and not torch.cuda.is_available(): raise ValueError("ERROR: cuda is not available, try running on CPU") if args.subcommand == "train": check_manual_seed(args) check_paths(args) train(args) else: stylize(args) if __name__ == "__main__": main() ignite-0.5.1/examples/fast_neural_style/transformer_net.py000066400000000000000000000073061465426447700241500ustar00rootroot00000000000000import torch class TransformerNet(torch.nn.Module): def __init__(self): super(TransformerNet, self).__init__() # Initial convolution layers self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1) self.in1 = torch.nn.InstanceNorm2d(32, affine=True) self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2) self.in2 = torch.nn.InstanceNorm2d(64, affine=True) self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2) self.in3 = torch.nn.InstanceNorm2d(128, affine=True) # Residual layers self.res1 = ResidualBlock(128) self.res2 = ResidualBlock(128) self.res3 = ResidualBlock(128) self.res4 = ResidualBlock(128) self.res5 = ResidualBlock(128) # Upsampling Layers self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2) self.in4 = torch.nn.InstanceNorm2d(64, affine=True) self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2) self.in5 = torch.nn.InstanceNorm2d(32, affine=True) self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1) # Non-linearities self.relu = torch.nn.ReLU() def forward(self, X): y = self.relu(self.in1(self.conv1(X))) y = self.relu(self.in2(self.conv2(y))) y = self.relu(self.in3(self.conv3(y))) y = self.res1(y) y = self.res2(y) y = self.res3(y) y = self.res4(y) y = self.res5(y) y = self.relu(self.in4(self.deconv1(y))) y = self.relu(self.in5(self.deconv2(y))) y = self.deconv3(y) return y class ConvLayer(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride): super(ConvLayer, self).__init__() reflection_padding = kernel_size // 2 self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding) self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride) def forward(self, x): out = self.reflection_pad(x) out = self.conv2d(out) return out class ResidualBlock(torch.nn.Module): """ResidualBlock introduced in: https://arxiv.org/abs/1512.03385 recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html """ def __init__(self, channels): super(ResidualBlock, self).__init__() self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1) self.in1 = torch.nn.InstanceNorm2d(channels, affine=True) self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1) self.in2 = torch.nn.InstanceNorm2d(channels, affine=True) self.relu = torch.nn.ReLU() def forward(self, x): residual = x out = self.relu(self.in1(self.conv1(x))) out = self.in2(self.conv2(out)) out = out + residual return out class UpsampleConvLayer(torch.nn.Module): """UpsampleConvLayer Upsamples the input and then does a convolution. This method gives better results compared to ConvTranspose2d. ref: http://distill.pub/2016/deconv-checkerboard/ """ def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None): super(UpsampleConvLayer, self).__init__() self.upsample = upsample reflection_padding = kernel_size // 2 self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding) self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride) def forward(self, x): x_in = x if self.upsample: x_in = torch.nn.functional.interpolate(x_in, mode="nearest", scale_factor=self.upsample) out = self.reflection_pad(x_in) out = self.conv2d(out) return out ignite-0.5.1/examples/fast_neural_style/utils.py000066400000000000000000000017051465426447700220750ustar00rootroot00000000000000from PIL import Image def load_image(filename, size=None, scale=None): img = Image.open(filename) if size is not None: img = img.resize((size, size), Image.LANCZOS) elif scale is not None: img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.LANCZOS) return img def save_image(filename, data): img = data.clone().clamp(0, 255).numpy() img = img.transpose(1, 2, 0).astype("uint8") img = Image.fromarray(img) img.save(filename) def gram_matrix(y): (b, ch, h, w) = y.size() features = y.view(b, ch, w * h) features_t = features.transpose(1, 2) gram = features.bmm(features_t) / (ch * h * w) return gram def normalize_batch(batch): # normalize using imagenet mean and std mean = batch.new_tensor([0.485, 0.456, 0.406]).view(-1, 1, 1) std = batch.new_tensor([0.229, 0.224, 0.225]).view(-1, 1, 1) batch = batch.div_(255.0) return (batch - mean) / std ignite-0.5.1/examples/fast_neural_style/vgg.py000066400000000000000000000026401465426447700215170ustar00rootroot00000000000000from collections import namedtuple import torch from torchvision import models from torchvision.models.vgg import VGG16_Weights class Vgg16(torch.nn.Module): def __init__(self, requires_grad=False): super(Vgg16, self).__init__() vgg_pretrained_features = models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() for x in range(4): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(4, 9): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(9, 16): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(16, 23): self.slice4.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h = self.slice1(X) h_relu1_2 = h h = self.slice2(h) h_relu2_2 = h h = self.slice3(h) h_relu3_3 = h h = self.slice4(h) h_relu4_3 = h vgg_outputs = namedtuple("VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3"]) out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3) return out ignite-0.5.1/examples/gan/000077500000000000000000000000001465426447700154025ustar00rootroot00000000000000ignite-0.5.1/examples/gan/README.md000066400000000000000000000004471465426447700166660ustar00rootroot00000000000000# Deep Convolution Generative Adversarial Networks with Ignite ported from [pytorch-examples](https://github.com/pytorch/examples/tree/master/dcgan) Usage: For example, run on CIFAR10 dataset: ``` python dcgan.py --dataset cifar10 --dataroot /tmp/cifar10 --output-dir /tmp/outputs-dcgan ``` ignite-0.5.1/examples/gan/dcgan.py000066400000000000000000000365351465426447700170440ustar00rootroot00000000000000import argparse import os import random import warnings from pathlib import Path import torch import torch.nn as nn import torch.optim as optim import torch.utils.data as data from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint, ProgressBar, Timer from ignite.metrics import RunningAverage try: import torchvision.datasets as dset import torchvision.transforms as transforms import torchvision.utils as vutils except ImportError: raise ModuleNotFoundError( "Please install torchvision to run this example, for example " "via conda by running 'conda install -c pytorch torchvision'. " ) PRINT_FREQ = 100 FAKE_IMG_FNAME = "fake_sample_epoch_{:04d}.png" REAL_IMG_FNAME = "real_sample_epoch_{:04d}.png" LOGS_FNAME = "logs.tsv" PLOT_FNAME = "plot.svg" SAMPLES_FNAME = "samples.svg" CKPT_PREFIX = "networks" class Net(nn.Module): """A base class for both generator and the discriminator. Provides a common weight initialization scheme. """ def weights_init(self): for m in self.modules(): classname = m.__class__.__name__ if "Conv" in classname: m.weight.data.normal_(0.0, 0.02) elif "BatchNorm" in classname: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def forward(self, x): return x class Generator(Net): """Generator network. Args: nf (int): Number of filters in the second-to-last deconv layer """ def __init__(self, z_dim, nf, nc): super(Generator, self).__init__() self.net = nn.Sequential( # input is Z, going into a convolution nn.ConvTranspose2d(in_channels=z_dim, out_channels=nf * 8, kernel_size=4, stride=1, padding=0, bias=False), nn.BatchNorm2d(nf * 8), nn.ReLU(inplace=True), # state size. (nf*8) x 4 x 4 nn.ConvTranspose2d(in_channels=nf * 8, out_channels=nf * 4, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(nf * 4), nn.ReLU(inplace=True), # state size. (nf*4) x 8 x 8 nn.ConvTranspose2d(in_channels=nf * 4, out_channels=nf * 2, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(nf * 2), nn.ReLU(inplace=True), # state size. (nf*2) x 16 x 16 nn.ConvTranspose2d(in_channels=nf * 2, out_channels=nf, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(nf), nn.ReLU(inplace=True), # state size. (nf) x 32 x 32 nn.ConvTranspose2d(in_channels=nf, out_channels=nc, kernel_size=4, stride=2, padding=1, bias=False), nn.Tanh(), # state size. (nc) x 64 x 64 ) self.weights_init() def forward(self, x): return self.net(x) class Discriminator(Net): """Discriminator network. Args: nf (int): Number of filters in the first conv layer. """ def __init__(self, nc, nf): super(Discriminator, self).__init__() self.net = nn.Sequential( # input is (nc) x 64 x 64 nn.Conv2d(in_channels=nc, out_channels=nf, kernel_size=4, stride=2, padding=1, bias=False), nn.LeakyReLU(0.2, inplace=True), # state size. (nf) x 32 x 32 nn.Conv2d(in_channels=nf, out_channels=nf * 2, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(nf * 2), nn.LeakyReLU(0.2, inplace=True), # state size. (nf*2) x 16 x 16 nn.Conv2d(in_channels=nf * 2, out_channels=nf * 4, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(nf * 4), nn.LeakyReLU(0.2, inplace=True), # state size. (nf*4) x 8 x 8 nn.Conv2d(in_channels=nf * 4, out_channels=nf * 8, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(nf * 8), nn.LeakyReLU(0.2, inplace=True), # state size. (nf*8) x 4 x 4 nn.Conv2d(in_channels=nf * 8, out_channels=1, kernel_size=4, stride=1, padding=0, bias=False), nn.Sigmoid(), ) self.weights_init() def forward(self, x): output = self.net(x) return output.view(-1, 1).squeeze(1) def check_manual_seed(seed): """If manual seed is not specified, choose a random one and communicate it to the user.""" seed = seed or random.randint(1, 10000) random.seed(seed) torch.manual_seed(seed) print(f"Using manual seed: {seed}") def check_dataset(dataset, dataroot): """ Args: dataset (str): Name of the dataset to use. See CLI help for details dataroot (str): root directory where the dataset will be stored. Returns: dataset (data.Dataset): torchvision Dataset object """ resize = transforms.Resize(64) crop = transforms.CenterCrop(64) to_tensor = transforms.ToTensor() normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) if dataset in {"imagenet", "folder", "lfw"}: dataset = dset.ImageFolder(root=dataroot, transform=transforms.Compose([resize, crop, to_tensor, normalize])) nc = 3 elif dataset == "lsun": dataset = dset.LSUN( root=dataroot, classes=["bedroom_train"], transform=transforms.Compose([resize, crop, to_tensor, normalize]) ) nc = 3 elif dataset == "cifar10": dataset = dset.CIFAR10( root=dataroot, download=True, transform=transforms.Compose([resize, to_tensor, normalize]) ) nc = 3 elif dataset == "mnist": dataset = dset.MNIST(root=dataroot, download=True, transform=transforms.Compose([resize, to_tensor, normalize])) nc = 1 elif dataset == "fake": dataset = dset.FakeData(size=256, image_size=(3, 64, 64), transform=to_tensor) nc = 3 else: raise RuntimeError(f"Invalid dataset name: {dataset}") return dataset, nc def main( dataset, dataroot, z_dim, g_filters, d_filters, batch_size, epochs, learning_rate, beta_1, saved_G, saved_D, seed, n_workers, device, alpha, output_dir, ): # seed check_manual_seed(seed) # data dataset, num_channels = check_dataset(dataset, dataroot) loader = data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=n_workers, drop_last=True) # netowrks netG = Generator(z_dim, g_filters, num_channels).to(device) netD = Discriminator(num_channels, d_filters).to(device) # criterion bce = nn.BCELoss() # optimizers optimizerG = optim.Adam(netG.parameters(), lr=learning_rate, betas=(beta_1, 0.999)) optimizerD = optim.Adam(netD.parameters(), lr=learning_rate, betas=(beta_1, 0.999)) # load pre-trained models if saved_G: netG.load_state_dict(torch.load(saved_G)) if saved_D: netD.load_state_dict(torch.load(saved_D)) # misc real_labels = torch.ones(batch_size, device=device) fake_labels = torch.zeros(batch_size, device=device) fixed_noise = torch.randn(batch_size, z_dim, 1, 1, device=device) def get_noise(): return torch.randn(batch_size, z_dim, 1, 1, device=device) # The main function, processing a batch of examples def step(engine, batch): # unpack the batch. It comes from a dataset, so we have pairs. Discard labels. real, _ = batch real = real.to(device) # ----------------------------------------------------------- # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) netD.zero_grad() # train with real output = netD(real) errD_real = bce(output, real_labels) D_x = output.mean().item() errD_real.backward() # get fake image from generator noise = get_noise() fake = netG(noise) # train with fake output = netD(fake.detach()) errD_fake = bce(output, fake_labels) D_G_z1 = output.mean().item() errD_fake.backward() # gradient update errD = errD_real + errD_fake optimizerD.step() # ----------------------------------------------------------- # (2) Update G network: maximize log(D(G(z))) netG.zero_grad() # Update generator. We want to make a step that will make it more likely that discriminator outputs "real" output = netD(fake) errG = bce(output, real_labels) D_G_z2 = output.mean().item() errG.backward() # gradient update optimizerG.step() return {"errD": errD.item(), "errG": errG.item(), "D_x": D_x, "D_G_z1": D_G_z1, "D_G_z2": D_G_z2} # ignite objects trainer = Engine(step) checkpoint_handler = ModelCheckpoint(output_dir, CKPT_PREFIX, n_saved=10, require_empty=False) timer = Timer(average=True) # attach running average metrics monitoring_metrics = ["errD", "errG", "D_x", "D_G_z1", "D_G_z2"] RunningAverage(alpha=alpha, output_transform=lambda x: x["errD"]).attach(trainer, "errD") RunningAverage(alpha=alpha, output_transform=lambda x: x["errG"]).attach(trainer, "errG") RunningAverage(alpha=alpha, output_transform=lambda x: x["D_x"]).attach(trainer, "D_x") RunningAverage(alpha=alpha, output_transform=lambda x: x["D_G_z1"]).attach(trainer, "D_G_z1") RunningAverage(alpha=alpha, output_transform=lambda x: x["D_G_z2"]).attach(trainer, "D_G_z2") # attach progress bar pbar = ProgressBar() pbar.attach(trainer, metric_names=monitoring_metrics) @trainer.on(Events.ITERATION_COMPLETED(every=PRINT_FREQ)) def print_logs(engine): fname = output_dir / LOGS_FNAME columns = ["iteration"] + list(engine.state.metrics.keys()) values = [str(engine.state.iteration)] + [str(round(value, 5)) for value in engine.state.metrics.values()] with open(fname, "a") as f: if f.tell() == 0: print("\t".join(columns), file=f) print("\t".join(values), file=f) message = f"[{engine.state.epoch}/{epochs}][{engine.state.iteration % len(loader)}/{len(loader)}]" for name, value in zip(columns, values): message += f" | {name}: {value}" pbar.log_message(message) # adding handlers using `trainer.on` decorator API @trainer.on(Events.EPOCH_COMPLETED) def save_fake_example(engine): fake = netG(fixed_noise) path = output_dir / FAKE_IMG_FNAME.format(engine.state.epoch) vutils.save_image(fake.detach(), path, normalize=True) # adding handlers using `trainer.on` decorator API @trainer.on(Events.EPOCH_COMPLETED) def save_real_example(engine): img, y = engine.state.batch path = output_dir / REAL_IMG_FNAME.format(engine.state.epoch) vutils.save_image(img, path, normalize=True) # adding handlers using `trainer.add_event_handler` method API trainer.add_event_handler( event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={"netG": netG, "netD": netD} ) # automatically adding handlers via a special `attach` method of `Timer` handler timer.attach( trainer, start=Events.EPOCH_STARTED, resume=Events.ITERATION_STARTED, pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED, ) # adding handlers using `trainer.on` decorator API @trainer.on(Events.EPOCH_COMPLETED) def print_times(engine): pbar.log_message(f"Epoch {engine.state.epoch} done. Time per batch: {timer.value():.3f}[s]") timer.reset() # adding handlers using `trainer.on` decorator API @trainer.on(Events.EPOCH_COMPLETED) def create_plots(engine): try: import matplotlib as mpl mpl.use("agg") import matplotlib.pyplot as plt import pandas as pd except ImportError: warnings.warn("Loss plots will not be generated -- pandas or matplotlib not found") else: df = pd.read_csv(output_dir / LOGS_FNAME, delimiter="\t", index_col="iteration") _ = df.plot(subplots=True, figsize=(20, 20)) _ = plt.xlabel("Iteration number") fig = plt.gcf() path = output_dir / PLOT_FNAME fig.savefig(path) # adding handlers using `trainer.on` decorator API @trainer.on(Events.EXCEPTION_RAISED) def handle_exception(engine, e): if isinstance(e, KeyboardInterrupt) and (engine.state.iteration > 1): engine.terminate() warnings.warn("KeyboardInterrupt caught. Exiting gracefully.") create_plots(engine) checkpoint_handler(engine, {"netG_exception": netG, "netD_exception": netD}) else: raise e # Setup is done. Now let's run the training trainer.run(loader, epochs) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--dataset", required=True, choices={"cifar10", "lsun", "imagenet", "folder", "lfw", "fake", "mnist"}, help="Type of the dataset to be used.", ) parser.add_argument("--dataroot", required=True, help="path to dataset") parser.add_argument("--workers", type=int, default=2, help="number of data loading workers") parser.add_argument("--batch-size", type=int, default=64, help="input batch size") parser.add_argument("--z-dim", type=int, default=100, help="size of the latent z vector") parser.add_argument( "--g-filters", type=int, default=64, help="Number of filters in the second-to-last generator deconv layer" ) parser.add_argument("--d-filters", type=int, default=64, help="Number of filters in first discriminator conv layer") parser.add_argument("--epochs", type=int, default=25, help="number of epochs to train for") parser.add_argument("--lr", type=float, default=0.0002, help="learning rate") parser.add_argument("--beta-1", type=float, default=0.5, help="beta_1 for adam") parser.add_argument("--no-cuda", action="store_true", help="disables cuda") parser.add_argument("--saved-G", default="", help="path to pickled generator (to continue training)") parser.add_argument("--saved-D", default="", help="path to pickled discriminator (to continue training)") parser.add_argument("--output-dir", default=".", help="directory to output images and model checkpoints") parser.add_argument("--seed", type=int, help="manual seed") parser.add_argument("--alpha", type=float, default=0.98, help="smoothing constant for exponential moving averages") args = parser.parse_args() dev = "cpu" if (not torch.cuda.is_available() or args.no_cuda) else "cuda:0" args.output_dir = Path(args.output_dir) try: args.output_dir.mkdir(parents=True) except FileExistsError: if (not args.output_dir.is_dir()) or (len(os.listdir(args.output_dir)) > 0): raise FileExistsError("Please provide a path to a non-existing or empty directory.") main( dataset=args.dataset, dataroot=args.dataroot, z_dim=args.z_dim, g_filters=args.g_filters, d_filters=args.d_filters, batch_size=args.batch_size, epochs=args.epochs, learning_rate=args.lr, beta_1=args.beta_1, saved_D=args.saved_D, saved_G=args.saved_G, seed=args.seed, device=dev, n_workers=args.workers, alpha=args.alpha, output_dir=args.output_dir, ) ignite-0.5.1/examples/mnist/000077500000000000000000000000001465426447700157675ustar00rootroot00000000000000ignite-0.5.1/examples/mnist/README.md000066400000000000000000000104541465426447700172520ustar00rootroot00000000000000# Basic MNIST Example with Ignite ported from [pytorch-examples](https://github.com/pytorch/examples/tree/master/mnist) #### Minimal requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` #### Usage: Run the example: ```bash python mnist.py ``` Same example with logging using TQDM progress bar ```bash python mnist_with_tqdm_logger.py ``` ### Logging with Tensorboard MNIST example with training and validation monitoring using Tensorboard #### Additional requirements: - Tensorboard: `pip install tensorboard` Run the example: ```bash python mnist_with_tensorboard.py --log_dir=/tmp/tensorboard_logs ``` Start tensorboard: ```bash tensorboard --logdir=/tmp/tensorboard_logs/ ``` ### Logging with Visdom MNIST example with training and validation monitoring using Visdom #### Additional requirements: - [Visdom](https://github.com/facebookresearch/visdom): `pip install visdom` #### Usage: Start visdom: ```bash python -m visdom.server ``` Run the example: ```bash python mnist_with_visdom.py ``` ### Logging with ClearML #### Additional requirements: - [ClearML python client](https://clear.ml/docs/latest/docs/): `pip install clearml` #### Usage: ```bash python mnist_with_clearml_logger.py ``` ### Training save & resume Example shows how to save a checkpoint of the trainer, model, optimizer, lr scheduler. User can resume the training from stored latest checkpoint. In addition, training crash can be emulated. We provided an option `--deterministic` which setups a deterministic trainer as [`DeterministicEngine`](https://pytorch.org/ignite/engine.html#ignite.engine.deterministic.DeterministicEngine). Trainer performs dataflow synchronization on epoch in order to ensure the same dataflow when training is resumed. Please, see the documentation for more details. #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` - [TensorboardX](https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` - Tensorboard: `pip install tensorboard` #### Usage: Training ```bash python mnist_save_resume_engine.py --log_dir=logs/run_1 --epochs=10 # or same in deterministic mode python mnist_save_resume_engine.py --log_dir=logs-det/run_1 --deterministic --epochs=10 ``` Resume the training ```bash python mnist_save_resume_engine.py --log_dir=logs/run_2 --resume_from=logs/run_1/checkpoint_5628.pt --epochs=10 # or same in deterministic mode python mnist_save_resume_engine.py --log_dir=logs-det/run_2 --resume_from=logs-det/run_1/checkpoint_5628.pt --deterministic --epochs=10 ``` Start tensorboard: ```bash tensorboard --logdir=. ``` The script logs batch stats (mean/std of images, median of targets), model weights' norms and computed gradients norms in `run.log` and `resume_run.log` to compare training behaviour in both cases. If set `--deterministic` option, we can observe the same values after resuming the training. | Non-deterministic | Deterministic | | --------------------------------- | ------------------------------------- | | ![img11](assets/logs_run_1_2.png) | ![img12](assets/logs-det_run_1_2.png) | Deterministic `run.log` vs `resume_run.log` ![img13](assets/run_vs_resume_run_logs_1_2.png) #### Usage with simulated crash Initial training with a crash ```bash python mnist_save_resume_engine.py --crash_iteration 5700 --log_dir=logs/run_3_crash --epochs 10 # or same in deterministic mode python mnist_save_resume_engine.py --crash_iteration 5700 --log_dir=logs-det/run_3_crash --epochs 10 --deterministic ``` Resume from the latest checkpoint ```bash python mnist_save_resume_engine.py --resume_from logs/run_3_crash/checkpoint_6.pt --log_dir=logs/run_4 --epochs 10 # or same in deterministic mode python mnist_save_resume_engine.py --resume_from logs-det/run_3_crash/checkpoint_6.pt --log_dir=logs-det/run_4 --epochs 10 --deterministic ``` | Non-deterministic | Deterministic | | --------------------------------- | ------------------------------------- | | ![img21](assets/logs_run_3_4.png) | ![img22](assets/logs-det_run_3_4.png) | Deterministic `run.log` vs `resume_run.log` ![img23](assets/run_vs_resume_run_logs_3_4.png) ignite-0.5.1/examples/mnist/assets/000077500000000000000000000000001465426447700172715ustar00rootroot00000000000000ignite-0.5.1/examples/mnist/assets/logs-det_run_1_2.png000066400000000000000000011032761465426447700230540ustar00rootroot00000000000000‰PNG  IHDR 0|ψφΗ (iCCPICC ProfileH‰•—XSΙ€η–$$$΄@€„ήDιUz l„$PBH"vdQΑ΅ bΑŠŠ¨Έ@velΨP”u±`CεM@WΏχήχΞχΝ½Μ9sΞΉs'wPb‹DY¨ΩΒ› ω3δ1ΩΩ9U-![¦~η'ύ>SG|²Ωι#¬ΘE.J‰(‹=σ,Η–μ,ιπ&°QωβΠhYΞ²Ίeζ„Ι˜ ω‚05"²δλά^Ζέ|ihܐύŽ$Φ 0@©\v`d=ΘΖΒ¬ˆπ!½wš ˜Φδ±bcQ8'zΘ?Zΐ“Ε 3[,ŸKfS*͌σςΉ™Οc ϋl,δΗ&(βDΫςρU ί“dΖ„ Ω;άzuΖύq/θϊΖΈ.°ΕΰL~ΈœΫjΏU:’ρ·Zω"Ϋ‘Qς(²/ΩςΗT¬UœGΌΘ*υ}-q₯ŽT+`€ηΗ<Ύ«ήΓ~΄Δc‡°σΨ)μ"Φ„Υ&vkΐZ°c2YOδkcxΆhy<™Πΰ§ωΨCsΚͺ&±«±λ±ϋ<Τςxy²—% 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σ©ΆηΆY±|&@½Χzvι₯¦ΛΗhξ=ΒςxVžυΏ^XΎίΧΧ%Ω±]<“Ι8σ{Ggή,ΐ)`ΏŽƒj—>ΛwύŸͺόqΰ\°‹g‚μy™.κλΈΆΆͺ_Rι·|Ώ*Ν fxυυυύMšωͺΡ66δg ο U#π          δ0ρNμχϋQ\\¬zrvΉ\8>>Žσδ\__ΦΦVυ’~&–=«P^„PXV‰ΒŠΪ‹PΥS©£ Aƒy?ͺO%ΣΚ΄­»΅βωwߏΡα9ψNKσ΅…@A….G)Κ«(‰Ιqe~ΏίαΫ…Γ˜;<% Σ#@½d[ΕΨAο©Y3~ΨZ+@]Ϋ±)Θλϋ+Ο‹μuNsα8:§ {Α«Ε~mOΰό`GζB$˜υub6fυ{μσ‡™Δ₯ηLθ1-         @^P •±ΏΏζζfυSZZŠ₯₯%΅^—.]BmmΠΐwΝ»Žυ fκIΞyέΏ­~mm“%Ϋ8ήυaΗj&L—5ώιQόB>J¨©Fey”,}Φ¨χψ‡H€²E€zΙŠ§f%(žš5γΝs³vžΟχΥΚρOBG ΡπF`ΏΆ§ρ8?ΨΓ‘Ή $&@}˜™;ϊ7―œ‡υ{μόcτF3|[πΑ˜r5255₯ζ­ΌŠ‘H€H€H€H€H€H€H€H€H€Ξ ͺͺ*Έέn(ϋάήήV«εt:UoΝΛ+lm__6Υ:ϊQ}ξλΚ ’   ΨK`{]ω=ΎumΗqΖ͊€|Ώo/-ζF$@‡@ΎλTεΏ8-ɚ’ œwA}©e*ύ—oχcλ©©½Gτΰl/OζF$@$@$@$@$@$@$@$@$ηJJJΠΫΫ«Φbzv‡‡‡y^#λΕ/tΦ£€ωͺυ .xJzpΎΰ€Υ'        °L ΨrJ&$         sHΰΰΰΪμ.²q³Ϊ΄'Ηη°…Y%          \'P˜λdωH€H€H€H€H€H€H€H€H€H€ΈΐΙΙ ¦X          ‹L€ΞΉυYw         HF€œ“Ρα=          S"πιΗcΕΥΧ¨IENDB`‚ignite-0.5.1/examples/mnist/mnist.py000066400000000000000000000110121465426447700174660ustar00rootroot00000000000000from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from tqdm import tqdm from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.metrics import Accuracy, Loss from ignite.utils import setup_logger class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.NLLLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) trainer.logger = setup_logger("trainer") val_metrics = {"accuracy": Accuracy(), "nll": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics=val_metrics, device=device) evaluator.logger = setup_logger("evaluator") pbar = tqdm(initial=0, leave=False, total=len(train_loader), desc=f"ITERATION - loss: {0:.2f}") @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(engine): pbar.desc = f"ITERATION - loss: {engine.state.output:.2f}" pbar.update(log_interval) @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): pbar.refresh() evaluator.run(train_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] tqdm.write( f"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): evaluator.run(val_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] tqdm.write( f"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) pbar.n = pbar.last_print_n = 0 @trainer.on(Events.EPOCH_COMPLETED | Events.COMPLETED) def log_time(engine): tqdm.write(f"{trainer.last_event_name.name} took {trainer.state.times[trainer.last_event_name.name]} seconds") trainer.run(train_loader, max_epochs=epochs) pbar.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--log_interval", type=int, default=10, help="how many batches to wait before logging training status" ) args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_interval) ignite-0.5.1/examples/mnist/mnist_save_resume_engine.py000066400000000000000000000260001465426447700234140ustar00rootroot00000000000000from argparse import ArgumentParser from pathlib import Path import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.optim.lr_scheduler import StepLR from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from tqdm import tqdm from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import Checkpoint, DiskSaver from ignite.metrics import Accuracy, Loss, RunningAverage from ignite.utils import manual_seed try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter except ImportError: raise ModuleNotFoundError( "This module requires either tensorboardX or torch >= 1.2.0. " "You may install tensorboardX with command: \n pip install tensorboardX \n" "or upgrade PyTorch using your package manager of choice (pip or conda)." ) # Basic model's definition class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): """Method to setup data loaders: train_loader and val_loader""" data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True, num_workers=4, ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False, num_workers=4, ) return train_loader, val_loader def log_model_weights(engine, model=None, fp=None, **kwargs): """Helper method to log norms of model weights: print and dump into a file""" assert model and fp output = {"total": 0.0} max_counter = 5 for name, p in model.named_parameters(): name = name.replace(".", "/") n = torch.norm(p) if max_counter > 0: output[name] = n output["total"] += n max_counter -= 1 output_items = " - ".join([f"{m}:{v:.4f}" for m, v in output.items()]) msg = f"{engine.state.epoch} | {engine.state.iteration}: {output_items}" with open(fp, "a") as h: h.write(msg) h.write("\n") def log_model_grads(engine, model=None, fp=None, **kwargs): """Helper method to log norms of model gradients: print and dump into a file""" assert model and fp output = {"grads/total": 0.0} max_counter = 5 for name, p in model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") n = torch.norm(p.grad) if max_counter > 0: output[f"grads/{name}"] = n output["grads/total"] += n max_counter -= 1 output_items = " - ".join([f"{m}:{v:.4f}" for m, v in output.items()]) msg = f"{engine.state.epoch} | {engine.state.iteration}: {output_items}" with open(fp, "a") as h: h.write(msg) h.write("\n") def log_data_stats(engine, fp=None, **kwargs): """Helper method to log mean/std of input batch of images and median of batch of targets.""" assert fp x, y = engine.state.batch output = { "batch xmean": x.mean().item(), "batch xstd": x.std().item(), "batch ymedian": y.median().item(), } output_items = " - ".join([f"{m}:{v:.4f}" for m, v in output.items()]) msg = f"{engine.state.epoch} | {engine.state.iteration}: {output_items}" with open(fp, "a") as h: h.write(msg) h.write("\n") def run( train_batch_size, val_batch_size, epochs, lr, momentum, log_interval, log_dir, checkpoint_every, resume_from, crash_iteration=-1, deterministic=False, ): # Setup seed to have same model's initialization: manual_seed(75) train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() writer = SummaryWriter(log_dir=log_dir) device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer criterion = nn.NLLLoss() optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) lr_scheduler = StepLR(optimizer, step_size=1, gamma=0.5) # Setup trainer and evaluator if deterministic: tqdm.write("Setup deterministic trainer") trainer = create_supervised_trainer(model, optimizer, criterion, device=device, deterministic=deterministic) running_loss = RunningAverage(output_transform=lambda x: x) running_loss.attach(trainer, "rloss") metrics = {"accuracy": Accuracy(), "nll": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics, device) # Apply learning rate scheduling @trainer.on(Events.EPOCH_COMPLETED) def lr_step(engine): lr_scheduler.step() pbar = tqdm(initial=0, leave=False, total=len(train_loader), desc=f"Epoch {0} - loss: {0:.4f} - lr: {lr:.4f}") @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(engine): lr = optimizer.param_groups[0]["lr"] rloss = engine.state.metrics["rloss"] pbar.desc = f"Epoch {engine.state.epoch} - loss: {rloss:.4f} - lr: {lr:.4f}" pbar.update(log_interval) writer.add_scalar("training/running_loss", rloss, engine.state.iteration) writer.add_scalar("lr", lr, engine.state.iteration) if crash_iteration > 0: @trainer.on(Events.ITERATION_COMPLETED(once=crash_iteration)) def _(engine): raise Exception(f"STOP at {engine.state.iteration}") if resume_from is not None: @trainer.on(Events.STARTED) def _(engine): pbar.n = engine.state.iteration % engine.state.epoch_length @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): pbar.refresh() evaluator.run(train_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] tqdm.write( f"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) writer.add_scalar("training/avg_loss", avg_nll, engine.state.epoch) writer.add_scalar("training/avg_accuracy", avg_accuracy, engine.state.epoch) # Compute and log validation metrics @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): evaluator.run(val_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] tqdm.write( f"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) pbar.n = pbar.last_print_n = 0 writer.add_scalar("valdation/avg_loss", avg_nll, engine.state.epoch) writer.add_scalar("valdation/avg_accuracy", avg_accuracy, engine.state.epoch) # Setup object to checkpoint objects_to_checkpoint = { "trainer": trainer, "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler, "train_running_loss": running_loss, "metrics": metrics, } training_checkpoint = Checkpoint( to_save=objects_to_checkpoint, save_handler=DiskSaver(log_dir, require_empty=False), n_saved=None, global_step_transform=lambda *_: trainer.state.epoch, ) trainer.add_event_handler(Events.EPOCH_COMPLETED(every=checkpoint_every), training_checkpoint) # Setup logger to print and dump into file: model weights, model grads and data stats # - first 3 iterations # - 4 iterations after checkpointing # This helps to compare resumed training with checkpointed training def log_event_filter(e, event): if event in [1, 2, 3]: return True elif 0 <= (event % (checkpoint_every * e.state.epoch_length)) < 5: return True return False fp = Path(log_dir) / ("run.log" if resume_from is None else "resume_run.log") fp = fp.as_posix() for h in [log_data_stats, log_model_weights, log_model_grads]: trainer.add_event_handler(Events.ITERATION_COMPLETED(event_filter=log_event_filter), h, model=model, fp=fp) if resume_from is not None: tqdm.write(f"Resume from the checkpoint: {resume_from}") checkpoint = torch.load(resume_from) Checkpoint.load_objects(to_load=objects_to_checkpoint, checkpoint=checkpoint) try: # Synchronize random states manual_seed(15) trainer.run(train_loader, max_epochs=epochs) except Exception as e: import traceback print(traceback.format_exc()) pbar.close() writer.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--log_interval", type=int, default=10, help="how many batches to wait before logging training status" ) parser.add_argument( "--log_dir", type=str, default="/tmp/mnist_save_resume", help="log directory for Tensorboard log output" ) parser.add_argument("--checkpoint_every", type=int, default=1, help="Checkpoint training every X epochs") parser.add_argument( "--resume_from", type=str, default=None, help="Path to the checkpoint .pt file to resume training from" ) parser.add_argument("--crash_iteration", type=int, default=-1, help="Iteration at which to raise an exception") parser.add_argument( "--deterministic", action="store_true", help="Deterministic training with dataflow synchronization" ) args = parser.parse_args() run( args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_interval, args.log_dir, args.checkpoint_every, args.resume_from, args.crash_iteration, args.deterministic, ) ignite-0.5.1/examples/mnist/mnist_with_clearml_logger.py000066400000000000000000000133001465426447700235610ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using ClearML. Requirements: ClearML: `pip install clearml` Usage: Run the example: ```bash python mnist_with_clearml_logger.py ``` """ from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import Checkpoint from ignite.handlers.clearml_logger import ( ClearMLLogger, ClearMLSaver, global_step_from_engine, GradsHistHandler, GradsScalarHandler, WeightsHistHandler, WeightsScalarHandler, ) from ignite.metrics import Accuracy, Loss from ignite.utils import setup_logger class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.CrossEntropyLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) trainer.logger = setup_logger("Trainer") metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)} train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) train_evaluator.logger = setup_logger("Train Evaluator") validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) validation_evaluator.logger = setup_logger("Val Evaluator") @trainer.on(Events.EPOCH_COMPLETED) def compute_metrics(engine): train_evaluator.run(train_loader) validation_evaluator.run(val_loader) clearml_logger = ClearMLLogger(project_name="examples", task_name="ignite") clearml_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), tag="training", output_transform=lambda loss: {"batchloss": loss}, ) for tag, evaluator in [("training metrics", train_evaluator), ("validation metrics", validation_evaluator)]: clearml_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag=tag, metric_names=["loss", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) clearml_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), optimizer=optimizer ) clearml_logger.attach( trainer, log_handler=WeightsScalarHandler(model, whitelist=["fc1"]), event_name=Events.ITERATION_COMPLETED(every=100), ) def is_conv(n, _): return "conv" in n clearml_logger.attach( trainer, log_handler=WeightsHistHandler(model, whitelist=is_conv), event_name=Events.ITERATION_COMPLETED(every=100), ) clearml_logger.attach( trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100) ) clearml_logger.attach( trainer, log_handler=GradsHistHandler(model, whitelist=["fc2.weight"]), event_name=Events.ITERATION_COMPLETED(every=100), ) handler = Checkpoint( {"model": model}, ClearMLSaver(), n_saved=1, score_function=lambda e: e.state.metrics["accuracy"], score_name="val_acc", filename_prefix="best", global_step_transform=global_step_from_engine(trainer), ) validation_evaluator.add_event_handler(Events.EPOCH_COMPLETED, handler) # kick everything off trainer.run(train_loader, max_epochs=epochs) clearml_logger.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum) ignite-0.5.1/examples/mnist/mnist_with_neptune_logger.py000066400000000000000000000132411465426447700236240ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using Neptune. Requirements: Neptune: `pip install neptune` Usage: Run the example: ```bash python mnist_with_neptune_logger.py ``` Go to https://neptune.ai and explore your run. Note: You can view example runs here: https://app.neptune.ai/o/common/org/pytorch-ignite-integration/ """ from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import Checkpoint from ignite.handlers.neptune_logger import ( global_step_from_engine, GradsScalarHandler, NeptuneLogger, NeptuneSaver, WeightsScalarHandler, ) from ignite.metrics import Accuracy, Loss from ignite.utils import setup_logger class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.CrossEntropyLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) trainer.logger = setup_logger("Trainer") metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)} train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) train_evaluator.logger = setup_logger("Train Evaluator") validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) validation_evaluator.logger = setup_logger("Val Evaluator") @trainer.on(Events.EPOCH_COMPLETED) def compute_metrics(engine): train_evaluator.run(train_loader) validation_evaluator.run(val_loader) npt_logger = NeptuneLogger( api_token="ANONYMOUS", project="common/pytorch-ignite-integration", name="ignite-mnist-example", ) npt_logger.experiment["params"] = { "train_batch_size": train_batch_size, "val_batch_size": val_batch_size, "epochs": epochs, "lr": lr, "momentum": momentum, } npt_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), tag="training", output_transform=lambda loss: {"batchloss": loss}, ) for tag, evaluator in [("training", train_evaluator), ("validation", validation_evaluator)]: npt_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag=tag, metric_names=["loss", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) npt_logger.attach_opt_params_handler(trainer, event_name=Events.ITERATION_COMPLETED(every=100), optimizer=optimizer) npt_logger.attach( trainer, log_handler=WeightsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100) ) npt_logger.attach(trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)) def score_function(engine): return engine.state.metrics["accuracy"] handler = Checkpoint( {"model": model}, NeptuneSaver(npt_logger), n_saved=2, filename_prefix="best", score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer), ) validation_evaluator.add_event_handler(Events.COMPLETED, handler) # kick everything off trainer.run(train_loader, max_epochs=epochs) npt_logger.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum) ignite-0.5.1/examples/mnist/mnist_with_tensorboard.py000066400000000000000000000132571465426447700231400ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using Tensorboard. Requirements: TensorboardX (https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` or PyTorch >= 1.2 which supports Tensorboard Tensorboard: `pip install tensorflow` (or just install tensorboard without the rest of tensorflow) Usage: Start tensorboard: ```bash tensorboard --logdir=/tmp/tensorboard_logs/ ``` Run the example: ```bash python mnist_with_tensorboard.py --log_dir=/tmp/tensorboard_logs ``` """ from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.metrics import Accuracy, Loss try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter except ImportError: raise ModuleNotFoundError( "This module requires either tensorboardX or torch >= 1.2.0. " "You may install tensorboardX with command: \n pip install tensorboardX \n" "or upgrade PyTorch using your package manager of choice (pip or conda)." ) class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval, log_dir): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() writer = SummaryWriter(log_dir=log_dir) device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.NLLLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) val_metrics = {"accuracy": Accuracy(), "nll": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics=val_metrics, device=device) @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(engine): print( f"Epoch[{engine.state.epoch}] Iteration[{engine.state.iteration}/{len(train_loader)}] " f"Loss: {engine.state.output:.2f}" ) writer.add_scalar("training/loss", engine.state.output, engine.state.iteration) @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): evaluator.run(train_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] print( f"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) writer.add_scalar("training/avg_loss", avg_nll, engine.state.epoch) writer.add_scalar("training/avg_accuracy", avg_accuracy, engine.state.epoch) @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): evaluator.run(val_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] print( f"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) writer.add_scalar("valdation/avg_loss", avg_nll, engine.state.epoch) writer.add_scalar("valdation/avg_accuracy", avg_accuracy, engine.state.epoch) # kick everything off trainer.run(train_loader, max_epochs=epochs) writer.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--log_interval", type=int, default=10, help="how many batches to wait before logging training status" ) parser.add_argument( "--log_dir", type=str, default="tensorboard_logs", help="log directory for Tensorboard log output" ) args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_interval, args.log_dir) ignite-0.5.1/examples/mnist/mnist_with_tensorboard_logger.py000066400000000000000000000152021465426447700244670ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using TensorboardX and Tensorboard. Requirements: Optionally TensorboardX (https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` Tensorboard: `pip install tensorflow` (or just install tensorboard without the rest of tensorflow) Usage: Start tensorboard: ```bash tensorboard --logdir=/tmp/tensorboard_logs/ ``` Run the example: ```bash python mnist_with_tensorboard_logger.py --log_dir=/tmp/tensorboard_logs ``` """ import sys from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import ModelCheckpoint from ignite.handlers.tensorboard_logger import ( global_step_from_engine, GradsHistHandler, GradsScalarHandler, TensorboardLogger, WeightsHistHandler, WeightsScalarHandler, ) from ignite.metrics import Accuracy, Loss from ignite.utils import setup_logger class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_dir): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.CrossEntropyLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) trainer.logger = setup_logger("Trainer") if sys.version_info > (3,): from ignite.metrics.gpu_info import GpuInfo try: GpuInfo().attach(trainer) except RuntimeError: print( "INFO: By default, in this example it is possible to log GPU information (used memory, utilization). " "As there is no pynvml python package installed, GPU information won't be logged. Otherwise, please " "install it : `pip install pynvml`" ) metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)} train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) train_evaluator.logger = setup_logger("Train Evaluator") validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) validation_evaluator.logger = setup_logger("Val Evaluator") @trainer.on(Events.EPOCH_COMPLETED) def compute_metrics(engine): train_evaluator.run(train_loader) validation_evaluator.run(val_loader) tb_logger = TensorboardLogger(log_dir=log_dir) tb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), tag="training", output_transform=lambda loss: {"batchloss": loss}, metric_names="all", ) for tag, evaluator in [("training", train_evaluator), ("validation", validation_evaluator)]: tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag=tag, metric_names=["loss", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) tb_logger.attach_opt_params_handler(trainer, event_name=Events.ITERATION_COMPLETED(every=100), optimizer=optimizer) tb_logger.attach( trainer, log_handler=WeightsScalarHandler(model, whitelist=["fc1"]), event_name=Events.ITERATION_COMPLETED(every=100), ) def is_conv(n, _): return "conv" in n tb_logger.attach( trainer, log_handler=WeightsHistHandler(model, whitelist=is_conv), event_name=Events.ITERATION_COMPLETED(every=100), ) tb_logger.attach(trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)) tb_logger.attach( trainer, log_handler=GradsHistHandler(model, whitelist=["fc2.weight"]), event_name=Events.ITERATION_COMPLETED(every=100), ) def score_function(engine): return engine.state.metrics["accuracy"] model_checkpoint = ModelCheckpoint( log_dir, n_saved=2, filename_prefix="best", score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer), ) validation_evaluator.add_event_handler(Events.COMPLETED, model_checkpoint, {"model": model}) # kick everything off trainer.run(train_loader, max_epochs=epochs) tb_logger.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--log_dir", type=str, default="tensorboard_logs", help="log directory for Tensorboard log output" ) args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_dir) ignite-0.5.1/examples/mnist/mnist_with_tensorboard_on_tpu.py000066400000000000000000000142051465426447700245160ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using Tensorboard on TPU Requirements: - PyTorch >= 1.5 - PyTorch XLA >= 1.5 - Tensorboard: `pip install tensorflow` (or just install tensorboard without the rest of tensorflow) Usage: Start tensorboard: ```bash tensorboard --logdir=/tmp/tensorboard_logs/ ``` Run the example: ```bash python mnist_with_tensorboard_on_tpu.py --log_dir=/tmp/tensorboard_logs ``` """ from argparse import ArgumentParser import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.metrics import Accuracy, Loss, RunningAverage try: import torch_xla.core.xla_model as xm except ImportError: raise ModuleNotFoundError( "In order to run PyTorch on TPU we need to install PyTorch XLA:" "\n\t- curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o xla-setup.py" "\n\t- python xla-setup.py --version 1.5" ) class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval, log_dir): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() writer = SummaryWriter(log_dir=log_dir) # Use TPU device device = xm.xla_device() model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.NLLLoss() # Create trainer and evaluator trainer = create_supervised_trainer( model, optimizer, criterion, device=device, output_transform=lambda x, y, y_pred, loss: [loss.item()] ) val_metrics = {"accuracy": Accuracy(), "nll": Loss(criterion)} evaluator = create_supervised_evaluator(model, metrics=val_metrics, device=device) tracker = xm.RateTracker() # Add RateTracker as an output of the training step @trainer.on(Events.ITERATION_COMPLETED) def add_rate_tracker(engine): tracker.add(len(engine.state.batch)) engine.state.output.append(tracker.global_rate()) # Setup output values of the training step as EMA metrics RunningAverage(output_transform=lambda x: x[0]).attach(trainer, "batch_loss") RunningAverage(output_transform=lambda x: x[1]).attach(trainer, "global_rate") # Let's log the EMA metrics every `log_interval` iterations @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(engine): writer.add_scalar("training/batch_loss", engine.state.metrics["batch_loss"], engine.state.iteration) writer.add_scalar("training/global_rate", engine.state.metrics["global_rate"], engine.state.iteration) @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): evaluator.run(train_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] print( f"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) writer.add_scalar("training/avg_loss", avg_nll, engine.state.epoch) writer.add_scalar("training/avg_accuracy", avg_accuracy, engine.state.epoch) @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): evaluator.run(val_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] print( f"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) writer.add_scalar("valdation/avg_loss", avg_nll, engine.state.epoch) writer.add_scalar("valdation/avg_accuracy", avg_accuracy, engine.state.epoch) # kick everything off trainer.run(train_loader, max_epochs=epochs) writer.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--log_interval", type=int, default=10, help="how many batches to wait before logging training status" ) parser.add_argument( "--log_dir", type=str, default="tensorboard_logs", help="log directory for Tensorboard log output" ) args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_interval, args.log_dir) ignite-0.5.1/examples/mnist/mnist_with_tqdm_logger.py000066400000000000000000000102341465426447700231120ustar00rootroot00000000000000from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import ProgressBar from ignite.metrics import Accuracy, Loss, RunningAverage class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum, display_gpu_info): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) trainer = create_supervised_trainer(model, optimizer, F.nll_loss, device=device) evaluator = create_supervised_evaluator( model, metrics={"accuracy": Accuracy(), "nll": Loss(F.nll_loss)}, device=device ) RunningAverage(output_transform=lambda x: x).attach(trainer, "loss") if display_gpu_info: from ignite.metrics import GpuInfo GpuInfo().attach(trainer, name="gpu") pbar = ProgressBar(persist=True) pbar.attach(trainer, metric_names="all") @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): evaluator.run(train_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] pbar.log_message( f"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): evaluator.run(val_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] pbar.log_message( f"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) pbar.n = pbar.last_print_n = 0 trainer.run(train_loader, max_epochs=epochs) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--display_gpu_info", action="store_true", help="Display gpu usage info. This needs python 3.X and pynvml package", ) args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.display_gpu_info) ignite-0.5.1/examples/mnist/mnist_with_visdom.py000066400000000000000000000134711465426447700221150ustar00rootroot00000000000000from argparse import ArgumentParser import numpy as np import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.metrics import Accuracy, Loss try: import visdom except ImportError: raise ModuleNotFoundError("No visdom package is found. Please install it with command: \n pip install visdom") class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def create_plot_window(vis, xlabel, ylabel, title): return vis.line(X=np.array([1]), Y=np.array([np.nan]), opts=dict(xlabel=xlabel, ylabel=ylabel, title=title)) def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval): vis = visdom.Visdom() # if not vis.check_connection(): # raise RuntimeError("Visdom server not running. Please run python -m visdom.server") train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) trainer = create_supervised_trainer(model, optimizer, F.nll_loss, device=device) evaluator = create_supervised_evaluator( model, metrics={"accuracy": Accuracy(), "nll": Loss(F.nll_loss)}, device=device ) train_loss_window = create_plot_window(vis, "#Iterations", "Loss", "Training Loss") train_avg_loss_window = create_plot_window(vis, "#Iterations", "Loss", "Training Average Loss") train_avg_accuracy_window = create_plot_window(vis, "#Iterations", "Accuracy", "Training Average Accuracy") val_avg_loss_window = create_plot_window(vis, "#Epochs", "Loss", "Validation Average Loss") val_avg_accuracy_window = create_plot_window(vis, "#Epochs", "Accuracy", "Validation Average Accuracy") @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(engine): print( f"Epoch[{engine.state.epoch}] Iteration[{engine.state.iteration}/{len(train_loader)}] " f"Loss: {engine.state.output:.2f}" ) vis.line( X=np.array([engine.state.iteration]), Y=np.array([engine.state.output]), update="append", win=train_loss_window, ) @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): evaluator.run(train_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] print( f"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) vis.line( X=np.array([engine.state.epoch]), Y=np.array([avg_accuracy]), win=train_avg_accuracy_window, update="append" ) vis.line(X=np.array([engine.state.epoch]), Y=np.array([avg_nll]), win=train_avg_loss_window, update="append") @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): evaluator.run(val_loader) metrics = evaluator.state.metrics avg_accuracy = metrics["accuracy"] avg_nll = metrics["nll"] print( f"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}" ) vis.line( X=np.array([engine.state.epoch]), Y=np.array([avg_accuracy]), win=val_avg_accuracy_window, update="append" ) vis.line(X=np.array([engine.state.epoch]), Y=np.array([avg_nll]), win=val_avg_loss_window, update="append") # kick everything off trainer.run(train_loader, max_epochs=epochs) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument( "--log_interval", type=int, default=10, help="how many batches to wait before logging training status" ) parser.add_argument("--log_file", type=str, default=None, help="log file to log output to") args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_interval) ignite-0.5.1/examples/mnist/mnist_with_visdom_logger.py000066400000000000000000000127211465426447700234510ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using Visdom. Requirements: Visdom (https://github.com/facebookresearch/visdom.git): `pip install git+https://github.com/facebookresearch/visdom.git` Usage: Start visdom server: ```bash visdom -logging_level 30 ``` Run the example: ```bash python mnist_with_visdom_logger.py ``` """ from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import ModelCheckpoint from ignite.handlers.visdom_logger import ( global_step_from_engine, GradsScalarHandler, VisdomLogger, WeightsScalarHandler, ) from ignite.metrics import Accuracy, Loss from ignite.utils import setup_logger class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_dir): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.CrossEntropyLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) trainer.logger = setup_logger("Trainer") metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)} train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) train_evaluator.logger = setup_logger("Train Evaluator") validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) validation_evaluator.logger = setup_logger("Val Evaluator") @trainer.on(Events.EPOCH_COMPLETED) def compute_metrics(engine): train_evaluator.run(train_loader) validation_evaluator.run(val_loader) vd_logger = VisdomLogger(env="mnist_training") vd_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), tag="training", output_transform=lambda loss: {"batchloss": loss}, ) for tag, evaluator in [("training", train_evaluator), ("validation", validation_evaluator)]: vd_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag=tag, metric_names=["loss", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) vd_logger.attach_opt_params_handler(trainer, event_name=Events.ITERATION_COMPLETED(every=100), optimizer=optimizer) vd_logger.attach(trainer, log_handler=WeightsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)) vd_logger.attach(trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)) def score_function(engine): return engine.state.metrics["accuracy"] model_checkpoint = ModelCheckpoint( log_dir, n_saved=2, filename_prefix="best", score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer), ) validation_evaluator.add_event_handler(Events.COMPLETED, model_checkpoint, {"model": model}) # kick everything off trainer.run(train_loader, max_epochs=epochs) vd_logger.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") parser.add_argument("--log_dir", type=str, default="mnist_visdom_logs", help="log directory for training output") args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_dir) ignite-0.5.1/examples/mnist/mnist_with_wandb_logger.py000066400000000000000000000125511465426447700232440ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using Weights & Biases Requirements: Weights & Biases: `pip install wandb` Usage: Make sure you are logged into Weights & Biases (use the `wandb` command). Run the example: ```bash python mnist_with_wandb_logger.py ``` Go to https://wandb.com and explore your experiment. """ from argparse import ArgumentParser import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events from ignite.handlers import ModelCheckpoint from ignite.handlers.wandb_logger import global_step_from_engine, WandBLogger from ignite.metrics import Accuracy, Loss from ignite.utils import setup_logger class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) def get_data_loaders(train_batch_size, val_batch_size): data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True ) val_loader = DataLoader( MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False ) return train_loader, val_loader def run(train_batch_size, val_batch_size, epochs, lr, momentum): train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) model = Net() device = "cpu" if torch.cuda.is_available(): device = "cuda" model.to(device) # Move model before creating optimizer optimizer = SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.CrossEntropyLoss() trainer = create_supervised_trainer(model, optimizer, criterion, device=device) trainer.logger = setup_logger("Trainer") metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)} train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) train_evaluator.logger = setup_logger("Train Evaluator") validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device) validation_evaluator.logger = setup_logger("Val Evaluator") @trainer.on(Events.EPOCH_COMPLETED) def compute_metrics(engine): train_evaluator.run(train_loader) validation_evaluator.run(val_loader) wandb_logger = WandBLogger( project="pytorch-ignite-integration", name="ignite-mnist-example", config={ "train_batch_size": train_batch_size, "val_batch_size": val_batch_size, "epochs": epochs, "lr": lr, "momentum": momentum, }, ) wandb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), tag="training", output_transform=lambda loss: {"batchloss": loss}, ) for tag, evaluator in [("training", train_evaluator), ("validation", validation_evaluator)]: wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag=tag, metric_names=["loss", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) wandb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=100), optimizer=optimizer ) wandb_logger.watch(model, log="all") def score_function(engine): return engine.state.metrics["accuracy"] model_checkpoint = ModelCheckpoint( wandb_logger.run.dir, n_saved=2, filename_prefix="best", score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer), ) validation_evaluator.add_event_handler(Events.COMPLETED, model_checkpoint, {"model": model}) # kick everything off trainer.run(train_loader, max_epochs=epochs) wandb_logger.close() if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)") parser.add_argument( "--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)") parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)") args = parser.parse_args() run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum) ignite-0.5.1/examples/notebooks/000077500000000000000000000000001465426447700166405ustar00rootroot00000000000000ignite-0.5.1/examples/notebooks/Cifar100_bench_amp.ipynb000066400000000000000000000133771465426447700231570ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "tSC1tFE5fDHM" }, "source": [ "# Benchmark mixed precision training on Cifar100\n", "\n", "In this notebook we will benchmark 1) native PyTorch mixed precision module [`torch.cuda.amp`](https://pytorch.org/docs/master/amp.html) and 2) NVidia/Apex package.\n", "\n", "We will train Wide-ResNet model on Cifar100 dataset using Turing enabled GPU and compare training times.\n", "\n", "**TL;DR**\n", "\n", "The ranking is the following:\n", "- 1st place: Nvidia/Apex \"O2\"\n", "- 2nd place: `torch.cuda.amp`: autocast and scaler\n", "- 3rd place: Nvidia/Apex \"O1\"\n", "- 4th place: fp32\n", "\n", "According to @mcarilli: \"Native amp is more like a faster, better integrated, locally enabled O1\"" ] }, { "cell_type": "markdown", "metadata": { "id": "VZJDqc7vfDHV" }, "source": [ "## Installations and setup\n", "\n", "1) Recently added [`torch.cuda.amp`](https://pytorch.org/docs/master/notes/amp_examples.html#working-with-multiple-models-losses-and-optimizers) module to perform automatic mixed precision training instead of using Nvidia/Apex package is available in PyTorch >=1.6.0.\n", "\n", "In this example we only need `pynvml` and `fire` packages, assuming that `torch` and `ignite` are already installed. We can install it using pip:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SkRXPuNRfDHX" }, "outputs": [], "source": [ "!pip install pytorch-ignite pynvml fire" ] }, { "cell_type": "markdown", "metadata": { "id": "9SPEV91DfDHZ" }, "source": [ "2) Let's install Nvidia/Apex package:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fGtxgbj8fDHb" }, "outputs": [], "source": [ "# Install Apex:\n", "# If torch cuda version and nvcc version match:\n", "!pip install --upgrade --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" git+https://github.com/NVIDIA/apex/\n", "# if above command is failing, please install apex without c++/cuda extensions:\n", "# !pip install --upgrade --no-cache-dir git+https://github.com/NVIDIA/apex/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QnihHXQpfDHb" }, "outputs": [], "source": [ "import torch\n", "import torchvision\n", "import ignite\n", "torch.__version__, torchvision.__version__, ignite.__version__" ] }, { "cell_type": "markdown", "metadata": { "id": "TooCbccqfDHg" }, "source": [ "3) The scripts we will execute are located in `ignite/examples/contrib/cifar100_amp_benchmark` of github repository. Let's clone the repository and setup PYTHONPATH to execute benchmark scripts:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6xqqj0q1fDHh" }, "outputs": [], "source": [ "!git clone https://github.com/pytorch/ignite.git /tmp/ignite\n", "scriptspath=\"/tmp/ignite/examples/cifar100_amp_benchmark/\"\n", "setup=f\"cd {scriptspath} && export PYTHONPATH=$PWD:$PYTHONPATH\"" ] }, { "cell_type": "markdown", "metadata": { "id": "gX30i_abfDHi" }, "source": [ "4) Download dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ulufk4tsfDHj" }, "outputs": [], "source": [ "from torchvision.datasets.cifar import CIFAR100\n", "CIFAR100(root=\"/tmp/cifar100/\", train=True, download=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "ai6nBHZKfDHl" }, "source": [ "## Training in fp32" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mHwsVTB6fDHq" }, "outputs": [], "source": [ "!{setup} && python benchmark_fp32.py /tmp/cifar100/ --batch_size=256 --max_epochs=20" ] }, { "cell_type": "markdown", "metadata": { "id": "n2p-EMwGfDHs" }, "source": [ "## Training with `torch.cuda.amp`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xkuW1EY-fDHs" }, "outputs": [], "source": [ "!{setup} && python benchmark_torch_cuda_amp.py /tmp/cifar100/ --batch_size=256 --max_epochs=20" ] }, { "cell_type": "markdown", "metadata": { "id": "2qjtyKnOfDHt" }, "source": [ "## Training with `Nvidia/apex`\n", "\n", "\n", "- we check 2 optimization levels: \"O1\" and \"O2\"\n", " - \"O1\" optimization level: automatic casts arount Pytorch functions and tensor methods\n", " - \"O2\" optimization level: fp16 training with fp32 batchnorm and fp32 master weights" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A6Pe4cW6fDHu" }, "outputs": [], "source": [ "!{setup} && python benchmark_nvidia_apex.py /tmp/cifar100/ --batch_size=256 --max_epochs=20 --opt=\"O1\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1aqdlPSgfDHu" }, "outputs": [], "source": [ "!{setup} && python benchmark_nvidia_apex.py /tmp/cifar100/ --batch_size=256 --max_epochs=20 --opt=\"O2\"" ] } ], "metadata": { "colab": { "name": "Cifar100_bench_amp.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 0 } ignite-0.5.1/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb000066400000000000000000001010741465426447700253370ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "colab": { "name": "Cifar10_Ax_hyperparam_tuning.ipynb", "provenance": [] } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "m4TggyOPfapy" }, "source": [ "# FastResNet Hyperparameters tuning with [Ax](https://ax.dev/) on CIFAR10\n", "\n", "In this notebook we provide an example of hyperparameter tuning with [Ax](https://ax.dev/) package. We will train a ResNet model from [awesome repository of David Page](https://github.com/davidcpage/cifar10-fast) on CIFAR10.\n", "\n", "\n", "### Why Ax ?\n", "\n", "This is a good question ... Maybe this page could better answer : https://ax.dev/docs/why-ax.html\n", "\n", "> Ax is a platform for optimizing any kind of experiment, including machine learning experiments, A/B tests, and simulations. Ax can optimize discrete configurations (e.g., variants of an A/B test) using multi-armed bandit optimization, and continuous (e.g., integer or floating point)-valued configurations using Bayesian optimization. This makes it suitable for a wide range of applications.\n", "\n", "There are also interesting packages as [ray-tune](https://ray.readthedocs.io/en/latest/tune.html), [optuna](https://github.com/pfnet/optuna) and many others. As a side note, optuna provides an example with Ignite [here](https://github.com/pfnet/optuna/blob/master/examples/ignite_simple.py).\n", "\n", "\n", "### Fast ResNet model\n", "\n", "We will reimplement a resnet model from David Page's [cifar-10 repository](https://github.com/davidcpage/cifar10-fast) which trains very fast (94% of test accuracy in 26 second on NVidia V100). For sake of simplicity, we will not apply all preprocessing used in the repository (please see [bag-of-trick notebook](https://github.com/davidcpage/cifar10-fast/blob/master/bag_of_tricks.ipynb) for details).\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cRMcUAbLfap7" }, "source": [ "### Setup dependencies\n", "\n", "Please install \n", "- `torchvision`\n", "- `Ax`\n", "- `tensorboard`\n" ] }, { "cell_type": "code", "metadata": { "id": "zSAI-Dknfap-" }, "source": [ "!pip install pytorch-ignite tensorboardX ax-platform" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "S_q9Q0zIfaqK" }, "source": [ "import sys\n", "sys.path.insert(0, \"../../\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "SwOlZhS6faqM" }, "source": [ "import torch\n", "import ignite\n", "\n", "torch.__version__, ignite.__version__" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "kWGvqsNofaqP" }, "source": [ "### Setup model\n", "\n", "Cifar10-fast model is inspired of ResNet family models and in order to run fast it uses various tricks like:\n", "- `conv + batch norm + activation + pool` -> `conv + pool + batch norm + activation`\n", "- `batchnorm` -> `ghost batchnorm` -> `frozen ghost batchnorm`\n", "- `ReLU` -> `CeLU`\n", "- data whitening as convolution non-learnable operation (we will not implement it)\n", "\n", "Network architecture looks like this:\n", "\n", "![fastresnet](https://github.com/abdulelahsm/ignite/blob/update-tutorials/examples/notebooks/assets/fastresnet_v2.svg?raw=1)\n", "\n", "\n", "Please see [bag-of-trick notebook](https://github.com/davidcpage/cifar10-fast/blob/master/bag_of_tricks.ipynb) for more detail.\n" ] }, { "cell_type": "code", "metadata": { "id": "Ga6ykOK4faqR" }, "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "\n", "class GhostBatchNorm(nn.BatchNorm2d):\n", " \"\"\"\n", " From : https://github.com/davidcpage/cifar10-fast/blob/master/bag_of_tricks.ipynb\n", "\n", " Batch norm seems to work best with batch size of around 32. The reasons presumably have to do \n", " with noise in the batch statistics and specifically a balance between a beneficial regularising effect \n", " at intermediate batch sizes and an excess of noise at small batches.\n", " \n", " Our batches are of size 512 and we can't afford to reduce them without taking a serious hit on training times, \n", " but we can apply batch norm separately to subsets of a training batch. This technique, known as 'ghost' batch \n", " norm, is usually used in a distributed setting but is just as useful when using large batches on a single node. \n", " It isn't supported directly in PyTorch but we can roll our own easily enough.\n", " \"\"\"\n", " def __init__(self, num_features, num_splits, eps=1e-05, momentum=0.1, weight=True, bias=True):\n", " super(GhostBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum)\n", " self.weight.data.fill_(1.0)\n", " self.bias.data.fill_(0.0)\n", " self.weight.requires_grad = weight\n", " self.bias.requires_grad = bias \n", " self.num_splits = num_splits\n", " self.register_buffer('running_mean', torch.zeros(num_features*self.num_splits))\n", " self.register_buffer('running_var', torch.ones(num_features*self.num_splits))\n", "\n", " def train(self, mode=True):\n", " if (self.training is True) and (mode is False):\n", " self.running_mean = torch.mean(self.running_mean.view(self.num_splits, self.num_features), dim=0).repeat(self.num_splits)\n", " self.running_var = torch.mean(self.running_var.view(self.num_splits, self.num_features), dim=0).repeat(self.num_splits)\n", " return super(GhostBatchNorm, self).train(mode)\n", " \n", " def forward(self, input):\n", " N, C, H, W = input.shape\n", " if self.training or not self.track_running_stats:\n", " return F.batch_norm(\n", " input.view(-1, C*self.num_splits, H, W), self.running_mean, self.running_var, \n", " self.weight.repeat(self.num_splits), self.bias.repeat(self.num_splits),\n", " True, self.momentum, self.eps).view(N, C, H, W) \n", " else:\n", " return F.batch_norm(\n", " input, self.running_mean[:self.num_features], self.running_var[:self.num_features], \n", " self.weight, self.bias, False, self.momentum, self.eps)\n", "\n", " \n", "class IdentityResidualBlock(nn.Module):\n", "\n", " def __init__(self, num_channels, \n", " conv_ksize=3, conv_pad=1,\n", " gbn_num_splits=16):\n", " super(IdentityResidualBlock, self).__init__()\n", " self.res1 = nn.Sequential(\n", " Conv2d(num_channels, num_channels, kernel_size=conv_ksize, padding=conv_pad, stride=1, bias=False),\n", " GhostBatchNorm(num_channels, num_splits=gbn_num_splits, weight=False),\n", " nn.CELU(alpha=0.3) \n", " )\n", " self.res2 = nn.Sequential(\n", " Conv2d(num_channels, num_channels, kernel_size=conv_ksize, padding=conv_pad, stride=1, bias=False),\n", " GhostBatchNorm(num_channels, num_splits=gbn_num_splits, weight=False),\n", " nn.CELU(alpha=0.3) \n", " )\n", "\n", " def forward(self, x):\n", " residual = x\n", " x = self.res1(x)\n", " x = self.res2(x)\n", " return x + residual\n", " \n", "\n", "# We override conv2d to get proper padding for kernel size = 2 \n", "class Conv2d(nn.Conv2d):\n", " \n", " def __init__(self, *args, **kwargs):\n", " super(Conv2d, self).__init__(*args, **kwargs)\n", " if self.kernel_size == (2, 2):\n", " self.forward = self.ksize_2_forward\n", " self.ksize_2_padding = (0, self.padding[0], 0, self.padding[1])\n", " self.padding = (0, 0)\n", " \n", " def ksize_2_forward(self, x):\n", " x = F.pad(x, pad=self.ksize_2_padding)\n", " return super(Conv2d, self).forward(x)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "eMJPLneEfaqT" }, "source": [ "class FastResNet(nn.Module):\n", " \n", " def __init__(self, num_classes=10, \n", " fmap_factor=64, conv_ksize=3, conv_pad=1, \n", " gbn_num_splits=512 // 32, \n", " classif_scale=0.0625):\n", " super(FastResNet, self).__init__()\n", " \n", " self.prep = nn.Sequential(\n", " Conv2d(3, fmap_factor, kernel_size=conv_ksize, padding=conv_pad, stride=1, bias=False),\n", " GhostBatchNorm(fmap_factor, num_splits=gbn_num_splits, weight=False),\n", " nn.CELU(alpha=0.3)\n", " )\n", "\n", " self.layer1 = nn.Sequential(\n", " Conv2d(fmap_factor, fmap_factor * 2, kernel_size=conv_ksize, padding=conv_pad, stride=1, bias=False),\n", " nn.MaxPool2d(kernel_size=2),\n", " GhostBatchNorm(fmap_factor * 2, num_splits=gbn_num_splits, weight=False),\n", " nn.CELU(alpha=0.3),\n", " IdentityResidualBlock(fmap_factor * 2,\n", " conv_ksize=conv_ksize, conv_pad=conv_pad, \n", " gbn_num_splits=gbn_num_splits)\n", " )\n", " \n", " self.layer2 = nn.Sequential(\n", " Conv2d(fmap_factor * 2, fmap_factor * 4, kernel_size=conv_ksize, padding=conv_pad, stride=1, bias=False),\n", " nn.MaxPool2d(kernel_size=2),\n", " GhostBatchNorm(fmap_factor * 4, num_splits=gbn_num_splits, weight=False),\n", " nn.CELU(alpha=0.3), \n", " )\n", " \n", " self.layer3 = nn.Sequential(\n", " Conv2d(fmap_factor * 4, fmap_factor * 8, kernel_size=conv_ksize, padding=conv_pad, stride=1, bias=False),\n", " nn.MaxPool2d(kernel_size=2),\n", " GhostBatchNorm(fmap_factor * 8, num_splits=gbn_num_splits, weight=False),\n", " nn.CELU(alpha=0.3),\n", " IdentityResidualBlock(fmap_factor * 8, \n", " conv_ksize=conv_ksize, conv_pad=conv_pad, \n", " gbn_num_splits=gbn_num_splits)\n", " )\n", " \n", " self.pool = nn.MaxPool2d(kernel_size=4)\n", " \n", " self.classifier = nn.Sequential(\n", " nn.Flatten(),\n", " nn.Linear(fmap_factor * 8, num_classes)\n", " )\n", " self.scale = torch.tensor(0.0625, requires_grad=False)\n", "\n", " def forward(self, x):\n", " x = self.prep(x)\n", " x = self.layer1(x)\n", " x = self.layer2(x)\n", " x = self.layer3(x)\n", " x = self.pool(x)\n", " y = self.classifier(x)\n", " return y * self.scale\n", " " ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "mSR5-EYdfaqT" }, "source": [ "model = FastResNet(10, fmap_factor=64)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JB5VpI-KfaqU" }, "source": [ "def print_num_params(model, display_all_modules=False):\n", " total_num_params = 0\n", " for n, p in model.named_parameters():\n", " num_params = 1\n", " for s in p.shape:\n", " num_params *= s\n", " if display_all_modules: print(f\"{n}: {num_params}\")\n", " total_num_params += num_params\n", " print(\"-\" * 50)\n", " print(f\"Total number of parameters: {total_num_params:.2e}\")\n", " \n", "\n", "print_num_params(model)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "tCJ9xVa1faqW" }, "source": [ "model = FastResNet(10, fmap_factor=64, conv_ksize=2)\n", "\n", "print_num_params(model)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tEe1st3Dfaqd" }, "source": [ "### Setup dataflow\n", "\n", "We will setup the dataflow using `torchvision` transformation and will not follow the suggestions of [bag-of-trick notebook](https://github.com/davidcpage/cifar10-fast/blob/master/bag_of_tricks.ipynb). Data augmentations used to transform the dataset are\n", "\n", "- Random Crop\n", "- Flip left-right\n", "- Cutout" ] }, { "cell_type": "code", "metadata": { "id": "LtUkVh21faqe" }, "source": [ "import torch\n", "from torchvision.transforms import Compose, Pad, RandomHorizontalFlip, RandomErasing, RandomCrop, Normalize" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "BuGB2zjWfaqe" }, "source": [ "from torch.utils.data import DataLoader\n", "from torchvision.transforms import ToTensor\n", "from torchvision.datasets.cifar import CIFAR10\n", "\n", "\n", "train_transform = Compose([\n", " Pad(4),\n", " RandomCrop(32),\n", " RandomHorizontalFlip(),\n", " ToTensor(), \n", " Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n", " RandomErasing(scale=(0.0625, 0.0625), ratio=(1.0, 1.0))\n", "])\n", "\n", "\n", "test_transform = Compose([\n", " ToTensor(), \n", " Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n", "])\n", "\n", "\n", "train_ds = CIFAR10(\"/tmp/cifar10\", train=True, download=True, transform=train_transform)\n", "test_ds = CIFAR10(\"/tmp/cifar10\", train=False, download=True, transform=train_transform)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "6r71eyznfaqf" }, "source": [ "def get_train_test_loaders():\n", " train_loader = DataLoader(train_ds, batch_size=512, num_workers=10, shuffle=True, drop_last=True, pin_memory=True)\n", " test_loader = DataLoader(test_ds, batch_size=512, num_workers=10, shuffle=False, drop_last=False, pin_memory=True)\n", " return train_loader, test_loader" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "NrCUZlGjfaqh" }, "source": [ "### Setup criterion, optimizer and lr scheduling\n", "\n", "Following cifar10-fast, we will use label smoothing trick for improving the training speed and generalization of neural nets in classification problems." ] }, { "cell_type": "code", "metadata": { "id": "8bX-LXoFfaqh" }, "source": [ "import torch.nn as nn\n", "import torch.optim as optim\n", "\n", "\n", "class CriterionWithLabelSmoothing(nn.Module):\n", " \n", " def __init__(self, criterion, alpha=0.2):\n", " super(CriterionWithLabelSmoothing, self).__init__()\n", " self.criterion = criterion\n", " if self.criterion.reduction != 'none':\n", " raise ValueError(\"Input criterion should have reduction equal none\")\n", " self.alpha = alpha\n", " \n", " def forward(self, logits, targets):\n", " loss = self.criterion(logits, targets)\n", " log_probs = torch.log_softmax(logits, dim=1)\n", " klloss = -log_probs.mean(dim=1) \n", " out = (1.0 - self.alpha) * loss + self.alpha * klloss\n", " return out.mean(dim=0)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "oI02itLjfaqh" }, "source": [ "def get_criterion(alpha):\n", " return CriterionWithLabelSmoothing(nn.CrossEntropyLoss(reduction='none'), alpha=0.2)\n", "\n", "\n", "def get_optimizer(model, momentum, weight_decay, nesterov):\n", " biases = [p for n, p in model.named_parameters() if \"bias\" in n]\n", " others = [p for n, p in model.named_parameters() if \"bias\" not in n]\n", " return optim.SGD(\n", " [{\"params\": others, \"lr\": 1.0, \"weight_decay\": weight_decay}, \n", " {\"params\": biases, \"lr\": 1.0, \"weight_decay\": weight_decay / 64}], \n", " momentum=momentum, nesterov=nesterov\n", " )\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "I8rCwiNgfaqi" }, "source": [ "There is an implementation difference of current PyTorch SGD and SGD from cifar10-fast. The latter uses Sutskever et al implementation:\n", "```\n", "new_w = w + mu * v - lr * (dw + weight_decay * w)\n", "v = mu * prev_v - lr * (dw + weight_decay * w)\n", "```\n", "and PyTorch's one is \n", "```\n", "new_w = w - lr * (mu * v + dw + weight_decay * w)\n", "v = mu * prev_v + dw + weight_decay * w\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "YexZcC92faqi" }, "source": [ "from ignite.handlers import PiecewiseLinear, ParamGroupScheduler" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "BavMV6RHfaqj" }, "source": [ "def get_lr_scheduler(optimizer, lr_max_value, lr_max_value_epoch, num_epochs, epoch_length):\n", " milestones_values = [\n", " (0, 0.0), \n", " (epoch_length * lr_max_value_epoch, lr_max_value), \n", " (epoch_length * num_epochs - 1, 0.0)\n", " ]\n", " lr_scheduler1 = PiecewiseLinear(optimizer, \"lr\", milestones_values=milestones_values, param_group_index=0)\n", "\n", " milestones_values = [\n", " (0, 0.0), \n", " (epoch_length * lr_max_value_epoch, lr_max_value * 64), \n", " (epoch_length * num_epochs - 1, 0.0)\n", " ]\n", " lr_scheduler2 = PiecewiseLinear(optimizer, \"lr\", milestones_values=milestones_values, param_group_index=1)\n", "\n", " lr_scheduler = ParamGroupScheduler(\n", " [lr_scheduler1, lr_scheduler2],\n", " [\"lr scheduler (non-biases)\", \"lr scheduler (biases)\"]\n", " )\n", " \n", " return lr_scheduler" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bDGjGeHofaqu" }, "source": [ "%matplotlib inline\n", "\n", "num_epochs = 25\n", "lr_max_value = 0.4\n", "milestones_values = [(0, 0.0), (num_epochs // 5, lr_max_value), (num_epochs - 1, 0.0)]\n", "\n", "PiecewiseLinear.plot_values(num_epochs, param_name=\"lr\", milestones_values=milestones_values)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ydjoAss_faqv" }, "source": [], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ubg08s_yfaqv" }, "source": [ "### Setup hyperparameter tuning\n", "\n", "Now we are ready to setup hyperparameter tuning to optimize the following parameters in order to get higher accuracy on test dataset while training limited by 12 epochs:\n", "\n", "- learning rate peak value: `[0.1, 1.0]`\n", "- SGD momentum: `[0.7, 1.0]`\n", "- weight decay: `[0.0, 1e-3]`\n", "- label smoothing `alpha`: `[0.1, 0.5]`\n", "- number of features (`fmap_factor`): `[16, 24, 32, 40, 48, 56, 64, 72, 80]`\n", "- convolution kernel size: `3` or `2`\n", "- ..." ] }, { "cell_type": "code", "metadata": { "id": "Yt3W860Hfaqw" }, "source": [ "from ax.plot.contour import plot_contour\n", "from ax.plot.trace import optimization_trace_single_method\n", "from ax.service.managed_loop import optimize\n", "from ax.utils.notebook.plotting import render, init_notebook_plotting\n", "\n", "init_notebook_plotting()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6AHf4Tqwfaq2" }, "source": [ "First, we need to create evaluation function that receives experiment parameters and returns test accuracy.\n", "\n", "Input parameters search space is defined as a list of dictionaries that have the following required keys: \n", "- \"name\" - parameter name, \n", "- \"type\" - parameter type (\"range\", \"choice\" or \"fixed\"), \n", "- \"bounds\" for range parameters, \n", "- \"values\" for choice parameters, and \n", "- \"value\" for fixed parameters.\n", "\n", "Experiment parameters object provided for a single experiment is a dictionary `parameter name: value or values`. \n", "\n", "\n", "Links: \n", "- [Ax Parameters API](https://ax.dev/api/core.html#module-ax.core.parameter)\n", "- [Ax optimize function](https://ax.dev/api/service.html#ax.service.managed_loop.optimize)\n", "- [Ax parameters search space example](https://ax.dev/tutorials/gpei_hartmann_service.html#2.-Set-up-experiment)" ] }, { "cell_type": "code", "metadata": { "id": "d0OloBgffaq3" }, "source": [ "from ignite.engine import create_supervised_trainer, create_supervised_evaluator, Events, convert_tensor\n", "from ignite.metrics import Accuracy\n", "from ignite.handlers import TensorboardLogger, ProgressBar\n", "from ignite.handlers.tensorboard_logger import OutputHandler, OptimizerParamsHandler, GradsHistHandler, \\\n", " global_step_from_engine" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LS0hvziTfaq3" }, "source": [ "# Transfer batch to GPU and set floating-point 16\n", "def prepare_batch_fp16(batch, device=None, non_blocking=True):\n", " x, y = batch\n", " return (convert_tensor(x, device=device, non_blocking=non_blocking).half(),\n", " convert_tensor(y, device=device, non_blocking=non_blocking))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ii-gTogTfaq6" }, "source": [ "torch.backends.cudnn.benchmark = True" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Vecp6Lu1faq6" }, "source": [ "num_epochs = 17\n", "\n", "\n", "def run_experiment(parameters):\n", " device = 'cuda'\n", " fast_mode = parameters.get(\"fast_mode\", True)\n", " \n", " # setup model\n", " model = FastResNet(\n", " num_classes=10, \n", " fmap_factor=parameters.get(\"fmap_factor\"), \n", " conv_ksize=parameters.get(\"conv_ksize\"),\n", " classif_scale=parameters.get(\"classif_scale\")\n", " ).to(device).half()\n", " \n", " # setup dataloaders \n", " train_loader, test_loader = get_train_test_loaders()\n", " \n", " # setup solver\n", " criterion = get_criterion(parameters.get(\"alpha\")).to(device)\n", " optimizer = get_optimizer(\n", " model, \n", " parameters.get(\"momentum\"), \n", " parameters.get(\"weight_decay\"),\n", " parameters.get(\"nesterov\")\n", " )\n", " lr_scheduler = get_lr_scheduler(\n", " optimizer, \n", " parameters.get(\"lr_max_value\"),\n", " parameters.get(\"lr_max_value_epoch\"), \n", " num_epochs=num_epochs,\n", " epoch_length=len(train_loader)\n", " )\n", " \n", " # setup ignite trainer\n", " trainer = create_supervised_trainer(model, optimizer, criterion, \n", " device=device, non_blocking=True,\n", " prepare_batch=prepare_batch_fp16)\n", " \n", " # setup learning rate scheduler\n", " trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler)\n", " \n", " # setup tensorboard logger\n", " exp_log_name = f\"exp_{parameters.get('fmap_factor')}_{parameters.get('conv_ksize')}_\" + \\\n", " f\"{parameters.get('alpha'):.2}_{parameters.get('lr_max_value'):.4}\"\n", " tb_logger = TensorboardLogger(log_dir=f\"/tmp/tb_logs/{exp_log_name}\")\n", " \n", " if not fast_mode:\n", " # - log learning rate\n", " tb_logger.attach(trainer, OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED)\n", "\n", " # - log training batch loss\n", " tb_logger.attach(trainer, OutputHandler(tag=\"training\", output_transform=lambda x: {\"batch loss\": x}), \n", " event_name=Events.ITERATION_COMPLETED)\n", "\n", " # - log model grads\n", " tb_logger.attach(trainer, GradsHistHandler(model), event_name=Events.EPOCH_COMPLETED) \n", " \n", " # setup a progress bar\n", " ProgressBar().attach(trainer, event_name=Events.EPOCH_COMPLETED, closing_event_name=Events.COMPLETED) \n", " \n", " # setup evaluator\n", " def output_transform(output):\n", " y_pred, y = output\n", " y_pred = y_pred.float()\n", " return y_pred, y\n", "\n", " metrics = {\n", " \"test accuracy\": Accuracy(output_transform=output_transform)\n", " }\n", " evaluator = create_supervised_evaluator(model, metrics=metrics, \n", " device=device, non_blocking=True, \n", " prepare_batch=prepare_batch_fp16)\n", " \n", " # evaluate trained model each 3 epochs\n", " @trainer.on(Events.EPOCH_COMPLETED)\n", " def run_evaluation(engine):\n", " c1 = (engine.state.epoch - 1) % 3 == 0\n", " c2 = engine.state.epoch == engine.state.max_epochs\n", " if (c1 and not fast_mode) or c2:\n", " evaluator.run(test_loader)\n", " \n", " if not fast_mode:\n", " # - log test accuracy\n", " tb_logger.attach(evaluator, \n", " OutputHandler(tag=\"validation\", metric_names=\"all\", \n", " global_step_transform=global_step_from_engine(trainer)), \n", " event_name=Events.EPOCH_COMPLETED)\n", "\n", " trainer.run(train_loader, max_epochs=num_epochs) \n", " test_acc = evaluator.state.metrics['test accuracy']\n", " \n", " # dump hparams/result to Tensorboard\n", " tb_logger.writer.add_hparams(parameters, {'hparam/test_accuracy': test_acc})\n", "\n", " tb_logger.close() \n", " return test_acc" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wHDqJ59Yfaq7" }, "source": [ "Original training configurations gives us the following result:" ] }, { "cell_type": "code", "metadata": { "id": "rDitZVaxfaq7" }, "source": [ "batch_size = 512\n", "num_epochs = 20\n", "\n", "run_experiment(\n", " parameters={\n", " \"fmap_factor\": 64,\n", " \"conv_ksize\": 3,\n", " \"classif_scale\": 0.0625,\n", " \"alpha\": 0.2,\n", " \"momentum\": 0.9,\n", " \"weight_decay\": 5e-4,\n", " \"nesterov\": True,\n", " \"lr_max_value\": 1.0,\n", " \"lr_max_value_epoch\": num_epochs // 5,\n", " \"fast_mode\": False\n", " }\n", ")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mExJHmKefaq8" }, "source": [ "#### Setup parameters search space" ] }, { "cell_type": "code", "metadata": { "id": "UovND_RDfaq8" }, "source": [ "parameters_space = [\n", " {\n", " \"name\": \"fmap_factor\",\n", " \"type\": \"range\",\n", " \"bounds\": [48, 80],\n", " },\n", " {\n", " \"name\": \"conv_ksize\",\n", " \"type\": \"choice\",\n", " \"values\": [2, 3],\n", " },\n", " {\n", " \"name\": \"classif_scale\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.00625, 0.250],\n", " },\n", " {\n", " \"name\": \"alpha\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.1, 0.5],\n", " },\n", " {\n", " \"name\": \"momentum\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.7, 1.0],\n", " },\n", " {\n", " \"name\": \"weight_decay\",\n", " \"type\": \"range\",\n", " \"bounds\": [1e-4, 1e-3],\n", " \"value_type\": \"float\",\n", " }, \n", " {\n", " \"name\": \"nesterov\",\n", " \"type\": \"choice\",\n", " \"values\": [True, False],\n", " },\n", " {\n", " \"name\": \"lr_max_value\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.1, 1.0],\n", " },\n", " {\n", " \"name\": \"lr_max_value_epoch\",\n", " \"type\": \"range\",\n", " \"bounds\": [1, 10],\n", " },\n", "]\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qySwubl7faq8" }, "source": [], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "z2VfeJJ6faq9" }, "source": [ "### Start tuning" ] }, { "cell_type": "code", "metadata": { "id": "ZCDlqoEIfaq9" }, "source": [ "num_epochs = exp_num_epochs = 20\n", "\n", "\n", "best_parameters, values, experiment, model = optimize(\n", " parameters=parameters_space,\n", " evaluation_function=run_experiment,\n", " objective_name='test accuracy',\n", " total_trials=30\n", ")\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SoK4IRuxfaq-" }, "source": [ "We found the best parameters that give the following outcome:" ] }, { "cell_type": "code", "metadata": { "id": "AeK3X7eofaq-" }, "source": [ "means, covariances = values\n", "print(f\"\\nBest parameters: {best_parameters}\\n\")\n", "print(f\"Test accuracy: {means} Β± {covariances}\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5IE4nWH0faq-" }, "source": [ "Let's plot contours showing test accuracy as a function of the two hyperparameters." ] }, { "cell_type": "code", "metadata": { "id": "I4OExhm6faq_" }, "source": [ "render(plot_contour(model=model, param_x='lr_max_value', param_y='momentum', metric_name='test accuracy'))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qC7r9vjlfarA" }, "source": [ "Let's retrain the model with best found parameters and compare with previous baseline: " ] }, { "cell_type": "code", "metadata": { "id": "rVAR5tvJfarA" }, "source": [ "batch_size = 512\n", "num_epochs = 20\n", "\n", "best_parameters_copy = dict(best_parameters)\n", "best_parameters_copy['fast_mode'] = False\n", "\n", "run_experiment(\n", " parameters=best_parameters_copy\n", ")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "P0ob7uehfarC" }, "source": [ "In Tensorboard we can observer a tab \"HPARAMS\":\n", "\n", "![hparams](https://github.com/abdulelahsm/ignite/blob/update-tutorials/examples/notebooks/assets/ax_hparams.png?raw=1)" ] } ] }ignite-0.5.1/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb000066400000000000000000001376461465426447700247130ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "CycleGAN_with_nvdia_apex.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "jcIvuoy0zlFM" }, "source": [ "# CycleGAN with Ignite and Nvidia/Apex\n", "\n", "In this notebook we provide an implementation of [CycleGAN](https://arxiv.org/abs/1703.10593) and its automatic mixed precision training on \"Horse 2 Zebra\" dataset using Ignite and Nvidia/Apex.\n", "\n", "**Update (05/2020):** thanks to **@mcarilli** a bug in PyTorch (https://github.com/pytorch/pytorch/issues/37157) with `ConvTranspose2d` was fixed since v1.6.0 (or master at the moment of writing) and this enables CycleGAN automatic mixed precision training.\n", "\n", "### CycleGAN in a Nutshell\n", "\n", "CycleGAN is unpaired image-to-image translation task from $A$ to $B$ and represented by two generative networks $G$ and $F$:\n", "$$\n", "\\hat{y} = G(x) \\in B,\\text{ for } x \\in A \\\\\n", "\\hat{x} = F(y) \\in A,\\text{ for } y \\in B\n", "$$\n", "\n", "and two discriminators $D_A$ and $D_B$. Training of the networks is done by minimizing the loss is a sum of 3 components:\n", "$$\n", "\\mathcal{L}(G, F, D_A, D_B) = \\mathcal{L}_{GAN}(G, D_B) + \\mathcal{L}_{GAN}(F, D_A) + \\lambda \\mathcal{L}_{cyc}(G, F)\n", "$$\n", "with GAN loss:\n", "$$\n", "\\mathcal{L}_{GAN}(G, D_B) = \\text{mean}_{x \\in A}\\left[ (D_B(G(x)) - 1)^2 \\right]+ \\text{mean}_{y \\in B}\\left[ (D_B(y) - 1)^2 \\right] \\\\\n", "\\mathcal{L}_{GAN}(F, D_A) = \\text{mean}_{y \\in B}\\left[ (D_A(F(y)) - 1)^2 \\right]+ \\text{mean}_{x \\in A}\\left[ (D_A(x) - 1)^2 \\right]\n", "$$\n", "and forward and backward cycle consistency term:\n", "$$\n", "\\mathcal{L}_{cyc}(G, F) = \\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right] + \\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]\n", "$$\n", "\n", "Optionally, one can add identity loss terms. See [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md#what-is-the-identity-loss-322-373-362)." ] }, { "cell_type": "code", "metadata": { "id": "nDtYYPqIPVYP" }, "source": [ "!nvidia-smi" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Uboc36tszlFO" }, "source": [ "## Requirements\n", "\n", "1) Let's download the dataset:" ] }, { "cell_type": "code", "metadata": { "id": "JPiG3vNazlFQ" }, "source": [ "!wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip -O/tmp/horse2zebra.zip\n", "!7z x /tmp/horse2zebra.zip -o/tmp/" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-MkAbMR2zlFb" }, "source": [ "2.a) We need install PyTorch >= 1.6.0 and torchvision" ] }, { "cell_type": "code", "metadata": { "id": "4G7O0bTNzlFc" }, "source": [ "!pip install --pre --upgrade torch torchvision -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ooFlZUKnzlFj" }, "source": [ "import torch\n", "torch.__version__" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hGa3fccizlFq" }, "source": [ "2.b) Next we need to install [nvidia/apex](https://github.com/NVIDIA/apex)" ] }, { "cell_type": "code", "metadata": { "id": "4XZA8zY-zlFz" }, "source": [ "# Install Apex:\n", "# If torch cuda version and nvcc version match:\n", "!pip install --upgrade --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" git+https://github.com/NVIDIA/apex/\n", "# if above command is failing, please install apex without c++/cuda extensions:\n", "# !pip install --upgrade --no-cache-dir git+https://github.com/NVIDIA/apex/" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qkMTpk84z-8q" }, "source": [ "3) Install latest tensorboardX and nightly `pytorch-ignite`" ] }, { "cell_type": "code", "metadata": { "id": "ndF-Xc_C0OaY" }, "source": [ "!pip install --pre --upgrade pytorch-ignite tensorboardX" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "kJ1Y88VY0PSI" }, "source": [ "import ignite\n", "ignite.__version__" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2X5yTc8yzlF4" }, "source": [ "import random\n", "import torch\n", "\n", "seed = 17\n", "random.seed(seed)\n", "_ = torch.manual_seed(seed)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "19UMiJOazlF9" }, "source": [ "## Dataflow\n", "\n", "Let's setup the datase" ] }, { "cell_type": "code", "metadata": { "id": "vvjeGiN5zlF-" }, "source": [ "from torch.utils.data import Dataset, DataLoader\n", "from PIL import Image\n", "\n", "class FilesDataset(Dataset):\n", " \n", " def __init__(self, path, extension=\"*.jpg\"):\n", " self.path = Path(path)\n", " assert self.path.exists(), \"Path '{}' is not found\".format(path)\n", " self.images = list(self.path.rglob(extension))\n", " assert len(self.images) > 0, \"No images with extension {} found at '{}'\".format(extension, path)\n", " \n", " def __len__(self):\n", " return len(self.images)\n", " \n", " def __getitem__(self, i):\n", " return Image.open(self.images[i]).convert('RGB')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_pnfNZIEzlGC" }, "source": [ "from pathlib import Path\n", "\n", "root = Path(\"/tmp/horse2zebra\")\n", "\n", "train_A = FilesDataset(root / \"trainA\")\n", "train_B = FilesDataset(root / \"trainB\")\n", "\n", "test_A = FilesDataset(root / \"testA\") \n", "test_B = FilesDataset(root / \"testB\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Ot_JOE3KzlGG" }, "source": [ "Some details on the datasets:" ] }, { "cell_type": "code", "metadata": { "id": "wPeNl2ufzlGH" }, "source": [ "print(\"Dataset sizes: \\ntrain A: {} | B: {}\\ntest A: {} | B: {}\\n\\t\".format(len(train_A), len(train_B), len(test_A), len(test_B)))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "b0EKmJivzlGM" }, "source": [ "print(f\"Train random image sizes (A): {train_A[0].size}, {train_A[1].size}, {train_A[10].size}, {train_A[-1].size}\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "7waPGeQOzlGQ" }, "source": [ "print(f\"Train random image sizes (B): {train_B[0].size}, {train_B[1].size}, {train_B[10].size}, {train_B[-1].size}\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2vPHwFKBzlGV" }, "source": [ "import matplotlib.pylab as plt\n", "%matplotlib inline" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_OqBOrx2zlGb" }, "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Train dataset 'Horses'\")\n", "plt.imshow(train_A[10])\n", "plt.subplot(122)\n", "plt.title(\"Train dataset 'Zebras'\")\n", "plt.imshow(train_B[10])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9HtGaHU7zlGf" }, "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Test dataset 'Horses'\")\n", "plt.imshow(test_A[0])\n", "plt.subplot(122)\n", "plt.title(\"Test dataset 'Zebras'\")\n", "plt.imshow(test_B[0])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "73DFE_28zlGj" }, "source": [ "Let's create a dataset composed of random image pairs of datasets A and B" ] }, { "cell_type": "code", "metadata": { "id": "Q-R7-6B9zlGk" }, "source": [ "import random\n", "\n", "\n", "class Image2ImageDataset(Dataset):\n", " \n", " def __init__(self, ds_a, ds_b):\n", " self.dataset_a = ds_a\n", " self.dataset_b = ds_b\n", " \n", " def __len__(self):\n", " return max(len(self.dataset_a), len(self.dataset_b))\n", "\n", " def __getitem__(self, i):\n", " dp_a = self.dataset_a[i % len(self.dataset_a)]\n", " j = random.randint(0, len(self.dataset_b) - 1)\n", " dp_b = self.dataset_b[j]\n", " return {\n", " 'A': dp_a,\n", " 'B': dp_b\n", " }\n", "\n", "\n", "class TransformedDataset(Dataset):\n", " \n", " def __init__(self, ds, transform):\n", " self.dataset = ds\n", " self.transform = transform\n", " \n", " def __len__(self):\n", " return len(self.dataset)\n", " \n", " def __getitem__(self, i):\n", " return {k: self.transform(v) for k, v in self.dataset[i].items()}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4FIgl0aOzlGn" }, "source": [ "train_ab_ds = Image2ImageDataset(train_A, train_B)\n", "test_ab_ds = Image2ImageDataset(test_A, test_B)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "xQRxOp_PzlGr" }, "source": [ "dp = train_ab_ds[20]\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Train dataset 'Horses'\")\n", "plt.imshow(dp['A'])\n", "plt.subplot(122)\n", "plt.title(\"Train dataset 'Zebras'\")\n", "plt.imshow(dp['B'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "YiDAQaCqzlGu" }, "source": [ "dp = test_ab_ds[20]\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Test dataset 'Horses'\")\n", "plt.imshow(dp['A'])\n", "plt.subplot(122)\n", "plt.title(\"Test dataset 'Zebras'\")\n", "plt.imshow(dp['B'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "yfIjl0MYzlGy" }, "source": [ "from torchvision.transforms import Compose, ColorJitter, RandomHorizontalFlip, ToTensor, Normalize, RandomCrop\n", "\n", "# To accelerate the training we reduce the image size to 200x200 pix instead of 256x256\n", "train_transform = Compose([\n", " RandomCrop(200),\n", " RandomHorizontalFlip(),\n", " ColorJitter(),\n", " ToTensor(),\n", " Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n", "])\n", "transformed_train_ab_ds = TransformedDataset(train_ab_ds, transform=train_transform)\n", "\n", "# Please select appropriate batch_size value according to your infrastructure\n", "batch_size = 10\n", "train_ab_loader = DataLoader(transformed_train_ab_ds, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True, num_workers=4)\n", "\n", "\n", "test_transform = Compose([\n", " ToTensor(),\n", " Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n", "])\n", "transformed_test_ab_ds = TransformedDataset(test_ab_ds, transform=test_transform)\n", "batch_size = 10\n", "test_ab_loader = DataLoader(transformed_test_ab_ds, batch_size=batch_size, shuffle=False, drop_last=False, pin_memory=True, num_workers=4)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "CBL5LfStzlG2" }, "source": [ "import torchvision.utils as vutils\n", "\n", "# Plot some training images\n", "real_batch = next(iter(train_ab_loader))\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.axis(\"off\")\n", "plt.title(\"Training Images from A\")\n", "plt.imshow( \n", " vutils.make_grid(real_batch['A'][:64], padding=2, normalize=True).cpu().numpy().transpose((1, 2, 0))\n", ")\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.axis(\"off\")\n", "plt.title(\"Training Images from B\")\n", "plt.imshow(\n", " vutils.make_grid(real_batch['B'][:64], padding=2, normalize=True).cpu().numpy().transpose((1, 2, 0))\n", ")\n", "real_batch = None\n", "torch.cuda.empty_cache()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5EzYQJC5zlG6" }, "source": [ "## Generator and Discriminator networks\n", "\n", "- Generator network architecture contains 9 residual blocks\n", "Using paper's notations:\n", "```\n", "c7s1-64,d128,d256,R256,R256,R256,R256,R256,R256,R256,R256,R256,u128,u64,c7s1-3,tanh\n", "```\n", "where `c7s1-k` denotes a 7x7 Convolution-InstanceNorm-ReLU layer with `k` filters and stride 1. `dk` denotes a 3x3 Convolution-InstanceNorm-ReLU layer with `k` filters and stride 2. Reflection padding was used to reduce artifacts. `Rk` denotes a residual block that contains two 3x3 convolutional layers with the same number of filters on both layer. `uk` denotes a 3x3 fractional-strided-Convolution-InstanceNorm-ReLU layer with `k` filters and stride 1/2." ] }, { "cell_type": "code", "metadata": { "id": "32CwGT7FzlG7" }, "source": [ "import torch\n", "import torch.nn as nn\n", "\n", "\n", "def get_conv_inorm_relu(in_planes, out_planes, kernel_size, stride, reflection_pad=True, with_relu=True):\n", " layers = []\n", " padding = (kernel_size - 1) // 2\n", " if reflection_pad:\n", " layers.append(nn.ReflectionPad2d(padding=padding))\n", " padding = 0\n", " layers += [\n", " nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding),\n", " nn.InstanceNorm2d(out_planes, affine=False, track_running_stats=False),\n", " ]\n", " if with_relu:\n", " layers.append(nn.ReLU(inplace=True))\n", " return nn.Sequential(*layers)\n", "\n", "\n", "def get_conv_transposed_inorm_relu(in_planes, out_planes, kernel_size, stride):\n", " return nn.Sequential(\n", " nn.ConvTranspose2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=1, output_padding=1),\n", " nn.InstanceNorm2d(out_planes, affine=False, track_running_stats=False),\n", " nn.ReLU(inplace=True)\n", " )\n", "\n", "\n", "class ResidualBlock(nn.Module):\n", " \n", " def __init__(self, in_planes):\n", " super(ResidualBlock, self).__init__()\n", " self.conv1 = get_conv_inorm_relu(in_planes, in_planes, kernel_size=3, stride=1)\n", " self.conv2 = get_conv_inorm_relu(in_planes, in_planes, kernel_size=3, stride=1, with_relu=False) \n", "\n", " def forward(self, x):\n", " residual = x\n", " x = self.conv1(x)\n", " x = self.conv2(x) \n", " return x + residual\n", "\n", "\n", "class Generator(nn.Module):\n", " \n", " def __init__(self):\n", " super(Generator, self).__init__()\n", " \n", " self.c7s1_64 = get_conv_inorm_relu(3, 64, kernel_size=7, stride=1)\n", " self.d128 = get_conv_inorm_relu(64, 128, kernel_size=3, stride=2, reflection_pad=False)\n", " self.d256 = get_conv_inorm_relu(128, 256, kernel_size=3, stride=2, reflection_pad=False)\n", "\n", " self.resnet9 = nn.Sequential(*[ResidualBlock(256) for i in range(9)])\n", "\n", " self.u128 = get_conv_transposed_inorm_relu(256, 128, kernel_size=3, stride=2)\n", " self.u64 = get_conv_transposed_inorm_relu(128, 64, kernel_size=3, stride=2)\n", " self.c7s1_3 = get_conv_inorm_relu(64, 3, kernel_size=7, stride=1, with_relu=False)\n", " # Replace instance norm by tanh activation\n", " self.c7s1_3[-1] = nn.Tanh()\n", "\n", " def forward(self, x):\n", " # Encoding\n", " x = self.c7s1_64(x)\n", " x = self.d128(x)\n", " x = self.d256(x)\n", " \n", " # 9 residual blocks\n", " x = self.resnet9(x)\n", "\n", " # Decoding\n", " x = self.u128(x)\n", " x = self.u64(x)\n", " y = self.c7s1_3(x)\n", " return y\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "O0guWKr0zlHA" }, "source": [ "Let's check the network:" ] }, { "cell_type": "code", "metadata": { "id": "YH0MVc8AzlHC" }, "source": [ "x = torch.rand(4, 3, 256, 256)\n", "g = Generator()\n", "y = g(x)\n", "y.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8xFV-ExhzlHF" }, "source": [ "- Discriminator network is a PatchGAN (with receptive field 70x70):\n", "```\n", "C64-C128-C256-C512\n", "```\n", "where `Ck` denote a 4x4 Convolution-InstanceNorm-LeakyReLU layer with `k` filters and stride 2. After the last layer, \n", "a convolution to produce a 1-dimensional output is applied. No `InstanceNorm` for the first `C64` layer. Leaky ReLUs are with a slope of `0.2`. \n", "\n", "Good explanation is given in [this comment](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/39#issuecomment-305575964) on what is a PatchGAN:\n", "```\n", "In fact, a \"PatchGAN\" is just a convnet! Or you could say all convnets are patchnets: the power of convnets is that they process each image patch identically and independently, which makes things very cheap (# params, time, memory), and, amazingly, turns out to work.\n", "\n", "The difference between a PatchGAN and regular GAN discriminator is that rather the regular GAN maps from a 256x256 image to a single scalar output, which signifies \"real\" or \"fake\", whereas the PatchGAN maps from 256x256 to an NxN array of outputs X, where each X_ij signifies whether the patch ij in the image is real or fake. Which is patch ij in the input? Well, output X_ij is just a neuron in a convnet, and we can trace back its receptive field to see which input pixels it is sensitive to. In the CycleGAN architecture, the receptive fields of the discriminator turn out to be 70x70 patches in the input image!\n", "\n", "This is all mathematically equivalent to if we had manually chopped up the image into 70x70 overlapping patches, run a regular discriminator over each patch, and averaged the results.\n", "\n", "Maybe it would have been better if we called it a \"Fully Convolutional GAN\" like in FCNs... it's the same idea :)\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "1oj5XaM0zlHG" }, "source": [ "def get_conv_inorm_lrelu(in_planes, out_planes, stride=2, negative_slope=0.2):\n", " return nn.Sequential(\n", " nn.Conv2d(in_planes, out_planes, kernel_size=4, stride=stride, padding=1),\n", " nn.InstanceNorm2d(out_planes, affine=False, track_running_stats=False),\n", " nn.LeakyReLU(negative_slope=negative_slope, inplace=True)\n", " )\n", "\n", "\n", "class Discriminator(nn.Module):\n", "\n", " def __init__(self):\n", " super(Discriminator, self).__init__()\n", " self.c64 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)\n", " self.relu = nn.LeakyReLU(0.2, inplace=True)\n", " self.c128 = get_conv_inorm_lrelu(64, 128)\n", " self.c256 = get_conv_inorm_lrelu(128, 256)\n", " self.c512 = get_conv_inorm_lrelu(256, 512, stride=1)\n", " self.last_conv = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1)\n", "\n", " def forward(self, x):\n", " x = self.c64(x)\n", " x = self.relu(x)\n", "\n", " x = self.c128(x)\n", " x = self.c256(x)\n", " x = self.c512(x)\n", " y = self.last_conv(x)\n", " return y\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qc05lzCMzlHK" }, "source": [ "Let's check the network:" ] }, { "cell_type": "code", "metadata": { "id": "YxBiH0lezlHL" }, "source": [ "x = torch.rand(4, 3, 256, 256)\n", "d = Discriminator()\n", "y = d(x)\n", "y.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ZIidCoiHzlHQ" }, "source": [ "According to the paper, the weights are initialized from a Gaussian distribution $\\mathcal{N}(0,0.02)$" ] }, { "cell_type": "code", "metadata": { "id": "e3XCVI4szlHQ" }, "source": [ "def init_weights(module):\n", " assert isinstance(module, nn.Module)\n", " if hasattr(module, \"weight\") and module.weight is not None:\n", " torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n", " if hasattr(module, \"bias\") and module.bias is not None:\n", " torch.nn.init.constant_(module.bias, 0.0)\n", " for c in module.children():\n", " init_weights(c)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "C88zzv4ozlHS" }, "source": [ "g = None; d = None" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "uORvKFM3zlHW" }, "source": [ "## Networks' automatic mixed precision training" ] }, { "cell_type": "code", "metadata": { "id": "98Mn0IY8zlHX" }, "source": [ "assert torch.backends.cudnn.enabled, \"NVIDIA/Apex:Amp requires cudnn backend to be enabled.\"\n", "torch.backends.cudnn.benchmark = True" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fdX_qVVdzlHa" }, "source": [ "device = \"cuda\"\n", "\n", "generator_A2B = Generator().to(device)\n", "init_weights(generator_A2B)\n", "\n", "discriminator_B = Discriminator().to(device)\n", "init_weights(discriminator_B)\n", "\n", "generator_B2A = Generator().to(device)\n", "init_weights(generator_B2A)\n", "discriminator_A = Discriminator().to(device)\n", "init_weights(discriminator_A)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "LpUwBW4PzlHd" }, "source": [ "Similarly, we train the networks from scratch, with a learning rate of `0.0002`." ] }, { "cell_type": "code", "metadata": { "id": "pd_n1OIYzlHe" }, "source": [ "from itertools import chain\n", "import torch.optim as optim\n", "\n", "lr = 0.0002\n", "beta1 = 0.5\n", "\n", "optimizer_G = optim.Adam(chain(generator_A2B.parameters(), generator_B2A.parameters()), lr=lr, betas=(beta1, 0.999))\n", "optimizer_D = optim.Adam(chain(discriminator_A.parameters(), discriminator_B.parameters()), lr=lr, betas=(beta1, 0.999))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FvVz2Hl9zlHg" }, "source": [ "Let's define some helper functions:\n", "- to turn on/off gradients" ] }, { "cell_type": "code", "metadata": { "id": "5URhsn2JzlHh" }, "source": [ "def toggle_grad(model, on_or_off):\n", " # https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py#L674\n", " for param in model.parameters():\n", " param.requires_grad = on_or_off\n", "\n", "\n", "try:\n", " from apex import amp\n", "except ImportError:\n", " raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to run this example.\")\n", "\n", "\n", "# Initialize Amp\n", "models, optimizers = amp.initialize([generator_A2B, generator_B2A, discriminator_A, discriminator_B], \n", " [optimizer_G, optimizer_D],\n", " opt_level=\"O2\", num_losses=2)\n", "\n", "generator_A2B, generator_B2A, discriminator_A, discriminator_B = models\n", "optimizer_G, optimizer_D = optimizers" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7h6GXCU_zlHk" }, "source": [ "### Fake images buffer trick\n", "\n", "According to the paper, to reduce model oscillation and the discriminators are updated using a history of generated images rather than the ones produced by the latest generators. There is an image buffer that stores the X previously created images." ] }, { "cell_type": "code", "metadata": { "id": "msRrTIZXzlHl" }, "source": [ "buffer_size = 50\n", "fake_a_buffer = []\n", "fake_b_buffer = []\n", "\n", "\n", "def buffer_insert_and_get(buffer, batch):\n", " output_batch = []\n", " for b in batch:\n", " b = b.unsqueeze(0)\n", " # if buffer is not fully filled:\n", " if len(buffer) < buffer_size:\n", " output_batch.append(b)\n", " buffer.append(b.cpu())\n", " elif random.uniform(0, 1) > 0.5:\n", " # Add newly created image into the buffer and put ont from the buffer into the output\n", " random_index = random.randint(0, buffer_size - 1) \n", " output_batch.append(buffer[random_index].clone().to(device))\n", " buffer[random_index] = b.cpu()\n", " else:\n", " output_batch.append(b)\n", " return torch.cat(output_batch, dim=0)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "yd33Z6QmzlHn" }, "source": [ "Next, let's define a single iteration via `update_fn` function. This function is then used by `ignite.engine.Engine` to update models while running over the input data.\n", "\n", "As suggested, we divide the objective by 2 while optimizing D, which slows down the rate at which D learns, relative to the rate of G. \n", "\n", "According to the paper:\n", "- generator A is trained minimize $\\text{mean}_{x \\in A}[(D_B(G(x)) βˆ’ 1)^2]$ and cycle loss $\\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right]$\n", "- generator B is trained minimize $\\text{mean}_{y \\in B}[(D_A(F(y)) βˆ’ 1)^2]$ and cycle loss $\\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]$\n", "- discriminators A is trained to minimize $\\text{mean}_{x \\in A}[(D_A(x) βˆ’ 1)^2] + \\text{mean}_{y \\in B}[D_A(F(y))^2]$.\n", "- discriminator B is trained to minimize $\\text{mean}_{y \\in B}[(D_B(y) βˆ’ 1)^2] + \\text{mean}_{x \\in A}[D_B(G(x))^2]$." ] }, { "cell_type": "code", "metadata": { "id": "C2tu38mmzlHp" }, "source": [ "from ignite.utils import convert_tensor\n", "import torch.nn.functional as F\n", "\n", "\n", "lambda_value = 10.0\n", "\n", "\n", "def discriminator_forward_pass(discriminator, batch_real, batch_fake, fake_buffer):\n", " decision_real = discriminator(batch_real)\n", " batch_fake = buffer_insert_and_get(fake_buffer, batch_fake) \n", " decision_fake = discriminator(batch_fake)\n", " return decision_real, decision_fake\n", "\n", "\n", "def compute_loss_generator(batch_decision, batch_real, batch_rec, lambda_value):\n", " # loss gan\n", " target = torch.ones_like(batch_decision)\n", " loss_gan = F.mse_loss(batch_decision, target)\n", " # loss cycle\n", " loss_cycle = F.l1_loss(batch_rec, batch_real) * lambda_value \n", " return loss_gan + loss_cycle\n", "\n", "\n", "def compute_loss_discriminator(decision_real, decision_fake):\n", " # loss = mean (D_b(y) βˆ’ 1)^2 + mean D_b(G(x))^2 \n", " loss = F.mse_loss(decision_fake, torch.zeros_like(decision_fake))\n", " loss += F.mse_loss(decision_real, torch.ones_like(decision_real))\n", " return loss\n", "\n", "\n", "def update_fn(engine, batch):\n", " generator_A2B.train()\n", " generator_B2A.train()\n", " discriminator_A.train()\n", " discriminator_B.train()\n", "\n", " real_a = convert_tensor(batch['A'], device=device, non_blocking=True)\n", " real_b = convert_tensor(batch['B'], device=device, non_blocking=True)\n", " \n", " # Update generators:\n", "\n", " # Disable grads computation for the discriminators:\n", " toggle_grad(discriminator_A, False)\n", " toggle_grad(discriminator_B, False) \n", " \n", " fake_b = generator_A2B(real_a)\n", " rec_a = generator_B2A(fake_b)\n", " fake_a = generator_B2A(real_b)\n", " rec_b = generator_A2B(fake_a)\n", " decision_fake_a = discriminator_A(fake_a)\n", " decision_fake_b = discriminator_B(fake_b)\n", "\n", " # Compute loss for generators and update generators\n", " # loss_a2b = GAN loss: mean (D_B(G(x)) βˆ’ 1)^2 + Forward cycle loss: || F(G(x)) - x ||_1 \n", " loss_a2b = compute_loss_generator(decision_fake_b, real_a, rec_a, lambda_value) \n", "\n", " # loss_b2a = GAN loss: mean (D_A(F(x)) βˆ’ 1)^2 + Backward cycle loss: || G(F(y)) - y ||_1\n", " loss_b2a = compute_loss_generator(decision_fake_a, real_b, rec_b, lambda_value)\n", "\n", " # total generators loss:\n", " loss_generators = loss_a2b + loss_b2a\n", "\n", " optimizer_G.zero_grad() \n", " with amp.scale_loss(loss_generators, optimizer_G, loss_id=0) as scaled_loss:\n", " scaled_loss.backward()\n", " optimizer_G.step()\n", "\n", " decision_fake_a = rec_a = decision_fake_b = rec_b = None\n", " \n", " # Update discriminators:\n", "\n", " # Enable grads computation for the discriminators:\n", " toggle_grad(discriminator_A, True)\n", " toggle_grad(discriminator_B, True)\n", "\n", " decision_real_a, decision_fake_a = discriminator_forward_pass(discriminator_A, real_a, fake_a.detach(), fake_a_buffer) \n", " decision_real_b, decision_fake_b = discriminator_forward_pass(discriminator_B, real_b, fake_b.detach(), fake_b_buffer) \n", " # Compute loss for discriminators and update discriminators\n", " # loss_a = mean (D_a(y) βˆ’ 1)^2 + mean D_a(F(x))^2\n", " loss_a = compute_loss_discriminator(decision_real_a, decision_fake_a)\n", "\n", " # loss_b = mean (D_b(y) βˆ’ 1)^2 + mean D_b(G(x))^2\n", " loss_b = compute_loss_discriminator(decision_real_b, decision_fake_b)\n", " \n", " # total discriminators loss:\n", " loss_discriminators = 0.5 * (loss_a + loss_b)\n", " \n", " optimizer_D.zero_grad()\n", " with amp.scale_loss(loss_discriminators, optimizer_D, loss_id=1) as scaled_loss:\n", " scaled_loss.backward()\n", " optimizer_D.step()\n", " \n", " return {\n", " \"loss_generators\": loss_generators.item(),\n", " \"loss_generator_a2b\": loss_a2b.item(),\n", " \"loss_generator_b2a\": loss_b2a.item(),\n", " \"loss_discriminators\": loss_discriminators.item(),\n", " \"loss_discriminator_a\": loss_a.item(),\n", " \"loss_discriminator_b\": loss_b.item(),\n", " }\n", " " ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "kYUpOC9KzlHy" }, "source": [ "Let's check `update_fn`" ] }, { "cell_type": "code", "metadata": { "id": "-l85TCzFzlHy" }, "source": [ "real_batch = next(iter(train_ab_loader))\n", "\n", "res = update_fn(engine=None, batch=real_batch)\n", "\n", "real_batch = None\n", "torch.cuda.empty_cache()\n", "\n", "res" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "VbePH1sDzlH3" }, "source": [ "Now let's define a trainer and add some practical handlers:\n", "- log to tensorboard: losses, lr, generated images\n", "- progress bar\n", "- models/optimizers checkpointing\n", "\n", "\n", "Optionally, we also log to online platform [W&B](https://app.wandb.ai). Current logs can be seen at https://app.wandb.ai/vfdev-5/ignite-cyclegan-apex\n", "\n", "```\n", "pip install --upgrade wandb\n", "wandb login your-token\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "lTVeh3TVR1k3" }, "source": [ "!pip install --upgrade wandb\n", "# !wandb login your-token" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "3CmksFkIzlH5" }, "source": [ "from ignite.engine import Engine, Events\n", "from ignite.metrics import RunningAverage\n", "\n", "from ignite.handlers import TensorboardLogger, WandBLogger\n", "from ignite.handlers.tensorboard_logger import OutputHandler, OptimizerParamsHandler" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Hkq6KF3PzlH6" }, "source": [ "from functools import partial\n", "\n", "\n", "trainer = Engine(update_fn)\n", "\n", "metric_names = [\n", " 'loss_discriminators', \n", " 'loss_generators', \n", " 'loss_discriminator_a',\n", " 'loss_discriminator_b',\n", " 'loss_generator_a2b',\n", " 'loss_generator_b2a' \n", "]\n", "\n", "def output_transform(out, name):\n", " return out[name]\n", "\n", "for name in metric_names:\n", " # here we cannot use lambdas as they do not store argument `name`\n", " RunningAverage(output_transform=partial(output_transform, name=name)).attach(trainer, name)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "WnkfedNdzlH9" }, "source": [ "from datetime import datetime\n", "\n", "exp_name = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", "tb_logger = TensorboardLogger(log_dir=\"/tmp/cycle_gan_horse2zebra_tb_logs/{}\".format(exp_name))\n", "\n", "tb_logger.attach(trainer, \n", " log_handler=OutputHandler('training', metric_names), \n", " event_name=Events.ITERATION_COMPLETED)\n", "\n", "print(\"Experiment name: \", exp_name)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "PYJbyPYCzlH_" }, "source": [ "from pathlib import Path\n", "\n", "try:\n", "\n", " wb_run_name = \"cycle_gan_horse2zebra\"\n", " wb_dir = Path(\"/tmp/cycle_gan_horse2zebra_wandb\")\n", " if not wb_dir.exists():\n", " wb_dir.mkdir()\n", " wb_logger = WandBLogger(\n", " project=\"ignite-cyclegan-apex\",\n", " name=wb_run_name,\n", " sync_tensorboard=True,\n", " dir=wb_dir.as_posix(),\n", " reinit=True\n", " )\n", "except RuntimeError:\n", " wb_logger = None" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ol8pi8H0zlIB" }, "source": [ "Let's create an evaluator to inference on train/test images and log the generated images to Tensorboard:" ] }, { "cell_type": "code", "metadata": { "id": "lQHD_FjRzlIB" }, "source": [ "from ignite.engine import Engine\n", "\n", "\n", "def evaluate_fn(engine, batch):\n", " generator_A2B.eval()\n", " generator_B2A.eval() \n", " with torch.no_grad():\n", " real_a = convert_tensor(batch['A'], device=device, non_blocking=True)\n", " real_b = convert_tensor(batch['B'], device=device, non_blocking=True)\n", " \n", " fake_b = generator_A2B(real_a)\n", " rec_a = generator_B2A(fake_b)\n", "\n", " fake_a = generator_B2A(real_b)\n", " rec_b = generator_A2B(fake_a)\n", " \n", " return {\n", " 'real_a': real_a,\n", " 'real_b': real_b,\n", " 'fake_a': fake_a,\n", " 'fake_b': fake_b,\n", " 'rec_a': rec_a,\n", " 'rec_b': rec_b, \n", " }\n", "\n", "\n", "evaluator = Engine(evaluate_fn)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "dqOfxVF6zlIE" }, "source": [ "from torch.utils.data import Subset\n", "\n", "eval_batch_size = 5\n", "\n", "train_random_indices = [random.randint(0, len(train_ab_ds) - 1) for _ in range(eval_batch_size)]\n", "small_train_ds = Subset(train_ab_ds, train_random_indices)\n", "small_train_ds = TransformedDataset(small_train_ds, transform=test_transform)\n", "\n", "test_random_indices = [random.randint(0, len(test_ab_ds) - 1) for _ in range(eval_batch_size)]\n", "small_test_ds = Subset(test_ab_ds, test_random_indices)\n", "small_test_ds = TransformedDataset(small_test_ds, transform=test_transform)\n", "\n", "eval_train_loader = DataLoader(small_train_ds, batch_size=eval_batch_size, shuffle=False, drop_last=False, pin_memory=True, num_workers=4)\n", "eval_test_loader = DataLoader(small_test_ds, batch_size=eval_batch_size, shuffle=False, drop_last=False, pin_memory=True, num_workers=4)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LKChxbQkzlIG" }, "source": [ "@trainer.on(Events.EPOCH_STARTED)\n", "def run_evaluation(engine):\n", " evaluator.run(eval_train_loader)\n", " evaluator.run(eval_test_loader)\n", "\n", "\n", "def log_generated_images(engine, logger, event_name):\n", "\n", " tag = \"Train\" if engine.state.dataloader == eval_train_loader else \"Test\"\n", " output = engine.state.output\n", " state = trainer.state\n", " global_step = state.get_event_attrib_value(event_name)\n", "\n", " # create a grid:\n", " # [real a1, real a2, ...]\n", " # [fake a1, fake a2, ...]\n", " # [rec a1, rec a2, ...]\n", " \n", " s = output['real_a'].shape[0]\n", " res_a = vutils.make_grid(torch.cat([\n", " output['real_a'],\n", " output['fake_b'],\n", " output['rec_a'],\n", " ]), padding=2, normalize=True, nrow=s).cpu()\n", "\n", " logger.writer.add_image(tag=\"{} Horses2Zebras (real, fake, rec)\".format(tag), \n", " img_tensor=res_a, global_step=global_step, dataformats='CHW')\n", "\n", " s = output['real_b'].shape[0]\n", " res_b = vutils.make_grid(torch.cat([\n", " output['real_b'],\n", " output['fake_a'],\n", " output['rec_b'],\n", " ]), padding=2, normalize=True, nrow=s).cpu()\n", " logger.writer.add_image(tag=\"{} Zebras2Horses (real, fake, rec)\".format(tag), \n", " img_tensor=res_b, global_step=global_step, dataformats='CHW')\n", "\n", " \n", "tb_logger.attach(evaluator,\n", " log_handler=log_generated_images, \n", " event_name=Events.COMPLETED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "BMM_OzwozlII" }, "source": [ "We also follow suggested lr scheduling: the same learning rate for the first 100 epochs and linearly decay the rate to zero over the next 100 epochs." ] }, { "cell_type": "code", "metadata": { "id": "iUm5u9RdzlII" }, "source": [ "from ignite.handlers import PiecewiseLinear, ParamGroupScheduler\n", "\n", "lr = 0.0002\n", "\n", "milestones_values = [\n", " (0, lr),\n", " (100, lr),\n", " (200, 0.0)\n", "]\n", "gen_lr_scheduler = PiecewiseLinear(optimizer_D, param_name='lr', milestones_values=milestones_values)\n", "desc_lr_scheduler = PiecewiseLinear(optimizer_G, param_name='lr', milestones_values=milestones_values)\n", "\n", "lr_scheduler = ParamGroupScheduler([gen_lr_scheduler, desc_lr_scheduler], \n", " names=['gen_lr_scheduler', 'desc_lr_scheduler'])\n", "\n", "trainer.add_event_handler(Events.EPOCH_STARTED, lr_scheduler)\n", "\n", "\n", "tb_logger.attach(trainer,\n", " log_handler=OptimizerParamsHandler(optimizer_G, \"lr\"), \n", " event_name=Events.EPOCH_STARTED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8c6wzCMyzlIL" }, "source": [ "Let's setup models/optimizers checkpointing:" ] }, { "cell_type": "code", "metadata": { "id": "GAkSTvqZzlIM" }, "source": [ "from ignite.handlers import ModelCheckpoint, TerminateOnNan" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1PbOwr3fzlIO" }, "source": [ "!rm -rf \"/tmp/cycle_gan_checkpoints\" \n", "!mkdir \"/tmp/cycle_gan_checkpoints\"\n", "\n", "from ignite.handlers import ModelCheckpoint, TerminateOnNan\n", "\n", "\n", "checkpoint_handler = ModelCheckpoint(dirname=\"/tmp/cycle_gan_checkpoints\", filename_prefix=\"\")\n", "\n", "to_save = {\n", " \"generator_A2B\": generator_A2B,\n", " \"discriminator_B\": discriminator_B,\n", " \"generator_B2A\": generator_B2A,\n", " \"discriminator_A\": discriminator_A,\n", " \n", " \"optimizer_G\": optimizer_G,\n", " \"optimizer_D\": optimizer_D,\n", "}\n", "\n", "trainer.add_event_handler(Events.ITERATION_COMPLETED(every=500), checkpoint_handler, to_save)\n", "trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan())" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "HmY0vbZ3zlIQ" }, "source": [ "from ignite.handlers import ProgressBar\n", "\n", "# Iteration-wise progress bar\n", "ProgressBar(bar_format=\"\").attach(trainer)\n", "# Epoch-wise progress bar with display of training losses\n", "ProgressBar(persist=True, bar_format=\"\").attach(trainer, metric_names=['loss_discriminators', 'loss_generators'], \n", " event_name=Events.EPOCH_STARTED, closing_event_name=Events.COMPLETED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ON5YXeyG5-ZM" }, "source": [ "# Display in Firefox may not work properly. Use Chrome.\n", "%load_ext tensorboard\n", "\n", "%tensorboard --logdir=/tmp/cycle_gan_horse2zebra_tb_logs" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KsUWFlljzlIT" }, "source": [ "trainer.run(train_ab_loader, max_epochs=200)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "J9iJ2qgFzlIa" }, "source": [ "tb_logger.close()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SqM3CDjGzlIc" }, "source": [ "### Inference with trained generator\n", "\n", "Let's display saved checkpoint, load weights for \"Horses to Zebras\" generator and run an inference on a test image" ] }, { "cell_type": "code", "metadata": { "id": "63fPaeBszlId" }, "source": [ "!ls /tmp/cycle_gan_checkpoints/" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "plx8tVwizlIf" }, "source": [ "checkpoint_path = \"/tmp/cycle_gan_checkpoints/checkpoint_26500.pt\"\n", "\n", "# let's save this checkpoint to W&B\n", "if wb_logger is not None:\n", " wb_logger.save(checkpoint_path)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "B7XfWZ_OzlIh" }, "source": [ "checkpoint_state_dict = torch.load(checkpoint_path)\n", "generator_A2B.load_state_dict(checkpoint_state_dict[\"generator_A2B\"])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "pQ9qevDdzlIk" }, "source": [ "def normalize(x):\n", " vmin = x.min()\n", " vmax = x.max()\n", " x.clamp_(min=vmin, max=vmax)\n", " x.add_(-vmin).div_(vmax - vmin + 1e-5)\n", " return x" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "o2Y5LezkzlIn" }, "source": [ "i = random.randint(0, len(test_ab_ds) - 1)\n", "img = test_ab_ds[i]['A']\n", "x = test_transform(img)\n", "x = x.unsqueeze(0).to(device)\n", "\n", "\n", "with torch.no_grad():\n", " y_pred = generator_A2B(x)\n", " \n", "\n", "img_pred = (255 * normalize(y_pred[0, ...])).cpu().numpy().transpose((1, 2, 0)).astype('uint8')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "GuTQLGu-zlIp" }, "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Horse\")\n", "plt.imshow(img)\n", "plt.subplot(122)\n", "plt.title(\"Generated zebra\")\n", "plt.imshow(img_pred)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FgUb_vT7zlIr" }, "source": [ "Let's apply for fun our zebra-filter on an image with deep learning gurus:" ] }, { "cell_type": "code", "metadata": { "id": "NlJhc0nQzlIs" }, "source": [ "!wget https://www.kdnuggets.com/wp-content/uploads/photo.jpg -O/tmp/dl_durus.jpg" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bX8tSIxMzlIt" }, "source": [ "from PIL import Image\n", "\n", "img = Image.open(\"/tmp/dl_durus.jpg\")\n", "x = test_transform(img)\n", "x = x.unsqueeze(0).to(device)\n", "\n", "\n", "with torch.no_grad():\n", " y_pred = generator_A2B(x)\n", "\n", "\n", "img_pred = (255 * normalize(y_pred[0, ...])).cpu().numpy().transpose((1, 2, 0)).astype('uint8')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "BYVxaD_SzlIv" }, "source": [ "plt.figure(figsize=(15, 8))\n", "plt.subplot(121)\n", "plt.title(\"4 Deep-learning gurus\")\n", "plt.imshow(img)\n", "plt.subplot(122)\n", "plt.title(\"Zebras\")\n", "plt.imshow(img_pred)" ], "execution_count": null, "outputs": [] } ] } ignite-0.5.1/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb000066400000000000000000001365651465426447700253730ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "CycleGAN_with_torch_cuda_amp.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "XwaQH08zFRZW" }, "source": [ "# CycleGAN with Ignite and `torch.cuda.amp`\n", "\n", "In this notebook we provide an implementation of [CycleGAN](https://arxiv.org/abs/1703.10593) and its training on \"Horse 2 Zebra\" dataset using Ignite. This notebook is almost similar to another our [notebook on CycleGAN with Nvidia/Apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_ignite_and_nvdia_apex.ipynb).\n", "\n", "In contrast, we will use recently added [`torch.cuda.amp`](https://pytorch.org/docs/master/notes/amp_examples.html#working-with-multiple-models-losses-and-optimizers) module to perform automatic mixed precision training instead of using Nvidia/Apex package. This module is available in pytorch (>=1.6.0) release.\n", "\n", "\n", "### CycleGAN in a Nutshell\n", "\n", "CycleGAN is unpaired image-to-image translation task from $A$ to $B$ and represented by two generative networks $G$ and $F$:\n", "$$\n", "\\hat{y} = G(x) \\in B,\\text{ for } x \\in A \\\\\n", "\\hat{x} = F(y) \\in A,\\text{ for } y \\in B\n", "$$\n", "\n", "and two discriminators $D_A$ and $D_B$. Training of the networks is done by minimizing the loss is a sum of 3 components:\n", "$$\n", "\\mathcal{L}(G, F, D_A, D_B) = \\mathcal{L}_{GAN}(G, D_B) + \\mathcal{L}_{GAN}(F, D_A) + \\lambda \\mathcal{L}_{cyc}(G, F)\n", "$$\n", "with GAN loss:\n", "$$\n", "\\mathcal{L}_{GAN}(G, D_B) = \\text{mean}_{x \\in A}\\left[ (D_B(G(x)) - 1)^2 \\right]+ \\text{mean}_{y \\in B}\\left[ (D_B(y) - 1)^2 \\right] \\\\\n", "\\mathcal{L}_{GAN}(F, D_A) = \\text{mean}_{y \\in B}\\left[ (D_A(F(y)) - 1)^2 \\right]+ \\text{mean}_{x \\in A}\\left[ (D_A(x) - 1)^2 \\right]\n", "$$\n", "and forward and backward cycle consistency term:\n", "$$\n", "\\mathcal{L}_{cyc}(G, F) = \\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right] + \\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]\n", "$$\n", "\n", "Optionally, one can add identity loss terms. See [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md#what-is-the-identity-loss-322-373-362)." ] }, { "cell_type": "code", "metadata": { "id": "eqe1kXPcXj1U" }, "source": [ "!nvidia-smi" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6Y60e3m2FRZY" }, "source": [ "## Requirements\n", "\n", "1) Let's download the dataset:" ] }, { "cell_type": "code", "metadata": { "id": "l3LdmHAuFRZa" }, "source": [ "!wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip -O/tmp/horse2zebra.zip\n", "!7z x /tmp/horse2zebra.zip -o/tmp/" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "quI5HJekFRZm" }, "source": [ "2) Next we need to install `torchvision` and PyTorch Nightly Release (>=1.6.0)\n", "\n", "```python\n", "# Torchvision:\n", "pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "3c6PHUZeFRZu" }, "source": [ "!pip install --upgrade --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DU14wTLeZBh3" }, "source": [ "3) We install nightly version of pytorch-ignite" ] }, { "cell_type": "code", "metadata": { "id": "dWN63EToZA-G" }, "source": [ "!pip install --pre pytorch-ignite" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "p8M6GlpmQ5jZ" }, "source": [ "import torch\n", "import ignite\n", "torch.__version__, ignite.__version__" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "d3LCqCufFRZ6" }, "source": [ "import random\n", "import torch\n", "\n", "seed = 17\n", "random.seed(seed)\n", "_ = torch.manual_seed(seed)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "20G39vEqFRaA" }, "source": [ "## Dataflow\n", "\n", "Let's setup the datase" ] }, { "cell_type": "code", "metadata": { "id": "nOr3nd4qFRaB" }, "source": [ "from torch.utils.data import Dataset, DataLoader\n", "from PIL import Image\n", "\n", "class FilesDataset(Dataset):\n", " \n", " def __init__(self, path, extension=\"*.jpg\"):\n", " self.path = Path(path)\n", " assert self.path.exists(), \"Path '{}' is not found\".format(path)\n", " self.images = list(self.path.rglob(extension))\n", " assert len(self.images) > 0, \"No images with extension {} found at '{}'\".format(extension, path)\n", " \n", " def __len__(self):\n", " return len(self.images)\n", " \n", " def __getitem__(self, i):\n", " return Image.open(self.images[i]).convert('RGB')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "byG73rBHFRaG" }, "source": [ "from pathlib import Path\n", "\n", "root = Path(\"/tmp/horse2zebra\")\n", "\n", "train_A = FilesDataset(root / \"trainA\")\n", "train_B = FilesDataset(root / \"trainB\")\n", "\n", "test_A = FilesDataset(root / \"testA\") \n", "test_B = FilesDataset(root / \"testB\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Kv1oXmSJFRaK" }, "source": [ "Some details on the datasets:" ] }, { "cell_type": "code", "metadata": { "id": "UZ4rY5S9FRaL" }, "source": [ "print(\"Dataset sizes: \\ntrain A: {} | B: {}\\ntest A: {} | B: {}\\n\\t\".format(len(train_A), len(train_B), len(test_A), len(test_B)))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "R4lyZ6GSFRaR" }, "source": [ "print(\"Train random image sizes (A): {}, {}, {}, {}\".format(train_A[0].size, train_A[1].size, train_A[10].size, train_A[-1].size))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "e-61gbfVFRaX" }, "source": [ "print(\"Train random image sizes (B): {}, {}, {}, {}\".format(train_B[0].size, train_B[1].size, train_B[10].size, train_B[-1].size))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fAxkND8jFRac" }, "source": [ "import matplotlib.pylab as plt\n", "%matplotlib inline" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Xx7cAEI0FRah" }, "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Train dataset 'Horses'\")\n", "plt.imshow(train_A[10])\n", "plt.subplot(122)\n", "plt.title(\"Train dataset 'Zebras'\")\n", "plt.imshow(train_B[10])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "S_v_FuvZFRao" }, "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Test dataset 'Horses'\")\n", "plt.imshow(test_A[0])\n", "plt.subplot(122)\n", "plt.title(\"Test dataset 'Zebras'\")\n", "plt.imshow(test_B[0])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gc6ZuUWwFRas" }, "source": [ "Let's create a dataset composed of random image pairs of datasets A and B" ] }, { "cell_type": "code", "metadata": { "id": "B699THXXFRat" }, "source": [ "import random\n", "\n", "\n", "class Image2ImageDataset(Dataset):\n", " \n", " def __init__(self, ds_a, ds_b):\n", " self.dataset_a = ds_a\n", " self.dataset_b = ds_b\n", " \n", " def __len__(self):\n", " return max(len(self.dataset_a), len(self.dataset_b))\n", "\n", " def __getitem__(self, i):\n", " dp_a = self.dataset_a[i % len(self.dataset_a)]\n", " j = random.randint(0, len(self.dataset_b) - 1)\n", " dp_b = self.dataset_b[j]\n", " return {\n", " 'A': dp_a,\n", " 'B': dp_b\n", " }\n", "\n", "\n", "class TransformedDataset(Dataset):\n", " \n", " def __init__(self, ds, transform):\n", " self.dataset = ds\n", " self.transform = transform\n", " \n", " def __len__(self):\n", " return len(self.dataset)\n", " \n", " def __getitem__(self, i):\n", " return {k: self.transform(v) for k, v in self.dataset[i].items()}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "RF6OQwxoFRax" }, "source": [ "train_ab_ds = Image2ImageDataset(train_A, train_B)\n", "test_ab_ds = Image2ImageDataset(test_A, test_B)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "eIcsATwrFRa1" }, "source": [ "dp = train_ab_ds[20]\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Train dataset 'Horses'\")\n", "plt.imshow(dp['A'])\n", "plt.subplot(122)\n", "plt.title(\"Train dataset 'Zebras'\")\n", "plt.imshow(dp['B'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "X3zfGZoOFRa6" }, "source": [ "dp = test_ab_ds[20]\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Test dataset 'Horses'\")\n", "plt.imshow(dp['A'])\n", "plt.subplot(122)\n", "plt.title(\"Test dataset 'Zebras'\")\n", "plt.imshow(dp['B'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "q2Hda4tjFRa-" }, "source": [ "from torchvision.transforms import Compose, ColorJitter, RandomHorizontalFlip, ToTensor, Normalize, RandomCrop\n", "\n", "# To accelerate the training we reduce the image size to 200x200 pix instead of 256x256\n", "train_transform = Compose([\n", " RandomCrop(200),\n", " RandomHorizontalFlip(),\n", " ColorJitter(),\n", " ToTensor(),\n", " Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n", "])\n", "transformed_train_ab_ds = TransformedDataset(train_ab_ds, transform=train_transform)\n", "\n", "# Please select appropriate batch_size value according to your infrastructure\n", "batch_size = 10\n", "train_ab_loader = DataLoader(transformed_train_ab_ds, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True, num_workers=4)\n", "\n", "\n", "test_transform = Compose([\n", " ToTensor(),\n", " Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n", "])\n", "transformed_test_ab_ds = TransformedDataset(test_ab_ds, transform=test_transform)\n", "batch_size = 10\n", "test_ab_loader = DataLoader(transformed_test_ab_ds, batch_size=batch_size, shuffle=False, drop_last=False, pin_memory=True, num_workers=4)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "HtfXU9uaFRbB" }, "source": [ "import torchvision.utils as vutils\n", "\n", "# Plot some training images\n", "real_batch = next(iter(train_ab_loader))\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.axis(\"off\")\n", "plt.title(\"Training Images from A\")\n", "plt.imshow( \n", " vutils.make_grid(real_batch['A'][:64], padding=2, normalize=True).cpu().numpy().transpose((1, 2, 0))\n", ")\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.axis(\"off\")\n", "plt.title(\"Training Images from B\")\n", "plt.imshow(\n", " vutils.make_grid(real_batch['B'][:64], padding=2, normalize=True).cpu().numpy().transpose((1, 2, 0))\n", ")\n", "real_batch = None\n", "torch.cuda.empty_cache()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "RlZ39Id-FRbF" }, "source": [ "## Generator and Discriminator networks\n", "\n", "- Generator network architecture contains 9 residual blocks\n", "Using paper's notations:\n", "```\n", "c7s1-64,d128,d256,R256,R256,R256,R256,R256,R256,R256,R256,R256,u128,u64,c7s1-3,tanh\n", "```\n", "where `c7s1-k` denotes a 7x7 Convolution-InstanceNorm-ReLU layer with `k` filters and stride 1. `dk` denotes a 3x3 Convolution-InstanceNorm-ReLU layer with `k` filters and stride 2. Reflection padding was used to reduce artifacts. `Rk` denotes a residual block that contains two 3x3 convolutional layers with the same number of filters on both layer. `uk` denotes a 3x3 fractional-strided-Convolution-InstanceNorm-ReLU layer with `k` filters and stride 1/2." ] }, { "cell_type": "code", "metadata": { "id": "Ri6_NvWfFRbG" }, "source": [ "import torch\n", "import torch.nn as nn\n", "\n", "\n", "def get_conv_inorm_relu(in_planes, out_planes, kernel_size, stride, reflection_pad=True, with_relu=True):\n", " layers = []\n", " padding = (kernel_size - 1) // 2\n", " if reflection_pad:\n", " layers.append(nn.ReflectionPad2d(padding=padding))\n", " padding = 0\n", " layers += [\n", " nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding),\n", " nn.InstanceNorm2d(out_planes, affine=False, track_running_stats=False),\n", " ]\n", " if with_relu:\n", " layers.append(nn.ReLU(inplace=True))\n", " return nn.Sequential(*layers)\n", "\n", "\n", "def get_conv_transposed_inorm_relu(in_planes, out_planes, kernel_size, stride):\n", " return nn.Sequential(\n", " nn.ConvTranspose2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=1, output_padding=1),\n", " nn.InstanceNorm2d(out_planes, affine=False, track_running_stats=False),\n", " nn.ReLU(inplace=True)\n", " )\n", "\n", "\n", "class ResidualBlock(nn.Module):\n", " \n", " def __init__(self, in_planes):\n", " super(ResidualBlock, self).__init__()\n", " self.conv1 = get_conv_inorm_relu(in_planes, in_planes, kernel_size=3, stride=1)\n", " self.conv2 = get_conv_inorm_relu(in_planes, in_planes, kernel_size=3, stride=1, with_relu=False) \n", "\n", " def forward(self, x):\n", " residual = x\n", " x = self.conv1(x)\n", " x = self.conv2(x) \n", " return x + residual\n", "\n", "\n", "class Generator(nn.Module):\n", " \n", " def __init__(self):\n", " super(Generator, self).__init__()\n", " \n", " self.c7s1_64 = get_conv_inorm_relu(3, 64, kernel_size=7, stride=1)\n", " self.d128 = get_conv_inorm_relu(64, 128, kernel_size=3, stride=2, reflection_pad=False)\n", " self.d256 = get_conv_inorm_relu(128, 256, kernel_size=3, stride=2, reflection_pad=False)\n", "\n", " self.resnet9 = nn.Sequential(*[ResidualBlock(256) for i in range(9)])\n", "\n", " self.u128 = get_conv_transposed_inorm_relu(256, 128, kernel_size=3, stride=2)\n", " self.u64 = get_conv_transposed_inorm_relu(128, 64, kernel_size=3, stride=2)\n", " self.c7s1_3 = get_conv_inorm_relu(64, 3, kernel_size=7, stride=1, with_relu=False)\n", " # Replace instance norm by tanh activation\n", " self.c7s1_3[-1] = nn.Tanh()\n", "\n", " def forward(self, x):\n", " # Encoding\n", " x = self.c7s1_64(x)\n", " x = self.d128(x)\n", " x = self.d256(x)\n", " \n", " # 9 residual blocks\n", " x = self.resnet9(x)\n", "\n", " # Decoding\n", " x = self.u128(x)\n", " x = self.u64(x)\n", " y = self.c7s1_3(x)\n", " return y\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "snM1h6BnFRbK" }, "source": [ "Let's check the network:" ] }, { "cell_type": "code", "metadata": { "id": "rfOTRPt4FRbL" }, "source": [ "x = torch.rand(4, 3, 256, 256)\n", "g = Generator()\n", "y = g(x)\n", "y.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Mb_6xzMvFRbP" }, "source": [ "- Discriminator network is a PatchGAN (with receptive field 70x70):\n", "```\n", "C64-C128-C256-C512\n", "```\n", "where `Ck` denote a 4x4 Convolution-InstanceNorm-LeakyReLU layer with `k` filters and stride 2. After the last layer, \n", "a convolution to produce a 1-dimensional output is applied. No `InstanceNorm` for the first `C64` layer. Leaky ReLUs are with a slope of `0.2`. \n", "\n", "Good explanation is given in [this comment](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/39#issuecomment-305575964) on what is a PatchGAN:\n", "```\n", "In fact, a \"PatchGAN\" is just a convnet! Or you could say all convnets are patchnets: the power of convnets is that they process each image patch identically and independently, which makes things very cheap (# params, time, memory), and, amazingly, turns out to work.\n", "\n", "The difference between a PatchGAN and regular GAN discriminator is that rather the regular GAN maps from a 256x256 image to a single scalar output, which signifies \"real\" or \"fake\", whereas the PatchGAN maps from 256x256 to an NxN array of outputs X, where each X_ij signifies whether the patch ij in the image is real or fake. Which is patch ij in the input? Well, output X_ij is just a neuron in a convnet, and we can trace back its receptive field to see which input pixels it is sensitive to. In the CycleGAN architecture, the receptive fields of the discriminator turn out to be 70x70 patches in the input image!\n", "\n", "This is all mathematically equivalent to if we had manually chopped up the image into 70x70 overlapping patches, run a regular discriminator over each patch, and averaged the results.\n", "\n", "Maybe it would have been better if we called it a \"Fully Convolutional GAN\" like in FCNs... it's the same idea :)\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "rdb5-RAYFRbR" }, "source": [ "def get_conv_inorm_lrelu(in_planes, out_planes, stride=2, negative_slope=0.2):\n", " return nn.Sequential(\n", " nn.Conv2d(in_planes, out_planes, kernel_size=4, stride=stride, padding=1),\n", " nn.InstanceNorm2d(out_planes, affine=False, track_running_stats=False),\n", " nn.LeakyReLU(negative_slope=negative_slope, inplace=True)\n", " )\n", "\n", "\n", "class Discriminator(nn.Module):\n", "\n", " def __init__(self):\n", " super(Discriminator, self).__init__()\n", " self.c64 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)\n", " self.relu = nn.LeakyReLU(0.2, inplace=True)\n", " self.c128 = get_conv_inorm_lrelu(64, 128)\n", " self.c256 = get_conv_inorm_lrelu(128, 256)\n", " self.c512 = get_conv_inorm_lrelu(256, 512, stride=1)\n", " self.last_conv = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1)\n", "\n", " def forward(self, x):\n", " x = self.c64(x)\n", " x = self.relu(x)\n", "\n", " x = self.c128(x)\n", " x = self.c256(x)\n", " x = self.c512(x)\n", " y = self.last_conv(x)\n", " return y\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xu2K1U7JFRbU" }, "source": [ "Let's check the network:" ] }, { "cell_type": "code", "metadata": { "id": "On4vpLc_FRbV" }, "source": [ "x = torch.rand(4, 3, 256, 256)\n", "d = Discriminator()\n", "y = d(x)\n", "y.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "orcYUiDfFRbZ" }, "source": [ "According to the paper, the weights are initialized from a Gaussian distribution $\\mathcal{N}(0,0.02)$" ] }, { "cell_type": "code", "metadata": { "id": "2vId72FGFRba" }, "source": [ "def init_weights(module):\n", " assert isinstance(module, nn.Module)\n", " if hasattr(module, \"weight\") and module.weight is not None:\n", " torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n", " if hasattr(module, \"bias\") and module.bias is not None:\n", " torch.nn.init.constant_(module.bias, 0.0)\n", " for c in module.children():\n", " init_weights(c)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IMoUXZ5yFRbd" }, "source": [ "g = None; d = None" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lUG71td9FRbg" }, "source": [ "## Networks training" ] }, { "cell_type": "code", "metadata": { "id": "ozU5CQ9JFRbh" }, "source": [ "assert torch.backends.cudnn.enabled\n", "torch.backends.cudnn.benchmark = True" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "-zZ5ZuXGFRbl" }, "source": [ "device = \"cuda\"\n", "\n", "generator_A2B = Generator().to(device)\n", "init_weights(generator_A2B)\n", "\n", "discriminator_B = Discriminator().to(device)\n", "init_weights(discriminator_B)\n", "\n", "generator_B2A = Generator().to(device)\n", "init_weights(generator_B2A)\n", "discriminator_A = Discriminator().to(device)\n", "init_weights(discriminator_A)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "f90Ew1kaFRbo" }, "source": [ "Similarly, we train the networks from scratch, with a learning rate of `0.0002`." ] }, { "cell_type": "code", "metadata": { "id": "YbgTEAMCFRbo" }, "source": [ "from itertools import chain\n", "import torch.optim as optim\n", "\n", "lr = 0.0002\n", "beta1 = 0.5\n", "\n", "optimizer_G = optim.Adam(chain(generator_A2B.parameters(), generator_B2A.parameters()), lr=lr, betas=(beta1, 0.999))\n", "optimizer_D = optim.Adam(chain(discriminator_A.parameters(), discriminator_B.parameters()), lr=lr, betas=(beta1, 0.999))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Gs9la9yqFRbr" }, "source": [ "Let's define some helper functions:\n", "- to turn on/off gradients" ] }, { "cell_type": "code", "metadata": { "id": "p82seQ9JFRbs" }, "source": [ "def toggle_grad(model, on_or_off):\n", " # https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py#L674\n", " for param in model.parameters():\n", " param.requires_grad = on_or_off" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wTF4p83kFRbv" }, "source": [ "### Fake images buffer trick\n", "\n", "According to the paper, to reduce model oscillation and the discriminators are updated using a history of generated images rather than the ones produced by the latest generators. There is an image buffer that stores the X previously created images." ] }, { "cell_type": "code", "metadata": { "id": "jyghMWUPFRbw" }, "source": [ "buffer_size = 50\n", "fake_a_buffer = []\n", "fake_b_buffer = []\n", "\n", "\n", "def buffer_insert_and_get(buffer, batch):\n", " output_batch = []\n", " for b in batch:\n", " b = b.unsqueeze(0)\n", " # if buffer is not fully filled:\n", " if len(buffer) < buffer_size:\n", " output_batch.append(b)\n", " buffer.append(b.cpu())\n", " elif random.uniform(0, 1) > 0.5:\n", " # Add newly created image into the buffer and put ont from the buffer into the output\n", " random_index = random.randint(0, buffer_size - 1) \n", " output_batch.append(buffer[random_index].clone().to(device))\n", " buffer[random_index] = b.cpu()\n", " else:\n", " output_batch.append(b)\n", " return torch.cat(output_batch, dim=0)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "K4k46BvuFRb_" }, "source": [ "Next, let's define a single iteration via `update_fn` function. This function is then used by `ignite.engine.Engine` to update models while running over the input data.\n", "\n", "As suggested, we divide the objective by 2 while optimizing D, which slows down the rate at which D learns, relative to the rate of G. \n", "\n", "According to the paper:\n", "- generator A is trained minimize $\\text{mean}_{x \\in A}[(D_B(G(x)) βˆ’ 1)^2]$ and cycle loss $\\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right]$\n", "- generator B is trained minimize $\\text{mean}_{y \\in B}[(D_A(F(y)) βˆ’ 1)^2]$ and cycle loss $\\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]$\n", "- discriminators A is trained to minimize $\\text{mean}_{x \\in A}[(D_A(x) βˆ’ 1)^2] + \\text{mean}_{y \\in B}[D_A(F(y))^2]$.\n", "- discriminator B is trained to minimize $\\text{mean}_{y \\in B}[(D_B(y) βˆ’ 1)^2] + \\text{mean}_{x \\in A}[D_B(G(x))^2]$." ] }, { "cell_type": "markdown", "metadata": { "id": "JE8dLeEfIl_Z" }, "source": [ "We will use [`torch.cuda.amp.autocast`](https://pytorch.org/docs/master/amp.html#torch.cuda.amp.autocast) and [`torch.cuda.amp.GradScaler`](https://pytorch.org/docs/master/amp.html#torch.cuda.amp.GradScaler) to perform automatic mixed precision training. Our code follows a [typical mixed precision training example](https://pytorch.org/docs/master/notes/amp_examples.html#typical-mixed-precision-training)." ] }, { "cell_type": "code", "metadata": { "id": "vrJls4p-FRcA" }, "source": [ "from torch.cuda.amp import autocast, GradScaler\n", "\n", "from ignite.utils import convert_tensor\n", "import torch.nn.functional as F\n", "\n", "amp_enabled = True\n", "lambda_value = 10.0\n", "amp_scaler = GradScaler(enabled=amp_enabled)\n", "\n", "\n", "def discriminator_forward_pass(discriminator, batch_real, batch_fake, fake_buffer):\n", " decision_real = discriminator(batch_real)\n", " batch_fake = buffer_insert_and_get(fake_buffer, batch_fake) \n", " decision_fake = discriminator(batch_fake)\n", " return decision_real, decision_fake\n", "\n", "\n", "def compute_loss_generator(batch_decision, batch_real, batch_rec, lambda_value):\n", " # loss gan\n", " target = torch.ones_like(batch_decision)\n", " loss_gan = F.mse_loss(batch_decision, target)\n", " # loss cycle\n", " loss_cycle = F.l1_loss(batch_rec, batch_real) * lambda_value \n", " return loss_gan + loss_cycle\n", "\n", "\n", "def compute_loss_discriminator(decision_real, decision_fake):\n", " # loss = mean (D_b(y) βˆ’ 1)^2 + mean D_b(G(x))^2 \n", " loss = F.mse_loss(decision_fake, torch.zeros_like(decision_fake))\n", " loss += F.mse_loss(decision_real, torch.ones_like(decision_real))\n", " return loss\n", "\n", "\n", "def update_fn(engine, batch):\n", " generator_A2B.train()\n", " generator_B2A.train()\n", " discriminator_A.train()\n", " discriminator_B.train() \n", "\n", " real_a = convert_tensor(batch['A'], device=device, non_blocking=True)\n", " real_b = convert_tensor(batch['B'], device=device, non_blocking=True)\n", "\n", " # Update generators\n", "\n", " # Disable grads computation for the discriminators:\n", " toggle_grad(discriminator_A, False)\n", " toggle_grad(discriminator_B, False) \n", "\n", " with autocast(enabled=amp_enabled):\n", " fake_b = generator_A2B(real_a)\n", " rec_a = generator_B2A(fake_b)\n", " fake_a = generator_B2A(real_b)\n", " rec_b = generator_A2B(fake_a)\n", " decision_fake_a = discriminator_A(fake_a)\n", " decision_fake_b = discriminator_B(fake_b)\n", "\n", " # Compute loss for generators and update generators\n", " # loss_a2b = GAN loss: mean (D_b(G(x)) βˆ’ 1)^2 + Forward cycle loss: || F(G(x)) - x ||_1 \n", " loss_a2b = compute_loss_generator(decision_fake_b, real_a, rec_a, lambda_value) \n", "\n", " # loss_b2a = GAN loss: mean (D_a(F(x)) βˆ’ 1)^2 + Backward cycle loss: || G(F(y)) - y ||_1\n", " loss_b2a = compute_loss_generator(decision_fake_a, real_b, rec_b, lambda_value)\n", "\n", " # total generators loss:\n", " loss_generators = loss_a2b + loss_b2a\n", "\n", " optimizer_G.zero_grad()\n", " amp_scaler.scale(loss_generators).backward()\n", " amp_scaler.step(optimizer_G)\n", "\n", " decision_fake_a = rec_a = decision_fake_b = rec_b = None\n", " \n", " # Enable grads computation for the discriminators:\n", " toggle_grad(discriminator_A, True)\n", " toggle_grad(discriminator_B, True) \n", "\n", " with autocast(enabled=amp_enabled):\n", " decision_real_a, decision_fake_a = discriminator_forward_pass(discriminator_A, real_a, fake_a.detach(), fake_a_buffer) \n", " decision_real_b, decision_fake_b = discriminator_forward_pass(discriminator_B, real_b, fake_b.detach(), fake_b_buffer) \n", " # Compute loss for discriminators and update discriminators\n", " # loss_a = mean (D_a(y) βˆ’ 1)^2 + mean D_a(F(x))^2\n", " loss_a = compute_loss_discriminator(decision_real_a, decision_fake_a)\n", "\n", " # loss_b = mean (D_b(y) βˆ’ 1)^2 + mean D_b(G(x))^2\n", " loss_b = compute_loss_discriminator(decision_real_b, decision_fake_b)\n", " \n", " # total discriminators loss:\n", " loss_discriminators = 0.5 * (loss_a + loss_b)\n", " \n", " optimizer_D.zero_grad()\n", " amp_scaler.scale(loss_discriminators).backward()\n", " amp_scaler.step(optimizer_D)\n", " amp_scaler.update()\n", " \n", " return {\n", " \"loss_generators\": loss_generators.item(),\n", " \"loss_generator_a2b\": loss_a2b.item(),\n", " \"loss_generator_b2a\": loss_b2a.item(),\n", " \"loss_discriminators\": loss_discriminators.item(),\n", " \"loss_discriminator_a\": loss_a.item(),\n", " \"loss_discriminator_b\": loss_b.item(),\n", " }\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "3DF1hNTrFRcD" }, "source": [ "Let's check `update_fn`" ] }, { "cell_type": "code", "metadata": { "id": "rLZextmDzzw_" }, "source": [ "real_batch = next(iter(train_ab_loader))\n", "\n", "res = update_fn(engine=None, batch=real_batch)\n", "\n", "real_batch = None\n", "torch.cuda.empty_cache()\n", "\n", "res" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SxVF6kGYFRcK" }, "source": [ "Now let's define a trainer and add some practical handlers:\n", "- log to tensorboard: losses, lr, generated images\n", "- progress bar\n", "- models/optimizers checkpointing\n", "\n", "Optionally, we also log to online platform [W&B](https://app.wandb.ai). Current logs can be seen at https://app.wandb.ai/vfdev-5/ignite-cyclegan-torch-amp\n", "```\n", "pip install --upgrade wandb\n", "wandb login your-token\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "camPNT4TcCFu" }, "source": [ "!pip install --upgrade wandb\n", "# !wandb login your-token" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "TO58FENsFRcM" }, "source": [ "from ignite.engine import Engine, Events\n", "from ignite.metrics import RunningAverage\n", "\n", "from ignite.handlers import TensorboardLogger, WandBLogger\n", "from ignite.handlers.tensorboard_logger import OutputHandler, OptimizerParamsHandler" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "jbeZjaBtFRcO" }, "source": [ "from functools import partial\n", "\n", "\n", "trainer = Engine(update_fn)\n", "\n", "metric_names = [\n", " 'loss_discriminators', \n", " 'loss_generators', \n", " 'loss_discriminator_a',\n", " 'loss_discriminator_b',\n", " 'loss_generator_a2b',\n", " 'loss_generator_b2a' \n", "]\n", "\n", "def output_transform(out, name):\n", " return out[name]\n", "\n", "for name in metric_names:\n", " # here we cannot use lambdas as they do not store argument `name`\n", " RunningAverage(output_transform=partial(output_transform, name=name)).attach(trainer, name)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qODNF0imFRcQ" }, "source": [ "from datetime import datetime\n", "\n", "exp_name = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", "tb_logger = TensorboardLogger(log_dir=\"/tmp/cycle_gan_horse2zebra_tb_logs/{}\".format(exp_name))\n", "\n", "tb_logger.attach(trainer, \n", " log_handler=OutputHandler('training', metric_names), \n", " event_name=Events.ITERATION_COMPLETED)\n", "\n", "print(\"Experiment name: \", exp_name)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ljTkKgMYFRcT" }, "source": [ "from pathlib import Path\n", "\n", "try:\n", " wb_run_name = \"cycle_gan_horse2zebra\"\n", " wb_dir = Path(\"/tmp/cycle_gan_horse2zebra_wandb\")\n", " if not wb_dir.exists():\n", " wb_dir.mkdir()\n", " wb_logger = WandBLogger(\n", " project=\"ignite-cyclegan-torch-amp\",\n", " name=wb_run_name,\n", " sync_tensorboard=True,\n", " dir=wb_dir.as_posix(),\n", " reinit=True\n", " )\n", "except RuntimeError:\n", " wb_logger = None" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DbjedQjLFRcV" }, "source": [ "Let's create an evaluator to inference on train/test images and log the generated images to Tensorboard:" ] }, { "cell_type": "code", "metadata": { "id": "rCcdC6q9FRcV" }, "source": [ "from ignite.engine import Engine\n", "\n", "\n", "def evaluate_fn(engine, batch):\n", " generator_A2B.eval()\n", " generator_B2A.eval() \n", " with torch.no_grad():\n", " real_a = convert_tensor(batch['A'], device=device, non_blocking=True)\n", " real_b = convert_tensor(batch['B'], device=device, non_blocking=True)\n", " \n", " fake_b = generator_A2B(real_a)\n", " rec_a = generator_B2A(fake_b)\n", "\n", " fake_a = generator_B2A(real_b)\n", " rec_b = generator_A2B(fake_a)\n", " \n", " return {\n", " 'real_a': real_a,\n", " 'real_b': real_b,\n", " 'fake_a': fake_a,\n", " 'fake_b': fake_b,\n", " 'rec_a': rec_a,\n", " 'rec_b': rec_b, \n", " }\n", "\n", "\n", "evaluator = Engine(evaluate_fn)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "UIecBsPKFRcX" }, "source": [ "from torch.utils.data import Subset\n", "\n", "eval_batch_size = 5\n", "\n", "train_random_indices = [random.randint(0, len(train_ab_ds) - 1) for _ in range(eval_batch_size)]\n", "small_train_ds = Subset(train_ab_ds, train_random_indices)\n", "small_train_ds = TransformedDataset(small_train_ds, transform=test_transform)\n", "\n", "test_random_indices = [random.randint(0, len(test_ab_ds) - 1) for _ in range(eval_batch_size)]\n", "small_test_ds = Subset(test_ab_ds, test_random_indices)\n", "small_test_ds = TransformedDataset(small_test_ds, transform=test_transform)\n", "\n", "eval_train_loader = DataLoader(small_train_ds, batch_size=eval_batch_size, shuffle=False, drop_last=False, pin_memory=True, num_workers=4)\n", "eval_test_loader = DataLoader(small_test_ds, batch_size=eval_batch_size, shuffle=False, drop_last=False, pin_memory=True, num_workers=4)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "H6XkEchHFRca" }, "source": [ "@trainer.on(Events.EPOCH_STARTED)\n", "def run_evaluation(engine):\n", " evaluator.run(eval_train_loader)\n", " evaluator.run(eval_test_loader)\n", "\n", "\n", "def log_generated_images(engine, logger, event_name):\n", "\n", " tag = \"Train\" if engine.state.dataloader == eval_train_loader else \"Test\"\n", " output = engine.state.output\n", " state = trainer.state\n", " global_step = state.get_event_attrib_value(event_name)\n", "\n", " # create a grid:\n", " # [real a1, real a2, ...]\n", " # [fake a1, fake a2, ...]\n", " # [rec a1, rec a2, ...]\n", " \n", " s = output['real_a'].shape[0]\n", " res_a = vutils.make_grid(torch.cat([\n", " output['real_a'],\n", " output['fake_b'],\n", " output['rec_a'],\n", " ]), padding=2, normalize=True, nrow=s).cpu()\n", "\n", " logger.writer.add_image(tag=\"{} Horses2Zebras (real, fake, rec)\".format(tag), \n", " img_tensor=res_a, global_step=global_step, dataformats='CHW')\n", "\n", " s = output['real_b'].shape[0]\n", " res_b = vutils.make_grid(torch.cat([\n", " output['real_b'],\n", " output['fake_a'],\n", " output['rec_b'],\n", " ]), padding=2, normalize=True, nrow=s).cpu()\n", " logger.writer.add_image(tag=\"{} Zebras2Horses (real, fake, rec)\".format(tag), \n", " img_tensor=res_b, global_step=global_step, dataformats='CHW')\n", "\n", " \n", "tb_logger.attach(evaluator,\n", " log_handler=log_generated_images, \n", " event_name=Events.COMPLETED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "zrGYmv7RFRcc" }, "source": [ "We also follow suggested lr scheduling: the same learning rate for the first 100 epochs and linearly decay the rate to zero over the next 100 epochs." ] }, { "cell_type": "code", "metadata": { "id": "SSIcXzReFRcc" }, "source": [ "from ignite.handlers import PiecewiseLinear, ParamGroupScheduler\n", "\n", "lr = 0.0002\n", "\n", "milestones_values = [\n", " (0, lr),\n", " (100, lr),\n", " (200, 0.0)\n", "]\n", "gen_lr_scheduler = PiecewiseLinear(optimizer_D, param_name='lr', milestones_values=milestones_values)\n", "desc_lr_scheduler = PiecewiseLinear(optimizer_G, param_name='lr', milestones_values=milestones_values)\n", "\n", "lr_scheduler = ParamGroupScheduler([gen_lr_scheduler, desc_lr_scheduler], \n", " names=['gen_lr_scheduler', 'desc_lr_scheduler'])\n", "\n", "trainer.add_event_handler(Events.EPOCH_STARTED, lr_scheduler)\n", "\n", "\n", "tb_logger.attach(trainer,\n", " log_handler=OptimizerParamsHandler(optimizer_G, \"lr\"), \n", " event_name=Events.EPOCH_STARTED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xWJcV2veFRcf" }, "source": [ "Let's setup models/optimizers checkpointing:" ] }, { "cell_type": "code", "metadata": { "id": "7ZONS845FRcg" }, "source": [ "from ignite.handlers import ModelCheckpoint, TerminateOnNan" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "F-emWk-YFRci" }, "source": [ "!rm -rf \"/tmp/cycle_gan_checkpoints\" \n", "!mkdir \"/tmp/cycle_gan_checkpoints\"\n", "\n", "from ignite.handlers import ModelCheckpoint, TerminateOnNan\n", "\n", "\n", "checkpoint_handler = ModelCheckpoint(dirname=\"/tmp/cycle_gan_checkpoints\", filename_prefix=\"\")\n", "\n", "to_save = {\n", " \"generator_A2B\": generator_A2B,\n", " \"discriminator_B\": discriminator_B,\n", " \"generator_B2A\": generator_B2A,\n", " \"discriminator_A\": discriminator_A,\n", " \n", " \"optimizer_G\": optimizer_G,\n", " \"optimizer_D\": optimizer_D,\n", "}\n", "\n", "trainer.add_event_handler(Events.ITERATION_COMPLETED(every=500), checkpoint_handler, to_save)\n", "trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan())" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "RtQKr6yxFRck" }, "source": [ "from ignite.handlers import ProgressBar\n", "\n", "# Iteration-wise progress bar\n", "ProgressBar(bar_format=\"\").attach(trainer)\n", "# Epoch-wise progress bar with display of training losses\n", "ProgressBar(persist=True, bar_format=\"\").attach(trainer, metric_names=['loss_discriminators', 'loss_generators'], \n", " event_name=Events.EPOCH_STARTED, closing_event_name=Events.COMPLETED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bM-6vr8pcmOW" }, "source": [ "# Display in Firefox may not work properly. Use Chrome.\n", "%load_ext tensorboard\n", "\n", "%tensorboard --logdir=/tmp/cycle_gan_horse2zebra_tb_logs" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "CcxhA9rHFRcn" }, "source": [ "trainer.run(train_ab_loader, max_epochs=200)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "XtXSfbHqFRct" }, "source": [ "tb_logger.close()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7CbI9ngDFRcx" }, "source": [ "### Inference with trained generator\n", "\n", "Let's display saved checkpoint, load weights for \"Horses to Zebras\" generator and run an inference on a test image" ] }, { "cell_type": "code", "metadata": { "id": "2xNAdA-WFRcx" }, "source": [ "!ls /tmp/cycle_gan_checkpoints/" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "TOhSsWRzFRcz" }, "source": [ "checkpoint_path = \"/tmp/cycle_gan_checkpoints/checkpoint_26500.pt\"\n", "\n", "# let's save this checkpoint to W&B\n", "if wb_logger is not None:\n", " wb_logger.save(checkpoint_path)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "j5BFfZceFRc2" }, "source": [ "checkpoint_state_dict = torch.load(checkpoint_path)\n", "generator_A2B.load_state_dict(checkpoint_state_dict[\"generator_A2B\"])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VGLb14xyFRc4" }, "source": [ "def normalize(x):\n", " vmin = x.min()\n", " vmax = x.max()\n", " x.clamp_(min=vmin, max=vmax)\n", " x.add_(-vmin).div_(vmax - vmin + 1e-5)\n", " return x" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "BO_DBOhSFRc7" }, "source": [ "i = random.randint(0, len(test_ab_ds) - 1)\n", "img = test_ab_ds[i]['A']\n", "x = test_transform(img)\n", "x = x.unsqueeze(0).to(device)\n", "\n", "\n", "with torch.no_grad():\n", " y_pred = generator_A2B(x)\n", " \n", "\n", "img_pred = (255 * normalize(y_pred[0, ...])).cpu().numpy().transpose((1, 2, 0)).astype('uint8')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "vseNrx2YFRc-" }, "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(121)\n", "plt.title(\"Horse\")\n", "plt.imshow(img)\n", "plt.subplot(122)\n", "plt.title(\"Generated zebra\")\n", "plt.imshow(img_pred)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "VqFJ0FrPFRdA" }, "source": [ "Let's apply for fun our zebra-filter on an image with deep learning gurus:" ] }, { "cell_type": "code", "metadata": { "id": "6BlVkvybFRdA" }, "source": [ "!wget https://www.kdnuggets.com/wp-content/uploads/photo.jpg -O/tmp/dl_durus.jpg" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "eB7aAeO1FRdC" }, "source": [ "from PIL import Image\n", "\n", "img = Image.open(\"/tmp/dl_durus.jpg\")\n", "x = test_transform(img)\n", "x = x.unsqueeze(0).to(device)\n", "\n", "\n", "with torch.no_grad():\n", " y_pred = generator_A2B(x)\n", "\n", "\n", "img_pred = (255 * normalize(y_pred[0, ...])).cpu().numpy().transpose((1, 2, 0)).astype('uint8')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "8Mcypu0lFRdD" }, "source": [ "plt.figure(figsize=(15, 8))\n", "plt.subplot(121)\n", "plt.title(\"4 Deep-learning gurus\")\n", "plt.imshow(img)\n", "plt.subplot(122)\n", "plt.title(\"Zebras\")\n", "plt.imshow(img_pred)" ], "execution_count": null, "outputs": [] } ] }ignite-0.5.1/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb000066400000000000000000001305101465426447700260210ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "colab": { "name": "EfficientNet_Cifar100_finetuning.ipynb", "provenance": [] }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "JZE22y3LsXpd" }, "source": [ "# Finetuning of ImageNet pretrained EfficientNet-B0 on CIFAR-100\n", "\n", "In 2019, new ConvNets architectures have been proposed in [\"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks\"](https://arxiv.org/pdf/1905.11946.pdf) paper. According to the paper, model's compound scaling starting from a 'good' baseline provides an network that achieves state-of-the-art on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet.\n", "\n", "![efficientnets](https://github.com/abdulelahsm/ignite/blob/update-tutorials/examples/notebooks/assets/efficientnets.png?raw=1)\n", "\n", "Following the paper, EfficientNet-B0 model pretrained on ImageNet and finetuned on CIFAR100 dataset gives 88% test accuracy. Let's reproduce this result with Ignite. [Official implementation](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) of EfficientNet uses Tensorflow, \n", "for our case we will borrow the code from [katsura-jp/efficientnet-pytorch](https://github.com/katsura-jp/efficientnet-pytorch), \n", "[rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models) and [lukemelas/EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch/) repositories (kudos to authors!). We will download pretrained weights from [lukemelas/EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch/) repository.\n", "\n", "## Network architecture review\n", "The architecture of EfficientNet-B0 is the following:\n", "```\n", "1 - Stem - Conv3x3|BN|Swish\n", "\n", "2 - Blocks - MBConv1, k3x3 \n", " - MBConv6, k3x3 repeated 2 times\n", " - MBConv6, k5x5 repeated 2 times\n", " - MBConv6, k3x3 repeated 3 times\n", " - MBConv6, k5x5 repeated 3 times\n", " - MBConv6, k5x5 repeated 4 times\n", " - MBConv6, k3x3\n", " totally 16 blocks\n", "\n", "3 - Head - Conv1x1|BN|Swish \n", " - Pooling\n", " - Dropout\n", " - FC\n", "```\n", "\n", "where \n", "```\n", "Swish(x) = x * sigmoid(x)\n", "```\n", "and `MBConvX` stands for mobile inverted bottleneck convolution, X - denotes expansion ratio:\n", "``` \n", "MBConv1 : \n", " -> DepthwiseConv|BN|Swish -> SqueezeExcitation -> Conv|BN\n", "\n", "MBConv6 : \n", " -> Conv|BN|Swish -> DepthwiseConv|BN|Swish -> SqueezeExcitation -> Conv|BN\n", "\n", "MBConv6+IdentitySkip : \n", " -.-> Conv|BN|Swish -> DepthwiseConv|BN|Swish -> SqueezeExcitation -> Conv|BN-(+)->\n", " \\___________________________________________________________________________/\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "hP_tseP1sXpl" }, "source": [ "## Installations\n", "\n", "1) Torchvision\n", "\n", "Please install torchvision in order to get CIFAR100 dataset: \n", "```\n", "conda install -y torchvision -c pytorch\n", "```\n", "\n", "2) Let's install Nvidia/Apex package:\n", "\n", "We will train with automatic mixed precision using [nvidia/apex](https://github.com/NVIDIA/apex) pacakge" ] }, { "cell_type": "code", "metadata": { "pycharm": { "name": "#%%\n" }, "id": "br990PJfgCHz" }, "source": [ "# Install Apex:\n", "# If torch cuda version and nvcc version match:\n", "!pip install --upgrade --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" git+https://github.com/NVIDIA/apex/\n", "# if above command is failing, please install apex without c++/cuda extensions:\n", "# !pip install --upgrade --no-cache-dir git+https://github.com/NVIDIA/apex/" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "id": "naLdioPTgCH2" }, "source": [ "3) Install tensorboardX and `pytorch-ignite`" ] }, { "cell_type": "code", "metadata": { "id": "watnTwx-sXpm" }, "source": [ "!pip install pytorch-ignite tensorboardX" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "FudN7dJBsXpp" }, "source": [ "import random\n", "import torch\n", "import ignite\n", "\n", "seed = 17\n", "random.seed(seed)\n", "_ = torch.manual_seed(seed)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "EnytGeC5sXpq" }, "source": [ "torch.__version__, ignite.__version__" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IZEFHfBwsXpr" }, "source": [ "## Model\n", "\n", "\n", "Let's define some helpful modules:\n", "- Flatten \n", "- Swish \n", "\n", "The reason why Swish is not implemented in `torch.nn` can be found [here](https://github.com/pytorch/pytorch/pull/3182).\n" ] }, { "cell_type": "code", "metadata": { "id": "rREuFNq1sXps" }, "source": [ "import torch\n", "import torch.nn as nn\n", "\n", "\n", "class Swish(nn.Module):\n", " \n", " def forward(self, x):\n", " return x * torch.sigmoid(x)\n", "\n", "\n", "class Flatten(nn.Module):\n", " \n", " def forward(self, x):\n", " return x.reshape(x.shape[0], -1)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tlOJJKnVsXpt" }, "source": [ "Let's visualize Swish transform vs ReLU:" ] }, { "cell_type": "code", "metadata": { "id": "SiQ5NmqasXpu" }, "source": [ "import matplotlib.pylab as plt\n", "%matplotlib inline\n", "\n", "d = torch.linspace(-10.0, 10.0)\n", "s = Swish()\n", "res = s(d)\n", "res2 = torch.relu(d)\n", "\n", "plt.title(\"Swish transformation\")\n", "plt.plot(d.numpy(), res.numpy(), label='Swish')\n", "plt.plot(d.numpy(), res2.numpy(), label='ReLU')\n", "plt.legend()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "i6Sp-82PsXpv" }, "source": [ "Now let's define `SqueezeExcitation` module" ] }, { "cell_type": "code", "metadata": { "id": "rmvJRZQosXpw" }, "source": [ "class SqueezeExcitation(nn.Module):\n", " \n", " def __init__(self, inplanes, se_planes):\n", " super(SqueezeExcitation, self).__init__()\n", " self.reduce_expand = nn.Sequential(\n", " nn.Conv2d(inplanes, se_planes, \n", " kernel_size=1, stride=1, padding=0, bias=True),\n", " Swish(),\n", " nn.Conv2d(se_planes, inplanes, \n", " kernel_size=1, stride=1, padding=0, bias=True),\n", " nn.Sigmoid()\n", " )\n", "\n", " def forward(self, x):\n", " x_se = torch.mean(x, dim=(-2, -1), keepdim=True)\n", " x_se = self.reduce_expand(x_se)\n", " return x_se * x\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-CAxubs8sXpw" }, "source": [ "Next, we can define `MBConv`.\n", "\n", "**Note on implementation**: in Tensorflow (and PyTorch ports) convolutions use `SAME` padding option which in PyTorch requires\n", "a specific padding computation and additional operation to apply. We will use built-in padding argument of the convolution." ] }, { "cell_type": "code", "metadata": { "id": "6-AL5AYysXpw" }, "source": [ "from torch.nn import functional as F\n", "\n", "\n", "class MBConv(nn.Module):\n", "\n", " def __init__(self, inplanes, planes, kernel_size, stride, \n", " expand_rate=1.0, se_rate=0.25, \n", " drop_connect_rate=0.2):\n", " super(MBConv, self).__init__()\n", "\n", " expand_planes = int(inplanes * expand_rate)\n", " se_planes = max(1, int(inplanes * se_rate))\n", "\n", " self.expansion_conv = None \n", " if expand_rate > 1.0:\n", " self.expansion_conv = nn.Sequential(\n", " nn.Conv2d(inplanes, expand_planes, \n", " kernel_size=1, stride=1, padding=0, bias=False),\n", " nn.BatchNorm2d(expand_planes, momentum=0.01, eps=1e-3),\n", " Swish()\n", " )\n", " inplanes = expand_planes\n", "\n", " self.depthwise_conv = nn.Sequential(\n", " nn.Conv2d(inplanes, expand_planes,\n", " kernel_size=kernel_size, stride=stride, \n", " padding=kernel_size // 2, groups=expand_planes,\n", " bias=False),\n", " nn.BatchNorm2d(expand_planes, momentum=0.01, eps=1e-3),\n", " Swish()\n", " )\n", "\n", " self.squeeze_excitation = SqueezeExcitation(expand_planes, se_planes)\n", " \n", " self.project_conv = nn.Sequential(\n", " nn.Conv2d(expand_planes, planes, \n", " kernel_size=1, stride=1, padding=0, bias=False),\n", " nn.BatchNorm2d(planes, momentum=0.01, eps=1e-3),\n", " )\n", "\n", " self.with_skip = stride == 1\n", " self.drop_connect_rate = drop_connect_rate\n", " \n", " def _drop_connect(self, x): \n", " keep_prob = 1.0 - self.drop_connect_rate\n", " drop_mask = torch.rand(x.shape[0], 1, 1, 1) + keep_prob\n", " drop_mask = drop_mask.type_as(x)\n", " drop_mask.floor_()\n", " return drop_mask * x / keep_prob\n", " \n", " def forward(self, x):\n", " z = x\n", " if self.expansion_conv is not None:\n", " x = self.expansion_conv(x)\n", "\n", " x = self.depthwise_conv(x)\n", " x = self.squeeze_excitation(x)\n", " x = self.project_conv(x)\n", " \n", " # Add identity skip\n", " if x.shape == z.shape and self.with_skip: \n", " if self.training and self.drop_connect_rate is not None:\n", " x = self._drop_connect(x)\n", " x += z\n", " return x" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OlPTQlFRsXpx" }, "source": [ "And finally, we can implement generic `EfficientNet`:" ] }, { "cell_type": "code", "metadata": { "id": "CV2NfxZIsXpx" }, "source": [ "from collections import OrderedDict\n", "import math\n", "\n", "\n", "def init_weights(module): \n", " if isinstance(module, nn.Conv2d): \n", " nn.init.kaiming_normal_(module.weight, a=0, mode='fan_out')\n", " elif isinstance(module, nn.Linear):\n", " init_range = 1.0 / math.sqrt(module.weight.shape[1])\n", " nn.init.uniform_(module.weight, a=-init_range, b=init_range)\n", " \n", " \n", "class EfficientNet(nn.Module):\n", " \n", " def _setup_repeats(self, num_repeats):\n", " return int(math.ceil(self.depth_coefficient * num_repeats))\n", " \n", " def _setup_channels(self, num_channels):\n", " num_channels *= self.width_coefficient\n", " new_num_channels = math.floor(num_channels / self.divisor + 0.5) * self.divisor\n", " new_num_channels = max(self.divisor, new_num_channels)\n", " if new_num_channels < 0.9 * num_channels:\n", " new_num_channels += self.divisor\n", " return new_num_channels\n", "\n", " def __init__(self, num_classes=100, \n", " width_coefficient=1.0,\n", " depth_coefficient=1.0,\n", " se_rate=0.25,\n", " dropout_rate=0.2,\n", " drop_connect_rate=0.2):\n", " super(EfficientNet, self).__init__()\n", " \n", " self.width_coefficient = width_coefficient\n", " self.depth_coefficient = depth_coefficient\n", " self.divisor = 8\n", " \n", " list_channels = [32, 16, 24, 40, 80, 112, 192, 320, 1280]\n", " list_channels = [self._setup_channels(c) for c in list_channels]\n", " \n", " list_num_repeats = [1, 2, 2, 3, 3, 4, 1]\n", " list_num_repeats = [self._setup_repeats(r) for r in list_num_repeats] \n", " \n", " expand_rates = [1, 6, 6, 6, 6, 6, 6]\n", " strides = [1, 2, 2, 2, 1, 2, 1]\n", " kernel_sizes = [3, 3, 5, 3, 5, 5, 3]\n", "\n", " # Define stem:\n", " self.stem = nn.Sequential(\n", " nn.Conv2d(3, list_channels[0], kernel_size=3, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(list_channels[0], momentum=0.01, eps=1e-3),\n", " Swish()\n", " )\n", " \n", " # Define MBConv blocks\n", " blocks = []\n", " counter = 0\n", " num_blocks = sum(list_num_repeats)\n", " for idx in range(7):\n", " \n", " num_channels = list_channels[idx]\n", " next_num_channels = list_channels[idx + 1]\n", " num_repeats = list_num_repeats[idx]\n", " expand_rate = expand_rates[idx]\n", " kernel_size = kernel_sizes[idx]\n", " stride = strides[idx]\n", " drop_rate = drop_connect_rate * counter / num_blocks\n", " \n", " name = \"MBConv{}_{}\".format(expand_rate, counter)\n", " blocks.append((\n", " name,\n", " MBConv(num_channels, next_num_channels, \n", " kernel_size=kernel_size, stride=stride, expand_rate=expand_rate, \n", " se_rate=se_rate, drop_connect_rate=drop_rate)\n", " ))\n", " counter += 1\n", " for i in range(1, num_repeats): \n", " name = \"MBConv{}_{}\".format(expand_rate, counter)\n", " drop_rate = drop_connect_rate * counter / num_blocks \n", " blocks.append((\n", " name,\n", " MBConv(next_num_channels, next_num_channels, \n", " kernel_size=kernel_size, stride=1, expand_rate=expand_rate, \n", " se_rate=se_rate, drop_connect_rate=drop_rate) \n", " ))\n", " counter += 1\n", " \n", " self.blocks = nn.Sequential(OrderedDict(blocks))\n", " \n", " # Define head\n", " self.head = nn.Sequential(\n", " nn.Conv2d(list_channels[-2], list_channels[-1], \n", " kernel_size=1, bias=False),\n", " nn.BatchNorm2d(list_channels[-1], momentum=0.01, eps=1e-3),\n", " Swish(),\n", " nn.AdaptiveAvgPool2d(1),\n", " Flatten(),\n", " nn.Dropout(p=dropout_rate),\n", " nn.Linear(list_channels[-1], num_classes)\n", " )\n", "\n", " self.apply(init_weights)\n", " \n", " def forward(self, x):\n", " f = self.stem(x)\n", " f = self.blocks(f)\n", " y = self.head(f)\n", " return y" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9tEhbJWnsXpy" }, "source": [ "All EfficientNet models can be defined using the following parametrization:\n", "```\n", "# (width_coefficient, depth_coefficient, resolution, dropout_rate)\n", "'efficientnet-b0': (1.0, 1.0, 224, 0.2),\n", "'efficientnet-b1': (1.0, 1.1, 240, 0.2),\n", "'efficientnet-b2': (1.1, 1.2, 260, 0.3),\n", "'efficientnet-b3': (1.2, 1.4, 300, 0.3),\n", "'efficientnet-b4': (1.4, 1.8, 380, 0.4),\n", "'efficientnet-b5': (1.6, 2.2, 456, 0.4),\n", "'efficientnet-b6': (1.8, 2.6, 528, 0.5),\n", "'efficientnet-b7': (2.0, 3.1, 600, 0.5),\n", "``` \n", "Let's define and train the third one: `EfficientNet-B0`" ] }, { "cell_type": "code", "metadata": { "id": "YCuCoTHBsXpy" }, "source": [ "model = EfficientNet(num_classes=1000, \n", " width_coefficient=1.0, depth_coefficient=1.0, \n", " dropout_rate=0.2)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "HjUCNwu3sXpz" }, "source": [ "Number of parameters:" ] }, { "cell_type": "code", "metadata": { "id": "WfBynpOEsXpz" }, "source": [ "def print_num_params(model, display_all_modules=False):\n", " total_num_params = 0\n", " for n, p in model.named_parameters():\n", " num_params = 1\n", " for s in p.shape:\n", " num_params *= s\n", " if display_all_modules: print(\"{}: {}\".format(n, num_params))\n", " total_num_params += num_params\n", " print(\"-\" * 50)\n", " print(\"Total number of parameters: {:.2e}\".format(total_num_params))\n", " \n", "\n", "print_num_params(model)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DxiAjmuMsXp0" }, "source": [ "Let's compare the number of parameters with some of ResNets:" ] }, { "cell_type": "code", "metadata": { "id": "rAUg5NwnsXp0" }, "source": [ "from torchvision.models.resnet import resnet18, resnet34, resnet50" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9ZMMOAjpsXp0" }, "source": [ "print_num_params(resnet18(pretrained=False, num_classes=100))\n", "print_num_params(resnet34(pretrained=False, num_classes=100))\n", "print_num_params(resnet50(pretrained=False, num_classes=100))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0CcItj-CsXp0" }, "source": [], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0NhRnYnAsXp1" }, "source": [ "### Model's graph with Tensorboard\n", "\n", "We can optionally inspect model's graph with the code below. For that we need to install\n", "`tensorboardX` package.\n", "Otherwise go directly to the next section." ] }, { "cell_type": "code", "metadata": { "id": "F2y5WvBisXp1" }, "source": [ "from tensorboardX.pytorch_graph import graph\n", "\n", "import random\n", "from IPython.display import clear_output, Image, display, HTML\n", "\n", "\n", "def show_graph(graph_def):\n", " \"\"\"Visualize TensorFlow graph.\"\"\"\n", " if hasattr(graph_def, 'as_graph_def'):\n", " graph_def = graph_def.as_graph_def()\n", " strip_def = graph_def\n", " code = \"\"\"\n", " \n", " \n", " \n", "

\n", " \"\"\".format(data=repr(str(strip_def)), id='graph'+str(random.randint(0, 1000)))\n", "\n", " iframe = \"\"\"\n", " \n", " \"\"\".format(code.replace('\"', '"'))\n", " display(HTML(iframe))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "_oOEmRUJsXp1" }, "source": [ "x = torch.rand(4, 3, 224, 224)\n", "\n", "# Error : module 'torch.onnx' has no attribute 'set_training'\n", "# uncomment when it will be fixed \n", "\n", "# graph_def = graph(model, x, operator_export_type='RAW')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "vW1y3eTMsXp1" }, "source": [ "# Display in Firefox may not work properly. Use Chrome.\n", "# show_graph(graph_def[0])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "irrGiMKWsXp4" }, "source": [ "### Load pretrained weights\n", "\n", "Let's load pretrained weights and check the model on a single image." ] }, { "cell_type": "code", "metadata": { "id": "gd3CHmnhsXp4" }, "source": [ "!mkdir /tmp/efficientnet_weights\n", "!wget http://storage.googleapis.com/public-models/efficientnet-b0-08094119.pth -O/tmp/efficientnet_weights/efficientnet-b0-08094119.pth" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0f1BnJ4TsXp4" }, "source": [ "from collections import OrderedDict\n", "\n", "model_state = torch.load(\"/tmp/efficientnet_weights/efficientnet-b0-08094119.pth\")\n", "\n", "# A basic remapping is required\n", "mapping = {\n", " k: v for k, v in zip(model_state.keys(), model.state_dict().keys())\n", "}\n", "mapped_model_state = OrderedDict([\n", " (mapping[k], v) for k, v in model_state.items()\n", "])\n", "\n", "model.load_state_dict(mapped_model_state, strict=False)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "-9mxclp2sXp5" }, "source": [ "!wget https://raw.githubusercontent.com/lukemelas/EfficientNet-PyTorch/master/examples/simple/img.jpg -O/tmp/giant_panda.jpg\n", "!wget https://raw.githubusercontent.com/lukemelas/EfficientNet-PyTorch/master/examples/simple/labels_map.txt -O/tmp/labels_map.txt" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "3QUPjDRBsXp5" }, "source": [ "import json\n", "\n", "with open(\"/tmp/labels_map.txt\", \"r\") as h:\n", " labels = json.load(h)\n", "\n", "from PIL import Image\n", "import torchvision.transforms as transforms\n", "\n", "\n", "img = Image.open(\"/tmp/giant_panda.jpg\")\n", "# Preprocess image\n", "image_size = 224\n", "tfms = transforms.Compose([transforms.Resize(image_size), \n", " transforms.CenterCrop(image_size), \n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),])\n", "x = tfms(img).unsqueeze(0)\n", "\n", "plt.imshow(img)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "l5xn0U7bsXp6" }, "source": [ "# Classify\n", "model.eval()\n", "with torch.no_grad():\n", " y_pred = model(x)\n", "\n", "# Print predictions\n", "print('-----')\n", "for idx in torch.topk(y_pred, k=5).indices.squeeze(0).tolist():\n", " prob = torch.softmax(y_pred, dim=1)[0, idx].item()\n", " print('{label:<75} ({p:.2f}%)'.format(label=labels[str(idx)], p=prob*100))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6p_g9Z4KsXp6" }, "source": [ "## Dataflow\n", "\n", "Let's setup the dataflow:\n", "- load CIFAR100 train and test datasets\n", "- setup train/test image transforms\n", "- setup train/test data loaders\n", "\n", "According to the paper authors borrowed training settings from other publications and the dataflow for CIFAR100 is the following:\n", "\n", "- input images to the network during training are resized to 224x224\n", "- horizontally flipped randomly and augmented using cutout.\n", "- each mini-batch contained 256 examples\n" ] }, { "cell_type": "code", "metadata": { "id": "Xm1nwsuVsXp7" }, "source": [ "from torchvision.datasets.cifar import CIFAR100 \n", "from torchvision.transforms import Compose, RandomCrop, Pad, RandomHorizontalFlip, Resize\n", "from torchvision.transforms import ToTensor, Normalize\n", "\n", "from torch.utils.data import Subset" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "t4dcDFtxsXp7" }, "source": [ "path = \"/tmp/cifar100\"\n", "\n", "from PIL.Image import BICUBIC\n", "\n", "\n", "train_transform = Compose([\n", " Resize(256, BICUBIC),\n", " RandomCrop(224),\n", " RandomHorizontalFlip(),\n", " ToTensor(),\n", " Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", "])\n", "\n", "test_transform = Compose([\n", " Resize(224, BICUBIC), \n", " ToTensor(),\n", " Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", "])\n", "\n", "\n", "train_dataset = CIFAR100(root=path, train=True, transform=train_transform, download=True)\n", "test_dataset = CIFAR100(root=path, train=False, transform=test_transform, download=False)\n", "\n", "train_eval_indices = [random.randint(0, len(train_dataset) - 1) for i in range(len(test_dataset))]\n", "train_eval_dataset = Subset(train_dataset, train_eval_indices)\n", "\n", "\n", "len(train_dataset), len(test_dataset), len(train_eval_dataset)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "pwOEeRFOsXp7" }, "source": [ "from torch.utils.data import DataLoader\n", "\n", "\n", "batch_size = 172\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=20, \n", " shuffle=True, drop_last=True, pin_memory=True)\n", "\n", "test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=20, \n", " shuffle=False, drop_last=False, pin_memory=True)\n", "\n", "eval_train_loader = DataLoader(train_eval_dataset, batch_size=batch_size, num_workers=20, \n", " shuffle=False, drop_last=False, pin_memory=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "UXCG-s1JsXp7" }, "source": [ "import torchvision.utils as vutils\n", "\n", "# Plot some training images\n", "batch = next(iter(train_loader))\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.axis(\"off\")\n", "plt.title(\"Training Images\")\n", "plt.imshow( \n", " vutils.make_grid(batch[0][:16], padding=2, normalize=True).cpu().numpy().transpose((1, 2, 0))\n", ")\n", "\n", "batch = None\n", "torch.cuda.empty_cache()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0ty_zhLUsXp8" }, "source": [ "## Finetunning model" ] }, { "cell_type": "markdown", "metadata": { "id": "dvVxC-_TsXp9" }, "source": [ "As we are interested to finetune the model to CIFAR-100, we will replace the classification fully-connected layer (ImageNet-1000 vs CIFAR-100)." ] }, { "cell_type": "code", "metadata": { "id": "O7D5BIlxsXp9" }, "source": [ "model.head[6].in_features, model.head[6].out_features" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "mwLQro9csXp-" }, "source": [ "model.head[6] = nn.Linear(1280, 100)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1tcFlIpGsXp-" }, "source": [ "model.head[6].in_features, model.head[6].out_features" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "3uS2NMt8sXp_" }, "source": [ "We will finetune the model on GPU with AMP fp32/fp16 using nvidia/apex package." ] }, { "cell_type": "code", "metadata": { "id": "Fv8lR4HlsXp_" }, "source": [ "assert torch.cuda.is_available()\n", "assert torch.backends.cudnn.enabled, \"NVIDIA/Apex:Amp requires cudnn backend to be enabled.\"\n", "torch.backends.cudnn.benchmark = True\n", "\n", "device = \"cuda\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "TUhjJfLTsXp_" }, "source": [ "model = model.to(device)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "2YB8-FGXsXqB" }, "source": [ "Let's setup cross-entropy as criterion and SGD as optimizer.\n", "\n", "We will split model parameters into 2 groups: \n", "\n", " 1) feature extractor (pretrained weights)\n", " 2) classifier (random weights)\n", "\n", "and define different learning rates for these groups (via learning rate scheduler)." ] }, { "cell_type": "code", "metadata": { "id": "TGxSb_MOsXqB" }, "source": [ "from itertools import chain\n", "\n", "import torch.optim as optim\n", "import torch.nn.functional as F\n", "\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "lr = 0.01\n", "\n", "optimizer = optim.SGD([\n", " {\n", " \"params\": chain(model.stem.parameters(), model.blocks.parameters()),\n", " \"lr\": lr * 0.1,\n", " },\n", " {\n", " \"params\": model.head[:6].parameters(),\n", " \"lr\": lr * 0.2,\n", " }, \n", " {\n", " \"params\": model.head[6].parameters(), \n", " \"lr\": lr\n", " }], \n", " momentum=0.9, weight_decay=0.001, nesterov=True)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "V6stcy6ksXqB" }, "source": [ "from torch.optim.lr_scheduler import ExponentialLR\n", "\n", "lr_scheduler = ExponentialLR(optimizer, gamma=0.975)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "yPuwNYeZsXqB" }, "source": [ "try:\n", " from apex import amp\n", "except ImportError:\n", " raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to run this example.\")\n", "\n", "\n", "# Initialize Amp\n", "model, optimizer = amp.initialize(model, optimizer, opt_level=\"O2\", num_losses=1)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5UtGQtQFsXqC" }, "source": [ "Next, let's define a single iteration function `update_fn`. This function is then used by `ignite.engine.Engine` to update model while running over the input data." ] }, { "cell_type": "code", "metadata": { "id": "MjMUQITRsXqC" }, "source": [ "from ignite.utils import convert_tensor\n", "\n", "\n", "def update_fn(engine, batch):\n", " model.train()\n", "\n", " x = convert_tensor(batch[0], device=device, non_blocking=True)\n", " y = convert_tensor(batch[1], device=device, non_blocking=True)\n", " \n", " y_pred = model(x)\n", " \n", " # Compute loss \n", " loss = criterion(y_pred, y) \n", "\n", " with amp.scale_loss(loss, optimizer) as scaled_loss:\n", " scaled_loss.backward()\n", "\n", " optimizer.step()\n", " \n", " return {\n", " \"batchloss\": loss.item(),\n", " } " ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E772HMiasXqC" }, "source": [ "Let's check `update_fn`" ] }, { "cell_type": "code", "metadata": { "id": "MLFVuQaDsXqD" }, "source": [ "batch = next(iter(train_loader))\n", "\n", "res = update_fn(engine=None, batch=batch)\n", "\n", "batch = None\n", "torch.cuda.empty_cache()\n", "\n", "res" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ibK059y1sXqD" }, "source": [ "Now let's define a trainer and add some practical handlers:\n", "- log to tensorboard: losses, metrics, lr\n", "- progress bar\n", "- models/optimizers checkpointing" ] }, { "cell_type": "code", "metadata": { "id": "rqrmwVuTsXqD" }, "source": [ "from ignite.engine import Engine, Events, create_supervised_evaluator\n", "from ignite.metrics import RunningAverage, Accuracy, Precision, Recall, Loss, TopKCategoricalAccuracy\n", "\n", "from ignite.handlers import TensorboardLogger\n", "from ignite.handlers.tensorboard_logger import OutputHandler, OptimizerParamsHandler" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "B2JI5oXosXqD" }, "source": [ "trainer = Engine(update_fn)\n", "\n", "\n", "def output_transform(out):\n", " return out['batchloss']\n", "\n", "\n", "RunningAverage(output_transform=output_transform).attach(trainer, \"batchloss\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2_WWUKuHsXqD" }, "source": [ "from datetime import datetime\n", "\n", "exp_name = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", "log_path = \"/tmp/finetune_efficientnet_cifar100/{}\".format(exp_name)\n", "tb_logger = TensorboardLogger(log_dir=log_path)\n", "\n", "\n", "tb_logger.attach(trainer, \n", " log_handler=OutputHandler('training', ['batchloss', ]), \n", " event_name=Events.ITERATION_COMPLETED)\n", "\n", "print(\"Experiment name: \", exp_name)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "R6BPVSZqsXqE" }, "source": [ "Let's setup learning rate scheduling:" ] }, { "cell_type": "code", "metadata": { "id": "ZVHrbB5esXqE" }, "source": [ "trainer.add_event_handler(Events.EPOCH_COMPLETED, lambda engine: lr_scheduler.step())\n", "\n", "# Log optimizer parameters\n", "tb_logger.attach(trainer,\n", " log_handler=OptimizerParamsHandler(optimizer, \"lr\"), \n", " event_name=Events.EPOCH_STARTED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Bp_hy7Y0sXqE" }, "source": [ "from ignite.handlers import ProgressBar\n", "\n", "# Iteration-wise progress bar\n", "# ProgressBar(bar_format=\"\").attach(trainer, metric_names=['batchloss',])\n", "\n", "# Epoch-wise progress bar with display of training losses\n", "ProgressBar(persist=True, bar_format=\"\").attach(trainer, \n", " event_name=Events.EPOCH_STARTED, \n", " closing_event_name=Events.COMPLETED)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "EM6NNDh8sXqE" }, "source": [ "Let's create two evaluators to compute metrics on train/test images and log them to Tensorboard:" ] }, { "cell_type": "code", "metadata": { "id": "yApMSd47sXqF" }, "source": [ "metrics = {\n", " 'Loss': Loss(criterion),\n", " 'Accuracy': Accuracy(),\n", " 'Precision': Precision(average=True),\n", " 'Recall': Recall(average=True),\n", " 'Top-5 Accuracy': TopKCategoricalAccuracy(k=5)\n", "}\n", "\n", "\n", "evaluator = create_supervised_evaluator(model, metrics=metrics, \n", " device=device, non_blocking=True)\n", "\n", "train_evaluator = create_supervised_evaluator(model, metrics=metrics, \n", " device=device, non_blocking=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_Xn8INiCsXqF" }, "source": [ "from ignite.handlers import global_step_from_engine\n", "\n", "\n", "def run_evaluation(engine):\n", " train_evaluator.run(eval_train_loader)\n", " evaluator.run(test_loader)\n", "\n", "\n", "trainer.add_event_handler(Events.EPOCH_STARTED(every=3), run_evaluation)\n", "trainer.add_event_handler(Events.COMPLETED, run_evaluation)\n", "\n", "\n", "# Log train eval metrics:\n", "tb_logger.attach_output_handler(\n", " train_evaluator,\n", " event_name=Events.EPOCH_COMPLETED,\n", " tag=\"training\",\n", " metric_names=list(metrics.keys()),\n", " global_step_transform=global_step_from_engine(trainer)\n", ")\n", "\n", "# Log val metrics:\n", "tb_logger.attach_output_handler(\n", " evaluator,\n", " event_name=Events.EPOCH_COMPLETED,\n", " tag=\"test\",\n", " metric_names=list(metrics.keys()),\n", " global_step_transform=global_step_from_engine(trainer)\n", ")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Sx_J-SHisXqG" }, "source": [ "Now let's setup the best model checkpointing, early stopping:" ] }, { "cell_type": "code", "metadata": { "id": "tC5UBpXfsXqG" }, "source": [ "import logging\n", "\n", "# Setup engine & logger\n", "def setup_logger(logger):\n", " handler = logging.StreamHandler()\n", " formatter = logging.Formatter(\"%(asctime)s %(name)-12s %(levelname)-8s %(message)s\")\n", " handler.setFormatter(formatter)\n", " logger.addHandler(handler)\n", " logger.setLevel(logging.INFO)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LKXnwpxBsXqG" }, "source": [ "from ignite.handlers import Checkpoint, DiskSaver, EarlyStopping, TerminateOnNan\n", "\n", "\n", "trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan())\n", "\n", "\n", "# Store the best model\n", "def default_score_fn(engine):\n", " score = engine.state.metrics['Accuracy']\n", " return score\n", "\n", "# Force filename to model.pt to ease the rerun of the notebook\n", "disk_saver = DiskSaver(dirname=log_path)\n", "best_model_handler = Checkpoint(to_save={'model': model}, \n", " save_handler=disk_saver, \n", " filename_pattern=\"{name}.{ext}\", \n", " n_saved=1)\n", "evaluator.add_event_handler(Events.COMPLETED, best_model_handler)\n", "\n", "# Add early stopping\n", "es_patience = 10\n", "es_handler = EarlyStopping(patience=es_patience, score_function=default_score_fn, trainer=trainer)\n", "evaluator.add_event_handler(Events.COMPLETED, es_handler)\n", "setup_logger(es_handler.logger)\n", "\n", "\n", "# Clear cuda cache between training/testing\n", "def empty_cuda_cache(engine):\n", " torch.cuda.empty_cache()\n", " import gc\n", " gc.collect()\n", "\n", "\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, empty_cuda_cache)\n", "evaluator.add_event_handler(Events.COMPLETED, empty_cuda_cache)\n", "train_evaluator.add_event_handler(Events.COMPLETED, empty_cuda_cache)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LBP3QVQXsXqG" }, "source": [ "num_epochs = 100\n", "\n", "\n", "trainer.run(train_loader, max_epochs=num_epochs)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gsXB45JhsXqH" }, "source": [ "Finetunning results:\n", "\n", "- Test dataset:" ] }, { "cell_type": "code", "metadata": { "id": "mX-t-Sq6sXqH" }, "source": [ "evaluator.state.metrics" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Ax_YKq4isXqH" }, "source": [ "- Training subset:" ] }, { "cell_type": "code", "metadata": { "id": "i4CA6YcRsXqI" }, "source": [ "train_evaluator.state.metrics" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ghxtoGLVsXqI" }, "source": [ "Obviously, our training settings is not the optimal one and the delta between our result and the paper's one is about 5%." ] }, { "cell_type": "markdown", "metadata": { "id": "C-7ccZ0QsXqJ" }, "source": [ "## Inference\n", "\n", "Let's load the best model and recompute evaluation metrics on test dataset with a very basic Test-Time-Augmentation to boost the performances.\n" ] }, { "cell_type": "code", "metadata": { "id": "x4BU5IOIsXqK" }, "source": [ "best_model = EfficientNet()\n", "best_model.load_state_dict(torch.load(log_path + \"/model.pt\"))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "co3E6dcesXqK" }, "source": [ "metrics = {\n", " 'Accuracy': Accuracy(),\n", " 'Precision': Precision(average=True),\n", " 'Recall': Recall(average=True),\n", "}\n", "\n", "\n", "def inference_update_with_tta(engine, batch):\n", " best_model.eval()\n", " with torch.no_grad():\n", " x, y = batch \n", " # Let's compute final prediction as a mean of predictions on x and flipped x\n", " y_pred1 = best_model(x)\n", " y_pred2 = best_model(x.flip(dims=(-1, )))\n", " y_pred = 0.5 * (y_pred1 + y_pred2)\n", "\n", " return y_pred, y\n", "\n", "\n", "inferencer = Engine(inference_update_with_tta)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "u68ntSHDsXqK" }, "source": [ "for name, metric in metrics.items():\n", " metric.attach(inferencer, name)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "W3YDadJ5sXqK" }, "source": [ "ProgressBar(desc=\"Inference\").attach(inferencer)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4fQTu7DOsXqK" }, "source": [ "result_state = inferencer.run(test_loader, max_epochs=1)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1vlgsopvsXqL" }, "source": [ "Finally, we obtain similar scores:" ] }, { "cell_type": "code", "metadata": { "id": "F6vCp6lpsXqL" }, "source": [ "result_state.metrics" ], "execution_count": null, "outputs": [] } ] } ignite-0.5.1/examples/notebooks/FashionMNIST.ipynb000066400000000000000000000602011465426447700221040ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "lMov-LUSabxx" }, "source": [ "# Convolutional Neural Networks for Classifying Fashion-MNIST Dataset using Ignite\n", "This is a tutorial on using Ignite to train neural network models, setup experiments and validate models.\n", "\n", "In this notebook, we will be doing classification of images using Convolutional Neural Networks \n", "\n", "We will be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) Fashion-MNIST is a set of 28x28 grayscale images of clothes.\n", "\n", "![Fashion MNIST dataset](https://github.com/abdulelahsm/ignite/blob/update-tutorials/examples/notebooks/assets/fashion-mnist.png?raw=1)\n", "\n", "Lets get started!" ] }, { "cell_type": "markdown", "metadata": { "id": "kXZxwFvCGqz0" }, "source": [ "## Required Dependencies\n", "\n", "We assume that `torch` and `ignite` are already installed. We can install it using `pip`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A7LJdJfU6pra" }, "outputs": [], "source": [ "!pip install pytorch-ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "7PiU02-fabxz" }, "source": [ "### Importing libraries\n", "\n", "General Data-Science Libraries like numpy, matplotlib and seaborn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-FlbGAEe6prv" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": { "id": "xp_4isvHabx5" }, "source": [ "We import `torch`, `nn` and `functional` modules to create our models.\n", "\n", "We also import `datasets` and `transforms` from torchvision for loading the dataset and applying transforms to the images in the dataset.\n", "\n", "We import `Dataloader` for making train and validation loader for loading data into our model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "E-OllAGT6pr-" }, "outputs": [], "source": [ "import torch\n", "from torch import nn, optim\n", "import torch.nn.functional as F\n", "from torch.utils.data import DataLoader\n", "from torchvision import datasets, transforms" ] }, { "cell_type": "markdown", "metadata": { "id": "Pj7oLY36abx8" }, "source": [ "`Ignite` is a High-level library to help with training neural networks in PyTorch. It comes with an `Engine` to setup a training loop, various metrics, handlers and a helpful contrib section! \n", "\n", "Below we import the following:\n", "* **Engine**: Runs a given process_function over each batch of a dataset, emitting events as it goes.\n", "* **Events**: Allows users to attach functions to an `Engine` to fire functions at a specific event. Eg: `EPOCH_COMPLETED`, `ITERATION_STARTED`, etc.\n", "* **Accuracy**: Metric to calculate accuracy over a dataset, for binary, multiclass, multilabel cases. \n", "* **Loss**: General metric that takes a loss function as a parameter, calculate loss over a dataset.\n", "* **RunningAverage**: General metric to attach to Engine during training. \n", "* **ModelCheckpoint**: Handler to checkpoint models. \n", "* **EarlyStopping**: Handler to stop training based on a score function. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YodbjXPi6psK" }, "outputs": [], "source": [ "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n", "from ignite.metrics import Accuracy, Loss, RunningAverage, ConfusionMatrix\n", "from ignite.handlers import ModelCheckpoint, EarlyStopping" ] }, { "cell_type": "markdown", "metadata": { "id": "vO53-X98abx-" }, "source": [ "The code below first sets up transform using `torhvision transfroms` for converting images to pytorch tensors and normalizing the images.\n", "\n", "Next, We use `torchvision datasets` for dowloading the fashion mnist dataset and applying transforms which we defined above.\n", "\n", "* `trainset` contains the training data.\n", "* `validationset` contains the validation data\n", "\n", "Next, We use `pytorch dataloader` for making dataloader from the train and validation sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y2dqyxnr6psU" }, "outputs": [], "source": [ "# transform to normalize the data\n", "transform = transforms.Compose([transforms.ToTensor(),\n", " transforms.Normalize((0.5,), (0.5,))])\n", "\n", "# Download and load the training data\n", "trainset = datasets.FashionMNIST('./data', download=True, train=True, transform=transform)\n", "train_loader = DataLoader(trainset, batch_size=64, shuffle=True)\n", "\n", "# Download and load the test data\n", "validationset = datasets.FashionMNIST('./data', download=True, train=False, transform=transform)\n", "val_loader = DataLoader(validationset, batch_size=64, shuffle=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "Cvzm-Oe1abyB" }, "source": [ "### CNN Model" ] }, { "cell_type": "markdown", "metadata": { "id": "uf4AVJ44abyC" }, "source": [ "Explanation of Model Architecture\n", "\n", "* [Convolutional layers](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html), the Convolutional layer is used to create a convolution kernel that is convolved with the layer input to produce a tensor of outputs.\n", "* [Maxpooling layers](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html), the Maxpooling layer is used to downsample an input representation keeping the most active pixels from the previous layer.\n", "* The usual [Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) + [Dropout](https://pytorch.org/docs/stable/generated/torch.nn.Dropout2d.html) layers to avoid overfitting and produce a 10-dim output.\n", "* We had used [Relu](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html) Non Linearity for the model and [logsoftmax](https://pytorch.org/docs/stable/generated/torch.nn.LogSoftmax.html) at the last layer because we are going to use the [NLLL loss](https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html).\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AST_DtTC6psh" }, "outputs": [], "source": [ "class CNN(nn.Module):\n", " \n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " \n", " self.convlayer1 = nn.Sequential(\n", " nn.Conv2d(1, 32, 3,padding=1),\n", " nn.BatchNorm2d(32),\n", " nn.ReLU(),\n", " nn.MaxPool2d(kernel_size=2, stride=2)\n", " )\n", " \n", " self.convlayer2 = nn.Sequential(\n", " nn.Conv2d(32,64,3),\n", " nn.BatchNorm2d(64),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2)\n", " )\n", " \n", " self.fc1 = nn.Linear(64*6*6,600)\n", " self.drop = nn.Dropout2d(0.25)\n", " self.fc2 = nn.Linear(600, 120)\n", " self.fc3 = nn.Linear(120, 10)\n", " \n", " def forward(self, x):\n", " x = self.convlayer1(x)\n", " x = self.convlayer2(x)\n", " x = x.view(-1,64*6*6)\n", " x = self.fc1(x)\n", " x = self.drop(x)\n", " x = self.fc2(x)\n", " x = self.fc3(x)\n", " \n", " return F.log_softmax(x,dim=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "oqvY1QLBabyE" }, "source": [ "### Creating Model, Optimizer and Loss" ] }, { "cell_type": "markdown", "metadata": { "id": "8z2zUS5zabyF" }, "source": [ "Below we create an instance of the CNN model. The model is placed on a device and then a loss function of `negative log likelihood loss` and `Adam optimizer` with learning rate of 0.001 are setup. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JXyVBBYw6pst" }, "outputs": [], "source": [ "# creating model,and defining optimizer and loss\n", "model = CNN()\n", "# moving model to gpu if available\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "model.to(device)\n", "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", "criterion = nn.NLLLoss()" ] }, { "cell_type": "markdown", "metadata": { "id": "o69S4bEvabyI" }, "source": [ "### Training and Evaluating using Ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "PALFpjYoabyJ" }, "source": [ "### Instantiating Training and Evaluating Engines\n", "\n", "Below we create 3 engines, a trainer, an evaluator for the training set and an evaluator for the validation set, by using the `create_supervised_trainer` and `create_supervised_evaluator` and passing the required arguments.\n", "\n", "We import the metrics from `ignite.metrics` which we want to calculate for the model. Like `Accuracy`, `ConfusionMatrix`, and `Loss` and we pass them to `evaluator` engines which will calculate these metrics for each iteration.\n", "\n", "* `training_history`: it stores the training loss and accuracy\n", "* `validation_history`:it stores the validation loss and accuracy\n", "* `last_epoch`: it stores the last epoch untill the model is trained\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a7EaSDMW6ps5" }, "outputs": [], "source": [ "# defining the number of epochs\n", "epochs = 12\n", "# creating trainer,evaluator\n", "trainer = create_supervised_trainer(model, optimizer, criterion, device=device)\n", "metrics = {\n", " 'accuracy':Accuracy(),\n", " 'nll':Loss(criterion),\n", " 'cm':ConfusionMatrix(num_classes=10)\n", "}\n", "train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)\n", "val_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)\n", "training_history = {'accuracy':[],'loss':[]}\n", "validation_history = {'accuracy':[],'loss':[]}\n", "last_epoch = []" ] }, { "cell_type": "markdown", "metadata": { "id": "aonym06RabyL" }, "source": [ "### Metrics - RunningAverage\n", "\n", "To start, we will attach a metric of `RunningAverage` to track a running average of the scalar loss output for each batch. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4LXaAfga6ptD" }, "outputs": [], "source": [ "RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')" ] }, { "cell_type": "markdown", "metadata": { "id": "2vDAID6BabyP" }, "source": [ "### EarlyStopping - Tracking Validation Loss\n", "\n", "Now we will setup a `EarlyStopping` handler for this training process. EarlyStopping requires a score_function that allows the user to define whatever criteria to stop trainig. In this case, if the loss of the validation set does not decrease in 10 epochs, the training process will stop early. Since the `EarlyStopping` handler relies on the validation loss, it's attached to the `val_evaluator`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0mcIq0lc6ptN" }, "outputs": [], "source": [ "def score_function(engine):\n", " val_loss = engine.state.metrics['nll']\n", " return -val_loss\n", "\n", "handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer)\n", "val_evaluator.add_event_handler(Events.COMPLETED, handler)" ] }, { "cell_type": "markdown", "metadata": { "id": "GZzf0wtLabyR" }, "source": [ "### Attaching Custom Functions to Engine at specific Events\n", "\n", "Below you will see ways to define your own custom functions and attaching them to various `Events` of the training process.\n", "\n", "The functions below both achieve similar tasks, they print the results of the evaluator run on a dataset. One function does that on the training evaluator and dataset, while the other on the validation. Another difference is how these functions are attached in the trainer engine.\n", "\n", "The first method involves using a decorator, the syntax is simple - `@` `trainer.on(Events.EPOCH_COMPLETED)`, means that the decorated function will be attached to the trainer and called at the end of each epoch. \n", "\n", "The second method involves using the add_event_handler method of trainer - `trainer.add_event_handler(Events.EPOCH_COMPLETED, custom_function)`. This achieves the same result as the above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UFQNaZDx6ptV" }, "outputs": [], "source": [ "@trainer.on(Events.EPOCH_COMPLETED)\n", "def log_training_results(trainer):\n", " train_evaluator.run(train_loader)\n", " metrics = train_evaluator.state.metrics\n", " accuracy = metrics['accuracy']*100\n", " loss = metrics['nll']\n", " last_epoch.append(0)\n", " training_history['accuracy'].append(accuracy)\n", " training_history['loss'].append(loss)\n", " print(\"Training Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}\"\n", " .format(trainer.state.epoch, accuracy, loss))\n", "\n", "def log_validation_results(trainer):\n", " val_evaluator.run(val_loader)\n", " metrics = val_evaluator.state.metrics\n", " accuracy = metrics['accuracy']*100\n", " loss = metrics['nll']\n", " validation_history['accuracy'].append(accuracy)\n", " validation_history['loss'].append(loss)\n", " print(\"Validation Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}\"\n", " .format(trainer.state.epoch, accuracy, loss))\n", " \n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, log_validation_results) " ] }, { "cell_type": "markdown", "metadata": { "id": "z4ZHDOs1abyT" }, "source": [ "### Confusion Matrix" ] }, { "cell_type": "markdown", "metadata": { "id": "j5NOsTQxabyU" }, "source": [ "Confusion matrix gives us a better idea of what our classification model is getting right and what types of errors it is making.\n", "\n", "We visualize the `confusion matrix` using the `seaborn.heatmap` from `seaborn` library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kVrVyE8X6pth" }, "outputs": [], "source": [ "@trainer.on(Events.COMPLETED)\n", "def log_confusion_matrix(trainer):\n", " val_evaluator.run(val_loader)\n", " metrics = val_evaluator.state.metrics\n", " cm = metrics['cm']\n", " cm = cm.numpy()\n", " cm = cm.astype(int)\n", " classes = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot']\n", " fig, ax = plt.subplots(figsize=(10,10)) \n", " ax= plt.subplot()\n", " sns.heatmap(cm, annot=True, ax = ax,fmt=\"d\")\n", " # labels, title and ticks\n", " ax.set_xlabel('Predicted labels')\n", " ax.set_ylabel('True labels') \n", " ax.set_title('Confusion Matrix') \n", " ax.xaxis.set_ticklabels(classes,rotation=90)\n", " ax.yaxis.set_ticklabels(classes,rotation=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "HX-NImoSabyW" }, "source": [ "### ModelCheckpoint\n", "\n", "Lastly, we want to checkpoint this model. It's important to do so, as training processes can be time consuming and if for some reason something goes wrong during training, a model checkpoint can be helpful to restart training from the point of failure.\n", "\n", "Below we will use Ignite's `ModelCheckpoint` handler to checkpoint models at the end of each epoch." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qGVhrmoZ6ptp" }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint('./saved_models', 'fashionMNIST', n_saved=2, create_dir=True, save_as_state_dict=True, require_empty=False)\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'fashionMNIST': model})" ] }, { "cell_type": "markdown", "metadata": { "id": "4YK6lGPHabyY" }, "source": [ "### Run Engine\n", "\n", "Next, we will run the trainer for 12 epochs and monitor results. Below we can see that custom functions defined above helps prints the `loss` and `accuracy` per epoch. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lW6m6Stp6pty" }, "outputs": [], "source": [ "trainer.run(train_loader, max_epochs=epochs)" ] }, { "cell_type": "markdown", "metadata": { "id": "hTwbdtI_abyg" }, "source": [ "### Plotting the loss and accuracy\n", "Next, we will plot the loss and accuracy which we have stored in the `training_history` and `validation_history` dictionary to see how loss and accuracy are changing with each epoch." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SptDgBR66pt4" }, "outputs": [], "source": [ "plt.plot(training_history['accuracy'],label=\"Training Accuracy\")\n", "plt.plot(validation_history['accuracy'],label=\"Validation Accuracy\")\n", "plt.xlabel('No. of Epochs')\n", "plt.ylabel('Accuracy')\n", "plt.legend(frameon=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "g4iomcl36pt-" }, "outputs": [], "source": [ "plt.plot(training_history['loss'],label=\"Training Loss\")\n", "plt.plot(validation_history['loss'],label=\"Validation Loss\")\n", "plt.xlabel('No. of Epochs')\n", "plt.ylabel('Loss')\n", "plt.legend(frameon=False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "NW9ze2HCabyp" }, "source": [ "### Loading the saved model from the disk\n", "Loading the saved pytorch model from the disk for inferencing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SU4y96CI6puH" }, "outputs": [], "source": [ "# loading the saved model\n", "def fetch_last_checkpoint_model_filename(model_save_path):\n", " import os\n", " from pathlib import Path\n", " checkpoint_files = os.listdir(model_save_path)\n", " checkpoint_files = [f for f in checkpoint_files if '.pt' in f]\n", " checkpoint_iter = [\n", " int(x.split('_')[2].split('.')[0])\n", " for x in checkpoint_files]\n", " last_idx = np.array(checkpoint_iter).argmax()\n", " return Path(model_save_path) / checkpoint_files[last_idx]\n", "\n", "model.load_state_dict(torch.load(fetch_last_checkpoint_model_filename('./saved_models')))\n", "print(\"Model Loaded\")" ] }, { "cell_type": "markdown", "metadata": { "id": "QNRWgV0Kabys" }, "source": [ "### Inferencing the model \n", "Below code will be used for inferencing from the model and visualizing the results.\n", "\n", "Here we do iteration from the `val_loader` and then select the class with highest probability and then compare it with actul class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uWojxHNy6puN" }, "outputs": [], "source": [ "# classes of fashion mnist dataset\n", "classes = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot']\n", "# creating iterator for iterating the dataset\n", "dataiter = iter(val_loader)\n", "images, labels = dataiter.next()\n", "images_arr = []\n", "labels_arr = []\n", "pred_arr = []\n", "# moving model to cpu for inference \n", "model.to(\"cpu\")\n", "# iterating on the dataset to predict the output\n", "for i in range(0,10):\n", " images_arr.append(images[i].unsqueeze(0))\n", " labels_arr.append(labels[i].item())\n", " ps = torch.exp(model(images_arr[i]))\n", " ps = ps.data.numpy().squeeze()\n", " pred_arr.append(np.argmax(ps))\n", "# plotting the results\n", "fig = plt.figure(figsize=(25,4))\n", "for i in range(10):\n", " ax = fig.add_subplot(2, 20/2, i+1, xticks=[], yticks=[])\n", " ax.imshow(images_arr[i].resize_(1, 28, 28).numpy().squeeze())\n", " ax.set_title(\"{} ({})\".format(classes[pred_arr[i]], classes[labels_arr[i]]),\n", " color=(\"green\" if pred_arr[i]==labels_arr[i] else \"red\"))" ] }, { "cell_type": "markdown", "metadata": { "id": "cWXOEpQ4abyv" }, "source": [ "### Refrences \n", "* [Pytorch Ignite Text CNN example notebook](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)\n", "* [Pytorch Ignite MNIST example](https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist.py)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "FashionMNIST.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9-final" } }, "nbformat": 4, "nbformat_minor": 0 } ignite-0.5.1/examples/notebooks/FastaiLRFinder_MNIST.ipynb000066400000000000000000000211131465426447700234500ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "5w-QlZE9mvdY" }, "source": [ "# MNIST example with 3-conv. layer network\n", "\n", "This example demonstrates the usage of `FastaiLRFinder` with a 3-conv. layer network on the MNIST dataset.\n", "\n", "## Required Dependencies\n", "\n", "We assume that `torch` and `ignite` are already installed. We can install it using pip:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wdmoIYIvmvdp" }, "outputs": [], "source": [ "!pip install pytorch-ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "lP73mDV_nHV9" }, "source": [ "## Import libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lMphyBmmmvdw", "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import torchvision.transforms as transforms\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import MNIST" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "z0epEdj4mvds" }, "outputs": [], "source": [ "import ignite\n", "ignite.__version__" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NEl3QFzZmvd1" }, "outputs": [], "source": [ "from ignite.engine import create_supervised_trainer, create_supervised_evaluator\n", "from ignite.metrics import Loss, Accuracy\n", "from ignite.handlers import ProgressBar\n", "from ignite.handlers import FastaiLRFinder" ] }, { "cell_type": "markdown", "metadata": { "id": "L_wmAdFgmvdx" }, "source": [ "## Loading MNIST" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eZeKOgKymvdx" }, "outputs": [], "source": [ "mnist_pwd = \"data\"\n", "batch_size= 256" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Hgq0uZHAmvdy" }, "outputs": [], "source": [ "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])\n", "\n", "trainset = MNIST(mnist_pwd, train=True, download=True, transform=transform)\n", "trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4)\n", "\n", "testset = MNIST(mnist_pwd, train=False, download=True, transform=transform)\n", "testloader = DataLoader(testset, batch_size=batch_size * 2, shuffle=False, num_workers=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "JTLEOUqtmvdy" }, "source": [ "## Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6KqS5A4Umvd0", "scrolled": true }, "outputs": [], "source": [ "class Net(nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n", " self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n", " self.conv2_drop = nn.Dropout2d()\n", " self.fc1 = nn.Linear(320, 50)\n", " self.fc2 = nn.Linear(50, 10)\n", "\n", " def forward(self, x):\n", " x = F.relu(F.max_pool2d(self.conv1(x), 2))\n", " x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n", " x = x.view(-1, 320)\n", " x = F.relu(self.fc1(x))\n", " x = F.dropout(x, training=self.training)\n", " x = self.fc2(x)\n", " return F.log_softmax(x, dim=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "U_EHmN2bmvd2" }, "source": [ "## Training loss (fastai)\n", "\n", "This learning rate test range follows the same procedure used by fastai. The model is trained for `num_iter` iterations while the learning rate is increased from its initial value specified by the optimizer algorithm to `end_lr`. The increase can be linear (`step_mode=\"linear\"`) or exponential (`step_mode=\"exp\"`); linear provides good results for small ranges while exponential is recommended for larger ranges." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WxaXjwDhmvd2" }, "outputs": [], "source": [ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "criterion = nn.NLLLoss()\n", "model = Net()\n", "model.to(device) # Move model before creating optimizer\n", "optimizer = optim.SGD(model.parameters(), lr=3e-4, momentum=0.9)\n", "\n", "# save the initial state for the model and the optimizer\n", "# to reset them later\n", "init_model_state = model.state_dict()\n", "init_opt_state = optimizer.state_dict()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CTGJPVI6mvd2" }, "outputs": [], "source": [ "trainer = create_supervised_trainer(model, optimizer, criterion, device=device)\n", "ProgressBar(persist=True).attach(trainer, output_transform=lambda x: {\"batch loss\": x})\n", "\n", "lr_finder = FastaiLRFinder()\n", "to_save={'model': model, 'optimizer': optimizer}\n", "with lr_finder.attach(trainer, to_save, diverge_th=1.5) as trainer_with_lr_finder:\n", " trainer_with_lr_finder.run(trainloader)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the found optimal learning rate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oN0VkPapmvd5" }, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "ax = lr_finder.plot()\n", "plt.show()\n", "\n", "print(\"Suggested LR\", lr_finder.lr_suggestion())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note, that model and optimizer were restored to their initial states when `FastaiLRFinder` finished.*" ] }, { "cell_type": "markdown", "metadata": { "id": "EHHNoOi8mvd6" }, "source": [ "Let's compare training results: \n", "- a) using a fixed learning rate (`lr=3e-4`)\n", "- b) with applying optimal learning rate." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# with lr=3e-4\n", "trainer.run(trainloader, max_epochs=5)\n", "\n", "evaluator = create_supervised_evaluator(model, metrics={\"acc\": Accuracy(), \"loss\": Loss(nn.NLLLoss())}, device=device)\n", "evaluator.run(testloader)\n", "\n", "print(evaluator.state.metrics)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After training we need to get the model and the optimizer back to their initial state, to do that, we will load the initial state again." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Reinit model / optimizer\n", "model.load_state_dict(init_model_state)\n", "optimizer.load_state_dict(init_opt_state)" ] }, { "cell_type": "markdown", "metadata": { "id": "NcT19wqkmvd6" }, "source": [ "Let's now apply suggested learning rate to the optimizer, and train the model again with optimal learning rate." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lr_finder.apply_suggested_lr(optimizer)\n", "print(optimizer.param_groups[0]['lr'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DJqgyaFnmvd7" }, "outputs": [], "source": [ "trainer.run(trainloader, max_epochs=5)\n", "\n", "evaluator.run(testloader)\n", "print(evaluator.state.metrics)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "FastaiLRFinder_MNIST.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 1 } ignite-0.5.1/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb000066400000000000000000000215711465426447700247450ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" }, "colab": { "name": "HandlersTimeProfiler_MNIST.ipynb", "provenance": [] }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "_1FzCFk3a1S3" }, "source": [ "# Profiling MNIST example with 3-conv. layer network\n", "\n", "This example demonstrates the usage of `HandlersTimeProfiler`. The example uses MNIST dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "BniLCaIAa1S9" }, "source": [ "## Install requirements\n", "\n", "We assume that `torch` and `ignite` (nightly) are already installed. We can install it using `pip`:" ] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "52PH_jwua1S_" }, "source": [ "!pip install --upgrade --pre pytorch-ignite" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "AgTadgBza1TA" }, "source": [ "## Import libraries" ] }, { "cell_type": "code", "metadata": { "pycharm": { "is_executing": false }, "id": "Y0sJP9iFa1TB" }, "source": [ "%matplotlib inline\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import torchvision.transforms as transforms\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import MNIST\n", "\n", "# A hack to fix the horizontal spill in large output\n", "# ref: https://stackoverflow.com/a/59058418/6574605\n", "from IPython.core.display import HTML\n", "display(HTML(\"\"))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "iK_9cOP6a1TI" }, "source": [ "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n", "from ignite.metrics import Loss, Accuracy\n", "from ignite.handlers import ProgressBar, HandlersTimeProfiler" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5gzxqMMTa1TE" }, "source": [ "## Loading MNIST" ] }, { "cell_type": "code", "metadata": { "id": "ChcbbEo_a1TF" }, "source": [ "mnist_pwd = \"data\"\n", "batch_size= 256" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "YcV6pX-7a1TG" }, "source": [ "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])\n", "\n", "trainset = MNIST(mnist_pwd, train=True, download=True, transform=transform)\n", "trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4)\n", "\n", "testset = MNIST(mnist_pwd, train=False, download=True, transform=transform)\n", "testloader = DataLoader(testset, batch_size=batch_size * 2, shuffle=False, num_workers=0)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "eN7ewRAza1TG" }, "source": [ "## Model" ] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "RuawaUVva1TH" }, "source": [ "class Net(nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n", " self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n", " self.conv2_drop = nn.Dropout2d()\n", " self.fc1 = nn.Linear(320, 50)\n", " self.fc2 = nn.Linear(50, 10)\n", "\n", " def forward(self, x):\n", " x = F.relu(F.max_pool2d(self.conv1(x), 2))\n", " x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n", " x = x.view(-1, 320)\n", " x = F.relu(self.fc1(x))\n", " x = F.dropout(x, training=self.training)\n", " x = self.fc2(x)\n", " return F.log_softmax(x, dim=1)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0BFfdk5ka1TJ" }, "source": [ "## Training Loss" ] }, { "cell_type": "code", "metadata": { "id": "nXkNMyJua1TK" }, "source": [ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "criterion = nn.NLLLoss()\n", "model = Net()\n", "model.to(device) # Move model before creating optimizer\n", "optimizer = optim.SGD(model.parameters(), lr=3e-4, momentum=0.9)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "dU37erdra1TK" }, "source": [ "trainer = create_supervised_trainer(model, optimizer, criterion, device=device)\n", "evaluator = create_supervised_evaluator(model, metrics={\"acc\": Accuracy(), \"loss\": Loss(nn.NLLLoss())}, device=device)\n", "\n", "# Attach handlers profiler\n", "profiler = HandlersTimeProfiler()\n", "profiler.attach(trainer)\n", "\n", "# Init and attach progressbar\n", "pbar = ProgressBar(persist=True)\n", "pbar.attach(trainer, metric_names=\"all\")\n", "\n", "# Evaluate on each epoch using event handler\n", "@trainer.on(Events.EPOCH_COMPLETED)\n", "def log_validation_results(engine):\n", " evaluator.run(testloader)\n", " metrics = evaluator.state.metrics\n", " avg_accuracy = metrics[\"acc\"]\n", " avg_nll = metrics[\"loss\"]\n", " pbar.log_message(\n", " \"Validation Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}\".format(\n", " engine.state.epoch, avg_accuracy, avg_nll\n", " )\n", " )\n", "\n", " pbar.n = pbar.last_print_n = 0\n", "\n", "trainer.run(trainloader, max_epochs=10)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "T_AIlhvqa1TK" }, "source": [ "We can see the summary of the profiling results from our run using the `get_results()` method of the profiler as shown below. The output shows total, average and other details of execution time for each handler attached." ] }, { "cell_type": "code", "metadata": { "id": "S8J8iWyUa1TL" }, "source": [ "profiler.print_results(profiler.get_results())" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "F0yJpyC7a1TM" }, "source": [ "Profiling results can be exported to a CSV file by using the `write_results()` method of profiler." ] }, { "cell_type": "code", "metadata": { "id": "_6a0t-Xha1TM" }, "source": [ "profiler.write_results(\"./results.csv\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-hG8hYQba1TM" }, "source": [ "Following code shows the preview of few rows of the CSV. Each handler has its separate column and the numbers of rows for each column will be equal to the number of times the handler invoked." ] }, { "cell_type": "code", "metadata": { "id": "9SraFwsDa1TM" }, "source": [ "import pandas as pd" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "f4ZiVInXa1TO" }, "source": [ "results = pd.read_csv(\"./results.csv\")\n", "results.head()" ], "execution_count": null, "outputs": [] } ] }ignite-0.5.1/examples/notebooks/MNIST_on_TPU.ipynb000066400000000000000000000324231465426447700220250ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "MNIST_on_TPU.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "TPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "ECija61OXJT4" }, "source": [ "## Getting Started with PyTorch-Ignite on Cloud TPUs\n", "\n", "This notebook is based on [\"Getting Started with PyTorch on Cloud TPUs\"](https://colab.research.google.com/github/pytorch/xla/blob/master/contrib/colab/getting-started.ipynb#scrollTo=RKLajLqUni6H) and will show you how to:\n", "\n", "- Install PyTorch/XLA on Colab, which lets you use PyTorch with TPUs.\n", "- Train a basic model on MNIST with PyTorch-Ignite.\n", "\n", "PyTorch/XLA is a package that lets PyTorch connect to Cloud TPUs and use TPU cores as devices. Colab provides a free Cloud TPU system (a remote CPU host + four TPU chips with two cores each) and installing PyTorch/XLA only takes a couple minutes.\n", "\n", "\n", "

  Use Colab Cloud TPU  

\n", "\n", "* On the main menu, click Runtime and select **Change runtime type**. Set \"TPU\" as the hardware accelerator.\n", "* The cell below makes sure you have access to a TPU on Colab.\n" ] }, { "cell_type": "code", "metadata": { "id": "0nIBhUekU593" }, "source": [ "import os\n", "assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator'" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "v-_pVHuPW_zn" }, "source": [ "## Installing PyTorch/XLA\n", "\n", "Run the following cell (or copy it into your own notebook!) to install PyTorch, Torchvision, and PyTorch/XLA. It will take a couple minutes to run.\n", "\n", "The PyTorch/XLA package lets PyTorch connect to Cloud TPUs. (It's named PyTorch/XLA, not PyTorch/TPU, because XLA is the name of the TPU compiler.) In particular, PyTorch/XLA makes TPU cores available as PyTorch devices. This lets PyTorch create and manipulate tensors on TPUs." ] }, { "cell_type": "code", "metadata": { "id": "4mnaxX3eXNGd" }, "source": [ "VERSION = !curl -s https://api.github.com/repos/pytorch/xla/releases/latest | grep -Po '\"tag_name\": \"v\\K.*?(?=\")'\n", "VERSION = VERSION[0].rstrip('0').rstrip('.') # remove trailing zero\n", "!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-{VERSION}-cp37-cp37m-linux_x86_64.whl" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "iKFb5vdtgODx" }, "source": [ "## Required Dependencies\n", "\n", "We assume that `torch` and `ignite` are already installed. We can install it using `pip`:" ] }, { "cell_type": "code", "metadata": { "id": "6bsFP_2vcTlP" }, "source": [ "!pip install pytorch-ignite" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "CHYlKKHUaoOm" }, "source": [ "## Train a basic model on MNIST with PyTorch-Ignite." ] }, { "cell_type": "markdown", "metadata": { "id": "0ki0DNMKaN9S" }, "source": [ "PyTorch XLA API is so simple as PyTorch uses Cloud TPUs just like it uses CPU or CUDA devices. With only minor changes we can train models with PyTorch and Ignite. We will use the code of this example : https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_tensorboard_on_tpu.py\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Gn4XhksofbPd" }, "source": [ "### Import librairies" ] }, { "cell_type": "code", "metadata": { "id": "EFAJ65OmZPjK" }, "source": [ "import torch\n", "print(\"PyTorch version:\", torch.__version__)\n", "\n", "# imports the torch_xla package\n", "import torch_xla\n", "import torch_xla.core.xla_model as xm\n", "print(\"PyTorch xla version:\", torch_xla.__version__)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KW4s6SiOVEwF" }, "source": [ "# Import PyTorch, Torchvision and Tensorboard\n", "from torch.utils.data import DataLoader\n", "from torch import nn\n", "import torch.nn.functional as F\n", "from torch.optim import SGD\n", "from torchvision.datasets import MNIST\n", "from torchvision.transforms import Compose, ToTensor, Normalize\n", "\n", "from torch.utils.tensorboard import SummaryWriter\n", "\n", "# Import PyTorch-Ignite\n", "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n", "from ignite.metrics import Accuracy, Loss, RunningAverage\n", "from ignite.handlers import ProgressBar" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ksx7UGGZhHGD" }, "source": [ "### Data processing" ] }, { "cell_type": "code", "metadata": { "id": "QnT6QuTkhC2d" }, "source": [ "# Dataloaders\n", "def get_data_loaders(train_batch_size, val_batch_size):\n", " data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])\n", "\n", " train_loader = DataLoader(\n", " MNIST(download=True, root=\".\", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True\n", " )\n", "\n", " val_loader = DataLoader(\n", " MNIST(download=False, root=\".\", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False\n", " )\n", " return train_loader, val_loader" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "EG9C4-yShJ5o" }, "source": [ "train_batch_size = 64\n", "val_batch_size = train_batch_size * 2\n", "\n", "train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hUSL88A5giaO" }, "source": [ "### Create a model" ] }, { "cell_type": "code", "metadata": { "id": "Biefe9RaZb7_" }, "source": [ "# Setup a basic CNN\n", "class Net(nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n", " self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n", " self.conv2_drop = nn.Dropout2d()\n", " self.fc1 = nn.Linear(320, 50)\n", " self.fc2 = nn.Linear(50, 10)\n", "\n", " def forward(self, x):\n", " x = F.relu(F.max_pool2d(self.conv1(x), 2))\n", " x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n", " x = x.view(-1, 320)\n", " x = F.relu(self.fc1(x))\n", " x = F.dropout(x, training=self.training)\n", " x = self.fc2(x)\n", " return F.log_softmax(x, dim=-1)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2eN7bb8yhQbK" }, "source": [ "model = Net()\n", "device = xm.xla_device()\n", "model = model.to(device) # Move model before creating optimizer" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "egKdsX-mhZ1h" }, "source": [ "### Optimizer and trainers" ] }, { "cell_type": "code", "metadata": { "id": "JvCwVzkDeWVG" }, "source": [ "optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)\n", "\n", "# Create trainer and evaluator\n", "trainer = create_supervised_trainer(\n", " model, \n", " optimizer, \n", " F.nll_loss, \n", " device=device, \n", " output_transform=lambda x, y, y_pred, loss: [loss.item(), ]\n", ")\n", "\n", "evaluator = create_supervised_evaluator(\n", " model, metrics={\"accuracy\": Accuracy(), \"nll\": Loss(F.nll_loss)}, device=device\n", ")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "clTM9TrThd7L" }, "source": [ "### Handlers" ] }, { "cell_type": "code", "metadata": { "id": "X8hiRkm2cr8m" }, "source": [ "# Setup event's handlers\n", "log_interval = 10\n", "\n", "log_dir = \"/tmp/tb_logs\"\n", "\n", "# writer\n", "writer = SummaryWriter(log_dir=log_dir)\n", "\n", "tracker = xm.RateTracker()\n", "\n", "# Add RateTracker as an output of the training step\n", "@trainer.on(Events.ITERATION_COMPLETED)\n", "def add_rate_tracker(engine):\n", " tracker.add(len(engine.state.batch))\n", " engine.state.output.append(tracker.global_rate())\n", "\n", "# Setup output values of the training step as EMA metrics\n", "RunningAverage(output_transform=lambda x: x[0]).attach(trainer, \"batch_loss\")\n", "RunningAverage(output_transform=lambda x: x[1]).attach(trainer, \"global_rate\")\n", "\n", "# Let's log the EMA metrics every `log_interval` iterations\n", "@trainer.on(Events.ITERATION_COMPLETED(every=log_interval))\n", "def log_training_loss(engine):\n", " writer.add_scalar(\"training/batch_loss\", engine.state.metrics[\"batch_loss\"], engine.state.iteration)\n", " writer.add_scalar(\"training/global_rate\", engine.state.metrics[\"global_rate\"], engine.state.iteration)\n", "\n", "# Setup a progress bar (tqdm) and display batch loss metric in the bar\n", "pbar = ProgressBar()\n", "pbar.attach(trainer, [\"batch_loss\", \"global_rate\"])\n", "\n", "# Let's compute training metrics: average accuracy and loss\n", "@trainer.on(Events.EPOCH_COMPLETED)\n", "def log_training_results(engine):\n", " evaluator.run(train_loader)\n", " metrics = evaluator.state.metrics\n", " avg_accuracy = metrics[\"accuracy\"]\n", " avg_nll = metrics[\"nll\"]\n", " pbar.log_message(\n", " f\"Training Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}\"\n", " )\n", " writer.add_scalar(\"training/avg_loss\", avg_nll, engine.state.epoch)\n", " writer.add_scalar(\"training/avg_accuracy\", avg_accuracy, engine.state.epoch)\n", "\n", "# Let's compute training metrics: average accuracy and loss\n", "@trainer.on(Events.EPOCH_COMPLETED)\n", "def log_validation_results(engine):\n", " evaluator.run(val_loader)\n", " metrics = evaluator.state.metrics\n", " avg_accuracy = metrics[\"accuracy\"]\n", " avg_nll = metrics[\"nll\"]\n", " print(\n", " f\"Validation Results - Epoch: {engine.state.epoch} Avg accuracy: {avg_accuracy:.2f} Avg loss: {avg_nll:.2f}\"\n", " )\n", " writer.add_scalar(\"valdation/avg_loss\", avg_nll, engine.state.epoch)\n", " writer.add_scalar(\"valdation/avg_accuracy\", avg_accuracy, engine.state.epoch)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IxFocNa7hnhy" }, "source": [ "# Display in Firefox may not work properly. Use Chrome.\n", "%load_ext tensorboard\n", "\n", "%tensorboard --logdir=\"/tmp/tb_logs\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qbC-Th_JcsS1" }, "source": [ "# kick everything off\n", "!rm -rf /tmp/tb_logs/*\n", "\n", "trainer.run(train_loader, max_epochs=10)" ], "execution_count": null, "outputs": [] } ] } ignite-0.5.1/examples/notebooks/TextCNN.ipynb000066400000000000000000001022241465426447700211670ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "RfRKTxQO51bK" }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "HCS-d1T3znj2" }, "source": [ "# Convolutional Neural Networks for Sentence Classification using Ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "rjZMYxFoznj9" }, "source": [ "This is a tutorial on using Ignite to train neural network models, set up experiments and validate models.\n", "\n", "In this experiment, we'll be replicating [\n", "Convolutional Neural Networks for Sentence Classification by Yoon Kim](https://arxiv.org/abs/1408.5882)! This paper uses CNN for text classification, a task typically reserved for RNNs, Logistic Regression, Naive Bayes.\n", "\n", "We want to be able to classify IMDB movie reviews and predict whether the review is positive or negative. IMDB Movie Review dataset comprises of 25000 positive and 25000 negative examples. The dataset comprises of text and label pairs. This is binary classification problem. We'll be using PyTorch to create the model, torchtext to import data and Ignite to train and monitor the models!\n", "\n", "Lets get started! " ] }, { "cell_type": "markdown", "metadata": { "id": "sovYyC0Zznj-" }, "source": [ "## Required Dependencies \n", "\n", "In this example we only need `torchtext` and `spacy` package, assuming that `torch` and `ignite` are already installed. We can install it using `pip`:\n", "\n", "`pip install torchtext==0.9.1 spacy`\n", "\n", "`python -m spacy download en_core_web_sm`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7XHAD9x7znj_" }, "outputs": [], "source": [ "!pip install pytorch-ignite torchtext==0.9.1 spacy\n", "!python -m spacy download en_core_web_sm" ] }, { "cell_type": "markdown", "metadata": { "id": "lZty7-RWznkA" }, "source": [ "## Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VKTazeAkznkB" }, "outputs": [], "source": [ "import random" ] }, { "cell_type": "markdown", "metadata": { "id": "wxThg0YTznkD" }, "source": [ "`torchtext` is a library that provides multiple datasets for NLP tasks, similar to `torchvision`. Below we import the following:\n", "* **datasets**: A module to download NLP datasets.\n", "* **GloVe**: A module to download and use pretrained GloVe embedings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XrXE-f7jznkD" }, "outputs": [], "source": [ "from torchtext import datasets\n", "from torchtext.vocab import GloVe" ] }, { "cell_type": "markdown", "metadata": { "id": "ivAnTyEfznkE" }, "source": [ "We import torch, nn and functional modules to create our models! " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gbEFAWr0znkE" }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F" ] }, { "cell_type": "markdown", "metadata": { "id": "Q22BGKi8znkF" }, "source": [ "`Ignite` is a High-level library to help with training neural networks in PyTorch. It comes with an `Engine` to set up a training loop, various metrics, handlers and a helpful contrib section! \n", "\n", "Below we import the following:\n", "* **Engine**: Runs a given process_function over each batch of a dataset, emitting events as it goes.\n", "* **Events**: Allows users to attach functions to an `Engine` to fire functions at a specific event. Eg: `EPOCH_COMPLETED`, `ITERATION_STARTED`, etc.\n", "* **Accuracy**: Metric to calculate accuracy over a dataset, for binary, multiclass, multilabel cases. \n", "* **Loss**: General metric that takes a loss function as a parameter, calculate loss over a dataset.\n", "* **RunningAverage**: General metric to attach to Engine during training. \n", "* **ModelCheckpoint**: Handler to checkpoint models. \n", "* **EarlyStopping**: Handler to stop training based on a score function. \n", "* **ProgressBar**: Handler to create a tqdm progress bar." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "enczLgLTznkH" }, "outputs": [], "source": [ "from ignite.engine import Engine, Events\n", "from ignite.metrics import Accuracy, Loss, RunningAverage\n", "from ignite.handlers import ModelCheckpoint, EarlyStopping\n", "from ignite.handlers import ProgressBar\n", "from ignite.utils import manual_seed\n", "\n", "SEED = 1234\n", "manual_seed(SEED)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-39hgxiUMCq9" }, "outputs": [], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" ] }, { "cell_type": "markdown", "metadata": { "id": "WZYyXYB5znkH" }, "source": [ "## Processing Data" ] }, { "cell_type": "markdown", "metadata": { "id": "irv_ebeDb8yV" }, "source": [ "We first set up a tokenizer using `torchtext.data.utils`.\n", "The job of a tokenizer to split a sentence into \"tokens\". You can read more about it at [wikipedia](https://en.wikipedia.org/wiki/Lexical_analysis).\n", "We will use the tokenizer from the \"spacy\" library which is a popular choice. Feel free to switch to \"basic_english\" if you want to use the default one or any other that you want.\n", "\n", "docs: https://pytorch.org/text/stable/data_utils.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YNRd5Z_KMANB" }, "outputs": [], "source": [ "from torchtext.data.utils import get_tokenizer\n", "tokenizer = get_tokenizer(\"spacy\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZknfGdqedSjN" }, "outputs": [], "source": [ "tokenizer(\"Ignite is a high-level library for training and evaluating neural networks.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ZvAmyqHygcZg" }, "source": [ "Next, the IMDB training and test datasets are downloaded. The `torchtext.datasets` API returns the train/test dataset split directly without the preprocessing information. Each split is an iterator which yields the raw texts and labels line-by-line." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "E_jNgWXHhMBQ" }, "outputs": [], "source": [ "train_iter, test_iter = datasets.IMDB(split=('train','test'))" ] }, { "cell_type": "markdown", "metadata": { "id": "xNKvG9b7jadd" }, "source": [ "Now we set up the train, validation and test splits. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VzJG7Uh_L9q-" }, "outputs": [], "source": [ "# We are using only 1000 samples for faster training\n", "# set to -1 to use full data\n", "N = 1000 \n", "\n", "# We will use 80% of the `train split` for training and the rest for validation\n", "train_frac = 0.8\n", "_temp = list(train_iter)\n", "\n", "\n", "random.shuffle(_temp)\n", "_temp = _temp[:(N if N > 0 else len(_temp) )]\n", "n_train = int(len(_temp)*train_frac)\n", "\n", "train_list = _temp[:n_train]\n", "validation_list = _temp[n_train:]\n", "test_list = list(test_iter)\n", "test_list = test_list[:(N if N > 0 else len(test_list))]" ] }, { "cell_type": "markdown", "metadata": { "id": "X-qYvdeplMIs" }, "source": [ "Let's explore a data sample to see what it looks like.\n", "Each data sample is a tuple of the format `(label, text)`.\n", "\n", "The value of label is either 'pos' or 'neg'.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qrlLB7PxkIW_" }, "outputs": [], "source": [ "random_sample = random.sample(train_list,1)[0]\n", "print(' text:', random_sample[1])\n", "print('label:', random_sample[0])" ] }, { "cell_type": "markdown", "metadata": { "id": "mN5cHrazmMDG" }, "source": [ "Now that we have the datasets splits, let's build our vocabulary. For this, we will use the `Vocab` class from `torchtext.vocab`. It is important that we build our vocabulary based on the train dataset as validation and test are **unseen** in our experimenting. \n", "\n", "`Vocab` allows us to use pretrained **GloVE** 100 dimensional word vectors. This means each word is described by 100 floats! If you want to read more about this, here are a few resources.\n", "* [StanfordNLP - GloVe](https://github.com/stanfordnlp/GloVe)\n", "* [DeepLearning.ai Lecture](https://www.coursera.org/lecture/nlp-sequence-models/glove-word-vectors-IxDTG)\n", "* [Stanford CS224N Lecture by Richard Socher](https://www.youtube.com/watch?v=ASn7ExxLZws)\n", "\n", "Note than the GloVE download size is around 900MB, so it might take some time to download. \n", "\n", "An instance of the `Vocab` class has the following attributes:\n", "* `extend` is used to extend the vocabulary\n", "* `freqs` is a dictionary of the frequency of each word\n", "* `itos` is a list of all the words in the vocabulary.\n", "* `stoi` is a dictionary mapping every word to an index.\n", "* `vectors` is a torch.Tensor of the downloaded embeddings\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T_ukillQMKsh" }, "outputs": [], "source": [ "from collections import Counter\n", "from torchtext.vocab import Vocab\n", "\n", "counter = Counter()\n", "\n", "for (label, line) in train_list:\n", " counter.update(tokenizer(line))\n", "\n", "vocab = Vocab(\n", " counter,\n", " min_freq=10,\n", " vectors=GloVe(name='6B', dim=100, cache='/tmp/glove/')\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VYYGwfYsM2Pr" }, "outputs": [], "source": [ "print(\"The length of the new vocab is\", len(vocab))\n", "new_stoi = vocab.stoi\n", "print(\"The index of '' is\", new_stoi[''])\n", "new_itos = vocab.itos\n", "print(\"The token at index 2 is\", new_itos[2])" ] }, { "cell_type": "markdown", "metadata": { "id": "4Y72cqB6Qhqt" }, "source": [ "We now create `text_transform` and `label_transform`, which are callable objects, such as a `lambda` func here, to process the raw text and label data from the dataset iterators (or iterables like a `list`). You can add the special symbols such as `` and `` to the sentence in `text_transform`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "z_9hw21lP1nG" }, "outputs": [], "source": [ "text_transform = lambda x: [vocab[token] for token in tokenizer(x)]\n", "label_transform = lambda x: 1 if x == 'pos' else 0\n", "\n", "# Print out the output of text_transform\n", "print(\"input to the text_transform:\", \"here is an example\")\n", "print(\"output of the text_transform:\", text_transform(\"here is an example\"))" ] }, { "cell_type": "markdown", "metadata": { "id": "xtZSEqjJQPxM" }, "source": [ "For generating the data batches we will use `torch.utils.data.DataLoader`. You could customize the data batch by defining a function with the `collate_fn` argument in the DataLoader. Here, in the `collate_batch` func, we process the raw text data and add padding to dynamically match the longest sentence in a batch." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NHHIEfpRP4TV" }, "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", "from torch.nn.utils.rnn import pad_sequence\n", "\n", "def collate_batch(batch):\n", " label_list, text_list = [], []\n", " for (_label, _text) in batch:\n", " label_list.append(label_transform(_label))\n", " processed_text = torch.tensor(text_transform(_text))\n", " text_list.append(processed_text)\n", " return torch.tensor(label_list), pad_sequence(text_list, padding_value=3.0)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3IQd3EVbQvTo" }, "outputs": [], "source": [ "batch_size = 8 # A batch size of 8\n", "\n", "def create_iterators(batch_size=8):\n", " \"\"\"Heler function to create the iterators\"\"\"\n", " dataloaders = []\n", " for split in [train_list, validation_list, test_list]:\n", " dataloader = DataLoader(\n", " split, batch_size=batch_size,\n", " collate_fn=collate_batch\n", " )\n", " dataloaders.append(dataloader)\n", " return dataloaders\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CudYIZitUNgx" }, "outputs": [], "source": [ "train_iterator, valid_iterator, test_iterator = create_iterators()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "787zNPm6RtKE" }, "outputs": [], "source": [ "next(iter(train_iterator))" ] }, { "cell_type": "markdown", "metadata": { "id": "d2azJGL6znkM" }, "source": [ "Let's actually explore what the output of the iterator is, this way we'll know what the input of the model is, how to compare the label to the output and how to set up our process_functions for Ignite's `Engine`.\n", "* `batch[0][0]` is the label of a single example. We can see that `vocab.stoi` was used to map the label that originally text into a float.\n", "* `batch[1][0]` is the text of a single example. Similar to label, `vocab.stoi` was used to convert each token of the example's text into indices.\n", "\n", "Now let's print the lengths of the sentences of the first 10 batches of `train_iterator`. We see here that all the batches are of different lengths, this means that the iterator is working as expected." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ga2xDXfyznkN" }, "outputs": [], "source": [ "batch = next(iter(train_iterator))\n", "print('batch[0][0] : ', batch[0][0])\n", "print('batch[1][0] : ', batch[1][[0] != 1])\n", "\n", "lengths = []\n", "for i, batch in enumerate(train_iterator):\n", " x = batch[1]\n", " lengths.append(x.shape[0])\n", " if i == 10:\n", " break\n", "\n", "print ('Lengths of first 10 batches : ', lengths)" ] }, { "cell_type": "markdown", "metadata": { "id": "KsUrKRr3znkO" }, "source": [ "## TextCNN Model" ] }, { "cell_type": "markdown", "metadata": { "id": "pldMpTv-znkO" }, "source": [ "Here is the replication of the model, here are the operations of the model:\n", "* **Embedding**: Embeds a batch of text of shape (N, L) to (N, L, D), where N is batch size, L is maximum length of the batch, D is the embedding dimension. \n", "\n", "* **Convolutions**: Runs parallel convolutions across the embedded words with kernel sizes of 3, 4, 5 to mimic trigrams, four-grams, five-grams. This results in outputs of (N, L - k + 1, D) per convolution, where k is the kernel_size. \n", "\n", "* **Activation**: ReLu activation is applied to each convolution operation.\n", "\n", "* **Pooling**: Runs parallel maxpooling operations on the activated convolutions with window sizes of L - k + 1, resulting in 1 value per channel i.e. a shape of (N, 1, D) per pooling. \n", "\n", "* **Concat**: The pooling outputs are concatenated and squeezed to result in a shape of (N, 3D). This is a single embedding for a sentence.\n", "\n", "* **Dropout**: Dropout is applied to the embedded sentence. \n", "\n", "* **Fully Connected**: The dropout output is passed through a fully connected layer of shape (3D, 1) to give a single output for each example in the batch. sigmoid is applied to the output of this layer.\n", "\n", "* **load_embeddings**: This is a method defined for TextCNN to load embeddings based on user input. There are 3 modes - rand which results in randomly initialized weights, static which results in frozen pretrained weights, nonstatic which results in trainable pretrained weights. \n", "\n", "\n", "Let's note that this model works for variable text lengths! The idea to embed the words of a sentence, use convolutions, maxpooling and concantenation to embed the sentence as a single vector! This single vector is passed through a fully connected layer with sigmoid to output a single value. This value can be interpreted as the probability a sentence is positive (closer to 1) or negative (closer to 0).\n", "\n", "The minimum length of text expected by the model is the size of the smallest kernel size of the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "63z1tffDznkO" }, "outputs": [], "source": [ "class TextCNN(nn.Module):\n", " def __init__(\n", " self,\n", " vocab_size,\n", " embedding_dim, \n", " kernel_sizes, \n", " num_filters, \n", " num_classes, d_prob, mode):\n", " super(TextCNN, self).__init__()\n", " self.vocab_size = vocab_size\n", " self.embedding_dim = embedding_dim\n", " self.kernel_sizes = kernel_sizes\n", " self.num_filters = num_filters\n", " self.num_classes = num_classes\n", " self.d_prob = d_prob\n", " self.mode = mode\n", " self.embedding = nn.Embedding(\n", " vocab_size, embedding_dim, padding_idx=0)\n", " self.load_embeddings()\n", " self.conv = nn.ModuleList([nn.Conv1d(in_channels=embedding_dim,\n", " out_channels=num_filters,\n", " kernel_size=k, stride=1) for k in kernel_sizes])\n", " self.dropout = nn.Dropout(d_prob)\n", " self.fc = nn.Linear(len(kernel_sizes) * num_filters, num_classes)\n", "\n", " def forward(self, x):\n", " batch_size, sequence_length = x.shape\n", " x = self.embedding(x.T).transpose(1, 2)\n", " x = [F.relu(conv(x)) for conv in self.conv]\n", " x = [F.max_pool1d(c, c.size(-1)).squeeze(dim=-1) for c in x]\n", " x = torch.cat(x, dim=1)\n", " x = self.fc(self.dropout(x))\n", " return torch.sigmoid(x).squeeze()\n", "\n", " def load_embeddings(self):\n", " if 'static' in self.mode:\n", " self.embedding.weight.data.copy_(vocab.vectors)\n", " if 'non' not in self.mode:\n", " self.embedding.weight.data.requires_grad = False\n", " print('Loaded pretrained embeddings, weights are not trainable.')\n", " else:\n", " self.embedding.weight.data.requires_grad = True\n", " print('Loaded pretrained embeddings, weights are trainable.')\n", " elif self.mode == 'rand':\n", " print('Randomly initialized embeddings are used.')\n", " else:\n", " raise ValueError('Unexpected value of mode. Please choose from static, nonstatic, rand.')" ] }, { "cell_type": "markdown", "metadata": { "id": "6G3TW4c-znkO" }, "source": [ "## Creating Model, Optimizer and Loss" ] }, { "cell_type": "markdown", "metadata": { "id": "D7nH55oXznkP" }, "source": [ "Below we create an instance of the TextCNN model and load embeddings in **static** mode. The model is placed on a device and then a loss function of Binary Cross Entropy and Adam optimizer are set up. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HM_7LQE3znkP" }, "outputs": [], "source": [ "vocab_size, embedding_dim = vocab.vectors.shape\n", "\n", "model = TextCNN(vocab_size=vocab_size,\n", " embedding_dim=embedding_dim,\n", " kernel_sizes=[3, 4, 5],\n", " num_filters=100,\n", " num_classes=1, \n", " d_prob=0.5,\n", " mode='static')\n", "model.to(device)\n", "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-3)\n", "criterion = nn.BCELoss()" ] }, { "cell_type": "markdown", "metadata": { "id": "xjxbAwvIznkP" }, "source": [ "## Training and Evaluating using Ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "L8Rl7spqznkQ" }, "source": [ "### Trainer Engine - process_function\n", "\n", "Ignite's Engine allows user to define a process_function to process a given batch, this is applied to all the batches of the dataset. This is a general class that can be applied to train and validate models! A process_function has two parameters engine and batch. \n", "\n", "\n", "Let's walk through what the function of the trainer does:\n", "\n", "* Sets model in train mode. \n", "* Sets the gradients of the optimizer to zero.\n", "* Generate x and y from batch.\n", "* Performs a forward pass to calculate y_pred using model and x.\n", "* Calculates loss using y_pred and y.\n", "* Performs a backward pass using loss to calculate gradients for the model parameters.\n", "* model parameters are optimized using gradients and optimizer.\n", "* Returns scalar loss. \n", "\n", "Below is a single operation during the trainig process. This process_function will be attached to the training engine." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q4ncIcYcznkQ" }, "outputs": [], "source": [ "def process_function(engine, batch):\n", " model.train()\n", " optimizer.zero_grad()\n", " y, x = batch\n", " x = x.to(device)\n", " y = y.to(device)\n", " y_pred = model(x)\n", " loss = criterion(y_pred, y.float())\n", " loss.backward()\n", " optimizer.step()\n", " return loss.item()" ] }, { "cell_type": "markdown", "metadata": { "id": "HiiQr_GYznkQ" }, "source": [ "### Evaluator Engine - process_function\n", "\n", "Similar to the training process function, we set up a function to evaluate a single batch. Here is what the eval_function does:\n", "\n", "* Sets model in eval mode.\n", "* With torch.no_grad(), no gradients are calculated for any succeding steps.\n", "* Generates x and y from batch.\n", "* Performs a forward pass on the model to calculate y_pred based on model and x.\n", "* Returns y_pred and y.\n", "\n", "Ignite suggests attaching metrics to evaluators and not trainers because during the training the model parameters are constantly changing and it is best to evaluate model on a stationary model. This information is important as there is a difference in the functions for training and evaluating. Training returns a single scalar loss. Evaluating returns y_pred and y as that output is used to calculate metrics per batch for the entire dataset.\n", "\n", "All metrics in Ignite require y_pred and y as outputs of the function attached to the Engine. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b9-G-9iVznkR" }, "outputs": [], "source": [ "def eval_function(engine, batch):\n", " model.eval()\n", " with torch.no_grad():\n", " y, x = batch\n", " y = y.to(device)\n", " x = x.to(device)\n", " y = y.float()\n", " y_pred = model(x)\n", " return y_pred, y" ] }, { "cell_type": "markdown", "metadata": { "id": "dcmIEZuNznkS" }, "source": [ "### Instantiating Training and Evaluating Engines\n", "\n", "Below we create 3 engines, a trainer, a training evaluator and a validation evaluator. You'll notice that train_evaluator and validation_evaluator use the same function, we'll see later why this was done! " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k1CxFQs_znkS" }, "outputs": [], "source": [ "trainer = Engine(process_function)\n", "train_evaluator = Engine(eval_function)\n", "validation_evaluator = Engine(eval_function)" ] }, { "cell_type": "markdown", "metadata": { "id": "ZVu91uVtznkS" }, "source": [ "### Metrics - RunningAverage, Accuracy and Loss\n", "\n", "To start, we'll attach a metric of Running Average to track a running average of the scalar loss output for each batch. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "P-98lPU9znkS" }, "outputs": [], "source": [ "RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')" ] }, { "cell_type": "markdown", "metadata": { "id": "Mufkp6mnznkS" }, "source": [ "Now there are two metrics that we want to use for evaluation - accuracy and loss. This is a binary problem, so for Loss we can simply pass the Binary Cross Entropy function as the loss_function. \n", "\n", "For Accuracy, Ignite requires y_pred and y to be comprised of 0's and 1's only. Since our model outputs from a sigmoid layer, values are between 0 and 1. We'll need to write a function that transforms `engine.state.output` which is comprised of y_pred and y. \n", "\n", "Below `thresholded_output_transform` does just that, it rounds y_pred to convert y_pred to 0's and 1's, and then returns rounded y_pred and y. This function is the output_transform function used to transform the `engine.state.output` to achieve `Accuracy`'s desired purpose.\n", "\n", "Now, we attach `Loss` and `Accuracy` (with `thresholded_output_transform`) to train_evaluator and validation_evaluator. \n", "\n", "To attach a metric to engine, the following format is used:\n", "* `Metric(output_transform=output_transform, ...).attach(engine, 'metric_name')`\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KAK6nXEbznkS" }, "outputs": [], "source": [ "def thresholded_output_transform(output):\n", " y_pred, y = output\n", " y_pred = torch.round(y_pred)\n", " return y_pred, y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QkcC2R4qznkT" }, "outputs": [], "source": [ "Accuracy(output_transform=thresholded_output_transform).attach(train_evaluator, 'accuracy')\n", "Loss(criterion).attach(train_evaluator, 'bce')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tLtT5f11znkT" }, "outputs": [], "source": [ "Accuracy(output_transform=thresholded_output_transform).attach(validation_evaluator, 'accuracy')\n", "Loss(criterion).attach(validation_evaluator, 'bce')" ] }, { "cell_type": "markdown", "metadata": { "id": "FbS2h_2eznkU" }, "source": [ "### Progress Bar\n", "\n", "Next we create an instance of Ignite's progess bar and attach it to the trainer and pass it a key of `engine.state.metrics` to track. In this case, the progress bar will be tracking `engine.state.metrics['loss']`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qteztuB3znkU" }, "outputs": [], "source": [ "pbar = ProgressBar(persist=True, bar_format=\"\")\n", "pbar.attach(trainer, ['loss'])" ] }, { "cell_type": "markdown", "metadata": { "id": "x4DxUwXfznkU" }, "source": [ "### EarlyStopping - Tracking Validation Loss\n", "\n", "Now we'll set up a Early Stopping handler for this training process. EarlyStopping requires a score_function that allows the user to define whatever criteria to stop training. In this case, if the loss of the validation set does not decrease in 5 epochs, the training process will stop early. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wPM6-USgznkU" }, "outputs": [], "source": [ "def score_function(engine):\n", " val_loss = engine.state.metrics['bce']\n", " return -val_loss\n", "\n", "handler = EarlyStopping(patience=5, score_function=score_function, trainer=trainer)\n", "validation_evaluator.add_event_handler(Events.COMPLETED, handler)" ] }, { "cell_type": "markdown", "metadata": { "id": "LfeL6EkhznkU" }, "source": [ "### Attaching Custom Functions to Engine at specific Events\n", "\n", "Below you'll see ways to define your own custom functions and attaching them to various `Events` of the training process.\n", "\n", "The functions below both achieve similar tasks, they print the results of the evaluator run on a dataset. One function does that on the training evaluator and dataset, while the other on the validation. Another difference is how these functions are attached in the trainer engine.\n", "\n", "The first method involves using a decorator, the syntax is simple - `@` `trainer.on(Events.EPOCH_COMPLETED)`, means that the decorated function will be attached to the trainer and called at the end of each epoch. \n", "\n", "The second method involves using the add_event_handler method of trainer - `trainer.add_event_handler(Events.EPOCH_COMPLETED, custom_function)`. This achieves the same result as the above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3XsmcAA2znkV" }, "outputs": [], "source": [ "@trainer.on(Events.EPOCH_COMPLETED)\n", "def log_training_results(engine):\n", " train_evaluator.run(train_iterator)\n", " metrics = train_evaluator.state.metrics\n", " avg_accuracy = metrics['accuracy']\n", " avg_bce = metrics['bce']\n", " pbar.log_message(\n", " \"Training Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}\"\n", " .format(engine.state.epoch, avg_accuracy, avg_bce))\n", " \n", "def log_validation_results(engine):\n", " validation_evaluator.run(valid_iterator)\n", " metrics = validation_evaluator.state.metrics\n", " avg_accuracy = metrics['accuracy']\n", " avg_bce = metrics['bce']\n", " pbar.log_message(\n", " \"Validation Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}\"\n", " .format(engine.state.epoch, avg_accuracy, avg_bce))\n", " pbar.n = pbar.last_print_n = 0\n", "\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, log_validation_results)" ] }, { "cell_type": "markdown", "metadata": { "id": "IAQEr88cznkW" }, "source": [ "### ModelCheckpoint\n", "\n", "Lastly, we want to checkpoint this model. It's important to do so, as training processes can be time consuming and if for some reason something goes wrong during training, a model checkpoint can be helpful to restart training from the point of failure.\n", "\n", "Below we'll use Ignite's `ModelCheckpoint` handler to checkpoint models at the end of each epoch. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9Gl6WT0YznkW" }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint('/tmp/models', 'textcnn', n_saved=2, create_dir=True, save_as_state_dict=True)\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'textcnn': model})" ] }, { "cell_type": "markdown", "metadata": { "id": "LxCIriIEznkW" }, "source": [ "### Run Engine\n", "\n", "Next, we'll run the trainer for 20 epochs and monitor results. Below we can see that progess bar prints the loss per iteration, and prints the results of training and validation as we specified in our custom function. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sPe46cQOznkX" }, "outputs": [], "source": [ "trainer.run(train_iterator, max_epochs=20)" ] }, { "cell_type": "markdown", "metadata": { "id": "OpqXiZUsznkY" }, "source": [ "That's it! We have successfully trained and evaluated a Convolutational Neural Network for Text Classification. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }ignite-0.5.1/examples/notebooks/VAE.ipynb000066400000000000000000000652271465426447700203320ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1-final" }, "colab": { "name": "VAE.ipynb", "provenance": [], "toc_visible": true }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "2wrkQRklOAeB" }, "source": [ "# Variational Auto Encoders using Ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "0Kvs_LwjOAeL" }, "source": [ "This is a tutorial on using Ignite to train neural network models, setup experiments and validate models.\n", "\n", "In this experiment, we'll be replicating [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114) by Kingma and Welling. This paper uses an encoder-decoder architecture to encode images to a vector and then reconstruct the images.\n", "\n", "We want to be able to encode and reconstruct MNIST images. MNIST is the classic machine learning dataset, it contains black and white images of digits 0 to 9. There are 50000 training images and 10000 test images. The dataset comprises of image and label pairs. \n", "\n", "We'll be using PyTorch to create the model, torchvision to import data and Ignite to train and monitor the models!\n", "\n", "Please note that a lot of this code has been borrowed from [official PyTorch example](https://github.com/pytorch/examples/tree/master/vae). Similar to that it uses ReLUs and the adam optimizer, instead of sigmoids and adagrad.\n", "\n", "Let's get started!" ] }, { "cell_type": "markdown", "metadata": { "id": "QFH5UZFhOAeM" }, "source": [ "## Required Dependencies\n", "\n", "In this example we only need `torchvision` package, assuming that `torch` and `ignite` are already installed. We can install it using `pip`:" ] }, { "cell_type": "markdown", "metadata": { "id": "HVhQpUjYOAeO" }, "source": [ "```\n", "pip install torchvision\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "1s2up1PkOAeP" }, "source": [ "!pip install pytorch-ignite torchvision" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gnx9qzjIOAeQ" }, "source": [ "## Import Libraries" ] }, { "cell_type": "code", "metadata": { "id": "9hKV-yeYOAeS" }, "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IMn3CAHqOAeS" }, "source": [ "We import `torch`, `nn` and `functional` modules to create our models! `DataLoader` to create iterators for the downloaded datasets.\n", "\n", "The code below also checks whether there are GPUs available on the machine and assigns the device to GPU if there are." ] }, { "cell_type": "code", "metadata": { "id": "_dLPBDj-OAeT" }, "source": [ "import torch\n", "from torch.utils.data import DataLoader\n", "from torch import nn, optim\n", "from torch.nn import functional as F\n", "SEED = 1234\n", "\n", "torch.manual_seed(SEED)\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DaX-nwZAOAeU" }, "source": [ "`torchvision` is a library that provides multiple datasets for computer vision tasks. Below we import the following:\n", "\n", "* **MNIST**: A module to download the MNIST datasets.\n", "* **save_image**: Saves tensors as images.\n", "* **make_grid**: Takes a concatenation of tensors and makes a grid of images.\n", "* **ToTensor**: Converts images to Tensors.\n", "* **Compose**: Collects transformations. " ] }, { "cell_type": "code", "metadata": { "id": "0oKaBceaOAeU" }, "source": [ "from torchvision.datasets import MNIST\n", "from torchvision.utils import save_image, make_grid\n", "from torchvision.transforms import Compose, ToTensor" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8q_cNs_xOAeW" }, "source": [ "`Ignite` is a High-level library to help with training neural networks in PyTorch. It comes with an `Engine` to setup a training loop, various metrics, handlers and a helpful contrib section! \n", "\n", "Below we import the following:\n", "* **Engine**: Runs a given process_function over each batch of a dataset, emitting events as it goes.\n", "* **Events**: Allows users to attach functions to an `Engine` to fire functions at a specific event. Eg: `EPOCH_COMPLETED`, `ITERATION_STARTED`, etc.\n", "* **MeanSquaredError**: Metric to calculate mean squared error. \n", "* **Loss**: General metric that takes a loss function as a parameter, calculate loss over a dataset.\n", "* **RunningAverage**: General metric to attach to Engine during training. \n", "* **ModelCheckpoint**: Handler to checkpoint models." ] }, { "cell_type": "code", "metadata": { "id": "EzibOregOAeW" }, "source": [ "from ignite.engine import Engine, Events\n", "from ignite.metrics import MeanSquaredError, Loss, RunningAverage" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mqtxETPkOAeW" }, "source": [ "## Processing Data" ] }, { "cell_type": "markdown", "metadata": { "id": "IzrrCIkaOAeX" }, "source": [ "Below the only transformation we use is to convert convert the images to Tensor, MNIST downloads the dataset on to your machine.\n", "\n", "* `train_data` is a list of tuples of image tensors and labels. `val_data` is the same, just a different number of images. \n", "* `image` is a 28 x 28 tensor with 1 channel, meaning a 28 x 28 grayscale image.\n", "* `label` is a single integer value, denoting what the image is showing." ] }, { "cell_type": "code", "metadata": { "id": "Np63e2xCOAeX" }, "source": [ "data_transform = Compose([ToTensor()])\n", "\n", "train_data = MNIST(download=True, root=\"/tmp/mnist/\", transform=data_transform, train=True)\n", "val_data = MNIST(download=True, root=\"/tmp/mnist/\", transform=data_transform, train=False)\n", "\n", "image = train_data[0][0]\n", "label = train_data[0][1]\n", "\n", "print ('len(train_data) : ', len(train_data))\n", "print ('len(val_data) : ', len(val_data))\n", "print ('image.shape : ', image.shape)\n", "print ('label : ', label)\n", "\n", "img = plt.imshow(image.squeeze().numpy(), cmap='gray')" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "euf13Tx6OAeX" }, "source": [ "Now let's setup iterators of the training and validation datasets. We can take advantage of PyTorch's `DataLoader` that allows us to specify the dataset, batch size, number of workers, device, and other helpful parameters. \n", "\n", "Let's see what the output of the iterators are:\n", "* We see that each batch consists of 32 images and their corresponding labels.\n", "* Examples are shuffled.\n", "* Data is placed on GPU if available, otherwise it uses CPU." ] }, { "cell_type": "code", "metadata": { "id": "0_HaWmxTOAeY" }, "source": [ "kwargs = {'num_workers': 1, 'pin_memory': True} if device == 'cuda' else {}\n", "\n", "train_loader = DataLoader(train_data, batch_size=32, shuffle=True, **kwargs)\n", "val_loader = DataLoader(val_data, batch_size=32, shuffle=True, **kwargs)\n", "\n", "for batch in train_loader:\n", " x, y = batch\n", " break\n", "\n", "print ('x.shape : ', x.shape)\n", "print ('y.shape : ', y.shape)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "GFBK6G7HOAeY" }, "source": [ "To visualize how well our model reconstruct images, let's save the above value of x as a set of images we can use to compare against the generation reconstructions from our model." ] }, { "cell_type": "code", "metadata": { "id": "_hhtM6UhOAeY" }, "source": [ "fixed_images = x.to(device)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "m2yFi8vhOAeZ" }, "source": [ "## VAE Model" ] }, { "cell_type": "markdown", "metadata": { "id": "hauIEN0XOAeZ" }, "source": [ "VAE is a model comprised of fully connected layers that take a flattened image, pass them through fully connected layers reducing the image to a low dimensional vector. The vector is then passed through a mirrored set of fully connected weights from the encoding steps, to generate a vector of the same size as the input." ] }, { "cell_type": "code", "metadata": { "id": "oQ5uwtzaOAea" }, "source": [ "class VAE(nn.Module):\n", " def __init__(self):\n", " super(VAE, self).__init__()\n", " self.fc1 = nn.Linear(784, 400)\n", " self.fc21 = nn.Linear(400, 20)\n", " self.fc22 = nn.Linear(400, 20)\n", " self.fc3 = nn.Linear(20, 400)\n", " self.fc4 = nn.Linear(400, 784)\n", "\n", " def encode(self, x):\n", " h1 = F.relu(self.fc1(x))\n", " return self.fc21(h1), self.fc22(h1)\n", "\n", " def reparameterize(self, mu, logvar):\n", " std = torch.exp(0.5*logvar)\n", " eps = torch.randn_like(std)\n", " return eps.mul(std).add_(mu)\n", "\n", " def decode(self, z):\n", " h3 = F.relu(self.fc3(z))\n", " return torch.sigmoid(self.fc4(h3))\n", "\n", " def forward(self, x):\n", " mu, logvar = self.encode(x)\n", " z = self.reparameterize(mu, logvar)\n", " return self.decode(z), mu, logvar" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mV27j9EDOAea" }, "source": [ "## Creating Model, Optimizer and Loss" ] }, { "cell_type": "markdown", "metadata": { "id": "kJ6yD5fBOAeb" }, "source": [ "Below we create an instance of the VAE model. The model is placed on a device and then loss functions of Binary Cross Entropy + KL Divergence is used and Adam optimizer are setup. " ] }, { "cell_type": "code", "metadata": { "id": "0rAgYpT3OAeb" }, "source": [ "model = VAE().to(device)\n", "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n", "\n", "def kld_loss(x_pred, x, mu, logvar):\n", " # see Appendix B from VAE paper:\n", " # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n", " # https://arxiv.org/abs/1312.6114\n", " # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n", " return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n", "\n", "bce_loss = nn.BCELoss(reduction='sum')" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1ye6HrDeOAeb" }, "source": [ "## Training and Evaluating using Ignite" ] }, { "cell_type": "markdown", "metadata": { "id": "v-QR-oALOAeb" }, "source": [ "### Trainer Engine - process_function\n", "\n", "Ignite's `Engine` allows user to define a `process_function` to process a given batch, this is applied to all the batches of the dataset. This is a general class that can be applied to train and validate models! A `process_function` has two parameters engine and batch. \n", "\n", "\n", "Let's walk through what the function of the trainer does:\n", "\n", "* Sets model in train mode. \n", "* Sets the gradients of the optimizer to zero.\n", "* Generate `x` from batch.\n", "* Flattens `x` into shape `(-1, 784)`.\n", "* Performs a forward pass to reconstuct `x` as `x_pred` using model and x. Model also return `mu`, `logvar`.\n", "* Calculates loss using `x_pred`, `x`, `logvar` and `mu`.\n", "* Performs a backward pass using loss to calculate gradients for the model parameters.\n", "* model parameters are optimized using gradients and optimizer.\n", "* Returns scalar loss. \n", "\n", "Below is a single operation during the trainig process. This process_function will be attached to the training engine." ] }, { "cell_type": "code", "metadata": { "id": "VwuW-CbeOAec" }, "source": [ "def process_function(engine, batch):\n", " model.train()\n", " optimizer.zero_grad()\n", " x, _ = batch\n", " x = x.to(device)\n", " x = x.view(-1, 784)\n", " x_pred, mu, logvar = model(x)\n", " BCE = bce_loss(x_pred, x)\n", " KLD = kld_loss(x_pred, x, mu, logvar)\n", " loss = BCE + KLD\n", " loss.backward()\n", " optimizer.step()\n", " return loss.item(), BCE.item(), KLD.item()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "p8KEf_jHOAec" }, "source": [ "### Evaluator Engine - process_function\n", "\n", "Similar to the training process function, we setup a function to evaluate a single batch. Here is what the `eval_function` does:\n", "\n", "* Sets model in eval mode.\n", "* Generates `x` from batch.\n", "* With `torch.no_grad()`, no gradients are calculated for any succeding steps.\n", "* Flattens `x` into shape `(-1, 784)`.\n", "* Performs a forward pass to reconstuct `x` as `x_pred` using model and x. Model also return `mu`, `logvar`.\n", "* Returns `x_pred`, `x`, `mu` and `logvar`.\n", "\n", "Ignite suggests attaching metrics to evaluators and not trainers because during the training the model parameters are constantly changing and it is best to evaluate model on a stationary model. This information is important as there is a difference in the functions for training and evaluating. Training returns a single scalar loss. Evaluating returns `y_pred` and `y` as that output is used to calculate metrics per batch for the entire dataset.\n", "\n", "All metrics in `Ignite` require `y_pred` and `y` as outputs of the function attached to the `Engine`. " ] }, { "cell_type": "code", "metadata": { "id": "PceHJQJ4OAec" }, "source": [ "def evaluate_function(engine, batch):\n", " model.eval()\n", " with torch.no_grad():\n", " x, _ = batch\n", " x = x.to(device)\n", " x = x.view(-1, 784)\n", " x_pred, mu, logvar = model(x)\n", " kwargs = {'mu': mu, 'logvar': logvar}\n", " return x_pred, x, kwargs" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "GdfA_leJOAec" }, "source": [ "### Instantiating Training and Evaluating Engines\n", "\n", "Below we create 2 engines, a `trainer` and `evaluator` using the functions defined above. We also define dictionaries to keep track of the history of the metrics on the training and validation sets. " ] }, { "cell_type": "code", "metadata": { "id": "EH494wHUOAed" }, "source": [ "trainer = Engine(process_function)\n", "evaluator = Engine(evaluate_function)\n", "training_history = {'bce': [], 'kld': [], 'mse': []}\n", "validation_history = {'bce': [], 'kld': [], 'mse': []}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "B_nxE8WdOAed" }, "source": [ "### Metrics - RunningAverage, MeanSquareError and Loss\n", "\n", "To start, we'll attach a metric of `RunningAverage` to track a running average of the scalar `loss`, `binary cross entropy` and `KL Divergence` output for each batch. " ] }, { "cell_type": "code", "metadata": { "id": "22b2EySTOAee" }, "source": [ "RunningAverage(output_transform=lambda x: x[0]).attach(trainer, 'loss')\n", "RunningAverage(output_transform=lambda x: x[1]).attach(trainer, 'bce')\n", "RunningAverage(output_transform=lambda x: x[2]).attach(trainer, 'kld')" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "fxmDDcc7OAee" }, "source": [ "Now there are two metrics that we want to use for evaluation - `mean squared error`, `binary cross entropy` and `KL Divergence`. If you noticed earlier, out `eval_function` returns `x_pred`, `x` and a few other values, `MeanSquaredError` only expects two values per batch. \n", "\n", "For each batch, the `engine.state.output` will be `x_pred`, `x` and `kwargs`, this is why we use `output_transform` to only extract values from `engine.state.output` based on the the metric need.\n", "\n", "As for `Loss`, we pass our defined `loss_function` and simply attach it to the `evaluator` as `engine.state.output` outputs all the parameters needed for `loss_function`." ] }, { "cell_type": "code", "metadata": { "id": "TxlHHgCjOAee" }, "source": [ "MeanSquaredError(output_transform=lambda x: [x[0], x[1]]).attach(evaluator, 'mse')\n", "Loss(bce_loss, output_transform=lambda x: [x[0], x[1]]).attach(evaluator, 'bce')\n", "Loss(kld_loss).attach(evaluator, 'kld')" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "AS5MFiXLOAee" }, "source": [ "### Attaching Custom Functions to Engine at specific Events\n", "\n", "Below you'll see ways to define your own custom functions and attaching them to various `Events` of the training process.\n", "\n", "The first method involves using a decorator, the syntax is simple - `@` `trainer.on(Events.EPOCH_COMPLETED)`, means that the decorated function will be attached to the `trainer` and called at the end of each epoch. \n", "\n", "The second method involves using the `add_event_handler` method of `trainer` - `trainer.add_event_handler(Events.EPOCH_COMPLETED, custom_function)`. This achieves the same result as the above.\n", "\n", "\n", "The function below print the loss during training at the end of each epoch. " ] }, { "cell_type": "code", "metadata": { "id": "uwPNFSwtOAee" }, "source": [ "@trainer.on(Events.EPOCH_COMPLETED)\n", "def print_trainer_logs(engine):\n", " avg_loss = engine.state.metrics['loss']\n", " avg_bce = engine.state.metrics['bce']\n", " avg_kld = engine.state.metrics['kld']\n", " print(\"Trainer Results - Epoch {} - Avg loss: {:.2f} Avg bce: {:.2f} Avg kld: {:.2f}\"\n", " .format(engine.state.epoch, avg_loss, avg_bce, avg_kld))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "s0tpWvp0OAef" }, "source": [ "The function below prints the logs of the `evaluator` and updates the history of metrics for training and validation datasets, we see that it takes parameters `DataLoader` and `mode`. Using this way we are repurposing a function and attaching it twice to the `trainer`, once to evaluate of the training dataset and other on the validation dataset." ] }, { "cell_type": "code", "metadata": { "id": "DuSfcPc3OAeg" }, "source": [ "def print_logs(engine, dataloader, mode, history_dict):\n", " evaluator.run(dataloader, max_epochs=1)\n", " metrics = evaluator.state.metrics\n", " avg_mse = metrics['mse']\n", " avg_bce = metrics['bce']\n", " avg_kld = metrics['kld']\n", " avg_loss = avg_bce + avg_kld\n", " print(\n", " mode + \" Results - Epoch {} - Avg mse: {:.2f} Avg loss: {:.2f} Avg bce: {:.2f} Avg kld: {:.2f}\"\n", " .format(engine.state.epoch, avg_mse, avg_loss, avg_bce, avg_kld))\n", " for key in evaluator.state.metrics.keys():\n", " history_dict[key].append(evaluator.state.metrics[key])\n", "\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, print_logs, train_loader, 'Training', training_history)\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED, print_logs, val_loader, 'Validation', validation_history)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "13po7FGPOAeg" }, "source": [ "The function below uses the set of images (`fixed_images`) and the VAE model to generate reconstructed images, the images are then formed into a grid, saved to your local machine and displayed in the notebook below. We attach this function to the start of the training process and at the end of each epoch, this way we'll be able to visualize how much better the model gets at reconstructing images. " ] }, { "cell_type": "code", "metadata": { "id": "2l8hhfITOAeg" }, "source": [ "def compare_images(engine, save_img=False):\n", " epoch = engine.state.epoch\n", " reconstructed_images = model(fixed_images.view(-1, 784))[0].view(-1, 1, 28, 28)\n", " comparison = torch.cat([fixed_images, reconstructed_images])\n", " if save_img:\n", " save_image(comparison.detach().cpu(), 'reconstructed_epoch_' + str(epoch) + '.png', nrow=8)\n", " comparison_image = make_grid(comparison.detach().cpu(), nrow=8)\n", " fig = plt.figure(figsize=(5, 5));\n", " output = plt.imshow(comparison_image.permute(1, 2, 0));\n", " plt.title('Epoch ' + str(epoch));\n", " plt.show();\n", "\n", "trainer.add_event_handler(Events.STARTED, compare_images, save_img=False)\n", "trainer.add_event_handler(Events.EPOCH_COMPLETED(every=5), compare_images, save_img=False)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "v1RsmwNROAeh" }, "source": [ "### Run Engine\n", "\n", "Next, we'll run the `trainer` for 20 epochs and monitor results." ] }, { "cell_type": "code", "metadata": { "id": "OgaJSdpVOAeh" }, "source": [ "e = trainer.run(train_loader, max_epochs=20)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "4dJ_jY4_OAei" }, "source": [ "### Plotting Results\n", "\n", "Below we see plot the metrics collected on the training and validation sets. We plot the history of `Binary Cross Entropy`, `Mean Squared Error` and `KL Divergence`." ] }, { "cell_type": "code", "metadata": { "id": "pu9ab4LUOAei" }, "source": [ "plt.plot(range(20), training_history['bce'], 'dodgerblue', label='training')\n", "plt.plot(range(20), validation_history['bce'], 'orange', label='validation')\n", "plt.xlim(0, 20);\n", "plt.xlabel('Epoch')\n", "plt.ylabel('BCE')\n", "plt.title('Binary Cross Entropy on Training/Validation Set')\n", "plt.legend();" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "XIuOnrwgOAei" }, "source": [ "plt.plot(range(20), training_history['kld'], 'dodgerblue', label='training')\n", "plt.plot(range(20), validation_history['kld'], 'orange', label='validation')\n", "plt.xlim(0, 20);\n", "plt.xlabel('Epoch')\n", "plt.ylabel('KLD')\n", "plt.title('KL Divergence on Training/Validation Set')\n", "plt.legend();" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1gN0gWiKOAei" }, "source": [ "plt.plot(range(20), training_history['mse'], 'dodgerblue', label='training')\n", "plt.plot(range(20), validation_history['mse'], 'orange', label='validation')\n", "plt.xlim(0, 20);\n", "plt.xlabel('Epoch')\n", "plt.ylabel('MSE')\n", "plt.title('Mean Squared Error on Training/Validation Set')\n", "plt.legend();" ], "execution_count": null, "outputs": [] } ] }ignite-0.5.1/examples/notebooks/assets/000077500000000000000000000000001465426447700201425ustar00rootroot00000000000000ignite-0.5.1/examples/notebooks/assets/ax_hparams.png000066400000000000000000041234001465426447700227770ustar00rootroot00000000000000‰PNG  IHDR <φ]αGχ 'iCCPICC ProfileH‰•—XSΙ€η–$$$΄@€„ήD)₯ΧΑFH %†„ bGX *"XΡUΧΘbΓ,‚½?QQΦΕ‚ •7I]ύή{ί;ωζή?gΜ9ηάΉσέ@5Š-e‘jd sΕΡΑ~ΜΔ€d&© π‡"ΝζHDΎQQαΚπύŸςξ΄…rΥVζληώ*κ\ž„9•+αdC>ξΜ‰s τB½ΙΜ\d"ŒhŠa€Meœ`W§*8\nν9%*›-N@E3“ύ¨”BΆrBΘ͐½8|6ςgΘc²³g@V΅„l™ϊŸτψLρΙf§°"Ή($’,φμ³[²³€Γs˜ΐFε‹C’e9Λκ–9#LΖTΘη…©‘5 _pεφ2~Β—†Δ ΩΰHόaΝ”Κe„AΦƒl,̊{₯ ‚XaνΡXA.+V1εŠgDωGgρ$1ΓΜΛη’ΩK3γ|‡|nβσXΓ>›ςω± Š8Ρφ'—¨Θ +ΰ γ†βΗΚDΉ~ΡCφΫEYQCφX3/+X¦7†ά&Ι‹Ϋ— ›"_ˆr£b±αšμΠ(E Έ5ώ 0ΆT0dA[oC/ό§θ l ι€l‡4Γ#δ=Bxωΰ/H< η'οε<¨2’U\mAšΌ7O>"<œ Β@ό/•ŽΜCΰ§Ω90Φ,Ψd}?ι˜ͺΓ:b 1€B "ZαΊΈëlΈ+ξ6Χ7{ΒBαα:‘“p{Ί @όCδL0tΒƒ†²Kύ>;άzuΒύpOθϊΖΈ.°ΕΗΓ™|qo8·Τ~«t$γo΅ςEΆ#£δQd²ε¨X«8x‘UκϋZ(βJ©–HϏyψW?.Ό‡ύh‰-Εbη°“Ψ¬kLμ8ֈ΅bGe<²6ΛΧΖπlΡςx2‘ΑOσ±‡ζ”UMbWkΧcχy¨δςfεΚ^’ΩbA:?—ι wk“%δŒΓt°³w@Άχ+Ά–7 ωžŽ0.~Σεœΐ­*ΣΏιΨp:ςϊ»o:“ΧpΩ―ΰh;G*ΞSθpΩ…(@Ύ):ΐξ]–0#ΰ <€‘ Δ‚$0 Φ™Χ©ΜsΑ"PJΐJ°T‚Ν`Ψφ‚ 4ƒ“ΰ,ΈΪΑup•nπτw`ABCθˆbˆ˜!6ˆβŠx!H8$!)H:"D€Θ\d1R‚”!•ΘV€ω9‚œD. 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prep_whiten->prep_conv prep_norm norm prep_conv->prep_norm prep_act act prep_norm->prep_act layer1_conv conv prep_act->layer1_conv layer1_pool pool layer1_conv->layer1_pool layer1_norm norm layer1_pool->layer1_norm layer1_act act layer1_norm->layer1_act layer1_residual_in in layer1_act->layer1_residual_in layer1_residual_res1_conv conv layer1_residual_in->layer1_residual_res1_conv layer1_residual_add add layer1_residual_in->layer1_residual_add layer1_residual_res1_norm norm layer1_residual_res1_conv->layer1_residual_res1_norm layer1_residual_res1_act act layer1_residual_res1_norm->layer1_residual_res1_act layer1_residual_res2_conv conv layer1_residual_res1_act->layer1_residual_res2_conv layer1_residual_res2_norm norm layer1_residual_res2_conv->layer1_residual_res2_norm layer1_residual_res2_act act layer1_residual_res2_norm->layer1_residual_res2_act layer1_residual_out out layer1_residual_res2_act->layer1_residual_out layer1_residual_out->layer1_residual_add layer2_conv conv layer1_residual_add->layer2_conv layer2_pool pool layer2_conv->layer2_pool layer2_norm norm layer2_pool->layer2_norm layer2_act act layer2_norm->layer2_act layer3_conv conv layer2_act->layer3_conv layer3_pool pool layer3_conv->layer3_pool layer3_norm norm layer3_pool->layer3_norm layer3_act act layer3_norm->layer3_act layer3_residual_in in layer3_act->layer3_residual_in layer3_residual_res1_conv conv layer3_residual_in->layer3_residual_res1_conv layer3_residual_add add layer3_residual_in->layer3_residual_add layer3_residual_res1_norm norm layer3_residual_res1_conv->layer3_residual_res1_norm layer3_residual_res1_act act layer3_residual_res1_norm->layer3_residual_res1_act layer3_residual_res2_conv conv layer3_residual_res1_act->layer3_residual_res2_conv layer3_residual_res2_norm norm layer3_residual_res2_conv->layer3_residual_res2_norm layer3_residual_res2_act act layer3_residual_res2_norm->layer3_residual_res2_act layer3_residual_out out layer3_residual_res2_act->layer3_residual_out 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Features: - Distributed training with native automatic mixed precision - Experiments tracking with [ClearML](https://github.com/allegroai/clearml) | Model | Training Top-1 Accuracy | Training Top-5 Accuracy | Test Top-1 Accuracy | Test Top-5 Accuracy | | --------- | ----------------------- | ----------------------- | ------------------- | ------------------- | | ResNet-50 | 78% | 92% | 77% | 94% | Experiment | Model | Training Top-1 Accuracy | Training Top-5 Accuracy | Test Top-1 Accuracy | Test Top-5 Accuracy | ClearML Link ---|---|---|---|---|---|--- configs/???.py | ## Setup ``` pip install -r requirements.txt ``` ### Docker For docker users, you can use the following images to run the example: ```bash docker pull pytorchignite/vision:latest ``` and install other requirements as suggested above ## Usage Please, export the `DATASET_PATH` environment variable for the ImageNet dataset. ```bash export DATASET_PATH=/path/to/imagenet # e.g. export DATASET_PATH=/data/ where "train", "val", "meta.bin" are located ``` ### Training #### Single GPU - Adjust batch size for your GPU type in the configuration file: `configs/baseline_resnet50.py` or `configs/baseline_resnet50.py` Run the following command: ```bash CUDA_VISIBLE_DEVICES=0 python -u main.py training configs/baseline_resnet50.py ``` #### Multiple GPUs - Adjust total batch size for your GPUs in the configuration file: `configs/baseline_resnet50.py` or `configs/baseline_resnet50.py` ```bash OMP_NUM_THREADS=1 torchrun --nproc_per_node=2 main.py training configs/baseline_resnet50.py ``` ## Acknowledgements Trainings were done using credits provided by [trainml.ai](trainml.ai) platform. ignite-0.5.1/examples/references/classification/imagenet/configs/000077500000000000000000000000001465426447700251725ustar00rootroot00000000000000ignite-0.5.1/examples/references/classification/imagenet/configs/baseline_resnet50.py000066400000000000000000000052231465426447700310550ustar00rootroot00000000000000# Basic training configuration import os from functools import partial import albumentations as A import torch.nn as nn import torch.optim as optim import torch.optim.lr_scheduler as lrs from albumentations.pytorch import ToTensorV2 as ToTensor from dataflow import denormalize, get_train_val_loaders from torchvision.models.resnet import resnet50 import ignite.distributed as idist # ############################## # Global configs # ############################## seed = 19 device = "cuda" debug = False # config to measure time passed to prepare batches and report measured time before the training benchmark_dataflow = True benchmark_dataflow_num_iters = 100 train_crop_size = 224 val_crop_size = 320 batch_size = 64 * idist.get_world_size() # total batch size num_workers = 8 val_interval = 2 # ############################## # Setup Dataflow # ############################## assert "DATASET_PATH" in os.environ data_path = os.environ["DATASET_PATH"] mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] train_transforms = A.Compose( [ A.RandomResizedCrop(train_crop_size, train_crop_size, scale=(0.08, 1.0)), A.HorizontalFlip(), A.CoarseDropout(max_height=32, max_width=32), A.HueSaturationValue(), A.Normalize(mean=mean, std=std), ToTensor(), ] ) val_transforms = A.Compose( [ # https://github.com/facebookresearch/FixRes/blob/b27575208a7c48a3a6e0fa9efb57baa4021d1305/imnet_resnet50_scratch/transforms.py#L76 A.Resize(int((256 / 224) * val_crop_size), int((256 / 224) * val_crop_size)), A.CenterCrop(val_crop_size, val_crop_size), A.Normalize(mean=mean, std=std), ToTensor(), ] ) train_loader, val_loader, train_eval_loader = get_train_val_loaders( data_path, train_transforms=train_transforms, val_transforms=val_transforms, batch_size=batch_size, num_workers=num_workers, val_batch_size=batch_size, limit_train_num_samples=batch_size * 6 if debug else None, limit_val_num_samples=batch_size * 6 if debug else None, ) # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=mean, std=std) # ############################## # Setup Model # ############################## model = resnet50(weights=None) # ############################## # Setup Solver # ############################## num_epochs = 105 criterion = nn.CrossEntropyLoss() le = len(train_loader) base_lr = 0.1 * (batch_size / 256.0) optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=1e-4) lr_scheduler = lrs.MultiStepLR(optimizer, milestones=[30 * le, 60 * le, 90 * le, 100 * le], gamma=0.1) ignite-0.5.1/examples/references/classification/imagenet/configs/check_baseline_resnet50.py000066400000000000000000000052531465426447700322150ustar00rootroot00000000000000# Basic training configuration import os from functools import partial import albumentations as A import torch.nn as nn import torch.optim as optim import torch.optim.lr_scheduler as lrs from albumentations.pytorch import ToTensorV2 as ToTensor from dataflow import denormalize, get_train_val_loaders from torchvision.models.resnet import resnet50 import ignite.distributed as idist # ############################## # Global configs # ############################## seed = 19 device = "cuda" debug = True # config to measure time passed to prepare batches and report measured time before the training benchmark_dataflow = True benchmark_dataflow_num_iters = 100 train_crop_size = 224 val_crop_size = 320 batch_size = 64 * idist.get_world_size() # total batch size num_workers = 8 val_interval = 2 start_by_validation = True # ############################## # Setup Dataflow # ############################## assert "DATASET_PATH" in os.environ data_path = os.environ["DATASET_PATH"] mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] train_transforms = A.Compose( [ A.RandomResizedCrop(train_crop_size, train_crop_size, scale=(0.08, 1.0)), A.HorizontalFlip(), A.CoarseDropout(max_height=32, max_width=32), A.HueSaturationValue(), A.Normalize(mean=mean, std=std), ToTensor(), ] ) val_transforms = A.Compose( [ # https://github.com/facebookresearch/FixRes/blob/b27575208a7c48a3a6e0fa9efb57baa4021d1305/imnet_resnet50_scratch/transforms.py#L76 A.Resize(int((256 / 224) * val_crop_size), int((256 / 224) * val_crop_size)), A.CenterCrop(val_crop_size, val_crop_size), A.Normalize(mean=mean, std=std), ToTensor(), ] ) train_loader, val_loader, train_eval_loader = get_train_val_loaders( data_path, train_transforms=train_transforms, val_transforms=val_transforms, batch_size=batch_size, num_workers=num_workers, val_batch_size=batch_size, limit_train_num_samples=batch_size * 6 if debug else None, limit_val_num_samples=batch_size * 6 if debug else None, ) # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=mean, std=std) # ############################## # Setup Model # ############################## model = resnet50(weights=None) # ############################## # Setup Solver # ############################## num_epochs = 2 criterion = nn.CrossEntropyLoss() le = len(train_loader) base_lr = 0.1 * (batch_size / 256.0) optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=1e-4) lr_scheduler = lrs.MultiStepLR(optimizer, milestones=[30 * le, 60 * le, 90 * le, 100 * le], gamma=0.1) ignite-0.5.1/examples/references/classification/imagenet/dataflow.py000066400000000000000000000062531465426447700257230ustar00rootroot00000000000000from pathlib import Path from typing import Callable, Optional, Tuple import cv2 import torch from torch.utils.data import DataLoader from torch.utils.data.dataset import Subset from torchvision.datasets import ImageFolder import ignite.distributed as idist from ignite.utils import convert_tensor def opencv_loader(path): img = cv2.imread(path) assert img is not None, f"Image at '{path}' has a problem" return cv2.cvtColor(img, cv2.COLOR_BGR2RGB) def get_dataloader(dataset, sampler=None, shuffle=False, limit_num_samples=None, **kwargs): if limit_num_samples is not None: g = torch.Generator().manual_seed(limit_num_samples) indices = torch.randperm(len(dataset), generator=g)[:limit_num_samples] dataset = Subset(dataset, indices) return idist.auto_dataloader(dataset, sampler=sampler, shuffle=(sampler is None) and shuffle, **kwargs) def get_train_val_loaders( root_path: str, train_transforms: Callable, val_transforms: Callable, batch_size: int = 16, num_workers: int = 8, val_batch_size: Optional[int] = None, limit_train_num_samples: Optional[int] = None, limit_val_num_samples: Optional[int] = None, ) -> Tuple[DataLoader, DataLoader, DataLoader]: train_ds = ImageFolder( Path(root_path) / "train", transform=lambda sample: train_transforms(image=sample)["image"], loader=opencv_loader, ) val_ds = ImageFolder( Path(root_path) / "val", transform=lambda sample: val_transforms(image=sample)["image"], loader=opencv_loader ) if len(val_ds) < len(train_ds): g = torch.Generator().manual_seed(len(train_ds)) train_eval_indices = torch.randperm(len(train_ds), generator=g)[: len(val_ds)] train_eval_ds = Subset(train_ds, train_eval_indices) else: train_eval_ds = train_ds val_batch_size = batch_size * 4 if val_batch_size is None else val_batch_size train_loader = get_dataloader( train_ds, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True, limit_num_samples=limit_train_num_samples, ) val_loader = get_dataloader( val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, limit_num_samples=limit_val_num_samples, ) train_eval_loader = get_dataloader( train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, limit_num_samples=limit_val_num_samples, ) return train_loader, val_loader, train_eval_loader def denormalize(t, mean, std, max_pixel_value=255): assert isinstance(t, torch.Tensor), f"{type(t)}" assert t.ndim == 3 d = t.device mean = torch.tensor(mean, device=d).unsqueeze(-1).unsqueeze(-1) std = torch.tensor(std, device=d).unsqueeze(-1).unsqueeze(-1) tensor = std * t + mean tensor *= max_pixel_value return tensor def prepare_batch(batch, device, non_blocking): x, y = batch[0], batch[1] x = convert_tensor(x, device, non_blocking=non_blocking) y = convert_tensor(y, device, non_blocking=non_blocking) return x, y ignite-0.5.1/examples/references/classification/imagenet/main.py000066400000000000000000000361651465426447700250530ustar00rootroot00000000000000import os from functools import partial from pathlib import Path import fire import torch try: from torch.cuda.amp import autocast, GradScaler except ImportError: raise RuntimeError("Please, use recent PyTorch version, e.g. >=1.6.0") import dataflow as data import utils import vis from py_config_runner import ConfigObject, get_params, InferenceConfigSchema, TrainvalConfigSchema import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, Timer from ignite.metrics import Accuracy, Frequency, TopKCategoricalAccuracy from ignite.utils import manual_seed, setup_logger def training(local_rank, config, logger, with_clearml): rank = idist.get_rank() manual_seed(config.seed + local_rank) train_loader = config.train_loader val_loader = config.val_loader train_eval_loader = config.train_eval_loader model, optimizer, criterion = utils.initialize(config) # Setup trainer for this specific task trainer = create_trainer(model, optimizer, criterion, train_loader.sampler, config, logger, with_clearml) # Setup evaluators accuracy = Accuracy() val_metrics = { "Accuracy": accuracy, "Top-5 Accuracy": TopKCategoricalAccuracy(k=5), "Error": (1.0 - accuracy) * 100, } if ("val_metrics" in config) and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="val") train_evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="train") val_interval = config.get("val_interval", 1) # Run validation on every val_interval epoch, in the end of the training # and in the begining if config.start_by_validation is True event = Events.EPOCH_COMPLETED(every=val_interval) if config.num_epochs % val_interval != 0: event |= Events.COMPLETED if config.get("start_by_validation", False): event |= Events.STARTED @trainer.on(event) def run_validation(): epoch = trainer.state.epoch state = train_evaluator.run(train_eval_loader) utils.log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(val_loader) utils.log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) score_metric_name = "Accuracy" if "es_patience" in config: common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name) # Store 2 best models by validation accuracy: common.gen_save_best_models_by_val_score( save_handler=utils.get_save_handler(config.output_path.as_posix(), with_clearml), evaluator=evaluator, models=model, metric_name=score_metric_name, n_saved=2, trainer=trainer, tag="val", ) # Setup Tensorboard logger if rank == 0: tb_logger = common.setup_tb_logging( config.output_path.as_posix(), trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator}, ) # Log validation predictions as images # We define a custom event filter to log less frequently the images (to reduce storage size) # - we plot images with masks of the middle validation batch # - once every 3 validations and # - at the end of the training def custom_event_filter(_, val_iteration): c1 = val_iteration == 1 c2 = trainer.state.epoch % (config.get("val_interval", 1) * 3) == 0 c2 |= trainer.state.epoch == config.num_epochs return c1 and c2 # Image denormalization function to plot predictions with images mean = config.get("mean", (0.485, 0.456, 0.406)) std = config.get("std", (0.229, 0.224, 0.225)) img_denormalize = partial(data.denormalize, mean=mean, std=std) tb_logger.attach( evaluator, log_handler=vis.predictions_gt_images_handler( img_denormalize_fn=img_denormalize, n_images=12, another_engine=trainer, prefix_tag="validation" ), event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter), ) tb_logger.attach( train_evaluator, log_handler=vis.predictions_gt_images_handler( img_denormalize_fn=img_denormalize, n_images=12, another_engine=trainer, prefix_tag="training" ), event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter), ) trainer.run(train_loader, max_epochs=config.num_epochs) if idist.get_rank() == 0: tb_logger.close() def create_trainer(model, optimizer, criterion, train_sampler, config, logger, with_clearml): device = config.device prepare_batch = data.prepare_batch # Setup trainer accumulation_steps = config.get("accumulation_steps", 1) model_output_transform = config.get("model_output_transform", lambda x: x) with_amp = config.get("with_amp", True) scaler = GradScaler(enabled=with_amp) def training_step(engine, batch): model.train() x, y = prepare_batch(batch, device=device, non_blocking=True) with autocast(enabled=with_amp): y_pred = model(x) y_pred = model_output_transform(y_pred) loss = criterion(y_pred, y) / accumulation_steps output = {"supervised batch loss": loss.item(), "num_samples": len(x)} scaler.scale(loss).backward() if engine.state.iteration % accumulation_steps == 0: scaler.step(optimizer) scaler.update() optimizer.zero_grad() return output trainer = Engine(training_step) trainer.logger = logger throughput_metric = Frequency(output_transform=lambda x: x["num_samples"]) throughput_metric.attach(trainer, name="Throughput") timer = Timer(average=True) timer.attach( trainer, resume=Events.ITERATION_STARTED, pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED, ) @trainer.on(Events.ITERATION_COMPLETED(every=20)) def log_progress(): metrics = dict(trainer.state.metrics) epoch_length = trainer.state.epoch_length metrics["ETA (seconds)"] = int((epoch_length - (trainer.state.iteration % epoch_length)) * timer.value()) metrics_str = ", ".join([f"{k}: {v}" for k, v in metrics.items()]) metrics_format = ( f"[{trainer.state.epoch}/{trainer.state.max_epochs}] " + f"Iter={trainer.state.iteration % epoch_length}/{epoch_length}: " + f"{metrics_str}" ) trainer.logger.info(metrics_format) output_names = [ "supervised batch loss", ] lr_scheduler = config.lr_scheduler to_save = { "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler, "trainer": trainer, "amp": scaler, } save_every_iters = config.get("save_every_iters", 1000) common.setup_common_training_handlers( trainer, train_sampler, to_save=to_save, save_every_iters=save_every_iters, save_handler=utils.get_save_handler(config.output_path.as_posix(), with_clearml), lr_scheduler=lr_scheduler, output_names=output_names, # with_pbars=not with_clearml, with_pbars=False, log_every_iters=1, ) resume_from = config.get("resume_from", None) if resume_from is not None: checkpoint_fp = Path(resume_from) assert checkpoint_fp.exists(), f"Checkpoint '{checkpoint_fp.as_posix()}' is not found" logger.info(f"Resume from a checkpoint: {checkpoint_fp.as_posix()}") checkpoint = torch.load(checkpoint_fp.as_posix(), map_location="cpu") Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint) return trainer def create_evaluator(model, metrics, config, with_clearml, tag="val"): model_output_transform = config.get("model_output_transform", lambda x: x) with_amp = config.get("with_amp", True) prepare_batch = data.prepare_batch @torch.no_grad() def evaluate_step(engine, batch): model.eval() with autocast(enabled=with_amp): x, y = prepare_batch(batch, device=config.device, non_blocking=True) y_pred = model(x) y_pred = model_output_transform(y_pred) return y_pred, y evaluator = Engine(evaluate_step) for name, metric in metrics.items(): metric.attach(evaluator, name) if idist.get_rank() == 0 and (not with_clearml): common.ProgressBar(desc=f"Evaluation ({tag})", persist=False).attach(evaluator) return evaluator def setup_experiment_tracking(config, with_clearml, task_type="training"): from datetime import datetime assert task_type in ("training", "testing"), task_type output_path = "" if idist.get_rank() == 0: if with_clearml: from clearml import Task schema = TrainvalConfigSchema if task_type == "training" else InferenceConfigSchema task = Task.init("ImageNet Training", config.config_filepath.stem, task_type=task_type) task.connect_configuration(config.config_filepath.as_posix()) task.upload_artifact(config.script_filepath.name, config.script_filepath.as_posix()) task.upload_artifact(config.config_filepath.name, config.config_filepath.as_posix()) task.connect(get_params(config, schema)) output_path = Path(os.environ.get("CLEARML_OUTPUT_PATH", "/tmp")) output_path = output_path / "clearml" / datetime.now().strftime("%Y%m%d-%H%M%S") else: import shutil output_path = Path(os.environ.get("OUTPUT_PATH", "/tmp/output-imagenet")) output_path = output_path / task_type / config.config_filepath.stem output_path = output_path / datetime.now().strftime("%Y%m%d-%H%M%S") output_path.mkdir(parents=True, exist_ok=True) shutil.copyfile(config.script_filepath.as_posix(), output_path / config.script_filepath.name) shutil.copyfile(config.config_filepath.as_posix(), output_path / config.config_filepath.name) output_path = output_path.as_posix() return Path(idist.broadcast(output_path, src=0)) def run_training(config_filepath, backend="nccl", with_clearml=True): """Main entry to run training experiment Args: config_filepath (str): training configuration .py file backend (str): distributed backend: nccl, gloo or None to run without distributed config with_clearml (bool): if True, uses ClearML as experiment tracking system """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled torch.backends.cudnn.benchmark = True config_filepath = Path(config_filepath) assert config_filepath.exists(), f"File '{config_filepath.as_posix()}' is not found" with idist.Parallel(backend=backend) as parallel: logger = setup_logger(name="ImageNet Training", distributed_rank=idist.get_rank()) config = ConfigObject(config_filepath) TrainvalConfigSchema.validate(config) config.script_filepath = Path(__file__) output_path = setup_experiment_tracking(config, with_clearml=with_clearml) config.output_path = output_path utils.log_basic_info(logger, get_params(config, TrainvalConfigSchema)) try: parallel.run(training, config, logger=logger, with_clearml=with_clearml) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e def get_model_weights(config, logger, with_clearml): path = "" if with_clearml: from clearml import Model if idist.get_rank() > 0: idist.barrier() else: model_id = config.weights_path logger.info(f"Loading trained model: {model_id}") model = Model(model_id) assert model is not None, f"{model_id}" path = model.get_local_copy() idist.barrier() path = idist.broadcast(path, src=0) else: path = config.weights_path logger.info(f"Loading {path}") assert Path(path).exists(), f"{path} is not found" return torch.load(path) def evaluation(local_rank, config, logger, with_clearml): rank = idist.get_rank() device = idist.device() manual_seed(config.seed + local_rank) data_loader = config.data_loader model = config.model.to(device) # Load weights: state_dict = get_model_weights(config, logger, with_clearml) model.load_state_dict(state_dict) # Adapt model to dist config model = idist.auto_model(model) # Setup evaluators val_metrics = { "Accuracy": Accuracy(), "Top-5 Accuracy": TopKCategoricalAccuracy(k=5), } if ("val_metrics" in config) and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="val") # Setup Tensorboard logger if rank == 0: tb_logger = common.TensorboardLogger(log_dir=config.output_path.as_posix()) tb_logger.attach_output_handler(evaluator, event_name=Events.COMPLETED, tag="validation", metric_names="all") state = evaluator.run(data_loader) utils.log_metrics(logger, 0, state.times["COMPLETED"], "Validation", state.metrics) if idist.get_rank() == 0: tb_logger.close() def run_evaluation(config_filepath, backend="nccl", with_clearml=True): """Main entry to run model's evaluation: - compute validation metrics Args: config_filepath (str): evaluation configuration .py file backend (str): distributed backend: nccl, gloo, horovod or None to run without distributed config with_clearml (bool): if True, uses ClearML as experiment tracking system """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled torch.backends.cudnn.benchmark = True config_filepath = Path(config_filepath) assert config_filepath.exists(), f"File '{config_filepath.as_posix()}' is not found" with idist.Parallel(backend=backend) as parallel: logger = setup_logger(name="ImageNet Evaluation", distributed_rank=idist.get_rank()) config = ConfigObject(config_filepath) InferenceConfigSchema.validate(config) config.script_filepath = Path(__file__) output_path = setup_experiment_tracking(config, with_clearml=with_clearml, task_type="testing") config.output_path = output_path utils.log_basic_info(logger, get_params(config, InferenceConfigSchema)) try: parallel.run(evaluation, config, logger=logger, with_clearml=with_clearml) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e if __name__ == "__main__": fire.Fire({"training": run_training, "eval": run_evaluation}) ignite-0.5.1/examples/references/classification/imagenet/requirements.txt000066400000000000000000000002331465426447700270240ustar00rootroot00000000000000albumentations numpy opencv-python-headless fire pytorch-ignite tensorboard torch torchvision tqdm clearml image-dataset-viz py_config_runner>=0.2.0,<1.0.0ignite-0.5.1/examples/references/classification/imagenet/utils.py000066400000000000000000000034441465426447700252610ustar00rootroot00000000000000import torch import ignite import ignite.distributed as idist from ignite.handlers import DiskSaver def initialize(config): device = idist.device() model = config.model.to(device) optimizer = config.optimizer # Adapt model to dist config model = idist.auto_model(model) optimizer = idist.auto_optim(optimizer) criterion = config.criterion.to(device) return model, optimizer, criterion def log_basic_info(logger, config): logger.info(f"- PyTorch version: {torch.__version__}") logger.info(f"- Ignite version: {ignite.__version__}") if torch.cuda.is_available(): # explicitly import cudnn as # torch.backends.cudnn can not be pickled with hvd spawning procs from torch.backends import cudnn logger.info(f"- GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}") logger.info(f"- CUDA version: {torch.version.cuda}") logger.info(f"- CUDNN version: {cudnn.version()}") logger.info("\n") logger.info("Configuration:") for key, value in config.items(): logger.info(f"\t{key}: {value}") logger.info("\n") if idist.get_world_size() > 1: logger.info("\nDistributed setting:") logger.info(f"\tbackend: {idist.backend()}") logger.info(f"\tworld size: {idist.get_world_size()}") logger.info("\n") def log_metrics(logger, epoch, elapsed, tag, metrics): metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) logger.info(f"\nEpoch {epoch} - Evaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}") def get_save_handler(output_path, with_clearml): if with_clearml: from ignite.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=output_path) return DiskSaver(output_path) ignite-0.5.1/examples/references/classification/imagenet/vis.py000066400000000000000000000061611465426447700247210ustar00rootroot00000000000000from typing import Callable, Optional import numpy as np import torch try: from image_dataset_viz import render_datapoint except ImportError: raise ModuleNotFoundError( "Please install image-dataset-viz via pip install --upgrade git+https://github.com/vfdev-5/ImageDatasetViz.git" ) def tensor_to_numpy(t: torch.Tensor) -> np.ndarray: img = t.cpu().numpy().transpose((1, 2, 0)) return img.astype(np.uint8) def make_grid( batch_img: torch.Tensor, batch_preds: torch.Tensor, img_denormalize_fn: Callable, batch_gt: Optional[torch.Tensor] = None, ): """Create a grid from batch image and mask as i+l1+gt1 | i+l2+gt2 | i+l3+gt3 | i+l4+gt4 | ... where i+l+gt = image + predicted label + ground truth Args: batch_img (torch.Tensor) batch of images of any type batch_preds (torch.Tensor) batch of masks img_denormalize_fn (Callable): function to denormalize batch of images batch_gt (torch.Tensor, optional): batch of ground truth masks. """ assert isinstance(batch_img, torch.Tensor) and isinstance(batch_preds, torch.Tensor) assert len(batch_img) == len(batch_preds), f"{len(batch_img)} vs {len(batch_preds)}" assert batch_preds.ndim == 1, f"{batch_preds.ndim}" if batch_gt is not None: assert isinstance(batch_gt, torch.Tensor) assert len(batch_preds) == len(batch_gt) assert batch_gt.ndim == 1, f"{batch_gt.ndim}" b = batch_img.shape[0] h, w = batch_img.shape[2:] le = 1 out_image = np.zeros((h * le, w * b, 3), dtype="uint8") for i in range(b): img = batch_img[i] y_preds = batch_preds[i] img = img_denormalize_fn(img) img = tensor_to_numpy(img) pred_label = y_preds.cpu().item() target = f"p={pred_label}" if batch_gt is not None: gt_label = batch_gt[i] gt_label = gt_label.cpu().item() target += f" | gt={gt_label}" out_image[0:h, i * w : (i + 1) * w, :] = render_datapoint(img, target, text_size=12) return out_image def predictions_gt_images_handler(img_denormalize_fn, n_images=None, another_engine=None, prefix_tag=None): def wrapper(engine, logger, event_name): batch = engine.state.batch output = engine.state.output x, y = batch y_pred = output[0] if y.shape == y_pred.shape and y.ndim == 4: # Case of y of shape (B, C, H, W) y = torch.argmax(y, dim=1) y_pred = torch.argmax(y_pred, dim=1).byte() if n_images is not None: x = x[:n_images, ...] y = y[:n_images, ...] y_pred = y_pred[:n_images, ...] grid_pred_gt = make_grid(x, y_pred, img_denormalize_fn, batch_gt=y) state = engine.state if another_engine is None else another_engine.state global_step = state.get_event_attrib_value(event_name) tag = "predictions_with_gt" if prefix_tag is not None: tag = f"{prefix_tag}: {tag}" logger.writer.add_image(tag=tag, img_tensor=grid_pred_gt, global_step=global_step, dataformats="HWC") return wrapper ignite-0.5.1/examples/references/segmentation/000077500000000000000000000000001465426447700214535ustar00rootroot00000000000000ignite-0.5.1/examples/references/segmentation/pascal_voc2012/000077500000000000000000000000001465426447700240725ustar00rootroot00000000000000ignite-0.5.1/examples/references/segmentation/pascal_voc2012/README.md000066400000000000000000000100411465426447700253450ustar00rootroot00000000000000# Reproducible PASCAL VOC2012 training with PyTorch-Ignite In this example, we provide script and tools to perform reproducible experiments on training neural networks on PASCAL VOC2012 dataset. Features: - Distributed training with native automatic mixed precision - Experiments tracking with [ClearML](https://github.com/allegroai/clearml) Experiment | Model | Dataset | Val Avg IoU | ClearML Link ---|---|---|---|--- configs/baseline_dplv3_resnet101.py | DeepLabV3 Resnet101 | VOC Only | 0.659161 | [link](https://app.clear.ml/projects/0e9a3a92d3134283b7d5572d516d60c5/experiments/a7254f084a9e47ca9380dfd739f89520/output/execution) configs/baseline_dplv3_resnet101_sbd.py | DeepLabV3 Resnet101 | VOC+SBD | 0.6853087 | [link](https://app.clear.ml/projects/0e9a3a92d3134283b7d5572d516d60c5/experiments/dc4cee3377a74d19bc2d0e0e4d638c1f/output/execution) ## Setup ``` pip install -r requirements.txt ``` ### Docker For docker users, you can use the following images to run the example: ```bash docker pull pytorchignite/vision:latest ``` or ```bash docker pull pytorchignite/hvd-vision:latest ``` and install other requirements as suggested above ### Using Horovod as distributed framework We do not add `horovod` as a requirement into `requirements.txt`. Please, install it manually following the official guides or use `pytorchignite/hvd-vision:latest` docker image. ### (Optional) Download Pascal VOC2012 and SDB datasets Download and extract the datasets: ```bash python main.py download /path/to/datasets ``` This script will download and extract the following datasets into `/path/to/datasets` - The [Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) dataset - Optionally, the [SBD](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz) evaluation dataset ## Usage Please, export the `DATASET_PATH` environment variable for the Pascal VOC2012 dataset. ```bash export DATASET_PATH=/path/to/pascal_voc2012 # e.g. export DATASET_PATH=/data/ where VOCdevkit is located ``` Optionally, if using SBD dataset, export the `SBD_DATASET_PATH` environment variable: ```bash export SBD_DATASET_PATH=/path/to/SBD/ # e.g. export SBD_DATASET_PATH=/data/SBD/ where "cls img inst train.txt train_noval.txt val.txt" are located ``` ### Training #### Single GPU - Adjust batch size for your GPU type in the configuration file: `configs/baseline_dplv3_resnet101_sbd.py` or `configs/baseline_dplv3_resnet101.py` Run the following command: ```bash CUDA_VISIBLE_DEVICES=0 python -u main.py training configs/baseline_dplv3_resnet101_sbd.py # or without SBD # CUDA_VISIBLE_DEVICES=0 python -u main.py training configs/baseline_dplv3_resnet101.py ``` #### Multiple GPUs - Adjust total batch size for your GPUs in the configuration file: `configs/baseline_dplv3_resnet101_sbd.py` or `configs/baseline_dplv3_resnet101.py` ```bash torchrun --nproc_per_node=2 main.py training configs/baseline_dplv3_resnet101_sbd.py # or without SBD # torchrun --nproc_per_node=2 main.py training configs/baseline_dplv3_resnet101.py ``` #### Using Horovod as distributed framework - Adjust total batch size for your GPUs in the configuration file: `configs/baseline_dplv3_resnet101_sbd.py` or `configs/baseline_dplv3_resnet101.py` ```bash horovodrun -np=2 python -u main.py training configs/baseline_dplv3_resnet101_sbd.py --backend="horovod" # or without SBD # horovodrun -np=2 python -u main.py training configs/baseline_dplv3_resnet101.py --backend="horovod" ``` ### Evaluation #### Single GPU ```bash CUDA_VISIBLE_DEVICES=0 python -u main.py eval configs/eval_baseline_dplv3_resnet101_sbd.py ``` #### Multiple GPUs ```bash torchrun --nproc_per_node=2 main.py eval configs/eval_baseline_dplv3_resnet101_sbd.py ``` #### Using Horovod as distributed framework ```bash horovodrun -np=2 python -u main.py eval configs/eval_baseline_dplv3_resnet101_sbd.py --backend="horovod" ``` ## Acknowledgements Trainings were done using credits provided by AWS for open-source development via NumFOCUS and using [trainml.ai](trainml.ai) platform. ignite-0.5.1/examples/references/segmentation/pascal_voc2012/configs/000077500000000000000000000000001465426447700255225ustar00rootroot00000000000000ignite-0.5.1/examples/references/segmentation/pascal_voc2012/configs/baseline_dplv3_resnet101.py000066400000000000000000000057211465426447700325750ustar00rootroot00000000000000# Basic training configuration import os from functools import partial import albumentations as A import cv2 import torch.nn as nn import torch.optim as optim import torch.optim.lr_scheduler as lrs from albumentations.pytorch import ToTensorV2 as ToTensor from dataflow import get_train_val_loaders, ignore_mask_boundaries from torchvision.models.segmentation import deeplabv3_resnet101 # ############################## # Global configs # ############################## seed = 21 device = "cuda" debug = False # Use AMP with torch native with_amp = True num_classes = 21 batch_size = 18 # total batch size val_batch_size = batch_size * 2 num_workers = 12 # total num workers per node val_interval = 3 # grads accumulation: accumulation_steps = 4 val_img_size = 513 train_img_size = 480 # ############################## # Setup Dataflow # ############################## assert "DATASET_PATH" in os.environ data_path = os.environ["DATASET_PATH"] mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) train_transforms = A.Compose( [ A.RandomScale(scale_limit=(0.0, 1.5), interpolation=cv2.INTER_LINEAR, p=1.0), A.PadIfNeeded(val_img_size, val_img_size, border_mode=cv2.BORDER_CONSTANT), A.RandomCrop(train_img_size, train_img_size), A.HorizontalFlip(), A.Blur(blur_limit=3), A.Normalize(mean=mean, std=std), ignore_mask_boundaries, ToTensor(), ] ) val_transforms = A.Compose( [ A.PadIfNeeded(val_img_size, val_img_size, border_mode=cv2.BORDER_CONSTANT), A.Normalize(mean=mean, std=std), ignore_mask_boundaries, ToTensor(), ] ) train_loader, val_loader, train_eval_loader = get_train_val_loaders( root_path=data_path, train_transforms=train_transforms, val_transforms=val_transforms, batch_size=batch_size, num_workers=num_workers, val_batch_size=val_batch_size, limit_train_num_samples=100 if debug else None, limit_val_num_samples=100 if debug else None, ) # ############################## # Setup model # ############################## num_classes = 21 model = deeplabv3_resnet101(num_classes=num_classes) def model_output_transform(output): return output["out"] # ############################## # Setup solver # ############################## save_every_iters = len(train_loader) num_epochs = 100 criterion = nn.CrossEntropyLoss() lr = 0.007 weight_decay = 5e-4 momentum = 0.9 nesterov = False optimizer = optim.SGD( [{"params": model.backbone.parameters()}, {"params": model.classifier.parameters()}], lr=1.0, momentum=momentum, weight_decay=weight_decay, nesterov=nesterov, ) le = len(train_loader) def lambda_lr_scheduler(iteration, lr0, n, a): return lr0 * pow((1.0 - 1.0 * iteration / n), a) lr_scheduler = lrs.LambdaLR( optimizer, lr_lambda=[ partial(lambda_lr_scheduler, lr0=lr, n=num_epochs * le, a=0.9), partial(lambda_lr_scheduler, lr0=lr * 10.0, n=num_epochs * le, a=0.9), ], ) ignite-0.5.1/examples/references/segmentation/pascal_voc2012/configs/baseline_dplv3_resnet101_sbd.py000066400000000000000000000061051465426447700334220ustar00rootroot00000000000000# Basic training configuration import os from functools import partial import albumentations as A import cv2 import torch.nn as nn import torch.optim as optim import torch.optim.lr_scheduler as lrs from albumentations.pytorch import ToTensorV2 as ToTensor from dataflow import get_train_val_loaders, ignore_mask_boundaries from torchvision.models.segmentation import deeplabv3_resnet101 # ############################## # Global configs # ############################## seed = 21 device = "cuda" debug = False # Use AMP with torch native with_amp = True num_classes = 21 batch_size = 18 # total batch size val_batch_size = batch_size * 2 num_workers = 12 # total num workers per node val_interval = 3 # grads accumulation: accumulation_steps = 4 val_img_size = 513 train_img_size = 480 # ############################## # Setup Dataflow # ############################## assert "DATASET_PATH" in os.environ data_path = os.environ["DATASET_PATH"] assert "SBD_DATASET_PATH" in os.environ sbd_data_path = os.environ["SBD_DATASET_PATH"] mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) train_transforms = A.Compose( [ A.RandomScale(scale_limit=(0.0, 1.5), interpolation=cv2.INTER_LINEAR, p=1.0), A.PadIfNeeded(val_img_size, val_img_size, border_mode=cv2.BORDER_CONSTANT), A.RandomCrop(train_img_size, train_img_size), A.HorizontalFlip(), A.Blur(blur_limit=3), A.Normalize(mean=mean, std=std), ignore_mask_boundaries, ToTensor(), ] ) val_transforms = A.Compose( [ A.PadIfNeeded(val_img_size, val_img_size, border_mode=cv2.BORDER_CONSTANT), A.Normalize(mean=mean, std=std), ignore_mask_boundaries, ToTensor(), ] ) train_loader, val_loader, train_eval_loader = get_train_val_loaders( root_path=data_path, train_transforms=train_transforms, val_transforms=val_transforms, batch_size=batch_size, num_workers=num_workers, val_batch_size=val_batch_size, sbd_path=sbd_data_path, limit_train_num_samples=100 if debug else None, limit_val_num_samples=100 if debug else None, ) # ############################## # Setup model # ############################## num_classes = 21 model = deeplabv3_resnet101(num_classes=num_classes) def model_output_transform(output): return output["out"] # ############################## # Setup solver # ############################## save_every_iters = len(train_loader) num_epochs = 100 criterion = nn.CrossEntropyLoss() lr = 0.007 weight_decay = 5e-4 momentum = 0.9 nesterov = False optimizer = optim.SGD( [{"params": model.backbone.parameters()}, {"params": model.classifier.parameters()}], lr=1.0, momentum=momentum, weight_decay=weight_decay, nesterov=nesterov, ) le = len(train_loader) def lambda_lr_scheduler(iteration, lr0, n, a): return lr0 * pow((1.0 - 1.0 * iteration / n), a) lr_scheduler = lrs.LambdaLR( optimizer, lr_lambda=[ partial(lambda_lr_scheduler, lr0=lr, n=num_epochs * le, a=0.9), partial(lambda_lr_scheduler, lr0=lr * 10.0, n=num_epochs * le, a=0.9), ], ) eval_baseline_dplv3_resnet101_sbd.py000066400000000000000000000032671465426447700343600ustar00rootroot00000000000000ignite-0.5.1/examples/references/segmentation/pascal_voc2012/configs# Basic training configuration import os import albumentations as A import cv2 from albumentations.pytorch import ToTensorV2 as ToTensor from dataflow import get_inference_dataloader, ignore_mask_boundaries from torchvision.models.segmentation import deeplabv3_resnet101 # ############################## # Global configs # ############################## seed = 21 device = "cuda" debug = False # Use AMP with torch native with_amp = True num_classes = 21 batch_size = 9 # total batch size num_workers = 8 # total num workers per node val_img_size = 513 # ############################## # Setup Dataflow # ############################## assert "DATASET_PATH" in os.environ data_path = os.environ["DATASET_PATH"] assert "SBD_DATASET_PATH" in os.environ sbd_data_path = os.environ["SBD_DATASET_PATH"] mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) val_transforms = A.Compose( [ A.PadIfNeeded(val_img_size, val_img_size, border_mode=cv2.BORDER_CONSTANT), A.Normalize(mean=mean, std=std), ignore_mask_boundaries, ToTensor(), ] ) data_loader = get_inference_dataloader( root_path=data_path, mode="test", transforms=val_transforms, batch_size=batch_size, num_workers=num_workers, limit_num_samples=batch_size * 5 if debug else None, ) # ############################## # Setup model # ############################## num_classes = 21 model = deeplabv3_resnet101(num_classes=num_classes) def model_output_transform(output): return output["out"] # baseline_dplv3_resnet101_sbd: best_model_78_val_miou_bg=0.6871.pt weights_path = "d8b4687d86cf445a944853fdd6a6b999" # or can specify a path # weights_path = "/path/to/best_model.pt" ignite-0.5.1/examples/references/segmentation/pascal_voc2012/dataflow.py000066400000000000000000000156501465426447700262540ustar00rootroot00000000000000import cv2 import numpy as np import torch from PIL import Image from torch.utils.data import Dataset from torch.utils.data.dataset import Subset from torchvision.datasets.sbd import SBDataset from torchvision.datasets.voc import VOCSegmentation import ignite.distributed as idist from ignite.utils import convert_tensor class TransformedDataset(Dataset): def __init__(self, ds, transform_fn): assert isinstance(ds, Dataset) assert callable(transform_fn) self.ds = ds self.transform_fn = transform_fn def __len__(self): return len(self.ds) def __getitem__(self, index): dp = self.ds[index] return self.transform_fn(**dp) class VOCSegmentationOpencv(VOCSegmentation): target_names = [ "background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "plant", "sheep", "sofa", "train", "tv/monitor", ] def __init__(self, *args, return_meta=False, **kwargs): super(VOCSegmentationOpencv, self).__init__(*args, **kwargs) self.return_meta = return_meta def __getitem__(self, index): img = cv2.imread(self.images[index]) assert img is not None, f"Image at '{self.images[index]}' has a problem" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) mask = np.asarray(Image.open(self.masks[index])) if self.return_meta: return { "image": img, "mask": mask, "meta": {"index": index, "image_path": self.images[index], "mask_path": self.masks[index]}, } return {"image": img, "mask": mask} class SBDatasetOpencv(SBDataset): def __init__(self, *args, return_meta=False, **kwargs): super(SBDatasetOpencv, self).__init__(*args, **kwargs) assert self.mode == "segmentation", "SBDatasetOpencv should be in segmentation mode only" self.return_meta = return_meta def _get_segmentation_target(self, filepath): mat = self._loadmat(filepath) return mat["GTcls"][0]["Segmentation"][0] def __getitem__(self, index): img = cv2.imread(self.images[index]) assert img is not None, f"Image at '{self.images[index]}' has a problem" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) mask = self._get_target(self.masks[index]) if self.return_meta: return { "image": img, "mask": mask, "meta": {"index": index, "image_path": self.images[index], "mask_path": self.masks[index]}, } return {"image": img, "mask": mask} def get_train_dataset(root_path, return_meta=False): return VOCSegmentationOpencv( root=root_path, year="2012", image_set="train", download=False, return_meta=return_meta ) def get_val_dataset(root_path, return_meta=False): return VOCSegmentationOpencv(root=root_path, year="2012", image_set="val", download=False, return_meta=return_meta) def get_train_noval_sbdataset(root_path, return_meta=False): return SBDatasetOpencv(root_path, image_set="train_noval", mode="segmentation", return_meta=return_meta) def get_dataloader(dataset, sampler=None, shuffle=False, limit_num_samples=None, **kwargs): if limit_num_samples is not None: g = torch.Generator().manual_seed(limit_num_samples) indices = torch.randperm(len(dataset), generator=g)[:limit_num_samples] dataset = Subset(dataset, indices) return idist.auto_dataloader(dataset, sampler=sampler, shuffle=(sampler is None) and shuffle, **kwargs) def get_train_val_loaders( root_path, train_transforms, val_transforms, batch_size=16, num_workers=8, train_sampler=None, val_batch_size=None, sbd_path=None, limit_train_num_samples=None, limit_val_num_samples=None, ): train_ds = get_train_dataset(root_path) val_ds = get_val_dataset(root_path) if sbd_path is not None: sbd_train_ds = get_train_noval_sbdataset(sbd_path) train_ds = train_ds + sbd_train_ds if len(val_ds) < len(train_ds): g = torch.Generator().manual_seed(len(train_ds)) train_eval_indices = torch.randperm(len(train_ds), generator=g)[: len(val_ds)] train_eval_ds = Subset(train_ds, train_eval_indices) else: train_eval_ds = train_ds train_ds = TransformedDataset(train_ds, transform_fn=train_transforms) val_ds = TransformedDataset(val_ds, transform_fn=val_transforms) train_eval_ds = TransformedDataset(train_eval_ds, transform_fn=val_transforms) val_batch_size = batch_size * 4 if val_batch_size is None else val_batch_size train_loader = get_dataloader( train_ds, shuffle=True, sampler=train_sampler, batch_size=batch_size, num_workers=num_workers, drop_last=True, limit_num_samples=limit_train_num_samples, ) val_loader = get_dataloader( val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, limit_num_samples=limit_val_num_samples, ) train_eval_loader = get_dataloader( train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, limit_num_samples=limit_val_num_samples, ) return train_loader, val_loader, train_eval_loader def get_inference_dataloader( root_path, mode, transforms, batch_size=16, num_workers=8, pin_memory=True, limit_num_samples=None ): assert mode in ("train", "test"), "Mode should be 'train' or 'test'" get_dataset_fn = get_train_dataset if mode == "train" else get_val_dataset dataset = get_dataset_fn(root_path, return_meta=True) dataset = TransformedDataset(dataset, transform_fn=transforms) return get_dataloader( dataset, limit_num_samples=limit_num_samples, shuffle=False, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory, drop_last=False, ) def ignore_mask_boundaries(**kwargs): assert "mask" in kwargs, "Input should contain 'mask'" mask = kwargs["mask"] mask[mask == 255] = 0 kwargs["mask"] = mask return kwargs def denormalize(t, mean, std, max_pixel_value=255): assert isinstance(t, torch.Tensor), f"{type(t)}" assert t.ndim == 3 d = t.device mean = torch.tensor(mean, device=d).unsqueeze(-1).unsqueeze(-1) std = torch.tensor(std, device=d).unsqueeze(-1).unsqueeze(-1) tensor = std * t + mean tensor *= max_pixel_value return tensor def prepare_image_mask(batch, device, non_blocking): x, y = batch["image"], batch["mask"] x = convert_tensor(x, device, non_blocking=non_blocking) y = convert_tensor(y, device, non_blocking=non_blocking).long() return x, y ignite-0.5.1/examples/references/segmentation/pascal_voc2012/main.py000066400000000000000000000412121465426447700253700ustar00rootroot00000000000000import os from functools import partial from pathlib import Path import fire import torch try: from torch.cuda.amp import autocast, GradScaler except ImportError: raise RuntimeError("Please, use recent PyTorch version, e.g. >=1.6.0") import dataflow as data import utils import vis from py_config_runner import ConfigObject, get_params, InferenceConfigSchema, TrainvalConfigSchema import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events from ignite.handlers import Checkpoint from ignite.metrics import ConfusionMatrix, IoU, mIoU from ignite.utils import manual_seed, setup_logger def download_datasets(output_path): """Helper tool to download datasets Args: output_path (str): path where to download and unzip the dataset """ from torchvision.datasets.sbd import SBDataset from torchvision.datasets.voc import VOCSegmentation output_path = Path(output_path) output_path.mkdir(parents=True, exist_ok=True) print("Download Pascal VOC 2012 - Training") VOCSegmentation(output_path.as_posix(), image_set="train", download=True) print("Download Pascal VOC 2012 - Validation") VOCSegmentation(output_path.as_posix(), image_set="val", download=True) print("Download SBD - Training without Pascal VOC validation part") sbd_path = output_path / "SBD" sbd_path.mkdir(exist_ok=True) SBDataset(sbd_path.as_posix(), image_set="train_noval", mode="segmentation", download=True) print("Done") print(f"Pascal VOC 2012 is at : {(output_path / 'VOCdevkit').as_posix()}") print(f"SBD is at : {sbd_path.as_posix()}") def training(local_rank, config, logger, with_clearml): rank = idist.get_rank() manual_seed(config.seed + local_rank) train_loader = config.train_loader val_loader = config.val_loader train_eval_loader = config.train_eval_loader model, optimizer, criterion = utils.initialize(config) # Setup trainer for this specific task trainer = create_trainer(model, optimizer, criterion, train_loader.sampler, config, logger, with_clearml) # Setup evaluators num_classes = config.num_classes cm_metric = ConfusionMatrix(num_classes=num_classes) val_metrics = { "IoU": IoU(cm_metric), "mIoU_bg": mIoU(cm_metric), } if ("val_metrics" in config) and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="val") train_evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="train") val_interval = config.get("val_interval", 1) # Run validation on every val_interval epoch, in the end of the training # and in the begining if config.start_by_validation is True event = Events.EPOCH_COMPLETED(every=val_interval) if config.num_epochs % val_interval != 0: event |= Events.COMPLETED if config.get("start_by_validation", False): event |= Events.STARTED @trainer.on(event) def run_validation(): epoch = trainer.state.epoch state = train_evaluator.run(train_eval_loader) utils.log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(val_loader) utils.log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) score_metric_name = "mIoU_bg" if "es_patience" in config: common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name) # Store 2 best models by validation accuracy: common.gen_save_best_models_by_val_score( save_handler=utils.get_save_handler(config.output_path.as_posix(), with_clearml), evaluator=evaluator, models=model, metric_name=score_metric_name, n_saved=2, trainer=trainer, tag="val", ) # Setup Tensorboard logger if rank == 0: tb_logger = common.setup_tb_logging( config.output_path.as_posix(), trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator}, ) # Log validation predictions as images # We define a custom event filter to log less frequently the images (to reduce storage size) # - we plot images with masks of the middle validation batch # - once every 3 validations and # - at the end of the training def custom_event_filter(_, val_iteration): c1 = val_iteration == len(val_loader) // 2 c2 = trainer.state.epoch % (config.get("val_interval", 1) * 3) == 0 c2 |= trainer.state.epoch == config.num_epochs return c1 and c2 # Image denormalization function to plot predictions with images mean = config.get("mean", (0.485, 0.456, 0.406)) std = config.get("std", (0.229, 0.224, 0.225)) img_denormalize = partial(data.denormalize, mean=mean, std=std) tb_logger.attach( evaluator, log_handler=vis.predictions_gt_images_handler( img_denormalize_fn=img_denormalize, n_images=8, another_engine=trainer, prefix_tag="validation" ), event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter), ) # Log confusion matrix to ClearML: if with_clearml: trainer.add_event_handler(Events.COMPLETED, compute_and_log_cm, cm_metric, trainer.state.iteration) trainer.run(train_loader, max_epochs=config.num_epochs) if idist.get_rank() == 0: tb_logger.close() def compute_and_log_cm(cm_metric, iteration): cm = cm_metric.compute() # CM: values are normalized such that diagonal values represent class recalls cm = ConfusionMatrix.normalize(cm, "recall").cpu().numpy() if idist.get_rank() == 0: from clearml import Task clearml_logger = Task.current_task().get_logger() try: clearml_logger.report_confusion_matrix( title="Final Confusion Matrix", matrix=cm, iteration=iteration, xlabels=data.VOCSegmentationOpencv.target_names, ylabels=data.VOCSegmentationOpencv.target_names, extra_layout=None, ) except NameError: # Temporary clearml bug work-around: # https://github.com/allegroai/clearml/pull/936 pass def create_trainer(model, optimizer, criterion, train_sampler, config, logger, with_clearml): device = config.device prepare_batch = data.prepare_image_mask # Setup trainer accumulation_steps = config.get("accumulation_steps", 1) model_output_transform = config.get("model_output_transform", lambda x: x) with_amp = config.get("with_amp", True) scaler = GradScaler(enabled=with_amp) def forward_pass(batch): model.train() x, y = prepare_batch(batch, device=device, non_blocking=True) with autocast(enabled=with_amp): y_pred = model(x) y_pred = model_output_transform(y_pred) loss = criterion(y_pred, y) / accumulation_steps return loss def amp_backward_pass(engine, loss): scaler.scale(loss).backward() if engine.state.iteration % accumulation_steps == 0: scaler.step(optimizer) scaler.update() optimizer.zero_grad() def hvd_amp_backward_pass(engine, loss): scaler.scale(loss).backward() optimizer.synchronize() with optimizer.skip_synchronize(): scaler.step(optimizer) scaler.update() optimizer.zero_grad() if idist.backend() == "horovod" and with_amp: backward_pass = hvd_amp_backward_pass else: backward_pass = amp_backward_pass def training_step(engine, batch): loss = forward_pass(batch) output = {"supervised batch loss": loss.item()} backward_pass(engine, loss) return output trainer = Engine(training_step) trainer.logger = logger output_names = [ "supervised batch loss", ] lr_scheduler = config.lr_scheduler to_save = { "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler, "trainer": trainer, "amp": scaler, } save_every_iters = config.get("save_every_iters", 1000) common.setup_common_training_handlers( trainer, train_sampler, to_save=to_save, save_every_iters=save_every_iters, save_handler=utils.get_save_handler(config.output_path.as_posix(), with_clearml), lr_scheduler=lr_scheduler, output_names=output_names, with_pbars=not with_clearml, log_every_iters=1, ) resume_from = config.get("resume_from", None) if resume_from is not None: checkpoint_fp = Path(resume_from) assert checkpoint_fp.exists(), f"Checkpoint '{checkpoint_fp.as_posix()}' is not found" logger.info(f"Resume from a checkpoint: {checkpoint_fp.as_posix()}") checkpoint = torch.load(checkpoint_fp.as_posix(), map_location="cpu") Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint) return trainer def create_evaluator(model, metrics, config, with_clearml, tag="val"): model_output_transform = config.get("model_output_transform", lambda x: x) with_amp = config.get("with_amp", True) prepare_batch = data.prepare_image_mask @torch.no_grad() def evaluate_step(engine, batch): model.eval() with autocast(enabled=with_amp): x, y = prepare_batch(batch, device=config.device, non_blocking=True) y_pred = model(x) y_pred = model_output_transform(y_pred) return y_pred, y evaluator = Engine(evaluate_step) for name, metric in metrics.items(): metric.attach(evaluator, name) if idist.get_rank() == 0 and (not with_clearml): common.ProgressBar(desc=f"Evaluation ({tag})", persist=False).attach(evaluator) return evaluator def setup_experiment_tracking(config, with_clearml, task_type="training"): from datetime import datetime assert task_type in ("training", "testing"), task_type output_path = "" if idist.get_rank() == 0: if with_clearml: from clearml import Task schema = TrainvalConfigSchema if task_type == "training" else InferenceConfigSchema task = Task.init("Pascal-VOC12 Training", config.config_filepath.stem, task_type=task_type) task.connect_configuration(config.config_filepath.as_posix()) task.upload_artifact(config.script_filepath.name, config.script_filepath.as_posix()) task.upload_artifact(config.config_filepath.name, config.config_filepath.as_posix()) task.connect(get_params(config, schema)) output_path = Path(os.environ.get("CLEARML_OUTPUT_PATH", "/tmp")) output_path = output_path / "clearml" / datetime.now().strftime("%Y%m%d-%H%M%S") else: import shutil output_path = Path(os.environ.get("OUTPUT_PATH", "/tmp/output-pascal-voc12")) output_path = output_path / task_type / config.config_filepath.stem output_path = output_path / datetime.now().strftime("%Y%m%d-%H%M%S") output_path.mkdir(parents=True, exist_ok=True) shutil.copyfile(config.script_filepath.as_posix(), output_path / config.script_filepath.name) shutil.copyfile(config.config_filepath.as_posix(), output_path / config.config_filepath.name) output_path = output_path.as_posix() return Path(idist.broadcast(output_path, src=0)) def run_training(config_filepath, backend="nccl", with_clearml=True): """Main entry to run training experiment Args: config_filepath (str): training configuration .py file backend (str): distributed backend: nccl, gloo, horovod or None to run without distributed config with_clearml (bool): if True, uses ClearML as experiment tracking system """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled torch.backends.cudnn.benchmark = True config_filepath = Path(config_filepath) assert config_filepath.exists(), f"File '{config_filepath.as_posix()}' is not found" with idist.Parallel(backend=backend) as parallel: logger = setup_logger(name="Pascal-VOC12 Training", distributed_rank=idist.get_rank()) config = ConfigObject(config_filepath) TrainvalConfigSchema.validate(config) config.script_filepath = Path(__file__) output_path = setup_experiment_tracking(config, with_clearml=with_clearml) config.output_path = output_path utils.log_basic_info(logger, get_params(config, TrainvalConfigSchema)) try: parallel.run(training, config, logger=logger, with_clearml=with_clearml) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e def get_model_weights(config, logger, with_clearml): path = "" if with_clearml: from clearml import Model if idist.get_rank() > 0: idist.barrier() else: model_id = config.weights_path logger.info(f"Loading trained model: {model_id}") model = Model(model_id) assert model is not None, f"{model_id}" path = model.get_local_copy() idist.barrier() path = idist.broadcast(path, src=0) else: path = config.weights_path logger.info(f"Loading {path}") assert Path(path).exists(), f"{path} is not found" return torch.load(path) def evaluation(local_rank, config, logger, with_clearml): rank = idist.get_rank() device = idist.device() manual_seed(config.seed + local_rank) data_loader = config.data_loader model = config.model.to(device) # Load weights: state_dict = get_model_weights(config, logger, with_clearml) model.load_state_dict(state_dict) # Adapt model to dist config model = idist.auto_model(model) # Setup evaluators num_classes = config.num_classes cm_metric = ConfusionMatrix(num_classes=num_classes) val_metrics = { "IoU": IoU(cm_metric), "mIoU_bg": mIoU(cm_metric), } if ("val_metrics" in config) and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="val") # Setup Tensorboard logger if rank == 0: tb_logger = common.TensorboardLogger(log_dir=config.output_path.as_posix()) tb_logger.attach_output_handler(evaluator, event_name=Events.COMPLETED, tag="validation", metric_names="all") # Log confusion matrix to ClearML: if with_clearml: evaluator.add_event_handler(Events.COMPLETED, compute_and_log_cm, cm_metric, evaluator.state.iteration) state = evaluator.run(data_loader) utils.log_metrics(logger, 0, state.times["COMPLETED"], "Validation", state.metrics) if idist.get_rank() == 0: tb_logger.close() def run_evaluation(config_filepath, backend="nccl", with_clearml=True): """Main entry to run model's evaluation: - compute validation metrics Args: config_filepath (str): evaluation configuration .py file backend (str): distributed backend: nccl, gloo, horovod or None to run without distributed config with_clearml (bool): if True, uses ClearML as experiment tracking system """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled torch.backends.cudnn.benchmark = True config_filepath = Path(config_filepath) assert config_filepath.exists(), f"File '{config_filepath.as_posix()}' is not found" with idist.Parallel(backend=backend) as parallel: logger = setup_logger(name="Pascal-VOC12 Evaluation", distributed_rank=idist.get_rank()) config = ConfigObject(config_filepath) InferenceConfigSchema.validate(config) config.script_filepath = Path(__file__) output_path = setup_experiment_tracking(config, with_clearml=with_clearml, task_type="testing") config.output_path = output_path utils.log_basic_info(logger, get_params(config, InferenceConfigSchema)) try: parallel.run(evaluation, config, logger=logger, with_clearml=with_clearml) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e if __name__ == "__main__": fire.Fire({"download": download_datasets, "training": run_training, "eval": run_evaluation}) ignite-0.5.1/examples/references/segmentation/pascal_voc2012/requirements.txt000066400000000000000000000002331465426447700273540ustar00rootroot00000000000000albumentations numpy opencv-python-headless fire pytorch-ignite tensorboard torch torchvision tqdm clearml image-dataset-viz py_config_runner>=0.2.0,<1.0.0ignite-0.5.1/examples/references/segmentation/pascal_voc2012/utils.py000066400000000000000000000034441465426447700256110ustar00rootroot00000000000000import torch import ignite import ignite.distributed as idist from ignite.handlers import DiskSaver def initialize(config): device = idist.device() model = config.model.to(device) optimizer = config.optimizer # Adapt model to dist config model = idist.auto_model(model) optimizer = idist.auto_optim(optimizer) criterion = config.criterion.to(device) return model, optimizer, criterion def log_basic_info(logger, config): logger.info(f"- PyTorch version: {torch.__version__}") logger.info(f"- Ignite version: {ignite.__version__}") if torch.cuda.is_available(): # explicitly import cudnn as # torch.backends.cudnn can not be pickled with hvd spawning procs from torch.backends import cudnn logger.info(f"- GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}") logger.info(f"- CUDA version: {torch.version.cuda}") logger.info(f"- CUDNN version: {cudnn.version()}") logger.info("\n") logger.info("Configuration:") for key, value in config.items(): logger.info(f"\t{key}: {value}") logger.info("\n") if idist.get_world_size() > 1: logger.info("\nDistributed setting:") logger.info(f"\tbackend: {idist.backend()}") logger.info(f"\tworld size: {idist.get_world_size()}") logger.info("\n") def log_metrics(logger, epoch, elapsed, tag, metrics): metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) logger.info(f"\nEpoch {epoch} - Evaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}") def get_save_handler(output_path, with_clearml): if with_clearml: from ignite.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=output_path) return DiskSaver(output_path) ignite-0.5.1/examples/references/segmentation/pascal_voc2012/vis.py000066400000000000000000000102101465426447700252370ustar00rootroot00000000000000import numpy as np import torch from PIL import Image try: from image_dataset_viz import render_datapoint except ImportError: raise ModuleNotFoundError( "Please install image-dataset-viz via pip install --upgrade git+https://github.com/vfdev-5/ImageDatasetViz.git" ) def _getvocpallete(num_cls): n = num_cls pallete = [0] * (n * 3) for j in range(0, n): lab = j pallete[j * 3 + 0] = 0 pallete[j * 3 + 1] = 0 pallete[j * 3 + 2] = 0 i = 0 while lab > 0: pallete[j * 3 + 0] |= ((lab >> 0) & 1) << (7 - i) pallete[j * 3 + 1] |= ((lab >> 1) & 1) << (7 - i) pallete[j * 3 + 2] |= ((lab >> 2) & 1) << (7 - i) i = i + 1 lab >>= 3 return pallete vocpallete = _getvocpallete(256) def render_mask(mask): if isinstance(mask, np.ndarray): mask = Image.fromarray(mask) mask.putpalette(vocpallete) mask = mask.convert(mode="RGB") return mask def tensor_to_rgb(t): img = t.cpu().numpy().transpose((1, 2, 0)) return img.astype(np.uint8) def make_grid(batch_img, batch_mask, img_denormalize_fn, batch_gt_mask=None): """Create a grid from batch image and mask as img1 | img2 | img3 | img4 | ... i+m1 | i+m2 | i+m3 | i+m4 | ... mask1 | mask2 | mask3 | mask4 | ... i+M1 | i+M2 | i+M3 | i+M4 | ... Mask1 | Mask2 | Mask3 | Mask4 | ... i+m = image + mask blended with alpha=0.4 - maskN is predicted mask - MaskN is ground-truth mask if given Args: batch_img (torch.Tensor) batch of images of any type batch_mask (torch.Tensor) batch of masks img_denormalize_fn (Callable): function to denormalize batch of images batch_gt_mask (torch.Tensor, optional): batch of ground truth masks. """ assert isinstance(batch_img, torch.Tensor) and isinstance(batch_mask, torch.Tensor) assert len(batch_img) == len(batch_mask) if batch_gt_mask is not None: assert isinstance(batch_gt_mask, torch.Tensor) assert len(batch_mask) == len(batch_gt_mask) b = batch_img.shape[0] h, w = batch_img.shape[2:] le = 3 if batch_gt_mask is None else 3 + 2 out_image = np.zeros((h * le, w * b, 3), dtype="uint8") for i in range(b): img = batch_img[i] mask = batch_mask[i] img = img_denormalize_fn(img) img = tensor_to_rgb(img) mask = mask.cpu().numpy() mask = render_mask(mask) out_image[0:h, i * w : (i + 1) * w, :] = img out_image[1 * h : 2 * h, i * w : (i + 1) * w, :] = render_datapoint(img, mask, blend_alpha=0.4) out_image[2 * h : 3 * h, i * w : (i + 1) * w, :] = mask if batch_gt_mask is not None: gt_mask = batch_gt_mask[i] gt_mask = gt_mask.cpu().numpy() gt_mask = render_mask(gt_mask) out_image[3 * h : 4 * h, i * w : (i + 1) * w, :] = render_datapoint(img, gt_mask, blend_alpha=0.4) out_image[4 * h : 5 * h, i * w : (i + 1) * w, :] = gt_mask return out_image def predictions_gt_images_handler(img_denormalize_fn, n_images=None, another_engine=None, prefix_tag=None): def wrapper(engine, logger, event_name): batch = engine.state.batch output = engine.state.output x = batch["image"] y = batch["mask"] y_pred = output[0] if y.shape == y_pred.shape and y.ndim == 4: # Case of y of shape (B, C, H, W) y = torch.argmax(y, dim=1) y_pred = torch.argmax(y_pred, dim=1).byte() if n_images is not None: x = x[:n_images, ...] y = y[:n_images, ...] y_pred = y_pred[:n_images, ...] grid_pred_gt = make_grid(x, y_pred, img_denormalize_fn, batch_gt_mask=y) state = engine.state if another_engine is None else another_engine.state global_step = state.epoch tag = "predictions_with_gt" if prefix_tag is not None: tag = f"{prefix_tag}: {tag} - epoch={global_step}" logger.writer.add_image(tag=tag, img_tensor=grid_pred_gt, global_step=global_step, dataformats="HWC") return wrapper ignite-0.5.1/examples/reinforcement_learning/000077500000000000000000000000001465426447700213545ustar00rootroot00000000000000ignite-0.5.1/examples/reinforcement_learning/README.md000066400000000000000000000004221465426447700226310ustar00rootroot00000000000000# Reinforcement learning training examples with Ignite ported from [pytorch-examples](https://github.com/pytorch/examples/tree/master/reinforcement_learning) ```bash pip install gymnasium # For REINFORCE: python reinforce.py # For actor critic: python actor_critic.py ``` ignite-0.5.1/examples/reinforcement_learning/actor_critic.py000066400000000000000000000145451465426447700244040ustar00rootroot00000000000000import argparse from collections import deque, namedtuple import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.distributions import Categorical from ignite.engine import Engine, Events try: import gymnasium as gym except ImportError: raise ModuleNotFoundError("Please install opengym: pip install gymnasium") SavedAction = namedtuple("SavedAction", ["log_prob", "value"]) eps = np.finfo(np.float32).eps.item() class Policy(nn.Module): """ implements both actor and critic in one model """ def __init__(self): super(Policy, self).__init__() self.affine1 = nn.Linear(4, 128) # actor's layer self.action_head = nn.Linear(128, 2) # critic's layer self.value_head = nn.Linear(128, 1) # action & reward buffer self.saved_actions = [] self.rewards = [] def forward(self, x): """ forward of both actor and critic """ x = F.relu(self.affine1(x)) # actor: choses action to take from state s_t # by returning probability of each action action_prob = F.softmax(self.action_head(x), dim=-1) # critic: evaluates being in the state s_t state_values = self.value_head(x) # return values for both actor and critic as a tuple of 2 values: # 1. a list with the probability of each action over the action space # 2. the value from state s_t return action_prob, state_values def select_action(policy, observation): observation = torch.from_numpy(observation).float() probs, observation_value = policy(observation) # create a categorical distribution over the list of probabilities of actions m = Categorical(probs) # and sample an action using the distribution action = m.sample() # save to action buffer policy.saved_actions.append(SavedAction(m.log_prob(action), observation_value)) # the action to take (left or right) return action.item() def finish_episode(policy, optimizer, gamma): """ Training code. Calculates actor and critic loss and performs backprop. """ R = 0 saved_actions = policy.saved_actions policy_losses = [] # list to save actor (policy) loss value_losses = [] # list to save critic (value) loss returns = deque() # list to save the true values # calculate the true value using rewards returned from the environment for r in policy.rewards[::-1]: # calculate the discounted value R = r + gamma * R returns.appendleft(R) returns = torch.tensor(returns) returns = (returns - returns.mean()) / (returns.std() + eps) for (log_prob, value), R in zip(saved_actions, returns): advantage = R - value.item() # calculate actor (policy) loss policy_losses.append(-log_prob * advantage) # calculate critic (value) loss using L1 smooth loss value_losses.append(F.smooth_l1_loss(value, torch.tensor([R]))) # reset gradients optimizer.zero_grad() # sum up all the values of policy_losses and value_losses loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum() # perform backprop loss.backward() optimizer.step() # reset rewards and action buffer del policy.rewards[:] del policy.saved_actions[:] EPISODE_STARTED = Events.EPOCH_STARTED EPISODE_COMPLETED = Events.EPOCH_COMPLETED def main(env, args): policy = Policy() optimizer = optim.Adam(policy.parameters(), lr=3e-2) timesteps = range(10000) def run_single_timestep(engine, timestep): observation = engine.state.observation # select action from policy action = select_action(policy, observation) # take the action engine.state.observation, reward, done, _, _ = env.step(action) if args.render: env.render() policy.rewards.append(reward) engine.state.ep_reward += reward if done: engine.terminate_epoch() engine.state.timestep = timestep trainer = Engine(run_single_timestep) trainer.state.running_reward = 10 @trainer.on(EPISODE_STARTED) def reset_environment_state(): # reset environment and episode reward torch.manual_seed(args.seed + trainer.state.epoch) trainer.state.observation, _ = env.reset(seed=args.seed + trainer.state.epoch) trainer.state.ep_reward = 0 @trainer.on(EPISODE_COMPLETED) def update_model(): # update cumulative reward t = trainer.state.timestep trainer.state.running_reward = 0.05 * trainer.state.ep_reward + (1 - 0.05) * trainer.state.running_reward # perform backprop finish_episode(policy, optimizer, args.gamma) @trainer.on(EPISODE_COMPLETED(every=args.log_interval)) def log_episode(): i_episode = trainer.state.epoch print( f"Episode {i_episode}\tLast reward: {trainer.state.ep_reward:.2f}" f"\tAverage reward: {trainer.state.running_reward:.2f}" ) @trainer.on(EPISODE_COMPLETED) def should_finish_training(): # check if we have "solved" the cart pole problem running_reward = trainer.state.running_reward if running_reward > env.spec.reward_threshold: print( f"Solved! Running reward is now {running_reward} and " f"the last episode runs to {trainer.state.timestep} time steps!" ) trainer.should_terminate = True trainer.run(timesteps, max_epochs=args.max_episodes) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Ignite actor-critic example") parser.add_argument("--gamma", type=float, default=0.99, metavar="G", help="discount factor (default: 0.99)") parser.add_argument("--seed", type=int, default=543, metavar="N", help="random seed (default: 1)") parser.add_argument("--render", action="store_true", help="render the environment") parser.add_argument( "--log-interval", type=int, default=10, metavar="N", help="interval between training status logs (default: 10)" ) parser.add_argument( "--max-episodes", type=int, default=1000000, metavar="N", help="Number of episodes for the training (default: 1000000)", ) args = parser.parse_args() env = gym.make("CartPole-v1") main(env, args) ignite-0.5.1/examples/reinforcement_learning/reinforce.py000066400000000000000000000107071465426447700237070ustar00rootroot00000000000000import argparse from collections import deque import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.distributions import Categorical from ignite.engine import Engine, Events try: import gymnasium as gym except ImportError: raise ModuleNotFoundError("Please install opengym: pip install gymnasium") eps = np.finfo(np.float32).eps.item() class Policy(nn.Module): def __init__(self): super(Policy, self).__init__() self.affine1 = nn.Linear(4, 128) self.dropout = nn.Dropout(p=0.6) self.affine2 = nn.Linear(128, 2) self.saved_log_probs = [] self.rewards = [] def forward(self, x): x = self.affine1(x) x = self.dropout(x) x = F.relu(x) action_scores = self.affine2(x) return F.softmax(action_scores, dim=1) def select_action(policy, observation): state = torch.from_numpy(observation).float().unsqueeze(0) probs = policy(state) m = Categorical(probs) action = m.sample() policy.saved_log_probs.append(m.log_prob(action)) return action.item() def finish_episode(policy, optimizer, gamma): R = 0 policy_loss = [] returns = deque() for r in policy.rewards[::-1]: R = r + gamma * R returns.appendleft(R) returns = torch.tensor(returns) returns = (returns - returns.mean()) / (returns.std() + eps) for log_prob, R in zip(policy.saved_log_probs, returns): policy_loss.append(-log_prob * R) optimizer.zero_grad() policy_loss = torch.cat(policy_loss).sum() policy_loss.backward() optimizer.step() del policy.rewards[:] del policy.saved_log_probs[:] EPISODE_STARTED = Events.EPOCH_STARTED EPISODE_COMPLETED = Events.EPOCH_COMPLETED def main(env, args): policy = Policy() optimizer = optim.Adam(policy.parameters(), lr=1e-2) timesteps = range(10000) def run_single_timestep(engine, timestep): observation = engine.state.observation action = select_action(policy, observation) engine.state.observation, reward, done, _, _ = env.step(action) if args.render: env.render() policy.rewards.append(reward) engine.state.ep_reward += reward if done: engine.terminate_epoch() engine.state.timestep = timestep trainer = Engine(run_single_timestep) trainer.state.running_reward = 10 @trainer.on(EPISODE_STARTED) def reset_environment_state(): torch.manual_seed(args.seed + trainer.state.epoch) trainer.state.observation, _ = env.reset(seed=args.seed + trainer.state.epoch) trainer.state.ep_reward = 0 @trainer.on(EPISODE_COMPLETED) def update_model(): trainer.state.running_reward = 0.05 * trainer.state.ep_reward + (1 - 0.05) * trainer.state.running_reward finish_episode(policy, optimizer, args.gamma) @trainer.on(EPISODE_COMPLETED(every=args.log_interval)) def log_episode(): i_episode = trainer.state.epoch print( f"Episode {i_episode}\tLast reward: {trainer.state.ep_reward:.2f}" f"\tAverage length: {trainer.state.running_reward:.2f}" ) @trainer.on(EPISODE_COMPLETED) def should_finish_training(): running_reward = trainer.state.running_reward if running_reward > env.spec.reward_threshold: print( f"Solved! Running reward is now {running_reward} and " f"the last episode runs to {trainer.state.timestep} time steps!" ) trainer.should_terminate = True trainer.run(timesteps, max_epochs=args.max_episodes) if __name__ == "__main__": parser = argparse.ArgumentParser(description="PyTorch REINFORCE example") parser.add_argument("--gamma", type=float, default=0.99, metavar="G", help="discount factor (default: 0.99)") parser.add_argument("--seed", type=int, default=543, metavar="N", help="random seed (default: 543)") parser.add_argument("--render", action="store_true", help="render the environment") parser.add_argument( "--log-interval", type=int, default=10, metavar="N", help="interval between training status logs (default: 10)" ) parser.add_argument( "--max-episodes", type=int, default=1000000, metavar="N", help="Number of episodes for the training (default: 1000000)", ) args = parser.parse_args() env = gym.make("CartPole-v1") main(env, args) ignite-0.5.1/examples/siamese_network/000077500000000000000000000000001465426447700200345ustar00rootroot00000000000000ignite-0.5.1/examples/siamese_network/README.md000066400000000000000000000003721465426447700213150ustar00rootroot00000000000000# Siamese Network example on MNIST dataset This example is ported over from [pytorch/examples/siamese_network](https://github.com/pytorch/examples/tree/main/siamese_network) Usage: ``` pip install -r requirements.txt python siamese_network.py ``` ignite-0.5.1/examples/siamese_network/requirements.txt000066400000000000000000000000401465426447700233120ustar00rootroot00000000000000torch torchvision pytorch-igniteignite-0.5.1/examples/siamese_network/siamese_network.py000066400000000000000000000276721465426447700236230ustar00rootroot00000000000000import argparse import numpy as np import torch import torch.nn as nn import torch.optim as optim import torchvision from torch.optim.lr_scheduler import StepLR from torch.utils.data import DataLoader, Dataset from torchvision import datasets from ignite.engine import Engine, Events from ignite.handlers import ProgressBar from ignite.handlers.param_scheduler import LRScheduler from ignite.metrics import Accuracy, RunningAverage from ignite.utils import manual_seed class SiameseNetwork(nn.Module): # update Siamese Network implementation in accordance with the dataset """ Siamese network for image similarity estimation. The network is composed of two identical networks, one for each input. The output of each network is concatenated and passed to a linear layer. The output of the linear layer passed through a sigmoid function. `"FaceNet" `_ is a variant of the Siamese network. This implementation varies from FaceNet as we use the `ResNet-18` model from `"Deep Residual Learning for Image Recognition" ` as our feature extractor. In addition we use CIFAR10 dataset along with TripletMarginLoss """ def __init__(self): super(SiameseNetwork, self).__init__() # get resnet model self.resnet = torchvision.models.resnet34(weights=None) fc_in_features = self.resnet.fc.in_features # changing the FC layer of resnet model to a linear layer self.resnet.fc = nn.Identity() # add linear layers to compare between the features of the two images self.fc = nn.Sequential( nn.Linear(fc_in_features, 256), nn.ReLU(inplace=True), nn.Linear(256, 10), nn.ReLU(inplace=True), ) # initialise relu activation self.relu = nn.ReLU() # initialize the weights self.resnet.apply(self.init_weights) self.fc.apply(self.init_weights) def init_weights(self, m): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) m.bias.data.fill_(0.01) def forward_once(self, x): output = self.resnet(x) output = output.view(output.size()[0], -1) return output def forward(self, input1, input2, input3): # pass the input through resnet output1 = self.forward_once(input1) output2 = self.forward_once(input2) output3 = self.forward_once(input3) # pass the output of resnet to sigmoid layer output1 = self.fc(output1) output2 = self.fc(output2) output3 = self.fc(output3) return output1, output2, output3 class MatcherDataset(Dataset): # following class implements data downloading and handles preprocessing def __init__(self, root, train, download=False): super(MatcherDataset, self).__init__() # get CIFAR10 dataset self.dataset = datasets.CIFAR10(root, train=train, download=download) # convert data from numpy array to Tensor self.data = torch.from_numpy(self.dataset.data) # shift the dimensions of dataset to match the initial input layer dimensions self.data = torch.movedim(self.data, (0, 1, 2, 3), (0, 2, 3, 1)) # convert targets list to torch Tensor self.dataset.targets = torch.tensor(self.dataset.targets) self.group_examples() def group_examples(self): """ To ease the accessibility of data based on the class, we will use `group_examples` to group examples based on class. The data classes have already been mapped to numeric values and so are the target outputs for each training input Every key in `grouped_examples` corresponds to a class in CIFAR10 dataset. For every key in `grouped_examples`, every value will conform to all of the indices for the CIFAR10 dataset examples that correspond to that key. """ # get the targets from CIFAR10 dataset np_arr = np.array(self.dataset.targets) # group examples based on class self.grouped_examples = {} for i in range(0, 10): self.grouped_examples[i] = np.where((np_arr == i))[0] def __len__(self): return self.data.shape[0] def __getitem__(self, index): """ For every sample in the batch we select 3 images. First one is the anchor image which is the image obtained from the current index. We also obtain the label of anchor image. Now we select two random images, one belonging to the same class as that of the anchor image (named as positive_image) and the other belonging to a different class than that of the anchor image (named as negative_image). We return the anchor image, positive image, negative image and anchor label. """ # obtain the anchor image anchor_image = self.data[index].float() # obtain the class label of the anchor image anchor_label = self.dataset.targets[index] anchor_label = int(anchor_label.item()) # find a label which is different from anchor_label labels = list(range(0, 10)) labels.remove(anchor_label) neg_index = torch.randint(0, 9, (1,)).item() neg_label = labels[neg_index] # get a random index from the range range of indices random_index = torch.randint(0, len(self.grouped_examples[anchor_label]), (1,)).item() # get the index of image in actual data using the anchor label and random index positive_index = self.grouped_examples[anchor_label][random_index] # choosing a random image using positive_index positive_image = self.data[positive_index].float() # get a random index from the range range of indices random_index = torch.randint(0, len(self.grouped_examples[neg_label]), (1,)).item() # get the index of image in actual data using the negative label and random index negative_index = self.grouped_examples[neg_label][random_index] # choosing a random image using negative_index negative_image = self.data[negative_index].float() return anchor_image, positive_image, negative_image, anchor_label def pairwise_distance(input1, input2): dist = input1 - input2 dist = torch.pow(dist, 2) return dist def calculate_loss(input1, input2): output = pairwise_distance(input1, input2) loss = torch.sum(output, 1) loss = torch.sqrt(loss) return loss def run(args, model, device, optimizer, train_loader, test_loader, lr_scheduler): # using Triplet Margin Loss criterion = nn.TripletMarginLoss(p=2, margin=2.8) # define model training step def train_step(engine, batch): model.train() anchor_image, positive_image, negative_image, anchor_label = batch anchor_image = anchor_image.to(device) positive_image, negative_image = positive_image.to(device), negative_image.to(device) anchor_label = anchor_label.to(device) optimizer.zero_grad() anchor_out, positive_out, negative_out = model(anchor_image, positive_image, negative_image) loss = criterion(anchor_out, positive_out, negative_out) loss.backward() optimizer.step() return loss # define model testing step def test_step(engine, batch): model.eval() with torch.no_grad(): anchor_image, _, _, anchor_label = batch anchor_image = anchor_image.to(device) anchor_label = anchor_label.to(device) other_image = [] other_label = [] y_true = [] for i in range(anchor_image.shape[0]): index = torch.randint(0, anchor_image.shape[0], (1,)).item() img = anchor_image[index] label = anchor_label[index] other_image.append(img) other_label.append(label) if anchor_label[i] == other_label[i]: y_true.append(1) else: y_true.append(0) other = torch.stack(other_image) other_label = torch.tensor(other_label) other, other_label = other.to(device), other_label.to(device) anchor_out, other_out, _ = model(anchor_image, other, other) test_loss = calculate_loss(anchor_out, other_out) y_pred = torch.where(test_loss < 3, 1, 0) y_true = torch.tensor(y_true) return [y_pred, y_true] # create engines for trainer and evaluator trainer = Engine(train_step) evaluator = Engine(test_step) # attach Running Average Loss metric to trainer and evaluator engines RunningAverage(output_transform=lambda x: x).attach(trainer, "loss") Accuracy(output_transform=lambda x: x).attach(evaluator, "accuracy") # attach progress bar to trainer with loss pbar1 = ProgressBar() pbar1.attach(trainer, metric_names=["loss"]) # attach progress bar to evaluator pbar2 = ProgressBar() pbar2.attach(evaluator) # attach LR Scheduler to trainer engine trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler) # event handler triggers evauator at end of every epoch @trainer.on(Events.EPOCH_COMPLETED(every=args.log_interval)) def test(engine): state = evaluator.run(test_loader) print(f'Test Accuracy: {state.metrics["accuracy"]}') # run the trainer trainer.run(train_loader, max_epochs=args.epochs) def main(): # adds training defaults and support for terminal arguments parser = argparse.ArgumentParser(description="PyTorch Siamese network Example") parser.add_argument( "--batch-size", type=int, default=256, metavar="N", help="input batch size for training (default: 64)" ) parser.add_argument( "--test-batch-size", type=int, default=256, metavar="N", help="input batch size for testing (default: 1000)" ) parser.add_argument("--epochs", type=int, default=10, metavar="N", help="number of epochs to train (default: 14)") parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)") parser.add_argument( "--gamma", type=float, default=0.95, metavar="M", help="Learning rate step gamma (default: 0.7)" ) parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training") parser.add_argument("--no-mps", action="store_true", default=False, help="disables macOS GPU training") parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass") parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)") parser.add_argument( "--log-interval", type=int, default=1, metavar="N", help="how many batches to wait before logging training status", ) parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model") parser.add_argument("--num-workers", default=4, help="number of processes generating parallel batches") args = parser.parse_args() # set manual seed manual_seed(args.seed) # set device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") # data loading train_dataset = MatcherDataset("../data", train=True, download=True) test_dataset = MatcherDataset("../data", train=False) train_loader = DataLoader(train_dataset, shuffle=True, batch_size=args.batch_size, num_workers=args.num_workers) test_loader = DataLoader(test_dataset, batch_size=args.test_batch_size, num_workers=args.num_workers) # set model parameters model = SiameseNetwork().to(device) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=15, gamma=args.gamma) lr_scheduler = LRScheduler(scheduler) # call run function run(args, model, device, optimizer, train_loader, test_loader, lr_scheduler) if __name__ == "__main__": main() ignite-0.5.1/examples/super_resolution/000077500000000000000000000000001465426447700202565ustar00rootroot00000000000000ignite-0.5.1/examples/super_resolution/README.md000066400000000000000000000044611465426447700215420ustar00rootroot00000000000000# Super-Resolution using an efficient sub-pixel convolutional neural network ported from [pytorch-examples](https://github.com/pytorch/examples/tree/main/super_resolution) This example illustrates how to use the efficient sub-pixel convolution layer described in ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al. 2016](https://arxiv.org/abs/1609.05158) for increasing spatial resolution within your network for tasks such as superresolution. ``` usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--crop_size CROPSIZE] [--batch_size BATCHSIZE] [--test_batch_size TESTBATCHSIZE] [--n_epochs NEPOCHS] [--lr LR] [--cuda] [--threads THREADS] [--seed SEED] [--debug] PyTorch Super Res Example optional arguments: -h, --help show this help message and exit --upscale_factor super resolution upscale factor --crop_size cropped size of the images for training --batch_size training batch size --test_batch_size testing batch size --n_epochs number of epochs to train for --lr Learning Rate. Default=0.01 --cuda use cuda --mps enable GPU on macOS --threads number of threads for data loader to use Default=4 --seed random seed to use. Default=123 --debug debug mode for testing ``` This example trains a super-resolution network on the [Caltech101 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Caltech101.html). A snapshot of the model after every epoch with filename `model_epoch_.pth` ## Example Usage: ### Train `python main.py --upscale_factor 3 --crop_size 180 --batch_size 4 --test_batch_size 100 --n_epochs 30 --lr 0.001` ### Super-Resolve `python super_resolve.py --input_image .jpg --model model_epoch_500.pth --output_filename out.png` ### Super-resolve example on a Cifar-10 image #### Input Image ![Cifar input image](./images/input_cifar.png) #### Output Images | Output image from Model | Output from bicubic sampling | |-------------------------------|------------------------------------| | ![Cifar output image](./images/out_cifar.png) | ![Cifar output from bicubic sampling](./images/bicubic_image_cifar.png)| ignite-0.5.1/examples/super_resolution/images/000077500000000000000000000000001465426447700215235ustar00rootroot00000000000000ignite-0.5.1/examples/super_resolution/images/bicubic_image_cifar.png000066400000000000000000000242221465426447700261410ustar00rootroot00000000000000‰PNG  IHDR``mϊΰo(YIDATxœέ}i“ΫH’εsχ€d¦€:zΊmΜΦvmΟΪcΊͺ€ΌHˆ?φC S₯ꙝž%e’ ρηG ("άύzQΝΝΝ͚•Z–²˜YΞωx<Žγ@€[ 71SCΒ @ ΄½ώώ#ύω]ˆχpsS#h¦@„[ wfN) ν¨ώcwύέσΧΗέm#’CγΪ¬VΧZkWS­α1ŒΓιtιΘΜ@—ž{qωΗHP‡ζ?^ΕΎ^κθ4Σ’eΛμf3α­΄RK?|ΐ?¦œ%₯ »+ύή3ψΟ!AΫαnͺ­΄e*ΣeΎ^Z-fjͺe)K© ΦV‡Για!΄JL?hρΏ¦ 7m΅,Σty;Ώ<]ί^Λ2›ͺ›ΦͺUMp8Z-¦ξψΗ‹ό  ?ΐ…mΗnΦκ²\―—Χ——ΟΏΌ=)Λfa'­ΕZum9!l^ θχS΅?X‚ϊͺ¬mς3O—ιςvy{ΉΌΎΤe†;(83‹ΦΪ_¦šΜ‘ΊgτkύΎ#όO’b…Ήi-ΛΌLΧΊ,‰‰Y˜Θ3Σ¦΅΄R΄ΥΑ__ζχΆ’7€ώ |ύΕ΄΅ΊΜσώw<ώ€zδG‚"άΝZ­eY–iš―ΣT3΄±{bR5!ΤZ[-jΒE‰δΓψπαγατH’X21±zγݝύfϋ?‡ω.>Λ4MΧλ΄\§θ 9 “ik΅Ί{„_Α`O§ΗOηΓΓ’”FbΚu$θύΡ½W™ηiš¦λ4]Σ|Ήj-d–ˆΗœr WZ™‘€ΛΫΫΫλΛp|σ‘8³0 @Ώ§―£Š•‹PΣR–ι:]/—ι:]―Σω|)ΣμΪρaΘ‡! 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Training settings parser = argparse.ArgumentParser(description="PyTorch Super Res Example") parser.add_argument("--crop_size", type=int, default=256, help="cropped size of the images for training") parser.add_argument("--upscale_factor", type=int, required=True, help="super resolution upscale factor") parser.add_argument("--batch_size", type=int, default=64, help="training batch size") parser.add_argument("--test_batch_size", type=int, default=10, help="testing batch size") parser.add_argument("--n_epochs", type=int, default=2, help="number of epochs to train for") parser.add_argument("--lr", type=float, default=0.01, help="Learning Rate. Default=0.01") parser.add_argument("--cuda", action="store_true", help="use cuda?") parser.add_argument("--mps", action="store_true", default=False, help="enables macOS GPU training") parser.add_argument("--threads", type=int, default=4, help="number of threads for data loader to use") parser.add_argument("--seed", type=int, default=123, help="random seed to use. Default=123") parser.add_argument("--debug", action="store_true", help="use debug") opt = parser.parse_args() print(opt) if opt.cuda and not torch.cuda.is_available(): raise Exception("No GPU found, please run without --cuda") if not opt.mps and torch.backends.mps.is_available(): raise Exception("Found mps device, please run with --mps to enable macOS GPU") torch.manual_seed(opt.seed) use_mps = opt.mps and torch.backends.mps.is_available() if opt.cuda: device = torch.device("cuda") elif use_mps: device = torch.device("mps") else: device = torch.device("cpu") print("===> Loading datasets") class SRDataset(torch.utils.data.Dataset): def __init__(self, dataset, scale_factor, crop_size=256): self.dataset = dataset self.scale_factor = scale_factor self.crop_size = crop_size def __getitem__(self, index): image, _ = self.dataset[index] img = image.convert("YCbCr") hr_image, _, _ = img.split() hr_image = center_crop(hr_image, self.crop_size) lr_image = hr_image.copy() if self.scale_factor != 1: size = self.crop_size // self.scale_factor lr_image = resize(lr_image, [size, size]) hr_image = to_tensor(hr_image) lr_image = to_tensor(lr_image) return lr_image, hr_image def __len__(self): return len(self.dataset) try: trainset = torchvision.datasets.Caltech101(root="./data", download=True) testset = torchvision.datasets.Caltech101(root="./data", download=False) except RuntimeError: print("Dataset download problem, exiting without error code") exit(0) trainset_sr = SRDataset(trainset, scale_factor=opt.upscale_factor, crop_size=opt.crop_size) testset_sr = SRDataset(testset, scale_factor=opt.upscale_factor, crop_size=opt.crop_size) training_data_loader = DataLoader(dataset=trainset_sr, num_workers=opt.threads, batch_size=opt.batch_size, shuffle=True) testing_data_loader = DataLoader(dataset=testset_sr, num_workers=opt.threads, batch_size=opt.test_batch_size) print("===> Building model") model = Net(upscale_factor=opt.upscale_factor).to(device) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=opt.lr) def train_step(engine, batch): model.train() input, target = batch[0].to(device), batch[1].to(device) optimizer.zero_grad() loss = criterion(model(input), target) loss.backward() optimizer.step() return loss.item() def validation_step(engine, batch): model.eval() with torch.no_grad(): x, y = batch[0].to(device), batch[1].to(device) y_pred = model(x) return y_pred, y trainer = Engine(train_step) evaluator = Engine(validation_step) psnr = PSNR(data_range=1) psnr.attach(evaluator, "psnr") validate_every = 1 if opt.debug: epoch_length = 10 validate_epoch_length = 1 else: epoch_length = len(training_data_loader) validate_epoch_length = len(testing_data_loader) @trainer.on(Events.EPOCH_COMPLETED(every=validate_every)) def log_validation(): evaluator.run(testing_data_loader, epoch_length=validate_epoch_length) metrics = evaluator.state.metrics print(f"Epoch: {trainer.state.epoch}, Avg. PSNR: {metrics['psnr']} dB") @trainer.on(Events.EPOCH_COMPLETED) def checkpoint(): model_out_path = "model_epoch_{}.pth".format(trainer.state.epoch) torch.save(model, model_out_path) print("Checkpoint saved to {}".format(model_out_path)) # Attach basic profiler basic_profiler = BasicTimeProfiler() basic_profiler.attach(trainer) ProgressBar().attach(trainer, output_transform=lambda x: {"loss": x}) trainer.run(training_data_loader, opt.n_epochs, epoch_length=epoch_length) results = basic_profiler.get_results() basic_profiler.print_results(results) ignite-0.5.1/examples/super_resolution/model.py000066400000000000000000000020251465426447700217270ustar00rootroot00000000000000import torch.nn as nn import torch.nn.init as init class Net(nn.Module): def __init__(self, upscale_factor): super(Net, self).__init__() self.relu = nn.ReLU() self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2)) self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)) self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1)) self.conv4 = nn.Conv2d(32, upscale_factor**2, (3, 3), (1, 1), (1, 1)) self.pixel_shuffle = nn.PixelShuffle(upscale_factor) self._initialize_weights() def forward(self, x): x = self.relu(self.conv1(x)) x = self.relu(self.conv2(x)) x = self.relu(self.conv3(x)) x = self.pixel_shuffle(self.conv4(x)) return x def _initialize_weights(self): init.orthogonal_(self.conv1.weight, init.calculate_gain("relu")) init.orthogonal_(self.conv2.weight, init.calculate_gain("relu")) init.orthogonal_(self.conv3.weight, init.calculate_gain("relu")) init.orthogonal_(self.conv4.weight) ignite-0.5.1/examples/super_resolution/super_resolve.py000066400000000000000000000024511465426447700235270ustar00rootroot00000000000000import argparse import numpy as np import torch from PIL import Image from torchvision.transforms.functional import to_tensor # Training settings parser = argparse.ArgumentParser(description="PyTorch Super Res Example") parser.add_argument("--input_image", type=str, required=True, help="input image to use") parser.add_argument("--model", type=str, required=True, help="model file to use") parser.add_argument("--output_filename", type=str, help="where to save the output image") parser.add_argument("--cuda", action="store_true", help="use cuda") opt = parser.parse_args() print(opt) img = Image.open(opt.input_image).convert("YCbCr") y, cb, cr = img.split() model = torch.load(opt.model) input = to_tensor(y).view(1, -1, y.size[1], y.size[0]) if opt.cuda: model = model.cuda() input = input.cuda() model.eval() with torch.no_grad(): out = model(input) out = out.cpu() out_img_y = out[0].detach().numpy() out_img_y *= 255.0 out_img_y = out_img_y.clip(0, 255) out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode="L") out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC) out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC) out_img = Image.merge("YCbCr", [out_img_y, out_img_cb, out_img_cr]).convert("RGB") out_img.save(opt.output_filename) print("output image saved to ", opt.output_filename) ignite-0.5.1/examples/transformers/000077500000000000000000000000001465426447700173625ustar00rootroot00000000000000ignite-0.5.1/examples/transformers/README.md000066400000000000000000000066521465426447700206520ustar00rootroot00000000000000# Transformers Example with Ignite In this example, we show how to use _Ignite_ to finetune a transformer model: - on 1 or more GPUs or TPUs - compute training/validation metrics - log learning rate, metrics etc - save the best model weights Configurations: - [x] single GPU - [x] multi GPUs on a single node - [x] TPUs on Colab ## Requirements: - pytorch-ignite: `pip install pytorch-ignite` - [transformers](https://github.com/huggingface/transformers): `pip install transformers` - [datasets](https://github.com/huggingface/datasets): `pip install datasets` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` - [tensorboardx](https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` - [python-fire](https://github.com/google/python-fire): `pip install fire` - Optional: [clearml](https://github.com/allegroai/clearml): `pip install clearml` Alternatively, install the all requirements using `pip install -r requirements.txt`. ## Usage: Run the example on a single GPU: ```bash python main.py run ``` If needed, please, adjust the batch size to your GPU device with `--batch_size` argument. The default model is `bert-base-uncased` incase you need to change that use the `--model` argument, for details on which models can be used refer [here](https://huggingface.co/transformers/v3.0.2/model_doc/auto.html#automodelforsequenceclassification) Example: ```bash #Using DistilBERT which has 40% less parameters than bert-base-uncased python main.py run --model="distilbert-base-uncased" ``` For details on accepted arguments: ```bash python main.py run -- --help ``` ### Distributed training #### Single node, multiple GPUs Let's start training on a single node with 2 gpus: ```bash # using torch.distributed.launch python -u -m torch.distributed.launch --nproc_per_node=2 --use_env main.py run --backend="nccl" ``` or ```bash # using function spawn inside the code python -u main.py run --backend="nccl" --nproc_per_node=2 ``` ##### Using [Horovod](https://horovod.readthedocs.io/en/latest/index.html) as distributed backend Please, make sure to have Horovod installed before running. Let's start training on a single node with 2 gpus: ```bash # horovodrun horovodrun -np=2 python -u main.py run --backend="horovod" ``` or ```bash # using function spawn inside the code python -u main.py run --backend="horovod" --nproc_per_node=2 ``` #### Colab or Kaggle kernels, on 8 TPUs ```python # setup TPU environment import os assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator' ``` ```bash VERSION = "nightly" !curl -q https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py !python pytorch-xla-env-setup.py --version $VERSION > /dev/null ``` ```python from main import run run(backend="xla-tpu", nproc_per_node=8) ``` ## ClearML fileserver If `ClearML` server is used (i.e. `--with_clearml` argument), the configuration to upload artifact must be done by modifying the `ClearML` configuration file `~/clearml.conf` generated by `clearml-init`. According to the [documentation](https://allegro.ai/clearml/docs/docs/examples/reporting/artifacts.html), the `output_uri` argument can be configured in `sdk.development.default_output_uri` to fileserver uri. If server is self-hosted, `ClearML` fileserver uri is `http://localhost:8081`. For more details, see https://allegro.ai/clearml/docs/docs/examples/reporting/artifacts.html ignite-0.5.1/examples/transformers/dataset.py000066400000000000000000000015271465426447700213660ustar00rootroot00000000000000import torch class TransformerDataset(torch.utils.data.Dataset): def __init__(self, texts, labels, tokenizer, max_length): self.texts = texts self.labels = labels self.tokenizer = tokenizer self.max_length = max_length def __getitem__(self, idx): text = str(self.texts[idx]) text = " ".join(text.split()) inputs = self.tokenizer( text, None, add_special_tokens=True, max_length=self.max_length, truncation=True, padding="max_length", return_tensors="pt", ) inputs = {k: v.type(torch.long).squeeze(0) for k, v in inputs.items()} labels_pt = torch.tensor(self.labels[idx], dtype=torch.float) return inputs, labels_pt def __len__(self): return len(self.labels) ignite-0.5.1/examples/transformers/main.py000066400000000000000000000347551465426447700206760ustar00rootroot00000000000000import os from datetime import datetime from pathlib import Path import fire import torch import torch.nn as nn import torch.optim as optim import utils from torch.cuda.amp import autocast, GradScaler import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, global_step_from_engine, PiecewiseLinear from ignite.metrics import Accuracy, Loss from ignite.utils import manual_seed, setup_logger os.environ["TOKENIZERS_PARALLELISM"] = "false" # remove tokenizer paralleism warning def training(local_rank, config): rank = idist.get_rank() manual_seed(config["seed"] + rank) device = idist.device() logger = setup_logger(name="IMDB-Training", distributed_rank=local_rank) log_basic_info(logger, config) output_path = config["output_dir"] if rank == 0: now = datetime.now().strftime("%Y%m%d-%H%M%S") folder_name = f"{config['model']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}" output_path = Path(output_path) / folder_name if not output_path.exists(): output_path.mkdir(parents=True) config["output_dir"] = output_path.as_posix() logger.info(f"Output path: {config['output_dir']}") if "cuda" in device.type: config["cuda device name"] = torch.cuda.get_device_name(local_rank) if config["with_clearml"]: from clearml import Task task = Task.init("IMDB-Training", task_name=output_path.stem) task.connect_configuration(config) # Log hyper parameters hyper_params = [ "model", "dropout", "n_fc", "batch_size", "max_length", "weight_decay", "num_epochs", "learning_rate", "num_warmup_epochs", ] task.connect({k: config[k] for k in hyper_params}) # Setup dataflow, model, optimizer, criterion train_loader, test_loader = get_dataflow(config) config["num_iters_per_epoch"] = len(train_loader) model, optimizer, criterion, lr_scheduler = initialize(config) # Create trainer for current task trainer = create_trainer(model, optimizer, criterion, lr_scheduler, train_loader.sampler, config, logger) # Let's now setup evaluator engine to perform model's validation and compute metrics metrics = { "Accuracy": Accuracy(output_transform=utils.thresholded_output_transform), "Loss": Loss(criterion), } # We define two evaluators as they wont have exactly similar roles: # - `evaluator` will save the best model based on validation score evaluator = create_evaluator(model, metrics, config, tag="val") train_evaluator = create_evaluator(model, metrics, config, tag="train") def run_validation(engine): epoch = trainer.state.epoch state = train_evaluator.run(train_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(test_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) trainer.add_event_handler( Events.EPOCH_COMPLETED(every=config["validate_every"]) | Events.COMPLETED | Events.STARTED, run_validation ) if rank == 0: # Setup TensorBoard logging on trainer and evaluators. Logged values are: # - Training metrics, e.g. running average loss values # - Learning rate # - Evaluation train/test metrics evaluators = {"training": train_evaluator, "test": evaluator} tb_logger = common.setup_tb_logging( output_path, trainer, optimizer, evaluators=evaluators, log_every_iters=config["log_every_iters"] ) # Store 2 best models by validation accuracy starting from num_epochs / 2: best_model_handler = Checkpoint( {"model": model}, utils.get_save_handler(config), filename_prefix="best", n_saved=2, global_step_transform=global_step_from_engine(trainer), score_name="test_accuracy", score_function=Checkpoint.get_default_score_fn("Accuracy"), ) evaluator.add_event_handler( Events.COMPLETED(lambda *_: trainer.state.epoch > config["num_epochs"] // 2), best_model_handler ) try: trainer.run(train_loader, max_epochs=config["num_epochs"]) except Exception as e: logger.exception("") raise e if rank == 0: tb_logger.close() def run( seed=543, data_dir="/tmp/data", output_dir="/tmp/output-imdb/", model="bert-base-uncased", model_dir="/tmp/model", tokenizer_dir="/tmp/tokenizer", num_classes=1, dropout=0.3, n_fc=768, max_length=256, batch_size=32, weight_decay=0.01, num_workers=4, num_epochs=3, learning_rate=5e-5, num_warmup_epochs=0, validate_every=1, checkpoint_every=1000, backend=None, resume_from=None, log_every_iters=15, nproc_per_node=None, with_clearml=False, with_amp=False, **spawn_kwargs, ): """Main entry to fintune a transformer model on the IMDB dataset for sentiment classification. Args: seed (int): random state seed to set. Default, 543. data_dir (str): dataset cache directory. Default, "/tmp/data". output_path (str): output path. Default, "/tmp/output-IMDB". model (str): model name (from transformers) to setup model,tokenize and config to train. Default, "bert-base-uncased". model_dir (str): cache directory to download the pretrained model. Default, "/tmp/model". tokenizer_dir (str) : tokenizer cache directory. Default, "/tmp/tokenizer". num_classes (int) : number of target classes. Default, 1 (binary classification). dropout (float) : dropout probability. Default, 0.3. n_fc (int) : number of neurons in the last fully connected layer. Default, 768. max_length (int) : maximum number of tokens for the inputs to the transformer model. Default,256 batch_size (int): total batch size. Default, 128 . weight_decay (float): weight decay. Default, 0.01 . num_workers (int): number of workers in the data loader. Default, 12. num_epochs (int): number of epochs to train the model. Default, 5. learning_rate (float): peak of piecewise linear learning rate scheduler. Default, 5e-5. num_warmup_epochs (int): number of warm-up epochs before learning rate decay. Default, 3. validate_every (int): run model's validation every ``validate_every`` epochs. Default, 3. checkpoint_every (int): store training checkpoint every ``checkpoint_every`` iterations. Default, 1000. backend (str, optional): backend to use for distributed configuration. Possible values: None, "nccl", "xla-tpu", "gloo" etc. Default, None. nproc_per_node (int, optional): optional argument to setup number of processes per node. It is useful, when main python process is spawning training as child processes. resume_from (str, optional): path to checkpoint to use to resume the training from. Default, None. log_every_iters (int): argument to log batch loss every ``log_every_iters`` iterations. It can be 0 to disable it. Default, 15. with_clearml (bool): if True, experiment ClearML logger is setup. Default, False. with_amp (bool): if True, enables native automatic mixed precision. Default, False. **spawn_kwargs: Other kwargs to spawn run in child processes: master_addr, master_port, node_rank, nnodes """ # check to see if the num_epochs is greater than or equal to num_warmup_epochs if num_warmup_epochs >= num_epochs: raise ValueError( "num_epochs cannot be less than or equal to num_warmup_epochs, please increase num_epochs or decrease " "num_warmup_epochs" ) # catch all local parameters config = locals() config.update(config["spawn_kwargs"]) del config["spawn_kwargs"] spawn_kwargs["nproc_per_node"] = nproc_per_node with idist.Parallel(backend=backend, **spawn_kwargs) as parallel: parallel.run(training, config) def get_dataflow(config): # - Get train/test datasets if idist.get_local_rank() > 0: # Ensure that only local rank 0 download the dataset # Thus each node will download a copy of the dataset idist.barrier() train_dataset, test_dataset = utils.get_dataset( config["data_dir"], config["model"], config["tokenizer_dir"], config["max_length"] ) if idist.get_local_rank() == 0: # Ensure that only local rank 0 download the dataset idist.barrier() # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu train_loader = idist.auto_dataloader( train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True ) test_loader = idist.auto_dataloader( test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False ) return train_loader, test_loader def initialize(config): model = utils.get_model( config["model"], config["model_dir"], config["dropout"], config["n_fc"], config["num_classes"] ) config["learning_rate"] *= idist.get_world_size() # Adapt model for distributed settings if configured model = idist.auto_model(model) optimizer = optim.AdamW(model.parameters(), lr=config["learning_rate"], weight_decay=config["weight_decay"]) optimizer = idist.auto_optim(optimizer) criterion = nn.BCEWithLogitsLoss() le = config["num_iters_per_epoch"] milestones_values = [ (0, 0.0), (le * config["num_warmup_epochs"], config["learning_rate"]), (le * config["num_epochs"], 0.0), ] lr_scheduler = PiecewiseLinear(optimizer, param_name="lr", milestones_values=milestones_values) return model, optimizer, criterion, lr_scheduler def log_metrics(logger, epoch, elapsed, tag, metrics): metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) logger.info(f"\nEpoch {epoch} - Evaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): logger.info(f"Train {config['model']} on IMDB") logger.info(f"- PyTorch version: {torch.__version__}") logger.info(f"- Ignite version: {ignite.__version__}") if torch.cuda.is_available(): # explicitly import cudnn as # torch.backends.cudnn can not be pickled with hvd spawning procs from torch.backends import cudnn logger.info(f"- GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}") logger.info(f"- CUDA version: {torch.version.cuda}") logger.info(f"- CUDNN version: {cudnn.version()}") logger.info("\n") logger.info("Configuration:") for key, value in config.items(): logger.info(f"\t{key}: {value}") logger.info("\n") if idist.get_world_size() > 1: logger.info("\nDistributed setting:") logger.info(f"\tbackend: {idist.backend()}") logger.info(f"\tworld size: {idist.get_world_size()}") logger.info("\n") def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, config, logger): device = idist.device() # Setup Ignite trainer: # - let's define training step # - add other common handlers: # - TerminateOnNan, # - handler to setup learning rate scheduling, # - ModelCheckpoint # - RunningAverage` on `train_step` output # - Two progress bars on epochs and optionally on iterations with_amp = config["with_amp"] scaler = GradScaler(enabled=with_amp) def train_step(engine, batch): input_batch = batch[0] labels = batch[1].view(-1, 1) if labels.device != device: input_batch = {k: v.to(device, non_blocking=True, dtype=torch.long) for k, v in batch[0].items()} labels = labels.to(device, non_blocking=True, dtype=torch.float) model.train() with autocast(enabled=with_amp): y_pred = model(input_batch) loss = criterion(y_pred, labels) optimizer.zero_grad() scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() return { "batch loss": loss.item(), } trainer = Engine(train_step) trainer.logger = logger to_save = {"trainer": trainer, "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler} metric_names = [ "batch loss", ] if config["log_every_iters"] == 0: # Disable logging training metrics: metric_names = None config["log_every_iters"] = 15 common.setup_common_training_handlers( trainer=trainer, train_sampler=train_sampler, to_save=to_save, save_every_iters=config["checkpoint_every"], save_handler=utils.get_save_handler(config), lr_scheduler=lr_scheduler, output_names=metric_names, log_every_iters=config["log_every_iters"], with_pbars=not config["with_clearml"], clear_cuda_cache=False, ) resume_from = config["resume_from"] if resume_from is not None: checkpoint_fp = Path(resume_from) assert checkpoint_fp.exists(), f"Checkpoint '{checkpoint_fp.as_posix()}' is not found" logger.info(f"Resume from a checkpoint: {checkpoint_fp.as_posix()}") checkpoint = torch.load(checkpoint_fp.as_posix(), map_location="cpu") Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint) return trainer def create_evaluator(model, metrics, config, tag="val"): with_amp = config["with_amp"] device = idist.device() @torch.no_grad() def evaluate_step(engine, batch): model.eval() input_batch = batch[0] labels = batch[1].view(-1, 1) if labels.device != device: input_batch = {k: v.to(device, non_blocking=True, dtype=torch.long) for k, v in batch[0].items()} labels = labels.to(device, non_blocking=True, dtype=torch.float) with autocast(enabled=with_amp): output = model(input_batch) return output, labels evaluator = Engine(evaluate_step) for name, metric in metrics.items(): metric.attach(evaluator, name) if idist.get_rank() == 0 and (not config["with_clearml"]): common.ProgressBar(desc=f"Evaluation ({tag})", persist=False).attach(evaluator) return evaluator if __name__ == "__main__": fire.Fire({"run": run}) ignite-0.5.1/examples/transformers/model.py000066400000000000000000000013541465426447700210370ustar00rootroot00000000000000import torch.nn as nn from transformers import AutoConfig, AutoModelForSequenceClassification class TransformerModel(nn.Module): def __init__(self, model_name, model_dir, dropout, n_fc, n_classes): super(TransformerModel, self).__init__() self.config = AutoConfig.from_pretrained( model_name, num_labels=n_classes, output_hidden_states=n_fc, classifier_dropout=dropout, output_attentions=True, ) self.transformer = AutoModelForSequenceClassification.from_pretrained( model_name, cache_dir=model_dir, config=self.config ) def forward(self, inputs): output = self.transformer(**inputs)["logits"] return output ignite-0.5.1/examples/transformers/requirements.txt000066400000000000000000000001031465426447700226400ustar00rootroot00000000000000pytorch-ignite transformers datasets tqdm tensorboardX fire clearmlignite-0.5.1/examples/transformers/utils.py000066400000000000000000000027521465426447700211020ustar00rootroot00000000000000import torch from dataset import TransformerDataset from datasets import load_dataset from model import TransformerModel from transformers import AutoTokenizer from ignite.handlers import DiskSaver def get_tokenizer(tokenizer_name, tokenizer_dir): tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, cache_dir=tokenizer_dir, do_lower_case=True) return tokenizer def get_model(model_name, model_dir, drop_out, n_fc, num_classes): model = TransformerModel(model_name, model_dir, drop_out, n_fc, num_classes) return model def get_dataset(cache_dir, tokenizer_name, tokenizer_dir, max_length): train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"], cache_dir=cache_dir) tokenizer = get_tokenizer(tokenizer_name, tokenizer_dir) train_texts, train_labels = train_dataset["text"], train_dataset["label"] test_texts, test_labels = test_dataset["text"], test_dataset["label"] train_dataset = TransformerDataset(train_texts, train_labels, tokenizer, max_length) test_dataset = TransformerDataset(test_texts, test_labels, tokenizer, max_length) return train_dataset, test_dataset def thresholded_output_transform(output): y_pred, y = output return torch.round(torch.sigmoid(y_pred)), y def get_save_handler(config): if config["with_clearml"]: from ignite.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=config["output_dir"]) return DiskSaver(config["output_dir"], require_empty=False) ignite-0.5.1/ignite/000077500000000000000000000000001465426447700142765ustar00rootroot00000000000000ignite-0.5.1/ignite/__init__.py000066400000000000000000000002661465426447700164130ustar00rootroot00000000000000import ignite.contrib import ignite.distributed import ignite.engine import ignite.exceptions import ignite.handlers import ignite.metrics import ignite.utils __version__ = "0.5.1" ignite-0.5.1/ignite/_utils.py000066400000000000000000000002661465426447700161530ustar00rootroot00000000000000# For compatibility from ignite.utils import apply_to_tensor, apply_to_type, convert_tensor, to_onehot __all__ = ["apply_to_tensor", "apply_to_type", "convert_tensor", "to_onehot"] ignite-0.5.1/ignite/base/000077500000000000000000000000001465426447700152105ustar00rootroot00000000000000ignite-0.5.1/ignite/base/__init__.py000066400000000000000000000000541465426447700173200ustar00rootroot00000000000000from ignite.base.mixins import Serializable ignite-0.5.1/ignite/base/mixins.py000066400000000000000000000017371465426447700171010ustar00rootroot00000000000000from collections import OrderedDict from collections.abc import Mapping from typing import Tuple class Serializable: _state_dict_all_req_keys: Tuple = () _state_dict_one_of_opt_keys: Tuple = () def state_dict(self) -> OrderedDict: raise NotImplementedError def load_state_dict(self, state_dict: Mapping) -> None: if not isinstance(state_dict, Mapping): raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}") for k in self._state_dict_all_req_keys: if k not in state_dict: raise ValueError( f"Required state attribute '{k}' is absent in provided state_dict '{state_dict.keys()}'" ) opts = [k in state_dict for k in self._state_dict_one_of_opt_keys] if len(opts) > 0 and ((not any(opts)) or (all(opts))): raise ValueError(f"state_dict should contain only one of '{self._state_dict_one_of_opt_keys}' keys") ignite-0.5.1/ignite/contrib/000077500000000000000000000000001465426447700157365ustar00rootroot00000000000000ignite-0.5.1/ignite/contrib/__init__.py000066400000000000000000000000001465426447700200350ustar00rootroot00000000000000ignite-0.5.1/ignite/contrib/engines/000077500000000000000000000000001465426447700173665ustar00rootroot00000000000000ignite-0.5.1/ignite/contrib/engines/__init__.py000066400000000000000000000001271465426447700214770ustar00rootroot00000000000000from ignite.contrib.engines.tbptt import create_supervised_tbptt_trainer, Tbptt_Events ignite-0.5.1/ignite/contrib/engines/common.py000066400000000000000000000674161465426447700212460ustar00rootroot00000000000000import numbers import warnings from functools import partial from typing import Any, Callable, cast, Dict, Iterable, Mapping, Optional, Sequence, Union import torch import torch.nn as nn from torch.optim.optimizer import Optimizer from torch.utils.data.distributed import DistributedSampler # https://github.com/pytorch/ignite/issues/2773 try: from torch.optim.lr_scheduler import LRScheduler as PyTorchLRScheduler except ImportError: from torch.optim.lr_scheduler import _LRScheduler as PyTorchLRScheduler import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers import ( Checkpoint, ClearMLLogger, DiskSaver, EarlyStopping, global_step_from_engine, MLflowLogger, NeptuneLogger, PolyaxonLogger, ProgressBar, TensorboardLogger, TerminateOnNan, VisdomLogger, WandBLogger, ) from ignite.handlers.base_logger import BaseLogger from ignite.handlers.checkpoint import BaseSaveHandler from ignite.handlers.param_scheduler import ParamScheduler from ignite.metrics import GpuInfo, RunningAverage from ignite.metrics.metric import RunningBatchWise from ignite.utils import deprecated def setup_common_training_handlers( trainer: Engine, train_sampler: Optional[DistributedSampler] = None, to_save: Optional[Mapping] = None, save_every_iters: int = 1000, output_path: Optional[str] = None, lr_scheduler: Optional[Union[ParamScheduler, PyTorchLRScheduler]] = None, with_gpu_stats: bool = False, output_names: Optional[Iterable[str]] = None, with_pbars: bool = True, with_pbar_on_iters: bool = True, log_every_iters: int = 100, stop_on_nan: bool = True, clear_cuda_cache: bool = True, save_handler: Optional[Union[Callable, BaseSaveHandler]] = None, **kwargs: Any, ) -> None: """Helper method to setup trainer with common handlers (it also supports distributed configuration): - :class:`~ignite.handlers.terminate_on_nan.TerminateOnNan` - handler to setup learning rate scheduling - :class:`~ignite.handlers.checkpoint.ModelCheckpoint` - :class:`~ignite.metrics.RunningAverage` on `update_function` output - Two progress bars on epochs and optionally on iterations Args: trainer: trainer engine. Output of trainer's `update_function` should be a dictionary or sequence or a single tensor. train_sampler: Optional distributed sampler used to call `set_epoch` method on epoch started event. to_save: dictionary with objects to save in the checkpoint. This argument is passed to :class:`~ignite.handlers.checkpoint.Checkpoint` instance. save_every_iters: saving interval. By default, `to_save` objects are stored each 1000 iterations. output_path: output path to indicate where `to_save` objects are stored. Argument is mutually exclusive with ``save_handler``. lr_scheduler: learning rate scheduler as native torch LRScheduler or ignite's parameter scheduler. with_gpu_stats: if True, :class:`~ignite.metrics.GpuInfo` is attached to the trainer. This requires `pynvml` package to be installed. output_names: list of names associated with `update_function` output dictionary. with_pbars: if True, two progress bars on epochs and optionally on iterations are attached. Default, True. with_pbar_on_iters: if True, a progress bar on iterations is attached to the trainer. Default, True. log_every_iters: logging interval for :class:`~ignite.metrics.GpuInfo` and for epoch-wise progress bar. Default, 100. stop_on_nan: if True, :class:`~ignite.handlers.terminate_on_nan.TerminateOnNan` handler is added to the trainer. Default, True. clear_cuda_cache: if True, `torch.cuda.empty_cache()` is called every end of epoch. Default, True. save_handler: Method or callable class to use to store ``to_save``. See :class:`~ignite.handlers.checkpoint.Checkpoint` for more details. Argument is mutually exclusive with ``output_path``. kwargs: optional keyword args to be passed to construct :class:`~ignite.handlers.checkpoint.Checkpoint`. """ if idist.get_world_size() > 1: _setup_common_distrib_training_handlers( trainer, train_sampler=train_sampler, to_save=to_save, save_every_iters=save_every_iters, output_path=output_path, lr_scheduler=lr_scheduler, with_gpu_stats=with_gpu_stats, output_names=output_names, with_pbars=with_pbars, with_pbar_on_iters=with_pbar_on_iters, log_every_iters=log_every_iters, stop_on_nan=stop_on_nan, clear_cuda_cache=clear_cuda_cache, save_handler=save_handler, **kwargs, ) else: if train_sampler is not None and isinstance(train_sampler, DistributedSampler): warnings.warn( "Argument train_sampler is a distributed sampler," " but either there is no distributed setting or world size is < 2. " "Train sampler argument will be ignored", UserWarning, ) _setup_common_training_handlers( trainer, to_save=to_save, save_every_iters=save_every_iters, output_path=output_path, lr_scheduler=lr_scheduler, with_gpu_stats=with_gpu_stats, output_names=output_names, with_pbars=with_pbars, with_pbar_on_iters=with_pbar_on_iters, log_every_iters=log_every_iters, stop_on_nan=stop_on_nan, clear_cuda_cache=clear_cuda_cache, save_handler=save_handler, **kwargs, ) setup_common_distrib_training_handlers = setup_common_training_handlers def _setup_common_training_handlers( trainer: Engine, to_save: Optional[Mapping] = None, save_every_iters: int = 1000, output_path: Optional[str] = None, lr_scheduler: Optional[Union[ParamScheduler, PyTorchLRScheduler]] = None, with_gpu_stats: bool = False, output_names: Optional[Iterable[str]] = None, with_pbars: bool = True, with_pbar_on_iters: bool = True, log_every_iters: int = 100, stop_on_nan: bool = True, clear_cuda_cache: bool = True, save_handler: Optional[Union[Callable, BaseSaveHandler]] = None, **kwargs: Any, ) -> None: if output_path is not None and save_handler is not None: raise ValueError( "Arguments output_path and save_handler are mutually exclusive. Please, define only one of them" ) if stop_on_nan: trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan()) if lr_scheduler is not None: if isinstance(lr_scheduler, PyTorchLRScheduler): trainer.add_event_handler(Events.ITERATION_COMPLETED, lambda engine: lr_scheduler.step()) else: trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler) if torch.cuda.is_available() and clear_cuda_cache: trainer.add_event_handler(Events.EPOCH_COMPLETED, empty_cuda_cache) if to_save is not None: if output_path is None and save_handler is None: raise ValueError( "If to_save argument is provided then output_path or save_handler arguments should be also defined" ) if output_path is not None: save_handler = DiskSaver(dirname=output_path, require_empty=False) checkpoint_handler = Checkpoint( to_save, cast(Union[Callable, BaseSaveHandler], save_handler), filename_prefix="training", **kwargs ) trainer.add_event_handler(Events.ITERATION_COMPLETED(every=save_every_iters), checkpoint_handler) if with_gpu_stats: GpuInfo().attach( trainer, name="gpu", event_name=Events.ITERATION_COMPLETED(every=log_every_iters) # type: ignore[arg-type] ) if output_names is not None: def output_transform(x: Any, index: int, name: str) -> Any: if isinstance(x, Mapping): return x[name] elif isinstance(x, Sequence): return x[index] elif isinstance(x, (torch.Tensor, numbers.Number)): return x else: raise TypeError( "Unhandled type of update_function's output. " f"It should either mapping or sequence, but given {type(x)}" ) for i, n in enumerate(output_names): RunningAverage(output_transform=partial(output_transform, index=i, name=n)).attach( trainer, n, usage=RunningBatchWise() ) if with_pbars: if with_pbar_on_iters: ProgressBar(persist=False).attach( trainer, metric_names="all", event_name=Events.ITERATION_COMPLETED(every=log_every_iters) ) ProgressBar(persist=True, bar_format="").attach( trainer, event_name=Events.EPOCH_STARTED, closing_event_name=Events.COMPLETED ) def _setup_common_distrib_training_handlers( trainer: Engine, train_sampler: Optional[DistributedSampler] = None, to_save: Optional[Mapping] = None, save_every_iters: int = 1000, output_path: Optional[str] = None, lr_scheduler: Optional[Union[ParamScheduler, PyTorchLRScheduler]] = None, with_gpu_stats: bool = False, output_names: Optional[Iterable[str]] = None, with_pbars: bool = True, with_pbar_on_iters: bool = True, log_every_iters: int = 100, stop_on_nan: bool = True, clear_cuda_cache: bool = True, save_handler: Optional[Union[Callable, BaseSaveHandler]] = None, **kwargs: Any, ) -> None: _setup_common_training_handlers( trainer, to_save=to_save, output_path=output_path, save_every_iters=save_every_iters, lr_scheduler=lr_scheduler, with_gpu_stats=with_gpu_stats, output_names=output_names, with_pbars=(idist.get_rank() == 0) and with_pbars, with_pbar_on_iters=with_pbar_on_iters, log_every_iters=log_every_iters, stop_on_nan=stop_on_nan, clear_cuda_cache=clear_cuda_cache, save_handler=save_handler, **kwargs, ) if train_sampler is not None: if not isinstance(train_sampler, DistributedSampler): raise TypeError("Train sampler should be torch DistributedSampler and have `set_epoch` method") @trainer.on(Events.EPOCH_STARTED) def distrib_set_epoch(engine: Engine) -> None: train_sampler.set_epoch(engine.state.epoch - 1) def empty_cuda_cache(_: Engine) -> None: torch.cuda.empty_cache() import gc gc.collect() @deprecated( "0.4.0", "0.6.0", ("Please use instead: setup_tb_logging, setup_visdom_logging or setup_mlflow_logging etc.",), raise_exception=True, ) def setup_any_logging( logger: BaseLogger, logger_module: Any, trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer], Dict[None, Optimizer]]], evaluators: Optional[Union[Engine, Dict[str, Engine]]], log_every_iters: int, ) -> None: pass def _setup_logging( logger: BaseLogger, trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer], Dict[None, Optimizer]]], evaluators: Optional[Union[Engine, Dict[str, Engine]]], log_every_iters: int, ) -> None: if optimizers is not None: if not isinstance(optimizers, (Optimizer, Mapping)): raise TypeError("Argument optimizers should be either a single optimizer or a dictionary or optimizers") if evaluators is not None: if not isinstance(evaluators, (Engine, Mapping)): raise TypeError("Argument evaluators should be either a single engine or a dictionary or engines") if log_every_iters is None: log_every_iters = 1 logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED(every=log_every_iters), tag="training", metric_names="all" ) if optimizers is not None: # Log optimizer parameters if isinstance(optimizers, Optimizer): optimizers = {None: optimizers} for k, optimizer in optimizers.items(): logger.attach_opt_params_handler( trainer, Events.ITERATION_STARTED(every=log_every_iters), optimizer, param_name="lr", tag=k ) if evaluators is not None: # Log evaluation metrics if isinstance(evaluators, Engine): evaluators = {"validation": evaluators} event_name = Events.ITERATION_COMPLETED if isinstance(logger, WandBLogger) else None gst = global_step_from_engine(trainer, custom_event_name=event_name) for k, evaluator in evaluators.items(): logger.attach_output_handler( evaluator, event_name=Events.COMPLETED, tag=k, metric_names="all", global_step_transform=gst ) def setup_tb_logging( output_path: str, trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> TensorboardLogger: """Method to setup TensorBoard logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: output_path: logging directory path trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.tensorboard_logger.TensorboardLogger` """ logger = TensorboardLogger(log_dir=output_path, **kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_visdom_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> VisdomLogger: """Method to setup Visdom logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.visdom_logger.VisdomLogger` """ logger = VisdomLogger(**kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_mlflow_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> MLflowLogger: """Method to setup MLflow logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.mlflow_logger.MLflowLogger` """ logger = MLflowLogger(**kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_neptune_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> NeptuneLogger: """Method to setup Neptune logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.neptune_logger.NeptuneLogger` """ logger = NeptuneLogger(**kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_wandb_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> WandBLogger: """Method to setup WandB logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.wandb_logger.WandBLogger` """ logger = WandBLogger(**kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_plx_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> PolyaxonLogger: """Method to setup Polyaxon logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.polyaxon_logger.PolyaxonLogger` """ logger = PolyaxonLogger(**kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_clearml_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> ClearMLLogger: """Method to setup ClearML logging on trainer and a list of evaluators. Logged metrics are: - Training metrics, e.g. running average loss values - Learning rate(s) - Evaluation metrics Args: trainer: trainer engine optimizers: single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators: single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters: interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None. kwargs: optional keyword args to be passed to construct the logger. Returns: :class:`~ignite.handlers.clearml_logger.ClearMLLogger` """ logger = ClearMLLogger(**kwargs) _setup_logging(logger, trainer, optimizers, evaluators, log_every_iters) return logger def setup_trains_logging( trainer: Engine, optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None, evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None, log_every_iters: int = 100, **kwargs: Any, ) -> ClearMLLogger: """``setup_trains_logging`` was renamed to :func:`~ignite.contrib.engines.common.setup_clearml_logging`.""" warnings.warn("setup_trains_logging was renamed to setup_clearml_logging.") return setup_clearml_logging(trainer, optimizers, evaluators, log_every_iters, **kwargs) get_default_score_fn = Checkpoint.get_default_score_fn def gen_save_best_models_by_val_score( save_handler: Union[Callable, BaseSaveHandler], evaluator: Engine, models: Union[torch.nn.Module, Dict[str, torch.nn.Module]], metric_name: str, n_saved: int = 3, trainer: Optional[Engine] = None, tag: str = "val", score_sign: float = 1.0, **kwargs: Any, ) -> Checkpoint: """Method adds a handler to ``evaluator`` to save ``n_saved`` of best models based on the metric (named by ``metric_name``) provided by ``evaluator`` (i.e. ``evaluator.state.metrics[metric_name]``). Models with highest metric value will be retained. The logic of how to store objects is delegated to ``save_handler``. Args: save_handler: Method or callable class to use to save engine and other provided objects. Function receives two objects: checkpoint as a dictionary and filename. If ``save_handler`` is callable class, it can inherit of :class:`~ignite.handlers.checkpoint.BaseSaveHandler` and optionally implement ``remove`` method to keep a fixed number of saved checkpoints. In case if user needs to save engine's checkpoint on a disk, ``save_handler`` can be defined with :class:`~ignite.handlers.DiskSaver`. evaluator: evaluation engine used to provide the score models: model or dictionary with the object to save. Objects should have implemented ``state_dict`` and ``load_state_dict`` methods. metric_name: metric name to use for score evaluation. This metric should be present in `evaluator.state.metrics`. n_saved: number of best models to store trainer: trainer engine to fetch the epoch when saving the best model. tag: score name prefix: `{tag}_{metric_name}`. By default, tag is "val". score_sign: sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0. kwargs: optional keyword args to be passed to construct :class:`~ignite.handlers.checkpoint.Checkpoint`. Returns: A :class:`~ignite.handlers.checkpoint.Checkpoint` handler. """ global_step_transform = None if trainer is not None: global_step_transform = global_step_from_engine(trainer) if isinstance(models, nn.Module): to_save: Dict[str, nn.Module] = {"model": models} else: to_save = models best_model_handler = Checkpoint( to_save, save_handler, filename_prefix="best", n_saved=n_saved, global_step_transform=global_step_transform, score_name=f"{tag}_{metric_name.lower()}", score_function=get_default_score_fn(metric_name, score_sign=score_sign), **kwargs, ) evaluator.add_event_handler(Events.COMPLETED, best_model_handler) return best_model_handler def save_best_model_by_val_score( output_path: str, evaluator: Engine, model: torch.nn.Module, metric_name: str, n_saved: int = 3, trainer: Optional[Engine] = None, tag: str = "val", score_sign: float = 1.0, **kwargs: Any, ) -> Checkpoint: """Method adds a handler to ``evaluator`` to save on a disk ``n_saved`` of best models based on the metric (named by ``metric_name``) provided by ``evaluator`` (i.e. ``evaluator.state.metrics[metric_name]``). Models with highest metric value will be retained. Args: output_path: output path to indicate where to save best models evaluator: evaluation engine used to provide the score model: model to store metric_name: metric name to use for score evaluation. This metric should be present in `evaluator.state.metrics`. n_saved: number of best models to store trainer: trainer engine to fetch the epoch when saving the best model. tag: score name prefix: `{tag}_{metric_name}`. By default, tag is "val". score_sign: sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0. kwargs: optional keyword args to be passed to construct :class:`~ignite.handlers.checkpoint.Checkpoint`. Returns: A :class:`~ignite.handlers.checkpoint.Checkpoint` handler. """ return gen_save_best_models_by_val_score( save_handler=DiskSaver(dirname=output_path, require_empty=False), evaluator=evaluator, models=model, metric_name=metric_name, n_saved=n_saved, trainer=trainer, tag=tag, score_sign=score_sign, **kwargs, ) def add_early_stopping_by_val_score( patience: int, evaluator: Engine, trainer: Engine, metric_name: str, score_sign: float = 1.0, ) -> EarlyStopping: """Method setups early stopping handler based on the score (named by `metric_name`) provided by `evaluator`. Metric value should increase in order to keep training and not early stop. Args: patience: number of events to wait if no improvement and then stop the training. evaluator: evaluation engine used to provide the score trainer: trainer engine to stop the run if no improvement. metric_name: metric name to use for score evaluation. This metric should be present in `evaluator.state.metrics`. score_sign: sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0. Returns: A :class:`~ignite.handlers.early_stopping.EarlyStopping` handler. """ es_handler = EarlyStopping( patience=patience, score_function=get_default_score_fn(metric_name, score_sign=score_sign), trainer=trainer ) evaluator.add_event_handler(Events.COMPLETED, es_handler) return es_handler ignite-0.5.1/ignite/contrib/engines/tbptt.py000066400000000000000000000106341465426447700211010ustar00rootroot00000000000000# coding: utf-8 import collections.abc as collections from typing import Callable, Mapping, Optional, Sequence, Union import torch import torch.nn as nn from torch.optim.optimizer import Optimizer from ignite.engine import _prepare_batch, Engine, EventEnum from ignite.utils import apply_to_tensor class Tbptt_Events(EventEnum): """Aditional tbptt events. Additional events for truncated backpropagation throught time dedicated trainer. """ TIME_ITERATION_STARTED = "time_iteration_started" TIME_ITERATION_COMPLETED = "time_iteration_completed" def _detach_hidden( hidden: Union[torch.Tensor, Sequence, Mapping, str, bytes] ) -> Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes]: """Cut backpropagation graph. Auxillary function to cut the backpropagation graph by detaching the hidden vector. """ return apply_to_tensor(hidden, torch.Tensor.detach) def create_supervised_tbptt_trainer( model: nn.Module, optimizer: Optimizer, loss_fn: nn.Module, tbtt_step: int, dim: int = 0, device: Optional[str] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, ) -> Engine: """Create a trainer for truncated backprop through time supervised models. Training recurrent model on long sequences is computationally intensive as it requires to process the whole sequence before getting a gradient. However, when the training loss is computed over many outputs (`X to many `_), there is an opportunity to compute a gradient over a subsequence. This is known as `truncated backpropagation through time `_. This supervised trainer apply gradient optimization step every `tbtt_step` time steps of the sequence, while backpropagating through the same `tbtt_step` time steps. Args: model: the model to train. optimizer: the optimizer to use. loss_fn: the loss function to use. tbtt_step: the length of time chunks (last one may be smaller). dim: axis representing the time dimension. device: device type specification (default: None). Applies to batches. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. Returns: a trainer engine with supervised update function. .. warning:: The internal use of `device` has changed. `device` will now *only* be used to move the input data to the correct device. The `model` should be moved by the user before creating an optimizer. For more information see: * `PyTorch Documentation `_ * `PyTorch's Explanation `_ """ def _update(engine: Engine, batch: Sequence[torch.Tensor]) -> float: loss_list = [] hidden = None x, y = batch for batch_t in zip(x.split(tbtt_step, dim=dim), y.split(tbtt_step, dim=dim)): x_t, y_t = prepare_batch(batch_t, device=device, non_blocking=non_blocking) # Fire event for start of iteration engine.fire_event(Tbptt_Events.TIME_ITERATION_STARTED) # Forward, backward and model.train() optimizer.zero_grad() if hidden is None: y_pred_t, hidden = model(x_t) else: hidden = _detach_hidden(hidden) y_pred_t, hidden = model(x_t, hidden) loss_t = loss_fn(y_pred_t, y_t) loss_t.backward() optimizer.step() # Setting state of engine for consistent behaviour engine.state.output = loss_t.item() loss_list.append(loss_t.item()) # Fire event for end of iteration engine.fire_event(Tbptt_Events.TIME_ITERATION_COMPLETED) # return average loss over the time splits return sum(loss_list) / len(loss_list) engine = Engine(_update) engine.register_events(*Tbptt_Events) return engine ignite-0.5.1/ignite/contrib/handlers/000077500000000000000000000000001465426447700175365ustar00rootroot00000000000000ignite-0.5.1/ignite/contrib/handlers/__init__.py000066400000000000000000000020611465426447700216460ustar00rootroot00000000000000from ignite.handlers import ( # ref # ref clearml_logger, EpochOutputStore, global_step_from_engine, mlflow_logger, neptune_logger, polyaxon_logger, tensorboard_logger, tqdm_logger, visdom_logger, wandb_logger, ) from ignite.handlers.clearml_logger import ClearMLLogger from ignite.handlers.lr_finder import FastaiLRFinder from ignite.handlers.mlflow_logger import MLflowLogger from ignite.handlers.neptune_logger import NeptuneLogger from ignite.handlers.param_scheduler import ( ConcatScheduler, CosineAnnealingScheduler, create_lr_scheduler_with_warmup, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, PiecewiseLinear, ) from ignite.handlers.polyaxon_logger import PolyaxonLogger from ignite.handlers.tensorboard_logger import TensorboardLogger from ignite.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler from ignite.handlers.tqdm_logger import ProgressBar from ignite.handlers.visdom_logger import VisdomLogger from ignite.handlers.wandb_logger import WandBLogger ignite-0.5.1/ignite/contrib/handlers/base_logger.py000066400000000000000000000022361465426447700223640ustar00rootroot00000000000000""" ``ignite.contrib.handlers.base_logger`` was moved to ``ignite.handlers.base_logger``. Note: ``ignite.contrib.handlers.base_logger`` was moved to ``ignite.handlers.base_logger``. Please refer to :mod:`~ignite.handlers.base_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/base_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.base_logger import ( BaseHandler, BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHandler, BaseWeightsScalarHandler, ) __all__ = [ "BaseHandler", "BaseWeightsHandler", "BaseOptimizerParamsHandler", "BaseOutputHandler", "BaseWeightsScalarHandler", "BaseLogger", ] BaseHandler = BaseHandler BaseWeightsHandler = BaseWeightsHandler BaseOptimizerParamsHandler = BaseOptimizerParamsHandler BaseOutputHandler = BaseOutputHandler BaseWeightsScalarHandler = BaseWeightsScalarHandler BaseLogger = BaseLogger ignite-0.5.1/ignite/contrib/handlers/clearml_logger.py000066400000000000000000000026011465426447700230650ustar00rootroot00000000000000""" ``ignite.contrib.handlers.clearml_logger`` was moved to ``ignite.handlers.clearml_logger``. Note: ``ignite.contrib.handlers.clearml_logger`` was moved to ``ignite.handlers.clearml_logger``. Please refer to :mod:`~ignite.handlers.clearml_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/clearml_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.clearml_logger import ( ClearMLLogger, ClearMLSaver, GradsHistHandler, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, WeightsHistHandler, WeightsScalarHandler, ) from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "ClearMLLogger", "ClearMLSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "WeightsHistHandler", "GradsScalarHandler", "GradsHistHandler", ] ClearMLLogger = ClearMLLogger ClearMLSaver = ClearMLSaver OptimizerParamsHandler = OptimizerParamsHandler OutputHandler = OutputHandler WeightsScalarHandler = WeightsScalarHandler WeightsHistHandler = WeightsHistHandler GradsScalarHandler = GradsScalarHandler GradsHistHandler = GradsHistHandler ignite-0.5.1/ignite/contrib/handlers/lr_finder.py000066400000000000000000000012761465426447700220620ustar00rootroot00000000000000""" ``ignite.contrib.handlers.lr_finder`` was moved to ``ignite.handlers.lr_finder``. Note: ``ignite.contrib.handlers.lr_finder`` was moved to ``ignite.handlers.lr_finder``. Please refer to :mod:`~ignite.handlers.lr_finder`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/lr_finder.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.lr_finder import FastaiLRFinder __all__ = [ "FastaiLRFinder", ] FastaiLRFinder = FastaiLRFinder ignite-0.5.1/ignite/contrib/handlers/mlflow_logger.py000066400000000000000000000016541465426447700227550ustar00rootroot00000000000000""" ``ignite.contrib.handlers.mlflow_logger`` was moved to ``ignite.handlers.mlflow_logger``. Note: ``ignite.contrib.handlers.mlflow_logger`` was moved to ``ignite.handlers.mlflow_logger``. Please refer to :mod:`~ignite.handlers.mlflow_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/mlflow_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.mlflow_logger import MLflowLogger, OptimizerParamsHandler, OutputHandler from ignite.handlers.utils import global_step_from_engine # noqa __all__ = ["MLflowLogger", "OutputHandler", "OptimizerParamsHandler"] MLflowLogger = MLflowLogger OutputHandler = OutputHandler OptimizerParamsHandler = OptimizerParamsHandler ignite-0.5.1/ignite/contrib/handlers/neptune_logger.py000066400000000000000000000024471465426447700231340ustar00rootroot00000000000000""" ``ignite.contrib.handlers.neptune_logger`` was moved to ``ignite.handlers.neptune_logger``. Note: ``ignite.contrib.handlers.neptune_logger`` was moved to ``ignite.handlers.neptune_logger``. Please refer to :mod:`~ignite.handlers.neptune_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/neptune_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.neptune_logger import ( _INTEGRATION_VERSION_KEY, GradsScalarHandler, NeptuneLogger, NeptuneSaver, OptimizerParamsHandler, OutputHandler, WeightsScalarHandler, ) from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "NeptuneLogger", "NeptuneSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", ] NeptuneLogger = NeptuneLogger NeptuneSaver = NeptuneSaver OptimizerParamsHandler = OptimizerParamsHandler OutputHandler = OutputHandler WeightsScalarHandler = WeightsScalarHandler GradsScalarHandler = GradsScalarHandler _INTEGRATION_VERSION_KEY = _INTEGRATION_VERSION_KEY ignite-0.5.1/ignite/contrib/handlers/param_scheduler.py000066400000000000000000000027551465426447700232570ustar00rootroot00000000000000""" ``ignite.contrib.handlers.param_scheduler`` was moved to ``ignite.handlers.param_scheduler``. Note: ``ignite.contrib.handlers.param_scheduler`` was moved to ``ignite.handlers.param_scheduler``. Please refer to :mod:`~ignite.handlers.param_scheduler`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/param_scheduler.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.param_scheduler import ( ConcatScheduler, CosineAnnealingScheduler, create_lr_scheduler_with_warmup, CyclicalScheduler, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, ParamScheduler, PiecewiseLinear, ) __all__ = [ "ConcatScheduler", "CosineAnnealingScheduler", "LinearCyclicalScheduler", "LRScheduler", "ParamGroupScheduler", "ParamScheduler", "PiecewiseLinear", "CyclicalScheduler", "create_lr_scheduler_with_warmup", ] ConcatScheduler = ConcatScheduler CosineAnnealingScheduler = CosineAnnealingScheduler LinearCyclicalScheduler = LinearCyclicalScheduler LRScheduler = LRScheduler ParamGroupScheduler = ParamGroupScheduler ParamScheduler = ParamScheduler PiecewiseLinear = PiecewiseLinear CyclicalScheduler = CyclicalScheduler create_lr_scheduler_with_warmup = create_lr_scheduler_with_warmup ignite-0.5.1/ignite/contrib/handlers/polyaxon_logger.py000066400000000000000000000017021465426447700233200ustar00rootroot00000000000000""" ``ignite.contrib.handlers.polyaxon_logger`` was moved to ``ignite.handlers.polyaxon_logger``. Note: ``ignite.contrib.handlers.polyaxon_logger`` was moved to ``ignite.handlers.polyaxon_logger``. Please refer to :mod:`~ignite.handlers.polyaxon_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/polyaxon_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.polyaxon_logger import OptimizerParamsHandler, OutputHandler, PolyaxonLogger from ignite.handlers.utils import global_step_from_engine # noqa __all__ = ["PolyaxonLogger", "OutputHandler", "OptimizerParamsHandler"] PolyaxonLogger = PolyaxonLogger OutputHandler = OutputHandler OptimizerParamsHandler = OptimizerParamsHandler ignite-0.5.1/ignite/contrib/handlers/tensorboard_logger.py000066400000000000000000000025541465426447700237770ustar00rootroot00000000000000""" ``ignite.contrib.handlers.tensorboard_logger`` was moved to ``ignite.handlers.tensorboard_logger``. Note: ``ignite.contrib.handlers.tensorboard_logger`` was moved to ``ignite.handlers.tensorboard_logger``. Please refer to :mod:`~ignite.handlers.tensorboard_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/tensorboard_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.tensorboard_logger import ( GradsHistHandler, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, TensorboardLogger, WeightsHistHandler, WeightsScalarHandler, ) from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "TensorboardLogger", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "WeightsHistHandler", "GradsScalarHandler", "GradsHistHandler", ] TensorboardLogger = TensorboardLogger OptimizerParamsHandler = OptimizerParamsHandler OutputHandler = OutputHandler WeightsScalarHandler = WeightsScalarHandler WeightsHistHandler = WeightsHistHandler GradsScalarHandler = GradsScalarHandler GradsHistHandler = GradsHistHandler ignite-0.5.1/ignite/contrib/handlers/time_profilers.py000066400000000000000000000015161465426447700231360ustar00rootroot00000000000000""" ``ignite.contrib.handlers.time_profilers.py`` was moved to ``ignite.handlers.time_profilers``. Note: ``ignite.contrib.handlers.time_profilers`` was moved to ``ignite.handlers.time_profilers``. Please refer to :mod:`~ignite.handlers.time_profilers`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/time_profilers.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler __all__ = [ "BasicTimeProfiler", "HandlersTimeProfiler", ] BasicTimeProfiler = BasicTimeProfiler HandlersTimeProfiler = HandlersTimeProfiler ignite-0.5.1/ignite/contrib/handlers/tqdm_logger.py000066400000000000000000000012771465426447700224230ustar00rootroot00000000000000""" ``ignite.contrib.handlers.tqdm_logger`` was moved to ``ignite.handlers.tqdm_logger``. Note: ``ignite.contrib.handlers.tqdm_logger`` was moved to ``ignite.handlers.tqdm_logger``. Please refer to :mod:`~ignite.handlers.tqdm_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/tqdm_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.tqdm_logger import ProgressBar __all__ = [ "ProgressBar", ] ProgressBar = ProgressBar ignite-0.5.1/ignite/contrib/handlers/visdom_logger.py000066400000000000000000000022741465426447700227550ustar00rootroot00000000000000""" ``ignite.contrib.handlers.visdom_logger`` was moved to ``ignite.handlers.visdom_logger``. Note: ``ignite.contrib.handlers.visdom_logger`` was moved to ``ignite.handlers.visdom_logger``. Please refer to :mod:`~ignite.handlers.visdom_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/visdom_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.utils import global_step_from_engine # noqa from ignite.handlers.visdom_logger import ( _DummyExecutor, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, VisdomLogger, WeightsScalarHandler, ) __all__ = [ "VisdomLogger", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", ] VisdomLogger = VisdomLogger OptimizerParamsHandler = OptimizerParamsHandler OutputHandler = OutputHandler WeightsScalarHandler = WeightsScalarHandler GradsScalarHandler = GradsScalarHandler _DummyExecutor = _DummyExecutor ignite-0.5.1/ignite/contrib/handlers/wandb_logger.py000066400000000000000000000016411465426447700225440ustar00rootroot00000000000000""" ``ignite.contrib.handlers.wandb_logger`` was moved to ``ignite.handlers.wandb_logger``. Note: ``ignite.contrib.handlers.wandb_logger`` was moved to ``ignite.handlers.wandb_logger``. Please refer to :mod:`~ignite.handlers.wandb_logger`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/handlers/wandb_logger.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.handlers.utils import global_step_from_engine # noqa from ignite.handlers.wandb_logger import OptimizerParamsHandler, OutputHandler, WandBLogger __all__ = ["WandBLogger", "OutputHandler", "OptimizerParamsHandler"] WandBLogger = WandBLogger OutputHandler = OutputHandler OptimizerParamsHandler = OptimizerParamsHandler ignite-0.5.1/ignite/contrib/metrics/000077500000000000000000000000001465426447700174045ustar00rootroot00000000000000ignite-0.5.1/ignite/contrib/metrics/__init__.py000066400000000000000000000006361465426447700215220ustar00rootroot00000000000000import ignite.metrics.regression from ignite.metrics import average_precision, cohen_kappa, gpu_info, precision_recall_curve, roc_auc from ignite.metrics.average_precision import AveragePrecision from ignite.metrics.cohen_kappa import CohenKappa from ignite.metrics.gpu_info import GpuInfo from ignite.metrics.precision_recall_curve import PrecisionRecallCurve from ignite.metrics.roc_auc import ROC_AUC, RocCurve ignite-0.5.1/ignite/contrib/metrics/average_precision.py000066400000000000000000000013671465426447700234520ustar00rootroot00000000000000""" ``ignite.contrib.metrics.average_precision`` was moved to ``ignite.metrics.average_precision``. Note: ``ignite.contrib.metrics.average_precision`` was moved to ``ignite.metrics.average_precision``. Please refer to :mod:`~ignite.metrics.average_precision`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to /ignite/metrics/average_precision.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.average_precision import AveragePrecision __all__ = [ "AveragePrecision", ] AveragePrecision = AveragePrecision ignite-0.5.1/ignite/contrib/metrics/cohen_kappa.py000066400000000000000000000012641465426447700222310ustar00rootroot00000000000000""" ``ignite.contrib.metrics.cohen_kappa`` was moved to ``ignite.metrics.cohen_kappa``. Note: ``ignite.contrib.metrics.cohen_kappa`` was moved to ``ignite.metrics.cohen_kappa``. Please refer to :mod:`~ignite.metrics.cohen_kappa`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/cohen_kappa.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.cohen_kappa import CohenKappa __all__ = [ "CohenKappa", ] CohenKappa = CohenKappa ignite-0.5.1/ignite/contrib/metrics/gpu_info.py000066400000000000000000000012231465426447700215620ustar00rootroot00000000000000""" ``ignite.contrib.metrics.gpu_info`` was moved to ``ignite.metrics.gpu_info``. Note: ``ignite.contrib.metrics.gpu_info`` was moved to ``ignite.metrics.gpu_info``. Please refer to :mod:`~ignite.metrics.gpu_info`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/gpu_info.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.gpu_info import GpuInfo __all__ = [ "GpuInfo", ] GpuInfo = GpuInfo ignite-0.5.1/ignite/contrib/metrics/precision_recall_curve.py000066400000000000000000000016741465426447700245070ustar00rootroot00000000000000""" ``ignite.contrib.metrics.precision_recall_curve`` was moved to ``ignite.metrics.precision_recall_curve``. Note: ``ignite.contrib.metrics.precision_recall_curve`` was moved to ``ignite.metrics.precision_recall_curve``. Please refer to :mod:`~ignite.metrics.precision_recall_curve`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/precision_recall_curve.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.precision_recall_curve import precision_recall_curve_compute_fn, PrecisionRecallCurve __all__ = [ "PrecisionRecallCurve", "precision_recall_curve_compute_fn", ] PrecisionRecallCurve = PrecisionRecallCurve precision_recall_curve_compute_fn = precision_recall_curve_compute_fn ignite-0.5.1/ignite/contrib/metrics/regression/000077500000000000000000000000001465426447700215645ustar00rootroot00000000000000ignite-0.5.1/ignite/contrib/metrics/regression/__init__.py000066400000000000000000000050251465426447700236770ustar00rootroot00000000000000from ignite.metrics.regression import ( canberra_metric, fractional_absolute_error, fractional_bias, geometric_mean_absolute_error, geometric_mean_relative_absolute_error, manhattan_distance, maximum_absolute_error, mean_absolute_relative_error, mean_error, mean_normalized_bias, median_absolute_error, median_absolute_percentage_error, median_relative_absolute_error, r2_score, wave_hedges_distance, ) from ignite.metrics.regression.canberra_metric import CanberraMetric from ignite.metrics.regression.fractional_absolute_error import FractionalAbsoluteError from ignite.metrics.regression.fractional_bias import FractionalBias from ignite.metrics.regression.geometric_mean_absolute_error import GeometricMeanAbsoluteError from ignite.metrics.regression.geometric_mean_relative_absolute_error import GeometricMeanRelativeAbsoluteError from ignite.metrics.regression.manhattan_distance import ManhattanDistance from ignite.metrics.regression.maximum_absolute_error import MaximumAbsoluteError from ignite.metrics.regression.mean_absolute_relative_error import MeanAbsoluteRelativeError from ignite.metrics.regression.mean_error import MeanError from ignite.metrics.regression.mean_normalized_bias import MeanNormalizedBias from ignite.metrics.regression.median_absolute_error import MedianAbsoluteError from ignite.metrics.regression.median_absolute_percentage_error import MedianAbsolutePercentageError from ignite.metrics.regression.median_relative_absolute_error import MedianRelativeAbsoluteError from ignite.metrics.regression.r2_score import R2Score from ignite.metrics.regression.wave_hedges_distance import WaveHedgesDistance __all__ = [ "CanberraMetric", "FractionalAbsoluteError", "FractionalBias", "GeometricMeanAbsoluteError", "GeometricMeanRelativeAbsoluteError", "ManhattanDistance", "MaximumAbsoluteError", "MeanAbsoluteRelativeError", "MeanError", "MeanNormalizedBias", "MedianAbsoluteError", "MedianAbsolutePercentageError", "MedianRelativeAbsoluteError", "R2Score", "WaveHedgesDistance", "canberra_metric", "fractional_absolute_error", "fractional_bias", "geometric_mean_absolute_error", "geometric_mean_relative_absolute_error", "manhattan_distance", "maximum_absolute_error", "mean_absolute_relative_error", "mean_error", "mean_normalized_bias", "median_absolute_error", "median_absolute_percentage_error", "median_relative_absolute_error", "r2_score", "wave_hedges_distance", ] ignite-0.5.1/ignite/contrib/metrics/regression/_base.py000066400000000000000000000043341465426447700232130ustar00rootroot00000000000000from abc import abstractmethod from typing import Tuple import torch from ignite.metrics import Metric from ignite.metrics.metric import reinit__is_reduced def _check_output_shapes(output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output c1 = y_pred.ndimension() == 2 and y_pred.shape[1] == 1 if not (y_pred.ndimension() == 1 or c1): raise ValueError(f"Input y_pred should have shape (N,) or (N, 1), but given {y_pred.shape}") c2 = y.ndimension() == 2 and y.shape[1] == 1 if not (y.ndimension() == 1 or c2): raise ValueError(f"Input y should have shape (N,) or (N, 1), but given {y.shape}") if y_pred.shape != y.shape: raise ValueError(f"Input data shapes should be the same, but given {y_pred.shape} and {y.shape}") def _check_output_types(output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output if y_pred.dtype not in (torch.float16, torch.float32, torch.float64): raise TypeError(f"Input y_pred dtype should be float 16, 32 or 64, but given {y_pred.dtype}") if y.dtype not in (torch.float16, torch.float32, torch.float64): raise TypeError(f"Input y dtype should be float 16, 32 or 64, but given {y.dtype}") def _torch_median(output: torch.Tensor) -> float: output = output.view(-1) len_ = len(output) if len_ % 2 == 0: return float((torch.kthvalue(output, len_ // 2)[0] + torch.kthvalue(output, len_ // 2 + 1)[0]) / 2) else: return float(torch.kthvalue(output, len_ // 2 + 1)[0]) class _BaseRegression(Metric): # Base class for all regression metrics # `update` method check the shapes and call internal overloaded # method `_update`. @reinit__is_reduced def update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: _check_output_shapes(output) _check_output_types(output) y_pred, y = output[0].detach(), output[1].detach() if y_pred.ndimension() == 2 and y_pred.shape[1] == 1: y_pred = y_pred.squeeze(dim=-1) if y.ndimension() == 2 and y.shape[1] == 1: y = y.squeeze(dim=-1) self._update((y_pred, y)) @abstractmethod def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: pass ignite-0.5.1/ignite/contrib/metrics/regression/canberra_metric.py000066400000000000000000000014621465426447700252610ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.canberra_metric`` was moved to ``ignite.metrics.regression.canberra_metric``. # noqa Note: ``ignite.contrib.metrics.regression.canberra_metric`` was moved to ``ignite.metrics.regression.canberra_metric``. # noqa Please refer to :mod:`~ignite.metrics.regression.canberra_metric`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/canberra_metric.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.canberra_metric import CanberraMetric __all__ = ["CanberraMetric"] CanberraMetric = CanberraMetric ignite-0.5.1/ignite/contrib/metrics/regression/fractional_absolute_error.py000066400000000000000000000016341465426447700273730ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.fractional_absolute_error`` was moved to ``ignite.metrics.regression.fractional_absolute_error``. # noqa Note: ``ignite.contrib.metrics.regression.fractional_absolute_error`` was moved to ``ignite.metrics.regression.fractional_absolute_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.fractional_absolute_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/fractional_absolute_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.fractional_absolute_error import FractionalAbsoluteError __all__ = ["FractionalAbsoluteError"] FractionalAbsoluteError = FractionalAbsoluteError ignite-0.5.1/ignite/contrib/metrics/regression/fractional_bias.py000066400000000000000000000014621465426447700252610ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.fractional_bias`` was moved to ``ignite.metrics.regression.fractional_bias``. # noqa Note: ``ignite.contrib.metrics.regression.fractional_bias`` was moved to ``ignite.metrics.regression.fractional_bias``. # noqa Please refer to :mod:`~ignite.metrics.regression.fractional_bias`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/fractional_bias.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.fractional_bias import FractionalBias __all__ = ["FractionalBias"] FractionalBias = FractionalBias ignite-0.5.1/ignite/contrib/metrics/regression/geometric_mean_absolute_error.py000066400000000000000000000017041465426447700302250ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.geometric_mean_absolute_error`` was moved to ``ignite.metrics.regression.geometric_mean_absolute_error``. # noqa Note: ``ignite.contrib.metrics.regression.geometric_mean_absolute_error`` was moved to ``ignite.metrics.regression.geometric_mean_absolute_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.geometric_mean_absolute_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/geometric_mean_absolute_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.geometric_mean_absolute_error import GeometricMeanAbsoluteError __all__ = ["GeometricMeanAbsoluteError"] GeometricMeanAbsoluteError = GeometricMeanAbsoluteError ignite-0.5.1/ignite/contrib/metrics/regression/geometric_mean_relative_absolute_error.py000066400000000000000000000020431465426447700321150ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.geometric_mean_relative_absolute_error`` was moved to ``ignite.metrics.regression.geometric_mean_relative_absolute_error``. # noqa Note: ``ignite.contrib.metrics.regression.geometric_mean_relative_absolute_error`` was moved to ``ignite.metrics.regression.geometric_mean_relative_absolute_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.geometric_mean_relative_absolute_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/geometric_mean_relative_absolute_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.geometric_mean_relative_absolute_error import GeometricMeanRelativeAbsoluteError __all__ = ["GeometricMeanRelativeAbsoluteError"] GeometricMeanRelativeAbsoluteError = GeometricMeanRelativeAbsoluteError ignite-0.5.1/ignite/contrib/metrics/regression/manhattan_distance.py000066400000000000000000000015231465426447700257640ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.manhattan_distance`` was moved to ``ignite.metrics.regression.manhattan_distance``. # noqa Note: ``ignite.contrib.metrics.regression.manhattan_distance`` was moved to ``ignite.metrics.regression.manhattan_distance``. # noqa Please refer to :mod:`~ignite.metrics.regression.manhattan_distance`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/manhattan_distance.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.manhattan_distance import ManhattanDistance __all__ = ["ManhattanDistance"] ManhattanDistance = ManhattanDistance ignite-0.5.1/ignite/contrib/metrics/regression/maximum_absolute_error.py000066400000000000000000000015731465426447700267300ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.maximum_absolute_error`` was moved to ``ignite.metrics.regression.maximum_absolute_error``. # noqa Note: ``ignite.contrib.metrics.regression.maximum_absolute_error`` was moved to ``ignite.metrics.regression.maximum_absolute_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.maximum_absolute_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/maximum_absolute_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.maximum_absolute_error import MaximumAbsoluteError __all__ = ["MaximumAbsoluteError"] MaximumAbsoluteError = MaximumAbsoluteError ignite-0.5.1/ignite/contrib/metrics/regression/mean_absolute_relative_error.py000066400000000000000000000016711465426447700300650ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.mean_absolute_relative_error`` was moved to ``ignite.metrics.regression.mean_absolute_relative_error``. # noqa Note: ``ignite.contrib.metrics.regression.mean_absolute_relative_error`` was moved to ``ignite.metrics.regression.mean_absolute_relative_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.mean_absolute_relative_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/mean_absolute_relative_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.mean_absolute_relative_error import MeanAbsoluteRelativeError __all__ = ["MeanAbsoluteRelativeError"] MeanAbsoluteRelativeError = MeanAbsoluteRelativeError ignite-0.5.1/ignite/contrib/metrics/regression/mean_error.py000066400000000000000000000013731465426447700242730ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.mean_error`` was moved to ``ignite.metrics.regression.mean_error``. # noqa Note: ``ignite.contrib.metrics.regression.mean_error`` was moved to ``ignite.metrics.regression.mean_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.mean_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/mean_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.mean_error import MeanError __all__ = ["MeanError"] MeanError = MeanError ignite-0.5.1/ignite/contrib/metrics/regression/mean_normalized_bias.py000066400000000000000000000015451465426447700263050ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.mean_normalized_bias`` was moved to ``ignite.metrics.regression.mean_normalized_bias``. # noqa Note: ``ignite.contrib.metrics.regression.mean_normalized_bias`` was moved to ``ignite.metrics.regression.mean_normalized_bias``. # noqa Please refer to :mod:`~ignite.metrics.regression.mean_normalized_bias`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/mean_normalized_bias.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.mean_normalized_bias import MeanNormalizedBias __all__ = ["MeanNormalizedBias"] MeanNormalizedBias = MeanNormalizedBias ignite-0.5.1/ignite/contrib/metrics/regression/median_absolute_error.py000066400000000000000000000015601465426447700265040ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.median_absolute_error`` was moved to ``ignite.metrics.regression.median_absolute_error``. # noqa Note: ``ignite.contrib.metrics.regression.median_absolute_error`` was moved to ``ignite.metrics.regression.median_absolute_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.median_absolute_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/median_absolute_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.median_absolute_error import MedianAbsoluteError __all__ = ["MedianAbsoluteError"] MedianAbsoluteError = MedianAbsoluteError ignite-0.5.1/ignite/contrib/metrics/regression/median_absolute_percentage_error.py000066400000000000000000000017451465426447700307060ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.median_absolute_percentage_error`` was moved to ``ignite.metrics.regression.median_absolute_percentage_error``. # noqa Note: ``ignite.contrib.metrics.regression.median_absolute_percentage_error`` was moved to ``ignite.metrics.regression.median_absolute_percentage_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.median_absolute_percentage_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/median_absolute_percentage_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.median_absolute_percentage_error import MedianAbsolutePercentageError __all__ = ["MedianAbsolutePercentageError"] MedianAbsolutePercentageError = MedianAbsolutePercentageError ignite-0.5.1/ignite/contrib/metrics/regression/median_relative_absolute_error.py000066400000000000000000000017171465426447700304030ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.median_relative_absolute_error`` was moved to ``ignite.metrics.regression.median_relative_absolute_error``. # noqa Note: ``ignite.contrib.metrics.regression.median_relative_absolute_error`` was moved to ``ignite.metrics.regression.median_relative_absolute_error``. # noqa Please refer to :mod:`~ignite.metrics.regression.median_relative_absolute_error`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/median_relative_absolute_error.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.median_relative_absolute_error import MedianRelativeAbsoluteError __all__ = ["MedianRelativeAbsoluteError"] MedianRelativeAbsoluteError = MedianRelativeAbsoluteError ignite-0.5.1/ignite/contrib/metrics/regression/r2_score.py000066400000000000000000000013451465426447700236570ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.r2_score`` was moved to ``ignite.metrics.regression.r2_score``. # noqa Note: ``ignite.contrib.metrics.regression.r2_score`` was moved to ``ignite.metrics.regression.r2_score``. # noqa Please refer to :mod:`~ignite.metrics.regression.r2_score`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/r2_score.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.r2_score import R2Score __all__ = ["R2Score"] R2Score = R2Score ignite-0.5.1/ignite/contrib/metrics/regression/wave_hedges_distance.py000066400000000000000000000015451465426447700262760ustar00rootroot00000000000000""" ``ignite.contrib.metrics.regression.wave_hedges_distance`` was moved to ``ignite.metrics.regression.wave_hedges_distance``. # noqa Note: ``ignite.contrib.metrics.regression.wave_hedges_distance`` was moved to ``ignite.metrics.regression.wave_hedges_distance``. # noqa Please refer to :mod:`~ignite.metrics.regression.wave_hedges_distance`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/regression/wave_hedges_distance.py" f" and will be removed in version {removed_in}" if removed_in else "" ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.regression.wave_hedges_distance import WaveHedgesDistance __all__ = ["WaveHedgesDistance"] WaveHedgesDistance = WaveHedgesDistance ignite-0.5.1/ignite/contrib/metrics/roc_auc.py000066400000000000000000000012571465426447700213760ustar00rootroot00000000000000""" ``ignite.contrib.metrics.roc_auc`` was moved to ``ignite.metrics.roc_auc``. Note: ``ignite.contrib.metrics.roc_auc`` was moved to ``ignite.metrics.roc_auc``. Please refer to :mod:`~ignite.metrics.roc_auc`. """ import warnings removed_in = "0.6.0" deprecation_warning = ( f"{__file__} has been moved to ignite/metrics/roc_auc.py" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) from ignite.metrics.roc_auc import ROC_AUC, RocCurve __all__ = ["RocCurve", "ROC_AUC"] RocCurve = RocCurve ROC_AUC = ROC_AUC ignite-0.5.1/ignite/distributed/000077500000000000000000000000001465426447700166205ustar00rootroot00000000000000ignite-0.5.1/ignite/distributed/__init__.py000066400000000000000000000002651465426447700207340ustar00rootroot00000000000000from ignite.distributed.auto import * from ignite.distributed.comp_models import native, xla from ignite.distributed.launcher import Parallel from ignite.distributed.utils import * ignite-0.5.1/ignite/distributed/auto.py000066400000000000000000000360511465426447700201470ustar00rootroot00000000000000import warnings from typing import Any, Iterator, List, Optional, Union import torch import torch.nn as nn from torch.optim.optimizer import Optimizer from torch.utils.data import DataLoader, Dataset, IterableDataset from torch.utils.data.distributed import DistributedSampler from torch.utils.data.sampler import Sampler from ignite.distributed import utils as idist from ignite.distributed.comp_models import horovod as idist_hvd, native as idist_native, xla as idist_xla from ignite.utils import setup_logger __all__ = ["auto_dataloader", "auto_model", "auto_optim", "DistributedProxySampler"] def auto_dataloader(dataset: Dataset, **kwargs: Any) -> Union[DataLoader, "_MpDeviceLoader"]: """Helper method to create a dataloader adapted for non-distributed and distributed configurations (supporting all available backends from :meth:`~ignite.distributed.utils.available_backends()`). Internally, we create a dataloader with provided kwargs while applying the following updates: - batch size is scaled by world size: ``batch_size / world_size`` if larger or equal world size. - number of workers is scaled by number of local processes: ``num_workers / nprocs`` if larger or equal world size. - if no sampler provided by user, a `torch DistributedSampler`_ is setup. - if a `torch DistributedSampler`_ is provided by user, it is used without wrapping it. - if another sampler is provided, it is wrapped by :class:`~ignite.distributed.auto.DistributedProxySampler`. - if the default device is 'cuda', `pin_memory` is automatically set to `True`. .. warning:: Custom batch sampler is not adapted for distributed configuration. Please, make sure that provided batch sampler is compatible with distributed configuration. Args: dataset: input torch dataset. If input dataset is `torch IterableDataset`_ then dataloader will be created without any distributed sampling. Please, make sure that the dataset itself produces different data on different ranks. kwargs: keyword arguments for `torch DataLoader`_. Returns: `torch DataLoader`_ or `XLA MpDeviceLoader`_ for XLA devices Examples: .. code-block:: python import ignite.distribted as idist train_loader = idist.auto_dataloader( train_dataset, batch_size=32, num_workers=4, shuffle=True, pin_memory="cuda" in idist.device().type, drop_last=True, ) .. _torch DataLoader: https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader .. _XLA MpDeviceLoader: https://pytorch.org/xla/release/2.0/index.html#running-on-multiple-xla-devices-with-multi-processing .. _torch DistributedSampler: https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler .. _torch IterableDataset: https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset """ rank = idist.get_rank() world_size = idist.get_world_size() logger = setup_logger(__name__ + ".auto_dataloader") if world_size > 1: if "batch_size" in kwargs and kwargs["batch_size"] >= world_size: kwargs["batch_size"] //= world_size nproc = idist.get_nproc_per_node() if "num_workers" in kwargs and kwargs["num_workers"] >= nproc: kwargs["num_workers"] = (kwargs["num_workers"] + nproc - 1) // nproc if "batch_sampler" not in kwargs: if isinstance(dataset, IterableDataset): logger.info( "Found iterable dataset, dataloader will be created without any distributed sampling. " "Please, make sure that the dataset itself produces different data on different ranks." ) else: sampler: Optional[Union[DistributedProxySampler, DistributedSampler, Sampler]] sampler = kwargs.get("sampler", None) if isinstance(sampler, DistributedSampler): if sampler.rank != rank: warnings.warn(f"Found distributed sampler with rank={sampler.rank}, but process rank is {rank}") if sampler.num_replicas != world_size: warnings.warn( f"Found distributed sampler with num_replicas={sampler.num_replicas}, " f"but world size is {world_size}" ) elif sampler is None: # removes "shuffle" from kwargs if sampler is used shuffle = kwargs.pop("shuffle", True) sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=shuffle) else: sampler = DistributedProxySampler(sampler, num_replicas=world_size, rank=rank) kwargs["sampler"] = sampler else: warnings.warn( "Found batch_sampler in provided kwargs. Please, make sure that it is compatible " "with distributed configuration" ) if idist.has_xla_support and idist.backend() == idist_xla.XLA_TPU and kwargs.get("pin_memory", False): # TODO: How about XLA GPU ? warnings.warn( "Found incompatible options: xla support and pin_memory args equal True. " "Argument `pin_memory=False` will be used to construct data loader." ) kwargs["pin_memory"] = False else: kwargs["pin_memory"] = kwargs.get("pin_memory", "cuda" in idist.device().type) logger.info(f"Use data loader kwargs for dataset '{repr(dataset)[:20].strip()}': \n\t{kwargs}") dataloader = DataLoader(dataset, **kwargs) if idist.has_xla_support and idist.backend() == idist_xla.XLA_TPU and world_size > 1: logger.info("DataLoader is wrapped by `MpDeviceLoader` on XLA") mp_device_loader_cls = _MpDeviceLoader try: from torch_xla.distributed.parallel_loader import MpDeviceLoader mp_device_loader_cls = MpDeviceLoader except ImportError: pass mp_dataloader = mp_device_loader_cls(dataloader, idist.device()) mp_dataloader.sampler = dataloader.sampler # type: ignore[attr-defined] return mp_dataloader return dataloader def auto_model(model: nn.Module, sync_bn: bool = False, **kwargs: Any) -> nn.Module: """Helper method to adapt provided model for non-distributed and distributed configurations (supporting all available backends from :meth:`~ignite.distributed.utils.available_backends()`). Internally, we perform to following: - send model to current :meth:`~ignite.distributed.utils.device()` if model's parameters are not on the device. - wrap the model to `torch DistributedDataParallel`_ for native torch distributed if world size is larger than 1. - wrap the model to `torch DataParallel`_ if no distributed context found and more than one CUDA devices available. - broadcast the initial variable states from rank 0 to all other processes if Horovod distributed framework is used. Args: model: model to adapt. sync_bn: if True, applies `torch convert_sync_batchnorm`_ to the model for native torch distributed only. Default, False. Note, if using Nvidia/Apex, batchnorm conversion should be applied before calling ``amp.initialize``. kwargs: kwargs to model's wrapping class: `torch DistributedDataParallel`_ or `torch DataParallel`_ if applicable. Please, make sure to use acceptable kwargs for given backend. Returns: torch.nn.Module Examples: .. code-block:: python import ignite.distribted as idist model = idist.auto_model(model) In addition with NVidia/Apex, it can be used in the following way: .. code-block:: python import ignite.distribted as idist model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level) model = idist.auto_model(model) .. _torch DistributedDataParallel: https://pytorch.org/docs/stable/generated/torch.nn.parallel. DistributedDataParallel.html .. _torch DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html .. _torch convert_sync_batchnorm: https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html# torch.nn.SyncBatchNorm.convert_sync_batchnorm .. versionchanged:: 0.4.2 - Added Horovod distributed framework. - Added ``sync_bn`` argument. .. versionchanged:: 0.4.3 Added kwargs to ``idist.auto_model``. """ logger = setup_logger(__name__ + ".auto_model") # Put model's parameters to device if its parameters are not on the device device = idist.device() if not all([p.device == device for p in model.parameters()]): model.to(device) # distributed data parallel model if idist.get_world_size() > 1: bnd = idist.backend() if idist.has_native_dist_support and bnd in (idist_native.NCCL, idist_native.GLOO, idist_native.MPI): if sync_bn: logger.info("Convert batch norm to sync batch norm") model = nn.SyncBatchNorm.convert_sync_batchnorm(model) if torch.cuda.is_available(): if "device_ids" in kwargs: raise ValueError(f"Argument kwargs should not contain 'device_ids', but got {kwargs}") lrank = idist.get_local_rank() logger.info(f"Apply torch DistributedDataParallel on model, device id: {lrank}") kwargs["device_ids"] = [ lrank, ] else: logger.info("Apply torch DistributedDataParallel on model") model = torch.nn.parallel.DistributedDataParallel(model, **kwargs) elif idist.has_hvd_support and bnd == idist_hvd.HOROVOD: import horovod.torch as hvd logger.info("Broadcast the initial variable states from rank 0 to all other processes") hvd.broadcast_parameters(model.state_dict(), root_rank=0) # not distributed but multiple GPUs reachable so data parallel model elif torch.cuda.device_count() > 1 and "cuda" in idist.device().type: logger.info("Apply torch DataParallel on model") model = torch.nn.parallel.DataParallel(model, **kwargs) return model def auto_optim(optimizer: Optimizer, **kwargs: Any) -> Optimizer: """Helper method to adapt optimizer for non-distributed and distributed configurations (supporting all available backends from :meth:`~ignite.distributed.utils.available_backends()`). Internally, this method is no-op for non-distributed and torch native distributed configuration. For XLA distributed configuration, we create a new class that inherits from provided optimizer. The goal is to override the `step()` method with specific `xm.optimizer_step`_ implementation. For Horovod distributed configuration, optimizer is wrapped with Horovod Distributed Optimizer and its state is broadcasted from rank 0 to all other processes. Args: optimizer: input torch optimizer kwargs: kwargs to Horovod backend's DistributedOptimizer. Returns: Optimizer Examples: .. code-block:: python import ignite.distributed as idist optimizer = idist.auto_optim(optimizer) .. _xm.optimizer_step: https://pytorch.org/xla/release/1.5/index.html#torch_xla.core.xla_model.optimizer_step .. versionchanged:: 0.4.2 Added Horovod distributed optimizer. .. versionchanged:: 0.4.7 Added kwargs to ``idist.auto_optim``. """ bnd = idist.backend() if idist.has_xla_support and bnd == idist_xla.XLA_TPU: cls = type(optimizer.__class__.__name__, (optimizer.__class__,), dict(_XLADistributedOptimizer.__dict__)) return cls(optimizer) if idist.has_hvd_support and bnd == idist_hvd.HOROVOD: import horovod.torch as hvd optimizer = hvd.DistributedOptimizer(optimizer, **kwargs) hvd.broadcast_optimizer_state(optimizer, root_rank=0) return optimizer return optimizer class DistributedProxySampler(DistributedSampler): """Distributed sampler proxy to adapt user's sampler for distributed data parallelism configuration. Code is based on https://github.com/pytorch/pytorch/issues/23430#issuecomment-562350407 Args: sampler: Input torch data sampler. num_replicas: Number of processes participating in distributed training. rank: Rank of the current process within ``num_replicas``. .. note:: Input sampler is assumed to have a constant size. """ def __init__(self, sampler: Sampler, num_replicas: Optional[int] = None, rank: Optional[int] = None) -> None: if not isinstance(sampler, Sampler): raise TypeError(f"Argument sampler should be instance of torch Sampler, but given: {type(sampler)}") if isinstance(sampler, DistributedSampler): raise TypeError("Argument sampler must not be a distributed sampler already") if not hasattr(sampler, "__len__"): raise TypeError("Argument sampler should have length") super(DistributedProxySampler, self).__init__( sampler, num_replicas=num_replicas, rank=rank, shuffle=False # type: ignore[arg-type] ) self.sampler = sampler def __iter__(self) -> Iterator: # deterministically shuffle based on epoch torch.manual_seed(self.epoch) indices: List = [] while len(indices) < self.total_size: indices += list(self.sampler) if len(indices) > self.total_size: indices = indices[: self.total_size] # subsample indices = indices[self.rank : self.total_size : self.num_replicas] if len(indices) != self.num_samples: raise RuntimeError(f"{len(indices)} vs {self.num_samples}") return iter(indices) if idist.has_xla_support: import torch_xla.core.xla_model as xm from torch_xla.distributed.parallel_loader import ParallelLoader class _MpDeviceLoader: # https://github.com/pytorch/xla/pull/2117 # From pytorch/xla if `torch_xla.distributed.parallel_loader.MpDeviceLoader` is not available def __init__(self, loader: Any, device: torch.device, **kwargs: Any) -> None: self._loader = loader self._device = device self._parallel_loader_kwargs = kwargs def __iter__(self) -> Iterator: parallel_loader = ParallelLoader(self._loader, [self._device], **self._parallel_loader_kwargs) return parallel_loader.per_device_loader(self._device) def __len__(self) -> int: return len(self._loader) class _XLADistributedOptimizer(Optimizer): def __init__(self, optimizer: Optimizer) -> None: super(self.__class__, self).__init__(optimizer.param_groups) # type: ignore[call-arg] self.wrapped_optimizer = optimizer def step(self, closure: Any = None) -> Any: xm.optimizer_step(self.wrapped_optimizer, barrier=True) ignite-0.5.1/ignite/distributed/comp_models/000077500000000000000000000000001465426447700211215ustar00rootroot00000000000000ignite-0.5.1/ignite/distributed/comp_models/__init__.py000066400000000000000000000025151465426447700232350ustar00rootroot00000000000000from typing import List, Tuple, Type, TYPE_CHECKING, Union from ignite.distributed.comp_models.base import _SerialModel from ignite.distributed.comp_models.horovod import has_hvd_support from ignite.distributed.comp_models.native import has_native_dist_support from ignite.distributed.comp_models.xla import has_xla_support if TYPE_CHECKING: from ignite.distributed.comp_models.horovod import _HorovodDistModel from ignite.distributed.comp_models.native import _NativeDistModel from ignite.distributed.comp_models.xla import _XlaDistModel def setup_available_computation_models() -> ( Tuple[Type[Union[_SerialModel, "_NativeDistModel", "_XlaDistModel", "_HorovodDistModel"]], ...] ): models: List[Type[Union[_SerialModel, "_NativeDistModel", "_XlaDistModel", "_HorovodDistModel"]]] = [ _SerialModel, ] if has_native_dist_support: from ignite.distributed.comp_models.native import _NativeDistModel models.append(_NativeDistModel) if has_xla_support: from ignite.distributed.comp_models.xla import _XlaDistModel models.append(_XlaDistModel) if has_hvd_support: from ignite.distributed.comp_models.horovod import _HorovodDistModel models.append(_HorovodDistModel) return tuple(models) registered_computation_models = setup_available_computation_models() ignite-0.5.1/ignite/distributed/comp_models/base.py000066400000000000000000000333051465426447700224110ustar00rootroot00000000000000from abc import ABCMeta, abstractmethod from numbers import Number from typing import Any, Callable, cast, List, Optional, Union import torch from packaging.version import Version _torch_version_gt_112 = Version(torch.__version__) > Version("1.12.0") class ComputationModel(metaclass=ABCMeta): """Base class for distributed computation models and defines interface methods. This class is public and should be used for other custom derived distributed models. """ # this is an additional local rank storage used when idist is setup from existing native torch dist context _ext_local_rank: Optional[int] = None def __init__(self) -> None: self._backend: Optional[str] = None self._nproc_per_node: Optional[int] = None self._nnodes: Optional[int] = None self._node: Optional[int] = None def _setup_attrs(self) -> None: if self._nproc_per_node is None: self._nproc_per_node = self._compute_nproc_per_node() if self.get_world_size() > 1 else 1 if self._nnodes is None: self._nnodes = self.get_world_size() // self._nproc_per_node if self._node is None: self._node = self.get_rank() // self._nproc_per_node @abstractmethod def _compute_nproc_per_node(self) -> int: pass @abstractmethod def get_local_rank(self) -> int: pass @abstractmethod def get_rank(self) -> int: pass @abstractmethod def get_world_size(self) -> int: pass @abstractmethod def get_nproc_per_node(self) -> int: pass @abstractmethod def get_nnodes(self) -> int: pass @abstractmethod def get_node_rank(self) -> int: pass @abstractmethod def device(self) -> torch.device: pass @abstractmethod def backend(self) -> Optional[str]: pass @abstractmethod def finalize(self) -> None: pass @staticmethod @abstractmethod def create_from_context() -> Optional["ComputationModel"]: pass @staticmethod @abstractmethod def create_from_backend(backend: str, **kwargs: Any) -> "ComputationModel": pass @staticmethod @abstractmethod def spawn(*args: Any, **kwargs: Any) -> None: pass _collective_op_dtype: Any = None @staticmethod def _encode_str(x: str, device: torch.device, size: int) -> torch.Tensor: name = torch.tensor(bytearray(x, "utf-8")).to(device) padded_x = torch.zeros(size + 1, device=device, dtype=torch.long) padded_x[: len(name)] = name padded_x[-1] = len(name) # output is tensor of shape (1, size + 1) return padded_x.unsqueeze(0) def _get_max_length(self, x: str, device: torch.device) -> int: size = torch.tensor([len(x)], device=device) size = self._do_all_reduce(size, op="MAX") return cast(int, size.item()) @staticmethod def _encode_input_data(x: Union[torch.Tensor, float, str, None], is_src: bool) -> List[int]: encoded_msg = [-1] * 512 if not is_src: # Discard input type if not source return encoded_msg if isinstance(x, torch.Tensor): shape = x.shape dtype = str(x.dtype) msg = [0, len(shape), *shape, len(dtype), *list(bytearray(dtype, "utf-8"))] encoded_msg[: len(msg)] = msg elif isinstance(x, Number): encoded_msg[0] = 1 elif isinstance(x, str): encoded_msg[0] = 2 return encoded_msg @staticmethod def _decode_as_placeholder(encoded_msg: List[int], device: torch.device) -> Union[torch.Tensor, float, str]: if encoded_msg[0] == 0: len_shape = encoded_msg[1] le = 2 + len_shape shape = encoded_msg[2:le] if len_shape > 0 else [] len_dtype = encoded_msg[le] dtype_str = bytearray(encoded_msg[le + 1 : le + 1 + len_dtype]).decode("utf-8") dtype = eval(dtype_str) return torch.empty(shape, device=device, dtype=dtype) elif encoded_msg[0] == 1: return 0.0 elif encoded_msg[0] == 2: return "" else: raise RuntimeError(f"Internal error: unhandled dtype {encoded_msg[0]}, encoded_msg={encoded_msg}") def _setup_placeholder( self, x: Union[torch.Tensor, float, str, None], device: torch.device, is_src: bool ) -> Union[torch.Tensor, float, str]: encoded_msg_per_rank = self._encode_input_data(x, is_src) encoded_msg_all_ranks = self._do_all_reduce(torch.tensor(encoded_msg_per_rank, device=device), op="MAX") if is_src: if x is None: raise RuntimeError("Internal error, x is None. Please, file an issue if you encounter this error.") return x encoded_msg = encoded_msg_all_ranks.cpu().tolist() return self._decode_as_placeholder(encoded_msg, device) @staticmethod def _decode_str(xs: torch.Tensor) -> List[str]: # xs.shape = (n, size + 1), e.g. (world_size, size + 1) out = [bytearray(x[: x[-1]].tolist()).decode("utf-8") for x in xs] return out def _apply_op( self, tensor: torch.Tensor, device: torch.device, fn: Callable, *args: Any, **kwargs: Any ) -> torch.Tensor: out_dtype = None tensor_device = None # check if the tensor is at specified device if tensor.device != device: tensor_device = tensor.device tensor = tensor.to(device) if self._collective_op_dtype is not None: # cast to _collective_op_dtype if current type is not floatX if tensor.dtype not in (torch.float32, torch.float64): out_dtype = tensor.dtype tensor = tensor.to(self._collective_op_dtype) tensor = fn(tensor, *args, **kwargs) if out_dtype is not None and tensor_device is not None: return tensor.to(dtype=out_dtype, device=tensor_device) if out_dtype is not None: return tensor.to(dtype=out_dtype) if tensor_device is not None: return tensor.to(device=tensor_device) return tensor def _collective_op( self, tensor: Union[torch.Tensor, Number, str], fn: Callable, *args: Any, **kwargs: Any ) -> Union[torch.Tensor, float, List[float], List[str]]: tensor_to_number = tensor_to_str = False device = self.device() if isinstance(tensor, (Number, float)): tensor_to_number = True dtype = self._collective_op_dtype if dtype is None and isinstance(tensor, float): dtype = torch.double tensor = torch.tensor(tensor, device=device, dtype=dtype) elif isinstance(tensor, str): tensor_to_str = True max_length = self._get_max_length(tensor, device) tensor = self._encode_str(tensor, device, size=max_length) tensor = self._apply_op(tensor, device, fn, *args, **kwargs) if tensor_to_number: return tensor.tolist() elif tensor_to_str: return self._decode_str(tensor) return tensor def all_reduce( self, tensor: Union[torch.Tensor, float], op: str = "sum", group: Optional[Any] = None ) -> Union[torch.Tensor, float]: if not isinstance(tensor, (torch.Tensor, Number)): raise TypeError(f"Unhandled input type {type(tensor)}") return cast(Union[torch.Tensor, float], self._collective_op(tensor, self._do_all_reduce, op, group=group)) def all_gather( self, tensor: Union[torch.Tensor, float, str, Any], group: Optional[Any] = None ) -> Union[torch.Tensor, float, List[float], List[str], List[Any]]: if not isinstance(tensor, (torch.Tensor, Number, str)): return self._do_all_gather_object(tensor, group=group) return self._collective_op(tensor, self._do_all_gather, group=group) def new_group(self, ranks: List[int], **kwargs: Any) -> Any: if isinstance(ranks, list) and all(isinstance(item, int) for item in ranks): return self._do_new_group(ranks, **kwargs) else: raise ValueError("Argument ranks should be list of int") def broadcast( self, tensor: Union[torch.Tensor, float, str, None], src: int = 0, safe_mode: bool = False ) -> Union[torch.Tensor, float, str]: if not (isinstance(tensor, (torch.Tensor, Number, str)) or tensor is None): raise TypeError(f"Unhandled input type {type(tensor)}") rank = self.get_rank() if tensor is None: if rank == src: raise ValueError("Source data can not be None") elif not safe_mode: raise ValueError("Argument safe_mode should be True if None is passed for non src rank") device = self.device() tensor_to_number = tensor_to_str = False if safe_mode: tensor = self._setup_placeholder(tensor, device, rank == src) if tensor is None: raise RuntimeError("Internal error, tensor is None. Please, file an issue if you encounter this error.") if isinstance(tensor, (Number, float)): # have to use Number and float to satisfy mypy tensor_to_number = True if rank != src: tensor = torch.empty(1, device=device, dtype=torch.float) else: tensor = torch.tensor([tensor], device=device, dtype=torch.float) elif isinstance(tensor, str): tensor_to_str = True max_length = self._get_max_length(tensor, device) if rank != src: tensor = torch.empty(1, max_length + 1, device=device, dtype=torch.long) else: tensor = self._encode_str(tensor, device, size=max_length) tensor = self._apply_op(tensor, device, self._do_broadcast, src) if tensor_to_number: return tensor.item() if tensor_to_str: list_str = self._decode_str(tensor) return list_str[0] return tensor @abstractmethod def _do_all_reduce(self, tensor: torch.Tensor, op: str = "SUM", group: Optional[Any] = None) -> torch.Tensor: pass @abstractmethod def _do_all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None) -> torch.Tensor: pass @abstractmethod def _do_all_gather_object(self, tensor: Any, group: Optional[Any] = None) -> List[Any]: pass @abstractmethod def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: pass @abstractmethod def barrier(self) -> None: pass @abstractmethod def _do_new_group(self, ranks: List[int], **kwargs: Any) -> Any: pass class _SerialModel(ComputationModel): """Private class defines non-distributed computation model for code compatibility with other distributed models.""" name = "serial" available_backends = () def __init__(self, _backend: Optional[str] = None, **kwargs: Any) -> None: super(_SerialModel, self).__init__() def get_local_rank(self) -> int: return 0 def get_rank(self) -> int: return 0 def get_world_size(self) -> int: return 1 def get_nproc_per_node(self) -> int: return 1 def get_nnodes(self) -> int: return 1 def get_node_rank(self) -> int: return 0 def device(self) -> torch.device: if torch.cuda.is_available(): return torch.device("cuda") if _torch_version_gt_112 and torch.backends.mps.is_available(): return torch.device("mps") return torch.device("cpu") def backend(self) -> Optional[str]: return None def finalize(self) -> None: pass def _compute_nproc_per_node(self) -> int: return 1 @staticmethod def create_from_context() -> "_SerialModel": return _SerialModel() @staticmethod def create_from_backend(backend: Optional[str] = None, **kwargs: Any) -> "_SerialModel": return _SerialModel() @staticmethod def spawn(*args: Any, **kwargs: Any) -> None: raise NotImplementedError("Serial computation model does not implement spawn method") def all_reduce( self, tensor: Union[torch.Tensor, float], op: str = "SUM", group: Optional[Any] = None ) -> Union[torch.Tensor, float]: return tensor def all_gather( self, tensor: Union[torch.Tensor, float, str, Any], group: Optional[Any] = None ) -> Union[torch.Tensor, float, List[float], List[str], List[Any]]: if isinstance(tensor, torch.Tensor): return tensor return cast(Union[List[float], List[str], List[Any]], [tensor]) def broadcast( self, tensor: Union[torch.Tensor, float, str, None], src: int = 0, safe_mode: bool = False ) -> Union[torch.Tensor, float, str]: if tensor is None: raise ValueError("Argument tensor should not be None") return tensor def _do_all_reduce(self, tensor: torch.Tensor, op: str = "SUM", group: Optional[Any] = None) -> torch.Tensor: return tensor def _do_all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None) -> torch.Tensor: return tensor def _do_all_gather_object(self, tensor: Any, group: Optional[Any] = None) -> Any: return tensor def _do_new_group(self, ranks: List[int], **kwargs: Any) -> Any: return ranks def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: return tensor def barrier(self) -> None: pass def new_group(self, ranks: List[int], **kwargs: Any) -> Any: if isinstance(ranks, list) and all(isinstance(item, int) for item in ranks): return self._do_new_group(ranks, **kwargs) else: raise ValueError("Argument ranks should be list of int") ignite-0.5.1/ignite/distributed/comp_models/horovod.py000066400000000000000000000177171465426447700231700ustar00rootroot00000000000000import warnings from typing import Any, Callable, cast, List, Mapping, Optional, Tuple import torch from ignite.distributed.comp_models.base import ComputationModel try: import horovod.torch as hvd try: # old API from horovod.run.runner import run as hvd_mp_spawn except ImportError: # new API: https://github.com/horovod/horovod/pull/2099 from horovod import run as hvd_mp_spawn has_hvd_support = True except ImportError: has_hvd_support = False if has_hvd_support: HOROVOD = "horovod" class _HorovodDistModel(ComputationModel): """Private class for `Horovod `_ distributed computation model.""" name = "horovod-dist" available_backends = (HOROVOD,) @staticmethod def _get_hvd_rank() -> int: try: rank = hvd.rank() except ValueError as e: rank = -1 return rank @staticmethod def create_from_context() -> Optional["_HorovodDistModel"]: rank = _HorovodDistModel._get_hvd_rank() # hvd must be initialized if not rank > -1: return None return _HorovodDistModel() @staticmethod def create_from_backend(backend: str = HOROVOD, **kwargs: Any) -> "_HorovodDistModel": if backend not in _HorovodDistModel.available_backends: raise ValueError(f"Backend should be one of '{_HorovodDistModel.available_backends}'") rank = _HorovodDistModel._get_hvd_rank() # hvd must be not initialized if rank > -1: raise RuntimeError("Can not re-initialize Horovod if it is already initialized") return _HorovodDistModel(backend, **kwargs) def __init__(self, backend: Optional[str] = None, **kwargs: Any) -> None: """This is a private method. Please, use `create_from_backend` or `create_from_context`""" super(_HorovodDistModel, self).__init__() if backend is not None: self._create_from_backend(backend, **kwargs) else: self._init_from_context() def _create_from_backend(self, backend: str, **kwargs: Any) -> None: self._backend: str = backend comm = kwargs.get("comm", None) hvd.init(comm=comm) self._setup_attrs() if torch.cuda.is_available(): torch.cuda.set_device(self.get_local_rank()) def _init_from_context(self) -> None: self._backend = HOROVOD self._setup_attrs() def _compute_nproc_per_node(self) -> int: return hvd.local_size() def get_local_rank(self) -> int: return hvd.local_rank() def get_rank(self) -> int: return hvd.rank() def get_world_size(self) -> int: return hvd.size() def get_nproc_per_node(self) -> int: return cast(int, self._nproc_per_node) def get_nnodes(self) -> int: return cast(int, self._nnodes) def get_node_rank(self) -> int: return cast(int, self._node) def device(self) -> torch.device: if torch.cuda.is_available(): index = torch.cuda.current_device() if index < self.get_local_rank(): warnings.warn( "Current device index is less than current local rank. " "Please, make sure to call torch.cuda.set_device(local_rank)." ) return torch.device(f"cuda:{index}") return torch.device("cpu") def backend(self) -> str: return self._backend def finalize(self) -> None: hvd.shutdown() @staticmethod def _dist_worker_task_fn(backend: str, fn: Callable, args: Tuple, kwargs_dict: Mapping) -> None: from ignite.distributed.utils import _set_model, finalize model = _HorovodDistModel.create_from_backend(backend) _set_model(model) fn(model.get_local_rank(), *args, **kwargs_dict) finalize() @staticmethod def spawn( fn: Callable, args: Tuple, kwargs_dict: Optional[Mapping] = None, nproc_per_node: int = 1, hosts: Optional[str] = None, backend: str = HOROVOD, **kwargs: Any, ) -> None: c1 = "nnodes" in kwargs and kwargs["nnodes"] > 1 c2 = "node_rank" in kwargs and kwargs["node_rank"] > 0 if c1 or c2: raise RuntimeError( "For multi-node configuration, please set 'hosts' argument instead according to horovod.run API." ) if "nnodes" in kwargs: # Remove 'nnodes=1' as it is an unexpected keyword argument for horovod.run del kwargs["nnodes"] if "node_rank" in kwargs: # Remove 'node_rank=0' as it is an unexpected keyword argument for horovod.run del kwargs["node_rank"] hvd_mp_spawn( _HorovodDistModel._dist_worker_task_fn, args=(HOROVOD, fn, args, kwargs_dict), num_proc=nproc_per_node, hosts=hosts, **kwargs, ) _reduce_op_map = { "SUM": hvd.mpi_ops.Sum, "AVERAGE": hvd.mpi_ops.Average, "ADASUM": hvd.mpi_ops.Adasum, } _manual_reduce_op_map = {"MIN": torch.min, "MAX": torch.max, "PRODUCT": torch.prod} def _do_all_reduce(self, tensor: torch.Tensor, op: str = "SUM", group: Optional[Any] = None) -> torch.Tensor: if group is not None: raise NotImplementedError("all_reduce with group for horovod is not implemented") if op in self._manual_reduce_op_map: op_fn = self._manual_reduce_op_map[op] return self._do_manual_all_reduce(tensor, op_fn) if op not in self._reduce_op_map: raise ValueError(f"Unsupported reduction operation: '{op}'") op = self._reduce_op_map[op] return hvd.allreduce(tensor, op=op) def _do_manual_all_reduce(self, tensor: torch.Tensor, op: Any) -> torch.Tensor: # We have to unsqueeze otherwise tensors will be gathered into a single tensor # without splitting (e.g. [1, 1, 1, 3, 3, 3] instead of [[1, 1, 1], [3, 3, 3]]) # and reduction op wont work as expected res = self._do_all_gather(tensor.unsqueeze(0)) reduced_res = op(res, dim=0) if isinstance(reduced_res, torch.Tensor): return reduced_res # output can also torch min/max_return_type: (min/max_vals, indices) return reduced_res[0] def _do_all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None) -> torch.Tensor: if group is not None: raise NotImplementedError("all_gather with group for horovod is not implemented") if tensor.ndimension() == 0: tensor = tensor.unsqueeze(0) return hvd.allgather(tensor) def _do_all_gather_object(self, tensor: Any, group: Optional[Any] = None) -> List[Any]: if group is not None: raise NotImplementedError("all_gather with group for horovod is not implemented") return hvd.allgather_object(tensor) def _do_new_group(self, ranks: List[int], **kwargs: Any) -> Any: return hvd.ProcessSet(ranks) def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: return hvd.broadcast(tensor, root_rank=src) def barrier(self) -> None: # https://github.com/horovod/horovod/issues/159#issuecomment-424834603 # hvd.allreduce(torch.tensor(0, device=self.device()), name="barrier") hvd.allreduce(torch.tensor(0, device="cpu"), name="barrier") ignite-0.5.1/ignite/distributed/comp_models/native.py000066400000000000000000000660711465426447700227730ustar00rootroot00000000000000import os import re import subprocess import warnings from typing import Any, Callable, cast, Dict, List, Mapping, Optional, Tuple, Union import torch import torch.distributed as dist import torch.multiprocessing as mp from packaging.version import Version from ignite.distributed.comp_models.base import ComputationModel has_native_dist_support = dist.is_available() if has_native_dist_support: NCCL = dist.Backend.NCCL GLOO = dist.Backend.GLOO MPI = dist.Backend.MPI class _NativeDistModel(ComputationModel): """Private class for PyTorch native distributed computation model. Supported `backends `_: - NCCL - GLOO - MPI In this implementation we assume the following mapping between backend and devices: - NCCL <-> GPU - GLOO <-> CPU or GPU - MPI <-> CPU """ name = "native-dist" available_backends = tuple(name for name in [NCCL, GLOO, MPI] if getattr(dist, f"is_{name}_available")()) @staticmethod def create_from_context() -> Optional["_NativeDistModel"]: if not (dist.is_available() and dist.is_initialized()): return None return _NativeDistModel() @staticmethod def create_from_backend( backend: str, init_method: Optional[str] = None, world_size: Optional[int] = None, rank: Optional[int] = None, **kwargs: Any, ) -> "_NativeDistModel": if backend not in _NativeDistModel.available_backends: raise ValueError(f"Backend should be one of '{_NativeDistModel.available_backends}'") if dist.is_available() and dist.is_initialized(): raise RuntimeError("Can not create new distributed process group if default one is already initialized") if init_method is None: if world_size is not None or rank is not None: raise ValueError("Arguments rank and world_size should be None if no init_method is provided") else: has_rank = rank is not None has_ws = world_size is not None if (has_rank or has_ws) and (not has_rank or not has_ws): raise ValueError(f"Both rank and world_size should be provided, but given {rank} and {world_size}") return _NativeDistModel( backend=backend, init_method=init_method, world_size=world_size, rank=rank, **kwargs ) def __init__( self, backend: Optional[str] = None, timeout: Optional[int] = None, init_method: Optional[str] = None, world_size: Optional[int] = None, rank: Optional[int] = None, **kwargs: Any, ) -> None: """This is a private method. Please, use `create_from_backend` or `create_from_context`""" super(_NativeDistModel, self).__init__() self._env_backup: Optional[Dict[str, str]] = None self._local_rank: Optional[int] = None self._master_port: Optional[int] = None self._master_addr: Optional[str] = None self._init_method: Optional[str] = None if backend is not None: self._create_from_backend( backend, timeout=timeout, init_method=init_method, world_size=world_size, rank=rank, **kwargs ) else: self._init_from_context() def _create_from_backend( self, backend: str, timeout: Optional[int] = None, init_method: Optional[str] = None, world_size: Optional[int] = None, rank: Optional[int] = None, **kwargs: Any, ) -> None: if backend == dist.Backend.NCCL and not torch.cuda.is_available(): raise RuntimeError("Nccl backend is required but no cuda capable devices") self._backend = backend self.setup_env_vars(rank, world_size) init_pg_kwargs: Dict[str, Any] = {} if timeout is not None: init_pg_kwargs["timeout"] = timeout if init_method is None: init_method = "env://" if "env" not in init_method: init_pg_kwargs["world_size"] = int(os.environ["WORLD_SIZE"]) init_pg_kwargs["rank"] = int(os.environ["RANK"]) self._init_method = init_method dist.init_process_group(backend, init_method=init_method, **init_pg_kwargs) if torch.cuda.is_available(): torch.cuda.set_device(self._local_rank) # Call barrier after init_process_group as in # https://github.com/facebookresearch/maskrcnn-benchmark/issues/172 # Define device ids for NCCL to avoid warnings # [W ProcessGroupNCCL.cpp:1569] Rank 0 using best-guess GPU 0 to perform barrier as devices used by # this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping # is incorrect.Specify device_ids in barrier() to force use of a particular device. if backend == dist.Backend.NCCL and Version(torch.__version__) >= Version("1.8.0"): device_ids = [torch.cuda.current_device()] dist.barrier(device_ids=device_ids) else: # For older versions there is no device_ids arg dist.barrier() self._setup_attrs() def _init_from_context(self) -> None: self._backend = dist.get_backend() self._identify_local_rank() self._setup_attrs() def _compute_nproc_per_node(self) -> int: local_rank = self.get_local_rank() # Create new cpu group to get nproc_per_node such we avoid using # badly configured NCCL gloo_group = dist.new_group(backend="gloo") tensor = torch.tensor([local_rank + 1]).to("cpu") dist.all_reduce(tensor, op=dist.ReduceOp.MAX, group=gloo_group) dist.destroy_process_group(gloo_group) return int(tensor.item()) def _get_all_hostnames(self) -> List[Tuple[str, ...]]: import socket device = "cpu" if torch.cuda.is_available(): index = torch.cuda.current_device() device = f"cuda:{index}" hostname = socket.gethostname() name = torch.tensor(bytearray(hostname, "utf-8")).to(device) padded_t_name = torch.zeros(256, device=device, dtype=torch.long) padded_t_name[: len(name)] = name out_t_names = [torch.zeros_like(padded_t_name) for _ in range(self.get_world_size())] dist.all_gather(out_t_names, padded_t_name) return [tuple(t.cpu().tolist()) for t in out_t_names] @staticmethod def _compute_node_and_local_ranks(rank: int, hostnames: List[Tuple[str, ...]]) -> Tuple[int, int]: from collections import Counter c: Counter = Counter(hostnames) sizes = torch.tensor([0] + list(c.values())) cumsum_sizes = torch.cumsum(sizes, dim=0) node_rank = (rank // cumsum_sizes[1:]).clamp(0, 1).sum().item() local_rank = rank - cumsum_sizes[node_rank].item() return int(local_rank), node_rank def _compute_local_rank_via_hostname(self) -> int: # get all hostnames hostnames = self._get_all_hostnames() local_rank, self._node = self._compute_node_and_local_ranks(self.get_rank(), hostnames) if local_rank < 0 or self._node < 0: raise ValueError( "Failed to correctly estimate local rank. " f"Debugging info: local rank: {local_rank}, node rank: {self._node}, hostnames: {hostnames}" ) return local_rank def _identify_local_rank(self) -> None: if "SLURM_JOB_ID" in os.environ: os.environ["LOCAL_RANK"] = os.environ["SLURM_LOCALID"] if "LOCAL_RANK" in os.environ: self._local_rank = int(os.environ["LOCAL_RANK"]) elif self._ext_local_rank is not None: self._local_rank = self._ext_local_rank else: warnings.warn( "Local rank information for native distributed setting will be initialized using " "a heuristic approach based on the hostnames. In some corner cases, determined " "local rank can be different from the real setup. To avoid this warning, " "please either set `os.environ['LOCAL_RANK']` " "or use `idist.set_local_rank(local_rank)` with correct local rank index." ) # use socket gethostname heuristic to determine number of nodes => local rank self._local_rank = self._compute_local_rank_via_hostname() def setup_env_vars(self, rank: Optional[int] = None, world_size: Optional[int] = None) -> None: self._env_backup = os.environ.copy() if "SLURM_JOB_ID" in os.environ: if rank is not None or world_size is not None: raise ValueError("Arguments rank and world_size should not be specified with SLURM") self._setup_env_in_slurm() else: env_vars = ["RANK", "LOCAL_RANK", "WORLD_SIZE"] all_env_vars_defined = [k in os.environ for k in env_vars] # check if all necessary env vars are set # if partially defined raise an error if any(all_env_vars_defined) and not all(all_env_vars_defined): raise RuntimeError(f"PyTorch distributed configuration should define env variables '{env_vars}'") os.environ["RANK"] = os.environ.get("RANK", f"{rank if rank is not None else 0}") os.environ["WORLD_SIZE"] = os.environ.get( "WORLD_SIZE", f"{world_size if world_size is not None else 1}" ) os.environ["LOCAL_RANK"] = os.environ.get("LOCAL_RANK", "0") os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "15000") os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "127.0.0.1") self._local_rank = int(os.environ["LOCAL_RANK"]) self._master_addr = os.environ["MASTER_ADDR"] self._master_port = int(os.environ["MASTER_PORT"]) def _setup_env_in_slurm(self) -> None: slurm_env_req_vars = [ "SLURM_JOB_ID", "SLURM_PROCID", "SLURM_LOCALID", "SLURM_NTASKS", "SLURM_JOB_NODELIST", "SLURM_JOB_NUM_NODES", ] for k in slurm_env_req_vars: if k not in os.environ: raise RuntimeError(f"SLURM distributed configuration is missing '{k}' in env variables") ddp_vars = _setup_ddp_vars_from_slurm_env(cast(Dict, os.environ)) # define DDP env vars required by PTH: for key, value in ddp_vars.items(): os.environ[key] = str(value) def get_local_rank(self) -> int: return cast(int, self._local_rank) def get_rank(self) -> int: return dist.get_rank() def get_world_size(self) -> int: return dist.get_world_size() def get_nproc_per_node(self) -> int: return cast(int, self._nproc_per_node) def get_nnodes(self) -> int: return cast(int, self._nnodes) def get_node_rank(self) -> int: return cast(int, self._node) def device(self) -> torch.device: if torch.cuda.is_available(): index = torch.cuda.current_device() if index < self.get_local_rank(): warnings.warn( "Current device index is less than current local rank. " "Please, make sure to call torch.cuda.set_device(local_rank)." ) return torch.device(f"cuda:{index}") return torch.device("cpu") def backend(self) -> str: return dist.get_backend() def finalize(self) -> None: dist.destroy_process_group() # restore backed-up env self._restore_env() def _restore_env(self) -> None: # restore backed-up env if self._env_backup is not None: os.environ.clear() os.environ.update(self._env_backup) @staticmethod def _dist_worker_task_fn( local_rank: int, backend: str, fn: Callable, args: Tuple, kw_dict: Mapping, world_size: int, nprocs_per_node: int, node_rank: int, master_addr: Optional[str], master_port: Optional[str], init_method: str, kw: Any, ) -> None: from ignite.distributed.utils import _set_model, finalize copy_env_vars = os.environ.copy() rank = node_rank * nprocs_per_node + local_rank os.environ["LOCAL_RANK"] = str(local_rank) os.environ["RANK"] = str(rank) os.environ["WORLD_SIZE"] = str(world_size) arg_world_size: Optional[int] = world_size arg_rank: Optional[int] = rank if init_method == "env://": os.environ["MASTER_ADDR"] = str(master_addr) os.environ["MASTER_PORT"] = str(master_port) arg_world_size = None arg_rank = None model = _NativeDistModel.create_from_backend( backend, init_method=init_method, world_size=arg_world_size, rank=arg_rank, **kw ) _set_model(model) fn(local_rank, *args, **kw_dict) finalize() os.environ.clear() os.environ.update(copy_env_vars) @staticmethod def spawn( fn: Callable, args: Tuple, kwargs_dict: Optional[Mapping] = None, nproc_per_node: int = 1, nnodes: int = 1, node_rank: int = 0, master_addr: Optional[str] = None, master_port: Optional[int] = None, backend: str = "nccl", init_method: Optional[str] = None, **kwargs: Any, ) -> None: world_size = nnodes * nproc_per_node spawn_kwargs = { "join": kwargs.get("join", True), "daemon": kwargs.get("daemon", False), } start_processes = mp.spawn # start_method and start_processes in pytorch >= 1.5 if Version(torch.__version__) >= Version("1.5.0"): import builtins if "__IPYTHON__" in builtins.__dict__: # use fork in jupyter default_start_method = "fork" else: default_start_method = "spawn" spawn_kwargs["start_method"] = kwargs.get("start_method", default_start_method) start_processes = mp.start_processes # TODO: `spawn` wrongfully does not adopt address and port from environment if `init_method` is "env://" if init_method in [None, "env://"]: init_method = "env://" if master_port is None: master_port = 2222 if master_addr is None: master_addr = "127.0.0.1" elif master_addr is not None: raise ValueError("master_addr should be None if init_method is provided other then 'env://'") elif master_port is not None: raise ValueError("master_port should be None if init_method is provided other then 'env://'") start_processes( _NativeDistModel._dist_worker_task_fn, nprocs=nproc_per_node, args=( backend, fn, args, kwargs_dict, world_size, nproc_per_node, node_rank, master_addr, master_port, init_method, kwargs, ), **spawn_kwargs, ) _reduce_op_map = { "SUM": dist.ReduceOp.SUM, "PRODUCT": dist.ReduceOp.PRODUCT, "MIN": dist.ReduceOp.MIN, "MAX": dist.ReduceOp.MAX, "AND": dist.ReduceOp.BAND, "OR": dist.ReduceOp.BOR, } def _do_all_reduce(self, tensor: torch.Tensor, op: str = "SUM", group: Optional[Any] = None) -> torch.Tensor: if op not in self._reduce_op_map: raise ValueError(f"Unsupported reduction operation: '{op}'") if group is not None and not isinstance(group, dist.ProcessGroup): raise ValueError("Argument group should be list of int or ProcessGroup") reduce_op = self._reduce_op_map[op] # We do if/else here for compatibility with older pytorch versions if group is not None: dist.all_reduce(tensor, reduce_op, group=group) else: dist.all_reduce(tensor, reduce_op) return tensor def _do_all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None) -> torch.Tensor: if group == dist.GroupMember.NON_GROUP_MEMBER: return tensor if group is None: group_size = self.get_world_size() elif isinstance(group, dist.ProcessGroup): group_size = group.size() else: raise ValueError("Argument group should be list of int or ProcessGroup") if tensor.ndimension() == 0: tensor = tensor.unsqueeze(0) output = [torch.zeros_like(tensor) for _ in range(group_size)] # We do if/else here for compatibility with older pytorch versions if group is not None: dist.all_gather(output, tensor, group=group) else: dist.all_gather(output, tensor) return torch.cat(output, dim=0) def _do_all_gather_object(self, tensor: Any, group: Optional[Any] = None) -> List[Any]: if Version(torch.__version__) < Version("1.7.0"): raise RuntimeError( "Current torch version does not implement dist.all_gather_object. " "Required version should be >=1.7.0" ) if group == dist.GroupMember.NON_GROUP_MEMBER: return tensor if group is None: group_size = self.get_world_size() elif isinstance(group, dist.ProcessGroup): group_size = group.size() else: raise ValueError("Argument group should be list of int or ProcessGroup") output = [None for _ in range(group_size)] # We do if/else here for compatibility with older pytorch versions if group is not None: dist.all_gather_object(output, tensor, group=group) else: dist.all_gather_object(output, tensor) return output def _do_new_group(self, ranks: List[int], **kwargs: Any) -> Any: return dist.new_group(ranks=ranks, **kwargs) def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: dist.broadcast(tensor, src=src) return tensor def barrier(self) -> None: dist.barrier() def _expand_hostlist(nodelist: str) -> List[str]: """Expand a compressed hostlist string and returns all hosts listed. Source : https://github.com/LLNL/py-hostlist/blob/master/hostlist/hostlist.py Args: nodelist: Compressed hostlist string .. note:: The host names can be composed by any character except the special ones `[`, `]`, `,`. Only one sequence `[...]` is supported per hostname. .. versionadded:: 0.4.6 """ result_hostlist = [] nodelist_match = r"([^,\[\]]+\[[^\[\]]*\][^,\[\]]*|[^,\[\]]*),?" nodelist = nodelist.replace(" ", "") for node in re.findall(nodelist_match, nodelist): node_match = r"(.+)\[((,?[0-9]+-?,?-?){0,})\](.*)?" match = re.search(node_match, node) if match is None: if node: result_hostlist.append(node) else: # holds the ranges of nodes as a string # now we can manipulate the string and cast it to a list of numbers num = str(match.group(2)).replace("[", "").replace("]", "") if len(num) == 0: raise ValueError(f"hostlist invalid : {nodelist}") num_list = num.split(",") # find range of node numbers ranges = [elem.split("-") if "-" in elem else [elem, elem] for elem in num_list] # if the node numbers contain leading zeros, store them to be lead_zeros = max([len(s) - len(s.lstrip("0")) for s, _ in ranges]) # list of expanded ranges of node numbers nodes_list = [list(range(int(s), int(e) + 1)) for s, e in ranges] # flat the list final_list = [item for sublist in nodes_list for item in sublist] # put final list in ascending order and append cluster name to each node number final_list = list(sorted(set(final_list))) # prepend leading zeros to numbers required hostlist_tmp = [str(elem).zfill(lead_zeros + 1) for elem in final_list] # append hostname to the node numbers hostlist_no_suffix = [match.group(1) + elem for elem in hostlist_tmp] # append suffix to hostlist if there is one final_hostlist = [elem + match.group(4) for elem in hostlist_no_suffix] result_hostlist += final_hostlist return result_hostlist def _setup_ddp_vars_from_slurm_env(environ: Dict[str, str]) -> Dict[str, Union[str, int]]: """Method to setup DDP env vars required by PyTorch from SLURM env""" # 1) Tools like enroot can have hooks to translate slurm env vars to RANK, LOCAL_RANK, WORLD_SIZE etc # See https://github.com/NVIDIA/enroot/blob/v3.1.0/conf/hooks/extra/50-slurm-pytorch.sh # 2) User can use torch.distributed.launch tool to schedule on N local GPUs using 1 node, 1 task by SLURM # To cover case 1), let's ensure that defined RANK == SLURM_PROCID, LOCAL_RANK == SLURM_LOCALID, # WORLD_SIZE == SLURM_NTASKS. We will use defined MASTER_ADDR and MASTER_PORT instead of defining # them by our means # To cover case 2), let's check that defined RANK >= SLURM_PROCID, LOCAL_RANK >= SLURM_LOCALID, # WORLD_SIZE >= SLURM_NTASKS, SLURM_JOB_NUM_NODES == 1 ddp_vars: Dict[str, Union[str, int, None]] = { "RANK": int(environ["SLURM_PROCID"]), "LOCAL_RANK": int(environ["SLURM_LOCALID"]), "WORLD_SIZE": int(environ["SLURM_NTASKS"]), "MASTER_ADDR": None, "MASTER_PORT": None, } pth_ddp_env_vars = {key: environ.get(key, None) for key in ddp_vars} defined_pth_ddp_env_vars = [v is not None for v in pth_ddp_env_vars.values()] if all(defined_pth_ddp_env_vars): nnodes = int(environ["SLURM_JOB_NUM_NODES"]) if nnodes > 1: # ensure that all pth_ddp_env_vars are consistent with slurm vars for key in ["RANK", "LOCAL_RANK", "WORLD_SIZE"]: slurm_var = cast(int, ddp_vars[key]) pth_var = int(cast(str, pth_ddp_env_vars[key])) if slurm_var != pth_var: raise RuntimeError( "Environment variable defined for PyTorch Distributed context is inconsistent with " f"equivalent SLURM env variable. {key}: {pth_var} vs {slurm_var}\n" f"SLURM vars: {ddp_vars}\n" f"PTH vars: {pth_ddp_env_vars}\n" ) else: # ensure that PTH RANK >= SLURM_PROCID, PTH LOCAL_RANK >= SLURM_LOCALID, # PTH WORLD_SIZE >= SLURM_NTASKS for key in ["RANK", "LOCAL_RANK", "WORLD_SIZE"]: slurm_var = cast(int, ddp_vars[key]) pth_var = int(cast(str, pth_ddp_env_vars[key])) if pth_var < slurm_var: raise RuntimeError( "Environment variable defined for PyTorch Distributed context is " "inconsistent with equivalent SLURM env variable. " f"We expect that {key}: {pth_var} >= {slurm_var}\n" f"SLURM vars: {ddp_vars}\n" f"PTH vars: {pth_ddp_env_vars}\n" ) ddp_vars[key] = pth_var # set up MASTER_ADDR and MASTER_PORT from PTH ddp_vars["MASTER_ADDR"] = cast(str, pth_ddp_env_vars["MASTER_ADDR"]) ddp_vars["MASTER_PORT"] = int(cast(str, pth_ddp_env_vars["MASTER_PORT"])) elif any(defined_pth_ddp_env_vars): # Let's warn user about PTH env variables that we could not taken into account warnings.warn( "We detected the following env variables: " f"{[(k, v) for k, v in pth_ddp_env_vars.items() if v is not None]},\n" "but will not take them into account as the following env vars are missing:" f"{[k for k, v in pth_ddp_env_vars.items() if v is None]},\n" ) if ddp_vars["MASTER_ADDR"] is None: nodelist = environ["SLURM_JOB_NODELIST"] try: # use scontrol to expand hostname list hostnames = subprocess.check_output(["scontrol", "show", "hostnames", nodelist]) method = "scontrol" except FileNotFoundError: # expand hostname list as scontrol hostnames = " ".join(_expand_hostlist(nodelist)).encode("utf-8") method = "ignite" # at least one hostname should be defined hostname_list = hostnames.split() if len(hostname_list) < 1: raise RuntimeError(f"No hostname detected in SLURM_JOB_NODELIST by {method} (nodelist={nodelist})") # master address is the first hostname of nodes list ddp_vars["MASTER_ADDR"] = str(hostname_list[0].decode("utf-8")) if ddp_vars["MASTER_PORT"] is None: # port should be the same over all process slurm_port = environ["SLURM_JOB_ID"] slurm_port = slurm_port[-4:] ddp_vars["MASTER_PORT"] = int(slurm_port) + 15000 return cast(Dict[str, Union[str, int]], ddp_vars) ignite-0.5.1/ignite/distributed/comp_models/xla.py000066400000000000000000000140361465426447700222630ustar00rootroot00000000000000from typing import Any, Callable, cast, List, Mapping, Optional, Tuple import torch from ignite.distributed.comp_models.base import ComputationModel try: import torch_xla import torch_xla.core.xla_model as xm import torch_xla.distributed.xla_multiprocessing as xmp has_xla_support = True except ImportError: has_xla_support = False if has_xla_support: XLA_TPU = "xla-tpu" class _XlaDistModel(ComputationModel): """Private class for PyTorch XLA basic distributed computation model. It handles single/multi-device computation model. Supported XLA devices: - CPU - TPU """ name = "xla-dist" available_backends = (XLA_TPU,) @staticmethod def create_from_context() -> Optional["_XlaDistModel"]: return _XlaDistModel() @staticmethod def create_from_backend(backend: str = XLA_TPU, **kwargs: Any) -> "_XlaDistModel": if backend not in _XlaDistModel.available_backends: raise ValueError(f"Backend should be one of '{_XlaDistModel.available_backends}'") return _XlaDistModel(backend=backend, **kwargs) def __init__(self, backend: Optional[str] = None, **kwargs: Any): """This is a private method. Please, use `create_from_backend` or `create_from_context`""" super(_XlaDistModel, self).__init__() if backend is not None: self._create_from_backend(backend, **kwargs) else: self._init_from_context() def _create_from_backend(self, backend: str, **kwargs: Any) -> None: xm.rendezvous("init") self._backend: str = backend self._setup_attrs() def _init_from_context(self) -> None: self._backend = XLA_TPU self._setup_attrs() def _compute_nproc_per_node(self) -> int: tensor = torch.tensor([self.get_local_rank() + 1.0], dtype=torch.float).to(self.device()) xm.all_reduce("max", [tensor]) return int(tensor.item()) def get_local_rank(self) -> int: return xm.get_local_ordinal() def get_rank(self) -> int: return xm.get_ordinal() def get_world_size(self) -> int: return xm.xrt_world_size() def get_nproc_per_node(self) -> int: return cast(int, self._nproc_per_node) def get_nnodes(self) -> int: return cast(int, self._nnodes) def get_node_rank(self) -> int: return cast(int, self._node) def device(self) -> torch.device: dev = torch_xla._XLAC._xla_get_default_device() return torch.device(dev) def backend(self) -> str: return self._backend def finalize(self) -> None: pass @staticmethod def _dist_worker_task_fn( local_rank: int, backend: str, fn: Callable, args: Tuple, kwargs_dict: Mapping ) -> None: from ignite.distributed.utils import _set_model, finalize model = _XlaDistModel.create_from_backend(backend) _set_model(model) fn(local_rank, *args, **kwargs_dict) finalize() @staticmethod def spawn( fn: Callable, args: Tuple, kwargs_dict: Optional[Mapping] = None, nproc_per_node: int = 1, nnodes: int = 1, node_rank: int = 0, backend: str = XLA_TPU, **kwargs: Any, ) -> None: if "start_method" not in kwargs: kwargs["start_method"] = "fork" xmp.spawn( _XlaDistModel._dist_worker_task_fn, args=(backend, fn, args, kwargs_dict), nprocs=nproc_per_node, **kwargs, ) _collective_op_dtype = torch.float32 _reduce_op_map = { "SUM": "sum", "PRODUCT": "mul", "MIN": "min", "MAX": "max", "AND": "and", "OR": "or", } def _do_all_reduce(self, tensor: torch.Tensor, op: str = "SUM", group: Optional[Any] = None) -> torch.Tensor: if group is not None and not self._check_group_type(group): raise ValueError("Argument group should be list of int") op = self._reduce_op_map[op] xm.all_reduce(op, [tensor], groups=group) return tensor def _do_all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None) -> torch.Tensor: # from https://github.com/jysohn23/xla/blob/model-parallel-colab/Gather_Scatter_Broadcast_PyTorch_XLA.ipynb if group is not None and (not isinstance(group, list) or not all(isinstance(item, int) for item in group)): raise ValueError("Argument group should be list of int") group_size = self.get_world_size() output = torch.zeros((group_size,) + tensor.shape, dtype=tensor.dtype, device=tensor.device) output[self.get_rank() % group_size] = tensor xm.all_reduce("sum", [output], groups=group) return output.reshape(-1, *output.shape[2:]) def _do_all_gather_object(self, tensor: Any, group: Optional[Any] = None) -> List[Any]: raise NotImplementedError("all_gather on object is not implemented for xla") def _do_new_group(self, ranks: List[int], **kwargs: Any) -> Any: return [ranks] def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: # from https://github.com/jysohn23/xla/blob/model-parallel-colab/Gather_Scatter_Broadcast_PyTorch_XLA.ipynb if src != self.get_rank(): tensor.fill_(0.0) xm.all_reduce("sum", [tensor]) return tensor def barrier(self) -> None: xm.rendezvous("barrier") def _check_group_type(self, group: Optional[Any]) -> bool: if isinstance(group, list) and all(isinstance(item, int) for item in group): return True return False ignite-0.5.1/ignite/distributed/launcher.py000066400000000000000000000322641465426447700210020ustar00rootroot00000000000000from typing import Any, Callable, Dict, Optional from ignite.distributed import utils as idist from ignite.utils import setup_logger __all__ = [ "Parallel", ] class Parallel: """Distributed launcher context manager to simplify distributed configuration setup for multiple backends: - backends from native torch distributed configuration: "nccl", "gloo" and "mpi" (if available) - XLA on TPUs via `pytorch/xla `_ (if installed) - using `Horovod distributed framework `_ (if installed) Namely, it can: 1) Spawn ``nproc_per_node`` child processes and initialize a processing group according to provided ``backend`` (useful for standalone scripts). 2) Only initialize a processing group given the ``backend`` (useful with tools like `torchrun`_, `horovodrun`_, etc). Args: backend: backend to use: `nccl`, `gloo`, `xla-tpu`, `horovod`. If None, no distributed configuration. nproc_per_node: optional argument, number of processes per node to specify. If not None, :meth:`~ignite.distributed.launcher.Parallel.run` will spawn ``nproc_per_node`` processes that run input function with its arguments. nnodes: optional argument, number of nodes participating in distributed configuration. If not None, :meth:`~ignite.distributed.launcher.Parallel.run` will spawn ``nproc_per_node`` processes that run input function with its arguments. Total world size is `nproc_per_node * nnodes`. This option is only supported by native torch distributed module. For other modules, please setup ``spawn_kwargs`` with backend specific arguments. node_rank: optional argument, current machine index. Mandatory argument if ``nnodes`` is specified and larger than one. This option is only supported by native torch distributed module. For other modules, please setup ``spawn_kwargs`` with backend specific arguments. master_addr: optional argument, master node TCP/IP address for torch native backends (`nccl`, `gloo`). Mandatory argument if ``nnodes`` is specified and larger than one. master_port: optional argument, master node port for torch native backends (`nccl`, `gloo`). Mandatory argument if ``master_addr`` is specified. init_method: optional argument to specify processing group initialization method for torch native backends (`nccl`, `gloo`). Default, "env://". See more info: `dist.init_process_group`_. spawn_kwargs: kwargs to ``idist.spawn`` function. Examples: 1) Single node or Multi-node, Multi-GPU training launched with `torchrun` or `horovodrun`_ tools Single node option with 4 GPUs .. code-block:: bash torchrun --nproc_per_node=4 main.py # or if installed horovod horovodrun -np=4 python main.py Multi-node option : 2 nodes with 8 GPUs each .. code-block:: bash ## node 0 torchrun --nnodes=2 --node_rank=0 --master_addr=master --master_port=3344 \ --nproc_per_node=8 main.py # or if installed horovod horovodrun -np 16 -H hostname1:8,hostname2:8 python main.py ## node 1 torchrun --nnodes=2 --node_rank=1 --master_addr=master --master_port=3344 \ --nproc_per_node=8 main.py User code is the same for both options: .. code-block:: python # main.py import ignite.distributed as idist def training(local_rank, config, **kwargs): # ... print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) # ... backend = "nccl" # or "horovod" if package is installed config = {"key": "value"} with idist.Parallel(backend=backend) as parallel: parallel.run(training, config, a=1, b=2) 2) Single node, Multi-GPU training launched with `python` .. code-block:: bash python main.py .. code-block:: python # main.py import ignite.distributed as idist def training(local_rank, config, **kwargs): # ... print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) # ... backend = "nccl" # or "horovod" if package is installed # no "init_method" was specified , "env://" will be used with idist.Parallel(backend=backend, nproc_per_node=4) as parallel: parallel.run(training, config, a=1, b=2) Initializing the process using ``file://`` .. code-block:: python with idist.Parallel(backend=backend, init_method='file:///d:/tmp/some_file', nproc_per_node=4) as parallel: parallel.run(training, config, a=1, b=2) Initializing the process using ``tcp://`` .. code-block:: python with idist.Parallel(backend=backend, init_method='tcp://10.1.1.20:23456', nproc_per_node=4) as parallel: parallel.run(training, config, a=1, b=2) 3) Single node, Multi-TPU training launched with `python` .. code-block:: bash python main.py .. code-block:: python # main.py import ignite.distributed as idist def training(local_rank, config, **kwargs): # ... print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) # ... config = {"key": "value"} with idist.Parallel(backend="xla-tpu", nproc_per_node=8) as parallel: parallel.run(training, config, a=1, b=2) 4) Multi-node, Multi-GPU training launched with `python`. For example, 2 nodes with 8 GPUs: Using torch native distributed framework: .. code-block:: bash # node 0 python main.py --node_rank=0 # node 1 python main.py --node_rank=1 .. code-block:: python # main.py import ignite.distributed as idist def training(local_rank, config, **kwargs): # ... print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) # ... dist_config = { "nproc_per_node": 8, "nnodes": 2, "node_rank": args.node_rank, "master_addr": "master", "master_port": 15000 } config = {"key": "value"} with idist.Parallel(backend="nccl", **dist_config) as parallel: parallel.run(training, config, a=1, b=2) .. _torchrun: https://pytorch.org/docs/stable/elastic/run.html#launcher-api .. _horovodrun: https://horovod.readthedocs.io/en/latest/api.html#module-horovod.run .. _dist.init_process_group: https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group .. versionchanged:: 0.4.2 ``backend`` now accepts `horovod` distributed framework. .. versionchanged:: 0.4.5 ``init_method`` added. """ def __init__( self, backend: Optional[str] = None, nproc_per_node: Optional[int] = None, nnodes: Optional[int] = None, node_rank: Optional[int] = None, master_addr: Optional[str] = None, master_port: Optional[int] = None, init_method: Optional[str] = None, **spawn_kwargs: Any, ) -> None: if backend is not None: if backend not in idist.available_backends(): raise ValueError(f"Unknown backend '{backend}'. Available backends: {idist.available_backends()}") else: arg_names = ["nproc_per_node", "nnodes", "node_rank", "master_addr", "master_port"] arg_values = [nproc_per_node, nnodes, node_rank, master_addr, master_port] for name, value in zip(arg_names, arg_values): if value is not None: raise ValueError(f"If backend is None, argument '{name}' should be also None, but given {value}") self.backend = backend self._spawn_params = None self.init_method = init_method if self.backend is not None: if nproc_per_node is not None: self._spawn_params = self._setup_spawn_params( nproc_per_node, nnodes, node_rank, master_addr, master_port, init_method, **spawn_kwargs ) # The logger will be setup after the idist.initialize() call self._logger = None @staticmethod def _setup_spawn_params( nproc_per_node: int, nnodes: Optional[int] = None, node_rank: Optional[int] = None, master_addr: Optional[str] = None, master_port: Optional[int] = None, init_method: Optional[str] = None, **spawn_kwargs: Any, ) -> Dict: if nproc_per_node < 1: raise ValueError(f"Argument nproc_per_node should positive, but given {nproc_per_node}") if nnodes is None: nnodes = 1 if nnodes < 1: raise ValueError(f"Argument nnodes should positive, but given {nnodes}") if node_rank is None: if nnodes > 1: raise ValueError("If number of nodes larger than one, arguments node_rank should be given") node_rank = 0 if node_rank >= nnodes or node_rank < 0: raise ValueError(f"Argument node_rank should be between 0 and {nnodes - 1}, but given {node_rank}") if nnodes > 1 and (master_addr is None or master_port is None) and init_method is None: raise ValueError( "If number of nodes larger than one, arguments master_addr and master_port or init_method " f"should be specified, but given master_addr={master_addr}, master_port={master_port} and " f"init_method={init_method}." ) params = { "nproc_per_node": nproc_per_node, "nnodes": nnodes, "node_rank": node_rank, "master_addr": master_addr, "master_port": master_port, "init_method": init_method, } params.update(spawn_kwargs) return {k: v for k, v in params.items() if v is not None} def run(self, func: Callable, *args: Any, **kwargs: Any) -> None: """Execute ``func`` with provided arguments in distributed context. Args: func: function to execute. First argument of the function should be `local_rank` - local process index. args: positional arguments of ``func`` (without `local_rank`). kwargs: keyword arguments of ``func``. Examples: .. code-block:: python def training(local_rank, config, **kwargs): # ... print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) # ... config = {"key": "value"} with idist.Parallel(backend=backend) as parallel: parallel.run(training, config, a=1, b=2) """ if self._spawn_params is not None and self.backend is not None: self._logger.info( # type: ignore[attr-defined] f"Spawn function '{func}' in {self._spawn_params['nproc_per_node']} processes" ) idist.spawn(self.backend, func, args=args, kwargs_dict=kwargs, **self._spawn_params) else: self._logger.info(f"- Run '{func}' in {idist.get_world_size()} processes") # type: ignore[attr-defined] local_rank = idist.get_local_rank() func(local_rank, *args, **kwargs) self._logger.info("End of run") # type: ignore[attr-defined] def __enter__(self) -> "Parallel": if self.backend is not None and self._spawn_params is None: idist.initialize(self.backend, init_method=self.init_method) # The logger can be setup from now since idist.initialize() has been called (if needed) self._logger = setup_logger(__name__ + "." + self.__class__.__name__) # type: ignore[assignment] if self.backend is not None: if self._spawn_params is None: self._logger.info( # type: ignore[attr-defined] f"Initialized processing group with backend: '{self.backend}'" ) else: self._logger.info( # type: ignore[attr-defined] f"Initialized distributed launcher with backend: '{self.backend}'" ) msg = "\n\t".join([f"{k}: {v}" for k, v in self._spawn_params.items() if v is not None]) self._logger.info(f"- Parameters to spawn processes: \n\t{msg}") # type: ignore[attr-defined] return self def __exit__(self, *args: Any, **kwargs: Any) -> None: if (self.backend is not None) and self._spawn_params is None: self._logger.info( # type: ignore[attr-defined] f"Finalized processing group with backend: '{self.backend}'" ) idist.finalize() ignite-0.5.1/ignite/distributed/utils.py000066400000000000000000000541201465426447700203340ustar00rootroot00000000000000import socket from contextlib import contextmanager from functools import wraps from typing import Any, Callable, List, Mapping, Optional, Tuple, Union import torch from ignite.distributed.comp_models import ( _SerialModel, has_hvd_support, has_native_dist_support, has_xla_support, registered_computation_models, ) from ignite.utils import setup_logger __all__ = [ "backend", "broadcast", "device", "available_backends", "model_name", "get_world_size", "get_rank", "get_local_rank", "get_nproc_per_node", "get_node_rank", "get_nnodes", "spawn", "initialize", "finalize", "show_config", "set_local_rank", "all_reduce", "all_gather", "barrier", "hostname", "has_xla_support", "has_native_dist_support", "has_hvd_support", "sync", "registered_computation_models", "one_rank_only", "new_group", "one_rank_first", ] _model = _SerialModel() _need_to_sync = True def sync(temporary: bool = False) -> None: """Helper method to force this module to synchronize with current distributed context. This method should be used when distributed context is manually created or destroyed. Args: temporary: If True, distributed model synchronization is done every call of ``idist.get_*`` methods. This may have a negative performance impact. """ global _model for comp_model_cls in registered_computation_models: if comp_model_cls == _SerialModel: continue model = comp_model_cls.create_from_context() if model is not None: _set_model(model, temporary=temporary) return _model = _SerialModel() def device() -> torch.device: """Returns current device according to current distributed configuration. - `torch.device("cpu")` if no distributed configuration or torch native gloo distributed configuration - `torch.device("cuda:local_rank")` if torch native nccl or horovod distributed configuration - `torch.device("xla:index")` if XLA distributed configuration Returns: torch.device .. versionchanged:: 0.4.2 Added Horovod distributed framework. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.device() def backend() -> Optional[str]: """Returns computation model's backend. - `None` for no distributed configuration - "nccl" or "gloo" or "mpi" for native torch distributed configuration - "xla-tpu" for XLA distributed configuration - "horovod" for Horovod distributed framework Returns: str or None .. versionchanged:: 0.4.2 Added Horovod distributed framework. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.backend() def available_backends() -> Tuple[str, ...]: """Returns available backends.""" out: Tuple[str, ...] = () for m in registered_computation_models: out += m.available_backends return out def model_name() -> str: """Returns distributed configuration name (given by ignite) - `serial` for no distributed configuration - `native-dist` for native torch distributed configuration - `xla-dist` for XLA distributed configuration - `horovod-dist` for Horovod distributed framework .. versionchanged:: 0.4.2 `horovod-dist` will be returned for Horovod distributed framework. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.name def get_world_size() -> int: """Returns world size of current distributed configuration. Returns 1 if no distributed configuration.""" if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.get_world_size() def get_rank() -> int: """Returns process rank within current distributed configuration. Returns 0 if no distributed configuration.""" if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.get_rank() def get_local_rank() -> int: """Returns local process rank within current distributed configuration. Returns 0 if no distributed configuration.""" if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.get_local_rank() def get_nproc_per_node() -> int: """Returns number of processes (or tasks) per node within current distributed configuration. Returns 1 if no distributed configuration. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.get_nproc_per_node() def get_nnodes() -> int: """Returns number of nodes within current distributed configuration. Returns 1 if no distributed configuration. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.get_nnodes() def get_node_rank() -> int: """Returns node rank within current distributed configuration. Returns 0 if no distributed configuration. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.get_node_rank() def hostname() -> str: """Returns host name for current process within current distributed configuration.""" return socket.gethostname() def spawn( backend: str, fn: Callable, args: Tuple, kwargs_dict: Optional[Mapping] = None, nproc_per_node: int = 1, **kwargs: Any, ) -> None: """Spawns ``nproc_per_node`` processes that run ``fn`` with ``args``/``kwargs_dict`` and initialize distributed configuration defined by ``backend``. Args: backend: backend to use: `nccl`, `gloo`, `xla-tpu`, `horovod` fn: function to called as the entrypoint of the spawned process. This function must be defined at the top level of a module so it can be pickled and spawned. This is a requirement imposed by multiprocessing. The function is called as ``fn(i, *args, **kwargs_dict)``, where `i` is the process index and args is the passed through tuple of arguments. args: arguments passed to `fn`. kwargs_dict: kwargs passed to `fn`. nproc_per_node: number of processes to spawn on a single node. Default, 1. kwargs: acceptable kwargs according to provided backend: - | "nccl" or "gloo" : ``nnodes`` (default, 1), ``node_rank`` (default, 0), ``master_addr`` | (default, "127.0.0.1"), ``master_port`` (default, 2222), ``init_method`` (default, "env://"), | `timeout` to `dist.init_process_group`_ function | and kwargs for `mp.start_processes`_ function. - | "xla-tpu" : ``nnodes`` (default, 1), ``node_rank`` (default, 0) and kwargs to `xmp.spawn`_ function. - | "horovod": ``hosts`` (default, None) and other kwargs to `hvd_run`_ function. Arguments ``nnodes=1`` | and ``node_rank=0`` are tolerated and ignored, otherwise an exception is raised. Examples: 1) Launch single node multi-GPU training using torch native distributed framework .. code-block:: python # >>> python main.py # main.py import ignite.distributed as idist def train_fn(local_rank, a, b, c, d=12): import torch.distributed as dist assert dist.is_available() and dist.is_initialized() assert dist.get_world_size() == 4 device = idist.device() assert device == torch.device(f"cuda:{local_rank}") idist.spawn("nccl", train_fn, args=(a, b, c), kwargs_dict={"d": 23}, nproc_per_node=4) 2) Launch multi-node multi-GPU training using torch native distributed framework .. code-block:: python # >>> (node 0): python main.py --node_rank=0 --nnodes=8 --master_addr=master --master_port=2222 # >>> (node 1): python main.py --node_rank=1 --nnodes=8 --master_addr=master --master_port=2222 # >>> ... # >>> (node 7): python main.py --node_rank=7 --nnodes=8 --master_addr=master --master_port=2222 # main.py import torch import ignite.distributed as idist def train_fn(local_rank, nnodes, nproc_per_node): import torch.distributed as dist assert dist.is_available() and dist.is_initialized() assert dist.get_world_size() == nnodes * nproc_per_node device = idist.device() assert device == torch.device(f"cuda:{local_rank}") idist.spawn( "nccl", train_fn, args=(nnodes, nproc_per_node), nproc_per_node=nproc_per_node, nnodes=nnodes, node_rank=node_rank, master_addr=master_addr, master_port=master_port ) 3) Launch single node multi-TPU training (for example on Google Colab) using PyTorch/XLA .. code-block:: python # >>> python main.py # main.py import ignite.distributed as idist def train_fn(local_rank, a, b, c, d=12): import torch_xla.core.xla_model as xm assert xm.get_world_size() == 8 device = idist.device() assert "xla" in device.type idist.spawn("xla-tpu", train_fn, args=(a, b, c), kwargs_dict={"d": 23}, nproc_per_node=8) .. _dist.init_process_group: https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group .. _mp.start_processes: https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn.spawn .. _xmp.spawn: https://pytorch.org/xla/release/1.6/index.html#torch_xla.distributed.xla_multiprocessing.spawn .. _hvd_run: https://horovod.readthedocs.io/en/latest/api.html#module-horovod.run .. versionchanged:: 0.4.2 ``backend`` now accepts `horovod` distributed framework. """ _assert_backend(backend) if kwargs_dict is None: kwargs_dict = {} for comp_model_cls in registered_computation_models: if backend not in comp_model_cls.available_backends: continue comp_model_cls.spawn( fn, args=args, kwargs_dict=kwargs_dict, nproc_per_node=nproc_per_node, backend=backend, **kwargs ) def all_reduce( tensor: Union[torch.Tensor, float], op: str = "SUM", group: Optional[Union[Any, List[int]]] = None ) -> Union[torch.Tensor, float]: """Helper method to perform all reduce operation. Args: tensor: tensor or number to collect across participating processes. op: reduction operation, "SUM" by default. Possible values: "SUM", "PRODUCT", "MIN", "MAX", "AND", "OR". Horovod backend supports only "SUM", "AVERAGE", "ADASUM", "MIN", "MAX", "PRODUCT". group: list of integer or the process group for each backend. If None, the default process group will be used. Returns: torch.Tensor or number .. versionchanged:: 0.4.11 added ``group`` """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) if isinstance(group, list) and all(isinstance(item, int) for item in group): group = _model.new_group(group) return _model.all_reduce(tensor, op, group=group) def all_gather( tensor: Union[torch.Tensor, float, str, Any], group: Optional[Union[Any, List[int]]] = None ) -> Union[torch.Tensor, float, List[float], List[str], List[Any]]: """Helper method to perform all gather operation. Args: tensor: tensor or number or str to collect across participating processes. If tensor, it should have the same shape across processes. group: list of integer or the process group for each backend. If None, the default process group will be used. Returns: If input is a tensor, returns a torch.Tensor of shape ``(world_size * tensor.shape[0], tensor.shape[1], ...)``. If input is a number, a torch.Tensor of shape ``(world_size, )`` is returned and finally a list of strings is returned if input is a string. If current process does not belong to `group`, the very ``tensor`` is returned. .. versionchanged:: 0.4.11 added ``group`` """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) if isinstance(group, list) and all(isinstance(item, int) for item in group): group = _model.new_group(group) return _model.all_gather(tensor, group=group) def broadcast( tensor: Union[torch.Tensor, float, str, None], src: int = 0, safe_mode: bool = False ) -> Union[torch.Tensor, float, str]: """Helper method to perform broadcast operation. Args: tensor: tensor or number or str to broadcast to participating processes. Make sure to respect data type of torch tensor input for all processes, otherwise execution will crash. Can use None for non-source data with ``safe_mode=True``. src: source rank. Default, 0. safe_mode: if True, non source input data can be ``None`` or anything (will be discarded), otherwise data type of the input ``tensor`` should be respected for all processes. Please, keep in mind, this mode is working only for dense tensors as source input if a tensor is provided. It also leads to some additional collectives before the broadcast, making it slower than without using this mode. Default, False. Returns: torch.Tensor or string or number Examples: .. code-block:: python y = None if idist.get_rank() == 0: t1 = torch.rand(4, 5, 6, device=idist.device()) s1 = "abc" x = 12.3456 y = torch.rand(1, 2, 3, device=idist.device()) else: t1 = torch.empty(4, 5, 6, device=idist.device()) s1 = "" x = 0.0 # Broadcast tensor t1 from rank 0 to all processes t1 = idist.broadcast(t1, src=0) assert isinstance(t1, torch.Tensor) # Broadcast string s1 from rank 0 to all processes s1 = idist.broadcast(s1, src=0) # >>> s1 = "abc" # Broadcast float number x from rank 0 to all processes x = idist.broadcast(x, src=0) # >>> x = 12.3456 # Broadcast any of those types from rank 0, # but other ranks do not define the placeholder y = idist.broadcast(y, src=0, safe_mode=True) assert isinstance(y, torch.Tensor) .. versionadded:: 0.4.2 .. versionchanged:: 0.4.5 added ``safe_mode`` """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.broadcast(tensor, src=src, safe_mode=safe_mode) def barrier() -> None: """Helper method to synchronize all processes.""" if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) _model.barrier() def new_group(ranks: List[int], **kwargs: Any) -> Any: """Helper method to make group for each backend from ranks. Args: ranks: subset of ranks to be grouped. kwargs: acceptable kwargs according to provided backend: - | "nccl" or "gloo" : ``backend (=None)``, ``pg_options (=None)``. Examples: Launch single node multi-GPU training with ``torchrun`` utility. .. code-block:: python import ignite.distributed as idist ranks = [0, 1] group = idist.new_group(ranks) .. versionadded:: 0.4.11 """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.new_group(ranks, **kwargs) def set_local_rank(index: int) -> None: """Method to hint the local rank in case if torch native distributed context is created by user without using :meth:`~ignite.distributed.utils.initialize` or :meth:`~ignite.distributed.utils.spawn`. Args: index: local rank or current process index Examples: User set up torch native distributed process group .. code-block:: python import ignite.distributed as idist def run(local_rank, *args, **kwargs): idist.set_local_rank(local_rank) # ... dist.init_process_group(**dist_info) # ... """ from ignite.distributed.comp_models.base import ComputationModel ComputationModel._ext_local_rank = index def _set_model(model: Any, temporary: bool = False) -> None: global _model, _need_to_sync _model = model _need_to_sync = True if not isinstance(_model, _SerialModel) and not temporary: _need_to_sync = False def _assert_backend(backend: str) -> None: backends = available_backends() if backend not in backends: raise ValueError(f"Backend should be one of '{backends}'") def initialize(backend: str, **kwargs: Any) -> None: """Initializes distributed configuration according to provided ``backend`` Args: backend: backend: `nccl`, `gloo`, `xla-tpu`, `horovod`. kwargs: acceptable kwargs according to provided backend: - | "nccl" or "gloo" : ``timeout(=timedelta(minutes=30))``, ``init_method(=None)``, | ``rank(=None)``, ``world_size(=None)``. | By default, ``init_method`` will be "env://". See more info about parameters: `torch_init`_. - | "horovod" : comm(=None), more info: `hvd_init`_. Examples: Launch single node multi-GPU training with ``torchrun`` utility. .. code-block:: python # >>> torchrun --nproc_per_node=4 main.py # main.py import ignite.distributed as idist def train_fn(local_rank, a, b, c): import torch.distributed as dist assert dist.is_available() and dist.is_initialized() assert dist.get_world_size() == 4 device = idist.device() assert device == torch.device(f"cuda:{local_rank}") backend = "nccl" # or "gloo" or "horovod" or "xla-tpu" idist.initialize(backend) # or for torch native distributed on Windows: # idist.initialize("nccl", init_method="file://tmp/shared") local_rank = idist.get_local_rank() train_fn(local_rank, a, b, c) idist.finalize() .. _torch_init: https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group .. _hvd_init: https://horovod.readthedocs.io/en/latest/api.html#module-horovod.torch .. versionchanged:: 0.4.2 ``backend`` now accepts `horovod` distributed framework. .. versionchanged:: 0.4.5 ``kwargs`` now accepts ``init_method``, ``rank``, ``world_size`` for PyTorch native distributed backend. """ if not (has_xla_support or has_native_dist_support or has_hvd_support): # nothing to do => serial model # maybe warn about this return _assert_backend(backend) for comp_model_cls in registered_computation_models: if backend not in comp_model_cls.available_backends: continue _set_model(comp_model_cls(backend, **kwargs)) def finalize() -> None: """Finalizes distributed configuration. For example, in case of native pytorch distributed configuration, it calls ``dist.destroy_process_group()``. """ _model.finalize() _set_model(_SerialModel()) def show_config() -> None: """Helper method to display distributed configuration via ``logging``.""" # setup parallel logger logger = setup_logger(__name__) logger.info(f"distributed configuration: {model_name()}") logger.info(f"backend: {backend()}") logger.info(f"device: {device().type}") logger.info(f"hostname: {hostname()}") logger.info(f"world size: {get_world_size()}") logger.info(f"rank: {get_rank()}") logger.info(f"local rank: {get_local_rank()}") logger.info(f"num processes per_node: {get_nproc_per_node()}") logger.info(f"num nodes: {get_nnodes()}") logger.info(f"node rank: {get_node_rank()}") def one_rank_only(rank: int = 0, with_barrier: bool = False) -> Callable: """Decorator to filter handlers wrt a rank number Args: rank: rank number of the handler (default: 0). with_barrier: synchronisation with a barrier (default: False). Examples: .. code-block:: python engine = ... @engine.on(...) @one_rank_only() # means @one_rank_only(rank=0) def some_handler(_): ... @engine.on(...) @one_rank_only(rank=1) def some_handler(_): ... """ def _one_rank_only(func: Callable) -> Callable: @wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Optional[Any]: ret = None if get_rank() == rank: ret = func(*args, **kwargs) if with_barrier: barrier() return ret return wrapper return _one_rank_only @contextmanager def one_rank_first(rank: int = 0, local: bool = False) -> Any: """Context manager that ensures a specific rank runs first before others in a distributed environment. Args: rank: rank of the process that should execute the code block inside the context manager first. Default, 0. local: flag to specify local rank or global rank. If True ``rank`` argument will define a local rank to run first. Default, False Examples: .. code-block:: python def download_dataset(): ... with idist.one_rank_first(): ds = download_dataset() dp = ds[0] .. versionadded:: 0.4.13 """ current_rank = get_local_rank() if local else get_rank() size = get_nproc_per_node() if local else get_world_size() if rank >= size or rank < 0: raise ValueError(f"rank should be between 0 and {size - 1}, but given {rank}") if current_rank != rank: barrier() yield if current_rank == rank: barrier() ignite-0.5.1/ignite/engine/000077500000000000000000000000001465426447700155435ustar00rootroot00000000000000ignite-0.5.1/ignite/engine/__init__.py000066400000000000000000001064261465426447700176650ustar00rootroot00000000000000from collections.abc import Mapping from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union import torch import ignite.distributed as idist from ignite.engine.deterministic import DeterministicEngine from ignite.engine.engine import Engine from ignite.engine.events import CallableEventWithFilter, EventEnum, Events, EventsList, RemovableEventHandle, State from ignite.metrics import Metric from ignite.utils import convert_tensor __all__ = [ "State", "create_supervised_trainer", "create_supervised_evaluator", "Engine", "DeterministicEngine", "Events", "EventsList", "EventEnum", "CallableEventWithFilter", "RemovableEventHandle", "supervised_training_step", "supervised_training_step_amp", "supervised_training_step_apex", "supervised_training_step_tpu", "supervised_evaluation_step", "supervised_evaluation_step_amp", ] def _prepare_batch( batch: Sequence[torch.Tensor], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False ) -> Tuple[Union[torch.Tensor, Sequence, Mapping, str, bytes], ...]: """Prepare batch for training or evaluation: pass to a device with options.""" x, y = batch return ( convert_tensor(x, device=device, non_blocking=non_blocking), convert_tensor(y, device=device, non_blocking=non_blocking), ) def supervised_training_step( model: torch.nn.Module, optimizer: torch.optim.Optimizer, loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(), gradient_accumulation_steps: int = 1, model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Callable: """Factory function for supervised training. Args: model: the model to train. optimizer: the optimizer to use. loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. Device can be CPU, GPU. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the form as required by the loss function output_transform: function that receives 'x', 'y', 'y_pred', 'loss' and returns value to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`. gradient_accumulation_steps: Number of steps the gradients should be accumulated across. (default: 1 (means no gradient accumulation)) model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: Callable: update function. Examples: .. code-block:: python from ignite.engine import Engine, supervised_training_step model = ... optimizer = ... loss_fn = ... update_fn = supervised_training_step(model, optimizer, loss_fn, 'cuda') trainer = Engine(update_fn) .. versionadded:: 0.4.5 .. versionchanged:: 0.4.7 Added Gradient Accumulation. .. versionchanged:: 0.4.11 Added `model_transform` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample .. versionchanged:: 0.5.0 Added support for ``mps`` device """ if gradient_accumulation_steps <= 0: raise ValueError( "Gradient_accumulation_steps must be strictly positive. " "No gradient accumulation if the value set to one (default)." ) def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: if (engine.state.iteration - 1) % gradient_accumulation_steps == 0: optimizer.zero_grad() model.train() x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) output = model_fn(model, x) y_pred = model_transform(output) loss = loss_fn(y_pred, y) if gradient_accumulation_steps > 1: loss = loss / gradient_accumulation_steps loss.backward() if engine.state.iteration % gradient_accumulation_steps == 0: optimizer.step() return output_transform(x, y, y_pred, loss * gradient_accumulation_steps) return update def supervised_training_step_amp( model: torch.nn.Module, optimizer: torch.optim.Optimizer, loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(), scaler: Optional["torch.cuda.amp.GradScaler"] = None, gradient_accumulation_steps: int = 1, model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Callable: """Factory function for supervised training using ``torch.cuda.amp``. Args: model: the model to train. optimizer: the optimizer to use. loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. Device can be CPU, GPU. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the form as required by the loss function output_transform: function that receives 'x', 'y', 'y_pred', 'loss' and returns value to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`. scaler: GradScaler instance for gradient scaling. (default: None) gradient_accumulation_steps: Number of steps the gradients should be accumulated across. (default: 1 (means no gradient accumulation)) model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: Callable: update function Examples: .. code-block:: python from ignite.engine import Engine, supervised_training_step_amp model = ... optimizer = ... loss_fn = ... scaler = torch.cuda.amp.GradScaler(2**10) update_fn = supervised_training_step_amp(model, optimizer, loss_fn, 'cuda', scaler=scaler) trainer = Engine(update_fn) .. versionadded:: 0.4.5 .. versionchanged:: 0.4.7 Added Gradient Accumulation. .. versionchanged:: 0.4.11 Added `model_transform` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample """ try: from torch.cuda.amp import autocast except ImportError: raise ImportError("Please install torch>=1.6.0 to use amp_mode='amp'.") if gradient_accumulation_steps <= 0: raise ValueError( "Gradient_accumulation_steps must be strictly positive. " "No gradient accumulation if the value set to one (default)." ) def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: if (engine.state.iteration - 1) % gradient_accumulation_steps == 0: optimizer.zero_grad() model.train() x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) with autocast(enabled=True): output = model_fn(model, x) y_pred = model_transform(output) loss = loss_fn(y_pred, y) if gradient_accumulation_steps > 1: loss = loss / gradient_accumulation_steps if scaler: scaler.scale(loss).backward() if engine.state.iteration % gradient_accumulation_steps == 0: scaler.step(optimizer) scaler.update() else: loss.backward() if engine.state.iteration % gradient_accumulation_steps == 0: optimizer.step() return output_transform(x, y, y_pred, loss * gradient_accumulation_steps) return update def supervised_training_step_apex( model: torch.nn.Module, optimizer: torch.optim.Optimizer, loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(), gradient_accumulation_steps: int = 1, model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Callable: """Factory function for supervised training using apex. Args: model: the model to train. optimizer: the optimizer to use. loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. Device can be CPU, GPU. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the form as required by the loss function output_transform: function that receives 'x', 'y', 'y_pred', 'loss' and returns value to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`. gradient_accumulation_steps: Number of steps the gradients should be accumulated across. (default: 1 (means no gradient accumulation)) model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: Callable: update function. Examples: .. code-block:: python from ignite.engine import Engine, supervised_training_step_apex model = ... optimizer = ... loss_fn = ... update_fn = supervised_training_step_apex(model, optimizer, loss_fn, 'cuda') trainer = Engine(update_fn) .. versionadded:: 0.4.5 .. versionchanged:: 0.4.7 Added Gradient Accumulation. .. versionchanged:: 0.4.11 Added `model_transform` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample """ try: from apex import amp as apex_amp except ModuleNotFoundError: raise ModuleNotFoundError("Please install apex from https://github.com/nvidia/apex to use amp_mode='apex'.") if gradient_accumulation_steps <= 0: raise ValueError( "Gradient_accumulation_steps must be strictly positive. " "No gradient accumulation if the value set to one (default)." ) def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: if (engine.state.iteration - 1) % gradient_accumulation_steps == 0: optimizer.zero_grad() model.train() x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) output = model_fn(model, x) y_pred = model_transform(output) loss = loss_fn(y_pred, y) if gradient_accumulation_steps > 1: loss = loss / gradient_accumulation_steps with apex_amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() if engine.state.iteration % gradient_accumulation_steps == 0: optimizer.step() return output_transform(x, y, y_pred, loss * gradient_accumulation_steps) return update def supervised_training_step_tpu( model: torch.nn.Module, optimizer: torch.optim.Optimizer, loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(), gradient_accumulation_steps: int = 1, model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Callable: """Factory function for supervised training using ``torch_xla``. Args: model: the model to train. optimizer: the optimizer to use. loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. Device can be CPU, TPU. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the form as required by the loss function output_transform: function that receives 'x', 'y', 'y_pred', 'loss' and returns value to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`. gradient_accumulation_steps: Number of steps the gradients should be accumulated across. (default: 1 (means no gradient accumulation)) model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: Callable: update function. Examples: .. code-block:: python from ignite.engine import Engine, supervised_training_step_tpu model = ... optimizer = ... loss_fn = ... update_fn = supervised_training_step_tpu(model, optimizer, loss_fn, 'xla') trainer = Engine(update_fn) .. versionadded:: 0.4.5 .. versionchanged:: 0.4.7 Added Gradient Accumulation argument for all supervised training methods. .. versionchanged:: 0.4.11 Added `model_transform` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample """ try: import torch_xla.core.xla_model as xm except ModuleNotFoundError: raise ModuleNotFoundError("torch_xla cannot be imported, please install PyTorch XLA.") if gradient_accumulation_steps <= 0: raise ValueError( "Gradient_accumulation_steps must be strictly positive. " "No gradient accumulation if the value set to one (default)." ) def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: if (engine.state.iteration - 1) % gradient_accumulation_steps == 0: optimizer.zero_grad() model.train() x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) output = model_fn(model, x) y_pred = model_transform(output) loss = loss_fn(y_pred, y) if gradient_accumulation_steps > 1: loss = loss / gradient_accumulation_steps loss.backward() if engine.state.iteration % gradient_accumulation_steps == 0: xm.optimizer_step(optimizer, barrier=True) return output_transform(x, y, y_pred, loss * gradient_accumulation_steps) return update def _check_arg( on_tpu: bool, on_mps: bool, amp_mode: Optional[str], scaler: Optional[Union[bool, "torch.cuda.amp.GradScaler"]] ) -> Tuple[Optional[str], Optional["torch.cuda.amp.GradScaler"]]: """Checking tpu, mps, amp and GradScaler instance combinations.""" if on_mps and amp_mode: raise ValueError("amp_mode cannot be used with mps device. Consider using amp_mode=None or device='cuda'.") if on_tpu and not idist.has_xla_support: raise RuntimeError("In order to run on TPU, please install PyTorch XLA") if amp_mode and on_tpu: raise ValueError("amp_mode cannot be used with xla device. Consider using amp_mode=None or device='cuda'.") if scaler: if amp_mode != "amp": raise ValueError(f"scaler argument is {scaler}, but amp_mode is {amp_mode}. Consider using amp_mode='amp'.") elif amp_mode == "amp" and isinstance(scaler, bool): try: from torch.cuda.amp import GradScaler except ImportError: raise ImportError("Please install torch>=1.6.0 to use scaler argument.") scaler = GradScaler(enabled=True) if on_tpu: return "tpu", None elif scaler and amp_mode == "amp": return amp_mode, scaler # type: ignore[return-value] else: return amp_mode, None def create_supervised_trainer( model: torch.nn.Module, optimizer: torch.optim.Optimizer, loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(), deterministic: bool = False, amp_mode: Optional[str] = None, scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False, gradient_accumulation_steps: int = 1, model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Engine: """Factory function for creating a trainer for supervised models. Args: model: the model to train. optimizer: the optimizer to use. loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. Device can be CPU, GPU or TPU. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the form as required by the loss function output_transform: function that receives 'x', 'y', 'y_pred', 'loss' and returns value to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`. deterministic: if True, returns deterministic engine of type :class:`~ignite.engine.deterministic.DeterministicEngine`, otherwise :class:`~ignite.engine.engine.Engine` (default: False). amp_mode: can be ``amp`` or ``apex``, model and optimizer will be casted to float16 using `torch.cuda.amp `_ for ``amp`` and using `apex `_ for ``apex``. (default: None) scaler: GradScaler instance for gradient scaling if `torch>=1.6.0` and ``amp_mode`` is ``amp``. If ``amp_mode`` is ``apex``, this argument will be ignored. If True, will create default GradScaler. If GradScaler instance is passed, it will be used instead. (default: False) gradient_accumulation_steps: Number of steps the gradients should be accumulated across. (default: 1 (means no gradient accumulation)) model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: a trainer engine with supervised update function. Examples: Create a trainer .. code-block:: python from ignite.engine import create_supervised_trainer from ignite.utils import convert_tensor from ignite.handlers.tqdm_logger import ProgressBar model = ... loss = ... optimizer = ... dataloader = ... def prepare_batch_fn(batch, device, non_blocking): x = ... # get x from batch y = ... # get y from batch # return a tuple of (x, y) that can be directly runned as # `loss_fn(model(x), y)` return ( convert_tensor(x, device, non_blocking), convert_tensor(y, device, non_blocking) ) def output_transform_fn(x, y, y_pred, loss): # return only the loss is actually the default behavior for # trainer engine, but you can return anything you want return loss.item() trainer = create_supervised_trainer( model, optimizer, loss, prepare_batch=prepare_batch_fn, output_transform=output_transform_fn ) pbar = ProgressBar() pbar.attach(trainer, output_transform=lambda x: {"loss": x}) trainer.run(dataloader, max_epochs=5) Note: If ``scaler`` is True, GradScaler instance will be created internally and trainer state has attribute named ``scaler`` for that instance and can be used for saving and loading. Note: `engine.state.output` for this engine is defined by `output_transform` parameter and is the loss of the processed batch by default. .. warning:: The internal use of `device` has changed. `device` will now *only* be used to move the input data to the correct device. The `model` should be moved by the user before creating an optimizer. For more information see: - `PyTorch Documentation `_ - `PyTorch's Explanation `_ .. warning:: If ``amp_mode='apex'`` , the model(s) and optimizer(s) must be initialized beforehand since ``amp.initialize`` should be called after you have finished constructing your model(s) and optimizer(s), but before you send your model through any DistributedDataParallel wrapper. See more: https://nvidia.github.io/apex/amp.html#module-apex.amp .. versionchanged:: 0.4.5 - Added ``amp_mode`` argument for automatic mixed precision. - Added ``scaler`` argument for gradient scaling. .. versionchanged:: 0.4.7 Added Gradient Accumulation argument for all supervised training methods. .. versionchanged:: 0.4.11 Added ``model_transform`` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample .. versionchanged:: 0.5.0 Added support for ``mps`` device """ device_type = device.type if isinstance(device, torch.device) else device on_tpu = "xla" in device_type if device_type is not None else False on_mps = "mps" in device_type if device_type is not None else False mode, _scaler = _check_arg(on_tpu, on_mps, amp_mode, scaler) if mode == "amp": _update = supervised_training_step_amp( model, optimizer, loss_fn, device, non_blocking, prepare_batch, model_transform, output_transform, _scaler, gradient_accumulation_steps, model_fn, ) elif mode == "apex": _update = supervised_training_step_apex( model, optimizer, loss_fn, device, non_blocking, prepare_batch, model_transform, output_transform, gradient_accumulation_steps, model_fn, ) elif mode == "tpu": _update = supervised_training_step_tpu( model, optimizer, loss_fn, device, non_blocking, prepare_batch, model_transform, output_transform, gradient_accumulation_steps, model_fn, ) else: _update = supervised_training_step( model, optimizer, loss_fn, device, non_blocking, prepare_batch, model_transform, output_transform, gradient_accumulation_steps, model_fn, ) trainer = Engine(_update) if not deterministic else DeterministicEngine(_update) if _scaler and scaler and isinstance(scaler, bool): trainer.state.scaler = _scaler # type: ignore[attr-defined] return trainer def supervised_evaluation_step( model: torch.nn.Module, device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any], Any] = lambda x, y, y_pred: (y_pred, y), model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Callable: """ Factory function for supervised evaluation. Args: model: the model to train. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the predictions: ``y_pred = model_transform(model(x))``. output_transform: function that receives 'x', 'y', 'y_pred' and returns value to be assigned to engine's state.output after each iteration. Default is returning `(y_pred, y,)` which fits output expected by metrics. If you change it you should use `output_transform` in metrics. model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: Inference function. Note: `engine.state.output` for this engine is defined by `output_transform` parameter and is a tuple of `(batch_pred, batch_y)` by default. .. warning:: The internal use of `device` has changed. `device` will now *only* be used to move the input data to the correct device. The `model` should be moved by the user before creating an optimizer. .. versionadded:: 0.4.5 .. versionchanged:: 0.4.12 Added ``model_transform`` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample """ def evaluate_step(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: model.eval() with torch.no_grad(): x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) output = model_fn(model, x) y_pred = model_transform(output) return output_transform(x, y, y_pred) return evaluate_step def supervised_evaluation_step_amp( model: torch.nn.Module, device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any], Any] = lambda x, y, y_pred: (y_pred, y), model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Callable: """ Factory function for supervised evaluation using ``torch.cuda.amp``. Args: model: the model to train. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the predictions: ``y_pred = model_transform(model(x))``. output_transform: function that receives 'x', 'y', 'y_pred' and returns value to be assigned to engine's state.output after each iteration. Default is returning `(y_pred, y,)` which fits output expected by metrics. If you change it you should use `output_transform` in metrics. model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: Inference function. Note: `engine.state.output` for this engine is defined by `output_transform` parameter and is a tuple of `(batch_pred, batch_y)` by default. .. warning:: The internal use of `device` has changed. `device` will now *only* be used to move the input data to the correct device. The `model` should be moved by the user before creating an optimizer. .. versionadded:: 0.4.5 .. versionchanged:: 0.4.12 Added ``model_transform`` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample """ try: from torch.cuda.amp import autocast except ImportError: raise ImportError("Please install torch>=1.6.0 to use amp_mode='amp'.") def evaluate_step(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: model.eval() with torch.no_grad(): x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) with autocast(enabled=True): output = model_fn(model, x) y_pred = model_transform(output) return output_transform(x, y, y_pred) return evaluate_step def create_supervised_evaluator( model: torch.nn.Module, metrics: Optional[Dict[str, Metric]] = None, device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, model_transform: Callable[[Any], Any] = lambda output: output, output_transform: Callable[[Any, Any, Any], Any] = lambda x, y, y_pred: (y_pred, y), amp_mode: Optional[str] = None, model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x), ) -> Engine: """ Factory function for creating an evaluator for supervised models. Args: model: the model to train. metrics: a map of metric names to Metrics. device: device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. model_transform: function that receives the output from the model and convert it into the predictions: ``y_pred = model_transform(model(x))``. output_transform: function that receives 'x', 'y', 'y_pred' and returns value to be assigned to engine's state.output after each iteration. Default is returning `(y_pred, y,)` which fits output expected by metrics. If you change it you should use `output_transform` in metrics. amp_mode: can be ``amp``, model will be casted to float16 using `torch.cuda.amp `_ model_fn: the model function that receives `model` and `x`, and returns `y_pred`. Returns: an evaluator engine with supervised inference function. Note: `engine.state.output` for this engine is defined by `output_transform` parameter and is a tuple of `(batch_pred, batch_y)` by default. .. warning:: The internal use of `device` has changed. `device` will now *only* be used to move the input data to the correct device. The `model` should be moved by the user before creating an optimizer. For more information see: - `PyTorch Documentation `_ - `PyTorch's Explanation `_ .. versionchanged:: 0.4.5 Added ``amp_mode`` argument for automatic mixed precision. .. versionchanged:: 0.4.12 Added ``model_transform`` to transform model's output .. versionchanged:: 0.4.13 Added `model_fn` to customize model's application on the sample .. versionchanged:: 0.5.0 Added support for ``mps`` device """ device_type = device.type if isinstance(device, torch.device) else device on_tpu = "xla" in device_type if device_type is not None else False on_mps = "mps" in device_type if device_type is not None else False mode, _ = _check_arg(on_tpu, on_mps, amp_mode, None) metrics = metrics or {} if mode == "amp": evaluate_step = supervised_evaluation_step_amp( model, device, non_blocking=non_blocking, prepare_batch=prepare_batch, model_transform=model_transform, output_transform=output_transform, model_fn=model_fn, ) else: evaluate_step = supervised_evaluation_step( model, device, non_blocking=non_blocking, prepare_batch=prepare_batch, model_transform=model_transform, output_transform=output_transform, model_fn=model_fn, ) evaluator = Engine(evaluate_step) for name, metric in metrics.items(): metric.attach(evaluator, name) return evaluator ignite-0.5.1/ignite/engine/deterministic.py000066400000000000000000000265601465426447700207710ustar00rootroot00000000000000import random import warnings from collections import OrderedDict from functools import wraps from typing import Any, Callable, Generator, Iterator, List, Optional import torch from torch.utils.data import DataLoader from torch.utils.data.sampler import BatchSampler from ignite.engine.engine import Engine from ignite.engine.events import Events from ignite.utils import manual_seed __all__ = ["update_dataloader", "keep_random_state", "ReproducibleBatchSampler", "DeterministicEngine"] def update_dataloader(dataloader: DataLoader, new_batch_sampler: BatchSampler) -> DataLoader: """Helper function to replace current batch sampler of the dataloader by a new batch sampler. Function returns new dataloader with new batch sampler. Args: dataloader: input dataloader new_batch_sampler: new batch sampler to use Returns: DataLoader """ params_keys = [k for k in dataloader.__dict__.keys() if not k.startswith("_")] for k in ["batch_size", "sampler", "drop_last", "batch_sampler", "dataset_kind"]: if k in params_keys: params_keys.remove(k) params = {k: getattr(dataloader, k) for k in params_keys} params["batch_sampler"] = new_batch_sampler return type(dataloader)(**params) class ReproducibleBatchSampler(BatchSampler): """Reproducible batch sampler. This class internally iterates and stores indices of the input batch sampler. This helps to start providing data batches from an iteration in a deterministic way. Args: batch_sampler: batch sampler same as used with `torch.utils.data.DataLoader`. start_iteration: optional start iteration. Examples: Setup dataloader with `ReproducibleBatchSampler` and start providing data batches from an iteration .. code-block:: python from ignite.engine.deterministic import update_dataloader dataloader = update_dataloader(dataloader, ReproducibleBatchSampler(dataloader.batch_sampler)) # rewind dataloader to a specific iteration: dataloader.batch_sampler.start_iteration = start_iteration """ def __init__(self, batch_sampler: BatchSampler, start_iteration: Optional[int] = None): if not isinstance(batch_sampler, BatchSampler): raise TypeError("Argument batch_sampler should be torch.utils.data.sampler.BatchSampler") self.batch_indices: List = [] self.batch_sampler = batch_sampler self.start_iteration = start_iteration self.sampler = self.batch_sampler.sampler def setup_batch_indices(self) -> None: """Setup batch indices.""" self.batch_indices = [] for batch in self.batch_sampler: self.batch_indices.append(batch) if self.start_iteration is not None: self.batch_indices = self.batch_indices[self.start_iteration :] self.start_iteration = None def __iter__(self) -> Generator: self.setup_batch_indices() for batch in self.batch_indices: yield batch def __len__(self) -> int: return len(self.batch_sampler) def _get_rng_states() -> List[Any]: output = [random.getstate(), torch.get_rng_state()] try: import numpy as np output.append(np.random.get_state()) except ImportError: pass return output def _set_rng_states(rng_states: List[Any]) -> None: random.setstate(rng_states[0]) if "cpu" not in rng_states[1].device.type: rng_states[1] = rng_states[1].cpu() torch.set_rng_state(rng_states[1]) try: import numpy as np np.random.set_state(rng_states[2]) except ImportError: pass def _repr_rng_state(rng_states: List[Any]) -> str: from hashlib import md5 out = " ".join([md5(str(list(s)).encode("utf-8")).hexdigest() for s in rng_states]) return out def keep_random_state(func: Callable) -> Callable: """Helper decorator to keep random state of torch, numpy and random intact while executing a function. For more details on usage, please see :ref:`Dataflow synchronization`. Args: func: function to decorate """ @wraps(func) def wrapper(*args: Any, **kwargs: Any) -> None: rng_states = _get_rng_states() func(*args, **kwargs) _set_rng_states(rng_states) return wrapper class DeterministicEngine(Engine): """Deterministic engine derived from :class:`~ignite.engine.engine.Engine`. "Deterministic" run is done by adding additional handlers to synchronize the dataflow and overriding some methods of :class:`~ignite.engine.engine.Engine`: .. code-block:: python for e in range(num_epochs): set_seed(seed_offset + e) if resume: setup_saved_rng_states() do_single_epoch_iterations(dataloader) If input data provider is `DataLoader`, its batch sampler is replaced by :class:`~ignite.engine.deterministic.ReproducibleBatchSampler`. .. code-block:: python for e in range(num_epochs): set_seed(seed_offset + e) setup_sampling(dataloader) if resume: setup_saved_rng_states() do_single_epoch_iterations(dataloader) Internally, `torch.backends.cudnn.deterministic = True` and `torch.backends.cudnn.benchmark = False` are also applied. For more details about dataflow synchronization, please see :ref:`Dataflow synchronization`. .. Note :: This class can produce exactly the same dataflow when resuming the run from an epoch (or more precisely from dataflow restart) and using torch `DataLoader` with `num_workers > 1` as data provider. Args: process_function: A function receiving a handle to the engine and the current batch in each iteration, and returns data to be stored in the engine's state. """ def __init__(self, process_function: Callable[[Engine, Any], Any]): super(DeterministicEngine, self).__init__(process_function) self.state_dict_user_keys.append("rng_states") if not hasattr(self.state, "rng_states"): setattr(self.state, "rng_states", None) self.add_event_handler(Events.STARTED, self._init_run) self.add_event_handler(Events.DATALOADER_STOP_ITERATION | Events.TERMINATE_SINGLE_EPOCH, self._setup_seed) def state_dict(self) -> OrderedDict: state_dict = super(DeterministicEngine, self).state_dict() state_dict["rng_states"] = _get_rng_states() return state_dict def _init_run(self) -> None: self.state.seed = int(torch.randint(0, int(1e9), (1,)).item()) if torch.cuda.is_available(): if hasattr(torch, "use_deterministic_algorithms"): torch.use_deterministic_algorithms(True, warn_only=True) else: torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def _setup_engine(self) -> None: if self.state.dataloader is None: raise ValueError( "Deterministic engine does not support the option of data=None. Please, provide data as iterable" ) self._dataloader_len = self._get_data_length(self.state.dataloader) # if input data is torch dataloader we replace batch sampler by a batch sampler # such that its random sampling indices are reproducible by prefetching them before data iteration if isinstance(self.state.dataloader, DataLoader): # attribute _dataset_kind is introduced since 1.3.0 => before 1.3.0 all datasets are map-like can_patch_dataloader = True if hasattr(self.state.dataloader, "_dataset_kind"): from torch.utils.data.dataloader import _DatasetKind _dataloader_kind = self.state.dataloader._dataset_kind can_patch_dataloader = _dataloader_kind == _DatasetKind.Map if can_patch_dataloader: if self._dataloader_len is not None and hasattr(self.state.dataloader.sampler, "epoch"): if self._dataloader_len != self.state.epoch_length: warnings.warn( "When defined engine's epoch length is different of input dataloader length, " "distributed sampler indices can not be setup in a reproducible manner" ) batch_sampler = self.state.dataloader.batch_sampler if not (batch_sampler is None or isinstance(batch_sampler, ReproducibleBatchSampler)): self.state.dataloader = update_dataloader( self.state.dataloader, ReproducibleBatchSampler(batch_sampler) # type: ignore[arg-type] ) iteration = self.state.iteration self._dataloader_iter = self._from_iteration(iteration) # Below we define initial counter value for _run_once_on_dataset to measure a single epoch if self.state.epoch_length is not None: iteration %= self.state.epoch_length self._init_iter = iteration # restore rng state if in the middle in_the_middle = self.state.iteration % self._dataloader_len > 0 if self._dataloader_len is not None else False rng_states = getattr(self.state, "rng_states", None) if rng_states is not None and in_the_middle: _set_rng_states(rng_states) setattr(self.state, "rng_states", None) def _from_iteration(self, iteration: int) -> Iterator: if self.state.dataloader is None: raise RuntimeError( "Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error." ) data = self.state.dataloader if isinstance(data, DataLoader): try: # following is unsafe for IterableDatasets iteration %= len(data.batch_sampler) # type: ignore[arg-type] # Synchronize dataflow according to state.iteration self._setup_seed() if iteration > 0: # batch sampler is ReproducibleBatchSampler data.batch_sampler.start_iteration = iteration # type: ignore[union-attr] return iter(data) except TypeError as e: # Probably we can do nothing with DataLoader built upon IterableDatasets pass self.logger.info("Resuming from iteration for provided data will fetch data until required iteration ...") if hasattr(data, "__len__"): iteration %= len(data) # type: ignore[arg-type] # Synchronize dataflow from the begining self._setup_seed(iteration=0) data_iter = iter(data) counter = 0 while counter < iteration: try: next(data_iter) counter += 1 except StopIteration: data_iter = iter(data) return data_iter def _setup_seed(self, _: Any = None, iter_counter: Optional[int] = None, iteration: Optional[int] = None) -> None: if iter_counter is None: le = self._dataloader_len if self._dataloader_len is not None else 1 elif not iter_counter > 0: raise ValueError("iter_counter should be positive value") else: le = iter_counter if iteration is None: iteration = self.state.iteration manual_seed(self.state.seed + iteration // le) # type: ignore[operator] ignite-0.5.1/ignite/engine/engine.py000066400000000000000000001527061465426447700173750ustar00rootroot00000000000000import functools import logging import time import warnings import weakref from collections import defaultdict, OrderedDict from collections.abc import Mapping from typing import Any, Callable, Dict, Generator, Iterable, Iterator, List, Optional, Tuple, Union from torch.utils.data import DataLoader from ignite.base import Serializable from ignite.engine.events import CallableEventWithFilter, EventEnum, Events, EventsList, RemovableEventHandle, State from ignite.engine.utils import _check_signature, _to_hours_mins_secs __all__ = ["Engine"] class Engine(Serializable): """Runs a given ``process_function`` over each batch of a dataset, emitting events as it goes. Args: process_function: A function receiving a handle to the engine and the current batch in each iteration, and returns data to be stored in the engine's state. Attributes: state: object that is used to pass internal and user-defined state between event handlers. It is created with the engine and its attributes (e.g. ``state.iteration``, ``state.epoch`` etc) are reset on every :meth:`~ignite.engine.engine.Engine.run`. last_event_name: last event name triggered by the engine. Note: :class:`~ignite.engine.engine.Engine` implementation has changed in v0.4.10 with "interrupt/resume" feature. Engine may behave differently on certain corner cases compared to the one from v0.4.9 and before. In such case, you can set ``Engine.interrupt_resume_enabled = False`` to restore previous behaviour. Examples: Create a basic trainer .. code-block:: python model = ... model = model.cuda() optimized = ... criterion = ... def train_step(engine, batch): model.train() inputs, targets = batch[0].cuda(), batch[1].cuda() optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() return loss.item() trainer = Engine(update_model) @trainer.on(Events.ITERATION_COMPLETED(every=100)) def log_training(engine): batch_loss = engine.state.output lr = optimizer.param_groups[0]['lr'] e = engine.state.epoch n = engine.state.max_epochs i = engine.state.iteration print(f"Epoch {e}/{n} : {i} - batch loss: {batch_loss}, lr: {lr}") trainer.run(data_loader, max_epochs=5) > Epoch 1/5 : 100 - batch loss: 0.10874069479016124, lr: 0.01 > ... > Epoch 2/5 : 1700 - batch loss: 0.4217900575859437, lr: 0.01 Create a basic evaluator to compute metrics .. code-block:: python from ignite.metrics import Accuracy def predict_on_batch(engine, batch) model.eval() with torch.no_grad(): x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) y_pred = model(x) return y_pred, y evaluator = Engine(predict_on_batch) Accuracy().attach(evaluator, "val_acc") evaluator.run(val_dataloader) Compute image mean/std on training dataset .. code-block:: python from ignite.metrics import Average def compute_mean_std(engine, batch): b, c, *_ = batch['image'].shape data = batch['image'].reshape(b, c, -1).to(dtype=torch.float64) mean = torch.mean(data, dim=-1).sum(dim=0) mean2 = torch.mean(data ** 2, dim=-1).sum(dim=0) return {"mean": mean, "mean^2": mean2} compute_engine = Engine(compute_mean_std) img_mean = Average(output_transform=lambda output: output['mean']) img_mean.attach(compute_engine, 'mean') img_mean2 = Average(output_transform=lambda output: output['mean^2']) img_mean2.attach(compute_engine, 'mean2') state = compute_engine.run(train_loader) state.metrics['std'] = torch.sqrt(state.metrics['mean2'] - state.metrics['mean'] ** 2) mean = state.metrics['mean'].tolist() std = state.metrics['std'].tolist() Resume engine's run from a state. User can load a `state_dict` and run engine starting from loaded state : .. code-block:: python # Restore from an epoch state_dict = {"epoch": 3, "max_epochs": 100, "epoch_length": len(data_loader)} # or an iteration # state_dict = {"iteration": 500, "max_epochs": 100, "epoch_length": len(data_loader)} trainer = Engine(...) trainer.load_state_dict(state_dict) trainer.run(data) """ _state_dict_all_req_keys = ("epoch_length", "max_epochs") _state_dict_one_of_opt_keys = ("iteration", "epoch") # Flag to disable engine._internal_run as generator feature for BC interrupt_resume_enabled = True def __init__(self, process_function: Callable[["Engine", Any], Any]): self._event_handlers: Dict[Any, List] = defaultdict(list) self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__) self._process_function = process_function self.last_event_name: Optional[Events] = None self.should_terminate = False self.should_terminate_single_epoch = False self.should_interrupt = False self.state = State() self._state_dict_user_keys: List[str] = [] self._allowed_events: List[EventEnum] = [] self._dataloader_iter: Optional[Iterator[Any]] = None self._init_iter: Optional[int] = None self.register_events(*Events) if self._process_function is None: raise ValueError("Engine must be given a processing function in order to run.") _check_signature(process_function, "process_function", self, None) # generator provided by self._internal_run_as_gen self._internal_run_generator: Optional[Generator[Any, None, State]] = None def register_events( self, *event_names: Union[List[str], List[EventEnum]], event_to_attr: Optional[dict] = None ) -> None: """Add events that can be fired. Registering an event will let the user trigger these events at any point. This opens the door to make the :meth:`~ignite.engine.engine.Engine.run` loop even more configurable. By default, the events from :class:`~ignite.engine.events.Events` are registered. Args: event_names: Defines the name of the event being supported. New events can be a str or an object derived from :class:`~ignite.engine.events.EventEnum`. See example below. event_to_attr: A dictionary to map an event to a state attribute. Examples: .. code-block:: python from ignite.engine import Engine, Events, EventEnum class CustomEvents(EventEnum): FOO_EVENT = "foo_event" BAR_EVENT = "bar_event" def process_function(e, batch): # ... trainer.fire_event("bwd_event") loss.backward() # ... trainer.fire_event("opt_event") optimizer.step() trainer = Engine(process_function) trainer.register_events(*CustomEvents) trainer.register_events("bwd_event", "opt_event") @trainer.on(Events.EPOCH_COMPLETED) def trigger_custom_event(): if required(...): trainer.fire_event(CustomEvents.FOO_EVENT) else: trainer.fire_event(CustomEvents.BAR_EVENT) @trainer.on(CustomEvents.FOO_EVENT) def do_foo_op(): # ... @trainer.on(CustomEvents.BAR_EVENT) def do_bar_op(): # ... Example with State Attribute: .. code-block:: python from enum import Enum from ignite.engine import Engine, EventEnum class TBPTT_Events(EventEnum): TIME_ITERATION_STARTED = "time_iteration_started" TIME_ITERATION_COMPLETED = "time_iteration_completed" TBPTT_event_to_attr = { TBPTT_Events.TIME_ITERATION_STARTED: 'time_iteration', TBPTT_Events.TIME_ITERATION_COMPLETED: 'time_iteration' } engine = Engine(process_function) engine.register_events(*TBPTT_Events, event_to_attr=TBPTT_event_to_attr) engine.run(data) # engine.state contains an attribute time_iteration, which can be accessed # using engine.state.time_iteration """ if not (event_to_attr is None or isinstance(event_to_attr, dict)): raise ValueError(f"Expected event_to_attr to be dictionary. Got {type(event_to_attr)}.") for index, e in enumerate(event_names): if not isinstance(e, (str, EventEnum)): raise TypeError(f"Value at {index} of event_names should be a str or EventEnum, but given {e}") self._allowed_events.append(e) if event_to_attr and e in event_to_attr: State.event_to_attr[e] = event_to_attr[e] # we need to update state attributes associated with new custom events self.state._update_attrs() def _handler_wrapper(self, handler: Callable, event_name: Any, event_filter: Callable) -> Callable: # signature of the following wrapper will be inspected during registering to check if engine is necessary # we have to build a wrapper with relevant signature : solution is functools.wraps @functools.wraps(handler) def wrapper(*args: Any, **kwargs: Any) -> Any: event = self.state.get_event_attrib_value(event_name) if event_filter(self, event): return handler(*args, **kwargs) # setup input handler as parent to make has_event_handler work setattr(wrapper, "_parent", weakref.ref(handler)) return wrapper def _assert_allowed_event(self, event_name: Any) -> None: if event_name not in self._allowed_events: self.logger.error(f"attempt to add event handler to an invalid event {event_name}") raise ValueError(f"Event {event_name} is not a valid event for this {self.__class__.__name__}.") def add_event_handler(self, event_name: Any, handler: Callable, *args: Any, **kwargs: Any) -> RemovableEventHandle: """Add an event handler to be executed when the specified event is fired. Args: event_name: An event or a list of events to attach the handler. Valid events are from :class:`~ignite.engine.events.Events` or any ``event_name`` added by :meth:`~ignite.engine.engine.Engine.register_events`. handler: the callable event handler that should be invoked. No restrictions on its signature. The first argument can be optionally `engine`, the :class:`~ignite.engine.engine.Engine` object, handler is bound to. args: optional args to be passed to ``handler``. kwargs: optional keyword args to be passed to ``handler``. Returns: :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler. Note: Note that other arguments can be passed to the handler in addition to the `*args` and `**kwargs` passed here, for example during :attr:`~ignite.engine.events.Events.EXCEPTION_RAISED`. Examples: .. code-block:: python engine = Engine(process_function) def print_epoch(engine): print(f"Epoch: {engine.state.epoch}") engine.add_event_handler(Events.EPOCH_COMPLETED, print_epoch) events_list = Events.EPOCH_COMPLETED | Events.COMPLETED def execute_something(): # do some thing not related to engine pass engine.add_event_handler(events_list, execute_something) Note: Since v0.3.0, Events become more flexible and allow to pass an event filter to the Engine. See :class:`~ignite.engine.events.Events` for more details. """ if isinstance(event_name, EventsList): for e in event_name: self.add_event_handler(e, handler, *args, **kwargs) return RemovableEventHandle(event_name, handler, self) if isinstance(event_name, CallableEventWithFilter) and event_name.filter is not None: event_filter = event_name.filter handler = self._handler_wrapper(handler, event_name, event_filter) self._assert_allowed_event(event_name) event_args: Tuple[Any, ...] = () if event_name == Events.EXCEPTION_RAISED: event_args += (Exception(),) elif event_name == Events.TERMINATE_SINGLE_EPOCH: event_args += (0,) try: _check_signature(handler, "handler", self, *(event_args + args), **kwargs) self._event_handlers[event_name].append((handler, (self,) + args, kwargs)) except ValueError: _check_signature(handler, "handler", *(event_args + args), **kwargs) self._event_handlers[event_name].append((handler, args, kwargs)) self.logger.debug(f"Added handler for event {event_name}") return RemovableEventHandle(event_name, handler, self) def has_event_handler(self, handler: Callable, event_name: Optional[Any] = None) -> bool: """Check if the specified event has the specified handler. Args: handler: the callable event handler. event_name: The event the handler attached to. Set this to ``None`` to search all events. """ if event_name is not None: if event_name not in self._event_handlers: return False events: Union[List[Any], Dict[Any, List]] = [event_name] else: events = self._event_handlers for e in events: for h, _, _ in self._event_handlers[e]: if self._compare_handlers(handler, h): return True return False @staticmethod def _compare_handlers(user_handler: Callable, registered_handler: Callable) -> bool: if hasattr(registered_handler, "_parent"): registered_handler = registered_handler._parent() return registered_handler == user_handler def remove_event_handler(self, handler: Callable, event_name: Any) -> None: """Remove event handler `handler` from registered handlers of the engine Args: handler: the callable event handler that should be removed event_name: The event the handler attached to. """ if event_name not in self._event_handlers: raise ValueError(f"Input event name '{event_name}' does not exist") new_event_handlers = [ (h, args, kwargs) for h, args, kwargs in self._event_handlers[event_name] if not self._compare_handlers(handler, h) ] if len(new_event_handlers) == len(self._event_handlers[event_name]): raise ValueError(f"Input handler '{handler}' is not found among registered event handlers") self._event_handlers[event_name] = new_event_handlers def on(self, event_name: Any, *args: Any, **kwargs: Any) -> Callable: """Decorator shortcut for :meth:`~ignite.engine.engine.Engine.add_event_handler`. Args: event_name: An event to attach the handler to. Valid events are from :class:`~ignite.engine.events.Events` or any ``event_name`` added by :meth:`~ignite.engine.engine.Engine.register_events`. args: optional args to be passed to `handler`. kwargs: optional keyword args to be passed to `handler`. Examples: .. code-block:: python engine = Engine(process_function) @engine.on(Events.EPOCH_COMPLETED) def print_epoch(): print(f"Epoch: {engine.state.epoch}") @engine.on(Events.EPOCH_COMPLETED | Events.COMPLETED) def execute_something(): # do some thing not related to engine pass """ def decorator(f: Callable) -> Callable: self.add_event_handler(event_name, f, *args, **kwargs) return f return decorator def _fire_event(self, event_name: Any, *event_args: Any, **event_kwargs: Any) -> None: """Execute all the handlers associated with given event. This method executes all handlers associated with the event `event_name`. Optional positional and keyword arguments can be used to pass arguments to **all** handlers added with this event. These arguments updates arguments passed using :meth:`~ignite.engine.engine.Engine.add_event_handler`. Args: event_name: event for which the handlers should be executed. Valid events are from :class:`~ignite.engine.events.Events` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. *event_args: optional args to be passed to all handlers. **event_kwargs: optional keyword args to be passed to all handlers. """ self.logger.debug(f"{self.state.epoch} | {self.state.iteration}, Firing handlers for event {event_name}") self.last_event_name = event_name for func, args, kwargs in self._event_handlers[event_name]: kwargs.update(event_kwargs) first, others = ((args[0],), args[1:]) if (args and args[0] == self) else ((), args) func(*first, *(event_args + others), **kwargs) def fire_event(self, event_name: Any) -> None: """Execute all the handlers associated with given event. This method executes all handlers associated with the event `event_name`. This is the method used in :meth:`~ignite.engine.engine.Engine.run` to call the core events found in :class:`~ignite.engine.events.Events`. Custom events can be fired if they have been registered before with :meth:`~ignite.engine.engine.Engine.register_events`. The engine `state` attribute should be used to exchange "dynamic" data among `process_function` and handlers. This method is called automatically for core events. If no custom events are used in the engine, there is no need for the user to call the method. Args: event_name: event for which the handlers should be executed. Valid events are from :class:`~ignite.engine.events.Events` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. """ self._assert_allowed_event(event_name) return self._fire_event(event_name) def interrupt(self) -> None: """Sends interrupt signal to the engine, so that it interrupts the run after the current iteration. The run can be resumed by calling :meth:`~ignite.engine.engine.Engine.run`. Data iteration will continue from the interrupted state. Examples: .. testcode:: from ignite.engine import Engine, Events data = range(10) max_epochs = 3 def check_input_data(e, b): print(f"Epoch {engine.state.epoch}, Iter {engine.state.iteration} | data={b}") i = (e.state.iteration - 1) % len(data) assert b == data[i] engine = Engine(check_input_data) @engine.on(Events.ITERATION_COMPLETED(every=11)) def call_interrupt(): engine.interrupt() print("Start engine run with interruptions:") state = engine.run(data, max_epochs=max_epochs) print("1 Engine run is interrupted at ", state.epoch, state.iteration) state = engine.run(data, max_epochs=max_epochs) print("2 Engine run is interrupted at ", state.epoch, state.iteration) state = engine.run(data, max_epochs=max_epochs) print("3 Engine ended the run at ", state.epoch, state.iteration) .. dropdown:: Output .. testoutput:: Start engine run with interruptions: Epoch 1, Iter 1 | data=0 Epoch 1, Iter 2 | data=1 Epoch 1, Iter 3 | data=2 Epoch 1, Iter 4 | data=3 Epoch 1, Iter 5 | data=4 Epoch 1, Iter 6 | data=5 Epoch 1, Iter 7 | data=6 Epoch 1, Iter 8 | data=7 Epoch 1, Iter 9 | data=8 Epoch 1, Iter 10 | data=9 Epoch 2, Iter 11 | data=0 1 Engine run is interrupted at 2 11 Epoch 2, Iter 12 | data=1 Epoch 2, Iter 13 | data=2 Epoch 2, Iter 14 | data=3 Epoch 2, Iter 15 | data=4 Epoch 2, Iter 16 | data=5 Epoch 2, Iter 17 | data=6 Epoch 2, Iter 18 | data=7 Epoch 2, Iter 19 | data=8 Epoch 2, Iter 20 | data=9 Epoch 3, Iter 21 | data=0 Epoch 3, Iter 22 | data=1 2 Engine run is interrupted at 3 22 Epoch 3, Iter 23 | data=2 Epoch 3, Iter 24 | data=3 Epoch 3, Iter 25 | data=4 Epoch 3, Iter 26 | data=5 Epoch 3, Iter 27 | data=6 Epoch 3, Iter 28 | data=7 Epoch 3, Iter 29 | data=8 Epoch 3, Iter 30 | data=9 3 Engine ended the run at 3 30 .. versionadded:: 0.4.10 """ if not self.interrupt_resume_enabled: raise RuntimeError( "Engine 'interrupt/resume' feature is disabled. " "Please, set Engine.interrupt_resume_enabled=True to enable it" ) self.logger.info("interrupt signaled. Engine will interrupt the run after current iteration is finished.") self.should_interrupt = True def terminate(self) -> None: """Sends terminate signal to the engine, so that it terminates completely the run. The run is terminated after the event on which ``terminate`` method was called. The following events are triggered: - ... - Terminating event - :attr:`~ignite.engine.events.Events.TERMINATE` - :attr:`~ignite.engine.events.Events.COMPLETED` Examples: .. testcode:: from ignite.engine import Engine, Events def func(engine, batch): print(engine.state.epoch, engine.state.iteration, " | ", batch) max_epochs = 4 data = range(10) engine = Engine(func) @engine.on(Events.ITERATION_COMPLETED(once=14)) def terminate(): print(f"-> terminate at iteration: {engine.state.iteration}") engine.terminate() print("Start engine run:") state = engine.run(data, max_epochs=max_epochs) print("1 Engine run is terminated at ", state.epoch, state.iteration) state = engine.run(data, max_epochs=max_epochs) print("2 Engine ended the run at ", state.epoch, state.iteration) .. dropdown:: Output .. testoutput:: Start engine run: 1 1 | 0 1 2 | 1 1 3 | 2 1 4 | 3 1 5 | 4 1 6 | 5 1 7 | 6 1 8 | 7 1 9 | 8 1 10 | 9 2 11 | 0 2 12 | 1 2 13 | 2 2 14 | 3 -> terminate at iteration: 14 1 Engine run is terminated at 2 14 3 15 | 0 3 16 | 1 3 17 | 2 3 18 | 3 3 19 | 4 3 20 | 5 3 21 | 6 3 22 | 7 3 23 | 8 3 24 | 9 4 25 | 0 4 26 | 1 4 27 | 2 4 28 | 3 4 29 | 4 4 30 | 5 4 31 | 6 4 32 | 7 4 33 | 8 4 34 | 9 2 Engine ended the run at 4 34 .. versionchanged:: 0.4.10 Behaviour changed, for details see https://github.com/pytorch/ignite/issues/2669 """ self.logger.info("Terminate signaled. Engine will stop after current iteration is finished.") self.should_terminate = True def terminate_epoch(self) -> None: """Sends terminate signal to the engine, so that it terminates the current epoch. The run continues from the next epoch. The following events are triggered: - ... - Event on which ``terminate_epoch`` method is called - :attr:`~ignite.engine.events.Events.TERMINATE_SINGLE_EPOCH` - :attr:`~ignite.engine.events.Events.EPOCH_COMPLETED` - :attr:`~ignite.engine.events.Events.EPOCH_STARTED` - ... """ self.logger.info( "Terminate current epoch is signaled. " "Current epoch iteration will stop after current iteration is finished." ) self.should_terminate_single_epoch = True def _handle_exception(self, e: BaseException) -> None: if Events.EXCEPTION_RAISED in self._event_handlers: self._fire_event(Events.EXCEPTION_RAISED, e) else: raise e @property def state_dict_user_keys(self) -> List: return self._state_dict_user_keys def state_dict(self) -> OrderedDict: """Returns a dictionary containing engine's state: "epoch_length", "max_epochs" and "iteration" and other state values defined by `engine.state_dict_user_keys` .. code-block:: python engine = Engine(...) engine.state_dict_user_keys.append("alpha") engine.state_dict_user_keys.append("beta") ... @engine.on(Events.STARTED) def init_user_value(_): engine.state.alpha = 0.1 engine.state.beta = 1.0 @engine.on(Events.COMPLETED) def save_engine(_): state_dict = engine.state_dict() assert "alpha" in state_dict and "beta" in state_dict torch.save(state_dict, "/tmp/engine.pt") Returns: OrderedDict: a dictionary containing engine's state """ keys: Tuple[str, ...] = self._state_dict_all_req_keys + (self._state_dict_one_of_opt_keys[0],) keys += tuple(self._state_dict_user_keys) return OrderedDict([(k, getattr(self.state, k)) for k in keys]) def load_state_dict(self, state_dict: Mapping) -> None: """Setups engine from `state_dict`. State dictionary should contain keys: `iteration` or `epoch`, `max_epochs` and `epoch_length`. If `engine.state_dict_user_keys` contains keys, they should be also present in the state dictionary. Iteration and epoch values are 0-based: the first iteration or epoch is zero. This method does not remove any custom attributes added by user. Args: state_dict: a dict with parameters .. code-block:: python # Restore from the 4rd epoch state_dict = {"epoch": 3, "max_epochs": 100, "epoch_length": len(data_loader)} # or 500th iteration # state_dict = {"iteration": 499, "max_epochs": 100, "epoch_length": len(data_loader)} trainer = Engine(...) trainer.load_state_dict(state_dict) trainer.run(data) """ super(Engine, self).load_state_dict(state_dict) for k in self._state_dict_user_keys: if k not in state_dict: raise ValueError( f"Required user state attribute '{k}' is absent in provided state_dict '{state_dict.keys()}'" ) self.state.max_epochs = state_dict["max_epochs"] self.state.epoch_length = state_dict["epoch_length"] for k in self._state_dict_user_keys: setattr(self.state, k, state_dict[k]) if "iteration" in state_dict: self.state.iteration = state_dict["iteration"] self.state.epoch = 0 if self.state.epoch_length is not None: self.state.epoch = self.state.iteration // self.state.epoch_length elif "epoch" in state_dict: self.state.epoch = state_dict["epoch"] if self.state.epoch_length is None: raise ValueError( "If epoch is provided in the state dict, epoch_length should not be None. " f"Input state_dict: {state_dict}" ) self.state.iteration = self.state.epoch_length * self.state.epoch @staticmethod def _is_done(state: State) -> bool: is_done_count = ( state.epoch_length is not None and state.max_epochs is not None and state.iteration >= state.epoch_length * state.max_epochs ) is_done_epochs = state.max_epochs is not None and state.epoch >= state.max_epochs return is_done_count or is_done_epochs def set_data(self, data: Union[Iterable, DataLoader]) -> None: """Method to set data. After calling the method the next batch passed to `processing_function` is from newly provided data. Please, note that epoch length is not modified. Args: data: Collection of batches allowing repeated iteration (e.g., list or `DataLoader`). Examples: User can switch data provider during the training: .. code-block:: python data1 = ... data2 = ... switch_iteration = 5000 def train_step(e, batch): # when iteration <= switch_iteration # batch is from data1 # when iteration > switch_iteration # batch is from data2 ... trainer = Engine(train_step) @trainer.on(Events.ITERATION_COMPLETED(once=switch_iteration)) def switch_dataloader(): trainer.set_data(data2) trainer.run(data1, max_epochs=100) """ self.state.dataloader = data self._dataloader_iter = iter(self.state.dataloader) def run( self, data: Optional[Iterable] = None, max_epochs: Optional[int] = None, epoch_length: Optional[int] = None, ) -> State: """Runs the ``process_function`` over the passed data. Engine has a state and the following logic is applied in this function: - At the first call, new state is defined by `max_epochs`, `epoch_length`, if provided. A timer for total and per-epoch time is initialized when Events.STARTED is handled. - If state is already defined such that there are iterations to run until `max_epochs` and no input arguments provided, state is kept and used in the function. - If state is defined and engine is "done" (no iterations to run until `max_epochs`), a new state is defined. - If state is defined, engine is NOT "done", then input arguments if provided override defined state. Args: data: Collection of batches allowing repeated iteration (e.g., list or `DataLoader`). If not provided, then ``epoch_length`` is required and ``batch`` argument of ``process_function`` will be ``None``. max_epochs: Max epochs to run for (default: None). If a new state should be created (first run or run again from ended engine), it's default value is 1. If run is resuming from a state, provided `max_epochs` will be taken into account and should be larger than `engine.state.max_epochs`. epoch_length: Number of iterations to count as one epoch. By default, it can be set as `len(data)`. If `data` is an iterator and `epoch_length` is not set, then it will be automatically determined as the iteration on which data iterator raises `StopIteration`. This argument should not change if run is resuming from a state. Returns: State: output state. Note: User can dynamically preprocess input batch at :attr:`~ignite.engine.events.Events.ITERATION_STARTED` and store output batch in `engine.state.batch`. Latter is passed as usually to `process_function` as argument: .. code-block:: python trainer = ... @trainer.on(Events.ITERATION_STARTED) def switch_batch(engine): engine.state.batch = preprocess_batch(engine.state.batch) Restart the training from the beginning. User can reset `max_epochs = None`: .. code-block:: python # ... trainer.run(train_loader, max_epochs=5) # Reset model weights etc. and restart the training trainer.state.max_epochs = None trainer.run(train_loader, max_epochs=2) """ if data is not None and not isinstance(data, Iterable): raise TypeError("Argument data should be iterable") if self.state.max_epochs is not None: # Check and apply overridden parameters if max_epochs is not None: if max_epochs < self.state.epoch: raise ValueError( "Argument max_epochs should be greater than or equal to the start " f"epoch defined in the state: {max_epochs} vs {self.state.epoch}. " "Please, set engine.state.max_epochs = None " "before calling engine.run() in order to restart the training from the beginning." ) self.state.max_epochs = max_epochs if epoch_length is not None: if epoch_length != self.state.epoch_length: raise ValueError( "Argument epoch_length should be same as in the state, " f"but given {epoch_length} vs {self.state.epoch_length}" ) if self.state.max_epochs is None or (self._is_done(self.state) and self._internal_run_generator is None): # Create new state if max_epochs is None: max_epochs = 1 if epoch_length is None: if data is None: raise ValueError("epoch_length should be provided if data is None") epoch_length = self._get_data_length(data) if epoch_length is not None and epoch_length < 1: raise ValueError("Input data has zero size. Please provide non-empty data") self.state.iteration = 0 self.state.epoch = 0 self.state.max_epochs = max_epochs self.state.epoch_length = epoch_length # Reset generator if previously used self._internal_run_generator = None self.logger.info(f"Engine run starting with max_epochs={max_epochs}.") else: self.logger.info( f"Engine run resuming from iteration {self.state.iteration}, " f"epoch {self.state.epoch} until {self.state.max_epochs} epochs" ) if self.state.epoch_length is None and data is None: raise ValueError("epoch_length should be provided if data is None") if self.should_terminate: # If engine was terminated and now is resuming from terminated state # we need to initialize iter_counter as 0 self._init_iter = 0 if self._dataloader_iter is None: self.state.dataloader = data if self.interrupt_resume_enabled: return self._internal_run() else: return self._internal_run_legacy() @staticmethod def _init_timers(state: State) -> None: state.times[Events.EPOCH_COMPLETED.name] = 0.0 state.times[Events.COMPLETED.name] = 0.0 def _get_data_length(self, data: Iterable) -> Optional[int]: try: if hasattr(data, "__len__"): return len(data) # type: ignore[arg-type] except TypeError: # _InfiniteConstantSampler can raise a TypeError on DataLoader length of a IterableDataset pass return None def _setup_dataloader_iter(self) -> None: if self.state.dataloader is None: if self.state.epoch_length is None: raise RuntimeError( "Internal error, self.state.epoch_length is None. " "Please, file an issue if you encounter this error." ) self._dataloader_iter = _get_none_data_iter(self.state.epoch_length) else: self._dataloader_iter = iter(self.state.dataloader) def _setup_engine(self) -> None: self._setup_dataloader_iter() if self._init_iter is None: iteration = self.state.iteration # Below we define initial counter value for _run_once_on_dataset to measure a single epoch if self.state.epoch_length is not None: iteration %= self.state.epoch_length self._init_iter = iteration def _internal_run(self) -> State: if self._internal_run_generator is None: self._internal_run_generator = self._internal_run_as_gen() try: return next(self._internal_run_generator) except StopIteration as out: self._internal_run_generator = None return out.value def _internal_run_as_gen(self) -> Generator[Any, None, State]: self.should_terminate = self.should_terminate_single_epoch = self.should_interrupt = False self._init_timers(self.state) try: try: start_time = time.time() self._fire_event(Events.STARTED) yield from self._maybe_terminate_or_interrupt() while not self._is_done(self.state) and not self.should_terminate: self.state.epoch += 1 handlers_start_time = time.time() self._fire_event(Events.EPOCH_STARTED) epoch_time_taken = time.time() - handlers_start_time yield from self._maybe_terminate_or_interrupt() if self._dataloader_iter is None: self._setup_engine() epoch_time_taken += yield from self._run_once_on_dataset_as_gen() # time is available for handlers but must be updated after fire self.state.times[Events.EPOCH_COMPLETED.name] = epoch_time_taken handlers_start_time = time.time() self._fire_event(Events.EPOCH_COMPLETED) epoch_time_taken += time.time() - handlers_start_time # update time wrt handlers self.state.times[Events.EPOCH_COMPLETED.name] = epoch_time_taken yield from self._maybe_terminate_or_interrupt() hours, mins, secs = _to_hours_mins_secs(epoch_time_taken) self.logger.info( f"Epoch[{self.state.epoch}] Complete. Time taken: {hours:02d}:{mins:02d}:{secs:06.3f}" ) except _EngineTerminateException: self._fire_event(Events.TERMINATE) time_taken = time.time() - start_time # time is available for handlers but must be updated after fire self.state.times[Events.COMPLETED.name] = time_taken handlers_start_time = time.time() self._fire_event(Events.COMPLETED) time_taken += time.time() - handlers_start_time # update time wrt handlers self.state.times[Events.COMPLETED.name] = time_taken hours, mins, secs = _to_hours_mins_secs(time_taken) self.logger.info(f"Engine run complete. Time taken: {hours:02d}:{mins:02d}:{secs:06.3f}") except BaseException as e: self._dataloader_iter = None self.logger.error(f"Engine run is terminating due to exception: {e}") self._handle_exception(e) self._dataloader_iter = None return self.state def _maybe_terminate_or_interrupt(self) -> Generator: if self.should_terminate: raise _EngineTerminateException() if self.should_terminate_single_epoch: raise _EngineTerminateSingleEpochException() if self.should_interrupt: self._fire_event(Events.INTERRUPT) self.should_interrupt = False yield self.state def _run_once_on_dataset_as_gen(self) -> Generator[State, None, float]: start_time = time.time() # We need to setup iter_counter > 0 if we resume from an iteration iter_counter = 0 if self._init_iter is None else self._init_iter self._init_iter = None should_exit = False try: if self._dataloader_iter is None: raise RuntimeError( "Internal error, self._dataloader_iter is None. " "Please, file an issue if you encounter this error." ) while True: self.state.batch = self.state.output = None try: # Avoid Events.GET_BATCH_STARTED triggered twice when data iter is restarted if self.last_event_name != Events.DATALOADER_STOP_ITERATION: # We should not trigger GET_BATCH_STARTED, GET_BATCH_COMPLETED, DATALOADER_STOP_ITERATION events # if no data was provided to engine.run(data=None, ...) if self.state.dataloader is not None: self._fire_event(Events.GET_BATCH_STARTED) yield from self._maybe_terminate_or_interrupt() self.state.batch = next(self._dataloader_iter) # We should not trigger GET_BATCH_STARTED, GET_BATCH_COMPLETED, DATALOADER_STOP_ITERATION events # if no data was provided to engine.run(data=None, ...) if self.state.dataloader is not None: self._fire_event(Events.GET_BATCH_COMPLETED) yield from self._maybe_terminate_or_interrupt() iter_counter += 1 should_exit = False except StopIteration: # Define self.state.epoch_length if it is not yet set if self.state.epoch_length is None: # Define epoch length and stop the epoch self.state.epoch_length = iter_counter break # Should exit while loop if we can not iterate if should_exit: if not self._is_done(self.state) and self.state.max_epochs is not None: total_iters = self.state.epoch_length * self.state.max_epochs warnings.warn( "Data iterator can not provide data anymore but required total number of " "iterations to run is not reached. " f"Current iteration: {self.state.iteration} vs Total iterations to run : {total_iters}" ) break # We should not trigger GET_BATCH_STARTED, GET_BATCH_COMPLETED, DATALOADER_STOP_ITERATION events # if no data was provided to engine.run(data=None, ...) if self.state.dataloader is not None: self._fire_event(Events.DATALOADER_STOP_ITERATION) yield from self._maybe_terminate_or_interrupt() self._setup_dataloader_iter() should_exit = True continue self.state.iteration += 1 self._fire_event(Events.ITERATION_STARTED) yield from self._maybe_terminate_or_interrupt() self.state.output = self._process_function(self, self.state.batch) self._fire_event(Events.ITERATION_COMPLETED) yield from self._maybe_terminate_or_interrupt() if self.state.epoch_length is not None and iter_counter == self.state.epoch_length: break except _EngineTerminateSingleEpochException: self._fire_event(Events.TERMINATE_SINGLE_EPOCH, iter_counter=iter_counter) self.should_terminate_single_epoch = False self._setup_dataloader_iter() except _EngineTerminateException as e: # we need to reraise this exception such that it is not handled # as a general exception by the code below raise e except Exception as e: self.logger.error(f"Current run is terminating due to exception: {e}") self._handle_exception(e) return time.time() - start_time def _maybe_terminate_legacy(self) -> None: if self.should_terminate: raise _EngineTerminateException() if self.should_terminate_single_epoch: raise _EngineTerminateSingleEpochException() def _internal_run_legacy(self) -> State: # internal_run without generator for BC self.should_terminate = self.should_terminate_single_epoch = self.should_interrupt = False self._init_timers(self.state) try: try: start_time = time.time() self._fire_event(Events.STARTED) self._maybe_terminate_legacy() while not self._is_done(self.state) and not self.should_terminate: self.state.epoch += 1 handlers_start_time = time.time() self._fire_event(Events.EPOCH_STARTED) epoch_time_taken = time.time() - handlers_start_time self._maybe_terminate_legacy() if self._dataloader_iter is None: self._setup_engine() epoch_time_taken += self._run_once_on_dataset_legacy() # time is available for handlers but must be updated after fire self.state.times[Events.EPOCH_COMPLETED.name] = epoch_time_taken handlers_start_time = time.time() self._fire_event(Events.EPOCH_COMPLETED) epoch_time_taken += time.time() - handlers_start_time # update time wrt handlers self.state.times[Events.EPOCH_COMPLETED.name] = epoch_time_taken self._maybe_terminate_legacy() hours, mins, secs = _to_hours_mins_secs(epoch_time_taken) self.logger.info( f"Epoch[{self.state.epoch}] Complete. Time taken: {hours:02d}:{mins:02d}:{secs:06.3f}" ) except _EngineTerminateException: self._fire_event(Events.TERMINATE) time_taken = time.time() - start_time # time is available for handlers but must be updated after fire self.state.times[Events.COMPLETED.name] = time_taken handlers_start_time = time.time() self._fire_event(Events.COMPLETED) time_taken += time.time() - handlers_start_time # update time wrt handlers self.state.times[Events.COMPLETED.name] = time_taken hours, mins, secs = _to_hours_mins_secs(time_taken) self.logger.info(f"Engine run complete. Time taken: {hours:02d}:{mins:02d}:{secs:06.3f}") except BaseException as e: self._dataloader_iter = None self.logger.error(f"Engine run is terminating due to exception: {e}") self._handle_exception(e) self._dataloader_iter = None return self.state def _run_once_on_dataset_legacy(self) -> float: start_time = time.time() # We need to setup iter_counter > 0 if we resume from an iteration iter_counter = 0 if self._init_iter is None else self._init_iter self._init_iter = None should_exit = False try: if self._dataloader_iter is None: raise RuntimeError( "Internal error, self._dataloader_iter is None. " "Please, file an issue if you encounter this error." ) while True: self.state.batch = self.state.output = None try: # Avoid Events.GET_BATCH_STARTED triggered twice when data iter is restarted if self.last_event_name != Events.DATALOADER_STOP_ITERATION: # We should not trigger GET_BATCH_STARTED, GET_BATCH_COMPLETED, DATALOADER_STOP_ITERATION events # if no data was provided to engine.run(data=None, ...) if self.state.dataloader is not None: self._fire_event(Events.GET_BATCH_STARTED) self._maybe_terminate_legacy() self.state.batch = next(self._dataloader_iter) # We should not trigger GET_BATCH_STARTED, GET_BATCH_COMPLETED, DATALOADER_STOP_ITERATION events # if no data was provided to engine.run(data=None, ...) if self.state.dataloader is not None: self._fire_event(Events.GET_BATCH_COMPLETED) self._maybe_terminate_legacy() iter_counter += 1 should_exit = False except StopIteration: # Define self.state.epoch_length if it is not yet set if self.state.epoch_length is None: # Define epoch length and stop the epoch self.state.epoch_length = iter_counter break # Should exit while loop if we can not iterate if should_exit: if not self._is_done(self.state) and self.state.max_epochs is not None: total_iters = self.state.epoch_length * self.state.max_epochs warnings.warn( "Data iterator can not provide data anymore but required total number of " "iterations to run is not reached. " f"Current iteration: {self.state.iteration} vs Total iterations to run : {total_iters}" ) break # We should not trigger GET_BATCH_STARTED, GET_BATCH_COMPLETED, DATALOADER_STOP_ITERATION events # if no data was provided to engine.run(data=None, ...) if self.state.dataloader is not None: self._fire_event(Events.DATALOADER_STOP_ITERATION) self._maybe_terminate_legacy() self._setup_dataloader_iter() should_exit = True continue self.state.iteration += 1 self._fire_event(Events.ITERATION_STARTED) self._maybe_terminate_legacy() self.state.output = self._process_function(self, self.state.batch) self._fire_event(Events.ITERATION_COMPLETED) self._maybe_terminate_legacy() if self.state.epoch_length is not None and iter_counter == self.state.epoch_length: break except _EngineTerminateSingleEpochException: self._fire_event(Events.TERMINATE_SINGLE_EPOCH, iter_counter=iter_counter) self.should_terminate_single_epoch = False self._setup_dataloader_iter() except _EngineTerminateException as e: # we need to reraise this exception such that it is not handled # as a general exception by the code below raise e except Exception as e: self.logger.error(f"Current run is terminating due to exception: {e}") self._handle_exception(e) return time.time() - start_time def _get_none_data_iter(size: int) -> Iterator: # Sized iterator for data as None for _ in range(size): yield None class _EngineTerminateSingleEpochException(Exception): """ Exception associated with Terminate Single Epoch event """ pass class _EngineTerminateException(Exception): """ Exception associated with Terminate event """ pass ignite-0.5.1/ignite/engine/events.py000066400000000000000000000511621465426447700174260ustar00rootroot00000000000000import numbers import warnings import weakref from collections.abc import Sequence from enum import Enum from types import DynamicClassAttribute from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, TYPE_CHECKING, Union from torch.utils.data import DataLoader from ignite.engine.utils import _check_signature if TYPE_CHECKING: from ignite.engine.engine import Engine __all__ = ["CallableEventWithFilter", "EventEnum", "Events", "State", "EventsList", "RemovableEventHandle"] class CallableEventWithFilter: """Single Event containing a filter, specifying whether the event should be run at the current event (if the event type is correct) Args: value: The actual enum value. Only needed for internal use. Do not touch! event_filter: A function taking the engine and the current event value as input and returning a boolean to indicate whether this event should be executed. Defaults to None, which will result to a function that always returns `True` name: The enum-name of the current object. Only needed for internal use. Do not touch! """ def __init__(self, value: str, event_filter: Optional[Callable] = None, name: Optional[str] = None) -> None: self.filter = event_filter if not hasattr(self, "_value_"): self._value_ = value if not hasattr(self, "_name_") and name is not None: self._name_ = name # copied to be compatible to enum @DynamicClassAttribute def name(self) -> str: """The name of the Enum member.""" return self._name_ @DynamicClassAttribute def value(self) -> str: """The value of the Enum member.""" return self._value_ def __call__( self, event_filter: Optional[Callable] = None, every: Optional[int] = None, once: Optional[Union[int, List]] = None, before: Optional[int] = None, after: Optional[int] = None, ) -> "CallableEventWithFilter": """ Makes the event class callable and accepts either an arbitrary callable as filter (which must take in the engine and current event value and return a boolean) or an every or once value Args: event_filter: a filter function to check if the event should be executed when the event type was fired every: a value specifying how often the event should be fired once: a value or list of values specifying when the event should be fired (if only once) before: a value specifying the number of occurrence that event should be fired before after: a value specifying the number of occurrence that event should be fired after Returns: CallableEventWithFilter: A new event having the same value but a different filter function """ if ( sum( ( event_filter is not None, once is not None, (every is not None or before is not None or after is not None), ) ) != 1 ): raise ValueError("Only one of the input arguments should be specified, except before, after and every") if (event_filter is not None) and not callable(event_filter): raise TypeError("Argument event_filter should be a callable") if (every is not None) and not (isinstance(every, numbers.Integral) and every > 0): raise ValueError("Argument every should be integer and greater than zero") if once is not None: c1 = isinstance(once, numbers.Integral) and once > 0 c2 = isinstance(once, Sequence) and len(once) > 0 and all(isinstance(e, int) and e > 0 for e in once) if not (c1 or c2): raise ValueError( f"Argument once should either be a positive integer or a list of positive integers, got {once}" ) if (before is not None) and not (isinstance(before, numbers.Integral) and before >= 0): raise ValueError("Argument before should be integer and greater or equal to zero") if (after is not None) and not (isinstance(after, numbers.Integral) and after >= 0): raise ValueError("Argument after should be integer and greater or equal to zero") if every is not None: if every == 1: # Just return the event itself event_filter = None else: event_filter = self.every_event_filter(every) if once is not None: event_filter = self.once_event_filter([once] if isinstance(once, int) else once) if before is not None or after is not None: if every is not None: event_filter = self.every_before_and_after_event_filter(every, before, after) else: event_filter = self.before_and_after_event_filter(before, after) # check signature: if event_filter is not None: _check_signature(event_filter, "event_filter", "engine", "event") return CallableEventWithFilter(self.value, event_filter, self.name) @staticmethod def every_event_filter(every: int) -> Callable: """A wrapper for every event filter.""" def wrapper(engine: "Engine", event: int) -> bool: if event % every == 0: return True return False return wrapper @staticmethod def once_event_filter(once: List) -> Callable: """A wrapper for once event filter.""" def wrapper(engine: "Engine", event: int) -> bool: if event in once: return True return False return wrapper @staticmethod def before_and_after_event_filter(before: Optional[int] = None, after: Optional[int] = None) -> Callable: """A wrapper for before and after event filter.""" before_: Union[int, float] = float("inf") if before is None else before after_: int = 0 if after is None else after def wrapper(engine: "Engine", event: int) -> bool: if event > after_ and event < before_: return True return False return wrapper @staticmethod def every_before_and_after_event_filter( every: int, before: Optional[int] = None, after: Optional[int] = None ) -> Callable: """A wrapper which triggers for every `every` iterations after `after` and before `before`.""" before_: Union[int, float] = float("inf") if before is None else before after_: int = 0 if after is None else after def wrapper(engine: "Engine", event: int) -> bool: if after_ < event < before_ and (event - after_ - 1) % every == 0: return True return False return wrapper @staticmethod def default_event_filter(engine: "Engine", event: int) -> bool: """Default event filter. This method is is deprecated and will be removed. Please, use None instead""" warnings.warn("Events.default_event_filter is deprecated and will be removed. Please, use None instead") return True def __repr__(self) -> str: out = f"Events.{self.name}" if self.filter is not None: out += f"(filter={self.filter})" return out def __eq__(self, other: Any) -> bool: if isinstance(other, CallableEventWithFilter): return self.name == other.name elif isinstance(other, str): return self.name == other else: return NotImplemented def __hash__(self) -> int: return hash(self._name_) def __or__(self, other: Any) -> "EventsList": return EventsList() | self | other class EventEnum(CallableEventWithFilter, Enum): """Base class for all :class:`~ignite.engine.events.Events`. User defined custom events should also inherit this class. Examples: Custom events based on the loss calculation and backward pass can be created as follows: .. code-block:: python from ignite.engine import EventEnum class BackpropEvents(EventEnum): BACKWARD_STARTED = 'backward_started' BACKWARD_COMPLETED = 'backward_completed' OPTIM_STEP_COMPLETED = 'optim_step_completed' def update(engine, batch): # ... loss = criterion(y_pred, y) engine.fire_event(BackpropEvents.BACKWARD_STARTED) loss.backward() engine.fire_event(BackpropEvents.BACKWARD_COMPLETED) optimizer.step() engine.fire_event(BackpropEvents.OPTIM_STEP_COMPLETED) # ... trainer = Engine(update) trainer.register_events(*BackpropEvents) @trainer.on(BackpropEvents.BACKWARD_STARTED) def function_before_backprop(engine): # ... """ def __new__(cls, value: str) -> "EventEnum": obj = CallableEventWithFilter.__new__(cls) obj._value_ = value return obj class Events(EventEnum): """Events that are fired by the :class:`~ignite.engine.engine.Engine` during execution. Built-in events: - STARTED : triggered when engine's run is started - EPOCH_STARTED : triggered when the epoch is started - GET_BATCH_STARTED : triggered before next batch is fetched - GET_BATCH_COMPLETED : triggered after the batch is fetched - ITERATION_STARTED : triggered when an iteration is started - ITERATION_COMPLETED : triggered when the iteration is ended - DATALOADER_STOP_ITERATION : engine's specific event triggered when dataloader has no more data to provide - EXCEPTION_RAISED : triggered when an exception is encountered - TERMINATE_SINGLE_EPOCH : triggered when the run is about to end the current epoch, after receiving a :meth:`~ignite.engine.engine.Engine.terminate_epoch()` or :meth:`~ignite.engine.engine.Engine.terminate()` call. - TERMINATE : triggered when the run is about to end completely, after receiving :meth:`~ignite.engine.engine.Engine.terminate()` call. - EPOCH_COMPLETED : triggered when the epoch is ended. Note that this is triggered even when :meth:`~ignite.engine.engine.Engine.terminate_epoch()` is called. - COMPLETED : triggered when engine's run is completed The table below illustrates which events are triggered when various termination methods are called. .. list-table:: :widths: 24 25 33 18 :header-rows: 1 * - Method - EVENT_COMPLETED - TERMINATE_SINGLE_EPOCH - TERMINATE * - no termination - βœ” - βœ— - βœ— * - :meth:`~ignite.engine.engine.Engine.terminate_epoch()` - βœ” - βœ” - βœ— * - :meth:`~ignite.engine.engine.Engine.terminate()` - βœ— - βœ” - βœ” Since v0.3.0, Events become more flexible and allow to pass an event filter to the Engine: .. code-block:: python engine = Engine() # a) custom event filter def custom_event_filter(engine, event): if event in [1, 2, 5, 10, 50, 100]: return True return False @engine.on(Events.ITERATION_STARTED(event_filter=custom_event_filter)) def call_on_special_event(engine): # do something on 1, 2, 5, 10, 50, 100 iterations # b) "every" event filter @engine.on(Events.ITERATION_STARTED(every=10)) def call_every(engine): # do something every 10th iteration # c) "once" event filter @engine.on(Events.ITERATION_STARTED(once=50)) def call_once(engine): # do something on 50th iteration # d) "before" and "after" event filter @engine.on(Events.EPOCH_STARTED(before=30, after=10)) def call_before(engine): # do something in 11 to 29 epoch # e) Mixing "every" and "before" / "after" event filters @engine.on(Events.EPOCH_STARTED(every=5, before=25, after=8)) def call_every_itr_before_after(engine): # do something on 9, 14, 19, 24 epochs Event filter function `event_filter` accepts as input `engine` and `event` and should return True/False. Argument `event` is the value of iteration or epoch, depending on which type of Events the function is passed. Since v0.4.0, user can also combine events with `|`-operator: .. code-block:: python events = Events.STARTED | Events.COMPLETED | Events.ITERATION_STARTED(every=3) engine = ... @engine.on(events) def call_on_events(engine): # do something Since v0.4.0, custom events defined by user should inherit from :class:`~ignite.engine.events.EventEnum` : .. code-block:: python class CustomEvents(EventEnum): FOO_EVENT = "foo_event" BAR_EVENT = "bar_event" """ EPOCH_STARTED = "epoch_started" """triggered when the epoch is started.""" EPOCH_COMPLETED = "epoch_completed" """Event attribute indicating epoch is ended.""" STARTED = "started" """triggered when engine's run is started.""" COMPLETED = "completed" """triggered when engine's run is completed""" ITERATION_STARTED = "iteration_started" """triggered when an iteration is started.""" ITERATION_COMPLETED = "iteration_completed" """triggered when the iteration is ended.""" EXCEPTION_RAISED = "exception_raised" """triggered when an exception is encountered.""" GET_BATCH_STARTED = "get_batch_started" """triggered before next batch is fetched.""" GET_BATCH_COMPLETED = "get_batch_completed" """triggered after the batch is fetched.""" DATALOADER_STOP_ITERATION = "dataloader_stop_iteration" """engine's specific event triggered when dataloader has no more data to provide""" TERMINATE = "terminate" """triggered when the run is about to end completely, after receiving terminate() call.""" TERMINATE_SINGLE_EPOCH = "terminate_single_epoch" """triggered when the run is about to end the current epoch, after receiving a terminate_epoch() call.""" INTERRUPT = "interrupt" """triggered when the run is interrupted, after receiving interrupt() call.""" def __or__(self, other: Any) -> "EventsList": return EventsList() | self | other class EventsList: """Collection of events stacked by operator `__or__`. .. code-block:: python events = Events.STARTED | Events.COMPLETED events |= Events.ITERATION_STARTED(every=3) engine = ... @engine.on(events) def call_on_events(engine): # do something or .. code-block:: python @engine.on(Events.STARTED | Events.COMPLETED | Events.ITERATION_STARTED(every=3)) def call_on_events(engine): # do something """ def __init__(self) -> None: self._events: List[Union[Events, CallableEventWithFilter]] = [] def _append(self, event: Union[Events, CallableEventWithFilter]) -> None: if not isinstance(event, (Events, CallableEventWithFilter)): raise TypeError(f"Argument event should be Events or CallableEventWithFilter, got: {type(event)}") self._events.append(event) def __getitem__(self, item: int) -> Union[Events, CallableEventWithFilter]: return self._events[item] def __iter__(self) -> Iterator[Union[Events, CallableEventWithFilter]]: return iter(self._events) def __len__(self) -> int: return len(self._events) def __or__(self, other: Union[Events, CallableEventWithFilter]) -> "EventsList": self._append(event=other) return self class State: """An object that is used to pass internal and user-defined state between event handlers. By default, state contains the following attributes: .. code-block:: python state.iteration # 1-based, the first iteration is 1 state.epoch # 1-based, the first epoch is 1 state.seed # seed to set at each epoch state.dataloader # data passed to engine state.epoch_length # optional length of an epoch state.max_epochs # number of epochs to run state.batch # batch passed to `process_function` state.output # output of `process_function` after a single iteration state.metrics # dictionary with defined metrics if any state.times # dictionary with total and per-epoch times fetched on # keys: Events.EPOCH_COMPLETED.name and Events.COMPLETED.name Args: kwargs: keyword arguments to be defined as State attributes. """ event_to_attr: Dict[Union[str, "Events", "CallableEventWithFilter"], str] = { Events.GET_BATCH_STARTED: "iteration", Events.GET_BATCH_COMPLETED: "iteration", Events.ITERATION_STARTED: "iteration", Events.ITERATION_COMPLETED: "iteration", Events.EPOCH_STARTED: "epoch", Events.EPOCH_COMPLETED: "epoch", Events.STARTED: "epoch", Events.COMPLETED: "epoch", } def __init__(self, **kwargs: Any) -> None: self.iteration = 0 self.epoch = 0 self.epoch_length: Optional[int] = None self.max_epochs: Optional[int] = None self.output: Optional[int] = None self.batch: Optional[int] = None self.metrics: Dict[str, Any] = {} self.dataloader: Optional[Union[DataLoader, Iterable[Any]]] = None self.seed: Optional[int] = None self.times: Dict[str, Optional[float]] = { Events.EPOCH_COMPLETED.name: None, Events.COMPLETED.name: None, } for k, v in kwargs.items(): setattr(self, k, v) self._update_attrs() def _update_attrs(self) -> None: for value in self.event_to_attr.values(): if not hasattr(self, value): setattr(self, value, 0) def get_event_attrib_value(self, event_name: Union[str, Events, CallableEventWithFilter]) -> int: """Get the value of Event attribute with given `event_name`.""" if event_name not in State.event_to_attr: raise RuntimeError(f"Unknown event name '{event_name}'") return getattr(self, State.event_to_attr[event_name]) def __repr__(self) -> str: s = "State:\n" for attr, value in self.__dict__.items(): if not isinstance(value, (numbers.Number, str)): value = type(value) s += f"\t{attr}: {value}\n" return s class RemovableEventHandle: """A weakref handle to remove a registered event. A handle that may be used to remove a registered event handler via the remove method, with-statement, or context manager protocol. Returned from :meth:`~ignite.engine.engine.Engine.add_event_handler`. Args: event_name: Registered event name. handler: Registered event handler, stored as weakref. engine: Target engine, stored as weakref. Examples: .. code-block:: python engine = Engine() def print_epoch(engine): print(f"Epoch: {engine.state.epoch}") with engine.add_event_handler(Events.EPOCH_COMPLETED, print_epoch): # print_epoch handler registered for a single run engine.run(data) # print_epoch handler is now unregistered """ def __init__( self, event_name: Union[CallableEventWithFilter, Enum, EventsList, Events], handler: Callable, engine: "Engine" ) -> None: self.event_name = event_name self.handler = weakref.ref(handler) self.engine = weakref.ref(engine) def remove(self) -> None: """Remove handler from engine.""" handler = self.handler() engine = self.engine() if handler is None or engine is None: return if hasattr(handler, "_parent"): handler = handler._parent() if handler is None: raise RuntimeError( "Internal error! Please fill an issue on https://github.com/pytorch/ignite/issues " "if encounter this error. Thank you!" ) if isinstance(self.event_name, EventsList): for e in self.event_name: if engine.has_event_handler(handler, e): engine.remove_event_handler(handler, e) else: if engine.has_event_handler(handler, self.event_name): engine.remove_event_handler(handler, self.event_name) def __enter__(self) -> "RemovableEventHandle": return self def __exit__(self, *args: Any, **kwargs: Any) -> None: self.remove() ignite-0.5.1/ignite/engine/utils.py000066400000000000000000000020611465426447700172540ustar00rootroot00000000000000import inspect from typing import Any, Callable, Tuple, Union def _check_signature(fn: Callable, fn_description: str, *args: Any, **kwargs: Any) -> None: # if handler with filter, check the handler rather than the decorator if hasattr(fn, "_parent"): signature = inspect.signature(fn._parent()) else: signature = inspect.signature(fn) try: # try without engine signature.bind(*args, **kwargs) except TypeError as exc: fn_params = list(signature.parameters) exception_msg = str(exc) passed_params = list(args) + list(kwargs) raise ValueError( f"Error adding {fn} '{fn_description}': " f"takes parameters {fn_params} but will be called with {passed_params}" f"({exception_msg})." ) def _to_hours_mins_secs(time_taken: Union[float, int]) -> Tuple[int, int, float]: """Convert seconds to hours, mins, seconds and milliseconds.""" mins, secs = divmod(time_taken, 60) hours, mins = divmod(mins, 60) return round(hours), round(mins), secs ignite-0.5.1/ignite/exceptions.py000066400000000000000000000002261465426447700170310ustar00rootroot00000000000000__all__ = ["NotComputableError"] class NotComputableError(RuntimeError): """ Exception class to raise if Metric cannot be computed. """ ignite-0.5.1/ignite/handlers/000077500000000000000000000000001465426447700160765ustar00rootroot00000000000000ignite-0.5.1/ignite/handlers/__init__.py000066400000000000000000000051211465426447700202060ustar00rootroot00000000000000from typing import Any, Callable, Optional from ignite.engine import Engine from ignite.engine.events import Events from ignite.handlers.checkpoint import Checkpoint, DiskSaver, ModelCheckpoint from ignite.handlers.clearml_logger import ClearMLLogger from ignite.handlers.early_stopping import EarlyStopping from ignite.handlers.ema_handler import EMAHandler from ignite.handlers.lr_finder import FastaiLRFinder from ignite.handlers.mlflow_logger import MLflowLogger from ignite.handlers.neptune_logger import NeptuneLogger from ignite.handlers.param_scheduler import ( BaseParamScheduler, ConcatScheduler, CosineAnnealingScheduler, create_lr_scheduler_with_warmup, CyclicalScheduler, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, ParamScheduler, PiecewiseLinear, ReduceLROnPlateauScheduler, ) from ignite.handlers.polyaxon_logger import PolyaxonLogger from ignite.handlers.state_param_scheduler import ( ExpStateScheduler, LambdaStateScheduler, MultiStepStateScheduler, PiecewiseLinearStateScheduler, StateParamScheduler, StepStateScheduler, ) from ignite.handlers.stores import EpochOutputStore from ignite.handlers.tensorboard_logger import TensorboardLogger from ignite.handlers.terminate_on_nan import TerminateOnNan from ignite.handlers.time_limit import TimeLimit from ignite.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler from ignite.handlers.timing import Timer from ignite.handlers.tqdm_logger import ProgressBar from ignite.handlers.utils import global_step_from_engine # noqa from ignite.handlers.visdom_logger import VisdomLogger from ignite.handlers.wandb_logger import WandBLogger __all__ = [ "ModelCheckpoint", "Checkpoint", "DiskSaver", "Timer", "EarlyStopping", "TerminateOnNan", "global_step_from_engine", "TimeLimit", "EpochOutputStore", "ConcatScheduler", "CosineAnnealingScheduler", "LinearCyclicalScheduler", "LRScheduler", "ParamGroupScheduler", "ParamScheduler", "PiecewiseLinear", "CyclicalScheduler", "create_lr_scheduler_with_warmup", "FastaiLRFinder", "EMAHandler", "BasicTimeProfiler", "HandlersTimeProfiler", "BaseParamScheduler", "StateParamScheduler", "LambdaStateScheduler", "PiecewiseLinearStateScheduler", "ExpStateScheduler", "StepStateScheduler", "MultiStepStateScheduler", "ReduceLROnPlateauScheduler", "ClearMLLogger", "MLflowLogger", "NeptuneLogger", "PolyaxonLogger", "TensorboardLogger", "ProgressBar", "VisdomLogger", "WandBLogger", ] ignite-0.5.1/ignite/handlers/base_logger.py000066400000000000000000000270211465426447700207230ustar00rootroot00000000000000"""Base logger and its helper handlers.""" import numbers import warnings from abc import ABCMeta, abstractmethod from collections import OrderedDict from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import torch import torch.nn as nn from torch.optim import Optimizer from ignite.engine import Engine, Events, EventsList, State from ignite.engine.events import CallableEventWithFilter, RemovableEventHandle class BaseHandler(metaclass=ABCMeta): """Base handler for defining various useful handlers.""" @abstractmethod def __call__(self, engine: Engine, logger: Any, event_name: Union[str, Events]) -> None: pass class BaseWeightsHandler(BaseHandler): """ Base handler for logging weights or their gradients. """ def __init__( self, model: nn.Module, tag: Optional[str] = None, whitelist: Optional[Union[List[str], Callable[[str, nn.Parameter], bool]]] = None, ): if not isinstance(model, torch.nn.Module): raise TypeError(f"Argument model should be of type torch.nn.Module, but given {type(model)}") self.model = model self.tag = tag weights = {} if whitelist is None: weights = dict(model.named_parameters()) elif callable(whitelist): for n, p in model.named_parameters(): if whitelist(n, p): weights[n] = p else: for n, p in model.named_parameters(): for item in whitelist: if n.startswith(item): weights[n] = p self.weights = weights.items() class BaseOptimizerParamsHandler(BaseHandler): """ Base handler for logging optimizer parameters """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): if not ( isinstance(optimizer, Optimizer) or (hasattr(optimizer, "param_groups") and isinstance(optimizer.param_groups, Sequence)) ): raise TypeError( "Argument optimizer should be torch.optim.Optimizer or has attribute 'param_groups' as list/tuple, " f"but given {type(optimizer)}" ) self.optimizer = optimizer self.param_name = param_name self.tag = tag class BaseOutputHandler(BaseHandler): """ Helper handler to log engine's output and/or metrics """ def __init__( self, tag: str, metric_names: Optional[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ): if metric_names is not None: if not (isinstance(metric_names, list) or (isinstance(metric_names, str) and metric_names == "all")): raise TypeError( f"metric_names should be either a list or equal 'all', got {type(metric_names)} instead." ) if output_transform is not None and not callable(output_transform): raise TypeError(f"output_transform should be a function, got {type(output_transform)} instead.") if output_transform is None and metric_names is None and state_attributes is None: raise ValueError("Either metric_names, output_transform or state_attributes should be defined") if global_step_transform is not None and not callable(global_step_transform): raise TypeError(f"global_step_transform should be a function, got {type(global_step_transform)} instead.") if global_step_transform is None: def global_step_transform(engine: Engine, event_name: Union[str, Events]) -> int: return engine.state.get_event_attrib_value(event_name) self.tag = tag self.metric_names = metric_names self.output_transform = output_transform self.global_step_transform = global_step_transform self.state_attributes = state_attributes def _setup_output_metrics_state_attrs( self, engine: Engine, log_text: Optional[bool] = False, key_tuple: Optional[bool] = True ) -> Dict[Any, Any]: """Helper method to setup metrics and state attributes to log""" metrics_state_attrs = OrderedDict() if self.metric_names is not None: if isinstance(self.metric_names, str) and self.metric_names == "all": metrics_state_attrs = OrderedDict(engine.state.metrics) else: for name in self.metric_names: if name not in engine.state.metrics: warnings.warn( f"Provided metric name '{name}' is missing " f"in engine's state metrics: {list(engine.state.metrics.keys())}" ) continue metrics_state_attrs[name] = engine.state.metrics[name] if self.output_transform is not None: output_dict = self.output_transform(engine.state.output) if not isinstance(output_dict, dict): output_dict = {"output": output_dict} metrics_state_attrs.update(output_dict) if self.state_attributes is not None: metrics_state_attrs.update({name: getattr(engine.state, name, None) for name in self.state_attributes}) metrics_state_attrs_dict: Dict[Any, Union[str, float, numbers.Number]] = OrderedDict() def key_tuple_tf(tag: str, name: str, *args: str) -> Tuple[str, ...]: return (tag, name) + args def key_str_tf(tag: str, name: str, *args: str) -> str: return "/".join((tag, name) + args) key_tf = key_tuple_tf if key_tuple else key_str_tf for name, value in metrics_state_attrs.items(): if isinstance(value, numbers.Number): metrics_state_attrs_dict[key_tf(self.tag, name)] = value elif isinstance(value, torch.Tensor) and value.ndimension() == 0: metrics_state_attrs_dict[key_tf(self.tag, name)] = value.item() elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): metrics_state_attrs_dict[key_tf(self.tag, name, str(i))] = v.item() else: if isinstance(value, str) and log_text: metrics_state_attrs_dict[key_tf(self.tag, name)] = value else: warnings.warn(f"Logger output_handler can not log metrics value type {type(value)}") return metrics_state_attrs_dict class BaseWeightsScalarHandler(BaseWeightsHandler): """ Helper handler to log model's weights or gradients as scalars. """ def __init__( self, model: nn.Module, reduction: Callable[[torch.Tensor], Union[float, torch.Tensor]] = torch.norm, tag: Optional[str] = None, whitelist: Optional[Union[List[str], Callable[[str, nn.Parameter], bool]]] = None, ): super(BaseWeightsScalarHandler, self).__init__(model, tag=tag, whitelist=whitelist) if not callable(reduction): raise TypeError(f"Argument reduction should be callable, but given {type(reduction)}") def _is_0D_tensor(t: Any) -> bool: return isinstance(t, torch.Tensor) and t.ndimension() == 0 # Test reduction function on a tensor o = reduction(torch.ones(4, 2)) if not (isinstance(o, numbers.Number) or _is_0D_tensor(o)): raise TypeError(f"Output of the reduction function should be a scalar, but got {type(o)}") self.reduction = reduction class BaseLogger(metaclass=ABCMeta): """ Base logger handler. See implementations: TensorboardLogger, VisdomLogger, PolyaxonLogger, MLflowLogger, ... """ def attach( self, engine: Engine, log_handler: Callable, event_name: Union[str, Events, CallableEventWithFilter, EventsList], *args: Any, **kwargs: Any, ) -> RemovableEventHandle: """Attach the logger to the engine and execute `log_handler` function at `event_name` events. Args: engine: engine object. log_handler: a logging handler to execute event_name: event to attach the logging handler to. Valid events are from :class:`~ignite.engine.events.Events` or :class:`~ignite.engine.events.EventsList` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. args: args forwarded to the `log_handler` method kwargs: kwargs forwarded to the `log_handler` method Returns: :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler. """ if isinstance(event_name, EventsList): for name in event_name: if name not in State.event_to_attr: raise RuntimeError(f"Unknown event name '{name}'") engine.add_event_handler(name, log_handler, self, name) return RemovableEventHandle(event_name, log_handler, engine) else: if event_name not in State.event_to_attr: raise RuntimeError(f"Unknown event name '{event_name}'") return engine.add_event_handler(event_name, log_handler, self, event_name, *args, **kwargs) def attach_output_handler(self, engine: Engine, event_name: Any, *args: Any, **kwargs: Any) -> RemovableEventHandle: """Shortcut method to attach `OutputHandler` to the logger. Args: engine: engine object. event_name: event to attach the logging handler to. Valid events are from :class:`~ignite.engine.events.Events` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. args: args to initialize `OutputHandler` kwargs: kwargs to initialize `OutputHandler` Returns: :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler. """ return self.attach(engine, self._create_output_handler(*args, **kwargs), event_name=event_name) def attach_opt_params_handler( self, engine: Engine, event_name: Any, *args: Any, **kwargs: Any ) -> RemovableEventHandle: """Shortcut method to attach `OptimizerParamsHandler` to the logger. Args: engine: engine object. event_name: event to attach the logging handler to. Valid events are from :class:`~ignite.engine.events.Events` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. args: args to initialize `OptimizerParamsHandler` kwargs: kwargs to initialize `OptimizerParamsHandler` Returns: :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler. .. versionchanged:: 0.4.3 Added missing return statement. """ return self.attach(engine, self._create_opt_params_handler(*args, **kwargs), event_name=event_name) @abstractmethod def _create_output_handler(self, engine: Engine, *args: Any, **kwargs: Any) -> Callable: pass @abstractmethod def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> Callable: pass def __enter__(self) -> "BaseLogger": return self def __exit__(self, type: Any, value: Any, traceback: Any) -> None: self.close() def close(self) -> None: pass ignite-0.5.1/ignite/handlers/checkpoint.py000066400000000000000000001275221465426447700206100ustar00rootroot00000000000000import collections.abc as collections import numbers import os import stat import tempfile from abc import ABCMeta, abstractmethod from collections import OrderedDict from pathlib import Path from typing import Any, Callable, cast, Dict, List, Mapping, NamedTuple, Optional, Union import torch import torch.nn as nn from packaging.version import Version if Version(torch.__version__) >= Version("1.9.0"): from torch.distributed.optim import ZeroRedundancyOptimizer HAVE_ZERO = True else: HAVE_ZERO = False import ignite.distributed as idist from ignite.base import Serializable from ignite.engine import Engine, Events from ignite.utils import _tree_apply2, _tree_map __all__ = ["Checkpoint", "DiskSaver", "ModelCheckpoint", "BaseSaveHandler"] class BaseSaveHandler(metaclass=ABCMeta): """Base class for save handlers Methods to override: - :meth:`~ignite.handlers.checkpoint.BaseSaveHandler.__call__` - :meth:`~ignite.handlers.checkpoint.BaseSaveHandler.remove` Note: In derived class, please, make sure that in distributed configuration overridden methods are called by a single process. Distributed configuration on XLA devices should be treated slightly differently: for saving checkpoint with `xm.save() `_ all processes should pass into the function. Otherwise, application gets stuck. """ @abstractmethod def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None: """Method to save `checkpoint` with `filename`. Additionally, metadata dictionary is provided. Metadata contains: - `basename`: file prefix (if provided) with checkpoint name, e.g. `epoch_checkpoint`. - `score_name`: score name if provided, e.g `val_acc`. - `priority`: checkpoint priority value (higher is better), e.g. `12` or `0.6554435` Args: checkpoint: checkpoint dictionary to save. filename: filename associated with checkpoint. metadata: metadata on checkpoint to save. """ @abstractmethod def remove(self, filename: str) -> None: """Method to remove saved checkpoint. Args: filename: filename associated with checkpoint. """ class Checkpoint(Serializable): """Checkpoint handler can be used to periodically save and load objects which have attribute ``state_dict/load_state_dict``. This class can use specific save handlers to store on the disk or a cloud storage, etc. The Checkpoint handler (if used with :class:`~ignite.handlers.DiskSaver`) also handles automatically moving data on TPU to CPU before writing the checkpoint. Args: to_save: Dictionary with the objects to save. Objects should have implemented ``state_dict`` and ``load_state_dict`` methods. If contains objects of type torch `DistributedDataParallel`_ or `DataParallel`_, their internal wrapped model is automatically saved (to avoid additional key ``module.`` in the state dictionary). save_handler: String, function or callable object. used to save engine and other provided objects. Function receives two objects: checkpoint as a dictionary and filename. If ``save_handler`` is callable class, it can inherit of :class:`~ignite.handlers.checkpoint.BaseSaveHandler` and optionally implement ``remove`` method to keep a fixed number of saved checkpoints. In case if user needs to save engine's checkpoint on a disk, ``save_handler`` can be defined with :class:`~ignite.handlers.DiskSaver` or a string specifying directory name can be passed to ``save_handler``. filename_prefix: Prefix for the file name to which objects will be saved. See Note for details. score_function: If not None, it should be a function taking a single argument, :class:`~ignite.engine.engine.Engine` object, and returning a score (`float`). Objects with highest scores will be retained. score_name: If ``score_function`` not None, it is possible to store its value using ``score_name``. If ``score_function`` is None, ``score_name`` can be used alone to define ``score_function`` as ``Checkpoint.get_default_score_fn(score_name)`` by default. n_saved: Number of objects that should be kept on disk. Older files will be removed. If set to `None`, all objects are kept. global_step_transform: global step transform function to output a desired global step. Input of the function is ``(engine, event_name)``. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.global_step_from_engine`. filename_pattern: If ``filename_pattern`` is provided, this pattern will be used to render checkpoint filenames. If the pattern is not defined, the default pattern would be used. See Note for details. include_self: Whether to include the `state_dict` of this object in the checkpoint. If `True`, then there must not be another object in ``to_save`` with key ``checkpointer``. greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise, the first model. Default, `False`. save_on_rank: Which rank to save the objects on, in the distributed configuration. If ``save_handler`` is string or :class:`~pathlib.Path`, this is also used to instantiate a :class:`~ignite.handlers.DiskSaver`. .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/ torch.nn.parallel.DistributedDataParallel.html .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html Note: This class stores a single file as a dictionary of provided objects to save. The filename is defined by ``filename_pattern`` and by default has the following structure: ``{filename_prefix}_{name}_{suffix}.{ext}`` where - ``filename_prefix`` is the argument passed to the constructor, - `name` is the key in ``to_save`` if a single object is to store, otherwise `name` is "checkpoint". - `suffix` is composed as following ``{global_step}_{score_name}={score}``. +----------------+------------+-----------------------+----------------------------------------------+ | score_function | score_name | global_step_transform | suffix | +================+============+=======================+==============================================+ | None | None | None | ``{engine.state.iteration}`` | +----------------+------------+-----------------------+----------------------------------------------+ | X | None | None | ``{score}`` | +----------------+------------+-----------------------+----------------------------------------------+ | X | None | X | ``{global_step}_{score}`` | +----------------+------------+-----------------------+----------------------------------------------+ | X | X | X | ``{global_step}_{score_name}={score}`` | +----------------+------------+-----------------------+----------------------------------------------+ | None | None | X | ``{global_step}`` | +----------------+------------+-----------------------+----------------------------------------------+ | X | X | None | ``{score_name}={score}`` | +----------------+------------+-----------------------+----------------------------------------------+ Above `global_step` defined by the output of `global_step_transform` and `score` defined by the output of `score_function`. By default, none of ``score_function``, ``score_name``, ``global_step_transform`` is defined, then suffix is setup by attached engine's current iteration. The filename will be `{filename_prefix}_{name}_{engine.state.iteration}.{ext}`. For example, ``score_name="neg_val_loss"`` and ``score_function`` that returns `-loss` (as objects with highest scores will be retained), then saved filename will be ``{filename_prefix}_{name}_neg_val_loss=-0.1234.pt``. Note: If ``filename_pattern`` is given, it will be used to render the filenames. ``filename_pattern`` is a string that can contain ``{filename_prefix}``, ``{name}``, ``{score}``, ``{score_name}`` and ``{global_step}`` as templates. For example, let ``filename_pattern="{global_step}-{name}-{score}.pt"`` then the saved filename will be ``30000-checkpoint-94.pt`` **Warning:** Please, keep in mind that if filename collide with already used one to saved a checkpoint, new checkpoint will replace the older one. This means that filename like ``checkpoint.pt`` will be saved every call and will always be overwritten by newer checkpoints. Note: To get the last stored filename, handler exposes attribute ``last_checkpoint``: .. code-block:: python handler = Checkpoint(...) ... print(handler.last_checkpoint) > checkpoint_12345.pt Note: This class is distributed configuration-friendly: it is not required to instantiate the class in rank 0 only process. This class supports automatically distributed configuration and if used with :class:`~ignite.handlers.DiskSaver`, checkpoint is stored by rank 0 process. .. warning:: When running on XLA devices or using :class:`~torch.distributed.optim.ZeroRedundancyOptimizer`, it should be run in all processes, otherwise application can get stuck while saving the checkpoint. .. code-block:: python # Wrong: # if idist.get_rank() == 0: # handler = Checkpoint(...) # trainer.add_event_handler(Events.ITERATION_COMPLETED(every=1000), handler) # Correct: handler = Checkpoint(...) trainer.add_event_handler(Events.ITERATION_COMPLETED(every=1000), handler) Examples: Attach the handler to make checkpoints during training: .. code-block:: python from ignite.engine import Engine, Events from ignite.handlers import Checkpoint trainer = ... model = ... optimizer = ... lr_scheduler = ... to_save = {'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'trainer': trainer} if (checkpoint_iters): # A: Output is "checkpoint_.pt" handler = Checkpoint( to_save, '/tmp/models', n_saved=2 ) trainer.add_event_handler(Events.ITERATION_COMPLETED(every=1000), handler) else: # B:Output is "checkpoint_.pt" gst = lambda *_: trainer.state.epoch handler = Checkpoint( to_save, '/tmp/models', n_saved=2, global_step_transform=gst ) trainer.add_event_handler(Events.EPOCH_COMPLETED, handler) trainer.run(data_loader, max_epochs=6) > A: ["checkpoint_7000.pt", "checkpoint_8000.pt", ] > B: ["checkpoint_5.pt", "checkpoint_6.pt", ] Attach the handler to an evaluator to save best model during the training according to computed validation metric: .. code-block:: python from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, global_step_from_engine trainer = ... evaluator = ... # Setup Accuracy metric computation on evaluator. # evaluator.state.metrics contain 'accuracy', # which will be used to define ``score_function`` automatically. # Run evaluation on epoch completed event # ... to_save = {'model': model} handler = Checkpoint( to_save, '/tmp/models', n_saved=2, filename_prefix='best', score_name="accuracy", global_step_transform=global_step_from_engine(trainer) ) evaluator.add_event_handler(Events.COMPLETED, handler) trainer.run(data_loader, max_epochs=10) > ["best_model_9_accuracy=0.77.pt", "best_model_10_accuracy=0.78.pt", ] Customise the ``save_handler``: .. code-block:: python handler = Checkpoint( to_save, save_handler=DiskSaver('/tmp/models', create_dir=True, **kwargs), n_saved=2 ) .. versionchanged:: 0.4.3 - Checkpoint can save model with same filename. - Added ``greater_or_equal`` argument. .. versionchanged:: 0.4.7 - `score_name` can be used to define `score_function` automatically without providing `score_function`. - `save_handler` automatically saves to disk if path to directory is provided. - `save_on_rank` saves objects on this rank in a distributed configuration. """ Item = NamedTuple("Item", [("priority", int), ("filename", str)]) _state_dict_all_req_keys = ("_saved",) def __init__( self, to_save: Mapping, save_handler: Union[str, Path, Callable, BaseSaveHandler], filename_prefix: str = "", score_function: Optional[Callable] = None, score_name: Optional[str] = None, n_saved: Union[int, None] = 1, global_step_transform: Optional[Callable] = None, filename_pattern: Optional[str] = None, include_self: bool = False, greater_or_equal: bool = False, save_on_rank: int = 0, ): if not isinstance(to_save, collections.Mapping): raise TypeError(f"Argument `to_save` should be a dictionary, but given {type(to_save)}") self._check_objects(to_save, "state_dict") if include_self: if not isinstance(to_save, collections.MutableMapping): raise TypeError( f"If `include_self` is True, then `to_save` must be mutable, but given {type(to_save)}." ) if "checkpointer" in to_save: raise ValueError(f"Cannot have key 'checkpointer' if `include_self` is True: {to_save}") if not ( isinstance(save_handler, str) or isinstance(save_handler, Path) or callable(save_handler) or isinstance(save_handler, BaseSaveHandler) ): raise TypeError( "Argument `save_handler` should be a string or Path object or callable or inherit from BaseSaveHandler" ) if global_step_transform is not None and not callable(global_step_transform): raise TypeError(f"global_step_transform should be a function, got {type(global_step_transform)} instead.") self.to_save = to_save self.filename_prefix = filename_prefix if isinstance(save_handler, str) or isinstance(save_handler, Path): self.save_handler = DiskSaver(save_handler, create_dir=True, save_on_rank=save_on_rank) else: self.save_handler = save_handler # type: ignore self.score_function = score_function self.score_name = score_name if self.score_name is not None and self.score_function is None: self.score_function = self.get_default_score_fn(self.score_name) self.n_saved = n_saved self.ext = "pt" self.global_step_transform = global_step_transform self.filename_pattern = filename_pattern self._saved: List["Checkpoint.Item"] = [] self.include_self = include_self self.greater_or_equal = greater_or_equal self.save_on_rank = save_on_rank def _get_filename_pattern(self, global_step: Optional[int]) -> str: if self.filename_pattern is None: filename_pattern = self.setup_filename_pattern( with_prefix=len(self.filename_prefix) > 0, with_score=self.score_function is not None, with_score_name=self.score_name is not None, with_global_step=global_step is not None, ) else: filename_pattern = self.filename_pattern return filename_pattern def reset(self) -> None: """Method to reset saved checkpoint names. Use this method if the engine will independently run multiple times: .. code-block:: python from ignite.handlers import Checkpoint trainer = ... checkpointer = Checkpoint(...) trainer.add_event_handler(Events.COMPLETED, checkpointer) trainer.add_event_handler(Events.STARTED, checkpointer.reset) # fold 0 trainer.run(data0, max_epochs=max_epochs) print("Last checkpoint:", checkpointer.last_checkpoint) # fold 1 trainer.run(data1, max_epochs=max_epochs) print("Last checkpoint:", checkpointer.last_checkpoint) .. versionadded:: 0.4.3 """ self._saved = [] @property def last_checkpoint(self) -> Optional[Union[str, Path]]: if len(self._saved) < 1: return None if not isinstance(self.save_handler, DiskSaver): return self._saved[-1].filename return self.save_handler.dirname / self._saved[-1].filename def _check_lt_n_saved(self, or_equal: bool = False) -> bool: if self.n_saved is None: return True return len(self._saved) < self.n_saved + int(or_equal) def _compare_fn(self, new: Union[int, float]) -> bool: if self.greater_or_equal: return new >= self._saved[0].priority else: return new > self._saved[0].priority def __call__(self, engine: Engine) -> None: global_step = None if self.global_step_transform is not None: global_step = self.global_step_transform(engine, engine.last_event_name) if self.score_function is not None: priority = self.score_function(engine) if not isinstance(priority, numbers.Number): raise ValueError("Output of score_function should be a number") else: if global_step is None: global_step = engine.state.get_event_attrib_value(Events.ITERATION_COMPLETED) priority = global_step if self._check_lt_n_saved() or self._compare_fn(priority): priority_str = f"{priority}" if isinstance(priority, numbers.Integral) else f"{priority:.4f}" checkpoint = self._setup_checkpoint() name = "checkpoint" if len(checkpoint) == 1: for k in checkpoint: name = k checkpoint = checkpoint[name] filename_pattern = self._get_filename_pattern(global_step) filename_dict = { "filename_prefix": self.filename_prefix, "ext": self.ext, "name": name, "score_name": self.score_name, "score": priority_str if (self.score_function is not None) else None, "global_step": global_step, } filename = filename_pattern.format(**filename_dict) metadata = { "basename": f"{self.filename_prefix}{'_' * int(len(self.filename_prefix) > 0)}{name}", "score_name": self.score_name, "priority": priority, } try: index = list(map(lambda it: it.filename == filename, self._saved)).index(True) to_remove = True except ValueError: index = 0 to_remove = not self._check_lt_n_saved() if to_remove: item = self._saved.pop(index) if isinstance(self.save_handler, BaseSaveHandler): self.save_handler.remove(item.filename) self._saved.append(Checkpoint.Item(priority, filename)) self._saved.sort(key=lambda it: it[0]) if self.include_self: # Now that we've updated _saved, we can add our own state_dict. checkpoint["checkpointer"] = self.state_dict() try: self.save_handler(checkpoint, filename, metadata) except TypeError: self.save_handler(checkpoint, filename) def _setup_checkpoint(self) -> Dict[str, Any]: if self.to_save is not None: def func(obj: Any, **kwargs: Any) -> Dict: if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)): obj = obj.module elif HAVE_ZERO and isinstance(obj, ZeroRedundancyOptimizer): obj.consolidate_state_dict(to=self.save_on_rank) if self.save_on_rank != idist.get_rank(): return {} return obj.state_dict() return cast(Dict[str, Any], _tree_map(func, self.to_save)) return {} @staticmethod def setup_filename_pattern( with_prefix: bool = True, with_score: bool = True, with_score_name: bool = True, with_global_step: bool = True ) -> str: """Helper method to get the default filename pattern for a checkpoint. Args: with_prefix: If True, the ``filename_prefix`` is added to the filename pattern: ``{filename_prefix}_{name}...``. Default, True. with_score: If True, ``score`` is added to the filename pattern: ``..._{score}.{ext}``. Default, True. At least one of ``with_score`` and ``with_global_step`` should be True. with_score_name: If True, ``score_name`` is added to the filename pattern: ``..._{score_name}={score}.{ext}``. If activated, argument ``with_score`` should be also True, otherwise an error is raised. Default, True. with_global_step: If True, ``{global_step}`` is added to the filename pattern: ``...{name}_{global_step}...``. At least one of ``with_score`` and ``with_global_step`` should be True. Examples: .. code-block:: python from ignite.handlers import Checkpoint filename_pattern = Checkpoint.setup_filename_pattern() print(filename_pattern) > "{filename_prefix}_{name}_{global_step}_{score_name}={score}.{ext}" .. versionadded:: 0.4.3 """ filename_pattern = "{name}" if not (with_global_step or with_score): raise ValueError("At least one of with_score and with_global_step should be True.") if with_global_step: filename_pattern += "_{global_step}" if with_score_name and with_score: filename_pattern += "_{score_name}={score}" elif with_score: filename_pattern += "_{score}" elif with_score_name: raise ValueError("If with_score_name is True, with_score should be also True") if with_prefix: filename_pattern = "{filename_prefix}_" + filename_pattern filename_pattern += ".{ext}" return filename_pattern @staticmethod def _check_objects(objs: Mapping, attr: str) -> None: def func(obj: Any, **kwargs: Any) -> None: if not hasattr(obj, attr): raise TypeError(f"Object {type(obj)} should have `{attr}` method") _tree_map(func, objs) @staticmethod def load_objects(to_load: Mapping, checkpoint: Union[str, Mapping, Path], **kwargs: Any) -> None: """Helper method to apply ``load_state_dict`` on the objects from ``to_load`` using states from ``checkpoint``. Args: to_load: a dictionary with objects, e.g. `{"model": model, "optimizer": optimizer, ...}` checkpoint: a path, a string filepath or a dictionary with state_dicts to load, e.g. `{"model": model_state_dict, "optimizer": opt_state_dict}`. If `to_load` contains a single key, then checkpoint can contain directly corresponding state_dict. kwargs: Keyword arguments accepted for `nn.Module.load_state_dict()`. Passing `strict=False` enables the user to load part of the pretrained model (useful for example, in Transfer Learning) Examples: .. code-block:: python import tempfile from pathlib import Path import torch from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint, Checkpoint trainer = Engine(lambda engine, batch: None) with tempfile.TemporaryDirectory() as tmpdirname: handler = ModelCheckpoint(tmpdirname, 'myprefix', n_saved=None, create_dir=True) model = torch.nn.Linear(3, 3) optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) to_save = {"weights": model, "optimizer": optimizer} trainer.add_event_handler(Events.EPOCH_COMPLETED(every=2), handler, to_save) trainer.run(torch.randn(10, 1), 5) to_load = to_save checkpoint_fp = Path(tmpdirname) / 'myprefix_checkpoint_40.pt' checkpoint = torch.load(checkpoint_fp) Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint) # or using a string for checkpoint filepath to_load = to_save checkpoint_fp = Path(tmpdirname) / 'myprefix_checkpoint_40.pt' Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint_fp) Note: If ``to_load`` contains objects of type torch `DistributedDataParallel`_ or `DataParallel`_, method ``load_state_dict`` will applied to their internal wrapped model (``obj.module``). .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/ torch.nn.parallel.DistributedDataParallel.html .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html """ if not isinstance(checkpoint, (collections.Mapping, str, Path)): raise TypeError(f"Argument checkpoint should be a string or a dictionary, but given {type(checkpoint)}") Checkpoint._check_objects(to_load, "load_state_dict") if isinstance(checkpoint, (str, Path)): checkpoint_obj = torch.load(checkpoint) else: checkpoint_obj = checkpoint def _load_object(obj: Any, chkpt_obj: Any) -> None: if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)): obj = obj.module if isinstance(obj, torch.nn.Module): obj.load_state_dict(chkpt_obj, **kwargs) else: obj.load_state_dict(chkpt_obj) if len(to_load) == 1: # single object and checkpoint is directly a state_dict key, obj = list(to_load.items())[0] if key not in checkpoint_obj: _load_object(obj, checkpoint_obj) return _tree_apply2(_load_object, to_load, checkpoint_obj) def reload_objects(self, to_load: Mapping, load_kwargs: Optional[Dict] = None, **filename_components: Any) -> None: """Helper method to apply ``load_state_dict`` on the objects from ``to_load``. Filename components such as name, score and global state can be configured. Args: to_load: a dictionary with objects, e.g. `{"model": model, "optimizer": optimizer, ...}` load_kwargs: Keyword arguments accepted for `nn.Module.load_state_dict()`. Passing `strict=False` enables the user to load part of the pretrained model (useful for example, in Transfer Learning) filename_components: Filename components used to define the checkpoint file path. Keyword arguments accepted are `name`, `score` and `global_state`. Examples: .. code-block:: python import tempfile import torch from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint trainer = Engine(lambda engine, batch: None) with tempfile.TemporaryDirectory() as tmpdirname: checkpoint = ModelCheckpoint(tmpdirname, 'myprefix', n_saved=None, create_dir=True) model = torch.nn.Linear(3, 3) optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) to_save = {"weights": model, "optimizer": optimizer} trainer.add_event_handler(Events.EPOCH_COMPLETED(every=2), checkpoint, to_save) trainer.run(torch.randn(10, 1), 5) to_load = to_save # load checkpoint myprefix_checkpoint_40.pt checkpoint.reload_objects(to_load=to_load, global_step=40) Note: If ``to_load`` contains objects of type torch `DistributedDataParallel`_ or `DataParallel`_, method ``load_state_dict`` will applied to their internal wrapped model (``obj.module``). Note: This method works only when the ``save_handler`` is of types string, :class:`~pathlib.Path` or :class:`~ignite.handlers.checkpoint.DiskSaver`. .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/ torch.nn.parallel.DistributedDataParallel.html .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html """ if not isinstance(self.save_handler, DiskSaver): raise AttributeError( f"Checkpoint's `save_handler` should be of type `DiskSaver`, given {type(self.save_handler)}" ) global_step = filename_components.get("global_step", None) filename_pattern = self._get_filename_pattern(global_step) checkpoint = self._setup_checkpoint() name = "checkpoint" if len(checkpoint) == 1: for k in checkpoint: name = k name = filename_components.get("name", name) score = filename_components.get("score", None) filename_dict = { "filename_prefix": self.filename_prefix, "ext": self.ext, "name": name, "score_name": self.score_name, "score": score, "global_step": global_step, } checkpoint_fp = filename_pattern.format(**filename_dict) path = self.save_handler.dirname / checkpoint_fp load_kwargs = {} if load_kwargs is None else load_kwargs Checkpoint.load_objects(to_load=to_load, checkpoint=path, **load_kwargs) def state_dict(self) -> OrderedDict: """Method returns state dict with saved items: list of ``(priority, filename)`` pairs. Can be used to save internal state of the class. """ # TODO: this method should use _state_dict_all_req_keys return OrderedDict([("_saved", [(p, f) for p, f in self._saved])]) def load_state_dict(self, state_dict: Mapping) -> None: """Method replaces internal state of the class with provided state dict data. Args: state_dict: a dict with "saved" key and list of ``(priority, filename)`` pairs as values. """ super().load_state_dict(state_dict) self._saved = [Checkpoint.Item(p, f) for p, f in state_dict["_saved"]] @staticmethod def get_default_score_fn(metric_name: str, score_sign: float = 1.0) -> Callable: """Helper method to get default score function based on the metric name. Args: metric_name: metric name to get the value from ``engine.state.metrics``. Engine is the one to which :class:`~ignite.handlers.checkpoint.Checkpoint` handler is added. score_sign: sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0. Examples: .. code-block:: python from ignite.handlers import Checkpoint best_acc_score = Checkpoint.get_default_score_fn("accuracy") best_model_handler = Checkpoint( to_save, save_handler, score_name="val_accuracy", score_function=best_acc_score ) evaluator.add_event_handler(Events.COMPLETED, best_model_handler) Usage with error-like metric: .. code-block:: python from ignite.handlers import Checkpoint neg_loss_score = Checkpoint.get_default_score_fn("loss", -1.0) best_model_handler = Checkpoint( to_save, save_handler, score_name="val_neg_loss", score_function=neg_loss_score ) evaluator.add_event_handler(Events.COMPLETED, best_model_handler) .. versionadded:: 0.4.3 """ if score_sign not in (1.0, -1.0): raise ValueError("Argument score_sign should be 1 or -1") def wrapper(engine: Engine) -> float: return score_sign * engine.state.metrics[metric_name] return wrapper class DiskSaver(BaseSaveHandler): """Handler that saves input checkpoint on a disk. Args: dirname: Directory path where the checkpoint will be saved atomic: if True, checkpoint is serialized to a temporary file, and then moved to final destination, so that files are guaranteed to not be damaged (for example if exception occurs during saving). create_dir: if True, will create directory ``dirname`` if it doesnt exist. require_empty: If True, will raise exception if there are any files in the directory ``dirname``. save_on_rank: The rank on which the checkpoint will be saved. Used in distributed configuration. kwargs: Accepted keyword arguments for `torch.save` or `xm.save`. .. versionchanged:: 0.4.2 Accept ``kwargs`` for `torch.save` or `xm.save`. .. versionchanged:: 0.4.10 Argument ``save_on_rank`` was added to specify the rank on which checkpoint should be saved. """ def __init__( self, dirname: Union[str, Path], atomic: bool = True, create_dir: bool = True, require_empty: bool = True, save_on_rank: int = 0, **kwargs: Any, ): self.dirname = Path(dirname).expanduser() self._atomic = atomic self.save_on_rank = save_on_rank if idist.get_rank() == save_on_rank: self._check_and_setup(self.dirname, create_dir, require_empty) self.kwargs = kwargs @staticmethod def _check_and_setup(dirname: Path, create_dir: bool, require_empty: bool) -> None: if create_dir: if not dirname.exists(): dirname.mkdir(parents=True) # Ensure that dirname exists if not dirname.exists(): raise ValueError(f"Directory path '{dirname}' is not found") if require_empty: matched = [fname for fname in os.listdir(dirname) if fname.endswith(".pt")] if len(matched) > 0: raise ValueError( f"Files {matched} with extension '.pt' are already present " f"in the directory {dirname}. If you want to use this " "directory anyway, pass `require_empty=False`." "" ) def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None: path = self.dirname / filename if idist.has_xla_support: import torch_xla.core.xla_model as xm # all tpu procs should enter here as internally performs sync across device self._save_func(checkpoint, path, xm.save) elif self.save_on_rank == idist.get_rank(): self._save_func(checkpoint, path, torch.save) def _save_func(self, checkpoint: Mapping, path: Path, func: Callable) -> None: if not self._atomic: func(checkpoint, path, **self.kwargs) else: tmp = tempfile.NamedTemporaryFile(delete=False, dir=self.dirname) tmp_file = tmp.file tmp_name = tmp.name try: func(checkpoint, tmp_file, **self.kwargs) except BaseException: tmp.close() os.remove(tmp_name) raise else: tmp.close() os.replace(tmp.name, path) # append group/others read mode os.chmod(path, os.stat(path).st_mode | stat.S_IRGRP | stat.S_IROTH) def remove(self, filename: str) -> None: if idist.get_rank() == self.save_on_rank: path = self.dirname / filename path.unlink() class ModelCheckpoint(Checkpoint): """ModelCheckpoint handler, inherits from :class:`~ignite.handlers.checkpoint.Checkpoint`, can be used to periodically save objects to disk only. If needed to store checkpoints to another storage type, please consider :class:`~ignite.handlers.checkpoint.Checkpoint`. It also provides `last_checkpoint` attribute to show the last saved checkpoint. This handler expects two arguments: - an :class:`~ignite.engine.engine.Engine` object - a `dict` mapping names (`str`) to objects that should be saved to disk. See Examples for further details. .. warning:: Behaviour of this class has been changed since v0.3.0. There is no more internal counter that has been used to indicate the number of save actions. User could see its value `step_number` in the filename, e.g. `{filename_prefix}_{name}_{step_number}.pt`. Actually, `step_number` is replaced by current engine's epoch if `score_function` is specified and current iteration otherwise. A single `pt` file is created instead of multiple files. Args: dirname: Directory path where objects will be saved. filename_prefix: Prefix for the file names to which objects will be saved. See Notes of :class:`~ignite.handlers.checkpoint.Checkpoint` for more details. score_function: if not None, it should be a function taking a single argument, an :class:`~ignite.engine.engine.Engine` object, and return a score (`float`). Objects with highest scores will be retained. score_name: if ``score_function`` not None, it is possible to store its value using `score_name`. See Examples of :class:`~ignite.handlers.checkpoint.Checkpoint` for more details. n_saved: Number of objects that should be kept on disk. Older files will be removed. If set to `None`, all objects are kept. atomic: If True, objects are serialized to a temporary file, and then moved to final destination, so that files are guaranteed to not be damaged (for example if exception occurs during saving). require_empty: If True, will raise exception if there are any files starting with ``filename_prefix`` in the directory ``dirname``. create_dir: If True, will create directory ``dirname`` if it does not exist. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.global_step_from_engine`. filename_pattern: If ``filename_pattern`` is provided, this pattern will be used to render checkpoint filenames. If the pattern is not defined, the default pattern would be used. See :class:`~ignite.handlers.checkpoint.Checkpoint` for details. include_self: Whether to include the `state_dict` of this object in the checkpoint. If `True`, then there must not be another object in ``to_save`` with key ``checkpointer``. greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise, the first model. Default, `False`. save_on_rank: Which rank to save the objects on, in the distributed configuration. Used to instantiate a :class:`~ignite.handlers.DiskSaver` and is also passed to the parent class. kwargs: Accepted keyword arguments for `torch.save` or `xm.save` in `DiskSaver`. .. versionchanged:: 0.4.2 Accept ``kwargs`` for `torch.save` or `xm.save` .. versionchanged:: 0.4.9 Accept ``filename_pattern`` and ``greater_or_equal`` for parity with :class:`~ignite.handlers.checkpoint.Checkpoint` .. versionchanged:: 0.4.10 Added `save_on_rank` arg to save objects on this rank in a distributed configuration Examples: .. testcode:: python import os from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint from torch import nn trainer = Engine(lambda engine, batch: None) handler = ModelCheckpoint('/tmp/models', 'myprefix', n_saved=2, create_dir=True, require_empty=False) model = nn.Linear(3, 3) trainer.add_event_handler(Events.EPOCH_COMPLETED(every=2), handler, {'mymodel': model}) trainer.run([0, 1, 2, 3, 4], max_epochs=6) print(sorted(os.listdir('/tmp/models'))) print(handler.last_checkpoint) .. testoutput:: python ['myprefix_mymodel_20.pt', 'myprefix_mymodel_30.pt'] /tmp/models/myprefix_mymodel_30.pt """ def __init__( self, dirname: Union[str, Path], filename_prefix: str = "", score_function: Optional[Callable] = None, score_name: Optional[str] = None, n_saved: Union[int, None] = 1, atomic: bool = True, require_empty: bool = True, create_dir: bool = True, global_step_transform: Optional[Callable] = None, filename_pattern: Optional[str] = None, include_self: bool = False, greater_or_equal: bool = False, save_on_rank: int = 0, **kwargs: Any, ): disk_saver = DiskSaver( dirname, atomic=atomic, create_dir=create_dir, require_empty=require_empty, save_on_rank=save_on_rank, **kwargs, ) super(ModelCheckpoint, self).__init__( to_save={}, save_handler=disk_saver, filename_prefix=filename_prefix, score_function=score_function, score_name=score_name, n_saved=n_saved, global_step_transform=global_step_transform, filename_pattern=filename_pattern, include_self=include_self, greater_or_equal=greater_or_equal, save_on_rank=save_on_rank, ) @property def last_checkpoint(self) -> Optional[Union[str, Path]]: if len(self._saved) < 1: return None if not isinstance(self.save_handler, DiskSaver): raise RuntimeError(f"Internal error, save_handler should be DiskSaver, but has {type(self.save_handler)}.") return self.save_handler.dirname / self._saved[-1].filename def __call__(self, engine: Engine, to_save: Mapping): # type: ignore if len(to_save) == 0: raise RuntimeError("No objects to checkpoint found.") self._check_objects(to_save, "state_dict") self.to_save = to_save super(ModelCheckpoint, self).__call__(engine) ignite-0.5.1/ignite/handlers/clearml_logger.py000066400000000000000000001111411465426447700214250ustar00rootroot00000000000000"""ClearML logger and its helper handlers.""" import os import tempfile import warnings from collections import defaultdict from datetime import datetime from enum import Enum from typing import Any, Callable, DefaultDict, List, Mapping, Optional, Tuple, Type, Union from torch.optim import Optimizer import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHandler, BaseWeightsScalarHandler, ) from ignite.handlers.checkpoint import DiskSaver from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "ClearMLLogger", "ClearMLSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "WeightsHistHandler", "GradsScalarHandler", "GradsHistHandler", "global_step_from_engine", ] class ClearMLLogger(BaseLogger): """ `ClearML `_ handler to log metrics, text, model/optimizer parameters, plots during training and validation. Also supports model checkpoints logging and upload to the storage solution of your choice (i.e. ClearML File server, S3 bucket etc.) .. code-block:: bash pip install clearml clearml-init Args: kwargs: Keyword arguments accepted from ``Task.init`` method. All arguments are optional. If a ClearML Task has already been created, kwargs will be ignored and the current ClearML Task will be used. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the trainer to log training loss at each iteration clearml_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. clearml_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. clearml_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration clearml_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) # Attach the logger to the trainer to log model's weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model) ) """ def __init__(self, **kwargs: Any): try: from clearml import Task from clearml.binding.frameworks.tensorflow_bind import WeightsGradientHistHelper except ImportError: raise ModuleNotFoundError( "This contrib module requires clearml to be installed. " "You may install clearml using: \n pip install clearml \n" ) experiment_kwargs = {k: v for k, v in kwargs.items() if k not in ("project_name", "task_name", "task_type")} if self.bypass_mode(): warnings.warn("ClearMLSaver: running in bypass mode") # Try to retrieve current the ClearML Task before trying to create a new one self._task = Task.current_task() if self._task is None: self._task = Task.init( project_name=kwargs.get("project_name"), task_name=kwargs.get("task_name"), task_type=kwargs.get("task_type", Task.TaskTypes.training), **experiment_kwargs, ) self.clearml_logger = self._task.get_logger() self.grad_helper = WeightsGradientHistHelper(logger=self.clearml_logger, report_freq=1) @classmethod def set_bypass_mode(cls, bypass: bool) -> None: """ Set ``clearml.Task`` to offline mode. Will bypass all outside communication, and will save all data and logs to a local session folder. Should only be used in "standalone mode", when there is no access to the *clearml-server*. Args: bypass: If ``True``, all outside communication is skipped. Data and logs will be stored in a local session folder. For more information, please refer to `ClearML docs `_. """ from clearml import Task setattr(cls, "_bypass", bypass) Task.set_offline(offline_mode=bypass) @classmethod def bypass_mode(cls) -> bool: """ Returns the bypass mode state. Note: `GITHUB_ACTIONS` env will automatically set bypass_mode to ``True`` unless overridden specifically with ``ClearMLLogger.set_bypass_mode(False)``. For more information, please refer to `ClearML docs `_. Return: If True, ``clearml.Task`` is on offline mode, and all outside communication is skipped. """ return getattr(cls, "_bypass", bool(os.environ.get("CI"))) def __getattr__(self, attr: Any) -> Any: """ Calls the corresponding method of ``clearml.Logger``. Args: attr: methods of the ``clearml.Logger`` class. """ return getattr(self.clearml_logger, attr) def get_task(self) -> Any: """ Returns the task context that the logger is reporting. Return: Returns the current task, equivalent to ``clearml.Task.current_task()``. """ return self._task def close(self) -> None: self.clearml_logger.flush() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.clearml_logger.global_step_from_engine`. state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: clearml_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently clearml_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.clearml_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on ClearML. clearml_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python clearml_logger.attach( trainer, log_handler=OutputHandler( tag="training", metric_names=["nll", "accuracy"], state_attributes=["alpha", "beta"], ), event_name=Events.ITERATION_COMPLETED ) Example of `global_step_transform` .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ): super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, ClearMLLogger): raise RuntimeError("Handler OutputHandler works only with ClearMLLogger") metrics = self._setup_output_metrics_state_attrs(engine) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) for key, value in metrics.items(): if len(key) == 2: logger.clearml_logger.report_scalar(title=key[0], series=key[1], iteration=global_step, value=value) elif len(key) == 3: logger.clearml_logger.report_scalar( title=f"{key[0]}/{key[1]}", series=key[2], iteration=global_step, value=value ) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration clearml_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently clearml_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, ClearMLLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with ClearMLLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { str(i): float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } for k, v in params.items(): logger.clearml_logger.report_scalar( title=f"{tag_prefix}{self.param_name}", series=k, value=v, iteration=global_step ) class WeightsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's weights as scalars. Handler, upon construction, iterates over named parameters of the model and keep reference to ones permitted by `whitelist`. Then at every call, applies reduction function to each parameter, produces a scalar and logs it. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" whitelist: specific weights to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if it should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's weights are logged. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the trainer to log model's weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, reduction=torch.norm) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log only `fc` weights clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler( model, whitelist=['fc'] ) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log weights which have `bias` in their names def has_bias_in_name(n, p): return 'bias' in n clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, whitelist=has_bias_in_name) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, ClearMLLogger): raise RuntimeError("Handler WeightsScalarHandler works only with ClearMLLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: title_name, _, series_name = name.partition(".") logger.clearml_logger.report_scalar( title=f"{tag_prefix}weights_{self.reduction.__name__}/{title_name}", series=series_name, value=self.reduction(p.data), iteration=global_step, ) class WeightsHistHandler(BaseWeightsHandler): """Helper handler to log model's weights as histograms. Args: model: model to log weights tag: common title for all produced plots. For example, 'generator' whitelist: specific weights to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if it should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's weights are logged. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the trainer to log model's weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log weights of `fc` layer weights = ['fc'] # Attach the logger to the trainer to log weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model, whitelist=weights) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log weights which name include 'conv'. weight_selector = lambda name, p: 'conv' in name # Attach the logger to the trainer to log weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model, whitelist=weight_selector) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, ClearMLLogger): raise RuntimeError("Handler 'WeightsHistHandler' works only with ClearMLLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: title_name, _, series_name = name.partition(".") logger.grad_helper.add_histogram( title=f"{tag_prefix}weights_{title_name}", series=series_name, step=global_step, hist_data=p.data.cpu().numpy(), ) class GradsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's gradients as scalars. Handler, upon construction, iterates over named parameters of the model and keep reference to ones permitted by the `whitelist`. Then at every call, applies reduction function to each parameter's gradient, produces a scalar and logs it. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" whitelist: specific gradients to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if its gradient should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's gradients are logged. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the trainer to log model's weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, reduction=torch.norm) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log gradient of `base` clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler( model, reduction=torch.norm, whitelist=['base'] ) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log gradient of weights which belong to a `fc` layer def is_in_fc_layer(n, p): return 'fc' in n clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, whitelist=is_in_fc_layer) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, ClearMLLogger): raise RuntimeError("Handler GradsScalarHandler works only with ClearMLLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: if p.grad is None: continue title_name, _, series_name = name.partition(".") logger.clearml_logger.report_scalar( title=f"{tag_prefix}grads_{self.reduction.__name__}/{title_name}", series=series_name, value=self.reduction(p.grad), iteration=global_step, ) class GradsHistHandler(BaseWeightsHandler): """Helper handler to log model's gradients as histograms. Args: model: model to log weights tag: common title for all produced plots. For example, 'generator' whitelist: specific gradients to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if its gradient should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's gradients are logged. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * # Create a logger clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Attach the logger to the trainer to log model's weights norm after each iteration clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log gradient of `fc.bias` clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model, whitelist=['fc.bias']) ) .. code-block:: python from ignite.handlers.clearml_logger import * clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) # Log gradient of weights which have shape (2, 1) def has_shape_2_1(n, p): return p.shape == (2,1) clearml_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model, whitelist=has_shape_2_1) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, ClearMLLogger): raise RuntimeError("Handler 'GradsHistHandler' works only with ClearMLLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: if p.grad is None: continue title_name, _, series_name = name.partition(".") logger.grad_helper.add_histogram( title=f"{tag_prefix}grads_{title_name}", series=series_name, step=global_step, hist_data=p.grad.cpu().numpy(), ) class ClearMLSaver(DiskSaver): """ Handler that saves input checkpoint as ClearML artifacts Args: logger: An instance of :class:`~ignite.handlers.clearml_logger.ClearMLLogger`, ensuring a valid ClearML ``Task`` has been initialized. If not provided, and a ClearML Task has not been manually initialized, a runtime error will be raised. output_uri: The default location for output models and other artifacts uploaded by ClearML. For more information, see ``clearml.Task.init``. dirname: Directory path where the checkpoint will be saved. If not provided, a temporary directory will be created. Examples: .. code-block:: python from ignite.handlers.clearml_logger import * from ignite.handlers import Checkpoint clearml_logger = ClearMLLogger( project_name="pytorch-ignite-integration", task_name="cnn-mnist" ) to_save = {"model": model} handler = Checkpoint( to_save, ClearMLSaver(), n_saved=1, score_function=lambda e: 123, score_name="acc", filename_prefix="best", global_step_transform=global_step_from_engine(trainer) ) validation_evaluator.add_event_handler(Events.EVENT_COMPLETED, handler) """ def __init__( self, logger: Optional[ClearMLLogger] = None, output_uri: Optional[str] = None, dirname: Optional[str] = None, *args: Any, **kwargs: Any, ): self._setup_check_clearml(logger, output_uri) if not dirname: dirname = "" if idist.get_rank() == 0: dirname = tempfile.mkdtemp(prefix=f"ignite_checkpoints_{datetime.now().strftime('%Y_%m_%d_%H_%M_%S_')}") if idist.get_world_size() > 1: dirname = idist.all_gather(dirname)[0] # type: ignore[index, assignment] warnings.warn(f"ClearMLSaver created a temporary checkpoints directory: {dirname}") idist.barrier() # Let's set non-atomic tmp dir saving behaviour if "atomic" not in kwargs: kwargs["atomic"] = False self._checkpoint_slots: DefaultDict[Union[str, Tuple[str, str]], List[Any]] = defaultdict(list) super(ClearMLSaver, self).__init__(dirname=dirname, *args, **kwargs) # type: ignore[misc] @idist.one_rank_only() def _setup_check_clearml(self, logger: ClearMLLogger, output_uri: str) -> None: try: from clearml import Task except ImportError: try: # Backwards-compatibility for legacy Trains SDK from trains import Task except ImportError: raise ModuleNotFoundError( "This contrib module requires clearml to be installed. " "You may install clearml using: \n pip install clearml \n" ) if logger and not isinstance(logger, ClearMLLogger): raise TypeError("logger must be an instance of ClearMLLogger") self._task = Task.current_task() if not self._task: raise RuntimeError( "ClearMLSaver requires a ClearML Task to be initialized. " "Please use the `logger` argument or call `clearml.Task.init()`." ) if output_uri: self._task.output_uri = output_uri class _CallbacksContext: def __init__( self, callback_type: Type[Enum], slots: List, checkpoint_key: str, filename: str, basename: str, metadata: Optional[Mapping] = None, ) -> None: self._callback_type = callback_type self._slots = slots self._checkpoint_key = str(checkpoint_key) self._filename = filename self._basename = basename self._metadata = metadata def pre_callback(self, action: str, model_info: Any) -> Any: if action != self._callback_type.save: # type: ignore[attr-defined] return model_info try: slot = self._slots.index(None) self._slots[slot] = model_info.upload_filename except ValueError: self._slots.append(model_info.upload_filename) slot = len(self._slots) - 1 model_info.upload_filename = f"{self._basename}_{slot}{os.path.splitext(self._filename)[1]}" model_info.local_model_id = f"{self._checkpoint_key}:{model_info.upload_filename}" return model_info def post_callback(self, action: str, model_info: Any) -> Any: if action != self._callback_type.save: # type: ignore[attr-defined] return model_info model_info.model.name = f"{model_info.task.name}: {self._filename}" prefix = "Checkpoint Metadata: " metadata_items = ", ".join(f"{k}={v}" for k, v in self._metadata.items()) if self._metadata else "none" metadata = f"{prefix}{metadata_items}" comment = "\n".join( metadata if line.startswith(prefix) else line for line in (model_info.model.comment or "").split("\n") ) if prefix not in comment: comment += "\n" + metadata model_info.model.comment = comment return model_info def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None: try: from clearml.binding.frameworks import WeightsFileHandler except ImportError: try: # Backwards-compatibility for legacy Trains SDK from trains.binding.frameworks import WeightsFileHandler except ImportError: raise ModuleNotFoundError( "This contrib module requires clearml to be installed. " "You may install clearml using: \n pip install clearml \n" ) try: basename = metadata["basename"] # type: ignore[index] except (TypeError, KeyError): warnings.warn("Checkpoint metadata missing or basename cannot be found") basename = "checkpoint" checkpoint_key = (str(self.dirname), basename) cb_context = self._CallbacksContext( callback_type=WeightsFileHandler.CallbackType, slots=self._checkpoint_slots[checkpoint_key], checkpoint_key=str(checkpoint_key), filename=filename, basename=basename, metadata=metadata, ) pre_cb_id = WeightsFileHandler.add_pre_callback(cb_context.pre_callback) post_cb_id = WeightsFileHandler.add_post_callback(cb_context.post_callback) try: super(ClearMLSaver, self).__call__(checkpoint, filename, metadata) finally: WeightsFileHandler.remove_pre_callback(pre_cb_id) WeightsFileHandler.remove_post_callback(post_cb_id) @idist.one_rank_only() def get_local_copy(self, filename: str) -> Optional[str]: """Get artifact local copy. .. warning:: In distributed configuration this method should be called on rank 0 process. Args: filename: artifact name. Returns: a local path to a downloaded copy of the artifact """ artifact = self._task.artifacts.get(filename) if artifact: return artifact.get_local_copy() self._task.get_logger().report_text(f"Can not find artifact {filename}") return None @idist.one_rank_only() def remove(self, filename: str) -> None: super(ClearMLSaver, self).remove(filename) for slots in self._checkpoint_slots.values(): try: slots[slots.index(filename)] = None except ValueError: pass else: break ignite-0.5.1/ignite/handlers/early_stopping.py000066400000000000000000000100531465426447700215060ustar00rootroot00000000000000from collections import OrderedDict from typing import Callable, cast, Mapping, Optional from ignite.base import Serializable from ignite.engine import Engine from ignite.utils import setup_logger __all__ = ["EarlyStopping"] class EarlyStopping(Serializable): """EarlyStopping handler can be used to stop the training if no improvement after a given number of events. Args: patience: Number of events to wait if no improvement and then stop the training. score_function: It should be a function taking a single argument, an :class:`~ignite.engine.engine.Engine` object, and return a score `float`. An improvement is considered if the score is higher. trainer: Trainer engine to stop the run if no improvement. min_delta: A minimum increase in the score to qualify as an improvement, i.e. an increase of less than or equal to `min_delta`, will count as no improvement. cumulative_delta: It True, `min_delta` defines an increase since the last `patience` reset, otherwise, it defines an increase after the last event. Default value is False. Examples: .. code-block:: python from ignite.engine import Engine, Events from ignite.handlers import EarlyStopping def score_function(engine): val_loss = engine.state.metrics['nll'] return -val_loss handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer) # Note: the handler is attached to an *Evaluator* (runs one epoch on validation dataset). evaluator.add_event_handler(Events.COMPLETED, handler) """ _state_dict_all_req_keys = ( "counter", "best_score", ) def __init__( self, patience: int, score_function: Callable, trainer: Engine, min_delta: float = 0.0, cumulative_delta: bool = False, ): if not callable(score_function): raise TypeError("Argument score_function should be a function.") if patience < 1: raise ValueError("Argument patience should be positive integer.") if min_delta < 0.0: raise ValueError("Argument min_delta should not be a negative number.") if not isinstance(trainer, Engine): raise TypeError("Argument trainer should be an instance of Engine.") self.score_function = score_function self.patience = patience self.min_delta = min_delta self.cumulative_delta = cumulative_delta self.trainer = trainer self.counter = 0 self.best_score: Optional[float] = None self.logger = setup_logger(__name__ + "." + self.__class__.__name__) def __call__(self, engine: Engine) -> None: score = self.score_function(engine) if self.best_score is None: self.best_score = score elif score <= self.best_score + self.min_delta: if not self.cumulative_delta and score > self.best_score: self.best_score = score self.counter += 1 self.logger.debug("EarlyStopping: %i / %i" % (self.counter, self.patience)) if self.counter >= self.patience: self.logger.info("EarlyStopping: Stop training") self.trainer.terminate() else: self.best_score = score self.counter = 0 def state_dict(self) -> "OrderedDict[str, float]": """Method returns state dict with ``counter`` and ``best_score``. Can be used to save internal state of the class. """ return OrderedDict([("counter", self.counter), ("best_score", cast(float, self.best_score))]) def load_state_dict(self, state_dict: Mapping) -> None: """Method replace internal state of the class with provided state dict data. Args: state_dict: a dict with "counter" and "best_score" keys/values. """ super().load_state_dict(state_dict) self.counter = state_dict["counter"] self.best_score = state_dict["best_score"] ignite-0.5.1/ignite/handlers/ema_handler.py000066400000000000000000000271161465426447700207160ustar00rootroot00000000000000import warnings from copy import deepcopy from typing import Optional, Union import torch.nn as nn from ignite.engine import CallableEventWithFilter, Engine, Events, EventsList from ignite.handlers.param_scheduler import BaseParamScheduler from ignite.handlers.state_param_scheduler import LambdaStateScheduler __all__ = ["EMAHandler"] class EMAWarmUp: def __init__(self, momentum_warmup: float, warmup_iters: int, momentum: float) -> None: self.momentum_warmup = momentum_warmup self.warmup_iters = warmup_iters self.momentum = momentum def __call__(self, event_index: int) -> float: denominator = max(1, self.warmup_iters - 1) curr_momentum = self.momentum_warmup + (self.momentum - self.momentum_warmup) * (event_index - 1) / denominator if self.momentum >= self.momentum_warmup: return min(self.momentum, curr_momentum) else: return max(self.momentum, curr_momentum) class EMAHandler: r"""Exponential moving average (EMA) handler can be used to compute a smoothed version of model. The EMA model is updated as follows: .. math:: \theta_{\text{EMA}, t+1} = (1 - \lambda) \cdot \theta_{\text{EMA}, t} + \lambda \cdot \theta_{t} where :math:`\theta_{\text{EMA}, t}` and :math:`\theta_{t}` are the EMA weights and online model weights at :math:`t`-th iteration, respectively; :math:`\lambda` is the update momentum. Current momentum can be retrieved from ``Engine.state.ema_momentum``. Args: model: the online model for which an EMA model will be computed. If ``model`` is ``DataParallel`` or ``DistributedDataParallel``, the EMA smoothing will be applied to ``model.module`` . momentum: the update momentum after warmup phase, should be float in range :math:`\left(0, 1 \right)`. momentum_warmup: the initial update momentum during warmup phase. warmup_iters: iterations of warmup. handle_buffers: how to handle model buffers during training. There are three options: 1. "copy" means copying the buffers of the online model; 2. "update" means applying EMA to the buffers of the online model; 3. "ema_train" means set the EMA model to ``train`` mode and skip copying or updating the buffers. Attributes: ema_model: the exponential moving averaged model. model: the online model that is tracked by EMAHandler. It is ``model.module`` if ``model`` in the initialization method is an instance of ``DistributedDataParallel``. momentum: the update momentum. handle_buffers: how to handle model buffers during training. Note: The EMA model is already in ``eval`` mode if ``handle_buffers`` is "copy" or "update". If model in the arguments is an ``nn.Module`` or ``DistributedDataParallel``, the EMA model is an ``nn.Module`` and it is on the same device as the online model. If the model is an ``nn.DataParallel``, then the EMA model is an ``nn.DataParallel``. Note: It is recommended to initialize and use an EMA handler in following steps: 1. Initialize ``model`` (``nn.Module`` or ``DistributedDataParallel``) and ``ema_handler`` (``EMAHandler``). 2. Build ``trainer`` (``ignite.engine.Engine``). 3. Resume from checkpoint for ``model`` and ``ema_handler.ema_model``. 4. Attach ``ema_handler`` to ``trainer``. Examples: .. code-block:: python device = torch.device("cuda:0") model = nn.Linear(2, 1).to(device) # update the ema every 5 iterations ema_handler = EMAHandler(model, momentum=0.0002) # get the ema model, which is an instance of nn.Module ema_model = ema_handler.ema_model trainer = Engine(train_step_fn) to_load = {"model": model, "ema_model", ema_model, "trainer", trainer} if resume_from is not None: Checkpoint.load_objects(to_load, checkpoint=resume_from) # update the EMA model every 5 iterations ema_handler.attach(trainer, name="ema_momentum", event=Events.ITERATION_COMPLETED(every=5)) # add other handlers to_save = to_load ckpt_handler = Checkpoint(to_save, DiskSaver(...), ...) trainer.add_event_handler(Events.EPOCH_COMPLETED, ckpt_handler) # current momentum can be retrieved from engine.state, # the attribute name is the `name` parameter used in the attach function @trainer.on(Events.ITERATION_COMPLETED): def print_ema_momentum(engine): print(f"current momentum: {engine.state.ema_momentum}" # use ema model for validation val_step_fn = get_val_step_fn(ema_model) evaluator = Engine(val_step_fn) @trainer.on(Events.EPOCH_COMPLETED) def run_validation(engine): engine.run(val_data_loader) trainer.run(...) The following example shows how to perform warm-up to the EMA momentum: .. code-block:: python device = torch.device("cuda:0") model = nn.Linear(2, 1).to(device) # linearly change the EMA momentum from 0.2 to 0.002 in the first 100 iterations, # then keep a constant EMA momentum of 0.002 afterwards ema_handler = EMAHandler(model, momentum=0.002, momentum_warmup=0.2, warmup_iters=100) engine = Engine(step_fn) ema_handler.attach(engine, name="ema_momentum") engine.run(...) The following example shows how to attach two handlers to the same trainer: .. code-block:: python generator = build_generator(...) discriminator = build_discriminator(...) gen_handler = EMAHandler(generator) disc_handler = EMAHandler(discriminator) step_fn = get_step_fn(...) engine = Engine(step_fn) # update EMA model of generator every 1 iteration gen_handler.attach(engine, "gen_ema_momentum", event=Events.ITERATION_COMPLETED) # update EMA model of discriminator every 2 iteration disc_handler.attach(engine, "dis_ema_momentum", event=Events.ITERATION_COMPLETED(every=2)) @engine.on(Events.ITERATION_COMPLETED) def print_ema_momentum(engine): print(f"current momentum for generator: {engine.state.gen_ema_momentum}") print(f"current momentum for discriminator: {engine.state.disc_ema_momentum}") engine.run(...) .. versionadded:: 0.4.6 """ def __init__( self, model: nn.Module, momentum: float = 0.0002, momentum_warmup: Optional[float] = None, warmup_iters: Optional[int] = None, handle_buffers: str = "copy", ) -> None: if not 0 < momentum < 1: raise ValueError(f"Invalid momentum: {momentum}") self.momentum = momentum self._momentum_lambda_obj: Optional[EMAWarmUp] = None if momentum_warmup is not None and warmup_iters is not None: self.momentum_scheduler: Optional[BaseParamScheduler] = None self._momentum_lambda_obj = EMAWarmUp(momentum_warmup, warmup_iters, momentum) if not isinstance(model, nn.Module): raise ValueError( f"model should be an instance of nn.Module or its subclasses, but got" f"model: {model.__class__.__name__}" ) if isinstance(model, nn.parallel.DistributedDataParallel): model = model.module self.model = model self.ema_model = deepcopy(self.model) for param in self.ema_model.parameters(): param.detach_() if handle_buffers not in ("copy", "update", "ema_train"): raise ValueError( f"handle_buffers can only be one of 'copy', 'update', 'ema_train', " f"but got {handle_buffers}" ) self.handle_buffers = handle_buffers if self.handle_buffers == "ema_train": self.ema_model.train() else: self.ema_model.eval() def _update_ema_model(self, engine: Engine, name: str) -> None: """Update weights of ema model""" momentum = getattr(engine.state, name) for ema_p, model_p in zip(self.ema_model.parameters(), self.model.parameters()): ema_p.mul_(1.0 - momentum).add_(model_p.data, alpha=momentum) if self.handle_buffers == "update": for ema_b, model_b in zip(self.ema_model.buffers(), self.model.buffers()): try: ema_b.mul_(1.0 - momentum).add_(model_b.data, alpha=momentum) except RuntimeError: # Handle the case where ema_b is torch.int64, torch.int32 etc., # where a runtime error will be thrown when performing the in-place operations with floats. # In this case, just copy the data ema_b.data = model_b.data elif self.handle_buffers == "copy": # assign the buffers for ema_b, model_b in zip(self.ema_model.buffers(), self.model.buffers()): ema_b.data = model_b.data else: pass def attach( self, engine: Engine, name: str = "ema_momentum", warn_if_exists: bool = True, event: Union[str, Events, CallableEventWithFilter, EventsList] = Events.ITERATION_COMPLETED, ) -> None: """Attach the handler to engine. After the handler is attached, the ``Engine.state`` will add an new attribute with name ``name`` if the attribute does not exist. Then, the current momentum can be retrieved from ``Engine.state`` when the engine runs. Note: There are two cases where a momentum with name ``name`` already exists: 1. the engine has loaded its state dict after resuming. In this case, there is no need to initialize the momentum again, and users can set ``warn_if_exists`` to False to suppress the warning message; 2. another handler has created a state attribute with the same name. In this case, users should choose another name for the ema momentum. Args: engine: trainer to which the handler will be attached. name: attribute name for retrieving EMA momentum from ``Engine.state``. It should be a unique name since a trainer can have multiple EMA handlers. warn_if_exists: if True, a warning will be thrown if the momentum with name ``name`` already exists. event: event when the EMA momentum and EMA model are updated. """ if hasattr(engine.state, name): if warn_if_exists: warnings.warn( f"Attribute '{name}' already exists in Engine.state. It might because 1. the engine has loaded its " f"state dict or 2. {name} is already created by other handlers. Turn off this warning by setting" f"warn_if_exists to False.", category=UserWarning, ) else: setattr(engine.state, name, self.momentum) if self._momentum_lambda_obj is not None: self.momentum_scheduler = LambdaStateScheduler(self._momentum_lambda_obj, param_name="ema_momentum") # first update the momentum and then update the EMA model self.momentum_scheduler.attach(engine, event) engine.add_event_handler(event, self._update_ema_model, name) ignite-0.5.1/ignite/handlers/fbresearch_logger.py000066400000000000000000000255601465426447700221230ustar00rootroot00000000000000"""FBResearch logger and its helper handlers.""" import datetime from typing import Any, Callable, List, Optional import torch from ignite import utils from ignite.engine import Engine, Events from ignite.handlers import Timer MB = 1024.0 * 1024.0 __all__ = ["FBResearchLogger"] class FBResearchLogger: """Logs training and validation metrics for research purposes. This logger is designed to attach to an Ignite Engine and log various metrics and system stats at configurable intervals, including learning rates, iteration times, and GPU memory usage. Args: logger: The logger to use for output. delimiter: The delimiter to use between metrics in the log output. show_output: Flag to enable logging of the output from the engine's process function. Examples: .. code-block:: python import logging import torch import torch.nn as nn import torch.optim as optim from ignite.engine import create_supervised_trainer, Events from ignite.handlers.fbresearch_logger import FBResearchLogger from ignite.utils import setup_logger model = nn.Linear(10, 5) opt = optim.SGD(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() data = [(torch.rand(4, 10), torch.randint(0, 5, size=(4, ))) for _ in range(100)] trainer = create_supervised_trainer( model, opt, criterion, output_transform=lambda x, y, y_pred, loss: {"total_loss": loss.item()} ) logger = setup_logger("trainer", level=logging.INFO) logger = FBResearchLogger(logger=logger, show_output=True) logger.attach(trainer, name="Train", every=20, optimizer=opt) trainer.run(data, max_epochs=4) Output: .. code-block:: text 2024-04-22 12:05:47,843 trainer INFO: Train: start epoch [1/4] ... Epoch [1/4] [20/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5999 Iter time: 0.0008 s Data prep .. ... Epoch [1/4] [40/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9297 Iter time: 0.0008 s Data prep .. ... Epoch [1/4] [60/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9985 Iter time: 0.0008 s Data prep .. ... Epoch [1/4] [80/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9785 Iter time: 0.0008 s Data prep .. ... Epoch [1/4] [100/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6211 Iter time: 0.0008 s Data prep . ... Train: Epoch [1/4] Total time: 0:00:00 (0.0008 s / it) ... Train: start epoch [2/4] ... Epoch [2/4] [19/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5981 Iter time: 0.0009 s Data prep .. ... Epoch [2/4] [39/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9013 Iter time: 0.0008 s Data prep .. ... Epoch [2/4] [59/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9811 Iter time: 0.0008 s Data prep .. ... Epoch [2/4] [79/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9434 Iter time: 0.0008 s Data prep .. ... Epoch [2/4] [99/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6116 Iter time: 0.0008 s Data prep .. ... Train: Epoch [2/4] Total time: 0:00:00 (0.0009 s / it) ... Train: start epoch [3/4] ... Epoch [3/4] [18/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5972 Iter time: 0.0008 s Data prep .. ... Epoch [3/4] [38/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8753 Iter time: 0.0008 s Data prep .. ... Epoch [3/4] [58/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9657 Iter time: 0.0009 s Data prep .. ... Epoch [3/4] [78/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9112 Iter time: 0.0008 s Data prep .. ... Epoch [3/4] [98/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6035 Iter time: 0.0008 s Data prep .. ... Train: Epoch [3/4] Total time: 0:00:00 (0.0009 s / it) ... Train: start epoch [4/4] ... Epoch [4/4] [17/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5969 Iter time: 0.0008 s Data prep .. ... Epoch [4/4] [37/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8516 Iter time: 0.0008 s Data prep .. ... Epoch [4/4] [57/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9521 Iter time: 0.0008 s Data prep .. ... Epoch [4/4] [77/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8816 Iter time: 0.0008 s Data prep .. ... Epoch [4/4] [97/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5966 Iter time: 0.0009 s Data prep .. ... Train: Epoch [4/4] Total time: 0:00:00 (0.0009 s / it) ... Train: run completed Total time: 0:00:00 """ def __init__(self, logger: Any, delimiter: str = " ", show_output: bool = False): self.delimiter = delimiter self.logger: Any = logger self.iter_timer: Timer = Timer(average=True) self.data_timer: Timer = Timer(average=True) self.show_output: bool = show_output def attach( self, engine: Engine, name: str, every: int = 1, output_transform: Optional[Callable] = None, state_attributes: Optional[List[str]] = None, optimizer: Optional[torch.optim.Optimizer] = None, ) -> None: """Attaches all the logging handlers to the given engine. Args: engine: The engine to attach the logging handlers to. name: The name of the engine (e.g., "Train", "Validate") to include in log messages. every: Frequency of iterations to log information. Logs are generated every 'every' iterations. output_transform: A function to select the value to log. state_attributes: A list of attributes to log. optimizer: The optimizer used during training to log current learning rates. """ self.name = name self.output_transform = output_transform self.state_attributes = state_attributes engine.add_event_handler(Events.EPOCH_STARTED, self.log_epoch_started, engine, name) engine.add_event_handler(Events.ITERATION_COMPLETED(every=every), self.log_every, engine, optimizer=optimizer) engine.add_event_handler(Events.EPOCH_COMPLETED, self.log_epoch_completed, engine, name) engine.add_event_handler(Events.COMPLETED, self.log_completed, engine, name) self.iter_timer.reset() self.iter_timer.attach( engine, start=Events.EPOCH_STARTED, resume=Events.ITERATION_STARTED, pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED, ) self.data_timer.reset() self.data_timer.attach( engine, start=Events.EPOCH_STARTED, resume=Events.GET_BATCH_STARTED, pause=Events.GET_BATCH_COMPLETED, step=Events.GET_BATCH_COMPLETED, ) def log_every(self, engine: Engine, optimizer: Optional[torch.optim.Optimizer] = None) -> None: """ Logs the training progress at regular intervals. Args: engine: The training engine. optimizer: The optimizer used for training. Defaults to None. """ assert engine.state.epoch_length is not None cuda_max_mem = "" if torch.cuda.is_available(): cuda_max_mem = f"GPU Max Mem: {torch.cuda.max_memory_allocated() / MB:.0f} MB" current_iter = engine.state.iteration % (engine.state.epoch_length + 1) iter_avg_time = self.iter_timer.value() eta_seconds = iter_avg_time * (engine.state.epoch_length - current_iter) outputs = [] if self.show_output and engine.state.output is not None: output = engine.state.output if self.output_transform is not None: output = self.output_transform(output) outputs = utils._to_str_list(output) lrs = "" if optimizer is not None: if len(optimizer.param_groups) == 1: lrs += f"lr: {optimizer.param_groups[0]['lr']:.5f}" else: for i, g in enumerate(optimizer.param_groups): lrs += f"lr [g{i}]: {g['lr']:.5f}" state_attrs = [] if self.state_attributes is not None: state_attrs = utils._to_str_list( {name: getattr(engine.state, name, None) for name in self.state_attributes} ) msg = self.delimiter.join( [ f"Epoch [{engine.state.epoch}/{engine.state.max_epochs}]", f"[{current_iter}/{engine.state.epoch_length}]:", f"ETA: {datetime.timedelta(seconds=int(eta_seconds))}", f"{lrs}", ] + outputs + [" ".join(state_attrs)] + [ f"Iter time: {iter_avg_time:.4f} s", f"Data prep time: {self.data_timer.value():.4f} s", cuda_max_mem, ] ) self.logger.info(msg) def log_epoch_started(self, engine: Engine, name: str) -> None: """ Logs the start of an epoch. Args: engine: The engine object. name: The name of the epoch. """ msg = f"{name}: start epoch [{engine.state.epoch}/{engine.state.max_epochs}]" self.logger.info(msg) def log_epoch_completed(self, engine: Engine, name: str) -> None: """ Logs the completion of an epoch. Args: engine: The engine object that triggered the event. name: The name of the event. Returns: None """ epoch_time = engine.state.times[Events.EPOCH_COMPLETED.name] epoch_info = ( f"Epoch [{engine.state.epoch}/{engine.state.max_epochs}]" if engine.state.max_epochs > 1 # type: ignore else "" ) msg = self.delimiter.join( [ f"{name}: {epoch_info}", f"Total time: {datetime.timedelta(seconds=int(epoch_time))}", # type: ignore f"({epoch_time / engine.state.epoch_length:.4f} s / it)", # type: ignore ] ) self.logger.info(msg) def log_completed(self, engine: Engine, name: str) -> None: """ Logs the completion of a run. Args: engine: The engine object representing the training/validation loop. name: The name of the run. """ if engine.state.max_epochs and engine.state.max_epochs > 1: total_time = engine.state.times[Events.COMPLETED.name] assert total_time is not None msg = self.delimiter.join( [ f"{name}: run completed", f"Total time: {datetime.timedelta(seconds=int(total_time))}", ] ) self.logger.info(msg) ignite-0.5.1/ignite/handlers/lr_finder.py000066400000000000000000000530621465426447700204220ustar00rootroot00000000000000# coding: utf-8 import contextlib import logging import tempfile import warnings from math import ceil from pathlib import Path from typing import Any, Callable, Dict, List, Mapping, Optional, Union import torch from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers import Checkpoint from ignite.handlers.param_scheduler import LRScheduler, ParamGroupScheduler, PiecewiseLinear class FastaiLRFinder: """Learning rate finder handler for supervised trainers. While attached, the handler increases the learning rate in between two boundaries in a linear or exponential manner. It provides valuable information on how well the network can be trained over a range of learning rates and what can be an optimal learning rate. Examples: .. code-block:: python from ignite.handlers import FastaiLRFinder trainer = ... model = ... optimizer = ... lr_finder = FastaiLRFinder() to_save = {"model": model, "optimizer": optimizer} with lr_finder.attach(trainer, to_save=to_save) as trainer_with_lr_finder: trainer_with_lr_finder.run(dataloader) # Get lr_finder results lr_finder.get_results() # Plot lr_finder results (requires matplotlib) lr_finder.plot() # get lr_finder suggestion for lr lr_finder.lr_suggestion() Note: When context manager is exited all LR finder's handlers are removed. Note: Please, also keep in mind that all other handlers attached the trainer will be executed during LR finder's run. Note: This class may require `matplotlib` package to be installed to plot learning rate range test: .. code-block:: bash pip install matplotlib References: Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 fastai/lr_find: https://github.com/fastai/fastai .. versionadded:: 0.4.6 """ _lr_schedule: Union[LRScheduler, PiecewiseLinear, ParamGroupScheduler] def __init__(self) -> None: self._diverge_flag = False self._history: Dict[str, List[Any]] = {} self._best_loss = None self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__) def _run( self, trainer: Engine, optimizer: Optimizer, output_transform: Callable, num_iter: int, start_lrs: List[float], end_lrs: List[float], step_mode: str, smooth_f: float, diverge_th: float, ) -> None: self._history = {"lr": [], "loss": []} self._best_loss = None self._diverge_flag = False # attach LRScheduler to trainer. if num_iter is None: num_iter = trainer.state.epoch_length * trainer.state.max_epochs else: max_iter = trainer.state.epoch_length * trainer.state.max_epochs # type: ignore[operator] if max_iter < num_iter: max_iter = num_iter trainer.state.max_epochs = ceil(num_iter / trainer.state.epoch_length) # type: ignore[operator] if not trainer.has_event_handler(self._reached_num_iterations): trainer.add_event_handler(Events.ITERATION_COMPLETED, self._reached_num_iterations, num_iter) # attach loss and lr logging if not trainer.has_event_handler(self._log_lr_and_loss): trainer.add_event_handler( Events.ITERATION_COMPLETED, self._log_lr_and_loss, output_transform, smooth_f, diverge_th ) self.logger.debug(f"Running LR finder for {num_iter} iterations") # Initialize the proper learning rate policy if step_mode.lower() == "exp": self._lr_schedule = LRScheduler(_ExponentialLR(optimizer, start_lrs, end_lrs, num_iter)) else: if len(start_lrs) == 1: self._lr_schedule = PiecewiseLinear( optimizer, param_name="lr", milestones_values=[(0, start_lrs[0]), (num_iter, end_lrs[0])], ) else: self._lr_schedule = ParamGroupScheduler( [ PiecewiseLinear( optimizer, param_name="lr", milestones_values=[(0, start_lrs[i]), (num_iter, end_lrs[i])], param_group_index=i, ) for i in range(len(optimizer.param_groups)) ] ) if not trainer.has_event_handler(self._lr_schedule): trainer.add_event_handler(Events.ITERATION_COMPLETED, self._lr_schedule, num_iter) def _reset(self, trainer: Engine) -> None: self.logger.debug("Completed LR finder run") trainer.remove_event_handler(self._lr_schedule, Events.ITERATION_COMPLETED) trainer.remove_event_handler(self._log_lr_and_loss, Events.ITERATION_COMPLETED) trainer.remove_event_handler(self._reached_num_iterations, Events.ITERATION_COMPLETED) def _log_lr_and_loss(self, trainer: Engine, output_transform: Callable, smooth_f: float, diverge_th: float) -> None: output = trainer.state.output loss = output_transform(output) if not isinstance(loss, float): if isinstance(loss, torch.Tensor): if (loss.ndimension() == 0) or (loss.ndimension() == 1 and len(loss) == 1): loss = loss.item() else: raise ValueError( "if output of the engine is torch.Tensor, then " "it must be 0d torch.Tensor or 1d torch.Tensor with 1 element, " f"but got torch.Tensor of shape {loss.shape}." ) else: raise TypeError( "output of the engine should be of type float or 0d torch.Tensor " "or 1d torch.Tensor with 1 element, " f"but got output of type {type(loss).__name__}" "You may wish to use the output_transform kwarg with the attach method e.g.\n" """ lr_finder = FastaiLRFinder() with lr_finder.attach(trainer, output_transform=lambda x:x["train_loss"]) as trainer_with_lr_finder: trainer_with_lr_finder.run(dataloader_train) """ ) loss = idist.all_reduce(loss) lr = self._lr_schedule.get_param() self._history["lr"].append(lr) if trainer.state.iteration == 1: self._best_loss = loss else: if smooth_f > 0: loss = smooth_f * loss + (1 - smooth_f) * self._history["loss"][-1] if loss < self._best_loss: self._best_loss = loss self._history["loss"].append(loss) # Check if the loss has diverged; if it has, stop the trainer if self._history["loss"][-1] > diverge_th * self._best_loss: # type: ignore[operator] self._diverge_flag = True self.logger.info("Stopping early, the loss has diverged") trainer.terminate() def _reached_num_iterations(self, trainer: Engine, num_iter: int) -> None: if trainer.state.iteration > num_iter: trainer.terminate() def _warning(self, _: Any) -> None: if not self._diverge_flag: warnings.warn( "Run completed without loss diverging, increase end_lr, decrease diverge_th or look" " at lr_finder.plot()", UserWarning, ) def _detach(self, trainer: Engine) -> None: """ Detaches lr_finder from trainer. Args: trainer: the trainer to detach form. """ if trainer.has_event_handler(self._run, Events.STARTED): trainer.remove_event_handler(self._run, Events.STARTED) if trainer.has_event_handler(self._warning, Events.COMPLETED): trainer.remove_event_handler(self._warning, Events.COMPLETED) if trainer.has_event_handler(self._reset, Events.COMPLETED): trainer.remove_event_handler(self._reset, Events.COMPLETED) def get_results(self) -> Dict[str, List[Any]]: """ Returns: Dictionary with loss and lr logs from the previous run """ return self._history def plot( self, skip_start: int = 10, skip_end: int = 5, log_lr: bool = True, display_suggestion: bool = True, ax: Optional[Any] = None, **kwargs: Any, ) -> None: """Plots the learning rate range test. This method requires ``matplotlib`` package to be installed: .. code-block:: bash pip install matplotlib Args: skip_start: number of batches to trim from the start. Default: 10. skip_end: number of batches to trim from the start. Default: 5. log_lr: True to plot the learning rate in a logarithmic scale; otherwise, plotted in a linear scale. Default: True. display_suggestion: if True, red dot shows the suggested learning rate. ax: Pre-existing axes for the plot. Default: None. kwargs: optional kwargs passed to ``plt.subplots`` if ``ax`` is not provided. .. code-block:: python ax = lr_finder.plot(skip_end=0) ax.figure.savefig("output.jpg") """ try: from matplotlib import pyplot as plt except ImportError: raise ModuleNotFoundError( "This method requires matplotlib to be installed. " "Please install it with command: \n pip install matplotlib" ) if not self._history: raise RuntimeError("learning rate finder didn't run yet so results can't be plotted") if skip_start < 0: raise ValueError("skip_start cannot be negative") if skip_end < 0: raise ValueError("skip_end cannot be negative") # Get the data to plot from the history dictionary. lrs = self._history["lr"] losses = self._history["loss"] num_groups = len(lrs[0]) if isinstance(lrs[0], list) else 1 legends = [f"suggested lr for param_groups {i}" for i in range(num_groups)] if ax is None: fig, ax = plt.subplots(**kwargs) # Check to show the suggested learning rate if display_suggestion: sug_lr = self.lr_suggestion() idx = self._history["lr"].index(sug_lr) if skip_start >= idx: warnings.warn( "skip_start is larger than the suggested LR found" " and it will not be visible on the plot. Please, make the value smaller.", UserWarning, ) corresponding_loss = self._history["loss"][int(idx)] # Check if optimizer has multiple param_groups if not isinstance(sug_lr, list): sug_lr = [ sug_lr, ] for lr in sug_lr: ax.scatter( lr, corresponding_loss, color="red" if len(sug_lr) == 1 else None, s=75, marker="o", zorder=3 ) # handle skip_end=0 properly if skip_end == 0: lrs = lrs[skip_start:] losses = losses[skip_start:] else: lrs = lrs[skip_start:-skip_end] losses = losses[skip_start:-skip_end] plt.legend(legends) # Plot loss as a function of the learning rate ax.plot(lrs, losses) if log_lr: ax.set_xscale("log") lr_min = min(lrs[0]) if isinstance(lrs[0], list) else lrs[0] lr_max = max(lrs[-1]) if isinstance(lrs[-1], list) else lrs[-1] ax.set_xlim([lr_min, lr_max]) ax.set_xlabel("Learning rate") ax.set_ylabel("Loss") plt.show() return ax def lr_suggestion(self) -> Any: """ Returns: Learning rate at the minimum numerical gradient (ignoring the increasing part of the curve) """ if not self._history: raise RuntimeError("learning rate finder didn't run yet so lr_suggestion can't be returned") loss = self._history["loss"] min_loss_idx = torch.tensor(loss).argmin() # Ignore the increasing part of the curve decreasing_losses = self._history["loss"][: int(min_loss_idx.item()) + 1] if len(decreasing_losses) < 3: raise RuntimeError( "FastaiLRFinder got unexpected curve shape, the curve should be somehow U-shaped, " "please decrease start_lr or increase end_lr to resolve this issue." ) losses = torch.tensor(decreasing_losses) grads = torch.tensor([0.5 * (losses[i + 1] - losses[i - 1]) for i in range(1, len(losses) - 1)]) min_grad_idx = grads.argmin() + 1 return self._history["lr"][int(min_grad_idx)] def apply_suggested_lr(self, optimizer: Optimizer) -> None: """ Applying the suggested learning rate(s) on the given optimizer. Args: optimizer: the optimizer to apply the suggested learning rate(s) on. Note: The given optimizer must be the same as the one we before found the suggested learning rate for. """ sug_lr = self.lr_suggestion() if not isinstance(sug_lr, list): sug_lr = [ sug_lr, ] if len(sug_lr) != len(optimizer.param_groups): raise RuntimeError( "The number of parameter groups does not match between " "given optimizer and the one used for estimating the " f"learning rate: {len(sug_lr)} vs {len(optimizer.param_groups)}" ) for i, lr in enumerate(sug_lr): optimizer.param_groups[i]["lr"] = lr @contextlib.contextmanager def attach( self, trainer: Engine, to_save: Mapping, output_transform: Callable = lambda output: output, num_iter: Optional[int] = None, start_lr: Optional[Union[float, List[float]]] = None, end_lr: Optional[Union[float, List[float]]] = 10.0, step_mode: str = "exp", smooth_f: float = 0.05, diverge_th: float = 5.0, ) -> Any: """Attaches lr_finder to a given trainer. It also resets model and optimizer at the end of the run. Args: trainer: lr_finder is attached to this trainer. Please, keep in mind that all attached handlers will be executed. to_save: dictionary with optimizer and other objects that needs to be restored after running the LR finder. For example, ``to_save={'optimizer': optimizer, 'model': model}``. It should contain "optimizer" key for the optimizer. Also all objects should implement ``state_dict`` and ``load_state_dict`` methods. output_transform: function that transforms the trainer's ``state.output`` after each iteration. It must return the loss of that iteration. num_iter: number of iterations for lr schedule between base lr and end_lr. Default, it will run for ``trainer.state.epoch_length * trainer.state.max_epochs``. start_lr: lower bound for lr search. Default, Learning Rate specified with the optimizer. end_lr: upper bound for lr search. Default, 10.0. step_mode: "exp" or "linear", which way should the lr be increased from ``start_lr`` to ``end_lr``. Default, "exp". smooth_f: loss smoothing factor in range ``[0, 1)``. Default, 0.05 diverge_th: Used for stopping the search when ``current loss > diverge_th * best_loss``. Default, 5.0. Returns: trainer_with_lr_finder (trainer used for finding the lr) Examples: .. code-block:: python to_save = {"model": model, "optimizer": optimizer} with lr_finder.attach(trainer, to_save=to_save) as trainer_with_lr_finder: trainer_with_lr_finder.run(dataloader) Note: lr_finder cannot be attached to more than one trainer at a time. """ if not isinstance(to_save, Mapping): raise TypeError(f"Argument to_save should be a mapping, but given {type(to_save)}") Checkpoint._check_objects(to_save, "state_dict") Checkpoint._check_objects(to_save, "load_state_dict") if "optimizer" not in to_save: raise ValueError("Mapping to_save should contain 'optimizer' key") if not isinstance(to_save["optimizer"], torch.optim.Optimizer): raise TypeError( f"Object to_save['optimizer'] should be torch optimizer, but given {type(to_save['optimizer'])}" ) if smooth_f < 0 or smooth_f >= 1: raise ValueError("smooth_f is outside the range [0, 1]") if diverge_th < 1: raise ValueError("diverge_th should be larger than 1") if step_mode not in ["exp", "linear"]: raise ValueError(f"step_mode should be 'exp' or 'linear', but given {step_mode}") if num_iter is not None: if not isinstance(num_iter, int): raise TypeError(f"if provided, num_iter should be an integer, but give {num_iter}") if num_iter <= 0: raise ValueError(f"if provided, num_iter should be positive, but give {num_iter}") optimizer = to_save["optimizer"] if start_lr is None: start_lrs = [pg["lr"] for pg in optimizer.param_groups] elif isinstance(start_lr, float): start_lrs = [start_lr] * len(optimizer.param_groups) elif isinstance(start_lr, list): if len(start_lr) != len(optimizer.param_groups): raise ValueError( "Number of values of start_lr should be equal to optimizer values." f"start_lr values:{len(start_lr)} optimizer values: {len(optimizer.param_groups)}" ) start_lrs = start_lr else: raise TypeError(f"start_lr should be a float or list of floats, but given {type(start_lr)}") if isinstance(end_lr, float): end_lrs = [end_lr] * len(optimizer.param_groups) elif isinstance(end_lr, list): if len(end_lr) != len(optimizer.param_groups): raise ValueError( "Number of values of end_lr should be equal to optimizer values." f"end_lr values:{len(end_lr)} optimizer values: {len(optimizer.param_groups)}" ) end_lrs = end_lr else: raise TypeError(f"end_lr should be a float or list of floats, but given {type(end_lr)}") for start, end in zip(start_lrs, end_lrs): if start >= end: raise ValueError(f"start_lr must be less than end_lr, start_lr={start_lr} vs end_lr={end_lr}") # store to_save with tempfile.TemporaryDirectory() as tmpdirname: obj = {k: o.state_dict() for k, o in to_save.items()} # add trainer obj["trainer"] = trainer.state_dict() cache_filepath = Path(tmpdirname) / "ignite_lr_finder_cache.pt" torch.save(obj, cache_filepath.as_posix()) # Attach handlers if not trainer.has_event_handler(self._run): trainer.add_event_handler( Events.STARTED, self._run, optimizer, output_transform, num_iter, start_lrs, end_lrs, step_mode, smooth_f, diverge_th, ) if not trainer.has_event_handler(self._warning): trainer.add_event_handler(Events.COMPLETED, self._warning) if not trainer.has_event_handler(self._reset): trainer.add_event_handler(Events.COMPLETED, self._reset) yield trainer self._detach(trainer) # restore to_save and reset trainer's state obj = torch.load(cache_filepath.as_posix()) trainer.load_state_dict(obj["trainer"]) for k, o in obj.items(): if k in to_save: to_save[k].load_state_dict(o) class _ExponentialLR(_LRScheduler): """Exponentially increases the learning rate between two boundaries over a number of iterations. Args: optimizer: wrapped optimizer. start_lrs: the initial learning rate for parameter groups. end_lrs: the final learning rate for parameter groups. num_iter: the number of iterations over which the test occurs. Default: 100. last_epoch: the index of last epoch. Default: -1. """ def __init__( self, optimizer: Optimizer, start_lrs: List[float], end_lrs: List[float], num_iter: int, last_epoch: int = -1 ): self.end_lrs = end_lrs self.num_iter = num_iter super(_ExponentialLR, self).__init__(optimizer, last_epoch) # override base_lrs self.base_lrs = start_lrs def get_lr(self) -> List[float]: curr_iter = self.last_epoch + 1 r = curr_iter / self.num_iter return [base_lr * (end_lr / base_lr) ** r for end_lr, base_lr in zip(self.end_lrs, self.base_lrs)] ignite-0.5.1/ignite/handlers/mlflow_logger.py000066400000000000000000000300331465426447700213060ustar00rootroot00000000000000"""MLflow logger and its helper handlers.""" import warnings from typing import Any, Callable, List, Optional, Union from torch.optim import Optimizer from ignite.engine import Engine, Events from ignite.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.handlers.utils import global_step_from_engine # noqa __all__ = ["MLflowLogger", "OutputHandler", "OptimizerParamsHandler", "global_step_from_engine"] class MLflowLogger(BaseLogger): """ `MLflow `_ tracking client handler to log parameters and metrics during the training and validation. This class requires `mlflow package `_ to be installed: .. code-block:: bash pip install mlflow Args: tracking_uri: MLflow tracking uri. See MLflow docs for more details Examples: .. code-block:: python from ignite.handlers.mlflow_logger import * # Create a logger mlflow_logger = MLflowLogger() # Log experiment parameters: mlflow_logger.log_params({ "seed": seed, "batch_size": batch_size, "model": model.__class__.__name__, "pytorch version": torch.__version__, "ignite version": ignite.__version__, "cuda version": torch.version.cuda, "device name": torch.cuda.get_device_name(0) }) # Attach the logger to the trainer to log training loss at each iteration mlflow_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {'loss': loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. mlflow_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. mlflow_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration mlflow_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) """ def __init__(self, tracking_uri: Optional[str] = None): try: import mlflow except ImportError: raise ModuleNotFoundError( "This contrib module requires mlflow to be installed. " "Please install it with command: \n pip install mlflow" ) if tracking_uri is not None: mlflow.set_tracking_uri(tracking_uri) self.active_run = mlflow.active_run() if self.active_run is None: self.active_run = mlflow.start_run() def __getattr__(self, attr: Any) -> Any: import mlflow return getattr(mlflow, attr) def close(self) -> None: import mlflow mlflow.end_run() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics. Args: tag: common title for all produced plots. For example, 'training' metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{'loss': loss1, 'another_loss': loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.mlflow_logger.global_step_from_engine`. state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.handlers.mlflow_logger import * # Create a logger mlflow_logger = MLflowLogger() # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: mlflow_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently mlflow_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.mlflow_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) mlflow_logger = MLflowLogger() def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on MLflow. mlflow_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python mlflow_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", metrics=["nll", "accuracy"], state_attributes=["alpha", "beta"], ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ) -> None: super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) def __call__(self, engine: Engine, logger: MLflowLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, MLflowLogger): raise TypeError("Handler 'OutputHandler' works only with MLflowLogger") rendered_metrics = self._setup_output_metrics_state_attrs(engine) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) # Additionally recheck metric names as MLflow rejects non-valid names with MLflowException from mlflow.utils.validation import _VALID_PARAM_AND_METRIC_NAMES metrics = {} for keys, value in rendered_metrics.items(): key = " ".join(keys) metrics[key] = value for key in list(metrics.keys()): if not _VALID_PARAM_AND_METRIC_NAMES.match(key): warnings.warn( f"MLflowLogger output_handler encountered an invalid metric name '{key}' that " "will be ignored and not logged to MLflow" ) del metrics[key] logger.log_metrics(metrics, step=global_step) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, 'generator' Examples: .. code-block:: python from ignite.handlers.mlflow_logger import * # Create a logger mlflow_logger = MLflowLogger() # Optionally, user can specify tracking_uri with corresponds to MLFLOW_TRACKING_URI # mlflow_logger = MLflowLogger(tracking_uri="uri") # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration mlflow_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently mlflow_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: MLflowLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, MLflowLogger): raise TypeError("Handler OptimizerParamsHandler works only with MLflowLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag} " if self.tag else "" params = { f"{tag_prefix}{self.param_name} group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } logger.log_metrics(params, step=global_step) ignite-0.5.1/ignite/handlers/neptune_logger.py000066400000000000000000000653461465426447700215030ustar00rootroot00000000000000"""Neptune logger and its helper handlers.""" import tempfile import warnings from typing import Any, Callable, List, Mapping, Optional, Union import torch from torch.optim import Optimizer import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsScalarHandler, ) from ignite.handlers.checkpoint import BaseSaveHandler from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "NeptuneLogger", "NeptuneSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", "global_step_from_engine", ] _INTEGRATION_VERSION_KEY = "source_code/integrations/neptune-pytorch-ignite" class NeptuneLogger(BaseLogger): """ `Neptune `_ handler to log metrics, model/optimizer parameters and gradients during training and validation. It can also log model checkpoints to Neptune. .. code-block:: bash pip install neptune Args: api_token: Neptune API token, found on https://neptune.ai -> User menu -> "Get your API token". If None, the value of the NEPTUNE_API_TOKEN environment variable is used. To keep your token secure, you should set it to the environment variable rather than including it in your code. project: Name of a Neptune project, in the form "workspace-name/project-name". For example "tom/mnist-classification". If None, the value of the NEPTUNE_PROJECT environment variable is used. **kwargs: Other arguments to be passed to the `init_run()` function. Examples: .. code-block:: python from ignite.handlers.neptune_logger import * # Create a logger # Note: We are using the API token for anonymous logging. You can pass your own token, or save it as an # environment variable and leave out the api_token argument. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project="common/pytorch-ignite-integration", name="cnn-mnist", # Optional, tags=["pytorch-ignite", "minst"], # Optional ) # Attach the logger to the trainer to log training loss at each iteration. npt_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss}, ) # Attach the logger to the evaluator on the training dataset and log NLL # and accuracy metrics after each epoch. # We set up `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. npt_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL and accuracy metrics after # each epoch. We set up `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. npt_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the trainer to log optimizer parameters, such as learning rate at each iteration. npt_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name="lr", # optional ) # Attach the logger to the trainer to log model's weights norm after each iteration. npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model), ) Explore runs with Neptune tracking here: https://app.neptune.ai/o/common/org/pytorch-ignite-integration/ You can also save model checkpoints to a Neptune: .. code-block:: python from ignite.handlers import Checkpoint def score_function(engine): return engine.state.metrics["accuracy"] to_save = {"model": model} handler = Checkpoint( to_save, NeptuneSaver(npt_logger), n_saved=2, filename_prefix="best", score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer), ) validation_evaluator.add_event_handler(Events.COMPLETED, handler) It is also possible to use the logger as a context manager: .. code-block:: python from ignite.handlers.neptune_logger import * with NeptuneLogger() as npt_logger: trainer = Engine(update_fn) # Attach the logger to the trainer to log training loss at each iteration npt_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss}, ) """ def __getattr__(self, attr: Any) -> Any: return getattr(self.experiment, attr) def __getitem__(self, key: str) -> Any: return self.experiment[key] def __setitem__(self, key: str, val: Any) -> Any: self.experiment[key] = val def __init__(self, api_token: Optional[str] = None, project: Optional[str] = None, **kwargs: Any) -> None: try: try: # neptune-client<1.0.0 package structure with warnings.catch_warnings(): # ignore the deprecation warnings warnings.simplefilter("ignore") import neptune.new as neptune except ImportError: # neptune>=1.0.0 package structure import neptune except ImportError: raise ModuleNotFoundError( "This contrib module requires the Neptune client library to be installed. " "Install neptune with the command: \n pip install neptune \n" ) run = neptune.init_run( api_token=api_token, project=project, **kwargs, ) from ignite import __version__ run[_INTEGRATION_VERSION_KEY] = __version__ self.experiment = run def close(self) -> None: self.experiment.stop() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics. Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.neptune_logger.global_step_from_engine`. state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.handlers.neptune_logger import * # Create a logger # We are using the api_token for the anonymous user neptuner but you can use your own. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: npt_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently npt_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.neptune_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) # We are using the api_token for the anonymous user neptuner but you can use your own. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite", "minst"] # Optional ) def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on NeptuneML. npt_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python npt_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", metrics=["nll", "accuracy"], state_attributes=["alpha", "beta"], ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ): super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) def __call__(self, engine: Engine, logger: NeptuneLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, NeptuneLogger): raise TypeError("Handler OutputHandler works only with NeptuneLogger") metrics = self._setup_output_metrics_state_attrs(engine, key_tuple=False) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) for key, value in metrics.items(): logger[key].append(value, step=global_step) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" Examples: .. code-block:: python from ignite.handlers.neptune_logger import * # Create a logger # We are using the api_token for the anonymous user neptuner but you can use your own. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration npt_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently npt_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: NeptuneLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, NeptuneLogger): raise TypeError("Handler OptimizerParamsHandler works only with NeptuneLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } for k, v in params.items(): logger[k].append(v, step=global_step) class WeightsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's weights as scalars. Handler, upon construction, iterates over named parameters of the model and keep reference to ones permitted by `whitelist`. Then at every call, applies reduction function to each parameter, produces a scalar and logs it. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" whitelist: specific weights to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if it should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's weights are logged. Examples: .. code-block:: python from ignite.handlers.neptune_logger import * # Create a logger # We are using the api_token for the anonymous user neptuner but you can use your own. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Attach the logger to the trainer to log model's weights norm after each iteration npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, reduction=torch.norm) ) .. code-block:: python from ignite.handlers.neptune_logger import * npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Log only `fc` weights npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler( model, whitelist=['fc'] ) ) .. code-block:: python from ignite.handlers.neptune_logger import * npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Log weights which have `bias` in their names def has_bias_in_name(n, p): return 'bias' in n npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, whitelist=has_bias_in_name) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: NeptuneLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, NeptuneLogger): raise TypeError("Handler WeightsScalarHandler works only with NeptuneLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: if p.grad is None: continue name = name.replace(".", "/") key = f"{tag_prefix}weights_{self.reduction.__name__}/{name}" logger[key].append(self.reduction(p.data), step=global_step) class GradsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's gradients as scalars. Handler, upon construction, iterates over named parameters of the model and keep reference to ones permitted by the `whitelist`. Then at every call, applies reduction function to each parameter's gradient, produces a scalar and logs it. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" whitelist: specific gradients to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if its gradient should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's gradients are logged. Examples: .. code-block:: python from ignite.handlers.neptune_logger import * # Create a logger # We are using the api_token for the anonymous user neptuner but you can use your own. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Attach the logger to the trainer to log model's weights norm after each iteration npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, reduction=torch.norm) ) .. code-block:: python from ignite.handlers.neptune_logger import * npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Log gradient of `base` npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler( model, reduction=torch.norm, whitelist=['base'] ) ) .. code-block:: python from ignite.handlers.neptune_logger import * npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) # Log gradient of weights which belong to a `fc` layer def is_in_fc_layer(n, p): return 'fc' in n npt_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, whitelist=is_in_fc_layer) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: NeptuneLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, NeptuneLogger): raise TypeError("Handler GradsScalarHandler works only with NeptuneLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: if p.grad is None: continue name = name.replace(".", "/") key = f"{tag_prefix}grads_{self.reduction.__name__}/{name}" logger[key].append(self.reduction(p.grad), step=global_step) class NeptuneSaver(BaseSaveHandler): """Handler that saves input checkpoint to the Neptune server. Args: neptune_logger: an instance of NeptuneLogger class. .. Note :: NeptuneSaver is currently not supported on Windows. Examples: .. code-block:: python from ignite.handlers.neptune_logger import * # Create a logger # We are using the api_token for the anonymous user neptuner but you can use your own. npt_logger = NeptuneLogger( api_token="ANONYMOUS", project_name="shared/pytorch-ignite-integration", experiment_name="cnn-mnist", # Optional, params={"max_epochs": 10}, # Optional, tags=["pytorch-ignite","minst"] # Optional ) ... evaluator = create_supervised_evaluator(model, metrics=metrics, ...) ... from ignite.handlers import Checkpoint def score_function(engine): return engine.state.metrics["accuracy"] to_save = {"model": model} # pass neptune logger to NeptuneServer handler = Checkpoint( to_save, NeptuneSaver(npt_logger), n_saved=2, filename_prefix="best", score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer) ) evaluator.add_event_handler(Events.COMPLETED, handler) # We need to close the logger when we are done npt_logger.close() For example, you can access model checkpoints and download them from here: https://ui.neptune.ai/o/shared/org/pytorch-ignite-integration/e/PYTOR1-18/charts """ @idist.one_rank_only() def __init__(self, neptune_logger: NeptuneLogger): self._logger = neptune_logger @idist.one_rank_only() def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None: # wont work on XLA # Imports for BC compatibility try: # neptune-client<1.0.0 package structure with warnings.catch_warnings(): # ignore the deprecation warnings warnings.simplefilter("ignore") from neptune.new.types import File except ImportError: # neptune>=1.0.0 package structure from neptune.types import File with tempfile.NamedTemporaryFile() as tmp: # we can not use tmp.name to open tmp.file twice on Win32 # https://docs.python.org/3/library/tempfile.html#tempfile.NamedTemporaryFile torch.save(checkpoint, tmp.file) # rewind the buffer tmp.file.seek(0) # hold onto the file stream for uploading. # NOTE: This won't load the whole file in memory and upload # the stream in smaller chunks. self._logger[filename].upload(File.from_stream(tmp.file)) @idist.one_rank_only(with_barrier=True) def remove(self, filename: str) -> None: del self._logger.experiment[filename] ignite-0.5.1/ignite/handlers/param_scheduler.py000066400000000000000000002052451465426447700216160ustar00rootroot00000000000000import itertools import math import numbers import tempfile import warnings from abc import ABCMeta, abstractmethod from collections import OrderedDict from copy import copy from pathlib import Path from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union import torch from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, ReduceLROnPlateau from torch.optim.optimizer import Optimizer # https://github.com/pytorch/ignite/issues/2773 try: from torch.optim.lr_scheduler import LRScheduler as PyTorchLRScheduler except ImportError: from torch.optim.lr_scheduler import _LRScheduler as PyTorchLRScheduler from ignite.engine import Engine class BaseParamScheduler(metaclass=ABCMeta): r"""An abstract class for updating an engine state or optimizer's parameter value during training. Args: param_name: name of engine state or optimizer's parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). .. versionadded:: 0.4.7 """ def __init__(self, param_name: str, save_history: bool = False): self.param_name = param_name self.event_index = 0 self._save_history = save_history self._state_attrs = ["event_index", "param_name", "save_history"] @property def save_history(self) -> bool: return self._save_history @save_history.setter def save_history(self, value: bool) -> None: self._save_history = value def state_dict(self) -> Dict[str, Any]: """Returns a dictionary containing a whole state of BaseParamScheduler. Returns: dict: a dictionary containing a whole state of BaseParamScheduler """ destination = OrderedDict() for name in self._state_attrs: if hasattr(self, name): val = getattr(self, name) if hasattr(val, "state_dict"): val = val.state_dict() destination[name] = copy(val) return destination def load_state_dict(self, state_dict: Mapping) -> None: """Copies parameters from :attr:`state_dict` into this BaseParamScheduler. Args: state_dict: a dict containing parameters. """ if not isinstance(state_dict, Mapping): raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}") for name in self._state_attrs: if name not in state_dict: raise ValueError( f"Required state attribute '{name}' is absent in provided state_dict '{state_dict.keys()}'" ) val = state_dict[name] obj = getattr(self, name) if isinstance(val, Mapping) and hasattr(obj, "load_state_dict"): obj.load_state_dict(val) else: setattr(self, name, val) @abstractmethod def get_param(self) -> Union[List[float], float]: """Method to get current parameter values Returns: list of params, or scalar param """ pass @classmethod @abstractmethod def simulate_values(cls, num_events: int, **scheduler_kwargs: Any) -> List[List[int]]: """Method to simulate scheduled values during `num_events` events. Args: num_events: number of events during the simulation. scheduler_kwargs: parameter scheduler configuration kwargs. Returns: event_index, value """ pass @classmethod def plot_values(cls, num_events: int, **scheduler_kwargs: Mapping) -> Any: """Method to plot simulated scheduled values during `num_events` events. This class requires `matplotlib package `_ to be installed: .. code-block:: bash pip install matplotlib Args: num_events: number of events during the simulation. scheduler_kwargs: parameter scheduler configuration kwargs. Returns: matplotlib.lines.Line2D Examples: .. code-block:: python import matplotlib.pylab as plt plt.figure(figsize=(10, 7)) LinearCyclicalScheduler.plot_values(num_events=50, param_name='lr', start_value=1e-1, end_value=1e-3, cycle_size=10)) """ try: import matplotlib.pyplot as plt except ImportError: raise ModuleNotFoundError( "This method requires matplotlib to be installed. " "Please install it with command: \n pip install matplotlib" ) values = cls.simulate_values(num_events=num_events, **scheduler_kwargs) label = scheduler_kwargs.get("param_name", "learning rate") ax = plt.plot([e for e, _ in values], [v for _, v in values], label=label) plt.legend() plt.grid(which="both") return ax class ParamScheduler(BaseParamScheduler): """An abstract class for updating an optimizer's parameter value during training. Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: name of optimizer's parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index: optimizer's parameters group to use Note: Parameter scheduler works independently of the internal state of the attached optimizer. More precisely, whatever the state of the optimizer (newly created or used by another scheduler) the scheduler sets defined absolute values. """ def __init__( self, optimizer: Optimizer, param_name: str, save_history: bool = False, param_group_index: Optional[int] = None, ): super(ParamScheduler, self).__init__(param_name, save_history) if not ( isinstance(optimizer, Optimizer) or (hasattr(optimizer, "param_groups") and isinstance(optimizer.param_groups, Sequence)) ): raise TypeError( "Argument optimizer should be torch.optim.Optimizer or has attribute 'param_groups' as list/tuple, " f"but given {type(optimizer)}" ) self.optimizer = optimizer self.param_group_index = param_group_index self._state_attrs += ["param_group_index"] def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: value = self._get_param() if isinstance(value, list): if len(value) != len(self.optimizer_param_groups): raise ValueError( "size of value is different than optimizer_param_groups " f"{len(value)} != {len(self.optimizer_param_groups)}" ) for i, param_group in enumerate(self.optimizer_param_groups): param_group[self.param_name] = value[i] else: for i, param_group in enumerate(self.optimizer_param_groups): param_group[self.param_name] = value if name is None: name = self.param_name if self.save_history and engine: if not hasattr(engine.state, "param_history") or engine.state.param_history is None: setattr(engine.state, "param_history", {}) engine.state.param_history.setdefault(name, []) # type: ignore[attr-defined] values = [pg[self.param_name] for pg in self.optimizer_param_groups] engine.state.param_history[name].append(values) # type: ignore[attr-defined] self.event_index += 1 @property def optimizer_param_groups(self) -> List[Dict[str, Any]]: if self.param_group_index is None: return self.optimizer.param_groups return [self.optimizer.param_groups[self.param_group_index]] @classmethod def simulate_values(cls, num_events: int, **scheduler_kwargs: Any) -> List[List[int]]: """Method to simulate scheduled values during `num_events` events. Args: num_events: number of events during the simulation. scheduler_kwargs: parameter scheduler configuration kwargs. Returns: event_index, value Examples: .. code-block:: python lr_values = np.array(LinearCyclicalScheduler.simulate_values(num_events=50, param_name='lr', start_value=1e-1, end_value=1e-3, cycle_size=10)) plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() """ keys_to_remove = ["optimizer", "save_history"] for key in keys_to_remove: if key in scheduler_kwargs: del scheduler_kwargs[key] values = [] scheduler = cls(optimizer=_get_fake_optimizer(), save_history=False, **scheduler_kwargs) for i in range(num_events): scheduler(engine=None) values.append([i, scheduler.optimizer_param_groups[0][scheduler.param_name]]) return values def _get_param(self) -> Union[List[float], float]: # `ParamScheduler` does nothing special, only returning what child class returns. # Intermediate child classes edit this method return self.get_param() class CyclicalScheduler(ParamScheduler): """An abstract class for updating an optimizer's parameter value over a cycle of some size. Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: name of optimizer's parameter to update. start_value: value at start of cycle. end_value: value at the middle of the cycle. cycle_size: length of cycle, value should be larger than 1. cycle_mult: ratio by which to change the cycle_size. at the end of each cycle (default=1.0). start_value_mult: ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult: ratio by which to change the end value at the end of each cycle (default=1.0). warmup_duration: duration of warm-up to be applied before each cycle. Through this warm-up, the parameter starts from the last cycle's end value and linearly goes to next cycle's start value. Default is no cyclic warm-up. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index: optimizer's parameters group to use. Note: If the scheduler is bound to an 'ITERATION_*' event, 'cycle_size' should usually be the number of batches in an epoch. .. versionadded:: 0.4.5 .. versionchanged:: 0.4.13 Added cyclic warm-up to the scheduler using ``warmup_duration``. """ def __init__( self, optimizer: Optimizer, param_name: str, start_value: float, end_value: float, cycle_size: int, cycle_mult: float = 1.0, start_value_mult: float = 1.0, end_value_mult: float = 1.0, warmup_duration: int = 0, save_history: bool = False, param_group_index: Optional[int] = None, ): super(CyclicalScheduler, self).__init__( optimizer, param_name, save_history=save_history, param_group_index=param_group_index ) self.start_value = start_value self.end_value = end_value self.cycle_size = cycle_size self.cycle_mult = cycle_mult self.cycle = 0 self.start_value_mult = start_value_mult self.end_value_mult = end_value_mult self.warmup_duration = warmup_duration self.total_cycle_size = self.warmup_duration + self.cycle_size if self.cycle_size < 2: raise ValueError(f"Argument cycle_size should be positive and larger than 1, but given {cycle_size}") self._state_attrs += [ "start_value", "end_value", "cycle_size", "cycle_mult", "cycle", "start_value_mult", "end_value_mult", "warmup_duration", "total_cycle_size", ] def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: if self.event_index != 0 and self.event_index == self.cycle_size: self.start_value *= self.start_value_mult if self.event_index != 0 and self.event_index == self.total_cycle_size: self.event_index = 0 self.cycle_size = int(self.cycle_size * self.cycle_mult) self.warmup_duration = int(self.warmup_duration * self.cycle_mult) self.total_cycle_size = self.warmup_duration + self.cycle_size self.cycle += 1 self.end_value *= self.end_value_mult return super(CyclicalScheduler, self).__call__(engine, name) def _get_param(self) -> Union[List[float], float]: """Applies warm-up if the scheduler is in the warm-up phase, otherwise returns what is returned by `self.get_param()` """ if self.event_index > self.cycle_size: warmup_progress = (self.event_index - self.cycle_size) / self.warmup_duration return self.end_value + (self.start_value - self.end_value) * warmup_progress return self.get_param() class LinearCyclicalScheduler(CyclicalScheduler): """Linearly adjusts param value to 'end_value' for a half-cycle, then linearly adjusts it back to 'start_value' for a half-cycle. Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: name of optimizer's parameter to update. start_value: value at start of cycle. end_value: value at the middle of the cycle. cycle_size: length of cycle. cycle_mult: ratio by which to change the cycle_size at the end of each cycle (default=1). start_value_mult: ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult: ratio by which to change the end value at the end of each cycle (default=1.0). warmup_duration: duration of warm-up to be applied before each cycle. Through this warm-up, the parameter starts from the last cycle's end value and linearly goes to next cycle's start value. Default is no cyclic warm-up. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index: optimizer's parameters group to use. monotonic: whether to schedule only one half of the cycle: descending or ascending. If True, this argument can not be used together with ``warmup_duration``. (default=False). Note: If the scheduler is bound to an 'ITERATION_*' event, 'cycle_size' should usually be the number of batches in an epoch. Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: 1 default_trainer = get_default_trainer() # Linearly increases the learning rate from 0.0 to 1.0 and back to 0.0 # over a cycle of 4 iterations scheduler = LinearCyclicalScheduler(default_optimizer, "lr", 0.0, 1.0, 4) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(default_optimizer.param_groups[0]["lr"]) default_trainer.run([0] * 9, max_epochs=1) .. testoutput:: 1 0.0 0.5 1.0 0.5 ... .. testcode:: 2 default_trainer = get_default_trainer() optimizer = torch.optim.SGD( [ {"params": default_model.base.parameters(), "lr": 0.001}, {"params": default_model.fc.parameters(), "lr": 0.01}, ] ) # Linearly increases the learning rate from 0.0 to 1.0 and back to 0.0 # over a cycle of 4 iterations scheduler1 = LinearCyclicalScheduler(optimizer, "lr (base)", 0.0, 1.0, 4, param_group_index=0) # Linearly increases the learning rate from 0.0 to 0.1 and back to 0.0 # over a cycle of 4 iterations scheduler2 = LinearCyclicalScheduler(optimizer, "lr (fc)", 0.0, 0.1, 4, param_group_index=1) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler1) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler2) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(optimizer.param_groups[0]["lr (base)"], optimizer.param_groups[1]["lr (fc)"]) default_trainer.run([0] * 9, max_epochs=1) .. testoutput:: 2 0.0 0.0 0.5 0.05 1.0 0.1 0.5 0.05 ... .. versionadded:: 0.4.5 .. versionchanged:: 0.4.13 Added cyclic warm-up to the scheduler using ``warmup_duration``. .. versionchanged:: 0.5.0 Added monotonic argument. """ def __init__(self, *args: Any, monotonic: bool = False, **kwagrs: Any): super(LinearCyclicalScheduler, self).__init__(*args, **kwagrs) self.monotonic = monotonic if self.warmup_duration > 0 and not self.monotonic: raise ValueError( "Invalid combination when warmup_duration > 0 and monotonic=False, " "please use either set warmup_duration=0 or monotonic=True" ) def get_param(self) -> float: """Method to get current optimizer's parameter value""" cycle_progress = self.event_index / self.cycle_size if self.monotonic: return self.start_value + (self.end_value - self.start_value) * cycle_progress else: return self.end_value + (self.start_value - self.end_value) * abs(cycle_progress - 0.5) * 2 class CosineAnnealingScheduler(CyclicalScheduler): """Anneals 'start_value' to 'end_value' over each cycle. The annealing takes the form of the first half of a cosine wave (as suggested in [Smith17]_). Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: name of optimizer's parameter to update. start_value: value at start of cycle. end_value: value at the end of the cycle. cycle_size: length of cycle. cycle_mult: ratio by which to change the cycle_size at the end of each cycle (default=1). start_value_mult: ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult: ratio by which to change the end value at the end of each cycle (default=1.0). warmup_duration: duration of warm-up to be applied before each cycle. Through this warm-up, the parameter starts from the last cycle's end value and linearly goes to next cycle's start value. Default is no cyclic warm-up. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index: optimizer's parameters group to use. Note: If the scheduler is bound to an 'ITERATION_*' event, 'cycle_size' should usually be the number of batches in an epoch. Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: 1 default_trainer = get_default_trainer() # CosineAnnealing increases the learning rate from 0.0 to 1.0 # over a cycle of 4 iterations scheduler = CosineAnnealingScheduler(default_optimizer, "lr", 0.0, 1.0, 4) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(default_optimizer.param_groups[0]["lr"]) default_trainer.run([0] * 9, max_epochs=1) .. testoutput:: 1 0.0 0.1464... 0.4999... 0.8535... ... .. testcode:: 2 default_trainer = get_default_trainer() optimizer = torch.optim.SGD( [ {"params": default_model.base.parameters(), "lr": 0.001}, {"params": default_model.fc.parameters(), "lr": 0.01}, ] ) # CosineAnnealing increases the learning rate from 0.0 to 1.0 # over a cycle of 4 iterations scheduler_1 = CosineAnnealingScheduler(optimizer, "lr (base)", 0.0, 1.0, 4, param_group_index=0) # CosineAnnealing increases the learning rate from 0.0 to 0.1 # over a cycle of 4 iterations scheduler_2 = CosineAnnealingScheduler(optimizer, "lr (fc)", 0.0, 0.1, 4, param_group_index=1) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler_1) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler_2) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(optimizer.param_groups[0]["lr (base)"], optimizer.param_groups[1]["lr (fc)"]) default_trainer.run([0] * 9, max_epochs=1) .. testoutput:: 2 0.0 0.0 0.1464... 0.01464... 0.4999... 0.04999... 0.8535... 0.08535... ... .. [Smith17] Smith, Leslie N. "Cyclical learning rates for training neural networks." Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017 .. versionadded:: 0.4.5 .. versionchanged:: 0.4.13 Added cyclic warm-up to the scheduler using ``warmup_duration``. """ def get_param(self) -> float: """Method to get current optimizer's parameter value""" cycle_progress = self.event_index / self.cycle_size return self.start_value + ((self.end_value - self.start_value) / 2) * (1 - math.cos(math.pi * cycle_progress)) class ConcatScheduler(ParamScheduler): """Concat a list of parameter schedulers. The `ConcatScheduler` goes through a list of schedulers given by `schedulers`. Duration of each scheduler is defined by `durations` list of integers. Args: schedulers: list of parameter schedulers. durations: list of number of events that lasts a parameter scheduler from schedulers. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() scheduler_1 = LinearCyclicalScheduler(default_optimizer, "lr", 0.0, 1.0, 8) scheduler_2 = CosineAnnealingScheduler(default_optimizer, "lr", 1.0, 0.2, 4) # Sets the Learning rate linearly from 0.0 to 1.0 over 4 iterations. Then # starts an annealing schedule from 1.0 to 0.2 over the next 4 iterations. # The annealing cycles are repeated indefinitely. combined_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=[4, ]) default_trainer.add_event_handler(Events.ITERATION_STARTED, combined_scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(default_optimizer.param_groups[0]["lr"]) default_trainer.run([0] * 8, max_epochs=1) .. testoutput:: 0.0 0.25 0.5 0.75 1.0 0.8828... 0.6000... 0.3171... .. versionadded:: 0.4.5 """ def __init__(self, schedulers: List[ParamScheduler], durations: List[int], save_history: bool = False): if not isinstance(schedulers, Sequence): raise TypeError(f"Argument schedulers should be a sequence, but given {schedulers}") if len(schedulers) < 2: raise ValueError( f"Argument schedulers should be of more than one parameter schedulers, but given {schedulers}" ) if not isinstance(durations, (list, tuple)): raise TypeError(f"Argument durations should be list/tuple, but given {durations}") if not all([isinstance(t, numbers.Integral) for t in durations]): raise ValueError(f"Argument durations should be list/tuple of integers, but given {durations}") if len(schedulers) != len(durations) + 1: raise ValueError( "Incorrect number schedulers or duration values, " f"given {len(schedulers)} and {len(durations)}" ) for i, scheduler in enumerate(schedulers): if not isinstance(scheduler, ParamScheduler) and not isinstance(scheduler, ParamGroupScheduler): raise TypeError( f"Value at index {i} of schedulers should be a parameter scheduler, but given {type(scheduler)}" ) self.schedulers = schedulers self.durations = durations tmp_optimizers = [s.optimizer for s in self.schedulers] tmp_list_optimizers = [s if isinstance(s, list) else [s] for s in tmp_optimizers] param_optimizers = list(itertools.chain(*tmp_list_optimizers)) optimizer = list(set(param_optimizers)) if len(optimizer) != 1: raise ValueError("schedulers should be related to same optimizer") tmp_param_names = [s.param_name for s in self.schedulers] tmp_list_param_names = [s if isinstance(s, list) else [s] for s in tmp_param_names] param_names = list(itertools.chain(*tmp_list_param_names)) param_name = list(set(param_names)) if len(param_name) != 1: raise ValueError("schedulers should be related to same param_name") # schedulers should have save_history sync with ParamGroupScheduler for s in schedulers: s.save_history = save_history super(ConcatScheduler, self).__init__( optimizer=optimizer[0], param_name=param_name[0], save_history=save_history ) self._scheduler_index = 0 self._setup_scheduler() self._state_attrs += ["_current_duration", "durations", "_scheduler_index"] def state_dict(self) -> Dict[str, Any]: """Returns a dictionary containing a whole state of ConcatScheduler. Returns: dict: a dictionary containing a whole state of ConcatScheduler """ state_dict = super(ConcatScheduler, self).state_dict() state_dict["schedulers"] = [] for s in self.schedulers: state_dict["schedulers"].append(s.state_dict()) return state_dict def load_state_dict(self, state_dict: Mapping) -> None: """Copies parameters from :attr:`state_dict` into this ConcatScheduler. Args: state_dict: a dict containing parameters. """ if not isinstance(state_dict, Mapping): raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}") if "schedulers" not in state_dict: raise ValueError( f"Required state attribute 'schedulers' is absent in provided state_dict '{state_dict.keys()}'" ) sds = state_dict["schedulers"] if len(sds) != len(self.schedulers): raise ValueError( f"Input state_dict contains {len(sds)} state_dicts of concatenated schedulers, " f"but {len(self.schedulers)} needed" ) for s, sd in zip(self.schedulers, sds): s.load_state_dict(sd) super(ConcatScheduler, self).load_state_dict(state_dict) self._current_scheduler = self.schedulers[self._scheduler_index] def _setup_scheduler(self) -> None: self._current_scheduler = self.schedulers[self._scheduler_index] self._current_duration = ( self.durations[self._scheduler_index] if self._scheduler_index < len(self.durations) else -1 ) def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: if self._current_duration == 0: self._scheduler_index += 1 self._setup_scheduler() self._current_scheduler(engine, name) self._current_duration -= 1 @property def optimizer_param_groups(self) -> List[Dict[str, Any]]: # We need to setup optimizer_param_groups as property # to synchonize with the latest _current_scheduler and its internal optimizer_param_groups return self._current_scheduler.optimizer_param_groups @property def save_history(self) -> bool: return self._current_scheduler.save_history @save_history.setter def save_history(self, value: bool) -> None: for s in self.schedulers: s.save_history = value def get_param(self) -> Union[List[float], float]: return self._current_scheduler.get_param() @classmethod def simulate_values( # type: ignore[override] cls, num_events: int, schedulers: List[ParamScheduler], durations: List[int], param_names: Optional[Union[List[str], Tuple[str]]] = None, ) -> List[List[int]]: """Method to simulate scheduled values during num_events events. Args: num_events: number of events during the simulation. schedulers: list of parameter schedulers. durations: list of number of events that lasts a parameter scheduler from schedulers. param_names: parameter name or list of parameter names to simulate values. By default, the first scheduler's parameter name is taken. Returns: list: list of [event_index, value_0, value_1, ...], where values correspond to `param_names`. """ if param_names is not None: if not isinstance(param_names, (list, tuple)): raise TypeError(f"Argument param_names should be list or tuple, but given {type(param_names)}") if not all(isinstance(item, str) for item in param_names): raise ValueError(f"Argument param_names should be list or tuple of strings, but given {param_names}") tmp_param_optimizers = [s.optimizer for s in schedulers] tmp_list_param_optimizers = [s if isinstance(s, list) else [s] for s in tmp_param_optimizers] param_optimizers = list(itertools.chain(*tmp_list_param_optimizers)) tmp_optimizer = list(set(param_optimizers)) if len(tmp_optimizer) != 1: raise ValueError("schedulers should be related to same optimizer") optimizer = tmp_optimizer[0] # This scheduler uses `ParamScheduler` which # should be replicated in order to simulate LR values and # not perturb original scheduler. with tempfile.TemporaryDirectory() as tmpdirname: cache_filepath = Path(tmpdirname) / "ignite_lr_scheduler_cache.pt" objs = {f"lr_scheduler_{i}": s.state_dict() for i, s in enumerate(schedulers)} # all schedulers should be related to the same optimizer objs["optimizer"] = optimizer.state_dict() torch.save(objs, cache_filepath.as_posix()) # do not save_history for s in schedulers: s.save_history = False output = [] scheduler = cls(schedulers=schedulers, save_history=False, durations=durations) if param_names is None: param_names = [scheduler.param_name] for i in range(num_events): scheduler(engine=None) values = [i] for param_name in param_names: params = [p[param_name] for p in scheduler.optimizer_param_groups] values = values + params output.append(values) objs = torch.load(cache_filepath.as_posix()) for i, s in enumerate(schedulers): s.load_state_dict(objs[f"lr_scheduler_{i}"]) optimizer.load_state_dict(objs["optimizer"]) return output class _CosineAnnealingWarmRestarts: def __init__(self, lr_scheduler: CosineAnnealingWarmRestarts): self._lr_scheduler = lr_scheduler @property def last_epoch(self) -> int: return self._lr_scheduler.last_epoch @last_epoch.setter def last_epoch(self, value: int) -> None: self._lr_scheduler.last_epoch = value @property def optimizer(self) -> torch.optim.Optimizer: return self._lr_scheduler.optimizer def get_lr(self, epoch: Optional[int] = None) -> List[float]: T_mult = self._lr_scheduler.T_mult eta_min = self._lr_scheduler.eta_min if epoch is None and self.last_epoch < 0: epoch = 0 if epoch is None: epoch = self.last_epoch + 1 self._lr_scheduler.T_cur = self._lr_scheduler.T_cur + 1 if self._lr_scheduler.T_cur >= self._lr_scheduler.T_i: self._lr_scheduler.T_cur = self._lr_scheduler.T_cur - self._lr_scheduler.T_i self._lr_scheduler.T_i = self._lr_scheduler.T_i * T_mult else: if epoch < 0: raise ValueError("Expected non-negative epoch, but got {}".format(epoch)) if epoch >= self._lr_scheduler.T_0: if T_mult == 1: self._lr_scheduler.T_cur = epoch % self._lr_scheduler.T_0 else: n = int(math.log((epoch / self._lr_scheduler.T_0 * (T_mult - 1) + 1), T_mult)) self._lr_scheduler.T_cur = epoch - self._lr_scheduler.T_0 * (T_mult**n - 1) / (T_mult - 1) self._lr_scheduler.T_i = self._lr_scheduler.T_0 * T_mult**n else: self._lr_scheduler.T_i = self._lr_scheduler.T_0 self._lr_scheduler.T_cur = epoch self.last_epoch = math.floor(epoch) return [ eta_min + (base_lr - eta_min) * (1 + math.cos(math.pi * self._lr_scheduler.T_cur / self._lr_scheduler.T_i)) / 2 for base_lr in self._lr_scheduler.base_lrs ] class LRScheduler(ParamScheduler): """A wrapper class to call `torch.optim.lr_scheduler` objects as `ignite` handlers. Args: lr_scheduler: lr_scheduler object to wrap. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). use_legacy: if True, scheduler should be attached to ``Events.ITERATION_COMPLETED``, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() from torch.optim.lr_scheduler import StepLR torch_lr_scheduler = StepLR(default_optimizer, step_size=3, gamma=0.1) scheduler = LRScheduler(torch_lr_scheduler) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(default_optimizer.param_groups[0]["lr"]) default_trainer.run([0] * 8, max_epochs=1) .. testoutput:: 0.1 0.1 0.1 0.010... 0.010... 0.010... 0.001... 0.001... .. versionadded:: 0.4.5 .. versionchanged:: 0.4.9 added `use_legacy` argument """ def __init__( self, lr_scheduler: PyTorchLRScheduler, save_history: bool = False, use_legacy: bool = False, ): if not isinstance(lr_scheduler, PyTorchLRScheduler): raise TypeError( "Argument lr_scheduler should be a subclass of " f"torch.optim.lr_scheduler.{PyTorchLRScheduler.__name__}, " f"but given {type(lr_scheduler)}" ) self.lr_scheduler: Union[PyTorchLRScheduler, _CosineAnnealingWarmRestarts] = lr_scheduler if isinstance(lr_scheduler, CosineAnnealingWarmRestarts): self.lr_scheduler = _CosineAnnealingWarmRestarts(lr_scheduler) super(LRScheduler, self).__init__( optimizer=self.lr_scheduler.optimizer, param_name="lr", save_history=save_history, ) if use_legacy: warnings.warn( "Please make sure to attach scheduler to Events.ITERATION_COMPLETED " "instead of Events.ITERATION_STARTED to make sure to use " "the first lr value from the optimizer, otherwise it will be skipped" ) self.lr_scheduler.last_epoch += 1 self._state_attrs += ["lr_scheduler"] def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: super(LRScheduler, self).__call__(engine, name) self.lr_scheduler.last_epoch += 1 def get_param(self) -> Union[float, List[float]]: """Method to get current optimizer's parameter value""" # Emulate context manager for pytorch>=1.4 self.lr_scheduler._get_lr_called_within_step = True # type: ignore[union-attr] lr_list = self.lr_scheduler.get_lr() self.lr_scheduler._get_lr_called_within_step = False # type: ignore[union-attr] if len(lr_list) == 1: return lr_list[0] else: return lr_list @classmethod def simulate_values( # type: ignore[override] cls, num_events: int, lr_scheduler: PyTorchLRScheduler, **kwargs: Any ) -> List[List[int]]: """Method to simulate scheduled values during num_events events. Args: num_events: number of events during the simulation. lr_scheduler: lr_scheduler object to wrap. Returns: event_index, value """ if not isinstance(lr_scheduler, PyTorchLRScheduler): raise TypeError( "Argument lr_scheduler should be a subclass of " f"torch.optim.lr_scheduler.{PyTorchLRScheduler.__name__}, " f"but given {type(lr_scheduler)}" ) # This scheduler uses `torch.optim.lr_scheduler.LRScheduler` which # should be replicated in order to simulate LR values and # not perturb original scheduler. with tempfile.TemporaryDirectory() as tmpdirname: cache_filepath = Path(tmpdirname) / "ignite_lr_scheduler_cache.pt" obj = { "lr_scheduler": lr_scheduler.state_dict(), "optimizer": lr_scheduler.optimizer.state_dict(), } torch.save(obj, cache_filepath.as_posix()) values = [] scheduler = cls(save_history=False, lr_scheduler=lr_scheduler, **kwargs) for i in range(num_events): scheduler(engine=None) params = [p[scheduler.param_name] for p in scheduler.optimizer_param_groups] values.append([i] + params) obj = torch.load(cache_filepath.as_posix()) lr_scheduler.load_state_dict(obj["lr_scheduler"]) lr_scheduler.optimizer.load_state_dict(obj["optimizer"]) return values def create_lr_scheduler_with_warmup( lr_scheduler: Union[ParamScheduler, PyTorchLRScheduler], warmup_start_value: float, warmup_duration: int, warmup_end_value: Optional[float] = None, save_history: bool = False, output_simulated_values: Optional[List] = None, ) -> "ConcatScheduler": """ Helper method to create a learning rate scheduler with a linear warm-up. Args: lr_scheduler: learning rate scheduler after the warm-up. warmup_start_value: learning rate start value of the warm-up phase. warmup_duration: warm-up phase duration, number of events. warmup_end_value: learning rate end value of the warm-up phase, (default=None). If None, warmup_end_value is set to optimizer initial lr. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). output_simulated_values: optional output of simulated learning rate values. If output_simulated_values is a list of None, e.g. `[None] * 100`, after the execution it will be filled by 100 simulated learning rate values. Returns: ConcatScheduler Note: If the first learning rate value provided by `lr_scheduler` is different from `warmup_end_value`, an additional event is added after the warm-up phase such that the warm-up ends with `warmup_end_value` value and then `lr_scheduler` provides its learning rate values as normally. Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: from torch.optim.lr_scheduler import ExponentialLR torch_lr_scheduler = ExponentialLR(optimizer=default_optimizer, gamma=0.98) default_trainer = get_default_trainer() scheduler = create_lr_scheduler_with_warmup(torch_lr_scheduler, warmup_start_value=0.0, warmup_end_value=0.1, warmup_duration=3) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(default_optimizer.param_groups[0]["lr"]) default_trainer.run([0] * 8, max_epochs=1) .. testoutput:: 0.0 0.05 0.1 0.098 0.09604 0.09411... 0.09223... 0.09039... .. versionadded:: 0.4.5 """ if not isinstance(lr_scheduler, (ParamScheduler, PyTorchLRScheduler)): raise TypeError( "Argument lr_scheduler should be a subclass of " f"torch.optim.lr_scheduler.{PyTorchLRScheduler.__name__} or ParamScheduler, " f"but given {type(lr_scheduler)}" ) if not isinstance(warmup_duration, numbers.Integral): raise TypeError(f"Argument warmup_duration should be integer, but given {warmup_duration}") if not (warmup_duration > 1): raise ValueError(f"Argument warmup_duration should be at least 2 events, but given {warmup_duration}") warmup_schedulers: List[ParamScheduler] = [] for param_group_index, param_group in enumerate(lr_scheduler.optimizer.param_groups): if warmup_end_value is None: param_group_warmup_end_value = param_group["lr"] else: param_group_warmup_end_value = warmup_end_value milestones_values = [(0, warmup_start_value), (warmup_duration - 1, param_group_warmup_end_value)] if isinstance(lr_scheduler, PyTorchLRScheduler): init_lr = param_group["lr"] if init_lr != param_group_warmup_end_value: milestones_values.append((warmup_duration, init_lr)) # We need to advance torch lr_scheduler to avoid duplicated lr value # given by PiecewiseLinear and LRScheduler. # We suggest to attach output scheduler on ITERATION_STARTED but # torch lr_scheduler works with ITERATION_COMPLETED # See also https://github.com/pytorch/ignite/pull/2496#issuecomment-1065984440 lr_scheduler.last_epoch += 1 lr_scheduler = LRScheduler(lr_scheduler, save_history=save_history) else: init_lr = lr_scheduler.get_param() if init_lr == param_group_warmup_end_value: if warmup_duration > 2: d = (param_group_warmup_end_value - warmup_start_value) / (warmup_duration - 1) milestones_values[-1] = (warmup_duration - 2, param_group_warmup_end_value - d) else: milestones_values.pop(-1) warmup_schedulers.append( PiecewiseLinear( lr_scheduler.optimizer, param_name="lr", milestones_values=milestones_values, param_group_index=param_group_index, save_history=save_history, ) ) warmup_scheduler = ParamGroupScheduler(warmup_schedulers, save_history=save_history) schedulers: List[Union[ParamScheduler, ParamGroupScheduler, PyTorchLRScheduler]] = [ warmup_scheduler, lr_scheduler, ] durations = [milestones_values[-1][0] + 1] combined_scheduler = ConcatScheduler(schedulers, durations=durations, save_history=save_history) if output_simulated_values is not None: if not isinstance(output_simulated_values, list): raise TypeError( "Argument output_simulated_values should be a list of None, e.g. `[None] * 100`, " f"but given {type(output_simulated_values)}." ) num_events = len(output_simulated_values) result = ConcatScheduler.simulate_values(num_events=num_events, schedulers=schedulers, durations=durations) for i in range(num_events): output_simulated_values[i] = result[i] return combined_scheduler class PiecewiseLinear(ParamScheduler): """ Piecewise linear parameter scheduler Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: name of optimizer's parameter to update. milestones_values: list of tuples (event index, parameter value) represents milestones and parameter. Milestones should be increasing integers. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index: optimizer's parameters group to use. .. code-block:: python scheduler = PiecewiseLinear(optimizer, "lr", milestones_values=[(10, 0.5), (20, 0.45), (21, 0.3), (30, 0.1), (40, 0.1)]) # Attach to the trainer trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) # # Sets the learning rate to 0.5 over the first 10 iterations, then decreases linearly from 0.5 to 0.45 between # 10th and 20th iterations. Next there is a jump to 0.3 at the 21st iteration and LR decreases linearly # from 0.3 to 0.1 between 21st and 30th iterations and remains 0.1 until the end of the iterations. Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: 1 default_trainer = get_default_trainer() milestones_values = [(1, 1.0), (3, 0.8), (5, 0.2)] scheduler = PiecewiseLinear( default_optimizer, "lr", milestones_values=milestones_values) # Sets lr equal to 1 for till the first iteration # Then linearly reduces lr from 1 to 0.8 till the third iteration # Then linearly reduces lr from 0.8 to 0.5 till the fifth iteration default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(default_optimizer.param_groups[0]["lr"]) default_trainer.run([0] * 6, max_epochs=1) .. testoutput:: 1 1.0 1.0 0.9 0.8 0.5 0.2 .. testcode:: 2 default_trainer = get_default_trainer() optimizer = torch.optim.SGD( [ {"params": default_model.base.parameters(), "lr": 0.1}, {"params": default_model.fc.parameters(), "lr": 1.0}, ] ) milestones_values1 = [(1, 0.1), (3, 0.08), (5, 0.02)] scheduler2 = PiecewiseLinear( optimizer, "lr", milestones_values=milestones_values1, param_group_index=0) # Sets lr equal to 0.1 for till the first iteration # Then linearly reduces lr from 0.1 to 0.08 till the third iteration # Then linearly reduces lr from 0.08 to 0.05 till the fifth iteration milestones_values2 = [(1, 1.0), (3, 0.8), (5, 0.2)] scheduler1 = PiecewiseLinear( optimizer, "lr", milestones_values=milestones_values2, param_group_index=1) # Sets lr equal to 1 for till the first iteration # Then linearly reduces lr from 1 to 0.8 till the third iteration # Then linearly reduces lr from 0.8 to 0.5 till the fifth iteration default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler1) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler2) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(optimizer.param_groups[0]["lr"], optimizer.param_groups[1]["lr"]) default_trainer.run([0] * 6, max_epochs=1) .. testoutput:: 2 0.1 1.0 0.1 1.0 0.09 0.9 0.08 0.8 0.05 0.5 0.02 0.2 .. versionadded:: 0.4.5 """ def __init__( self, optimizer: Optimizer, param_name: str, milestones_values: List[Tuple[int, float]], save_history: bool = False, param_group_index: Optional[int] = None, ): super(PiecewiseLinear, self).__init__(optimizer, param_name, save_history, param_group_index=param_group_index) if not isinstance(milestones_values, Sequence): raise TypeError( f"Argument milestones_values should be a list or tuple, but given {type(milestones_values)}" ) if len(milestones_values) < 1: raise ValueError( f"Argument milestones_values should be with at least one value, but given {milestones_values}" ) values: List[float] = [] milestones: List[int] = [] for pair in milestones_values: if not isinstance(pair, tuple) or len(pair) != 2: raise ValueError("Argument milestones_values should be a list of pairs (milestone, param_value)") if not isinstance(pair[0], numbers.Integral): raise TypeError(f"Value of a milestone should be integer, but given {type(pair[0])}") if len(milestones) > 0 and pair[0] < milestones[-1]: raise ValueError( f"Milestones should be increasing integers, but given {pair[0]} is smaller " f"than the previous milestone {milestones[-1]}" ) milestones.append(pair[0]) values.append(pair[1]) self.values = values self.milestones = milestones self._index = 0 self._state_attrs += ["values", "milestones", "_index"] def _get_start_end(self) -> Tuple[int, int, float, float]: if self.milestones[0] > self.event_index: return self.event_index - 1, self.event_index, self.values[0], self.values[0] elif self.milestones[-1] <= self.event_index: return (self.event_index, self.event_index + 1, self.values[-1], self.values[-1]) elif self.milestones[self._index] <= self.event_index < self.milestones[self._index + 1]: return ( self.milestones[self._index], self.milestones[self._index + 1], self.values[self._index], self.values[self._index + 1], ) else: self._index += 1 return self._get_start_end() def get_param(self) -> float: start_index, end_index, start_value, end_value = self._get_start_end() return start_value + (end_value - start_value) * (self.event_index - start_index) / (end_index - start_index) class ParamGroupScheduler: """ Scheduler helper to group multiple schedulers into one. Args: schedulers: list/tuple of parameter schedulers. names: list of names of schedulers. save_history: whether to save history or not. Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() optimizer = torch.optim.SGD( [ {"params": default_model.base.parameters(), "lr": 0.001}, {"params": default_model.fc.parameters(), "lr": 0.01}, ] ) # CosineAnnealing increases the learning rate from 0.0 to 1.0 # over a cycle of 4 iterations scheduler_1 = CosineAnnealingScheduler(optimizer, "lr", 0.0, 1.0, 4, param_group_index=0) # CosineAnnealing increases the learning rate from 0.0 to 0.1 # over a cycle of 4 iterations scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", 0.0, 0.1, 4, param_group_index=1) scheduler = ParamGroupScheduler(schedulers=[scheduler_1, scheduler_2], names=["lr (base)", "lr (fc)"]) default_trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def print_lr(): print(optimizer.param_groups[0]["lr"], optimizer.param_groups[1]["lr"]) default_trainer.run([0] * 8, max_epochs=1) .. testoutput:: 0.0 0.0 0.1464... 0.01464... 0.4999... 0.04999... 0.8535... 0.08535... ... .. versionadded:: 0.4.5 """ def __init__(self, schedulers: List[ParamScheduler], names: Optional[List[str]] = None, save_history: bool = False): if not isinstance(schedulers, Sequence): raise TypeError(f"Argument schedulers should be a list/tuple, but given {schedulers}") if not all(isinstance(scheduler, ParamScheduler) for scheduler in schedulers): raise ValueError( f"Argument schedulers should be a list/tuple of parameter schedulers, but given {schedulers}" ) if names is None: names = [s.param_name for s in schedulers] if not isinstance(names, (list, tuple)): raise TypeError(f"Argument names should be a list/tuple, but given {names}") if not all(isinstance(n, str) for n in names): raise ValueError(f"Argument names should be a list/tuple of parameter scheduler's names, but given {names}") if len(names) != len(schedulers): raise ValueError(f"{len(schedulers)} should be equal {len(names)}") self.schedulers = schedulers self.names = names # schedulers should have save_history sync with ParamGroupScheduler for s in schedulers: s.save_history = save_history self.optimizer = [s.optimizer for s in self.schedulers] self.param_name = [s.param_name for s in self.schedulers] def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: for scheduler, name in zip(self.schedulers, self.names): scheduler(engine, name) @property def optimizer_param_groups(self) -> List[Dict[str, Any]]: return [pg for scheduler in self.schedulers for pg in scheduler.optimizer_param_groups] @property def save_history(self) -> bool: return self.schedulers[0].save_history @save_history.setter def save_history(self, value: bool) -> None: for s in self.schedulers: s.save_history = value def state_dict(self) -> Dict[str, List[Any]]: """Returns a dictionary containing a whole state of ParamGroupScheduler. Returns: dict: a dictionary containing a whole state of ParamGroupScheduler """ state_dict: Dict[str, List[Any]] = OrderedDict() state_dict["schedulers"] = [] for n, s in zip(self.names, self.schedulers): state_dict["schedulers"].append((n, s.state_dict())) return state_dict def load_state_dict(self, state_dict: Mapping) -> None: """Copies parameters from :attr:`state_dict` into this ParamScheduler. Args: state_dict: a dict containing parameters. """ if not isinstance(state_dict, Mapping): raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}") if "schedulers" not in state_dict: raise ValueError( f"Required state attribute '{'schedulers'}' is absent in provided state_dict '{state_dict.keys()}'" ) sds = state_dict["schedulers"] if len(sds) != len(self.schedulers): raise ValueError( f"Input state_dict contains {len(sds)} state_dicts of param group schedulers, " f"but {len(self.schedulers)} needed" ) for req_n, s, (n, sd) in zip(self.names, self.schedulers, sds): if req_n != n: raise ValueError( f"Name of scheduler from input state dict does not correspond to required one, {n} vs {req_n}" ) s.load_state_dict(sd) @classmethod def simulate_values( cls, num_events: int, schedulers: List[ParamScheduler], **kwargs: Any ) -> List[List[Union[List[float], float, int]]]: """Method to simulate scheduled values during num_events events. Args: num_events: number of events during the simulation. schedulers: lr_scheduler object to wrap. kwargs: kwargs passed to construct an instance of :class:`ignite.handlers.param_scheduler.ParamGroupScheduler`. Returns: list: list of [event_index, scheduler_0_value, scheduler_1_value, ...], where scheduler_i_value corresponds to the simulated param of scheduler i at 'event_index'th event. """ # This scheduler uses `torch.optim.lr_scheduler.LRScheduler` which # should be replicated in order to simulate LR values and # not perturb original scheduler. with tempfile.TemporaryDirectory() as tmpdirname: cache_filepath = Path(tmpdirname) / "ignite_lr_scheduler_cache.pt" objs = {f"lr_scheduler_{i}": s.state_dict() for i, s in enumerate(schedulers)} # all schedulers should be related to the same optimizer objs["optimizer"] = schedulers[0].optimizer.state_dict() torch.save(objs, cache_filepath.as_posix()) values = [] scheduler = cls(schedulers=schedulers, **kwargs) for i in range(num_events): params = [scheduler.get_param() for scheduler in schedulers] values.append([i] + params) scheduler(engine=None) objs = torch.load(cache_filepath.as_posix()) for i, s in enumerate(schedulers): s.load_state_dict(objs[f"lr_scheduler_{i}"]) s.optimizer.load_state_dict(objs["optimizer"]) return values def get_param(self) -> List[Union[float, List[float]]]: """ Method to get current `schedulers`' parameter values .. versionadded:: 0.4.11 """ return [scheduler.get_param() for scheduler in self.schedulers] class ReduceLROnPlateauScheduler(ParamScheduler): """Reduce LR when a metric stops improving. Wrapper of `torch.optim.lr_scheduler.ReduceLROnPlateau `_. Args: optimizer: Wrapped optimizer. metric_name: metric whose improvement is monitored. Must be attached to the same engine. trainer: Trainer engine to log LR history in its `state.output.param_history`. Is used if `save_history` is true. Default: None. save_history: Whether to save history or not. If true, history will be logged in `trainer`'s `state.output.param_history`. Default: False. param_group_index: `optimizer`'s parameters group to use. Default: None. Use all `optimizer`'s paramater groups. scheduler_kwargs: Keyword arguments to be passed to the wrapped ``ReduceLROnPlateau``. Examples: .. code-block:: python # Metric "accuracy" should increase the best value by # more than 1 unit after at most 2 epochs, otherwise LR # would get multiplied by 0.5 . scheduler = ReduceLROnPlateauScheduler( default_optimizer, metric_name="accuracy", mode="max", factor=0.5, patience=1, threshold_mode='abs', threshold=1, trainer=trainer ) metric = Accuracy() default_evaluator.attach(metric, "accuracy") default_evaluator.add_event_handler(Events.COMPLETED, scheduler) .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() # Metric "loss" should decrease more than # 0.1 of best loss after at most # three iterations. Then best loss would get # updated, otherwise lr is multiplied by 0.5 scheduler = ReduceLROnPlateauScheduler( default_optimizer, "loss", save_history=True, mode="min", factor=0.5, patience=3, threshold_mode='rel', threshold=0.1, trainer=default_trainer ) metric_values = iter([10, 5, 3, 4, 4, 4, 5, 1]) default_evaluator.state.metrics = {"loss": None} @default_trainer.on(Events.ITERATION_COMPLETED) def set_metric_val(): default_evaluator.state.metrics["loss"] = next(metric_values) default_evaluator.add_event_handler(Events.COMPLETED, scheduler) @default_trainer.on(Events.ITERATION_COMPLETED) def trigger_eval(): default_evaluator.run([0.]) default_trainer.run([0.] * 8) print(default_trainer.state.param_history["lr"]) .. testoutput:: [[0.1], [0.1], [0.1], [0.1], [0.1], [0.1], [0.05], [0.05]] .. versionadded:: 0.4.9 """ def __init__( self, optimizer: Optimizer, metric_name: str, trainer: Optional[Engine] = None, save_history: bool = False, param_group_index: Optional[int] = None, **scheduler_kwargs: Any, ): super(ReduceLROnPlateauScheduler, self).__init__( optimizer, "lr", save_history=save_history, param_group_index=param_group_index ) self.metric_name = metric_name self.trainer = trainer self.optimizer = optimizer if "min_lr" in scheduler_kwargs and param_group_index is not None: min_lr = scheduler_kwargs["min_lr"] if not isinstance(min_lr, float): raise TypeError(f"When param_group_index is given, min_lr should be a float, but given {type(min_lr)}") _min_lr = min_lr min_lr = [0] * len(optimizer.param_groups) min_lr[param_group_index] = _min_lr else: min_lr = 0 _scheduler_kwargs = scheduler_kwargs.copy() _scheduler_kwargs["min_lr"] = min_lr if "verbose" in _scheduler_kwargs: warnings.warn( "Found verbose=True in provided scheduler_kwargs. " "It would be set to False. Please use save_history instead." ) _scheduler_kwargs["verbose"] = False self.scheduler = ReduceLROnPlateau(optimizer, **_scheduler_kwargs) self.scheduler._reduce_lr = self._reduce_lr # type: ignore[method-assign] self._state_attrs += ["metric_name", "scheduler"] def __call__(self, engine: Engine, name: Optional[str] = None) -> None: # type: ignore[override] if not hasattr(engine.state, "metrics") or self.metric_name not in engine.state.metrics: raise ValueError( "Argument engine should have in its 'state', attribute 'metrics' " f"which itself has the metric {self.metric_name}." ) self.scheduler.step(engine.state.metrics[self.metric_name]) super().__call__(self.trainer, name) def get_param(self) -> Union[float, List[float]]: lrs = [pg["lr"] for pg in self.optimizer_param_groups] return lrs[0] if len(lrs) == 1 else lrs def _reduce_lr(self, epoch: int) -> None: for i, param_group in enumerate(self.optimizer_param_groups): old_lr = float(param_group["lr"]) new_lr = max(old_lr * self.scheduler.factor, self.scheduler.min_lrs[i]) if old_lr - new_lr > self.scheduler.eps: param_group["lr"] = new_lr @classmethod def simulate_values( # type: ignore[override] cls, num_events: int, metric_values: List[float], init_lr: float, **scheduler_kwargs: Any ) -> List[List[int]]: """Method to simulate scheduled values during num_events events. Args: num_events: number of events during the simulation. metric_values: values to change LR based on. init_lr: initial LR to start with. scheduler_kwargs: kwargs passed to construct an instance of :class:`ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler`. Returns: event_index, value """ if len(metric_values) != num_events: raise ValueError( "Length of argument metric_values should be equal to num_events. " f"{len(metric_values)} != {num_events}" ) keys_to_remove = ["optimizer", "metric_name", "save_history"] for key in keys_to_remove: if key in scheduler_kwargs: del scheduler_kwargs[key] values = [] scheduler = cls( optimizer=_get_fake_optimizer(torch.optim.SGD, lr=init_lr), metric_name="metric", save_history=False, **scheduler_kwargs, ) engine = Engine(lambda _, __: None) for i in range(num_events): engine.state.metrics["metric"] = metric_values[i] scheduler(engine=engine) values.append([i, scheduler.optimizer_param_groups[0][scheduler.param_name]]) return values def _get_fake_optimizer( optimizer_cls: Optional[Union[Type[Optimizer], Type[torch.optim.SGD]]] = None, **kwargs: Any ) -> Union[Optimizer, torch.optim.SGD]: t = torch.zeros([1], requires_grad=True) if optimizer_cls is None: optimizer_cls = torch.optim.SGD kwargs["lr"] = 0.01 return optimizer_cls([t], **kwargs) ignite-0.5.1/ignite/handlers/polyaxon_logger.py000066400000000000000000000274301465426447700216660ustar00rootroot00000000000000"""Polyaxon logger and its helper handlers.""" from typing import Any, Callable, List, Optional, Union from torch.optim import Optimizer from ignite.engine import Engine, Events from ignite.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.handlers.utils import global_step_from_engine # noqa __all__ = ["PolyaxonLogger", "OutputHandler", "OptimizerParamsHandler", "global_step_from_engine"] class PolyaxonLogger(BaseLogger): """ `Polyaxon tracking client `_ handler to log parameters and metrics during the training and validation. This class requires `polyaxon `_ package to be installed: .. code-block:: bash pip install polyaxon // If you are using polyaxon v0.x pip install polyaxon-client Args: args: Positional arguments accepted from `Experiment `_. kwargs: Keyword arguments accepted from `Experiment `_. Examples: .. code-block:: python from ignite.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Log experiment parameters: plx_logger.log_inputs(**{ "seed": seed, "batch_size": batch_size, "model": model.__class__.__name__, "pytorch version": torch.__version__, "ignite version": ignite.__version__, "cuda version": torch.version.cuda, "device name": torch.cuda.get_device_name(0) }) # Attach the logger to the trainer to log training loss at each iteration plx_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. plx_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. plx_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration plx_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) # to manually end a run plx_logger.close() """ def __init__(self, *args: Any, **kwargs: Any): try: from polyaxon.tracking import Run self.experiment = Run(*args, **kwargs) except ImportError: try: from polyaxon_client.tracking import Experiment self.experiment = Experiment(*args, **kwargs) except ImportError: raise ModuleNotFoundError( "This contrib module requires polyaxon to be installed.\n" "For Polyaxon v1.x please install it with command: \n pip install polyaxon\n" "For Polyaxon v0.x please install it with command: \n pip install polyaxon-client" ) def close(self) -> None: try: self.experiment.end() except: pass def __getattr__(self, attr: Any) -> Any: return getattr(self.experiment, attr) def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics. Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.polyaxon_logger.global_step_from_engine`. state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: plx_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently plx_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.polyaxon_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) plx_logger = PolyaxonLogger() def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on Polyaxon. plx_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python plx_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", metrics=["nll", "accuracy"], state_attributes=["alpha", "beta"], ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ): super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) def __call__(self, engine: Engine, logger: PolyaxonLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, PolyaxonLogger): raise RuntimeError("Handler 'OutputHandler' works only with PolyaxonLogger") metrics = self._setup_output_metrics_state_attrs(engine, key_tuple=False) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) metrics.update({"step": global_step}) logger.log_metrics(**metrics) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" Examples: .. code-block:: python from ignite.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration plx_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently plx_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: PolyaxonLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, PolyaxonLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with PolyaxonLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } params["step"] = global_step logger.log_metrics(**params) ignite-0.5.1/ignite/handlers/state_param_scheduler.py000066400000000000000000000504111465426447700230070ustar00rootroot00000000000000import numbers import warnings from bisect import bisect_right from typing import Any, List, Sequence, Tuple, Union from ignite.engine import CallableEventWithFilter, Engine, Events, EventsList from ignite.handlers.param_scheduler import BaseParamScheduler class StateParamScheduler(BaseParamScheduler): """An abstract class for updating an engine state parameter values during training. Args: param_name: name of parameter to update. save_history: whether to log the parameter values to ``engine.state.param_history``, (default=False). create_new: whether to create ``param_name`` on ``engine.state`` taking into account whether ``param_name`` attribute already exists or not. Overrides existing attribute by default, (default=False). Note: Parameter scheduler works independently of the internal state of the attached engine. More precisely, whatever the state of the engine (newly created or used by another scheduler) the scheduler sets defined absolute values. .. versionadded:: 0.4.7 """ def __init__(self, param_name: str, save_history: bool = False, create_new: bool = False): super(StateParamScheduler, self).__init__(param_name, save_history) self.create_new = create_new def attach( self, engine: Engine, event: Union[str, Events, CallableEventWithFilter, EventsList] = Events.ITERATION_COMPLETED, ) -> None: """Attach the handler to the engine. Once the handler is attached, the ``Engine.state`` will have a new attribute with the name ``param_name``. Then the current value of the parameter can be retrieved from ``Engine.state`` when the engine is running. Args: engine: trainer to which the handler will be attached. event: trigger ``param_name`` value update. """ if hasattr(engine.state, self.param_name): if self.create_new: raise ValueError( f"Attribute '{self.param_name}' already exists in the engine.state. " f"This may be a conflict between multiple handlers. " f"Please choose another name." ) else: if not self.create_new: warnings.warn( f"Attribute '{self.param_name}' is not defined in the engine.state. " f"{type(self).__name__} will create it. Remove this warning by setting create_new=True." ) setattr(engine.state, self.param_name, None) if self.save_history: if not hasattr(engine.state, "param_history") or engine.state.param_history is None: setattr(engine.state, "param_history", {}) engine.state.param_history.setdefault(self.param_name, []) # type: ignore[attr-defined] engine.add_event_handler(event, self) def __call__(self, engine: Engine) -> None: self.event_index += 1 value = self.get_param() setattr(engine.state, self.param_name, value) if self.save_history: engine.state.param_history[self.param_name].append(value) # type: ignore[attr-defined] @classmethod def simulate_values(cls, num_events: int, **scheduler_kwargs: Any) -> List[List[int]]: """Method to simulate scheduled engine state parameter values during `num_events` events. Args: num_events: number of events during the simulation. scheduler_kwargs: parameter scheduler configuration kwargs. Returns: event_index, value Examples: .. code-block:: python import matplotlib.pyplot as plt import numpy as np step_state_param_values = np.array( StepStateScheduler.simulate_values( num_events=20, param_name="step_scheduled_param", initial_value=10, gamma=0.99, step_size=5 ) ) plt.plot(step_state_param_values[:, 0], step_state_param_values[:, 1], label="learning rate") plt.xlabel("events") plt.ylabel("values") plt.legend() """ for key in ["save_history"]: if key in scheduler_kwargs: del scheduler_kwargs[key] values = [] scheduler = cls(save_history=False, **scheduler_kwargs) engine = Engine(lambda e, b: None) for i in range(num_events): scheduler(engine=engine) values.append([i, getattr(engine.state, scheduler_kwargs["param_name"])]) return values class LambdaStateScheduler(StateParamScheduler): """Update a parameter during training by using a user defined callable object. User defined callable object is taking an event index as input and returns parameter value. Args: lambda_obj: user defined callable object. param_name: name of parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). create_new: whether to create ``param_name`` on ``engine.state`` taking into account whether ``param_name`` attribute already exists or not. Overrides existing attribute by default, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() class LambdaState: def __init__(self, initial_value, gamma): self.initial_value = initial_value self.gamma = gamma def __call__(self, event_index): return self.initial_value * self.gamma ** (event_index % 9) param_scheduler = LambdaStateScheduler( param_name="param", lambda_obj=LambdaState(1, 0.9), create_new=True ) # parameter is param, initial_value sets param to 1 and in this example gamma = 1 # using class 'LambdaState' user defined callable object can be created # update a parameter during training by using a user defined callable object # user defined callable object is taking an event index as input and returns parameter value # in this example, we update as initial_value * gamma ** (event_endex % 9) # in every Epoch the parameter is updated as 1 * 0.9 ** (Epoch % 9) # In Epoch 3, parameter param = 1 * 0.9 ** (3 % 9) = 0.729 # In Epoch 10, parameter param = 1 * 0.9 ** (10 % 9) = 0.9 param_scheduler.attach(default_trainer, Events.EPOCH_COMPLETED) @default_trainer.on(Events.EPOCH_COMPLETED) def print_param(): print(default_trainer.state.param) default_trainer.run([0], max_epochs=10) .. testoutput:: 0.9 0.81 0.7290... 0.6561 0.5904... 0.5314... 0.4782... 0.4304... 1.0 0.9 .. versionadded:: 0.4.7 """ def __init__(self, lambda_obj: Any, param_name: str, save_history: bool = False, create_new: bool = False): super(LambdaStateScheduler, self).__init__(param_name, save_history, create_new) if not callable(lambda_obj): raise ValueError("Expected lambda_obj to be callable.") self.lambda_obj = lambda_obj self._state_attrs += ["lambda_obj"] def get_param(self) -> Union[List[float], float]: return self.lambda_obj(self.event_index) class PiecewiseLinearStateScheduler(StateParamScheduler): """Piecewise linear state parameter scheduler. Args: milestones_values: list of tuples (event index, parameter value) represents milestones and parameter values. Milestones should be increasing integers. param_name: name of parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). create_new: whether to create ``param_name`` on ``engine.state`` taking into account whether ``param_name`` attribute already exists or not. Overrides existing attribute by default, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() param_scheduler = PiecewiseLinearStateScheduler( param_name="param", milestones_values=[(5, 1.0), (10, 0.8), (15, 0.6)], create_new=True ) # parameter is param, milestone (5, 1.0) sets param to 1.0 # milestone is (5, 1.0), param=1 for Epoch 1 to 5, # next milestone is (10, 0.8), param linearly reduces from 1.0 to 0.8 # Epoch 10, param = 0.8 # next milestone is (15,0.6), param linearly reduces from 0.8 to 0.6 # Epoch 15, param = 0.6 param_scheduler.attach(default_trainer, Events.EPOCH_COMPLETED) @default_trainer.on(Events.EPOCH_COMPLETED) def print_param(): print(default_trainer.state.param) default_trainer.run([0], max_epochs=15) .. testoutput:: 1.0 1.0 1.0 1.0 1.0 0.96 0.92 0.88 0.8400... 0.8 0.76 0.72 0.68 0.64 0.6 .. versionadded:: 0.4.7 """ def __init__( self, milestones_values: List[Tuple[int, float]], param_name: str, save_history: bool = False, create_new: bool = False, ): super(PiecewiseLinearStateScheduler, self).__init__(param_name, save_history, create_new) if not isinstance(milestones_values, Sequence): raise TypeError( f"Argument milestones_values should be a list or tuple, but given {type(milestones_values)}" ) if len(milestones_values) < 1: raise ValueError( f"Argument milestones_values should be with at least one value, but given {milestones_values}" ) values: List[float] = [] milestones: List[int] = [] for pair in milestones_values: if not isinstance(pair, tuple) or len(pair) != 2: raise ValueError("Argument milestones_values should be a list of pairs (milestone, param_value)") if not isinstance(pair[0], numbers.Integral): raise TypeError(f"Value of a milestone should be integer, but given {type(pair[0])}") if len(milestones) > 0 and pair[0] < milestones[-1]: raise ValueError( f"Milestones should be increasing integers, but given {pair[0]} is smaller " f"than the previous milestone {milestones[-1]}" ) milestones.append(pair[0]) values.append(pair[1]) self.values = values self.milestones = milestones self._index = 0 self._state_attrs += ["values", "milestones", "_index"] def _get_start_end(self) -> Tuple[int, int, float, float]: if self.milestones[0] > self.event_index: return self.event_index - 1, self.event_index, self.values[0], self.values[0] elif self.milestones[-1] <= self.event_index: return (self.event_index, self.event_index + 1, self.values[-1], self.values[-1]) elif self.milestones[self._index] <= self.event_index < self.milestones[self._index + 1]: return ( self.milestones[self._index], self.milestones[self._index + 1], self.values[self._index], self.values[self._index + 1], ) else: self._index += 1 return self._get_start_end() def get_param(self) -> Union[List[float], float]: start_index, end_index, start_value, end_value = self._get_start_end() return start_value + (end_value - start_value) * (self.event_index - start_index) / (end_index - start_index) class ExpStateScheduler(StateParamScheduler): """Update a parameter during training by using exponential function. The function decays the parameter value by gamma every step. Based on the closed form of ExponentialLR from PyTorch https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ExponentialLR.html Args: initial_value: Starting value of the parameter. gamma: Multiplicative factor of parameter value decay. param_name: name of parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). create_new: whether to create ``param_name`` on ``engine.state`` taking into account whether ``param_name`` attribute already exists or not. Overrides existing attribute by default, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() param_scheduler = ExpStateScheduler( param_name="param", initial_value=1, gamma=0.9, create_new=True ) # parameter is param, initial_value sets param to 1, gamma is set as 0.9 # Epoch 1, param changes from 1 to 1*0.9, param = 0.9 # Epoch 2, param changes from 0.9 to 0.9*0.9, param = 0.81 # Epoch 3, param changes from 0.81 to 0.81*0.9, param = 0.729 # Epoch 4, param changes from 0.81 to 0.729*0.9, param = 0.6561 param_scheduler.attach(default_trainer, Events.EPOCH_COMPLETED) @default_trainer.on(Events.EPOCH_COMPLETED) def print_param(): print(default_trainer.state.param) default_trainer.run([0], max_epochs=4) .. testoutput:: 0.9 0.81 0.7290... 0.6561 .. versionadded:: 0.4.7 """ def __init__( self, initial_value: float, gamma: float, param_name: str, save_history: bool = False, create_new: bool = False ): super(ExpStateScheduler, self).__init__(param_name, save_history, create_new) self.initial_value = initial_value self.gamma = gamma self._state_attrs += ["initial_value", "gamma"] def get_param(self) -> Union[List[float], float]: return self.initial_value * self.gamma**self.event_index class StepStateScheduler(StateParamScheduler): """Update a parameter during training by using a step function. This function decays the parameter value by gamma every step_size. Based on StepLR from PyTorch. https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html Args: initial_value: Starting value of the parameter. gamma: Multiplicative factor of parameter value decay. step_size: Period of parameter value decay. param_name: name of parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). create_new: whether to create ``param_name`` on ``engine.state`` taking into account whether ``param_name`` attribute already exists or not. Overrides existing attribute by default, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() param_scheduler = StepStateScheduler( param_name="param", initial_value=1, gamma=0.9, step_size=5, create_new=True ) # parameter is param, initial_value sets param to 1, gamma is set as 0.9 # Epoch 1 to 4, param does not change as step size is 5, # Epoch 5, param changes from 1 to 1*0.9, param = 0.9 # Epoch 5 to 9, param = 0.9 as step size is 5, # Epoch 10, param changes from 0.9 to 0.9*0.9, param = 0.81 # Epoch 10 to 14, param = 0.81, as step size is 5 # Epoch 15, param changes from 0.81 to 0.81*0.9, param = 0.729 # and so on ... the param change at Epoch = 5, 10, 15, 20, . . . param_scheduler.attach(default_trainer, Events.EPOCH_COMPLETED) @default_trainer.on(Events.EPOCH_COMPLETED(every=5)) def print_param(): print(default_trainer.state.param) default_trainer.run([0], max_epochs=25) .. testoutput:: 0.9 0.81 0.7290... 0.6561 0.5904... .. versionadded:: 0.4.7 """ def __init__( self, initial_value: float, gamma: float, step_size: int, param_name: str, save_history: bool = False, create_new: bool = False, ): super(StepStateScheduler, self).__init__(param_name, save_history, create_new) self.initial_value = initial_value self.gamma = gamma self.step_size = step_size self._state_attrs += ["initial_value", "gamma", "step_size"] def get_param(self) -> Union[List[float], float]: return self.initial_value * self.gamma ** (self.event_index // self.step_size) class MultiStepStateScheduler(StateParamScheduler): """Update a parameter during training by using a multi step function. The function decays the parameter value by gamma once the number of steps reaches one of the milestones. Based on MultiStepLR from PyTorch. https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.MultiStepLR.html Args: initial_value: Starting value of the parameter. gamma: Multiplicative factor of parameter value decay. milestones: List of step indices. Must be increasing. param_name: name of parameter to update. save_history: whether to log the parameter values to `engine.state.param_history`, (default=False). create_new: whether to create ``param_name`` on ``engine.state`` taking into account whether ``param_name`` attribute already exists or not. Overrides existing attribute by default, (default=False). Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: default_trainer = get_default_trainer() param_scheduler = MultiStepStateScheduler( param_name="param", initial_value=1, gamma=0.9, milestones=[3, 6, 9, 12], create_new=True ) # parameter is param, initial_value sets param to 1, gamma is set as 0.9 # Epoch 1 to 2, param does not change as milestone is 3 # Epoch 3, param changes from 1 to 1*0.9, param = 0.9 # Epoch 3 to 5, param does not change as milestone is 6 # Epoch 6, param changes from 0.9 to 0.9*0.9, param = 0.81 # Epoch 6 to 8, param does not change as milestone is 9 # Epoch 9, param changes from 0.81 to 0.81*0.9, param = 0.729 # Epoch 9 to 11, param does not change as milestone is 12 # Epoch 12, param changes from 0.729 to 0.729*0.9, param = 0.6561 param_scheduler.attach(default_trainer, Events.EPOCH_COMPLETED) @default_trainer.on(Events.EPOCH_COMPLETED) def print_param(): print(default_trainer.state.param) default_trainer.run([0], max_epochs=12) .. testoutput:: 1.0 1.0 0.9 0.9 0.9 0.81 0.81 0.81 0.7290... 0.7290... 0.7290... 0.6561 .. versionadded:: 0.4.7 """ def __init__( self, initial_value: float, gamma: float, milestones: List[int], param_name: str, save_history: bool = False, create_new: bool = False, ): super(MultiStepStateScheduler, self).__init__(param_name, save_history, create_new) self.initial_value = initial_value self.gamma = gamma self.milestones = milestones self._state_attrs += ["initial_value", "gamma", "milestones"] def get_param(self) -> Union[List[float], float]: return self.initial_value * self.gamma ** bisect_right(self.milestones, self.event_index) ignite-0.5.1/ignite/handlers/stores.py000066400000000000000000000050701465426447700177710ustar00rootroot00000000000000from typing import Any, Callable, List, Optional from ignite.engine import Engine, Events class EpochOutputStore: """EpochOutputStore handler to save output prediction and target history after every epoch, could be useful for e.g., visualization purposes. Note: This can potentially lead to a memory error if the output data is larger than available RAM. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output , e.g., lambda x: x[0] Attributes: data: a list of :class:`~ignite.engine.engine.Engine` outputs, optionally transformed by `output_transform`. Examples: .. code-block:: python eos = EpochOutputStore() trainer = create_supervised_trainer(model, optimizer, loss) train_evaluator = create_supervised_evaluator(model, metrics) eos.attach(train_evaluator, 'output') @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): train_evaluator.run(train_loader) output = train_evaluator.state.output # output = [(y_pred0, y0), (y_pred1, y1), ...] # do something with output, e.g., plotting .. versionadded:: 0.4.5 .. versionchanged:: 0.4.5 `attach` now accepts an optional argument `name` """ def __init__(self, output_transform: Callable = lambda x: x): self.data: List[Any] = [] self.output_transform = output_transform def reset(self) -> None: """Reset the attribute data to empty list.""" self.data = [] def update(self, engine: Engine) -> None: """Append the output of Engine to attribute data.""" output = self.output_transform(engine.state.output) self.data.append(output) def store(self, engine: Engine) -> None: """Store `self.data` on `engine.state.{self.name}`""" setattr(engine.state, self.name, self.data) def attach(self, engine: Engine, name: Optional[str] = None) -> None: """Attaching `reset` method at EPOCH_STARTED and `update` method at ITERATION_COMPLETED. If `name` is passed, will store `self.data` on `engine.state` under `name`. """ engine.add_event_handler(Events.EPOCH_STARTED, self.reset) engine.add_event_handler(Events.ITERATION_COMPLETED, self.update) if name: self.name = name engine.add_event_handler(Events.EPOCH_COMPLETED, self.store) ignite-0.5.1/ignite/handlers/tensorboard_logger.py000066400000000000000000000634151465426447700223420ustar00rootroot00000000000000"""TensorBoard logger and its helper handlers.""" from typing import Any, Callable, List, Optional, Union from torch.optim import Optimizer from ignite.engine import Engine, Events from ignite.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHandler, BaseWeightsScalarHandler, ) from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "TensorboardLogger", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "WeightsHistHandler", "GradsScalarHandler", "GradsHistHandler", "global_step_from_engine", ] class TensorboardLogger(BaseLogger): """ TensorBoard handler to log metrics, model/optimizer parameters, gradients during the training and validation. By default, this class favors `tensorboardX `_ package if installed: .. code-block:: bash pip install tensorboardX otherwise, it falls back to using `PyTorch's SummaryWriter `_ (>=v1.2.0). Args: args: Positional arguments accepted from `SummaryWriter `_. kwargs: Keyword arguments accepted from `SummaryWriter `_. For example, `log_dir` to setup path to the directory where to log. Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log training loss at each iteration tb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. tb_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration tb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model) ) # Attach the logger to the trainer to log model's weights as a histogram after each epoch tb_logger.attach( trainer, event_name=Events.EPOCH_COMPLETED, log_handler=WeightsHistHandler(model) ) # Attach the logger to the trainer to log model's gradients norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model) ) # Attach the logger to the trainer to log model's gradients as a histogram after each epoch tb_logger.attach( trainer, event_name=Events.EPOCH_COMPLETED, log_handler=GradsHistHandler(model) ) # We need to close the logger when we are done tb_logger.close() It is also possible to use the logger as context manager: .. code-block:: python from ignite.handlers.tensorboard_logger import * with TensorboardLogger(log_dir="experiments/tb_logs") as tb_logger: trainer = Engine(update_fn) # Attach the logger to the trainer to log training loss at each iteration tb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) """ def __init__(self, *args: Any, **kwargs: Any): try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter except ImportError: raise ModuleNotFoundError( "This contrib module requires either tensorboardX or torch >= 1.2.0. " "You may install tensorboardX with command: \n pip install tensorboardX \n" "or upgrade PyTorch using your package manager of choice (pip or conda)." ) self.writer = SummaryWriter(*args, **kwargs) def __getattr__(self, attr: Any) -> Any: return getattr(self.writer, attr) def close(self) -> None: self.writer.close() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output, engine's state attributes and/or metrics Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.tensorboard_logger.global_step_from_engine`. state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: tb_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.tensorboard_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on Tensorboard. tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python tb_logger.attach( trainer, log_handler=OutputHandler( tag="training", metric_names=["nll", "accuracy"], state_attributes=["alpha", "beta"], ), event_name=Events.ITERATION_COMPLETED ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, state_attributes: Optional[List[str]] = None, ): super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'OutputHandler' works only with TensorboardLogger") metrics = self._setup_output_metrics_state_attrs(engine, key_tuple=False) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) for key, value in metrics.items(): logger.writer.add_scalar(key, value, global_step) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration tb_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently tb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } for k, v in params.items(): logger.writer.add_scalar(k, v, global_step) class WeightsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's weights as scalars. Handler, upon construction, iterates over named parameters of the model and keep reference to ones permitted by `whitelist`. Then at every call, applies reduction function to each parameter, produces a scalar and logs it. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" whitelist: specific weights to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if it should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's weights are logged. Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, reduction=torch.norm) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log only `fc` weights tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler( model, whitelist=['fc'] ) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log weights which have `bias` in their names def has_bias_in_name(n, p): return 'bias' in n tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, whitelist=has_bias_in_name) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'WeightsScalarHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: name = name.replace(".", "/") logger.writer.add_scalar( f"{tag_prefix}weights_{self.reduction.__name__}/{name}", self.reduction(p.data), global_step, ) class WeightsHistHandler(BaseWeightsHandler): """Helper handler to log model's weights as histograms. Args: model: model to log weights tag: common title for all produced plots. For example, "generator" whitelist: specific weights to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if it should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's weights are logged. Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log weights of `fc` layer weights = ['fc'] # Attach the logger to the trainer to log weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model, whitelist=weights) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log weights which name include 'conv'. weight_selector = lambda name, p: 'conv' in name # Attach the logger to the trainer to log weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model, whitelist=weight_selector) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'WeightsHistHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: name = name.replace(".", "/") logger.writer.add_histogram( tag=f"{tag_prefix}weights/{name}", values=p.data.cpu().numpy(), global_step=global_step ) class GradsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's gradients as scalars. Handler, upon construction, iterates over named parameters of the model and keep reference to ones permitted by the `whitelist`. Then at every call, applies reduction function to each parameter's gradient, produces a scalar and logs it. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" whitelist: specific gradients to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if its gradient should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's gradients are logged. Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's gradients norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, reduction=torch.norm) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log gradient of `base` tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler( model, reduction=torch.norm, whitelist=['base'] ) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log gradient of weights which belong to a `fc` layer def is_in_fc_layer(n, p): return 'fc' in n tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, whitelist=is_in_fc_layer) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'GradsScalarHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_scalar( f"{tag_prefix}grads_{self.reduction.__name__}/{name}", self.reduction(p.grad), global_step ) class GradsHistHandler(BaseWeightsHandler): """Helper handler to log model's gradients as histograms. Args: model: model to log weights tag: common title for all produced plots. For example, "generator" whitelist: specific gradients to log. Should be list of model's submodules or parameters names, or a callable which gets weight along with its name and determines if its gradient should be logged. Names should be fully-qualified. For more information please refer to `PyTorch docs `_. If not given, all of model's gradients are logged. Examples: .. code-block:: python from ignite.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log gradient of `fc.bias` tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model, whitelist=['fc.bias']) ) .. code-block:: python from ignite.handlers.tensorboard_logger import * tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Log gradient of weights which have shape (2, 1) def has_shape_2_1(n, p): return p.shape == (2,1) tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model, whitelist=has_shape_2_1) ) .. versionchanged:: 0.4.9 optional argument `whitelist` added. """ def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'GradsHistHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.weights: if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_histogram( tag=f"{tag_prefix}grads/{name}", values=p.grad.cpu().numpy(), global_step=global_step ) ignite-0.5.1/ignite/handlers/terminate_on_nan.py000066400000000000000000000040671465426447700217770ustar00rootroot00000000000000import logging import numbers from typing import Callable, Union import torch from ignite.engine import Engine from ignite.utils import apply_to_type, setup_logger __all__ = ["TerminateOnNan"] class TerminateOnNan: """TerminateOnNan handler can be used to stop the training if the `process_function`'s output contains a NaN or infinite number or `torch.tensor`. The output can be of type: number, tensor or collection of them. The training is stopped if there is at least a single number/tensor have NaN or Infinite value. For example, if the output is `[1.23, torch.tensor(...), torch.tensor(float('nan'))]` the handler will stop the training. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into a number or `torch.tensor` or collection of them. This can be useful if, for example, you have a multi-output model and you want to check one or multiple values of the output. Examples: .. code-block:: python trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan()) """ def __init__(self, output_transform: Callable = lambda x: x): self.logger = setup_logger(__name__ + "." + self.__class__.__name__) self.logger.addHandler(logging.StreamHandler()) self._output_transform = output_transform def __call__(self, engine: Engine) -> None: output = self._output_transform(engine.state.output) def raise_error(x: Union[float, torch.Tensor]) -> None: if isinstance(x, numbers.Number): x = torch.tensor(x) if isinstance(x, torch.Tensor) and not bool(torch.isfinite(x).all()): raise RuntimeError("Infinite or NaN tensor found.") try: apply_to_type(output, (numbers.Number, torch.Tensor), raise_error) except RuntimeError: self.logger.warning(f"{self.__class__.__name__}: Output '{output}' contains NaN or Inf. Stop training") engine.terminate() ignite-0.5.1/ignite/handlers/time_limit.py000066400000000000000000000030371465426447700206070ustar00rootroot00000000000000import time from typing import Optional from ignite.engine import Engine __all__ = ["TimeLimit"] from ignite.utils import setup_logger class TimeLimit: """TimeLimit handler can be used to control training time for computing environments where session time is limited. Timer starts when handler is created and not training started. This handler gracefully terminates the training if time passed in the training exceeds a limit. Args: limit_sec: Maximum time before training terminates (in seconds). Defaults to 28800. Examples: .. code-block:: python from ignite.engine import Events from ignite.handlers import TimeLimit handler = TimeLimit() # 8 hours of training trainer.add_event_handler(Events.ITERATION_COMPLETED, handler) .. versionadded:: 0.4.3 """ def __init__(self, limit_sec: Optional[int] = 28800): if not isinstance(limit_sec, int): raise TypeError("Argument limit_sec should be an integer.") if limit_sec <= 0: raise ValueError("Argument limit_sec should be a positive integer.") self.limit_sec = limit_sec self.start_time = time.time() self.logger = setup_logger(__name__ + "." + self.__class__.__name__) def __call__(self, engine: Engine) -> None: elapsed_time = time.time() - self.start_time if elapsed_time > self.limit_sec: self.logger.info("Reached the time limit: {} sec. Stop training".format(self.limit_sec)) engine.terminate() ignite-0.5.1/ignite/handlers/time_profilers.py000066400000000000000000000730241465426447700215010ustar00rootroot00000000000000import functools from collections import OrderedDict from typing import Any, Callable, cast, Dict, List, Mapping, Sequence, Tuple, Union import torch from ignite.engine import Engine, EventEnum, Events from ignite.handlers.timing import Timer class BasicTimeProfiler: """ BasicTimeProfiler can be used to profile the handlers, events, data loading and data processing times. Examples: .. code-block:: python from ignite.handlers import BasicTimeProfiler trainer = Engine(train_updater) # Create an object of the profiler and attach an engine to it profiler = BasicTimeProfiler() profiler.attach(trainer) @trainer.on(Events.EPOCH_COMPLETED) def log_intermediate_results(): profiler.print_results(profiler.get_results()) trainer.run(dataloader, max_epochs=3) profiler.write_results('path_to_dir/time_profiling.csv') .. versionadded:: 0.4.6 """ events_to_ignore = [ Events.EXCEPTION_RAISED, Events.TERMINATE, Events.TERMINATE_SINGLE_EPOCH, Events.DATALOADER_STOP_ITERATION, Events.INTERRUPT, ] def __init__(self) -> None: self._dataflow_timer = Timer() self._processing_timer = Timer() self._event_handlers_timer = Timer() self.dataflow_times = torch.zeros(1) self.processing_times = torch.zeros(1) self.event_handlers_times: Dict[EventEnum, torch.Tensor] = {} self._events = [ Events.EPOCH_STARTED, Events.EPOCH_COMPLETED, Events.ITERATION_STARTED, Events.ITERATION_COMPLETED, Events.GET_BATCH_STARTED, Events.GET_BATCH_COMPLETED, Events.COMPLETED, ] self._fmethods = [ self._as_first_epoch_started, self._as_first_epoch_completed, self._as_first_iter_started, self._as_first_iter_completed, self._as_first_get_batch_started, self._as_first_get_batch_completed, self._as_first_completed, ] self._lmethods = [ self._as_last_epoch_started, self._as_last_epoch_completed, self._as_last_iter_started, self._as_last_iter_completed, self._as_last_get_batch_started, self._as_last_get_batch_completed, self._as_last_completed, ] def _reset(self, num_epochs: int, total_num_iters: int) -> None: self.dataflow_times = torch.zeros(total_num_iters) self.processing_times = torch.zeros(total_num_iters) self.event_handlers_times = { Events.STARTED: torch.zeros(1), Events.COMPLETED: torch.zeros(1), Events.EPOCH_STARTED: torch.zeros(num_epochs), Events.EPOCH_COMPLETED: torch.zeros(num_epochs), Events.ITERATION_STARTED: torch.zeros(total_num_iters), Events.ITERATION_COMPLETED: torch.zeros(total_num_iters), Events.GET_BATCH_COMPLETED: torch.zeros(total_num_iters), Events.GET_BATCH_STARTED: torch.zeros(total_num_iters), } def _as_first_started(self, engine: Engine) -> None: if hasattr(engine.state.dataloader, "__len__"): num_iters_per_epoch = len(engine.state.dataloader) # type: ignore[arg-type] else: if engine.state.epoch_length is None: raise ValueError( "As epoch_length is not set, we can not use BasicTimeProfiler in this case." "Please, set trainer.run(..., epoch_length=epoch_length) in order to fix this." ) num_iters_per_epoch = engine.state.epoch_length self.max_epochs = cast(int, engine.state.max_epochs) self.total_num_iters = self.max_epochs * num_iters_per_epoch self._reset(self.max_epochs, self.total_num_iters) self.event_handlers_names = { e: [ h.__qualname__ if hasattr(h, "__qualname__") else h.__class__.__name__ for (h, _, _) in engine._event_handlers[e] if "BasicTimeProfiler." not in repr(h) # avoid adding internal handlers into output ] for e in Events if e not in self.events_to_ignore } # Setup all other handlers: engine._event_handlers[Events.STARTED].append((self._as_last_started, (engine,), {})) for e, m in zip(self._events, self._fmethods): engine._event_handlers[e].insert(0, (m, (engine,), {})) for e, m in zip(self._events, self._lmethods): engine._event_handlers[e].append((m, (engine,), {})) # Let's go self._event_handlers_timer.reset() def _as_last_started(self, engine: Engine) -> None: self.event_handlers_times[Events.STARTED][0] = self._event_handlers_timer.value() def _as_first_epoch_started(self, engine: Engine) -> None: self._event_handlers_timer.reset() def _as_last_epoch_started(self, engine: Engine) -> None: t = self._event_handlers_timer.value() e = engine.state.epoch - 1 self.event_handlers_times[Events.EPOCH_STARTED][e] = t def _as_first_get_batch_started(self, engine: Engine) -> None: self._event_handlers_timer.reset() self._dataflow_timer.reset() def _as_last_get_batch_started(self, engine: Engine) -> None: t = self._event_handlers_timer.value() i = engine.state.iteration - 1 self.event_handlers_times[Events.GET_BATCH_STARTED][i] = t def _as_first_get_batch_completed(self, engine: Engine) -> None: self._event_handlers_timer.reset() def _as_last_get_batch_completed(self, engine: Engine) -> None: t = self._event_handlers_timer.value() i = engine.state.iteration - 1 self.event_handlers_times[Events.GET_BATCH_COMPLETED][i] = t d = self._dataflow_timer.value() self.dataflow_times[i] = d self._dataflow_timer.reset() def _as_first_iter_started(self, engine: Engine) -> None: self._event_handlers_timer.reset() def _as_last_iter_started(self, engine: Engine) -> None: t = self._event_handlers_timer.value() i = engine.state.iteration - 1 self.event_handlers_times[Events.ITERATION_STARTED][i] = t self._processing_timer.reset() def _as_first_iter_completed(self, engine: Engine) -> None: t = self._processing_timer.value() i = engine.state.iteration - 1 self.processing_times[i] = t self._event_handlers_timer.reset() def _as_last_iter_completed(self, engine: Engine) -> None: t = self._event_handlers_timer.value() i = engine.state.iteration - 1 self.event_handlers_times[Events.ITERATION_COMPLETED][i] = t def _as_first_epoch_completed(self, engine: Engine) -> None: self._event_handlers_timer.reset() def _as_last_epoch_completed(self, engine: Engine) -> None: t = self._event_handlers_timer.value() e = engine.state.epoch - 1 self.event_handlers_times[Events.EPOCH_COMPLETED][e] = t def _as_first_completed(self, engine: Engine) -> None: self._event_handlers_timer.reset() def _as_last_completed(self, engine: Engine) -> None: self.event_handlers_times[Events.COMPLETED][0] = self._event_handlers_timer.value() # Remove added handlers: engine.remove_event_handler(self._as_last_started, Events.STARTED) for e, m in zip(self._events, self._fmethods): engine.remove_event_handler(m, e) for e, m in zip(self._events, self._lmethods): engine.remove_event_handler(m, e) def attach(self, engine: Engine) -> None: """Attach BasicTimeProfiler to the given engine. Args: engine: the instance of Engine to attach """ if not isinstance(engine, Engine): raise TypeError(f"Argument engine should be ignite.engine.Engine, but given {type(engine)}") if not engine.has_event_handler(self._as_first_started): engine._event_handlers[Events.STARTED].insert(0, (self._as_first_started, (engine,), {})) @staticmethod def _compute_basic_stats(data: torch.Tensor) -> Dict[str, Union[str, float, Tuple[float, float]]]: # compute on non-zero data: data = data[data > 0] out: List[Tuple[str, Union[str, float, Tuple[float, float]]]] = [ ("total", torch.sum(data).item() if len(data) > 0 else "not yet triggered") ] if len(data) > 1: out.extend( [ ("min/index", (torch.min(data).item(), torch.argmin(data).item())), ("max/index", (torch.max(data).item(), torch.argmax(data).item())), ("mean", torch.mean(data).item()), ("std", torch.std(data).item()), ] ) return OrderedDict(out) def get_results(self) -> Dict[str, Dict[str, Any]]: """ Method to fetch the aggregated profiler results after the engine is run .. code-block:: python results = profiler.get_results() """ total_eh_time: Union[int, torch.Tensor] = sum( [(self.event_handlers_times[e]).sum() for e in Events if e not in self.events_to_ignore] ) event_handlers_stats = dict( [ (str(e.name).replace(".", "_"), self._compute_basic_stats(self.event_handlers_times[e])) for e in Events if e not in self.events_to_ignore ] + [("total_time", total_eh_time)] ) return OrderedDict( [ ("processing_stats", self._compute_basic_stats(self.processing_times)), ("dataflow_stats", self._compute_basic_stats(self.dataflow_times)), ("event_handlers_stats", event_handlers_stats), ( "event_handlers_names", {str(e.name).replace(".", "_") + "_names": v for e, v in self.event_handlers_names.items()}, ), ] ) def write_results(self, output_path: str) -> None: """ Method to store the unaggregated profiling results to a csv file Args: output_path: file output path containing a filename .. code-block:: python profiler.write_results('path_to_dir/awesome_filename.csv') Examples: .. code-block:: text ----------------------------------------------------------------- epoch iteration processing_stats dataflow_stats Event_STARTED ... 1.0 1.0 0.00003 0.252387 0.125676 1.0 2.0 0.00029 0.252342 0.125123 """ try: import pandas as pd except ImportError: raise ModuleNotFoundError("Need pandas to write results as files") iters_per_epoch = self.total_num_iters // self.max_epochs epochs = torch.arange(self.max_epochs, dtype=torch.float32).repeat_interleave(iters_per_epoch) + 1 iterations = torch.arange(self.total_num_iters, dtype=torch.float32) + 1 processing_stats = self.processing_times dataflow_stats = self.dataflow_times event_started = self.event_handlers_times[Events.STARTED].repeat_interleave(self.total_num_iters) event_completed = self.event_handlers_times[Events.COMPLETED].repeat_interleave(self.total_num_iters) event_epoch_started = self.event_handlers_times[Events.EPOCH_STARTED].repeat_interleave(iters_per_epoch) event_epoch_completed = self.event_handlers_times[Events.EPOCH_COMPLETED].repeat_interleave(iters_per_epoch) event_iter_started = self.event_handlers_times[Events.ITERATION_STARTED] event_iter_completed = self.event_handlers_times[Events.ITERATION_COMPLETED] event_batch_started = self.event_handlers_times[Events.GET_BATCH_STARTED] event_batch_completed = self.event_handlers_times[Events.GET_BATCH_COMPLETED] results_dump = torch.stack( [ epochs, iterations, processing_stats, dataflow_stats, event_started, event_completed, event_epoch_started, event_epoch_completed, event_iter_started, event_iter_completed, event_batch_started, event_batch_completed, ], dim=1, ).numpy() results_df = pd.DataFrame( data=results_dump, columns=[ "epoch", "iteration", "processing_stats", "dataflow_stats", "Event_STARTED", "Event_COMPLETED", "Event_EPOCH_STARTED", "Event_EPOCH_COMPLETED", "Event_ITERATION_STARTED", "Event_ITERATION_COMPLETED", "Event_GET_BATCH_STARTED", "Event_GET_BATCH_COMPLETED", ], ) results_df.to_csv(output_path, index=False) @staticmethod def print_results(results: Dict) -> str: """ Method to print the aggregated results from the profiler Args: results: the aggregated results from the profiler .. code-block:: python profiler.print_results(results) Examples: .. code-block:: text ---------------------------------------------------- | Time profiling stats (in seconds): | ---------------------------------------------------- total | min/index | max/index | mean | std Processing function: 157.46292 | 0.01452/1501 | 0.26905/0 | 0.07730 | 0.01258 Dataflow: 6.11384 | 0.00008/1935 | 0.28461/1551 | 0.00300 | 0.02693 Event handlers: 2.82721 - Events.STARTED: [] 0.00000 - Events.EPOCH_STARTED: [] 0.00006 | 0.00000/0 | 0.00000/17 | 0.00000 | 0.00000 - Events.ITERATION_STARTED: ['PiecewiseLinear'] 0.03482 | 0.00001/188 | 0.00018/679 | 0.00002 | 0.00001 - Events.ITERATION_COMPLETED: ['TerminateOnNan'] 0.20037 | 0.00006/866 | 0.00089/1943 | 0.00010 | 0.00003 - Events.EPOCH_COMPLETED: ['empty_cuda_cache', 'training..log_elapsed_time', ] 2.57860 | 0.11529/0 | 0.14977/13 | 0.12893 | 0.00790 - Events.COMPLETED: [] not yet triggered """ def to_str(v: Union[str, tuple]) -> str: if isinstance(v, str): return v elif isinstance(v, tuple): return f"{v[0]:.5f}/{v[1]}" return f"{v:.5f}" def odict_to_str(d: Mapping) -> str: out = " | ".join([to_str(v) for v in d.values()]) return out others = { k: odict_to_str(v) if isinstance(v, OrderedDict) else v for k, v in results["event_handlers_stats"].items() } others.update(results["event_handlers_names"]) output_message = """ ---------------------------------------------------- | Time profiling stats (in seconds): | ---------------------------------------------------- total | min/index | max/index | mean | std Processing function: {processing_stats} Dataflow: {dataflow_stats} Event handlers: {total_time:.5f} - Events.STARTED: {STARTED_names} {STARTED} - Events.EPOCH_STARTED: {EPOCH_STARTED_names} {EPOCH_STARTED} - Events.ITERATION_STARTED: {ITERATION_STARTED_names} {ITERATION_STARTED} - Events.ITERATION_COMPLETED: {ITERATION_COMPLETED_names} {ITERATION_COMPLETED} - Events.EPOCH_COMPLETED: {EPOCH_COMPLETED_names} {EPOCH_COMPLETED} - Events.COMPLETED: {COMPLETED_names} {COMPLETED} """.format( processing_stats=odict_to_str(results["processing_stats"]), dataflow_stats=odict_to_str(results["dataflow_stats"]), **others, ) print(output_message) return output_message class HandlersTimeProfiler: """ HandlersTimeProfiler can be used to profile the handlers, data loading and data processing times. Custom events are also profiled by this profiler Examples: .. code-block:: python from ignite.handlers import HandlersTimeProfiler trainer = Engine(train_updater) # Create an object of the profiler and attach an engine to it profiler = HandlersTimeProfiler() profiler.attach(trainer) @trainer.on(Events.EPOCH_COMPLETED) def log_intermediate_results(): profiler.print_results(profiler.get_results()) trainer.run(dataloader, max_epochs=3) profiler.write_results('path_to_dir/time_profiling.csv') .. versionadded:: 0.4.6 """ EVENT_FILTER_THESHOLD_TIME = 0.0001 def __init__(self) -> None: self._dataflow_timer = Timer() self._processing_timer = Timer() self._event_handlers_timer = Timer() self.dataflow_times: List[float] = [] self.processing_times: List[float] = [] self.event_handlers_times: Dict[EventEnum, Dict[str, List[float]]] = {} @staticmethod def _get_callable_name(handler: Callable) -> str: # get name of the callable handler return getattr(handler, "__qualname__", handler.__class__.__name__) def _create_wrapped_handler(self, handler: Callable, event: EventEnum) -> Callable: @functools.wraps(handler) def _timeit_handler(*args: Any, **kwargs: Any) -> None: self._event_handlers_timer.reset() handler(*args, **kwargs) t = self._event_handlers_timer.value() hname = self._get_callable_name(handler) # filter profiled time if the handler was attached to event with event filter if not hasattr(handler, "_parent") or t >= self.EVENT_FILTER_THESHOLD_TIME: self.event_handlers_times[event][hname].append(t) # required to revert back to original handler after profiling setattr(_timeit_handler, "_profiler_original", handler) return _timeit_handler def _timeit_processing(self) -> None: # handler used for profiling processing times t = self._processing_timer.value() self.processing_times.append(t) def _timeit_dataflow(self) -> None: # handler used for profiling dataflow times t = self._dataflow_timer.value() self.dataflow_times.append(t) def _reset(self, event_handlers_names: Mapping[EventEnum, List[str]]) -> None: # reset the variables used for profiling self.dataflow_times = [] self.processing_times = [] self.event_handlers_times = {e: {h: [] for h in event_handlers_names[e]} for e in event_handlers_names} @staticmethod def _is_internal_handler(handler: Callable) -> bool: # checks whether the handler is internal return any(n in repr(handler) for n in ["HandlersTimeProfiler.", "Timer."]) def _detach_profiler_handlers(self, engine: Engine) -> None: # reverts handlers to original handlers for e in engine._event_handlers: for i, (func, args, kwargs) in enumerate(engine._event_handlers[e]): if hasattr(func, "_profiler_original"): engine._event_handlers[e][i] = (func._profiler_original, args, kwargs) def _as_first_started(self, engine: Engine) -> None: # wraps original handlers for profiling self.event_handlers_names = { e: [ self._get_callable_name(h) for (h, _, _) in engine._event_handlers[e] if not self._is_internal_handler(h) ] for e in engine._allowed_events } self._reset(self.event_handlers_names) for e in engine._allowed_events: for i, (func, args, kwargs) in enumerate(engine._event_handlers[e]): if not self._is_internal_handler(func): engine._event_handlers[e][i] = (self._create_wrapped_handler(func, e), args, kwargs) # processing timer engine.add_event_handler(Events.ITERATION_STARTED, self._processing_timer.reset) engine._event_handlers[Events.ITERATION_COMPLETED].insert(0, (self._timeit_processing, (), {})) # dataflow timer engine.add_event_handler(Events.GET_BATCH_STARTED, self._dataflow_timer.reset) engine._event_handlers[Events.GET_BATCH_COMPLETED].insert(0, (self._timeit_dataflow, (), {})) # revert back the wrapped handlers with original handlers at the end engine.add_event_handler(Events.COMPLETED, self._detach_profiler_handlers) def attach(self, engine: Engine) -> None: """Attach HandlersTimeProfiler to the given engine. Args: engine: the instance of Engine to attach """ if not isinstance(engine, Engine): raise TypeError(f"Argument engine should be ignite.engine.Engine, but given {type(engine)}") if not engine.has_event_handler(self._as_first_started): engine._event_handlers[Events.STARTED].insert(0, (self._as_first_started, (engine,), {})) def get_results(self) -> List[List[Union[str, float, Tuple[Union[str, float], Union[str, float]]]]]: """ Method to fetch the aggregated profiler results after the engine is run .. code-block:: python results = profiler.get_results() """ total_eh_time = sum( [ sum(self.event_handlers_times[e][h]) for e in self.event_handlers_times for h in self.event_handlers_times[e] ] ) total_eh_time = round(float(total_eh_time), 5) def compute_basic_stats( times: Union[Sequence, torch.Tensor] ) -> List[Union[str, float, Tuple[Union[str, float], Union[str, float]]]]: data = torch.as_tensor(times, dtype=torch.float32) # compute on non-zero data: data = data[data > 0] total: Union[str, float] = round(torch.sum(data).item(), 5) if len(data) > 0 else "not triggered" min_index: Tuple[Union[str, float], Union[str, float]] = ("None", "None") max_index: Tuple[Union[str, float], Union[str, float]] = ("None", "None") mean: Union[str, float] = "None" std: Union[str, float] = "None" if len(data) > 0: min_index = (round(torch.min(data).item(), 5), torch.argmin(data).item()) max_index = (round(torch.max(data).item(), 5), torch.argmax(data).item()) mean = round(torch.mean(data).item(), 5) if len(data) > 1: std = round(torch.std(data).item(), 5) return [total, min_index, max_index, mean, std] event_handler_stats = [ [ h, getattr(e, "name", str(e)), *compute_basic_stats(torch.tensor(self.event_handlers_times[e][h], dtype=torch.float32)), ] for e in self.event_handlers_times for h in self.event_handlers_times[e] ] event_handler_stats.append(["Total", "", total_eh_time, "", "", "", ""]) event_handler_stats.append(["Processing", "None", *compute_basic_stats(self.processing_times)]) event_handler_stats.append(["Dataflow", "None", *compute_basic_stats(self.dataflow_times)]) return event_handler_stats def write_results(self, output_path: str) -> None: """ Method to store the unaggregated profiling results to a csv file Args: output_path: file output path containing a filename .. code-block:: python profiler.write_results('path_to_dir/awesome_filename.csv') Examples: .. code-block:: text ----------------------------------------------------------------- # processing_stats dataflow_stats training..log_elapsed_time (EPOCH_COMPLETED) ... 1 0.00003 0.252387 0.125676 2 0.00029 0.252342 0.125123 """ try: import pandas as pd except ImportError: raise ModuleNotFoundError("Need pandas to write results as files") processing_stats = torch.tensor(self.processing_times, dtype=torch.float32) dataflow_stats = torch.tensor(self.dataflow_times, dtype=torch.float32) cols = [processing_stats, dataflow_stats] headers = ["processing_stats", "dataflow_stats"] for e in self.event_handlers_times: for h in self.event_handlers_times[e]: headers.append(f"{h} ({getattr(e, 'name', str(e))})") cols.append(torch.tensor(self.event_handlers_times[e][h], dtype=torch.float32)) # Determine maximum length max_len = max([x.numel() for x in cols]) count_col = torch.arange(max_len, dtype=torch.float32) + 1 cols.insert(0, count_col) headers.insert(0, "#") # pad all tensors to have same length cols = [torch.nn.functional.pad(x, pad=(0, max_len - x.numel()), mode="constant", value=0) for x in cols] results_dump = torch.stack(cols, dim=1).numpy() results_df = pd.DataFrame(data=results_dump, columns=headers) results_df.to_csv(output_path, index=False) @staticmethod def print_results(results: List[List[Union[str, float]]]) -> None: """ Method to print the aggregated results from the profiler Args: results: the aggregated results from the profiler .. code-block:: python profiler.print_results(results) Examples: .. code-block:: text ----------------------------------------- ----------------------- -------------- ... Handler Event Name Total(s) ----------------------------------------- ----------------------- -------------- run..log_training_results EPOCH_COMPLETED 19.43245 run..log_validation_results EPOCH_COMPLETED 2.55271 run..log_time EPOCH_COMPLETED 0.00049 run..log_intermediate_results EPOCH_COMPLETED 0.00106 run..log_training_loss ITERATION_COMPLETED 0.059 run..log_time COMPLETED not triggered ----------------------------------------- ----------------------- -------------- Total 22.04571 ----------------------------------------- ----------------------- -------------- Processing took total 11.29543s [min/index: 0.00393s/1875, max/index: 0.00784s/0, mean: 0.00602s, std: 0.00034s] Dataflow took total 16.24365s [min/index: 0.00533s/1874, max/index: 0.01129s/937, mean: 0.00866s, std: 0.00113s] """ # adopted implementation of torch.autograd.profiler.build_table handler_column_width = max([len(item[0]) for item in results]) + 4 # type: ignore[arg-type] event_column_width = max([len(item[1]) for item in results]) + 4 # type: ignore[arg-type] DEFAULT_COLUMN_WIDTH = 14 headers = [ "Handler", "Event Name", "Total(s)", "Min(s)/IDX", "Max(s)/IDX", "Mean(s)", "Std(s)", ] # Have to use a list because nonlocal is Py3 only... SPACING_SIZE = 2 row_format_lst = [""] header_sep_lst = [""] line_length_lst = [-SPACING_SIZE] def add_column(padding: int, text_dir: str = ">") -> None: row_format_lst[0] += "{: " + text_dir + str(padding) + "}" + (" " * SPACING_SIZE) header_sep_lst[0] += "-" * padding + (" " * SPACING_SIZE) line_length_lst[0] += padding + SPACING_SIZE add_column(handler_column_width, text_dir="<") add_column(event_column_width, text_dir="<") for _ in headers[2:]: add_column(DEFAULT_COLUMN_WIDTH) row_format = row_format_lst[0] header_sep = header_sep_lst[0] result = [] def append(s: str) -> None: result.append(s) result.append("\n") result.append("\n") append(header_sep) append(row_format.format(*headers)) append(header_sep) for row in results[:-3]: # format min/idx and max/idx row[3] = "{}/{}".format(*row[3]) # type: ignore[misc] row[4] = "{}/{}".format(*row[4]) # type: ignore[misc] append(row_format.format(*row)) append(header_sep) # print total handlers time row append(row_format.format(*results[-3])) append(header_sep) summary_format = "{} took total {}s [min/index: {}, max/index: {}, mean: {}s, std: {}s]" for row in results[-2:]: row[3] = "{}s/{}".format(*row[3]) # type: ignore[misc] row[4] = "{}s/{}".format(*row[4]) # type: ignore[misc] del row[1] append(summary_format.format(*row)) print("".join(result)) ignite-0.5.1/ignite/handlers/timing.py000066400000000000000000000111411465426447700177350ustar00rootroot00000000000000from time import perf_counter from typing import Any, Optional from ignite.engine import Engine, Events __all__ = ["Timer"] class Timer: """Timer object can be used to measure (average) time between events. Args: average: if True, then when ``.value()`` method is called, the returned value will be equal to total time measured, divided by the value of internal counter. Attributes: total (float): total time elapsed when the Timer was running (in seconds). step_count (int): internal counter, useful to measure average time, e.g. of processing a single batch. Incremented with the ``.step()`` method. running (bool): flag indicating if timer is measuring time. Note: When using ``Timer(average=True)`` do not forget to call ``timer.step()`` every time an event occurs. See the examples below. Examples: Measuring total time of the epoch: .. code-block:: python from ignite.handlers import Timer import time work = lambda : time.sleep(0.1) idle = lambda : time.sleep(0.1) t = Timer(average=False) for _ in range(10): work() idle() t.value() # 2.003073937026784 Measuring average time of the epoch: .. code-block:: python t = Timer(average=True) for _ in range(10): work() idle() t.step() t.value() # 0.2003182829997968 Measuring average time it takes to execute a single ``work()`` call: .. code-block:: python t = Timer(average=True) for _ in range(10): t.resume() work() t.pause() idle() t.step() t.value() # 0.10016545779653825 Using the Timer to measure average time it takes to process a single batch of examples: .. code-block:: python from ignite.engine import Engine, Events from ignite.handlers import Timer trainer = Engine(training_update_function) timer = Timer(average=True) timer.attach( trainer, start=Events.STARTED, resume=Events.ITERATION_STARTED, pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED ) """ def __init__(self, average: bool = False): self._average = average self.reset() def attach( self, engine: Engine, start: Events = Events.STARTED, pause: Events = Events.COMPLETED, resume: Optional[Events] = None, step: Optional[Events] = None, ) -> "Timer": """Register callbacks to control the timer. Args: engine: Engine that this timer will be attached to. start: Event which should start (reset) the timer. pause: Event which should pause the timer. resume: Event which should resume the timer. step: Event which should call the `step` method of the counter. Returns: this timer """ engine.add_event_handler(start, self.reset) engine.add_event_handler(pause, self.pause) if resume is not None: engine.add_event_handler(resume, self.resume) if step is not None: engine.add_event_handler(step, self.step) return self def reset(self, *args: Any) -> "Timer": """Reset the timer to zero.""" self._t0 = perf_counter() self.total = 0.0 self.step_count = 0.0 self.running = True return self def pause(self, *args: Any) -> None: """Pause the current running timer.""" if self.running: self.total += self._elapsed() self.running = False def resume(self, *args: Any) -> None: """Resume the current running timer.""" if not self.running: self.running = True self._t0 = perf_counter() def value(self) -> float: """Return the average timer value.""" total = self.total if self.running: total += self._elapsed() if self._average: denominator = max(self.step_count, 1.0) else: denominator = 1.0 return total / denominator def step(self, *args: Any) -> None: """Increment the timer.""" self.step_count += 1.0 def _elapsed(self) -> float: return perf_counter() - self._t0 ignite-0.5.1/ignite/handlers/tqdm_logger.py000066400000000000000000000313601465426447700207570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """TQDM logger.""" from collections import OrderedDict from typing import Any, Callable, List, Optional, Union from ignite.engine import Engine, Events from ignite.engine.events import CallableEventWithFilter, RemovableEventHandle from ignite.handlers.base_logger import BaseLogger, BaseOutputHandler class ProgressBar(BaseLogger): """ TQDM progress bar handler to log training progress and computed metrics. Args: persist: set to ``True`` to persist the progress bar after completion (default = ``False``) bar_format : Specify a custom bar string formatting. May impact performance. [default: '{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]']. Set to ``None`` to use ``tqdm`` default bar formatting: '{l_bar}{bar}{r_bar}', where l_bar='{desc}: {percentage:3.0f}%|' and r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'. For more details on the formatting, see `tqdm docs `_. tqdm_kwargs: kwargs passed to tqdm progress bar. By default, progress bar description displays "Epoch [5/10]" where 5 is the current epoch and 10 is the number of epochs; however, if ``max_epochs`` are set to 1, the progress bar instead displays "Iteration: [5/10]". If tqdm_kwargs defines `desc`, e.g. "Predictions", than the description is "Predictions [5/10]" if number of epochs is more than one otherwise it is simply "Predictions". Examples: Simple progress bar .. code-block:: python trainer = create_supervised_trainer(model, optimizer, loss) pbar = ProgressBar() pbar.attach(trainer) # Progress bar will looks like # Epoch [2/50]: [64/128] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ [06:17<12:34] Log output to a file instead of stderr (tqdm's default output) .. code-block:: python trainer = create_supervised_trainer(model, optimizer, loss) log_file = open("output.log", "w") pbar = ProgressBar(file=log_file) pbar.attach(trainer) Attach metrics that already have been computed at :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED` (such as :class:`~ignite.metrics.RunningAverage`) .. code-block:: python trainer = create_supervised_trainer(model, optimizer, loss) RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss') pbar = ProgressBar() pbar.attach(trainer, ['loss']) # Progress bar will looks like # Epoch [2/50]: [64/128] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ , loss=0.123 [06:17<12:34] Directly attach the engine's output .. code-block:: python trainer = create_supervised_trainer(model, optimizer, loss) pbar = ProgressBar() pbar.attach(trainer, output_transform=lambda x: {'loss': x}) # Progress bar will looks like # Epoch [2/50]: [64/128] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ , loss=0.123 [06:17<12:34] Example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python pbar.attach( trainer, metric_names=["nll", "accuracy"], state_attributes=["alpha", "beta"], ) Note: When attaching the progress bar to an engine, it is recommended that you replace every print operation in the engine's handlers triggered every iteration with ``pbar.log_message`` to guarantee the correct format of the stdout. Note: When using inside jupyter notebook, `ProgressBar` automatically uses `tqdm_notebook`. For correct rendering, please install `ipywidgets `_. Due to `tqdm notebook bugs `_, bar format may be needed to be set to an empty string value. .. versionchanged:: 0.4.7 `attach` now accepts an optional list of `state_attributes` """ _events_order: List[Union[Events, CallableEventWithFilter]] = [ Events.STARTED, Events.EPOCH_STARTED, Events.ITERATION_STARTED, Events.ITERATION_COMPLETED, Events.EPOCH_COMPLETED, Events.COMPLETED, ] def __init__( self, persist: bool = False, bar_format: Union[ str, None ] = "{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]", **tqdm_kwargs: Any, ): try: from tqdm.autonotebook import tqdm except ImportError: raise ModuleNotFoundError( "This contrib module requires tqdm to be installed. " "Please install it with command: \n pip install tqdm" ) self.pbar_cls = tqdm self.pbar = None self.persist = persist self.bar_format = bar_format self.tqdm_kwargs = tqdm_kwargs def _reset(self, pbar_total: Optional[int]) -> None: self.pbar = self.pbar_cls( total=pbar_total, leave=self.persist, bar_format=self.bar_format, initial=1, **self.tqdm_kwargs ) def _close(self, engine: Engine) -> None: if self.pbar is not None: # https://github.com/tqdm/notebook.py#L240-L250 # issue #1115 : notebook backend of tqdm checks if n < total (error or KeyboardInterrupt) # and the bar persists in 'danger' mode if self.pbar.total is not None: self.pbar.n = self.pbar.total self.pbar.close() self.pbar = None @staticmethod def _compare_lt( event1: Union[Events, CallableEventWithFilter], event2: Union[Events, CallableEventWithFilter] ) -> bool: i1 = ProgressBar._events_order.index(event1) i2 = ProgressBar._events_order.index(event2) return i1 < i2 def log_message(self, message: str) -> None: """ Logs a message, preserving the progress bar correct output format. Args: message: string you wish to log. """ from tqdm import tqdm tqdm.write(message, file=self.tqdm_kwargs.get("file", None)) def attach( # type: ignore[override] self, engine: Engine, metric_names: Optional[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, event_name: Union[Events, CallableEventWithFilter] = Events.ITERATION_COMPLETED, closing_event_name: Union[Events, CallableEventWithFilter] = Events.EPOCH_COMPLETED, state_attributes: Optional[List[str]] = None, ) -> None: """ Attaches the progress bar to an engine object. Args: engine: engine object. metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: a function to select what you want to print from the engine's output. This function may return either a dictionary with entries in the format of ``{name: value}``, or a single scalar, which will be displayed with the default name `output`. event_name: event's name on which the progress bar advances. Valid events are from :class:`~ignite.engine.events.Events`. closing_event_name: event's name on which the progress bar is closed. Valid events are from :class:`~ignite.engine.events.Events`. state_attributes: list of attributes of the ``trainer.state`` to plot. Note: Accepted output value types are numbers, 0d and 1d torch tensors and strings. """ desc = self.tqdm_kwargs.get("desc", None) if event_name not in engine._allowed_events: raise ValueError(f"Logging event {event_name.name} is not in allowed events for this engine") if isinstance(closing_event_name, CallableEventWithFilter): if closing_event_name.filter is not None: raise ValueError("Closing Event should not be a filtered event") if not self._compare_lt(event_name, closing_event_name): raise ValueError(f"Logging event {event_name} should be called before closing event {closing_event_name}") log_handler = _OutputHandler( desc, metric_names, output_transform, closing_event_name=closing_event_name, state_attributes=state_attributes, ) super(ProgressBar, self).attach(engine, log_handler, event_name) engine.add_event_handler(closing_event_name, self._close) def attach_opt_params_handler( # type: ignore[empty-body] self, engine: Engine, event_name: Union[str, Events], *args: Any, **kwargs: Any ) -> RemovableEventHandle: """Intentionally empty""" pass def _create_output_handler(self, *args: Any, **kwargs: Any) -> "_OutputHandler": return _OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> Callable: # type: ignore[empty-body] """Intentionally empty""" pass class _OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics pbar = ProgressBar() Args: description: progress bar description. metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{'loss': loss1, 'another_loss': loss2}` to label the plot with corresponding keys. closing_event_name: event's name on which the progress bar is closed. Valid events are from :class:`~ignite.engine.events.Events` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. state_attributes: list of attributes of the ``trainer.state`` to plot. """ def __init__( self, description: str, metric_names: Optional[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, closing_event_name: Union[Events, CallableEventWithFilter] = Events.EPOCH_COMPLETED, state_attributes: Optional[List[str]] = None, ): if metric_names is None and output_transform is None: # This helps to avoid 'Either metric_names or output_transform should be defined' of BaseOutputHandler metric_names = [] super(_OutputHandler, self).__init__( description, metric_names, output_transform, global_step_transform=None, state_attributes=state_attributes ) self.closing_event_name = closing_event_name @staticmethod def get_max_number_events(event_name: Union[str, Events, CallableEventWithFilter], engine: Engine) -> Optional[int]: if event_name in (Events.ITERATION_STARTED, Events.ITERATION_COMPLETED): return engine.state.epoch_length if event_name in (Events.EPOCH_STARTED, Events.EPOCH_COMPLETED): return engine.state.max_epochs return 1 def __call__(self, engine: Engine, logger: ProgressBar, event_name: Union[str, Events]) -> None: pbar_total = self.get_max_number_events(event_name, engine) if logger.pbar is None: logger._reset(pbar_total=pbar_total) max_epochs = engine.state.max_epochs default_desc = "Iteration" if max_epochs == 1 else "Epoch" desc = self.tag or default_desc max_num_of_closing_events = self.get_max_number_events(self.closing_event_name, engine) if max_num_of_closing_events and max_num_of_closing_events > 1: global_step = engine.state.get_event_attrib_value(self.closing_event_name) desc += f" [{global_step}/{max_num_of_closing_events}]" logger.pbar.set_description(desc) # type: ignore[attr-defined] rendered_metrics = self._setup_output_metrics_state_attrs(engine, log_text=True) metrics = OrderedDict() for key, value in rendered_metrics.items(): key = "_".join(key[1:]) # tqdm has tag as description metrics[key] = value if metrics: logger.pbar.set_postfix(metrics) # type: ignore[attr-defined] global_step = engine.state.get_event_attrib_value(event_name) if pbar_total is not None: global_step = (global_step - 1) % pbar_total + 1 logger.pbar.update(global_step - logger.pbar.n) # type: ignore[attr-defined] ignite-0.5.1/ignite/handlers/utils.py000066400000000000000000000015501465426447700176110ustar00rootroot00000000000000from typing import Any, Callable, Optional from ignite.engine import Engine from ignite.engine.events import Events def global_step_from_engine(engine: Engine, custom_event_name: Optional[Events] = None) -> Callable: """Helper method to setup `global_step_transform` function using another engine. This can be helpful for logging trainer epoch/iteration while output handler is attached to an evaluator. Args: engine: engine which state is used to provide the global step custom_event_name: registered event name. Optional argument, event name to use. Returns: global step based on provided engine """ def wrapper(_: Any, event_name: Events) -> int: if custom_event_name is not None: event_name = custom_event_name return engine.state.get_event_attrib_value(event_name) return wrapper ignite-0.5.1/ignite/handlers/visdom_logger.py000066400000000000000000000520571465426447700213210ustar00rootroot00000000000000"""Visdom logger and its helper handlers.""" import os from typing import Any, Callable, cast, Dict, List, Optional, Union import torch import torch.nn as nn from torch.optim import Optimizer from ignite.engine import Engine, Events from ignite.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsScalarHandler, ) from ignite.handlers.utils import global_step_from_engine # noqa __all__ = [ "VisdomLogger", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", "global_step_from_engine", ] class VisdomLogger(BaseLogger): """ VisdomLogger handler to log metrics, model/optimizer parameters, gradients during the training and validation. This class requires `visdom `_ package to be installed: .. code-block:: bash pip install git+https://github.com/fossasia/visdom.git Args: server: visdom server URL. It can be also specified by environment variable `VISDOM_SERVER_URL` port: visdom server's port. It can be also specified by environment variable `VISDOM_PORT` num_workers: number of workers to use in `concurrent.futures.ThreadPoolExecutor` to post data to visdom server. Default, `num_workers=1`. If `num_workers=0` and logger uses the main thread. If using Python 2.7 and `num_workers>0` the package `futures` should be installed: `pip install futures` kwargs: kwargs to pass into `visdom.Visdom `_. Note: We can also specify username/password using environment variables: VISDOM_USERNAME, VISDOM_PASSWORD .. warning:: Frequent logging, e.g. when logger is attached to `Events.ITERATION_COMPLETED`, can slow down the run if the main thread is used to send the data to visdom server (`num_workers=0`). To avoid this situation we can either log less frequently or set `num_workers=1`. Examples: .. code-block:: python from ignite.handlers.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the trainer to log training loss at each iteration vd_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. vd_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. vd_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration vd_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) # Attach the logger to the trainer to log model's weights norm after each iteration vd_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model) ) # Attach the logger to the trainer to log model's gradients norm after each iteration vd_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model) ) # We need to close the logger with we are done vd_logger.close() It is also possible to use the logger as context manager: .. code-block:: python from ignite.handlers.visdom_logger import * with VisdomLogger() as vd_logger: trainer = Engine(update_fn) # Attach the logger to the trainer to log training loss at each iteration vd_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, server: Optional[str] = None, port: Optional[int] = None, num_workers: int = 1, raise_exceptions: bool = True, **kwargs: Any, ): try: import visdom except ImportError: raise ModuleNotFoundError( "This contrib module requires visdom package. " "Please install it with command:\n" "pip install git+https://github.com/fossasia/visdom.git" ) if num_workers > 0: # If visdom is installed, one of its dependencies `tornado` # requires also `futures` to be installed. # Let's check anyway if we can import it. try: from concurrent.futures import ThreadPoolExecutor except ImportError: raise ModuleNotFoundError( "This contrib module requires concurrent.futures module" "Please install it with command:\n" "pip install futures" ) if server is None: server = cast(str, os.environ.get("VISDOM_SERVER_URL", "localhost")) if port is None: port = int(os.environ.get("VISDOM_PORT", 8097)) if "username" not in kwargs: username = os.environ.get("VISDOM_USERNAME", None) kwargs["username"] = username if "password" not in kwargs: password = os.environ.get("VISDOM_PASSWORD", None) kwargs["password"] = password self.vis = visdom.Visdom(server=server, port=port, raise_exceptions=raise_exceptions, **kwargs) if not self.vis.offline and not self.vis.check_connection(): # type: ignore[attr-defined] raise RuntimeError(f"Failed to connect to Visdom server at {server}. Did you run python -m visdom.server ?") self.executor: Union[_DummyExecutor, "ThreadPoolExecutor"] = _DummyExecutor() if num_workers > 0: self.executor = ThreadPoolExecutor(max_workers=num_workers) def _save(self) -> None: self.vis.save([self.vis.env]) # type: ignore[attr-defined] def close(self) -> None: self.executor.shutdown() self.vis.close() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class _BaseVisDrawer: def __init__(self, show_legend: bool = False): self.windows: Dict[str, Any] = {} self.show_legend = show_legend def add_scalar( self, logger: VisdomLogger, k: str, v: Union[str, float, torch.Tensor], event_name: Any, global_step: int ) -> None: """ Helper method to log a scalar with VisdomLogger. Args: logger: visdom logger k: scalar name which is used to set window title and y-axis label v: scalar value, y-axis value event_name: Event name which is used to setup x-axis label. Valid events are from :class:`~ignite.engine.events.Events` or any `event_name` added by :meth:`~ignite.engine.engine.Engine.register_events`. global_step: global step, x-axis value """ if k not in self.windows: self.windows[k] = { "win": None, "opts": {"title": k, "xlabel": str(event_name), "ylabel": k, "showlegend": self.show_legend}, } update = None if self.windows[k]["win"] is None else "append" kwargs = { "X": [global_step], "Y": [v], "env": logger.vis.env, # type: ignore[attr-defined] "win": self.windows[k]["win"], "update": update, "opts": self.windows[k]["opts"], "name": k, } future = logger.executor.submit(logger.vis.line, **kwargs) if self.windows[k]["win"] is None: self.windows[k]["win"] = future.result() class OutputHandler(BaseOutputHandler, _BaseVisDrawer): """Helper handler to log engine's output and/or metrics Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.visdom_logger.global_step_from_engine`. show_legend: flag to show legend in the window state_attributes: list of attributes of the ``trainer.state`` to plot. Examples: .. code-block:: python from ignite.handlers.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: vd_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently vd_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.visdom_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) vd_logger = VisdomLogger() def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on Visdom. vd_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python vd_logger.attach( trainer, log_handler=OutputHandler( tag="training", metric_names=["nll", "accuracy"], state_attributes=["alpha", "beta"], ), event_name=Events.ITERATION_COMPLETED ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) """ def __init__( self, tag: str, metric_names: Optional[str] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, show_legend: bool = False, state_attributes: Optional[List[str]] = None, ): super(OutputHandler, self).__init__( tag, metric_names, output_transform, global_step_transform, state_attributes ) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine: Engine, logger: VisdomLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler 'OutputHandler' works only with VisdomLogger") metrics = self._setup_output_metrics_state_attrs(engine, key_tuple=False) global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) for key, value in metrics.items(): self.add_scalar(logger, key, value, event_name, global_step) logger._save() class OptimizerParamsHandler(BaseOptimizerParamsHandler, _BaseVisDrawer): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" show_legend: flag to show legend in the window Examples: .. code-block:: python from ignite.handlers.visdom_logger import * # Create a logger vb_logger = VisdomLogger() # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration vd_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently vd_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__( self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None, show_legend: bool = False ): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine: Engine, logger: VisdomLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with VisdomLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } for k, v in params.items(): self.add_scalar(logger, k, v, event_name, global_step) logger._save() class WeightsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer): """Helper handler to log model's weights as scalars. Handler iterates over named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" show_legend: flag to show legend in the window Examples: .. code-block:: python from ignite.handlers.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the trainer to log model's weights norm after each iteration vd_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, reduction=torch.norm) ) """ def __init__( self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None, show_legend: bool = False ): super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine: Engine, logger: VisdomLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler 'WeightsScalarHandler' works only with VisdomLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.model.named_parameters(): name = name.replace(".", "/") k = f"{tag_prefix}weights_{self.reduction.__name__}/{name}" v = self.reduction(p.data) self.add_scalar(logger, k, v, event_name, global_step) logger._save() class GradsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer): """Helper handler to log model's gradients as scalars. Handler iterates over the gradients of named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar. Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" show_legend: flag to show legend in the window Examples: .. code-block:: python from ignite.handlers.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the trainer to log model's weights norm after each iteration vd_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, reduction=torch.norm) ) """ def __init__( self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None, show_legend: bool = False ): super(GradsScalarHandler, self).__init__(model, reduction, tag) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine: Engine, logger: VisdomLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler 'GradsScalarHandler' works only with VisdomLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") k = f"{tag_prefix}grads_{self.reduction.__name__}/{name}" v = self.reduction(p.grad) self.add_scalar(logger, k, v, event_name, global_step) logger._save() class _DummyExecutor: class _DummyFuture: def __init__(self, result: Any) -> None: self._output = result def result(self) -> Any: return self._output def __init__(self, *args: Any, **kwargs: Any) -> None: pass def submit(self, fn: Callable, **kwargs: Any) -> "_DummyFuture": return _DummyExecutor._DummyFuture(fn(**kwargs)) def shutdown(self, *args: Any, **kwargs: Any) -> None: pass ignite-0.5.1/ignite/handlers/wandb_logger.py000066400000000000000000000340571465426447700211130ustar00rootroot00000000000000"""WandB logger and its helper handlers.""" from typing import Any, Callable, List, Optional, Union from torch.optim import Optimizer from ignite.engine import Engine, Events from ignite.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.handlers.utils import global_step_from_engine # noqa __all__ = ["WandBLogger", "OutputHandler", "OptimizerParamsHandler", "global_step_from_engine"] class WandBLogger(BaseLogger): """`Weights & Biases `_ handler to log metrics, model/optimizer parameters, gradients during training and validation. It can also be used to log model checkpoints to the Weights & Biases cloud. .. code-block:: bash pip install wandb This class is also a wrapper for the wandb module. This means that you can call any wandb function using this wrapper. See examples on how to save model parameters and gradients. Args: args: Positional arguments accepted by `wandb.init`. kwargs: Keyword arguments accepted by `wandb.init`. Please see `wandb.init `_ for documentation of possible parameters. Examples: .. code-block:: python from ignite.handlers.wandb_logger import * # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) # Attach the logger to the trainer to log training loss at each iteration wandb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=lambda *_: trainer.state.iteration` to take iteration value # of the `trainer`: wandb_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=lambda *_: trainer.state.iteration` to take iteration value # of the `trainer` instead of `evaluator`. wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration wandb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) # We need to close the logger when we are done wandb_logger.close() If you want to log model gradients, the model call graph, etc., use the logger as wrapper of wandb. Refer to the documentation of wandb.watch for details: .. code-block:: python wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) model = torch.nn.Sequential(...) wandb_logger.watch(model) For model checkpointing, Weights & Biases creates a local run dir, and automatically synchronizes all files saved there at the end of the run. You can just use the `wandb_logger.run.dir` as path for the `ModelCheckpoint`: .. code-block:: python from ignite.handlers import ModelCheckpoint def score_function(engine): return engine.state.metrics['accuracy'] model_checkpoint = ModelCheckpoint( wandb_logger.run.dir, n_saved=2, filename_prefix='best', require_empty=False, score_function=score_function, score_name="validation_accuracy", global_step_transform=global_step_from_engine(trainer) ) evaluator.add_event_handler(Events.COMPLETED, model_checkpoint, {'model': model}) """ def __init__(self, *args: Any, **kwargs: Any): try: import wandb self._wandb = wandb except ImportError: raise ModuleNotFoundError( "This contrib module requires wandb to be installed. " "You man install wandb with the command:\n pip install wandb\n" ) if kwargs.get("init", True): kwargs.pop("init", None) wandb.init(*args, **kwargs) def __getattr__(self, attr: Any) -> Any: return getattr(self._wandb, attr) def close(self) -> None: self._wandb.finish() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs) class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.handlers.wandb_logger.global_step_from_engine`. sync: If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log. Examples: .. code-block:: python from ignite.handlers.wandb_logger import * # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=lambda *_: trainer.state.iteration,` to take iteration value # of the `trainer`: wandb_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ), event_name=Events.EPOCH_COMPLETED ) # or equivalently wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=lambda *_: trainer.state.iteration, ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.handlers.wandb_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on Weights & Biases. wandb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta`` are also logged along with the NLL and Accuracy after each iteration: .. code-block:: python wandb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", metrics=["nll", "accuracy"], state_attributes=["alpha", "beta"], ) Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) .. versionchanged:: 0.4.7 accepts an optional list of `state_attributes` """ def __init__( self, tag: str, metric_names: Optional[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None, sync: Optional[bool] = None, state_attributes: Optional[List[str]] = None, ): super().__init__(tag, metric_names, output_transform, global_step_transform, state_attributes) self.sync = sync def __call__(self, engine: Engine, logger: WandBLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, WandBLogger): raise RuntimeError(f"Handler '{self.__class__.__name__}' works only with WandBLogger.") global_step = self.global_step_transform(engine, event_name) if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) metrics = self._setup_output_metrics_state_attrs(engine, log_text=True, key_tuple=False) logger.log(metrics, step=global_step, sync=self.sync) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" sync: If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log. Examples: .. code-block:: python from ignite.handlers.wandb_logger import * # Create a logger. All parameters are optional. See documentation # on wandb.init for details. wandb_logger = WandBLogger( entity="shared", project="pytorch-ignite-integration", name="cnn-mnist", config={"max_epochs": 10}, tags=["pytorch-ignite", "minst"] ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration wandb_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently wandb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) """ def __init__( self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None, sync: Optional[bool] = None ): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) self.sync = sync def __call__(self, engine: Engine, logger: WandBLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, WandBLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with WandBLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } logger.log(params, step=global_step, sync=self.sync) ignite-0.5.1/ignite/metrics/000077500000000000000000000000001465426447700157445ustar00rootroot00000000000000ignite-0.5.1/ignite/metrics/__init__.py000066400000000000000000000060661465426447700200650ustar00rootroot00000000000000import ignite.metrics.regression from ignite.metrics.accumulation import Average, GeometricAverage, VariableAccumulation from ignite.metrics.accuracy import Accuracy from ignite.metrics.average_precision import AveragePrecision from ignite.metrics.classification_report import ClassificationReport from ignite.metrics.cohen_kappa import CohenKappa from ignite.metrics.confusion_matrix import ConfusionMatrix, DiceCoefficient, IoU, JaccardIndex, mIoU from ignite.metrics.cosine_similarity import CosineSimilarity from ignite.metrics.entropy import Entropy from ignite.metrics.epoch_metric import EpochMetric from ignite.metrics.fbeta import Fbeta from ignite.metrics.frequency import Frequency from ignite.metrics.gan.fid import FID from ignite.metrics.gan.inception_score import InceptionScore from ignite.metrics.gpu_info import GpuInfo from ignite.metrics.js_divergence import JSDivergence from ignite.metrics.kl_divergence import KLDivergence from ignite.metrics.loss import Loss from ignite.metrics.maximum_mean_discrepancy import MaximumMeanDiscrepancy from ignite.metrics.mean_absolute_error import MeanAbsoluteError from ignite.metrics.mean_pairwise_distance import MeanPairwiseDistance from ignite.metrics.mean_squared_error import MeanSquaredError from ignite.metrics.metric import BatchFiltered, BatchWise, EpochWise, Metric, MetricUsage from ignite.metrics.metric_group import MetricGroup from ignite.metrics.metrics_lambda import MetricsLambda from ignite.metrics.multilabel_confusion_matrix import MultiLabelConfusionMatrix from ignite.metrics.mutual_information import MutualInformation from ignite.metrics.nlp.bleu import Bleu from ignite.metrics.nlp.rouge import Rouge, RougeL, RougeN from ignite.metrics.precision import Precision from ignite.metrics.precision_recall_curve import PrecisionRecallCurve from ignite.metrics.psnr import PSNR from ignite.metrics.recall import Recall from ignite.metrics.roc_auc import ROC_AUC, RocCurve from ignite.metrics.root_mean_squared_error import RootMeanSquaredError from ignite.metrics.running_average import RunningAverage from ignite.metrics.ssim import SSIM from ignite.metrics.top_k_categorical_accuracy import TopKCategoricalAccuracy __all__ = [ "Metric", "Accuracy", "Loss", "MetricGroup", "MetricsLambda", "MeanAbsoluteError", "MeanPairwiseDistance", "MeanSquaredError", "ConfusionMatrix", "CosineSimilarity", "ClassificationReport", "TopKCategoricalAccuracy", "Average", "DiceCoefficient", "Entropy", "EpochMetric", "Fbeta", "FID", "GeometricAverage", "IoU", "InceptionScore", "mIoU", "JaccardIndex", "JSDivergence", "KLDivergence", "MaximumMeanDiscrepancy", "MultiLabelConfusionMatrix", "MutualInformation", "Precision", "PSNR", "Recall", "RootMeanSquaredError", "RunningAverage", "VariableAccumulation", "Frequency", "SSIM", "Bleu", "Rouge", "RougeN", "RougeL", "regression", "AveragePrecision", "CohenKappa", "GpuInfo", "PrecisionRecallCurve", "RocCurve", "ROC_AUC", ] ignite-0.5.1/ignite/metrics/accumulation.py000066400000000000000000000302331465426447700210030ustar00rootroot00000000000000import numbers from typing import Callable, Tuple, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["VariableAccumulation", "GeometricAverage", "Average"] class VariableAccumulation(Metric): """Single variable accumulator helper to compute (arithmetic, geometric, harmonic) average of a single variable. - ``update`` must receive output of the form `x`. - `x` can be a number or `torch.Tensor`. Note: The class stores input into two public variables: `accumulator` and `num_examples`. Number of samples is updated following the rule: - `+1` if input is a number - `+1` if input is a 1D `torch.Tensor` - `+batch_size` if input is a ND `torch.Tensor`. Batch size is the first dimension (`shape[0]`). Args: op: a callable to update accumulator. Method's signature is `(accumulator, output)`. For example, to compute arithmetic mean value, `op = lambda a, x: a + x`. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ required_output_keys = None _state_dict_all_req_keys = ("accumulator", "num_examples") def __init__( self, op: Callable, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): if not callable(op): raise TypeError(f"Argument op should be a callable, but given {type(op)}") self._op = op super(VariableAccumulation, self).__init__( output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @reinit__is_reduced def reset(self) -> None: self.accumulator = torch.tensor(0.0, dtype=torch.float64, device=self._device) self.num_examples = 0 def _check_output_type(self, output: Union[float, torch.Tensor]) -> None: if not isinstance(output, (numbers.Number, torch.Tensor)): raise TypeError(f"Output should be a number or torch.Tensor, but given {type(output)}") @reinit__is_reduced def update(self, output: Union[float, torch.Tensor]) -> None: self._check_output_type(output) if isinstance(output, torch.Tensor): output = output.detach() if not (output.device == self._device and output.dtype == self.accumulator.dtype): output = output.to(self.accumulator) self.accumulator = self._op(self.accumulator, output) if isinstance(output, torch.Tensor): self.num_examples += output.shape[0] if len(output.shape) > 1 else 1 else: self.num_examples += 1 @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Tuple[torch.Tensor, int]: return self.accumulator, self.num_examples class Average(VariableAccumulation): """Helper class to compute arithmetic average of a single variable. - ``update`` must receive output of the form `x`. - `x` can be a number or `torch.Tensor`. Note: Number of samples is updated following the rule: - `+1` if input is a number - `+1` if input is a 1D `torch.Tensor` - `+batch_size` if input is an ND `torch.Tensor`. Batch size is the first dimension (`shape[0]`). For input `x` being an ND `torch.Tensor` with N > 1, the first dimension is seen as the number of samples and is summed up and added to the accumulator: `accumulator += x.sum(dim=0)` ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = Average() metric.attach(default_evaluator, 'avg') # Case 1. input is er data = torch.tensor([0, 1, 2, 3, 4]) state = default_evaluator.run(data) print(state.metrics['avg']) .. testoutput:: 2.0 .. testcode:: metric = Average() metric.attach(default_evaluator, 'avg') # Case 2. input is a 1D torch.Tensor data = torch.tensor([ [0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3] ]) state = default_evaluator.run(data) print(state.metrics['avg']) .. testoutput:: tensor([1.5000, 1.5000, 1.5000], dtype=torch.float64) .. testcode:: metric = Average() metric.attach(default_evaluator, 'avg') # Case 3. input is a ND torch.Tensor data = [ torch.tensor([[0, 0, 0], [1, 1, 1]]), torch.tensor([[2, 2, 2], [3, 3, 3]]) ] state = default_evaluator.run(data) print(state.metrics['avg']) .. testoutput:: tensor([1.5000, 1.5000, 1.5000], dtype=torch.float64) .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): def _mean_op(a: Union[float, torch.Tensor], x: Union[float, torch.Tensor]) -> Union[float, torch.Tensor]: if isinstance(x, torch.Tensor) and x.ndim > 1: x = x.sum(dim=0) return a + x super(Average, self).__init__( op=_mean_op, output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Union[float, torch.Tensor]: if self.num_examples < 1: raise NotComputableError( f"{self.__class__.__name__} must have at least one example before it can be computed." ) return self.accumulator / self.num_examples class GeometricAverage(VariableAccumulation): """Helper class to compute geometric average of a single variable. - ``update`` must receive output of the form `x`. - `x` can be a positive number or a positive `torch.Tensor`, such that ``torch.log(x)`` is not `nan`. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Note: Number of samples is updated following the rule: - `+1` if input is a number - `+1` if input is a 1D `torch.Tensor` - `+batch_size` if input is a ND `torch.Tensor`. Batch size is the first dimension (`shape[0]`). For input `x` being an ND `torch.Tensor` with N > 1, the first dimension is seen as the number of samples and is aggregated and added to the accumulator: `accumulator *= prod(x, dim=0)` ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = GeometricAverage() metric.attach(default_evaluator, 'avg') # Case 1. input is er data = torch.tensor([1, 2, 3]) state = default_evaluator.run(data) print(state.metrics['avg']) .. testoutput:: 1.8171... .. testcode:: metric = GeometricAverage() metric.attach(default_evaluator, 'avg') # Case 2. input is a 1D torch.Tensor data = torch.tensor([ [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], ]) state = default_evaluator.run(data) print(state.metrics['avg']) .. testoutput:: tensor([2.2134, 2.2134, 2.2134], dtype=torch.float64) .. testcode:: metric = GeometricAverage() metric.attach(default_evaluator, 'avg') # Case 3. input is a ND torch.Tensor data = [ torch.tensor([[1, 1, 1], [2, 2, 2]]), torch.tensor([[3, 3, 3], [4, 4, 4]]) ] state = default_evaluator.run(data) print(state.metrics['avg']) .. testoutput:: tensor([2.2134, 2.2134, 2.2134], dtype=torch.float64) .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): def _geom_op(a: torch.Tensor, x: Union[float, torch.Tensor]) -> torch.Tensor: if not isinstance(x, torch.Tensor): x = torch.tensor(x) x = torch.log(x) if x.ndim > 1: x = x.sum(dim=0) return a + x super(GeometricAverage, self).__init__( op=_geom_op, output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @sync_all_reduce("accumulator", "num_examples") def compute(self) -> Union[float, torch.Tensor]: if self.num_examples < 1: raise NotComputableError( f"{self.__class__.__name__} must have at least one example before it can be computed." ) tensor = torch.exp(self.accumulator / self.num_examples) if tensor.numel() == 1: return tensor.item() return tensor ignite-0.5.1/ignite/metrics/accuracy.py000066400000000000000000000236701465426447700201200ustar00rootroot00000000000000from typing import Callable, Optional, Sequence, Tuple, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["Accuracy"] class _BaseClassification(Metric): def __init__( self, output_transform: Callable = lambda x: x, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): self._is_multilabel = is_multilabel self._type: Optional[str] = None self._num_classes: Optional[int] = None super(_BaseClassification, self).__init__( output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) def reset(self) -> None: self._type = None self._num_classes = None def _check_shape(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output if not (y.ndimension() == y_pred.ndimension() or y.ndimension() + 1 == y_pred.ndimension()): raise ValueError( "y must have shape of (batch_size, ...) and y_pred must have " "shape of (batch_size, num_categories, ...) or (batch_size, ...), " f"but given {y.shape} vs {y_pred.shape}." ) y_shape = y.shape y_pred_shape: Tuple[int, ...] = y_pred.shape if y.ndimension() + 1 == y_pred.ndimension(): y_pred_shape = (y_pred_shape[0],) + y_pred_shape[2:] if not (y_shape == y_pred_shape): raise ValueError("y and y_pred must have compatible shapes.") if self._is_multilabel and not (y.shape == y_pred.shape and y.ndimension() > 1 and y.shape[1] > 1): raise ValueError( "y and y_pred must have same shape of (batch_size, num_categories, ...) and num_categories > 1." ) def _check_binary_multilabel_cases(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output if not torch.equal(y, y**2): raise ValueError("For binary cases, y must be comprised of 0's and 1's.") if not torch.equal(y_pred, y_pred**2): raise ValueError("For binary cases, y_pred must be comprised of 0's and 1's.") def _check_type(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output if y.ndimension() + 1 == y_pred.ndimension(): num_classes = y_pred.shape[1] if num_classes == 1: update_type = "binary" self._check_binary_multilabel_cases((y_pred, y)) else: update_type = "multiclass" elif y.ndimension() == y_pred.ndimension(): self._check_binary_multilabel_cases((y_pred, y)) if self._is_multilabel: update_type = "multilabel" num_classes = y_pred.shape[1] else: update_type = "binary" num_classes = 1 else: raise RuntimeError( f"Invalid shapes of y (shape={y.shape}) and y_pred (shape={y_pred.shape}), check documentation." " for expected shapes of y and y_pred." ) if self._type is None: self._type = update_type self._num_classes = num_classes else: if self._type != update_type: raise RuntimeError(f"Input data type has changed from {self._type} to {update_type}.") if self._num_classes != num_classes: raise ValueError(f"Input data number of classes has changed from {self._num_classes} to {num_classes}") class Accuracy(_BaseClassification): r"""Calculates the accuracy for binary, multiclass and multilabel data. .. math:: \text{Accuracy} = \frac{ TP + TN }{ TP + TN + FP + FN } where :math:`\text{TP}` is true positives, :math:`\text{TN}` is true negatives, :math:`\text{FP}` is false positives and :math:`\text{FN}` is false negatives. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...). - `y` must be in the following shape (batch_size, ...). - `y` and `y_pred` must be in the following shape of (batch_size, num_categories, ...) and num_categories must be greater than 1 for multilabel cases. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. is_multilabel: flag to use in multilabel case. By default, False. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Binary case .. testcode:: 1 metric = Accuracy() metric.attach(default_evaluator, "accuracy") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["accuracy"]) .. testoutput:: 1 0.6666... Multiclass case .. testcode:: 2 metric = Accuracy() metric.attach(default_evaluator, "accuracy") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["accuracy"]) .. testoutput:: 2 0.5 Multilabel case .. testcode:: 3 metric = Accuracy(is_multilabel=True) metric.attach(default_evaluator, "accuracy") y_true = torch.tensor([ [0, 0, 1, 0, 1], [1, 0, 1, 0, 0], [0, 0, 0, 0, 1], [1, 0, 0, 0, 1], [0, 1, 1, 0, 1], ]) y_pred = torch.tensor([ [1, 1, 0, 0, 0], [1, 0, 1, 0, 0], [1, 0, 0, 0, 0], [1, 0, 1, 1, 1], [1, 1, 0, 0, 1], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["accuracy"]) .. testoutput:: 3 0.2 In binary and multilabel cases, the elements of `y` and `y_pred` should have 0 or 1 values. Thresholding of predictions can be done as below: .. testcode:: 4 def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y metric = Accuracy(output_transform=thresholded_output_transform) metric.attach(default_evaluator, "accuracy") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["accuracy"]) .. testoutput:: 4 0.6666... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_num_correct", "_num_examples") def __init__( self, output_transform: Callable = lambda x: x, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): super(Accuracy, self).__init__( output_transform=output_transform, is_multilabel=is_multilabel, device=device, skip_unrolling=skip_unrolling ) @reinit__is_reduced def reset(self) -> None: self._num_correct = torch.tensor(0, device=self._device) self._num_examples = 0 super(Accuracy, self).reset() @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_shape(output) self._check_type(output) y_pred, y = output[0].detach(), output[1].detach() if self._type == "binary": correct = torch.eq(y_pred.view(-1).to(y), y.view(-1)) elif self._type == "multiclass": indices = torch.argmax(y_pred, dim=1) correct = torch.eq(indices, y).view(-1) elif self._type == "multilabel": # if y, y_pred shape is (N, C, ...) -> (N x ..., C) num_classes = y_pred.size(1) last_dim = y_pred.ndimension() y_pred = torch.transpose(y_pred, 1, last_dim - 1).reshape(-1, num_classes) y = torch.transpose(y, 1, last_dim - 1).reshape(-1, num_classes) correct = torch.all(y == y_pred.type_as(y), dim=-1) self._num_correct += torch.sum(correct).to(self._device) self._num_examples += correct.shape[0] @sync_all_reduce("_num_examples", "_num_correct") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("Accuracy must have at least one example before it can be computed.") return self._num_correct.item() / self._num_examples ignite-0.5.1/ignite/metrics/average_precision.py000066400000000000000000000071561465426447700220140ustar00rootroot00000000000000from typing import Callable, Union import torch from ignite.metrics.epoch_metric import EpochMetric def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: from sklearn.metrics import average_precision_score y_true = y_targets.cpu().numpy() y_pred = y_preds.cpu().numpy() return average_precision_score(y_true, y_pred) class AveragePrecision(EpochMetric): """Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.average_precision_score `_ . Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. check_compute_fn: Default False. If True, `average_precision_score `_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. device: optional device specification for internal storage. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Note: AveragePrecision expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below: .. code-block:: python def activated_output_transform(output): y_pred, y = output y_pred = torch.softmax(y_pred, dim=1) return y_pred, y avg_precision = AveragePrecision(activated_output_transform) Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: y_pred = torch.tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]]) y_true = torch.tensor([[1, 1], [1, 1], [0, 1], [0, 1]]) avg_precision = AveragePrecision() avg_precision.attach(default_evaluator, 'average_precision') state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['average_precision']) .. testoutput:: 0.9166... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): try: from sklearn.metrics import average_precision_score # noqa: F401 except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") super(AveragePrecision, self).__init__( average_precision_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, skip_unrolling=skip_unrolling, ) ignite-0.5.1/ignite/metrics/classification_report.py000066400000000000000000000135441465426447700227130ustar00rootroot00000000000000import json from typing import Callable, Collection, Dict, List, Optional, Union import torch from ignite.metrics.fbeta import Fbeta from ignite.metrics.metrics_lambda import MetricsLambda from ignite.metrics.precision import Precision from ignite.metrics.recall import Recall __all__ = ["ClassificationReport"] def ClassificationReport( beta: int = 1, output_dict: bool = False, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), is_multilabel: bool = False, labels: Optional[List[str]] = None, ) -> MetricsLambda: r"""Build a text report showing the main classification metrics. The report resembles in functionality to `scikit-learn classification_report `_ The underlying implementation doesn't use the sklearn function. Args: beta: weight of precision in harmonic mean output_dict: If True, return output as dict, otherwise return a str output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. is_multilabel: If True, the tensors are assumed to be multilabel. device: optional device specification for internal storage. labels: Optional list of label indices to include in the report Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Multiclass case .. testcode:: 1 metric = ClassificationReport(output_dict=True) metric.attach(default_evaluator, "cr") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["cr"].keys()) print(state.metrics["cr"]["0"]) print(state.metrics["cr"]["1"]) print(state.metrics["cr"]["2"]) print(state.metrics["cr"]["macro avg"]) .. testoutput:: 1 dict_keys(['0', '1', '2', 'macro avg']) {'precision': 0.5, 'recall': 0.5, 'f1-score': 0.4999...} {'precision': 1.0, 'recall': 0.5, 'f1-score': 0.6666...} {'precision': 0.3333..., 'recall': 0.5, 'f1-score': 0.3999...} {'precision': 0.6111..., 'recall': 0.5, 'f1-score': 0.5222...} Multilabel case, the shapes must be (batch_size, num_categories, ...) .. testcode:: 2 metric = ClassificationReport(output_dict=True, is_multilabel=True) metric.attach(default_evaluator, "cr") y_true = torch.tensor([ [0, 0, 1], [0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 1], ]) y_pred = torch.tensor([ [1, 1, 0], [1, 0, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["cr"].keys()) print(state.metrics["cr"]["0"]) print(state.metrics["cr"]["1"]) print(state.metrics["cr"]["2"]) print(state.metrics["cr"]["macro avg"]) .. testoutput:: 2 dict_keys(['0', '1', '2', 'macro avg']) {'precision': 0.2, 'recall': 1.0, 'f1-score': 0.3333...} {'precision': 0.5, 'recall': 1.0, 'f1-score': 0.6666...} {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0} {'precision': 0.2333..., 'recall': 0.6666..., 'f1-score': 0.3333...} """ # setup all the underlying metrics precision = Precision(average=False, is_multilabel=is_multilabel, output_transform=output_transform, device=device) recall = Recall(average=False, is_multilabel=is_multilabel, output_transform=output_transform, device=device) fbeta = Fbeta(beta, average=False, precision=precision, recall=recall) averaged_precision = precision.mean() averaged_recall = recall.mean() averaged_fbeta = fbeta.mean() def _wrapper( re: torch.Tensor, pr: torch.Tensor, f: torch.Tensor, a_re: torch.Tensor, a_pr: torch.Tensor, a_f: torch.Tensor ) -> Union[Collection[str], Dict]: if pr.shape != re.shape: raise ValueError( "Internal error: Precision and Recall have mismatched shapes: " f"{pr.shape} vs {re.shape}. Please, open an issue " "with a reference on this error. Thank you!" ) dict_obj = {} for idx, p_label in enumerate(pr): dict_obj[_get_label_for_class(idx)] = { "precision": p_label.item(), "recall": re[idx].item(), f"f{beta}-score": f[idx].item(), } dict_obj["macro avg"] = { "precision": a_pr.item(), "recall": a_re.item(), f"f{beta}-score": a_f.item(), } return dict_obj if output_dict else json.dumps(dict_obj) # helper method to get a label for a given class def _get_label_for_class(idx: int) -> str: return labels[idx] if labels else str(idx) return MetricsLambda(_wrapper, recall, precision, fbeta, averaged_recall, averaged_precision, averaged_fbeta) ignite-0.5.1/ignite/metrics/cohen_kappa.py000066400000000000000000000077311465426447700205760ustar00rootroot00000000000000from typing import Callable, Optional, Union import torch from ignite.metrics.epoch_metric import EpochMetric class CohenKappa(EpochMetric): """Compute different types of Cohen's Kappa: Non-Wieghted, Linear, Quadratic. Accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.cohen_kappa_score `_ . Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. weights: a string is used to define the type of Cohen's Kappa whether Non-Weighted or Linear or Quadratic. Default, None. check_compute_fn: Default False. If True, `cohen_kappa_score `_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. device: optional device specification for internal storage. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = CohenKappa() metric.attach(default_evaluator, 'ck') y_true = torch.tensor([2, 0, 2, 2, 0, 1]) y_pred = torch.tensor([0, 0, 2, 2, 0, 2]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['ck']) .. testoutput:: 0.4285... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, weights: Optional[str] = None, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): try: from sklearn.metrics import cohen_kappa_score # noqa: F401 except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") if weights not in (None, "linear", "quadratic"): raise ValueError("Kappa Weighting type must be None or linear or quadratic.") # initalize weights self.weights = weights self.cohen_kappa_compute = self.get_cohen_kappa_fn() super(CohenKappa, self).__init__( self.cohen_kappa_compute, output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, skip_unrolling=skip_unrolling, ) def get_cohen_kappa_fn(self) -> Callable[[torch.Tensor, torch.Tensor], float]: """Return a function computing Cohen Kappa from scikit-learn.""" from sklearn.metrics import cohen_kappa_score def wrapper(y_targets: torch.Tensor, y_preds: torch.Tensor) -> float: y_true = y_targets.cpu().numpy() y_pred = y_preds.cpu().numpy() return cohen_kappa_score(y_true, y_pred, weights=self.weights) return wrapper ignite-0.5.1/ignite/metrics/confusion_matrix.py000066400000000000000000000434471465426447700217210ustar00rootroot00000000000000import numbers from typing import Callable, Optional, Sequence, Tuple, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce from ignite.metrics.metrics_lambda import MetricsLambda __all__ = ["ConfusionMatrix", "mIoU", "IoU", "DiceCoefficient", "cmAccuracy", "cmPrecision", "cmRecall", "JaccardIndex"] class ConfusionMatrix(Metric): """Calculates confusion matrix for multi-class data. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` must contain logits and has the following shape (batch_size, num_classes, ...). If you are doing binary classification, see Note for an example on how to get this. - `y` should have the following shape (batch_size, ...) and contains ground-truth class indices with or without the background class. During the computation, argmax of `y_pred` is taken to determine predicted classes. Args: num_classes: Number of classes, should be > 1. See notes for more details. average: confusion matrix values averaging schema: None, "samples", "recall", "precision". Default is None. If `average="samples"` then confusion matrix values are normalized by the number of seen samples. If `average="recall"` then confusion matrix values are normalized such that diagonal values represent class recalls. If `average="precision"` then confusion matrix values are normalized such that diagonal values represent class precisions. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Note: The confusion matrix is formatted such that columns are predictions and rows are targets. For example, if you were to plot the matrix, you could correctly assign to the horizontal axis the label "predicted values" and to the vertical axis the label "actual values". Note: In case of the targets `y` in `(batch_size, ...)` format, target indices between 0 and `num_classes` only contribute to the confusion matrix and others are neglected. For example, if `num_classes=20` and target index equal 255 is encountered, then it is filtered out. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: 1 metric = ConfusionMatrix(num_classes=3) metric.attach(default_evaluator, 'cm') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['cm']) .. testoutput:: 1 tensor([[1, 1, 0], [0, 2, 0], [0, 1, 0]]) If you are doing binary classification with a single output unit, you may have to transform your network output, so that you have one value for each class. E.g. you can transform your network output into a one-hot vector with: .. testcode:: 2 def binary_one_hot_output_transform(output): from ignite import utils y_pred, y = output y_pred = torch.sigmoid(y_pred).round().long() y_pred = utils.to_onehot(y_pred, 2) y = y.long() return y_pred, y metric = ConfusionMatrix(num_classes=2, output_transform=binary_one_hot_output_transform) metric.attach(default_evaluator, 'cm') y_true = torch.tensor([0, 1, 0, 1, 0]) y_pred = torch.tensor([0, 0, 1, 1, 0]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['cm']) .. testoutput:: 2 tensor([[2, 1], [1, 1]]) .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("confusion_matrix", "_num_examples") def __init__( self, num_classes: int, average: Optional[str] = None, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = True, ): if average is not None and average not in ("samples", "recall", "precision"): raise ValueError("Argument average can None or one of 'samples', 'recall', 'precision'") if num_classes <= 1: raise ValueError("Argument num_classes needs to be > 1") self.num_classes = num_classes self._num_examples = 0 self.average = average super(ConfusionMatrix, self).__init__( output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @reinit__is_reduced def reset(self) -> None: self.confusion_matrix = torch.zeros(self.num_classes, self.num_classes, dtype=torch.int64, device=self._device) self._num_examples = 0 def _check_shape(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if y_pred.ndimension() < 2: raise ValueError( f"y_pred must have shape (batch_size, num_classes (currently set to {self.num_classes}), ...), " f"but given {y_pred.shape}" ) if y_pred.shape[1] != self.num_classes: raise ValueError(f"y_pred does not have correct number of classes: {y_pred.shape[1]} vs {self.num_classes}") if not (y.ndimension() + 1 == y_pred.ndimension()): raise ValueError( f"y_pred must have shape (batch_size, num_classes (currently set to {self.num_classes}), ...) " "and y must have shape of (batch_size, ...), " f"but given {y.shape} vs {y_pred.shape}." ) y_shape = y.shape y_pred_shape: Tuple[int, ...] = y_pred.shape if y.ndimension() + 1 == y_pred.ndimension(): y_pred_shape = (y_pred_shape[0],) + y_pred_shape[2:] if y_shape != y_pred_shape: raise ValueError("y and y_pred must have compatible shapes.") @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_shape(output) y_pred, y = output[0].detach(), output[1].detach() self._num_examples += y_pred.shape[0] # target is (batch_size, ...) y_pred = torch.argmax(y_pred, dim=1).flatten() y = y.flatten() target_mask = (y >= 0) & (y < self.num_classes) y = y[target_mask] y_pred = y_pred[target_mask] indices = self.num_classes * y + y_pred m = torch.bincount(indices, minlength=self.num_classes**2).reshape(self.num_classes, self.num_classes) self.confusion_matrix += m.to(self.confusion_matrix) @sync_all_reduce("confusion_matrix", "_num_examples") def compute(self) -> torch.Tensor: if self._num_examples == 0: raise NotComputableError("Confusion matrix must have at least one example before it can be computed.") if self.average: self.confusion_matrix = self.confusion_matrix.float() if self.average == "samples": return self.confusion_matrix / self._num_examples else: return self.normalize(self.confusion_matrix, self.average) return self.confusion_matrix @staticmethod def normalize(matrix: torch.Tensor, average: str) -> torch.Tensor: """Normalize given `matrix` with given `average`.""" if average == "recall": return matrix / (matrix.sum(dim=1).unsqueeze(1) + 1e-15) elif average == "precision": return matrix / (matrix.sum(dim=0) + 1e-15) else: raise ValueError("Argument average should be one of 'samples', 'recall', 'precision'") def IoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: r"""Calculates Intersection over Union using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. .. math:: \text{J}(A, B) = \frac{ \lvert A \cap B \rvert }{ \lvert A \cup B \rvert } Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Returns: MetricsLambda Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = IoU(cm) metric.attach(default_evaluator, 'iou') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['iou']) .. testoutput:: tensor([0.5000, 0.5000, 0.0000], dtype=torch.float64) """ if not isinstance(cm, ConfusionMatrix): raise TypeError(f"Argument cm should be instance of ConfusionMatrix, but given {type(cm)}") if not (cm.average in (None, "samples")): raise ValueError("ConfusionMatrix should have average attribute either None or 'samples'") if ignore_index is not None: if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes): raise ValueError( f"ignore_index should be integer and in the range of [0, {cm.num_classes}), but given {ignore_index}" ) # Increase floating point precision and pass to CPU cm = cm.to(torch.double) iou: MetricsLambda = cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag() + 1e-15) if ignore_index is not None: ignore_idx: int = ignore_index # used due to typing issues with mympy def ignore_index_fn(iou_vector: torch.Tensor) -> torch.Tensor: if ignore_idx >= len(iou_vector): raise ValueError(f"ignore_index {ignore_idx} is larger than the length of IoU vector {len(iou_vector)}") indices = list(range(len(iou_vector))) indices.remove(ignore_idx) return iou_vector[indices] return MetricsLambda(ignore_index_fn, iou) else: return iou def mIoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: """Calculates mean Intersection over Union using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Returns: MetricsLambda Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = mIoU(cm, ignore_index=0) metric.attach(default_evaluator, 'miou') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['miou']) .. testoutput:: 0.24999... """ iou: MetricsLambda = IoU(cm=cm, ignore_index=ignore_index).mean() return iou def cmAccuracy(cm: ConfusionMatrix) -> MetricsLambda: """Calculates accuracy using :class:`~ignite.metrics.metric.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.to(torch.double) accuracy: MetricsLambda = cm.diag().sum() / (cm.sum() + 1e-15) return accuracy def cmPrecision(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda: """Calculates precision using :class:`~ignite.metrics.metric.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric average: if True metric value is averaged over all classes Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.to(torch.double) precision: MetricsLambda = cm.diag() / (cm.sum(dim=0) + 1e-15) if average: mean: MetricsLambda = precision.mean() return mean return precision def cmRecall(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda: """ Calculates recall using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric average: if True metric value is averaged over all classes Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.to(torch.double) recall: MetricsLambda = cm.diag() / (cm.sum(dim=1) + 1e-15) if average: mean: MetricsLambda = recall.mean() return mean return recall def DiceCoefficient(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: """Calculates Dice Coefficient for a given :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = DiceCoefficient(cm, ignore_index=0) metric.attach(default_evaluator, 'dice') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['dice']) .. testoutput:: tensor([0.6667, 0.0000], dtype=torch.float64) """ if not isinstance(cm, ConfusionMatrix): raise TypeError(f"Argument cm should be instance of ConfusionMatrix, but given {type(cm)}") if ignore_index is not None: if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes): raise ValueError( f"ignore_index should be integer and in the range of [0, {cm.num_classes}), but given {ignore_index}" ) # Increase floating point precision and pass to CPU cm = cm.to(torch.double) dice: MetricsLambda = 2.0 * cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) + 1e-15) if ignore_index is not None: ignore_idx: int = ignore_index # used due to typing issues with mympy def ignore_index_fn(dice_vector: torch.Tensor) -> torch.Tensor: if ignore_idx >= len(dice_vector): raise ValueError( f"ignore_index {ignore_idx} is larger than the length of Dice vector {len(dice_vector)}" ) indices = list(range(len(dice_vector))) indices.remove(ignore_idx) return dice_vector[indices] return MetricsLambda(ignore_index_fn, dice) else: return dice def JaccardIndex(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: r"""Calculates the Jaccard Index using :class:`~ignite.metrics.confusion_matrix.ConfusionMatrix` metric. Implementation is based on :meth:`~ignite.metrics.IoU`. .. math:: \text{J}(A, B) = \frac{ \lvert A \cap B \rvert }{ \lvert A \cup B \rvert } Args: cm: instance of confusion matrix metric ignore_index: index to ignore, e.g. background index Returns: MetricsLambda Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: cm = ConfusionMatrix(num_classes=3) metric = JaccardIndex(cm, ignore_index=0) metric.attach(default_evaluator, 'jac') y_true = torch.tensor([0, 1, 0, 1, 2]) y_pred = torch.tensor([ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['jac']) .. testoutput:: tensor([0.5000, 0.0000], dtype=torch.float64) """ return IoU(cm, ignore_index) ignite-0.5.1/ignite/metrics/cosine_similarity.py000066400000000000000000000105151465426447700220460ustar00rootroot00000000000000from typing import Callable, Sequence, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["CosineSimilarity"] class CosineSimilarity(Metric): r"""Calculates the mean of the `cosine similarity `_. .. math:: \text{cosine\_similarity} = \frac{1}{N} \sum_{i=1}^N \frac{x_i \cdot y_i}{\max ( \| x_i \|_2 \| y_i \|_2 , \epsilon)} where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor. - ``update`` must receive output of the form ``(y_pred, y)``. Args: eps: a small value to avoid division by zero. Default: 1e-8 output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = CosineSimilarity() metric.attach(default_evaluator, 'cosine_similarity') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]).float() target = torch.tensor([ [1, 5, 1 ,11], [1, 3, 5, 1], [2, 3, 1, 5], [1, 2, 4, 1] ]).float() state = default_evaluator.run([[preds, target]]) print(state.metrics['cosine_similarity']) .. testoutput:: 0.5080491304397583 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, eps: float = 1e-8, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): super().__init__(output_transform, device, skip_unrolling=skip_unrolling) self.eps = eps _state_dict_all_req_keys = ("_sum_of_cos_similarities", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_cos_similarities = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred = output[0].flatten(start_dim=1).detach() y = output[1].flatten(start_dim=1).detach() cos_similarities = torch.nn.functional.cosine_similarity(y_pred, y, dim=1, eps=self.eps) self._sum_of_cos_similarities += torch.sum(cos_similarities).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_cos_similarities", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("CosineSimilarity must have at least one example before it can be computed.") return self._sum_of_cos_similarities.item() / self._num_examples ignite-0.5.1/ignite/metrics/entropy.py000066400000000000000000000106371465426447700200250ustar00rootroot00000000000000from typing import Sequence import torch import torch.nn.functional as F from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["Entropy"] class Entropy(Metric): r"""Calculates the mean of `entropy `_. .. math:: H = \frac{1}{N} \sum_{i=1}^N \sum_{c=1}^C -p_{i,c} \log p_{i,c}, \quad p_{i,c} = \frac{\exp(z_{i,c})}{\sum_{c'=1}^C \exp(z_{i,c'})} where :math:`p_{i,c}` is the prediction probability of :math:`i`-th data belonging to the class :math:`c`. - ``update`` must receive output of the form ``(y_pred, y)`` while ``y`` is not used in this metric. - ``y_pred`` is expected to be the unnormalized logits for each class. :math:`(B, C)` (classification) or :math:`(B, C, ...)` (e.g., image segmentation) shapes are allowed. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = Entropy() metric.attach(default_evaluator, 'entropy') y_true = torch.tensor([0, 1, 2]) # not considered in the Entropy metric. y_pred = torch.tensor([ [ 0.0000, 0.6931, 1.0986], [ 1.3863, 1.6094, 1.6094], [ 0.0000, -2.3026, -2.3026] ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['entropy']) .. testoutput:: 0.8902875582377116 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_entropies", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_entropies = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred = output[0].detach() if y_pred.ndim >= 3: num_classes = y_pred.shape[1] # (B, C, ...) -> (B, ..., C) -> (B*..., C) # regarding as B*... predictions y_pred = y_pred.movedim(1, -1).reshape(-1, num_classes) elif y_pred.ndim == 1: raise ValueError(f"y_pred must be in the shape of (B, C) or (B, C, ...), got {y_pred.shape}.") prob = F.softmax(y_pred, dim=1) log_prob = F.log_softmax(y_pred, dim=1) self._update(prob, log_prob) def _update(self, prob: torch.Tensor, log_prob: torch.Tensor) -> None: entropy_sum = -torch.sum(prob * log_prob) self._sum_of_entropies += entropy_sum.to(self._device) self._num_examples += prob.shape[0] @sync_all_reduce("_sum_of_entropies", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("Entropy must have at least one example before it can be computed.") return self._sum_of_entropies.item() / self._num_examples ignite-0.5.1/ignite/metrics/epoch_metric.py000066400000000000000000000156121465426447700207640ustar00rootroot00000000000000import warnings from typing import Callable, cast, List, Optional, Tuple, Union import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced __all__ = ["EpochMetric"] class EpochMetric(Metric): """Class for metrics that should be computed on the entire output history of a model. Model's output and targets are restricted to be of shape ``(batch_size, n_targets)``. Output datatype should be `float32`. Target datatype should be `long` for classification and `float` for regression. .. warning:: Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM. In distributed configuration, all stored data (output and target) is mutually collected across all processes using all gather collective operation. This can potentially lead to a memory error. Compute method executes ``compute_fn`` on zero rank process only and final result is broadcasted to all processes. - ``update`` must receive output of the form ``(y_pred, y)``. Args: compute_fn: a callable which receives two tensors as the `predictions` and `targets` and returns a scalar. Input tensors will be on specified ``device`` (see arg below). output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. check_compute_fn: if True, ``compute_fn`` is run on the first batch of data to ensure there are no issues. If issues exist, user is warned that there might be an issue with the ``compute_fn``. Default, True. device: optional device specification for internal storage. Example: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: def mse_fn(y_preds, y_targets): return torch.mean(((y_preds - y_targets.type_as(y_preds)) ** 2)).item() metric = EpochMetric(mse_fn) metric.attach(default_evaluator, "mse") y_true = torch.tensor([0, 1, 2, 3, 4, 5]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["mse"]) .. testoutput:: 0.5729... Warnings: EpochMetricWarning: User is warned that there are issues with ``compute_fn`` on a batch of data processed. To disable the warning, set ``check_compute_fn=False``. .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_predictions", "_targets") def __init__( self, compute_fn: Callable[[torch.Tensor, torch.Tensor], float], output_transform: Callable = lambda x: x, check_compute_fn: bool = True, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ) -> None: if not callable(compute_fn): raise TypeError("Argument compute_fn should be callable.") self.compute_fn = compute_fn self._check_compute_fn = check_compute_fn super(EpochMetric, self).__init__( output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @reinit__is_reduced def reset(self) -> None: self._predictions: List[torch.Tensor] = [] self._targets: List[torch.Tensor] = [] self._result: Optional[float] = None def _check_shape(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output if y_pred.ndimension() not in (1, 2): raise ValueError("Predictions should be of shape (batch_size, n_targets) or (batch_size, ).") if y.ndimension() not in (1, 2): raise ValueError("Targets should be of shape (batch_size, n_targets) or (batch_size, ).") def _check_type(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output if len(self._predictions) < 1: return dtype_preds = self._predictions[-1].dtype if dtype_preds != y_pred.dtype: raise ValueError( f"Incoherent types between input y_pred and stored predictions: {dtype_preds} vs {y_pred.dtype}" ) dtype_targets = self._targets[-1].dtype if dtype_targets != y.dtype: raise ValueError(f"Incoherent types between input y and stored targets: {dtype_targets} vs {y.dtype}") @reinit__is_reduced def update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: self._check_shape(output) y_pred, y = output[0].detach(), output[1].detach() if y_pred.ndimension() == 2 and y_pred.shape[1] == 1: y_pred = y_pred.squeeze(dim=-1) if y.ndimension() == 2 and y.shape[1] == 1: y = y.squeeze(dim=-1) y_pred = y_pred.clone().to(self._device) y = y.clone().to(self._device) self._check_type((y_pred, y)) self._predictions.append(y_pred) self._targets.append(y) # Check once the signature and execution of compute_fn if len(self._predictions) == 1 and self._check_compute_fn: try: self.compute_fn(self._predictions[0], self._targets[0]) except Exception as e: warnings.warn(f"Probably, there can be a problem with `compute_fn`:\n {e}.", EpochMetricWarning) def compute(self) -> float: if len(self._predictions) < 1 or len(self._targets) < 1: raise NotComputableError("EpochMetric must have at least one example before it can be computed.") if self._result is None: _prediction_tensor = torch.cat(self._predictions, dim=0) _target_tensor = torch.cat(self._targets, dim=0) ws = idist.get_world_size() if ws > 1: # All gather across all processes _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor)) _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor)) self._result = 0.0 if idist.get_rank() == 0: # Run compute_fn on zero rank only self._result = self.compute_fn(_prediction_tensor, _target_tensor) if ws > 1: # broadcast result to all processes self._result = cast(float, idist.broadcast(self._result, src=0)) return self._result class EpochMetricWarning(UserWarning): pass ignite-0.5.1/ignite/metrics/fbeta.py000066400000000000000000000144341465426447700174050ustar00rootroot00000000000000from typing import Callable, Optional, Union import torch from ignite.metrics.metrics_lambda import MetricsLambda from ignite.metrics.precision import Precision from ignite.metrics.recall import Recall __all__ = ["Fbeta"] def Fbeta( beta: float, average: bool = True, precision: Optional[Precision] = None, recall: Optional[Recall] = None, output_transform: Optional[Callable] = None, device: Union[str, torch.device] = torch.device("cpu"), ) -> MetricsLambda: r"""Calculates F-beta score. .. math:: F_\beta = \left( 1 + \beta^2 \right) * \frac{ \text{precision} * \text{recall} } { \left( \beta^2 * \text{precision} \right) + \text{recall} } where :math:`\beta` is a positive real factor. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...). - `y` must be in the following shape (batch_size, ...). Args: beta: weight of precision in harmonic mean average: if True, F-beta score is computed as the unweighted average (across all classes in multiclass case), otherwise, returns a tensor with F-beta score for each class in multiclass case. precision: precision object metric with `average=False` to compute F-beta score recall: recall object metric with `average=False` to compute F-beta score output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. It is used only if precision or recall are not provided. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Returns: MetricsLambda, F-beta metric Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Binary case .. testcode:: 1 P = Precision(average=False) R = Recall(average=False) metric = Fbeta(beta=1.0, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 1 0.7499... Multiclass case .. testcode:: 2 P = Precision(average=False) R = Recall(average=False) metric = Fbeta(beta=1.0, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 2 0.5222... F-beta can be computed for each class as done below: .. testcode:: 3 P = Precision(average=False) R = Recall(average=False) metric = Fbeta(beta=1.0, average=False, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 3 tensor([0.5000, 0.6667, 0.4000], dtype=torch.float64) The elements of `y` and `y_pred` should have 0 or 1 values. Thresholding of predictions can be done as below: .. testcode:: 4 def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y P = Precision(average=False, output_transform=thresholded_output_transform) R = Recall(average=False, output_transform=thresholded_output_transform) metric = Fbeta(beta=1.0, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 4 0.7499... """ if not (beta > 0): raise ValueError(f"Beta should be a positive integer, but given {beta}") if precision is not None and output_transform is not None: raise ValueError("If precision argument is provided, output_transform should be None") if recall is not None and output_transform is not None: raise ValueError("If recall argument is provided, output_transform should be None") if precision is None: precision = Precision( output_transform=(lambda x: x) if output_transform is None else output_transform, # type: ignore[arg-type] average=False, device=device, ) elif precision._average: raise ValueError("Input precision metric should have average=False") if recall is None: recall = Recall( output_transform=(lambda x: x) if output_transform is None else output_transform, # type: ignore[arg-type] average=False, device=device, ) elif recall._average: raise ValueError("Input recall metric should have average=False") fbeta = (1.0 + beta**2) * precision * recall / (beta**2 * precision + recall + 1e-15) if average: fbeta = fbeta.mean().item() return fbeta ignite-0.5.1/ignite/metrics/frequency.py000066400000000000000000000077051465426447700203300ustar00rootroot00000000000000from typing import Callable, Union import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers.timing import Timer from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce class Frequency(Metric): """Provides metrics for the number of examples processed per second. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. code-block:: python # Compute number of tokens processed wps_metric = Frequency(output_transform=lambda x: x['ntokens']) wps_metric.attach(trainer, name='wps') # Logging with TQDM ProgressBar(persist=True).attach(trainer, metric_names=['wps']) # Progress bar will look like # Epoch [2/10]: [12/24] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ , wps=400 [00:17<1:23] To compute examples processed per second every 50th iteration: .. code-block:: python # Compute number of tokens processed wps_metric = Frequency(output_transform=lambda x: x['ntokens']) wps_metric.attach(trainer, name='wps', event_name=Events.ITERATION_COMPLETED(every=50)) # Logging with TQDM ProgressBar(persist=True).attach(trainer, metric_names=['wps']) # Progress bar will look like # Epoch [2/10]: [50/100] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ , wps=400 [00:17<00:35] .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ) -> None: super(Frequency, self).__init__(output_transform=output_transform, device=device, skip_unrolling=skip_unrolling) @reinit__is_reduced def reset(self) -> None: self._timer = Timer() self._acc = 0 self._n = 0 self._elapsed = 0.0 super(Frequency, self).reset() # type: ignore @reinit__is_reduced def update(self, output: int) -> None: self._acc += output self._n = self._acc self._elapsed = self._timer.value() @sync_all_reduce("_n", "_elapsed") def compute(self) -> float: time_divisor = 1.0 if idist.get_world_size() > 1: time_divisor *= idist.get_world_size() # Returns the average processed objects per second across all workers return self._n / self._elapsed * time_divisor def completed(self, engine: Engine, name: str) -> None: engine.state.metrics[name] = int(self.compute()) # TODO: see issue https://github.com/pytorch/ignite/issues/1405 def attach( # type: ignore self, engine: Engine, name: str, event_name: Events = Events.ITERATION_COMPLETED ) -> None: engine.add_event_handler(Events.EPOCH_STARTED, self.started) engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed) engine.add_event_handler(event_name, self.completed, name) ignite-0.5.1/ignite/metrics/gan/000077500000000000000000000000001465426447700165115ustar00rootroot00000000000000ignite-0.5.1/ignite/metrics/gan/__init__.py000066400000000000000000000002251465426447700206210ustar00rootroot00000000000000from ignite.metrics.gan.fid import FID from ignite.metrics.gan.inception_score import InceptionScore __all__ = [ "InceptionScore", "FID", ] ignite-0.5.1/ignite/metrics/gan/fid.py000066400000000000000000000234121465426447700176270ustar00rootroot00000000000000import warnings from typing import Callable, Optional, Sequence, Union import torch from packaging.version import Version from ignite.metrics.gan.utils import _BaseInceptionMetric, InceptionModel from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce __all__ = [ "FID", ] if Version(torch.__version__) <= Version("1.7.0"): torch_outer = torch.ger else: torch_outer = torch.outer def fid_score( mu1: torch.Tensor, mu2: torch.Tensor, sigma1: torch.Tensor, sigma2: torch.Tensor, eps: float = 1e-6 ) -> float: try: import numpy as np except ImportError: raise ModuleNotFoundError("fid_score requires numpy to be installed.") try: import scipy.linalg except ImportError: raise ModuleNotFoundError("fid_score requires scipy to be installed.") mu1, mu2 = mu1.cpu(), mu2.cpu() sigma1, sigma2 = sigma1.cpu(), sigma2.cpu() diff = mu1 - mu2 # Product might be almost singular covmean, _ = scipy.linalg.sqrtm(sigma1.mm(sigma2), disp=False) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError("Imaginary component {}".format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) if not np.isfinite(covmean).all(): tr_covmean = np.sum(np.sqrt(((np.diag(sigma1) * eps) * (np.diag(sigma2) * eps)) / (eps * eps))) return float(diff.dot(diff).item() + torch.trace(sigma1) + torch.trace(sigma2) - 2 * tr_covmean) class FID(_BaseInceptionMetric): r"""Calculates Frechet Inception Distance. .. math:: \text{FID} = |\mu_{1} - \mu_{2}| + \text{Tr}(\sigma_{1} + \sigma_{2} - {2}\sqrt{\sigma_1*\sigma_2}) where :math:`\mu_1` and :math:`\sigma_1` refer to the mean and covariance of the train data and :math:`\mu_2` and :math:`\sigma_2` refer to the mean and covariance of the test data. More details can be found in `Heusel et al. 2017`__ __ https://arxiv.org/pdf/1706.08500.pdf In addition, a faster and online computation approach can be found in `Mathiasen et al. 2020`__ __ https://arxiv.org/pdf/2009.14075.pdf Remark: This implementation is inspired by `pytorch_fid` package which can be found `here`__ __ https://github.com/mseitzer/pytorch-fid .. note:: The default Inception model requires the `torchvision` module to be installed. FID also requires `scipy` library for matrix square root calculations. Args: num_features: number of features predicted by the model or the reduced feature vector of the image. Default value is 1000. feature_extractor: a torch Module for extracting the features from the input data. It returns a tensor of shape (batch_size, num_features). If neither ``num_features`` nor ``feature_extractor`` are defined, by default we use an ImageNet pretrained Inception Model and use model's output logits as features. If only ``num_features`` is defined but ``feature_extractor`` is not defined, ``feature_extractor`` is assigned Identity Function. Please note that the model will be implicitly converted to device mentioned in the ``device`` argument. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = FID(num_features=1, feature_extractor=default_model) metric.attach(default_evaluator, "fid") y_true = torch.ones(10, 4) y_pred = torch.ones(10, 4) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["fid"]) .. testoutput:: 0.0 .. note:: The default `torchvision` model used is InceptionV3 pretrained on ImageNet. This can lead to differences in results with `pytorch_fid`. To find comparable results, the following model wrapper should be used: .. code:: import torch.nn as nn # wrapper class as feature_extractor class WrapperInceptionV3(nn.Module): def __init__(self, fid_incv3): super().__init__() self.fid_incv3 = fid_incv3 @torch.no_grad() def forward(self, x): y = self.fid_incv3(x) y = y[0] y = y[:, :, 0, 0] return y # use cpu rather than cuda to get comparable results device = "cpu" # pytorch_fid model dims = 2048 block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) # wrapper model to pytorch_fid model wrapper_model = WrapperInceptionV3(model) wrapper_model.eval(); # comparable metric pytorch_fid_metric = FID(num_features=dims, feature_extractor=wrapper_model) Important, `pytorch_fid` results depend on the batch size if the device is `cuda`. .. versionadded:: 0.4.6 """ _state_dict_all_req_keys = ("_num_examples", "_train_total", "_test_total", "_train_sigma", "_test_sigma") def __init__( self, num_features: Optional[int] = None, feature_extractor: Optional[torch.nn.Module] = None, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: try: import numpy as np # noqa: F401 except ImportError: raise ModuleNotFoundError("This module requires numpy to be installed.") try: import scipy # noqa: F401 except ImportError: raise ModuleNotFoundError("This module requires scipy to be installed.") if num_features is None and feature_extractor is None: num_features = 1000 feature_extractor = InceptionModel(return_features=False, device=device) self._eps = 1e-6 super(FID, self).__init__( num_features=num_features, feature_extractor=feature_extractor, output_transform=output_transform, device=device, ) @staticmethod def _online_update(features: torch.Tensor, total: torch.Tensor, sigma: torch.Tensor) -> None: total += features sigma += torch_outer(features, features) def _get_covariance(self, sigma: torch.Tensor, total: torch.Tensor) -> torch.Tensor: r""" Calculates covariance from mean and sum of products of variables """ sub_matrix = torch_outer(total, total) sub_matrix = sub_matrix / self._num_examples return (sigma - sub_matrix) / (self._num_examples - 1) @reinit__is_reduced def reset(self) -> None: self._train_sigma = torch.zeros( (self._num_features, self._num_features), dtype=torch.float64, device=self._device ) self._train_total = torch.zeros(self._num_features, dtype=torch.float64, device=self._device) self._test_sigma = torch.zeros( (self._num_features, self._num_features), dtype=torch.float64, device=self._device ) self._test_total = torch.zeros(self._num_features, dtype=torch.float64, device=self._device) self._num_examples: int = 0 super(FID, self).reset() # type: ignore @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: train, test = output train_features = self._extract_features(train) test_features = self._extract_features(test) if train_features.shape[0] != test_features.shape[0] or train_features.shape[1] != test_features.shape[1]: raise ValueError( f""" Number of Training Features and Testing Features should be equal ({train_features.shape} != {test_features.shape}) """ ) # Updates the mean and covariance for the train features for features in train_features: self._online_update(features, self._train_total, self._train_sigma) # Updates the mean and covariance for the test features for features in test_features: self._online_update(features, self._test_total, self._test_sigma) self._num_examples += train_features.shape[0] @sync_all_reduce("_num_examples", "_train_total", "_test_total", "_train_sigma", "_test_sigma") def compute(self) -> float: fid = fid_score( mu1=self._train_total / self._num_examples, mu2=self._test_total / self._num_examples, sigma1=self._get_covariance(self._train_sigma, self._train_total), sigma2=self._get_covariance(self._test_sigma, self._test_total), eps=self._eps, ) if torch.isnan(torch.tensor(fid)) or torch.isinf(torch.tensor(fid)): warnings.warn("The product of covariance of train and test features is out of bounds.") return fid ignite-0.5.1/ignite/metrics/gan/inception_score.py000066400000000000000000000127151465426447700222540ustar00rootroot00000000000000from typing import Callable, Optional, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.gan.utils import _BaseInceptionMetric, InceptionModel # These decorators helps with distributed settings from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce __all__ = ["InceptionScore"] class InceptionScore(_BaseInceptionMetric): r"""Calculates Inception Score. .. math:: \text{IS(G)} = \exp(\frac{1}{N}\sum_{i=1}^{N} D_{KL} (p(y|x^{(i)} \parallel \hat{p}(y)))) where :math:`p(y|x)` is the conditional probability of image being the given object and :math:`p(y)` is the marginal probability that the given image is real, `G` refers to the generated image and :math:`D_{KL}` refers to KL Divergence of the above mentioned probabilities. More details can be found in `Barratt et al. 2018`__. __ https://arxiv.org/pdf/1801.01973.pdf Args: num_features: number of features predicted by the model or number of classes of the model. Default value is 1000. feature_extractor: a torch Module for predicting the probabilities from the input data. It returns a tensor of shape (batch_size, num_features). If neither ``num_features`` nor ``feature_extractor`` are defined, by default we use an ImageNet pretrained Inception Model. If only ``num_features`` is defined but ``feature_extractor`` is not defined, ``feature_extractor`` is assigned Identity Function. Please note that the class object will be implicitly converted to device mentioned in the ``device`` argument. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``y_pred``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. .. note:: The default Inception model requires the `torchvision` module to be installed. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. code-block:: python metric = InceptionScore() metric.attach(default_evaluator, "is") y = torch.rand(10, 3, 299, 299) state = default_evaluator.run([y]) print(state.metrics["is"]) .. testcode:: metric = InceptionScore(num_features=1, feature_extractor=default_model) metric.attach(default_evaluator, "is") y = torch.zeros(10, 4) state = default_evaluator.run([y]) print(state.metrics["is"]) .. testoutput:: 1.0 .. versionadded:: 0.4.6 """ _state_dict_all_req_keys = ("_num_examples", "_prob_total", "_total_kl_d") def __init__( self, num_features: Optional[int] = None, feature_extractor: Optional[torch.nn.Module] = None, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: if num_features is None and feature_extractor is None: num_features = 1000 feature_extractor = InceptionModel(return_features=False, device=device) self._eps = 1e-16 super(InceptionScore, self).__init__( num_features=num_features, feature_extractor=feature_extractor, output_transform=output_transform, device=device, ) @reinit__is_reduced def reset(self) -> None: self._num_examples = 0 self._prob_total = torch.zeros(self._num_features, dtype=torch.float64, device=self._device) self._total_kl_d = torch.zeros(self._num_features, dtype=torch.float64, device=self._device) super(InceptionScore, self).reset() # type: ignore @reinit__is_reduced def update(self, output: torch.Tensor) -> None: probabilities = self._extract_features(output) prob_sum = torch.sum(probabilities, 0, dtype=torch.float64) log_prob = torch.log(probabilities + self._eps) if log_prob.dtype != probabilities.dtype: log_prob = log_prob.to(probabilities) kl_sum = torch.sum(probabilities * log_prob, 0, dtype=torch.float64) self._num_examples += probabilities.shape[0] self._prob_total += prob_sum self._total_kl_d += kl_sum @sync_all_reduce("_num_examples", "_prob_total", "_total_kl_d") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("InceptionScore must have at least one example before it can be computed.") mean_probs = self._prob_total / self._num_examples log_mean_probs = torch.log(mean_probs + self._eps) if log_mean_probs.dtype != self._prob_total.dtype: log_mean_probs = log_mean_probs.to(self._prob_total) excess_entropy = self._prob_total * log_mean_probs avg_kl_d = torch.sum(self._total_kl_d - excess_entropy) / self._num_examples return torch.exp(avg_kl_d).item() ignite-0.5.1/ignite/metrics/gan/utils.py000066400000000000000000000076241465426447700202340ustar00rootroot00000000000000from typing import Callable, Optional, Union import torch from packaging.version import Version from ignite.metrics.metric import Metric class InceptionModel(torch.nn.Module): r"""Inception Model pre-trained on the ImageNet Dataset. Args: return_features: set it to `True` if you want the model to return features from the last pooling layer instead of prediction probabilities. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. """ def __init__(self, return_features: bool, device: Union[str, torch.device] = "cpu") -> None: try: import torchvision from torchvision import models except ImportError: raise ModuleNotFoundError("This module requires torchvision to be installed.") super(InceptionModel, self).__init__() self._device = device if Version(torchvision.__version__) < Version("0.13.0"): model_kwargs = {"pretrained": True} else: model_kwargs = {"weights": models.Inception_V3_Weights.DEFAULT} self.model = models.inception_v3(**model_kwargs).to(self._device) if return_features: self.model.fc = torch.nn.Identity() else: self.model.fc = torch.nn.Sequential(self.model.fc, torch.nn.Softmax(dim=1)) self.model.eval() @torch.no_grad() def forward(self, data: torch.Tensor) -> torch.Tensor: if data.dim() != 4: raise ValueError(f"Inputs should be a tensor of dim 4, got {data.dim()}") if data.shape[1] != 3: raise ValueError(f"Inputs should be a tensor with 3 channels, got {data.shape}") if data.device != torch.device(self._device): data = data.to(self._device) return self.model(data) class _BaseInceptionMetric(Metric): def __init__( self, num_features: Optional[int], feature_extractor: Optional[torch.nn.Module], output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: if num_features is None: raise ValueError("Argument num_features must be provided, if feature_extractor is specified.") if feature_extractor is None: feature_extractor = torch.nn.Identity() if num_features <= 0: raise ValueError(f"Argument num_features must be greater to zero, got: {num_features}") if not isinstance(feature_extractor, torch.nn.Module): raise TypeError( f"Argument feature_extractor must be of type torch.nn.Module, got {type(self._feature_extractor)}" ) self._num_features = num_features self._feature_extractor = feature_extractor.to(device) super(_BaseInceptionMetric, self).__init__(output_transform=output_transform, device=device) def _check_feature_shapes(self, samples: torch.Tensor) -> None: if samples.dim() != 2: raise ValueError(f"feature_extractor output must be a tensor of dim 2, got: {samples.dim()}") if samples.shape[0] == 0: raise ValueError(f"Batch size should be greater than one, got: {samples.shape[0]}") if samples.shape[1] != self._num_features: raise ValueError( f"num_features returned by feature_extractor should be {self._num_features}, got: {samples.shape[1]}" ) def _extract_features(self, inputs: torch.Tensor) -> torch.Tensor: inputs = inputs.detach() if inputs.device != torch.device(self._device): inputs = inputs.to(self._device) with torch.no_grad(): outputs = self._feature_extractor(inputs).to(self._device, dtype=torch.float64) self._check_feature_shapes(outputs) return outputs ignite-0.5.1/ignite/metrics/gpu_info.py000066400000000000000000000101541465426447700201250ustar00rootroot00000000000000# -*- coding: utf-8 -*- import warnings from typing import Any, Dict, List, Tuple, Union import torch from ignite.engine import Engine, EventEnum, Events from ignite.metrics.metric import Metric class GpuInfo(Metric): """Provides GPU information: a) used memory percentage, b) gpu utilization percentage values as Metric on each iterations. .. Note :: In case if gpu utilization reports "N/A" on a given GPU, corresponding metric value is not set. Examples: .. code-block:: python # Default GPU measurements GpuInfo().attach(trainer, name='gpu') # metric names are 'gpu:X mem(%)', 'gpu:X util(%)' # Logging with TQDM ProgressBar(persist=True).attach(trainer, metric_names=['gpu:0 mem(%)', 'gpu:0 util(%)']) # Progress bar will looks like # Epoch [2/10]: [12/24] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ , gpu:0 mem(%)=79, gpu:0 util(%)=59 [00:17<1:23] # Logging with Tensorboard tb_logger.attach(trainer, log_handler=OutputHandler(tag="training", metric_names='all'), event_name=Events.ITERATION_COMPLETED) """ def __init__(self) -> None: try: from pynvml.smi import nvidia_smi except ImportError: raise ModuleNotFoundError( "This contrib module requires pynvml to be installed. " "Please install it with command: \n pip install pynvml" ) # Let's check available devices if not torch.cuda.is_available(): raise RuntimeError("This contrib module requires available GPU") # Let it fail if no libnvidia drivers or NMVL library found self.nvsmi = nvidia_smi.getInstance() super(GpuInfo, self).__init__() def reset(self) -> None: pass def update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: pass def compute(self) -> List[Dict[str, Any]]: data: Dict[str, List[Dict[str, Any]]] = self.nvsmi.DeviceQuery("memory.used, memory.total, utilization.gpu") if len(data) == 0 or ("gpu" not in data): warnings.warn("No GPU information available") return [] return data["gpu"] def completed(self, engine: Engine, name: str) -> None: data = self.compute() if len(data) < 1: warnings.warn("No GPU information available") return for i, data_by_rank in enumerate(data): mem_name = f"{name}:{i} mem(%)" if "fb_memory_usage" not in data_by_rank: warnings.warn(f"No GPU memory usage information available in {data_by_rank}") continue mem_report = data_by_rank["fb_memory_usage"] if not ("used" in mem_report and "total" in mem_report): warnings.warn( "GPU memory usage information does not provide used/total " f"memory consumption information in {mem_report}" ) continue engine.state.metrics[mem_name] = int(mem_report["used"] * 100.0 / mem_report["total"]) for i, data_by_rank in enumerate(data): util_name = f"{name}:{i} util(%)" if "utilization" not in data_by_rank: warnings.warn(f"No GPU utilization information available in {data_by_rank}") continue util_report = data_by_rank["utilization"] if not ("gpu_util" in util_report): warnings.warn(f"GPU utilization information does not provide 'gpu_util' information in {util_report}") continue try: engine.state.metrics[util_name] = int(util_report["gpu_util"]) except ValueError: # Do not set GPU utilization information pass # TODO: see issue https://github.com/pytorch/ignite/issues/1405 def attach( # type: ignore self, engine: Engine, name: str = "gpu", event_name: Union[str, EventEnum] = Events.ITERATION_COMPLETED ) -> None: engine.add_event_handler(event_name, self.completed, name) ignite-0.5.1/ignite/metrics/js_divergence.py000066400000000000000000000113461465426447700211320ustar00rootroot00000000000000import torch import torch.nn.functional as F from packaging.version import Version from ignite.exceptions import NotComputableError from ignite.metrics.kl_divergence import KLDivergence from ignite.metrics.metric import sync_all_reduce __all__ = ["JSDivergence"] TORCH_VERSION_GE_160 = Version(torch.__version__) >= Version("1.6.0") class JSDivergence(KLDivergence): r"""Calculates the mean of `Jensen-Shannon (JS) divergence `_. .. math:: \begin{align*} D_\text{JS}(\mathbf{p}_i \| \mathbf{q}_i) &= \frac{1}{2} D_\text{KL}(\mathbf{p}_i \| \mathbf{m}_i) + \frac{1}{2} D_\text{KL}(\mathbf{q}_i \| \mathbf{m}_i), \\ \mathbf{m}_i &= \frac{1}{2}(\mathbf{p}_i + \mathbf{q}_i), \\ D_\text{KL}(\mathbf{p}_i \| \mathbf{q}_i) &= \sum_{c=1}^C p_{i,c} \log \frac{p_{i,c}}{q_{i,c}}. \end{align*} where :math:`\mathbf{p}_i` and :math:`\mathbf{q}_i` are the ground truth and prediction probability tensors, and :math:`D_\text{KL}` is the KL-divergence. - ``update`` must receive output of the form ``(y_pred, y)``. - ``y_pred`` and ``y`` are expected to be the unnormalized logits for each class. :math:`(B, C)` (classification) or :math:`(B, C, ...)` (e.g., image segmentation) shapes are allowed. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = JSDivergence() metric.attach(default_evaluator, 'js-div') y_true = torch.tensor([ [ 0.0000, -2.3026, -2.3026], [ 1.3863, 1.6094, 1.6094], [ 0.0000, 0.6931, 1.0986] ]) y_pred = torch.tensor([ [ 0.0000, 0.6931, 1.0986], [ 1.3863, 1.6094, 1.6094], [ 0.0000, -2.3026, -2.3026] ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['js-div']) .. testoutput:: 0.16266516844431558 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def _update(self, y_pred: torch.Tensor, y: torch.Tensor) -> None: y_pred_prob = F.softmax(y_pred, dim=1) y_prob = F.softmax(y, dim=1) m_prob = (y_pred_prob + y_prob) / 2 m_log = m_prob.log() if TORCH_VERSION_GE_160: # log_target option can be used from 1.6.0 y_pred_log = F.log_softmax(y_pred, dim=1) y_log = F.log_softmax(y, dim=1) self._sum_of_kl += ( F.kl_div(m_log, y_pred_log, log_target=True, reduction="sum") + F.kl_div(m_log, y_log, log_target=True, reduction="sum") ).to(self._device) else: # y_pred and y are expected to be probabilities self._sum_of_kl += ( F.kl_div(m_log, y_pred_prob, reduction="sum") + F.kl_div(m_log, y_prob, reduction="sum") ).to(self._device) @sync_all_reduce("_sum_of_kl", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("JSDivergence must have at least one example before it can be computed.") return self._sum_of_kl.item() / (self._num_examples * 2) ignite-0.5.1/ignite/metrics/kl_divergence.py000066400000000000000000000120401465426447700211140ustar00rootroot00000000000000from typing import Sequence import torch import torch.nn.functional as F from packaging.version import Version from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["KLDivergence"] TORCH_VERSION_GE_160 = Version(torch.__version__) >= Version("1.6.0") class KLDivergence(Metric): r"""Calculates the mean of `Kullback-Leibler (KL) divergence `_. .. math:: D_\text{KL}(\mathbf{p}_i \| \mathbf{q}_i) = \sum_{c=1}^C p_{i,c} \log \frac{p_{i,c}}{q_{i,c}} where :math:`\mathbf{p}_i` and :math:`\mathbf{q}_i` are the ground truth and prediction probability tensors. - ``update`` must receive output of the form ``(y_pred, y)``. - ``y_pred`` and ``y`` are expected to be the unnormalized logits for each class. :math:`(B, C)` (classification) or :math:`(B, C, ...)` (e.g., image segmentation) shapes are allowed. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = KLDivergence() metric.attach(default_evaluator, 'kl-div') y_true = torch.tensor([ [ 0.0000, -2.3026, -2.3026], [ 1.3863, 1.6094, 1.6094], [ 0.0000, 0.6931, 1.0986] ]) y_pred = torch.tensor([ [ 0.0000, 0.6931, 1.0986], [ 1.3863, 1.6094, 1.6094], [ 0.0000, -2.3026, -2.3026] ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['kl-div']) .. testoutput:: 0.7220296859741211 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_kl", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_kl = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if y_pred.shape != y.shape: raise ValueError(f"y_pred and y must be in the same shape, got {y_pred.shape} != {y.shape}.") if y_pred.ndim >= 3: num_classes = y_pred.shape[1] # (B, C, ...) -> (B, ..., C) -> (B*..., C) # regarding as B*... predictions y_pred = y_pred.movedim(1, -1).reshape(-1, num_classes) y = y.movedim(1, -1).reshape(-1, num_classes) elif y_pred.ndim == 1: raise ValueError(f"y_pred must be in the shape of (B, C) or (B, C, ...), got {y_pred.shape}.") self._num_examples += y_pred.shape[0] self._update(y_pred, y) def _update(self, y_pred: torch.Tensor, y: torch.Tensor) -> None: y_pred = F.log_softmax(y_pred, dim=1) if TORCH_VERSION_GE_160: # log_target option can be used from 1.6.0 y = F.log_softmax(y, dim=1) kl_sum = F.kl_div(y_pred, y, log_target=True, reduction="sum") else: # y is expected to be a probability tensor y = F.softmax(y, dim=1) kl_sum = F.kl_div(y_pred, y, reduction="sum") self._sum_of_kl += kl_sum.to(self._device) @sync_all_reduce("_sum_of_kl", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("KLDivergence must have at least one example before it can be computed.") return self._sum_of_kl.item() / self._num_examples ignite-0.5.1/ignite/metrics/loss.py000066400000000000000000000113221465426447700172750ustar00rootroot00000000000000from typing import Callable, cast, Dict, Sequence, Tuple, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["Loss"] class Loss(Metric): """ Calculates the average loss according to the passed loss_fn. Args: loss_fn: a callable taking a prediction tensor, a target tensor, optionally other arguments, and returns the average loss over all observations in the batch. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. The output is expected to be a tuple `(prediction, target)` or (prediction, target, kwargs) where kwargs is a dictionary of extra keywords arguments. If extra keywords arguments are provided they are passed to `loss_fn`. batch_size: a callable taking a target tensor that returns the first dimension size (usually the batch size). device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether input should be unrolled or not before it is passed to to loss_fn. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Attributes: required_output_keys: dictionary defines required keys to be found in ``engine.state.output`` if the latter is a dictionary. Default, ``("y_pred", "y", "criterion_kwargs")``. This is useful when the criterion function requires additional arguments, which can be passed using ``criterion_kwargs``. See an example below. Examples: Let's implement a Loss metric that requires ``x``, ``y_pred``, ``y`` and ``criterion_kwargs`` as input for ``criterion`` function. In the example below we show how to setup standard metric like Accuracy and the Loss metric using an ``evaluator`` created with :meth:`~ignite.engine.create_supervised_evaluator` method. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: model = default_model criterion = nn.NLLLoss() metric = Loss(criterion) metric.attach(default_evaluator, 'loss') y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]) y_true = torch.tensor([2, 2]).long() state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['loss']) .. testoutput:: -0.3499999... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ required_output_keys = ("y_pred", "y", "criterion_kwargs") _state_dict_all_req_keys = ("_sum", "_num_examples") def __init__( self, loss_fn: Callable, output_transform: Callable = lambda x: x, batch_size: Callable = len, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): super(Loss, self).__init__(output_transform, device=device, skip_unrolling=skip_unrolling) self._loss_fn = loss_fn self._batch_size = batch_size @reinit__is_reduced def reset(self) -> None: self._sum = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[Union[torch.Tensor, Dict]]) -> None: if len(output) == 2: y_pred, y = cast(Tuple[torch.Tensor, torch.Tensor], output) kwargs: Dict = {} else: y_pred, y, kwargs = cast(Tuple[torch.Tensor, torch.Tensor, Dict], output) average_loss = self._loss_fn(y_pred, y, **kwargs).detach() if len(average_loss.shape) != 0: raise ValueError("loss_fn did not return the average loss.") n = self._batch_size(y) self._sum += average_loss.to(self._device) * n self._num_examples += n @sync_all_reduce("_sum", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("Loss must have at least one example before it can be computed.") return self._sum.item() / self._num_examples ignite-0.5.1/ignite/metrics/maximum_mean_discrepancy.py000066400000000000000000000144641465426447700233700ustar00rootroot00000000000000from typing import Callable, Sequence import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["MaximumMeanDiscrepancy"] class MaximumMeanDiscrepancy(Metric): r"""Calculates the mean of `maximum mean discrepancy (MMD) `_. .. math:: \begin{align*} \text{MMD}^2 (P,Q) &= \underset{\| f \| \leq 1}{\text{sup}} | \mathbb{E}_{X\sim P}[f(X)] - \mathbb{E}_{Y\sim Q}[f(Y)] |^2 \\ &\approx \frac{1}{B(B-1)} \sum_{i=1}^B \sum_{\substack{j=1 \\ j\neq i}}^B k(\mathbf{x}_i,\mathbf{x}_j) -\frac{2}{B^2}\sum_{i=1}^B \sum_{j=1}^B k(\mathbf{x}_i,\mathbf{y}_j) + \frac{1}{B(B-1)} \sum_{i=1}^B \sum_{\substack{j=1 \\ j\neq i}}^B k(\mathbf{y}_i,\mathbf{y}_j) \end{align*} where :math:`B` is the batch size, and :math:`\mathbf{x}_i` and :math:`\mathbf{y}_j` are feature vectors sampled from :math:`P` and :math:`Q`, respectively. :math:`k(\mathbf{x},\mathbf{y})=\exp(-\| \mathbf{x}-\mathbf{y} \|^2/ 2\sigma^2)` is the Gaussian RBF kernel. This metric computes the MMD for each batch and takes the average. More details can be found in `Gretton et al. 2012`__. __ https://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf - ``update`` must receive output of the form ``(x, y)``. - ``x`` and ``y`` are expected to be in the same shape :math:`(B, \ldots)`. Args: var: the bandwidth :math:`\sigma^2` of the kernel. Default: 1.0 output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, this metric requires the output as ``(x, y)``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(x, y)``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MaximumMeanDiscrepancy() metric.attach(default_evaluator, "mmd") x = torch.tensor([[-0.80324818, -0.95768364, -0.03807209], [-0.11059691, -0.38230813, -0.4111988], [-0.8864329, -0.02890403, -0.60119252], [-0.68732452, -0.12854739, -0.72095073], [-0.62604613, -0.52368328, -0.24112842]]) y = torch.tensor([[0.0686768, 0.80502737, 0.53321717], [0.83849465, 0.59099726, 0.76385441], [0.68688272, 0.56833803, 0.98100778], [0.55267761, 0.13084654, 0.45382906], [0.0754253, 0.70317304, 0.4756805]]) state = default_evaluator.run([[x, y]]) print(state.metrics["mmd"]) .. testoutput:: 1.072697639465332 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_xx_sum", "_yy_sum", "_xy_sum", "_num_batches") def __init__( self, var: float = 1.0, output_transform: Callable = lambda x: x, device: torch.device = torch.device("cpu"), skip_unrolling: bool = False, ): self.var = var super().__init__(output_transform, device, skip_unrolling=skip_unrolling) @reinit__is_reduced def reset(self) -> None: self._xx_sum = torch.tensor(0.0, device=self._device) self._yy_sum = torch.tensor(0.0, device=self._device) self._xy_sum = torch.tensor(0.0, device=self._device) self._num_batches = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: x, y = output[0].detach(), output[1].detach() if x.shape != y.shape: raise ValueError(f"x and y must be in the same shape, got {x.shape} != {y.shape}.") if x.ndim >= 3: x = x.flatten(start_dim=1) y = y.flatten(start_dim=1) elif x.ndim == 1: raise ValueError(f"x must be in the shape of (B, ...), got {x.shape}.") xx, yy, zz = torch.mm(x, x.t()), torch.mm(y, y.t()), torch.mm(x, y.t()) rx = xx.diag().unsqueeze(0).expand_as(xx) ry = yy.diag().unsqueeze(0).expand_as(yy) dxx = rx.t() + rx - 2.0 * xx dyy = ry.t() + ry - 2.0 * yy dxy = rx.t() + ry - 2.0 * zz v = self.var XX = torch.exp(-0.5 * dxx / v) YY = torch.exp(-0.5 * dyy / v) XY = torch.exp(-0.5 * dxy / v) # unbiased n = x.shape[0] XX = (XX.sum() - n) / (n * (n - 1)) YY = (YY.sum() - n) / (n * (n - 1)) XY = XY.sum() / (n * n) self._xx_sum += XX.to(self._device) self._yy_sum += YY.to(self._device) self._xy_sum += XY.to(self._device) self._num_batches += 1 @sync_all_reduce("_xx_sum", "_yy_sum", "_xy_sum", "_num_batches") def compute(self) -> float: if self._num_batches == 0: raise NotComputableError("MaximumMeanDiscrepacy must have at least one batch before it can be computed.") mmd2 = (self._xx_sum + self._yy_sum - 2.0 * self._xy_sum).clamp(min=0.0) / self._num_batches return mmd2.sqrt().item() ignite-0.5.1/ignite/metrics/mean_absolute_error.py000066400000000000000000000071571465426447700223570ustar00rootroot00000000000000from typing import Sequence, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["MeanAbsoluteError"] class MeanAbsoluteError(Metric): r"""Calculates `the mean absolute error `_. .. math:: \text{MAE} = \frac{1}{N} \sum_{i=1}^N \lvert y_{i} - x_{i} \rvert where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor. - ``update`` must receive output of the form ``(y_pred, y)``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanAbsoluteError() metric.attach(default_evaluator, 'mae') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['mae']) .. testoutput:: 2.9375 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_absolute_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_absolute_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() absolute_errors = torch.abs(y_pred - y.view_as(y_pred)) self._sum_of_absolute_errors += torch.sum(absolute_errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_absolute_errors", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError("MeanAbsoluteError must have at least one example before it can be computed.") return self._sum_of_absolute_errors.item() / self._num_examples ignite-0.5.1/ignite/metrics/mean_pairwise_distance.py000066400000000000000000000100101465426447700230030ustar00rootroot00000000000000from typing import Callable, Sequence, Union import torch from torch.nn.functional import pairwise_distance from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["MeanPairwiseDistance"] class MeanPairwiseDistance(Metric): """Calculates the mean :class:`~torch.nn.PairwiseDistance`. Average of pairwise distances computed on provided batches. - ``update`` must receive output of the form ``(y_pred, y)``. Args: p: the norm degree. Default: 2 eps: Small value to avoid division by zero. Default: 1e-6 output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanPairwiseDistance(p=4) metric.attach(default_evaluator, 'mpd') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['mpd']) .. testoutput:: 1.5955... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_distances", "_num_examples") def __init__( self, p: int = 2, eps: float = 1e-6, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ) -> None: super(MeanPairwiseDistance, self).__init__(output_transform, device=device, skip_unrolling=False) self._p = p self._eps = eps @reinit__is_reduced def reset(self) -> None: self._sum_of_distances = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() distances = pairwise_distance(y_pred, y, p=self._p, eps=self._eps) self._sum_of_distances += torch.sum(distances).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_distances", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError("MeanAbsoluteError must have at least one example before it can be computed.") return self._sum_of_distances.item() / self._num_examples ignite-0.5.1/ignite/metrics/mean_squared_error.py000066400000000000000000000071371465426447700222030ustar00rootroot00000000000000from typing import Sequence, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["MeanSquaredError"] class MeanSquaredError(Metric): r"""Calculates the `mean squared error `_. .. math:: \text{MSE} = \frac{1}{N} \sum_{i=1}^N \|y_{i} - x_{i}\|^2 where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor. - ``update`` must receive output of the form ``(y_pred, y)``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanSquaredError() metric.attach(default_evaluator, 'mse') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['mse']) .. testoutput:: 3.828125 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_squared_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_squared_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() squared_errors = torch.pow(y_pred - y.view_as(y_pred), 2) self._sum_of_squared_errors += torch.sum(squared_errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_squared_errors", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError("MeanSquaredError must have at least one example before it can be computed.") return self._sum_of_squared_errors.item() / self._num_examples ignite-0.5.1/ignite/metrics/metric.py000066400000000000000000001037011465426447700176030ustar00rootroot00000000000000from abc import ABCMeta, abstractmethod from collections import OrderedDict from collections.abc import Mapping from functools import wraps from numbers import Number from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, TYPE_CHECKING, Union import torch import ignite.distributed as idist from ignite.base.mixins import Serializable from ignite.engine import CallableEventWithFilter, Engine, Events from ignite.utils import _CollectionItem, _tree_apply2, _tree_map if TYPE_CHECKING: from ignite.metrics.metrics_lambda import MetricsLambda __all__ = [ "Metric", "MetricUsage", "EpochWise", "BatchWise", "BatchFiltered", "RunningEpochWise", "RunningBatchWise", "SingleEpochRunningBatchWise", ] class MetricUsage: """ Base class for all usages of metrics. A usage of metric defines the events when a metric starts to compute, updates and completes. Valid events are from :class:`~ignite.engine.events.Events`. Args: started: event when the metric starts to compute. This event will be associated to :meth:`~ignite.metrics.metric.Metric.started`. completed: event when the metric completes. This event will be associated to :meth:`~ignite.metrics.metric.Metric.completed`. iteration_completed: event when the metric updates. This event will be associated to :meth:`~ignite.metrics.metric.Metric.iteration_completed`. """ usage_name: str def __init__(self, started: Events, completed: Events, iteration_completed: CallableEventWithFilter) -> None: self.__started = started self.__completed = completed self.__iteration_completed = iteration_completed @property def STARTED(self) -> Events: return self.__started @property def COMPLETED(self) -> Events: return self.__completed @property def ITERATION_COMPLETED(self) -> CallableEventWithFilter: return self.__iteration_completed class EpochWise(MetricUsage): """ Epoch-wise usage of Metrics. It's the default and most common usage of metrics. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``EPOCH_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "epoch_wise" def __init__(self) -> None: super(EpochWise, self).__init__( started=Events.EPOCH_STARTED, completed=Events.EPOCH_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, ) class RunningEpochWise(EpochWise): """ Running epoch-wise usage of Metrics. It's the running version of the :class:`~.metrics.metric.EpochWise` metric usage. A metric with such a usage most likely accompanies an :class:`~.metrics.metric.EpochWise` one to compute a running measure of it e.g. running average. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``EPOCH_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``EPOCH_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "running_epoch_wise" def __init__(self) -> None: super(EpochWise, self).__init__( started=Events.STARTED, completed=Events.EPOCH_COMPLETED, iteration_completed=Events.EPOCH_COMPLETED, ) class BatchWise(MetricUsage): """ Batch-wise usage of Metrics. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``ITERATION_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``ITERATION_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "batch_wise" def __init__(self) -> None: super(BatchWise, self).__init__( started=Events.ITERATION_STARTED, completed=Events.ITERATION_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, ) class RunningBatchWise(BatchWise): """ Running batch-wise usage of Metrics. It's the running version of the :class:`~.metrics.metric.EpochWise` metric usage. A metric with such a usage could for example accompany a :class:`~.metrics.metric.BatchWise` one to compute a running measure of it e.g. running average. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``ITERATION_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "running_batch_wise" def __init__(self) -> None: super(BatchWise, self).__init__( started=Events.STARTED, completed=Events.ITERATION_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, ) class SingleEpochRunningBatchWise(BatchWise): """ Running batch-wise usage of Metrics in a single epoch. It's like :class:`~.metrics.metric.RunningBatchWise` metric usage with the difference that is used during a single epoch. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``ITERATION_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "single_epoch_running_batch_wise" def __init__(self) -> None: super(BatchWise, self).__init__( started=Events.EPOCH_STARTED, completed=Events.ITERATION_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, ) class BatchFiltered(MetricUsage): """ Batch filtered usage of Metrics. This usage is similar to epoch-wise but update event is filtered. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on filtered ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``EPOCH_COMPLETED``. Args: args: Positional arguments to setup :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED` kwargs: Keyword arguments to setup :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED` handled by :meth:`~ignite.metrics.metric.Metric.iteration_completed`. """ def __init__(self, *args: Any, **kwargs: Any) -> None: super(BatchFiltered, self).__init__( started=Events.EPOCH_STARTED, completed=Events.EPOCH_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED(*args, **kwargs), ) class Metric(Serializable, metaclass=ABCMeta): """ Base class for all Metrics. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: The following example shows a custom loss metric that expects input from a multi-output model. .. code-block:: python import torch import torch.nn as nn import torch.nn.functional as F from ignite.engine import create_supervised_evaluator from ignite.metrics import Loss class MyLoss(nn.Module): def __init__(self, ca: float = 1.0, cb: float = 1.0) -> None: super().__init__() self.ca = ca self.cb = cb def forward(self, y_pred: Tuple[torch.Tensor, torch.Tensor], y_true: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: a_true, b_true = y_true a_pred, b_pred = y_pred return self.ca * F.mse_loss(a_pred, a_true) + self.cb * F.cross_entropy(b_pred, b_true) def prepare_batch(batch, device, non_blocking): return torch.rand(4, 1), (torch.rand(4, 1), torch.rand(4, 2)) class MyModel(nn.Module): def forward(self, x): return torch.rand(4, 1), torch.rand(4, 2) model = MyModel() device = "cpu" loss = MyLoss(0.5, 1.0) metrics = { "Loss": Loss(loss, skip_unrolling=True) } train_evaluator = create_supervised_evaluator(model, metrics, device, prepare_batch=prepare_batch) data = range(10) train_evaluator.run(data) train_evaluator.state.metrics["Loss"] Attributes: required_output_keys: dictionary defines required keys to be found in ``engine.state.output`` if the latter is a dictionary. By default, ``("y_pred", "y")``. This is useful with custom metrics that can require other arguments than predictions ``y_pred`` and targets ``y``. See an example below. Examples: Let's implement a custom metric that requires ``y_pred``, ``y`` and ``x`` as input for ``update`` function. In the example below we show how to setup standard metric like Accuracy and the custom metric using by an ``evaluator`` created with :meth:`~ignite.engine.create_supervised_evaluator` method. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. code-block:: python # https://discuss.pytorch.org/t/how-access-inputs-in-custom-ignite-metric/91221/5 import torch import torch.nn as nn from ignite.metrics import Metric, Accuracy from ignite.engine import create_supervised_evaluator class CustomMetric(Metric): required_output_keys = ("y_pred", "y", "x") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def update(self, output): y_pred, y, x = output # ... def reset(self): # ... pass def compute(self): # ... pass model = ... metrics = { "Accuracy": Accuracy(), "CustomMetric": CustomMetric() } evaluator = create_supervised_evaluator( model, metrics=metrics, output_transform=lambda x, y, y_pred: {"x": x, "y": y, "y_pred": y_pred} ) res = evaluator.run(data) .. versionchanged:: 0.4.2 ``required_output_keys`` became public attribute. .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ # public class attribute required_output_keys: Optional[Tuple] = ("y_pred", "y") # for backward compatibility _required_output_keys = required_output_keys def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): self._output_transform = output_transform # Some metrics have a large performance regression when run on XLA devices, so for now, we disallow it. if torch.device(device).type == "xla": raise ValueError("Cannot create metric on an XLA device. Use device='cpu' instead.") self._device = torch.device(device) self._skip_unrolling = skip_unrolling self.reset() @abstractmethod def reset(self) -> None: """ Resets the metric to its initial state. By default, this is called at the start of each epoch. """ pass @abstractmethod def update(self, output: Any) -> None: """ Updates the metric's state using the passed batch output. By default, this is called once for each batch. Args: output: the is the output from the engine's process function. """ pass @abstractmethod def compute(self) -> Any: """ Computes the metric based on its accumulated state. By default, this is called at the end of each epoch. Returns: Any: | the actual quantity of interest. However, if a :class:`~collections.abc.Mapping` is returned, it will be (shallow) flattened into `engine.state.metrics` when :func:`~ignite.metrics.metric.Metric.completed` is called. Raises: NotComputableError: raised when the metric cannot be computed. """ pass def started(self, engine: Engine) -> None: """Helper method to start data gathering for metric's computation. It is automatically attached to the `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. Args: engine: the engine to which the metric must be attached """ self.reset() @torch.no_grad() def iteration_completed(self, engine: Engine) -> None: """Helper method to update metric's computation. It is automatically attached to the `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. Args: engine: the engine to which the metric must be attached Note: ``engine.state.output`` is used to compute metric values. The majority of implemented metrics accept the following formats for ``engine.state.output``: ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. ``y_pred`` and ``y`` can be torch tensors or list of tensors/numbers if applicable. .. versionchanged:: 0.4.5 ``y_pred`` and ``y`` can be torch tensors or list of tensors/numbers """ output = self._output_transform(engine.state.output) if isinstance(output, Mapping): if self.required_output_keys is None: raise TypeError( f"Transformed engine output for {self.__class__.__name__} metric should be a tuple/list, " f"but given {type(output)}" ) if not all([k in output for k in self.required_output_keys]): raise ValueError( "When transformed engine's output is a mapping, " f"it should contain {self.required_output_keys} keys, but given {list(output.keys())}" ) output = tuple(output[k] for k in self.required_output_keys) if ( (not self._skip_unrolling) and isinstance(output, Sequence) and all([_is_list_of_tensors_or_numbers(o) for o in output]) ): if not (len(output) == 2 and len(output[0]) == len(output[1])): raise ValueError( f"Output should have 2 items of the same length, " f"got {len(output)} and {len(output[0])}, {len(output[1])}" ) for o1, o2 in zip(output[0], output[1]): # o1 and o2 are list of tensors or numbers tensor_o1 = _to_batched_tensor(o1) tensor_o2 = _to_batched_tensor(o2, device=tensor_o1.device) self.update((tensor_o1, tensor_o2)) else: self.update(output) def completed(self, engine: Engine, name: str) -> None: """Helper method to compute metric's value and put into the engine. It is automatically attached to the `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. If metrics' value is torch tensor, it is explicitly sent to CPU device. Args: engine: the engine to which the metric must be attached name: the name of the metric used as key in dict `engine.state.metrics` .. versionchanged:: 0.4.3 Added dict in metrics results. .. versionchanged:: 0.4.5 metric's value is put on CPU if torch tensor. """ result = self.compute() if isinstance(result, Mapping): if name in result.keys(): raise ValueError(f"Argument name '{name}' is conflicting with mapping keys: {list(result.keys())}") for key, value in result.items(): engine.state.metrics[key] = value engine.state.metrics[name] = result else: if isinstance(result, torch.Tensor): if len(result.size()) == 0: result = result.item() elif "cpu" not in result.device.type: result = result.cpu() engine.state.metrics[name] = result def _check_usage(self, usage: Union[str, MetricUsage]) -> MetricUsage: if isinstance(usage, str): usages = [EpochWise, RunningEpochWise, BatchWise, RunningBatchWise, SingleEpochRunningBatchWise] for usage_cls in usages: if usage == usage_cls.usage_name: usage = usage_cls() break if not isinstance(usage, MetricUsage): raise ValueError( "Argument usage should be '(Running)EpochWise.usage_name' or " f"'((SingleEpoch)Running)BatchWise.usage_name', got {usage}" ) if not isinstance(usage, MetricUsage): raise TypeError(f"Unhandled usage type {type(usage)}") return usage def attach(self, engine: Engine, name: str, usage: Union[str, MetricUsage] = EpochWise()) -> None: """ Attaches current metric to provided engine. On the end of engine's run, `engine.state.metrics` dictionary will contain computed metric's value under provided name. Args: engine: the engine to which the metric must be attached name: the name of the metric to attach usage: the usage of the metric. Valid string values should be :attr:`ignite.metrics.metric.EpochWise.usage_name` (default) or :attr:`ignite.metrics.metric.BatchWise.usage_name`. Examples: .. code-block:: python metric = ... metric.attach(engine, "mymetric") assert "mymetric" in engine.run(data).metrics assert metric.is_attached(engine) Example with usage: .. code-block:: python metric = ... metric.attach(engine, "mymetric", usage=BatchWise.usage_name) assert "mymetric" in engine.run(data).metrics assert metric.is_attached(engine, usage=BatchWise.usage_name) """ usage = self._check_usage(usage) if not engine.has_event_handler(self.started, usage.STARTED): engine.add_event_handler(usage.STARTED, self.started) if not engine.has_event_handler(self.iteration_completed, usage.ITERATION_COMPLETED): engine.add_event_handler(usage.ITERATION_COMPLETED, self.iteration_completed) engine.add_event_handler(usage.COMPLETED, self.completed, name) def detach(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> None: """ Detaches current metric from the engine and no metric's computation is done during the run. This method in conjunction with :meth:`~ignite.metrics.metric.Metric.attach` can be useful if several metrics need to be computed with different periods. For example, one metric is computed every training epoch and another metric (e.g. more expensive one) is done every n-th training epoch. Args: engine: the engine from which the metric must be detached usage: the usage of the metric. Valid string values should be 'epoch_wise' (default) or 'batch_wise'. Examples: .. code-block:: python metric = ... engine = ... metric.detach(engine) assert "mymetric" not in engine.run(data).metrics assert not metric.is_attached(engine) Example with usage: .. code-block:: python metric = ... engine = ... metric.detach(engine, usage="batch_wise") assert "mymetric" not in engine.run(data).metrics assert not metric.is_attached(engine, usage="batch_wise") """ usage = self._check_usage(usage) if engine.has_event_handler(self.completed, usage.COMPLETED): engine.remove_event_handler(self.completed, usage.COMPLETED) if engine.has_event_handler(self.started, usage.STARTED): engine.remove_event_handler(self.started, usage.STARTED) if engine.has_event_handler(self.iteration_completed, usage.ITERATION_COMPLETED): engine.remove_event_handler(self.iteration_completed, usage.ITERATION_COMPLETED) def is_attached(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> bool: """ Checks if current metric is attached to provided engine. If attached, metric's computed value is written to `engine.state.metrics` dictionary. Args: engine: the engine checked from which the metric should be attached usage: the usage of the metric. Valid string values should be 'epoch_wise' (default) or 'batch_wise'. """ usage = self._check_usage(usage) return engine.has_event_handler(self.completed, usage.COMPLETED) def _state_dict_per_rank(self) -> OrderedDict: def func( x: Union[torch.Tensor, Metric, None, float], **kwargs: Any ) -> Union[torch.Tensor, float, OrderedDict, None]: if isinstance(x, Metric): return x._state_dict_per_rank() if x is None or isinstance(x, (int, float, torch.Tensor)): return x else: raise TypeError( "Found attribute of unsupported type. Currently, supported types include" " numeric types, tensor, Metric or sequence/mapping of metrics." ) state: OrderedDict[str, Union[torch.Tensor, List, Dict, None]] = OrderedDict() for attr_name in self._state_dict_all_req_keys: if attr_name not in self.__dict__: raise ValueError( f"Found a value in _state_dict_all_req_keys that is not among metric attributes: {attr_name}" ) attr = getattr(self, attr_name) state[attr_name] = _tree_map(func, attr) # type: ignore[assignment] return state __state_dict_key_per_rank: str = "__metric_state_per_rank" def state_dict(self) -> OrderedDict: """Method returns state dict with attributes of the metric specified in its `_state_dict_all_req_keys` attribute. Can be used to save internal state of the class. """ state = self._state_dict_per_rank() if idist.get_world_size() > 1: return OrderedDict([(Metric.__state_dict_key_per_rank, idist.all_gather(state))]) return OrderedDict([(Metric.__state_dict_key_per_rank, [state])]) def _load_state_dict_per_rank(self, state_dict: Mapping) -> None: super().load_state_dict(state_dict) def func(x: Any, y: Any) -> None: if isinstance(x, Metric): x._load_state_dict_per_rank(y) elif isinstance(x, _CollectionItem): value = x.value() if y is None or isinstance(y, _CollectionItem.types_as_collection_item): x.load_value(y) elif isinstance(value, Metric): value._load_state_dict_per_rank(y) else: raise ValueError(f"Unsupported type for provided state_dict data: {type(y)}") for attr_name in self._state_dict_all_req_keys: attr = getattr(self, attr_name) attr = _CollectionItem.wrap(self.__dict__, attr_name, attr) _tree_apply2(func, attr, state_dict[attr_name]) def load_state_dict(self, state_dict: Mapping) -> None: """Method replaces internal state of the class with provided state dict data. If there's an active distributed configuration, the process uses its rank to pick the proper value from the list of values saved under each attribute's name in the dict. Args: state_dict: a dict containing attributes of the metric specified in its `_state_dict_all_req_keys` attribute. """ if not isinstance(state_dict, Mapping): raise TypeError(f"Argument state_dict should be a dictionary, but given {type(state_dict)}") if not (len(state_dict) == 1 and Metric.__state_dict_key_per_rank in state_dict): raise ValueError( "Incorrect state_dict object. Argument state_dict should be a dictionary " "provided by Metric.state_dict(). " f"Expected single key: {Metric.__state_dict_key_per_rank}, but given {state_dict.keys()}" ) list_state_dicts_per_rank = state_dict[Metric.__state_dict_key_per_rank] rank = idist.get_rank() world_size = idist.get_world_size() if len(list_state_dicts_per_rank) != world_size: raise ValueError( "Incorrect state_dict object. Argument state_dict should be a dictionary " "provided by Metric.state_dict(). " f"Expected a list of state_dicts of size equal world_size: {world_size}, " f"but got {len(list_state_dicts_per_rank)}" ) state_dict = list_state_dicts_per_rank[rank] self._load_state_dict_per_rank(state_dict) def __add__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x + y, self, other) def __radd__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x + y, other, self) def __sub__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x - y, self, other) def __rsub__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x - y, other, self) def __mul__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x * y, self, other) def __rmul__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x * y, other, self) def __pow__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x**y, self, other) def __rpow__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x**y, other, self) def __mod__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x % y, self, other) def __truediv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x.__truediv__(y), self, other) def __rtruediv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x.__truediv__(y), other, self) def __floordiv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x // y, self, other) def __getattr__(self, attr: str) -> Callable: from ignite.metrics.metrics_lambda import MetricsLambda if attr.startswith("__") and attr.endswith("__"): return object.__getattribute__(self, attr) def fn(x: Metric, *args: Any, **kwargs: Any) -> Any: return getattr(x, attr)(*args, **kwargs) def wrapper(*args: Any, **kwargs: Any) -> "MetricsLambda": return MetricsLambda(fn, self, *args, **kwargs) return wrapper def __getitem__(self, index: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x: x[index], self) def __getstate__(self) -> Dict: return self.__dict__ def __setstate__(self, d: Dict) -> None: self.__dict__.update(d) def sync_all_reduce(*attrs: Any) -> Callable: """Helper decorator for distributed configuration to collect instance attribute value across all participating processes and apply the specified reduction operation. See :doc:`metrics` on how to use it. Args: attrs: attribute names of decorated class .. versionchanged:: 0.4.5 - Ability to handle different reduction operations (SUM, MAX, MIN, PRODUCT). """ def wrapper(func: Callable) -> Callable: @wraps(func) def another_wrapper(self: Metric, *args: Any, **kwargs: Any) -> Callable: if not isinstance(self, Metric): raise RuntimeError( "Decorator sync_all_reduce should be used on ignite.metric.Metric class methods only" ) ws = idist.get_world_size() unreduced_attrs = {} if len(attrs) > 0 and ws > 1: for attr in attrs: op_kwargs = {} if ":" in attr: attr, op = attr.split(":") valid_ops = ["MIN", "MAX", "SUM", "PRODUCT"] if op not in valid_ops: raise ValueError(f"Reduction operation is not valid (expected : {valid_ops}, got: {op}") op_kwargs["op"] = op if attr not in self.__dict__: raise ValueError(f"Metric {type(self)} has no attribute named `{attr}`.") t = getattr(self, attr) if not isinstance(t, (Number, torch.Tensor)): raise TypeError( "Attribute provided to sync_all_reduce should be a " f"number or tensor but `{attr}` has type {type(t)}" ) unreduced_attrs[attr] = t # Here `clone` is necessary since `idist.all_reduce` modifies `t` inplace in the case # `t` is a tensor and its `device` is same as that of the process. # TODO: Remove this dual behavior of `all_reduce` to always either return a new tensor or # modify it in-place. t_reduced = idist.all_reduce(cast(float, t) if isinstance(t, Number) else t.clone(), **op_kwargs) setattr(self, attr, t_reduced) result = func(self, *args, **kwargs) for attr, value in unreduced_attrs.items(): setattr(self, attr, value) return result return another_wrapper setattr(wrapper, "_decorated", True) return wrapper def reinit__is_reduced(func: Callable) -> Callable: """Helper decorator for distributed configuration. See :doc:`metrics` on how to use it. Args: func: A callable to reinit. """ @wraps(func) def wrapper(self: Metric, *args: Any, **kwargs: Any) -> None: func(self, *args, **kwargs) if "_result" in self.__dict__: self._result = None # type: ignore[attr-defined] setattr(wrapper, "_decorated", True) return wrapper def _is_list_of_tensors_or_numbers(x: Sequence[Union[torch.Tensor, float]]) -> bool: return isinstance(x, Sequence) and all([isinstance(t, (torch.Tensor, Number)) for t in x]) def _to_batched_tensor(x: Union[torch.Tensor, float], device: Optional[torch.device] = None) -> torch.Tensor: if isinstance(x, torch.Tensor): return x.unsqueeze(dim=0) return torch.tensor([x], device=device) ignite-0.5.1/ignite/metrics/metric_group.py000066400000000000000000000035771465426447700210310ustar00rootroot00000000000000from typing import Any, Callable, Dict, Sequence import torch from ignite.metrics import Metric class MetricGroup(Metric): """ A class for grouping metrics so that user could manage them easier. Args: metrics: a dictionary of names to metric instances. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. `output_transform` of each metric in the group is also called upon its update. Examples: We construct a group of metrics, attach them to the engine at once and retrieve their result. .. code-block:: python import torch metric_group = MetricGroup({'acc': Accuracy(), 'precision': Precision(), 'loss': Loss(nn.NLLLoss())}) metric_group.attach(default_evaluator, "eval_metrics") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) # Metrics individually available in `state.metrics` state.metrics["acc"], state.metrics["precision"], state.metrics["loss"] # And also altogether state.metrics["eval_metrics"] """ _state_dict_all_req_keys = ("metrics",) def __init__(self, metrics: Dict[str, Metric], output_transform: Callable = lambda x: x): self.metrics = metrics super(MetricGroup, self).__init__(output_transform=output_transform) def reset(self) -> None: for m in self.metrics.values(): m.reset() def update(self, output: Sequence[torch.Tensor]) -> None: for m in self.metrics.values(): m.update(m._output_transform(output)) def compute(self) -> Dict[str, Any]: return {k: m.compute() for k, m in self.metrics.items()} ignite-0.5.1/ignite/metrics/metrics_lambda.py000066400000000000000000000162151465426447700212710ustar00rootroot00000000000000import itertools from typing import Any, Callable, Optional, Union import torch from ignite.engine import Engine from ignite.metrics.metric import EpochWise, Metric, MetricUsage, reinit__is_reduced __all__ = ["MetricsLambda"] class MetricsLambda(Metric): """ Apply a function to other metrics to obtain a new metric. The result of the new metric is defined to be the result of applying the function to the result of argument metrics. When update, this metric recursively updates the metrics it depends on. When reset, all its dependency metrics would be resetted as well. When attach, all its dependency metrics would be attached automatically (but partially, e.g :meth:`~ignite.metrics.metric.Metric.is_attached()` will return False). Args: f: the function that defines the computation args: Sequence of other metrics or something else that will be fed to ``f`` as arguments. kwargs: Sequence of other metrics or something else that will be fed to ``f`` as keyword arguments. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: precision = Precision(average=False) recall = Recall(average=False) def Fbeta(r, p, beta): return torch.mean((1 + beta ** 2) * p * r / (beta ** 2 * p + r + 1e-20)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) F2 = MetricsLambda(Fbeta, recall, precision, 2) F3 = MetricsLambda(Fbeta, recall, precision, 3) F4 = MetricsLambda(Fbeta, recall, precision, 4) F1.attach(default_evaluator, "F1") F2.attach(default_evaluator, "F2") F3.attach(default_evaluator, "F3") F4.attach(default_evaluator, "F4") y_true = torch.tensor([1, 0, 1, 0, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["F1"]) print(state.metrics["F2"]) print(state.metrics["F3"]) print(state.metrics["F4"]) .. testoutput:: 0.8571... 0.9375... 0.9677... 0.9807... When check if the metric is attached, if one of its dependency metrics is detached, the metric is considered detached too. .. code-block:: python engine = ... precision = Precision(average=False) aP = precision.mean() aP.attach(engine, "aP") assert aP.is_attached(engine) # partially attached assert not precision.is_attached(engine) precision.detach(engine) assert not aP.is_attached(engine) # fully attached assert not precision.is_attached(engine) """ _state_dict_all_req_keys = ("_updated", "args", "kwargs") def __init__(self, f: Callable, *args: Any, **kwargs: Any) -> None: self.function = f self.args = list(args) # we need args to be a list instead of a tuple for state_dict/load_state_dict feature self.kwargs = kwargs self.engine: Optional[Engine] = None self._updated = False super(MetricsLambda, self).__init__(device="cpu") @reinit__is_reduced def reset(self) -> None: for i in itertools.chain(self.args, self.kwargs.values()): if isinstance(i, Metric): i.reset() self._updated = False @reinit__is_reduced def update(self, output: Any) -> None: if self.engine: raise ValueError( "MetricsLambda is already attached to an engine, " "and MetricsLambda can't use update API while it's attached." ) for i in itertools.chain(self.args, self.kwargs.values()): if isinstance(i, Metric): i.update(output) self._updated = True def compute(self) -> Any: materialized = [_get_value_on_cpu(i) for i in self.args] materialized_kwargs = {k: _get_value_on_cpu(v) for k, v in self.kwargs.items()} return self.function(*materialized, **materialized_kwargs) def _internal_attach(self, engine: Engine, usage: MetricUsage) -> None: self.engine = engine for index, metric in enumerate(itertools.chain(self.args, self.kwargs.values())): if isinstance(metric, MetricsLambda): metric._internal_attach(engine, usage) elif isinstance(metric, Metric): # NB : metrics is attached partially # We must not use is_attached() but rather if these events exist if not engine.has_event_handler(metric.started, usage.STARTED): engine.add_event_handler(usage.STARTED, metric.started) if not engine.has_event_handler(metric.iteration_completed, usage.ITERATION_COMPLETED): engine.add_event_handler(usage.ITERATION_COMPLETED, metric.iteration_completed) def attach(self, engine: Engine, name: str, usage: Union[str, MetricUsage] = EpochWise()) -> None: if self._updated: raise ValueError( "The underlying metrics are already updated, can't attach while using reset/update/compute API." ) usage = self._check_usage(usage) # recursively attach all its dependencies (partially) self._internal_attach(engine, usage) # attach only handler on EPOCH_COMPLETED engine.add_event_handler(usage.COMPLETED, self.completed, name) def detach(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> None: usage = self._check_usage(usage) # remove from engine super(MetricsLambda, self).detach(engine, usage) self.engine = None def is_attached(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> bool: usage = self._check_usage(usage) # check recursively the dependencies return super(MetricsLambda, self).is_attached(engine, usage) and self._internal_is_attached(engine, usage) def _internal_is_attached(self, engine: Engine, usage: MetricUsage) -> bool: # if no engine, metrics is not attached if engine is None: return False # check recursively if metrics are attached is_detached = False for metric in itertools.chain(self.args, self.kwargs.values()): if isinstance(metric, MetricsLambda): if not metric._internal_is_attached(engine, usage): is_detached = True elif isinstance(metric, Metric): if not engine.has_event_handler(metric.started, usage.STARTED): is_detached = True if not engine.has_event_handler(metric.iteration_completed, usage.ITERATION_COMPLETED): is_detached = True return not is_detached def _get_value_on_cpu(v: Any) -> Any: if isinstance(v, Metric): v = v.compute() if isinstance(v, torch.Tensor): v = v.cpu() return v ignite-0.5.1/ignite/metrics/multilabel_confusion_matrix.py000066400000000000000000000163141465426447700241240ustar00rootroot00000000000000from typing import Callable, Sequence, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["MultiLabelConfusionMatrix"] class MultiLabelConfusionMatrix(Metric): """Calculates a confusion matrix for multi-labelled, multi-class data. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` must contain 0s and 1s and has the following shape (batch_size, num_classes, ...). For example, `y_pred[i, j]` = 1 denotes that the j'th class is one of the labels of the i'th sample as predicted. - `y` should have the following shape (batch_size, num_classes, ...) with 0s and 1s. For example, `y[i, j]` = 1 denotes that the j'th class is one of the labels of the i'th sample according to the ground truth. - both `y` and `y_pred` must be torch Tensors having any of the following types: {torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64}. They must have the same dimensions. - The confusion matrix 'M' is of dimension (num_classes, 2, 2). * M[i, 0, 0] corresponds to count/rate of true negatives of class i * M[i, 0, 1] corresponds to count/rate of false positives of class i * M[i, 1, 0] corresponds to count/rate of false negatives of class i * M[i, 1, 1] corresponds to count/rate of true positives of class i - The classes present in M are indexed as 0, ... , num_classes-1 as can be inferred from above. Args: num_classes: Number of classes, should be > 1. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. normalized: whether to normalize confusion matrix by its sum or not. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Example: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MultiLabelConfusionMatrix(num_classes=3) metric.attach(default_evaluator, "mlcm") y_true = torch.tensor([ [0, 0, 1], [0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 1], ]) y_pred = torch.tensor([ [1, 1, 0], [1, 0, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["mlcm"]) .. testoutput:: tensor([[[0, 4], [0, 1]], [[3, 1], [0, 1]], [[1, 2], [2, 0]]]) .. versionadded:: 0.4.5 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("confusion_matrix", "_num_examples") def __init__( self, num_classes: int, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), normalized: bool = False, skip_unrolling: bool = False, ): if num_classes <= 1: raise ValueError("Argument num_classes needs to be > 1") self.num_classes = num_classes self._num_examples = 0 self.normalized = normalized super(MultiLabelConfusionMatrix, self).__init__( output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @reinit__is_reduced def reset(self) -> None: self.confusion_matrix = torch.zeros(self.num_classes, 2, 2, dtype=torch.int64, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_input(output) y_pred, y = output[0].detach(), output[1].detach() self._num_examples += y.shape[0] y_reshaped = y.transpose(0, 1).reshape(self.num_classes, -1) y_pred_reshaped = y_pred.transpose(0, 1).reshape(self.num_classes, -1) y_total = y_reshaped.sum(dim=1) y_pred_total = y_pred_reshaped.sum(dim=1) tp = (y_reshaped * y_pred_reshaped).sum(dim=1) fp = y_pred_total - tp fn = y_total - tp tn = y_reshaped.shape[1] - tp - fp - fn self.confusion_matrix += torch.stack([tn, fp, fn, tp], dim=1).reshape(-1, 2, 2).to(self._device) @sync_all_reduce("confusion_matrix", "_num_examples") def compute(self) -> torch.Tensor: if self._num_examples == 0: raise NotComputableError("Confusion matrix must have at least one example before it can be computed.") if self.normalized: conf = self.confusion_matrix.to(dtype=torch.float64) sums = conf.sum(dim=(1, 2)) return conf / sums[:, None, None] return self.confusion_matrix def _check_input(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if y_pred.ndimension() < 2: raise ValueError( f"y_pred must at least have shape (batch_size, num_classes (currently set to {self.num_classes}), ...)" ) if y.ndimension() < 2: raise ValueError( f"y must at least have shape (batch_size, num_classes (currently set to {self.num_classes}), ...)" ) if y_pred.shape[0] != y.shape[0]: raise ValueError(f"y_pred and y have different batch size: {y_pred.shape[0]} vs {y.shape[0]}") if y_pred.shape[1] != self.num_classes: raise ValueError(f"y_pred does not have correct number of classes: {y_pred.shape[1]} vs {self.num_classes}") if y.shape[1] != self.num_classes: raise ValueError(f"y does not have correct number of classes: {y.shape[1]} vs {self.num_classes}") if y.shape != y_pred.shape: raise ValueError("y and y_pred shapes must match.") valid_types = (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) if y_pred.dtype not in valid_types: raise ValueError(f"y_pred must be of any type: {valid_types}") if y.dtype not in valid_types: raise ValueError(f"y must be of any type: {valid_types}") if not torch.equal(y_pred, y_pred**2): raise ValueError("y_pred must be a binary tensor") if not torch.equal(y, y**2): raise ValueError("y must be a binary tensor") ignite-0.5.1/ignite/metrics/mutual_information.py000066400000000000000000000111241465426447700222310ustar00rootroot00000000000000import torch from ignite.exceptions import NotComputableError from ignite.metrics import Entropy from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce __all__ = ["MutualInformation"] class MutualInformation(Entropy): r"""Calculates the `mutual information `_ between input :math:`X` and prediction :math:`Y`. .. math:: \begin{align*} I(X;Y) &= H(Y) - H(Y|X) = H \left( \frac{1}{N}\sum_{i=1}^N \hat{\mathbf{p}}_i \right) - \frac{1}{N}\sum_{i=1}^N H(\hat{\mathbf{p}}_i), \\ H(\mathbf{p}) &= -\sum_{c=1}^C p_c \log p_c. \end{align*} where :math:`\hat{\mathbf{p}}_i` is the prediction probability vector for :math:`i`-th input, and :math:`H(\mathbf{p})` is the entropy of :math:`\mathbf{p}`. Intuitively, this metric measures how well input data are clustered by classes in the feature space [1]. [1] https://proceedings.mlr.press/v70/hu17b.html - ``update`` must receive output of the form ``(y_pred, y)`` while ``y`` is not used in this metric. - ``y_pred`` is expected to be the unnormalized logits for each class. :math:`(B, C)` (classification) or :math:`(B, C, ...)` (e.g., image segmentation) shapes are allowed. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MutualInformation() metric.attach(default_evaluator, 'mutual_information') y_true = torch.tensor([0, 1, 2]) # not considered in the MutualInformation metric. y_pred = torch.tensor([ [ 0.0000, 0.6931, 1.0986], [ 1.3863, 1.6094, 1.6094], [ 0.0000, -2.3026, -2.3026] ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mutual_information']) .. testoutput:: 0.18599730730056763 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_probabilities",) @reinit__is_reduced def reset(self) -> None: super().reset() self._sum_of_probabilities = torch.tensor(0.0, device=self._device) def _update(self, prob: torch.Tensor, log_prob: torch.Tensor) -> None: super()._update(prob, log_prob) # We can't use += below as _sum_of_probabilities can be a scalar and prob.sum(dim=0) is a vector self._sum_of_probabilities = self._sum_of_probabilities + prob.sum(dim=0).to(self._device) @sync_all_reduce("_sum_of_probabilities", "_sum_of_entropies", "_num_examples") def compute(self) -> float: n = self._num_examples if n == 0: raise NotComputableError("MutualInformation must have at least one example before it can be computed.") marginal_prob = self._sum_of_probabilities / n marginal_ent = -(marginal_prob * torch.log(marginal_prob)).sum() conditional_ent = self._sum_of_entropies / n mi = marginal_ent - conditional_ent mi = torch.clamp(mi, min=0.0) # mutual information cannot be negative return float(mi.item()) ignite-0.5.1/ignite/metrics/nlp/000077500000000000000000000000001465426447700165355ustar00rootroot00000000000000ignite-0.5.1/ignite/metrics/nlp/__init__.py000066400000000000000000000002501465426447700206430ustar00rootroot00000000000000from ignite.metrics.nlp.bleu import Bleu from ignite.metrics.nlp.rouge import Rouge, RougeL, RougeN __all__ = [ "Bleu", "Rouge", "RougeN", "RougeL", ] ignite-0.5.1/ignite/metrics/nlp/bleu.py000066400000000000000000000262401465426447700200420ustar00rootroot00000000000000import math from typing import Any, Callable, Sequence, Tuple, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce from ignite.metrics.nlp.utils import modified_precision __all__ = ["Bleu"] def _closest_ref_length(references: Sequence[Sequence[Any]], hyp_len: int) -> int: ref_lens = (len(reference) for reference in references) closest_ref_len = min(ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)) return closest_ref_len class _Smoother: """ Smoothing helper http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf """ def __init__(self, method: str): valid = ["no_smooth", "smooth1", "nltk_smooth2", "smooth2"] if method not in valid: raise ValueError(f"Smooth is not valid (expected: {valid}, got: {method})") self.smooth = method def __call__(self, numerators: torch.Tensor, denominators: torch.Tensor) -> Sequence[float]: method = getattr(self, self.smooth) return method(numerators, denominators) @staticmethod def smooth1(numerators: torch.Tensor, denominators: torch.Tensor) -> Sequence[float]: epsilon = 0.1 denominators_ = [max(1, d.item()) for d in denominators] return [n.item() / d if n != 0 else epsilon / d for n, d in zip(numerators, denominators_)] @staticmethod def nltk_smooth2(numerators: torch.Tensor, denominators: torch.Tensor) -> Sequence[float]: denominators_ = torch.tensor([max(1, d.item()) for d in denominators]) return _Smoother._smooth2(numerators, denominators_) @staticmethod def smooth2(numerators: torch.Tensor, denominators: torch.Tensor) -> Sequence[float]: return _Smoother._smooth2(numerators, denominators) @staticmethod def _smooth2(numerators: torch.Tensor, denominators: torch.Tensor) -> Sequence[float]: return [ (n.item() + 1) / (d.item() + 1) if i != 0 else n.item() / d.item() for i, (n, d) in enumerate(zip(numerators, denominators)) ] @staticmethod def no_smooth(numerators: torch.Tensor, denominators: torch.Tensor) -> Sequence[float]: denominators_ = [max(1, d) for d in denominators] return [n.item() / d for n, d in zip(numerators, denominators_)] class Bleu(Metric): r"""Calculates the `BLEU score `_. .. math:: \text{BLEU} = b_{p} \cdot \exp \left( \sum_{n=1}^{N} w_{n} \: \log p_{n} \right) where :math:`N` is the order of n-grams, :math:`b_{p}` is a sentence brevety penalty, :math:`w_{n}` are positive weights summing to one and :math:`p_{n}` are modified n-gram precisions. More details can be found in `Papineni et al. 2002`__. __ https://www.aclweb.org/anthology/P02-1040 In addition, a review of smoothing techniques can be found in `Chen et al. 2014`__ __ https://aclanthology.org/W14-3346.pdf - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` (list(list(str))) - a list of hypotheses sentences. - `y` (list(list(list(str))) - a corpus of lists of reference sentences w.r.t hypotheses. Remark : This implementation is inspired by nltk Args: ngram: order of n-grams. smooth: enable smoothing. Valid are ``no_smooth``, ``smooth1``, ``nltk_smooth2`` or ``smooth2``. Default: ``no_smooth``. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. average: specifies which type of averaging to use (macro or micro) for more details refer https://www.nltk.org/_modules/nltk/translate/bleu_score.html Default: "macro" Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. testcode:: from ignite.metrics.nlp import Bleu m = Bleu(ngram=4, smooth="smooth1") y_pred = "the the the the the the the" y = ["the cat is on the mat", "there is a cat on the mat"] m.update(([y_pred.split()], [[_y.split() for _y in y]])) print(m.compute()) .. testoutput:: tensor(0.0393, dtype=torch.float64) .. versionadded:: 0.4.5 .. versionchanged:: 0.4.7 - ``update`` method has changed and now works on batch of inputs. - added ``average`` option to handle micro and macro averaging modes. """ def __init__( self, ngram: int = 4, smooth: str = "no_smooth", output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), average: str = "macro", ): if ngram <= 0: raise ValueError(f"ngram order must be greater than zero (got: {ngram})") self.ngrams_order = ngram self.weights = [1 / self.ngrams_order] * self.ngrams_order self.smoother = _Smoother(method=smooth) if average not in ["macro", "micro"]: raise ValueError(f'Average must be either "macro" or "micro" (got: {average})') self.average = average if average == "micro": self._state_dict_all_req_keys = ("p_numerators", "p_denominators", "hyp_length_sum", "ref_length_sum") else: self._state_dict_all_req_keys = ("_sum_of_bleu", "_num_sentences") super(Bleu, self).__init__(output_transform=output_transform, device=device) def _n_gram_counter( self, references: Sequence[Sequence[Sequence[Any]]], candidates: Sequence[Sequence[Any]], p_numerators: torch.Tensor, p_denominators: torch.Tensor, ) -> Tuple[int, int]: if len(references) != len(candidates): raise ValueError( f"nb of candidates should be equal to nb of reference lists ({len(candidates)} != " f"{len(references)})" ) hyp_lengths = 0 ref_lengths = 0 # Iterate through each hypothesis and their corresponding references. for refs, hyp in zip(references, candidates): # For each order of ngram, calculate the numerator and # denominator for the corpus-level modified precision. for i in range(1, self.ngrams_order + 1): numerator, denominator = modified_precision(refs, hyp, i) p_numerators[i] += numerator p_denominators[i] += denominator # Calculate the hypothesis lengths hyp_lengths += len(hyp) # Calculate the closest reference lengths. ref_lengths += _closest_ref_length(refs, len(hyp)) return hyp_lengths, ref_lengths def _brevity_penalty_smoothing( self, p_numerators: torch.Tensor, p_denominators: torch.Tensor, hyp_length_sum: int, ref_length_sum: int ) -> float: # Returns 0 if there's no matching n-grams # We only need to check for p_numerators[1] == 0, since if there's # no unigrams, there won't be any higher order ngrams. if p_numerators[1] == 0: return 0 # If no smoother, returns 0 if there's at least one a not matching n-grams] if self.smoother.smooth == "no_smooth" and min(p_numerators[1:]).item() == 0: return 0 # Calculate corpus-level brevity penalty. if hyp_length_sum < ref_length_sum: bp = math.exp(1 - ref_length_sum / hyp_length_sum) if hyp_length_sum > 0 else 0.0 else: bp = 1.0 # Smoothing p_n = self.smoother(p_numerators[1:], p_denominators[1:]) # Compute the geometric mean s = [w_i * math.log(p_i) for w_i, p_i in zip(self.weights, p_n)] gm = bp * math.exp(math.fsum(s)) return gm def _sentence_bleu(self, references: Sequence[Sequence[Any]], candidates: Sequence[Any]) -> float: return self._corpus_bleu([references], [candidates]) def _corpus_bleu(self, references: Sequence[Sequence[Sequence[Any]]], candidates: Sequence[Sequence[Any]]) -> float: p_numerators: torch.Tensor = torch.zeros(self.ngrams_order + 1) p_denominators: torch.Tensor = torch.zeros(self.ngrams_order + 1) hyp_length_sum, ref_length_sum = self._n_gram_counter( references=references, candidates=candidates, p_numerators=p_numerators, p_denominators=p_denominators ) bleu_score = self._brevity_penalty_smoothing( p_numerators=p_numerators, p_denominators=p_denominators, hyp_length_sum=hyp_length_sum, ref_length_sum=ref_length_sum, ) return bleu_score @reinit__is_reduced def reset(self) -> None: if self.average == "macro": self._sum_of_bleu = torch.tensor(0.0, dtype=torch.double, device=self._device) self._num_sentences = 0 if self.average == "micro": self.p_numerators = torch.zeros(self.ngrams_order + 1) self.p_denominators = torch.zeros(self.ngrams_order + 1) self.hyp_length_sum = 0 self.ref_length_sum = 0 @reinit__is_reduced def update(self, output: Tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None: y_pred, y = output if self.average == "macro": for refs, hyp in zip(y, y_pred): self._sum_of_bleu += self._sentence_bleu(references=refs, candidates=hyp) self._num_sentences += 1 elif self.average == "micro": hyp_lengths, ref_lengths = self._n_gram_counter( references=y, candidates=y_pred, p_numerators=self.p_numerators, p_denominators=self.p_denominators ) self.hyp_length_sum += hyp_lengths self.ref_length_sum += ref_lengths @sync_all_reduce("_sum_of_bleu", "_num_sentences") def _compute_macro(self) -> torch.Tensor: if self._num_sentences == 0: raise NotComputableError("Bleu must have at least one example before it can be computed.") return self._sum_of_bleu / self._num_sentences @sync_all_reduce("p_numerators", "p_denominators", "hyp_length_sum", "ref_length_sum") def _compute_micro(self) -> float: bleu_score = self._brevity_penalty_smoothing( p_numerators=self.p_numerators, p_denominators=self.p_denominators, hyp_length_sum=self.hyp_length_sum, ref_length_sum=self.ref_length_sum, ) return bleu_score def compute(self) -> None: if self.average == "macro": return self._compute_macro() elif self.average == "micro": return self._compute_micro() ignite-0.5.1/ignite/metrics/nlp/rouge.py000066400000000000000000000360041465426447700202330ustar00rootroot00000000000000from abc import ABCMeta, abstractmethod from collections import namedtuple from typing import Any, Callable, List, Mapping, Optional, Sequence, Tuple, Union import torch from ignite.exceptions import NotComputableError # These decorators helps with distributed settings from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce from ignite.metrics.nlp.utils import lcs, ngrams __all__ = ["Rouge", "RougeN", "RougeL"] class Score(namedtuple("Score", ["match", "candidate", "reference"])): r""" Computes precision and recall for given matches, candidate and reference lengths. """ def precision(self) -> float: """ Calculates precision. """ return self.match / self.candidate if self.candidate > 0 else 0 def recall(self) -> float: """ Calculates recall. """ return self.match / self.reference if self.reference > 0 else 0 def compute_ngram_scores(candidate: Sequence[Any], reference: Sequence[Any], n: int = 4) -> Score: """ Compute the score based on ngram co-occurence of sequences of items Args: candidate: candidate sequence of items reference: reference sequence of items n: ngram order Returns: The score containing the number of ngram co-occurences .. versionadded:: 0.4.5 """ # ngrams of the candidate candidate_counter = ngrams(candidate, n) # ngrams of the references reference_counter = ngrams(reference, n) # ngram co-occurences in the candidate and the references match_counters = candidate_counter & reference_counter # the score is defined using Fraction return Score( match=sum(match_counters.values()), candidate=sum(candidate_counter.values()), reference=sum(reference_counter.values()), ) def compute_lcs_scores(candidate: Sequence[Any], reference: Sequence[Any]) -> Score: """ Compute the score based on longest common subsequence of sequences of items Args: candidate: candidate sequence of items reference: reference sequence of items Returns: The score containing the length of longest common subsequence .. versionadded:: 0.4.5 """ # lcs of candidate and reference match = lcs(candidate, reference) # the score is defined using Fraction return Score(match=match, candidate=len(candidate), reference=len(reference)) class MultiRefReducer(metaclass=ABCMeta): r""" Reducer interface for multi-reference """ @abstractmethod def __call__(self, scores: Sequence[Score]) -> Score: pass class MultiRefAverageReducer(MultiRefReducer): r""" Reducer for averaging the scores """ def __call__(self, scores: Sequence[Score]) -> Score: match = sum([score.match for score in scores]) candidate = sum([score.candidate for score in scores]) reference = sum([score.reference for score in scores]) return Score(match=match, candidate=candidate, reference=reference) class MultiRefBestReducer(MultiRefReducer): r""" Reducer for selecting the best score """ def __call__(self, scores: Sequence[Score]) -> Score: return max(scores, key=lambda x: x.recall()) class _BaseRouge(Metric): r""" Rouge interface for Rouge-L and Rouge-N """ _state_dict_all_req_keys = ("_recall", "_precision", "_fmeasure", "_num_examples") def __init__( self, multiref: str = "average", alpha: float = 0, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: super(_BaseRouge, self).__init__(output_transform=output_transform, device=device) self._alpha = alpha if not 0 <= self._alpha <= 1: raise ValueError(f"alpha must be in interval [0, 1] (got : {self._alpha})") self._multiref = multiref valid_multiref = ["best", "average"] if self._multiref not in valid_multiref: raise ValueError(f"multiref : valid values are {valid_multiref} (got : {self._multiref})") self._mutliref_reducer = self._get_multiref_reducer() def _get_multiref_reducer(self) -> MultiRefReducer: if self._multiref == "average": return MultiRefAverageReducer() return MultiRefBestReducer() @reinit__is_reduced def reset(self) -> None: self._recall = 0.0 self._precision = 0.0 self._fmeasure = 0.0 self._num_examples = 0 @reinit__is_reduced def update(self, output: Tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None: candidates, references = output for _candidate, _reference in zip(candidates, references): multiref_scores = [self._compute_score(candidate=_candidate, reference=_ref) for _ref in _reference] score = self._mutliref_reducer(multiref_scores) precision = score.precision() recall = score.recall() self._precision += precision self._recall += recall precision_recall = precision * recall if precision_recall > 0: # avoid zero division self._fmeasure += precision_recall / ((1 - self._alpha) * precision + self._alpha * recall) self._num_examples += 1 @sync_all_reduce("_precision", "_recall", "_fmeasure", "_num_examples") def compute(self) -> Mapping: if self._num_examples == 0: raise NotComputableError("Rouge metric must have at least one example before be computed") return { f"{self._metric_name()}-P": float(self._precision / self._num_examples), f"{self._metric_name()}-R": float(self._recall / self._num_examples), f"{self._metric_name()}-F": float(self._fmeasure / self._num_examples), } @abstractmethod def _compute_score(self, candidate: Sequence[Any], reference: Sequence[Any]) -> Score: pass @abstractmethod def _metric_name(self) -> str: pass class RougeN(_BaseRouge): r"""Calculates the Rouge-N score. The Rouge-N is based on the ngram co-occurences of candidates and references. More details can be found in `Lin 2004`__. __ https://www.aclweb.org/anthology/W04-1013.pdf - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` (list(list(str))) must be a sequence of tokens. - `y` (list(list(list(str))) must be a list of sequence of tokens. Args: ngram: ngram order (default: 4). multiref: reduces scores for multi references. Valid values are "best" and "average" (default: "average"). alpha: controls the importance between recall and precision (alpha -> 0: recall is more important, alpha -> 1: precision is more important) output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. testcode:: from ignite.metrics import RougeN m = RougeN(ngram=2, multiref="best") candidate = "the cat is not there".split() references = [ "the cat is on the mat".split(), "there is a cat on the mat".split() ] m.update(([candidate], [references])) print(m.compute()) .. testoutput:: {'Rouge-2-P': 0.5, 'Rouge-2-R': 0.4, 'Rouge-2-F': 0.4} .. versionadded:: 0.4.5 """ def __init__( self, ngram: int = 4, multiref: str = "average", alpha: float = 0, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super(RougeN, self).__init__(multiref=multiref, alpha=alpha, output_transform=output_transform, device=device) self._ngram = ngram if self._ngram < 1: raise ValueError(f"ngram order must be greater than zero (got : {self._ngram})") def _compute_score(self, candidate: Sequence[Any], reference: Sequence[Any]) -> Score: return compute_ngram_scores(candidate=candidate, reference=reference, n=self._ngram) def _metric_name(self) -> str: return f"Rouge-{self._ngram}" class RougeL(_BaseRouge): r"""Calculates the Rouge-L score. The Rouge-L is based on the length of the longest common subsequence of candidates and references. More details can be found in `Lin 2004`__. __ https://www.aclweb.org/anthology/W04-1013.pdf - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` (list(list(str))) must be a sequence of tokens. - `y` (list(list(list(str))) must be a list of sequence of tokens. Args: multiref: reduces scores for multi references. Valid values are "best" and "average" (default: "average"). alpha: controls the importance between recall and precision (alpha -> 0: recall is more important, alpha -> 1: precision is more important) output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. testcode:: from ignite.metrics import RougeL m = RougeL(multiref="best") candidate = "the cat is not there".split() references = [ "the cat is on the mat".split(), "there is a cat on the mat".split() ] m.update(([candidate], [references])) print(m.compute()) .. testoutput:: {'Rouge-L-P': 0.6, 'Rouge-L-R': 0.5, 'Rouge-L-F': 0.5} .. versionadded:: 0.4.5 """ def __init__( self, multiref: str = "average", alpha: float = 0, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super(RougeL, self).__init__(multiref=multiref, alpha=alpha, output_transform=output_transform, device=device) def _compute_score(self, candidate: Sequence[Any], reference: Sequence[Any]) -> Score: return compute_lcs_scores(candidate=candidate, reference=reference) def _metric_name(self) -> str: return "Rouge-L" class Rouge(Metric): r"""Calculates the Rouge score for multiples Rouge-N and Rouge-L metrics. More details can be found in `Lin 2004`__. __ https://www.aclweb.org/anthology/W04-1013.pdf - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` (list(list(str))) must be a sequence of tokens. - `y` (list(list(list(str))) must be a list of sequence of tokens. Args: variants: set of metrics computed. Valid inputs are "L" and integer 1 <= n <= 9. multiref: reduces scores for multi references. Valid values are "best" and "average" (default: "average"). alpha: controls the importance between recall and precision (alpha -> 0: recall is more important, alpha -> 1: precision is more important) output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. testcode:: from ignite.metrics import Rouge m = Rouge(variants=["L", 2], multiref="best") candidate = "the cat is not there".split() references = [ "the cat is on the mat".split(), "there is a cat on the mat".split() ] m.update(([candidate], [references])) print(m.compute()) .. testoutput:: {'Rouge-L-P': 0.6, 'Rouge-L-R': 0.5, 'Rouge-L-F': 0.5, 'Rouge-2-P': 0.5, 'Rouge-2-R': 0.4, 'Rouge-2-F': 0.4} .. versionadded:: 0.4.5 .. versionchanged:: 0.4.7 ``update`` method has changed and now works on batch of inputs. """ _state_dict_all_req_keys = ("internal_metrics",) def __init__( self, variants: Optional[Sequence[Union[str, int]]] = None, multiref: str = "average", alpha: float = 0, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): if variants is None or len(variants) == 0: variants = [1, 2, 4, "L"] self.internal_metrics: List[_BaseRouge] = [] for m in variants: variant: Optional[_BaseRouge] = None if isinstance(m, str) and m == "L": variant = RougeL(multiref=multiref, alpha=alpha, output_transform=output_transform, device=device) elif isinstance(m, int): variant = RougeN( ngram=m, multiref=multiref, alpha=alpha, output_transform=output_transform, device=device ) else: raise ValueError("variant must be 'L' or integer greater to zero") self.internal_metrics.append(variant) super(Rouge, self).__init__(output_transform=output_transform, device=device) @reinit__is_reduced def reset(self) -> None: for m in self.internal_metrics: m.reset() @reinit__is_reduced def update(self, output: Tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None: for m in self.internal_metrics: m.update(output) def compute(self) -> Mapping: results = {} for m in self.internal_metrics: results.update(m.compute()) return results ignite-0.5.1/ignite/metrics/nlp/utils.py000066400000000000000000000044611465426447700202540ustar00rootroot00000000000000from collections import Counter from typing import Any, Sequence, Tuple __all__ = ["ngrams", "lcs", "modified_precision"] def ngrams(sequence: Sequence[Any], n: int) -> Counter: """ Generate the ngrams from a sequence of items Args: sequence: sequence of items n: n-gram order Returns: A counter of ngram objects .. versionadded:: 0.4.5 """ return Counter([tuple(sequence[i : i + n]) for i in range(len(sequence) - n + 1)]) def lcs(seq_a: Sequence[Any], seq_b: Sequence[Any]) -> int: """ Compute the length of the longest common subsequence in two sequence of items https://en.wikipedia.org/wiki/Longest_common_subsequence_problem Args: seq_a: first sequence of items seq_b: second sequence of items Returns: The length of the longest common subsequence .. versionadded:: 0.4.5 """ m = len(seq_a) n = len(seq_b) dp = [[0] * (n + 1) for _ in range(m + 1)] for i in range(m + 1): for j in range(n + 1): if i == 0 or j == 0: dp[i][j] = 0 elif seq_a[i - 1] == seq_b[j - 1]: dp[i][j] = dp[i - 1][j - 1] + 1 else: dp[i][j] = max(dp[i - 1][j], dp[i][j - 1]) return dp[m][n] def modified_precision(references: Sequence[Sequence[Any]], candidate: Any, n: int) -> Tuple[int, int]: """ Compute the modified precision .. math:: p_{n} = \frac{m_{n}}{l_{n}} where m_{n} is the number of matched n-grams between translation T and its reference R, and l_{n} is the total number of n-grams in the translation T. More details can be found in `Papineni et al. 2002`__. __ https://www.aclweb.org/anthology/P02-1040.pdf Args: references: list of references R candidate: translation T n: n-gram order Returns: The length of the longest common subsequence .. versionadded:: 0.4.5 """ # ngrams of the candidate counts = ngrams(candidate, n) # union of ngrams of references max_counts: Counter = Counter() for reference in references: max_counts |= ngrams(reference, n) # clipped count of the candidate and references clipped_counts = counts & max_counts return sum(clipped_counts.values()), sum(counts.values()) ignite-0.5.1/ignite/metrics/precision.py000066400000000000000000000442641465426447700203230ustar00rootroot00000000000000import warnings from typing import Callable, cast, Optional, Sequence, Union import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.accuracy import _BaseClassification from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.utils import to_onehot __all__ = ["Precision"] class _BasePrecisionRecall(_BaseClassification): _state_dict_all_req_keys = ("_numerator", "_denominator", "_weight", "_updated") def __init__( self, output_transform: Callable = lambda x: x, average: Optional[Union[bool, str]] = False, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): if not (average is None or isinstance(average, bool) or average in ["macro", "micro", "weighted", "samples"]): raise ValueError( "Argument average should be None or a boolean or one of values" " 'macro', 'micro', 'weighted' and 'samples'." ) if average is True: self._average: Optional[Union[bool, str]] = "macro" else: self._average = average self.eps = 1e-20 self._updated = False super(_BasePrecisionRecall, self).__init__( output_transform=output_transform, is_multilabel=is_multilabel, device=device, skip_unrolling=skip_unrolling ) def _check_type(self, output: Sequence[torch.Tensor]) -> None: super()._check_type(output) if self._type in ["binary", "multiclass"] and self._average == "samples": raise ValueError("Argument average='samples' is incompatible with binary and multiclass input data.") y_pred, y = output if self._type == "multiclass" and y.dtype != torch.long: warnings.warn("`y` should be of dtype long when entry type is multiclass", RuntimeWarning) if ( self._type == "binary" and self._average is not False and (y.dtype != torch.long or y_pred.dtype != torch.long) ): warnings.warn( "`y` and `y_pred` should be of dtype long when entry type is binary and average!=False", RuntimeWarning ) def _prepare_output(self, output: Sequence[torch.Tensor]) -> Sequence[torch.Tensor]: y_pred, y = output[0].detach(), output[1].detach() if self._type == "binary" or self._type == "multiclass": num_classes = 2 if self._type == "binary" else y_pred.size(1) if self._type == "multiclass" and y.max() + 1 > num_classes: raise ValueError( f"y_pred contains fewer classes than y. Number of classes in the prediction is {num_classes}" f" and an element in y has invalid class = {y.max().item() + 1}." ) y = y.view(-1) if self._type == "binary" and self._average is False: y_pred = y_pred.view(-1) else: y = to_onehot(y.long(), num_classes=num_classes) indices = torch.argmax(y_pred, dim=1) if self._type == "multiclass" else y_pred.long() y_pred = to_onehot(indices.view(-1), num_classes=num_classes) elif self._type == "multilabel": # if y, y_pred shape is (N, C, ...) -> (N * ..., C) num_labels = y_pred.size(1) y_pred = torch.transpose(y_pred, 1, -1).reshape(-1, num_labels) y = torch.transpose(y, 1, -1).reshape(-1, num_labels) # Convert from int cuda/cpu to double on self._device y_pred = y_pred.to(dtype=torch.float64, device=self._device) y = y.to(dtype=torch.float64, device=self._device) correct = y * y_pred return y_pred, y, correct @reinit__is_reduced def reset(self) -> None: """ `numerator`, `denominator` and `weight` are three variables chosen to be abstract representatives of the ones that are measured for cases with different `average` parameters. `weight` is only used when `average='weighted'`. Actual value of these three variables is as follows. average='samples': numerator (torch.Tensor): sum of metric value for samples denominator (int): number of samples average='weighted': numerator (torch.Tensor): number of true positives per class/label denominator (torch.Tensor): number of predicted(for precision) or actual(for recall) positives per class/label. weight (torch.Tensor): number of actual positives per class average='micro': numerator (torch.Tensor): sum of number of true positives for classes/labels denominator (torch.Tensor): sum of number of predicted(for precision) or actual(for recall) positives for classes/labels. average='macro' or boolean or None: numerator (torch.Tensor): number of true positives per class/label denominator (torch.Tensor): number of predicted(for precision) or actual(for recall) positives per class/label. """ self._numerator: Union[int, torch.Tensor] = 0 self._denominator: Union[int, torch.Tensor] = 0 self._weight: Union[int, torch.Tensor] = 0 self._updated = False super(_BasePrecisionRecall, self).reset() @sync_all_reduce("_numerator", "_denominator") def compute(self) -> Union[torch.Tensor, float]: r""" Return value of the metric for `average` options `'weighted'` and `'macro'` is computed as follows. .. math:: \text{Precision/Recall} = \frac{ numerator }{ denominator } \cdot weight wherein `weight` is the internal variable `_weight` for `'weighted'` option and :math:`1/C` for the `macro` one. :math:`C` is the number of classes/labels. Return value of the metric for `average` options `'micro'`, `'samples'`, `False` and None is as follows. .. math:: \text{Precision/Recall} = \frac{ numerator }{ denominator } """ if not self._updated: raise NotComputableError( f"{self.__class__.__name__} must have at least one example before it can be computed." ) fraction = self._numerator / (self._denominator + (self.eps if self._average != "samples" else 0)) if self._average == "weighted": _weight = idist.all_reduce(self._weight.clone()) # type: ignore[union-attr] sum_of_weights = cast(torch.Tensor, _weight).sum() + self.eps return ((fraction @ _weight) / sum_of_weights).item() # type: ignore elif self._average == "micro" or self._average == "samples": return cast(torch.Tensor, fraction).item() elif self._average == "macro": return cast(torch.Tensor, fraction).mean().item() else: return fraction class Precision(_BasePrecisionRecall): r"""Calculates precision for binary, multiclass and multilabel data. .. math:: \text{Precision} = \frac{ TP }{ TP + FP } where :math:`\text{TP}` is true positives and :math:`\text{FP}` is false positives. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...). - `y` must be in the following shape (batch_size, ...). Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. average: available options are False default option. For multicalss and multilabel inputs, per class and per label metric is returned respectively. None like `False` option except that per class metric is returned for binary data as well. For compatibility with Scikit-Learn api. 'micro' Metric is computed counting stats of classes/labels altogether. .. math:: \text{Micro Precision} = \frac{\sum_{k=1}^C TP_k}{\sum_{k=1}^C TP_k+FP_k} where :math:`C` is the number of classes/labels (2 in binary case). :math:`k` in :math:`TP_k` and :math:`FP_k` means that the measures are computed for class/label :math:`k` (in a one-vs-rest sense in multiclass case). For binary and multiclass inputs, this is equivalent with accuracy, so use :class:`~ignite.metrics.accuracy.Accuracy`. 'samples' for multilabel input, at first, precision is computed on a per sample basis and then average across samples is returned. .. math:: \text{Sample-averaged Precision} = \frac{\sum_{n=1}^N \frac{TP_n}{TP_n+FP_n}}{N} where :math:`N` is the number of samples. :math:`n` in :math:`TP_n` and :math:`FP_n` means that the measures are computed for sample :math:`n`, across labels. Incompatible with binary and multiclass inputs. 'weighted' like macro precision but considers class/label imbalance. for binary and multiclass input, it computes metric for each class then returns average of them weighted by support of classes (number of actual samples in each class). For multilabel input, it computes precision for each label then returns average of them weighted by support of labels (number of actual positive samples in each label). .. math:: Precision_k = \frac{TP_k}{TP_k+FP_k} .. math:: \text{Weighted Precision} = \frac{\sum_{k=1}^C P_k * Precision_k}{N} where :math:`C` is the number of classes (2 in binary case). :math:`P_k` is the number of samples belonged to class :math:`k` in binary and multiclass case, and the number of positive samples belonged to label :math:`k` in multilabel case. macro computes macro precision which is unweighted average of metric computed across classes/labels. .. math:: \text{Macro Precision} = \frac{\sum_{k=1}^C Precision_k}{C} where :math:`C` is the number of classes (2 in binary case). True like macro option. For backward compatibility. is_multilabel: flag to use in multilabel case. By default, value is False. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Binary case. In binary and multilabel cases, the elements of `y` and `y_pred` should have 0 or 1 values. .. testcode:: 1 metric = Precision() weighted_metric = Precision(average='weighted') two_class_metric = Precision(average=None) # Returns precision for both classes metric.attach(default_evaluator, "precision") weighted_metric.attach(default_evaluator, "weighted precision") two_class_metric.attach(default_evaluator, "both classes precision") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) print(f"Precision: {state.metrics['precision']}") print(f"Weighted Precision: {state.metrics['weighted precision']}") print(f"Precision for class 0 and class 1: {state.metrics['both classes precision']}") .. testoutput:: 1 Precision: 0.75 Weighted Precision: 0.6666666666666666 Precision for class 0 and class 1: tensor([0.5000, 0.7500], dtype=torch.float64) Multiclass case .. testcode:: 2 metric = Precision() macro_metric = Precision(average=True) weighted_metric = Precision(average='weighted') metric.attach(default_evaluator, "precision") macro_metric.attach(default_evaluator, "macro precision") weighted_metric.attach(default_evaluator, "weighted precision") y_true = torch.tensor([2, 0, 2, 1, 0]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288] ]) state = default_evaluator.run([[y_pred, y_true]]) print(f"Precision: {state.metrics['precision']}") print(f"Macro Precision: {state.metrics['macro precision']}") print(f"Weighted Precision: {state.metrics['weighted precision']}") .. testoutput:: 2 Precision: tensor([0.5000, 0.0000, 0.3333], dtype=torch.float64) Macro Precision: 0.27777777777777773 Weighted Precision: 0.3333333333333333 Multilabel case, the shapes must be (batch_size, num_labels, ...) .. testcode:: 3 metric = Precision(is_multilabel=True) micro_metric = Precision(is_multilabel=True, average='micro') macro_metric = Precision(is_multilabel=True, average=True) weighted_metric = Precision(is_multilabel=True, average='weighted') samples_metric = Precision(is_multilabel=True, average='samples') metric.attach(default_evaluator, "precision") micro_metric.attach(default_evaluator, "micro precision") macro_metric.attach(default_evaluator, "macro precision") weighted_metric.attach(default_evaluator, "weighted precision") samples_metric.attach(default_evaluator, "samples precision") y_true = torch.tensor([ [0, 0, 1], [0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 1], ]) y_pred = torch.tensor([ [1, 1, 0], [1, 0, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(f"Precision: {state.metrics['precision']}") print(f"Micro Precision: {state.metrics['micro precision']}") print(f"Macro Precision: {state.metrics['macro precision']}") print(f"Weighted Precision: {state.metrics['weighted precision']}") print(f"Samples Precision: {state.metrics['samples precision']}") .. testoutput:: 3 Precision: tensor([0.2000, 0.5000, 0.0000], dtype=torch.float64) Micro Precision: 0.2222222222222222 Macro Precision: 0.2333333333333333 Weighted Precision: 0.175 Samples Precision: 0.2 Thresholding of predictions can be done as below: .. testcode:: 4 def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y metric = Precision(output_transform=thresholded_output_transform) metric.attach(default_evaluator, "precision") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["precision"]) .. testoutput:: 4 0.75 .. versionchanged:: 0.4.10 Some new options were added to `average` parameter. .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: r""" Update the metric state using prediction and target. Args: output: a binary tuple of tensors (y_pred, y) whose shapes follow the table below. N stands for the batch dimension, `...` for possible additional dimensions and C for class dimension. .. list-table:: :widths: 20 10 10 10 :header-rows: 1 * - Output member\\Data type - Binary - Multiclass - Multilabel * - y_pred - (N, ...) - (N, C, ...) - (N, C, ...) * - y - (N, ...) - (N, ...) - (N, C, ...) For binary and multilabel data, both y and y_pred should consist of 0's and 1's, but for multiclass data, y_pred and y should consist of probabilities and integers respectively. """ self._check_shape(output) self._check_type(output) y_pred, y, correct = self._prepare_output(output) if self._average == "samples": all_positives = y_pred.sum(dim=1) true_positives = correct.sum(dim=1) self._numerator += torch.sum(true_positives / (all_positives + self.eps)) self._denominator += y.size(0) elif self._average == "micro": self._denominator += y_pred.sum() self._numerator += correct.sum() else: # _average in [False, None, 'macro', 'weighted'] self._denominator += y_pred.sum(dim=0) self._numerator += correct.sum(dim=0) if self._average == "weighted": self._weight += y.sum(dim=0) self._updated = True ignite-0.5.1/ignite/metrics/precision_recall_curve.py000066400000000000000000000137301465426447700230430ustar00rootroot00000000000000from typing import Any, Callable, cast, Tuple, Union import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.epoch_metric import EpochMetric def precision_recall_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> Tuple[Any, Any, Any]: try: from sklearn.metrics import precision_recall_curve except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") y_true = y_targets.cpu().numpy() y_pred = y_preds.cpu().numpy() return precision_recall_curve(y_true, y_pred) class PrecisionRecallCurve(EpochMetric): """Compute precision-recall pairs for different probability thresholds for binary classification task by accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.precision_recall_curve `_ . Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. check_compute_fn: Default False. If True, `precision_recall_curve `_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Note: PrecisionRecallCurve expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below: .. code-block:: python def sigmoid_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y avg_precision = PrecisionRecallCurve(sigmoid_output_transform) Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997]) y_true = torch.tensor([0, 0, 1, 1]) prec_recall_curve = PrecisionRecallCurve() prec_recall_curve.attach(default_evaluator, 'prec_recall_curve') state = default_evaluator.run([[y_pred, y_true]]) print("Precision", [round(i, 4) for i in state.metrics['prec_recall_curve'][0].tolist()]) print("Recall", [round(i, 4) for i in state.metrics['prec_recall_curve'][1].tolist()]) print("Thresholds", [round(i, 4) for i in state.metrics['prec_recall_curve'][2].tolist()]) .. testoutput:: Precision [0.5, 0.6667, 1.0, 1.0, 1.0] Recall [1.0, 1.0, 1.0, 0.5, 0.0] Thresholds [0.0474, 0.5987, 0.7109, 0.9997] .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ) -> None: super(PrecisionRecallCurve, self).__init__( precision_recall_curve_compute_fn, # type: ignore[arg-type] output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, skip_unrolling=skip_unrolling, ) def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: ignore[override] if len(self._predictions) < 1 or len(self._targets) < 1: raise NotComputableError("PrecisionRecallCurve must have at least one example before it can be computed.") if self._result is None: # type: ignore _prediction_tensor = torch.cat(self._predictions, dim=0) _target_tensor = torch.cat(self._targets, dim=0) ws = idist.get_world_size() if ws > 1: # All gather across all processes _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor)) _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor)) if idist.get_rank() == 0: # Run compute_fn on zero rank only precision, recall, thresholds = cast(Tuple, self.compute_fn(_prediction_tensor, _target_tensor)) precision = torch.tensor(precision, device=_prediction_tensor.device) recall = torch.tensor(recall, device=_prediction_tensor.device) # thresholds can have negative strides, not compatible with torch tensors # https://discuss.pytorch.org/t/negative-strides-in-tensor-error/134287/2 thresholds = torch.tensor(thresholds.copy(), device=_prediction_tensor.device) else: precision, recall, thresholds = None, None, None if ws > 1: # broadcast result to all processes precision = idist.broadcast(precision, src=0, safe_mode=True) recall = idist.broadcast(recall, src=0, safe_mode=True) thresholds = idist.broadcast(thresholds, src=0, safe_mode=True) self._result = (precision, recall, thresholds) # type: ignore[assignment] return cast(Tuple[torch.Tensor, torch.Tensor, torch.Tensor], self._result) # type: ignore ignite-0.5.1/ignite/metrics/psnr.py000066400000000000000000000126251465426447700173060ustar00rootroot00000000000000from typing import Callable, Sequence, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["PSNR"] class PSNR(Metric): r"""Computes average `Peak signal-to-noise ratio (PSNR) `_. .. math:: \text{PSNR}(I, J) = 10 * \log_{10}\left(\frac{ MAX_{I}^2 }{ \text{ MSE } }\right) where :math:`\text{MSE}` is `mean squared error `_. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` and `y` **must** have (batch_size, ...) shape. - `y_pred` and `y` **must** have same dtype and same shape. Args: data_range: The data range of the target image (distance between minimum and maximum possible values). For other data types, please set the data range, otherwise an exception will be raised. output_transform: A callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. device: specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: psnr = PSNR(data_range=1.0) psnr.attach(default_evaluator, 'psnr') preds = torch.rand([4, 3, 16, 16]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['psnr']) .. testoutput:: 16.8671405... This metric by default accepts Grayscale or RGB images. But if you have YCbCr or YUV images, only Y channel is needed for computing PSNR. And, this can be done with ``output_transform``. For instance, .. testcode:: def get_y_channel(output): y_pred, y = output # y_pred and y are (B, 3, H, W) and YCbCr or YUV images # let's select y channel return y_pred[:, 0, ...], y[:, 0, ...] psnr = PSNR(data_range=219, output_transform=get_y_channel) psnr.attach(default_evaluator, 'psnr') preds = 219 * torch.rand([4, 3, 16, 16]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['psnr']) .. testoutput:: 16.7027966... .. versionadded:: 0.4.3 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_batchwise_psnr", "_num_examples") def __init__( self, data_range: Union[int, float], output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): super().__init__(output_transform=output_transform, device=device, skip_unrolling=skip_unrolling) self.data_range = data_range def _check_shape_dtype(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output if y_pred.dtype != y.dtype: raise TypeError( f"Expected y_pred and y to have the same data type. Got y_pred: {y_pred.dtype} and y: {y.dtype}." ) if y_pred.shape != y.shape: raise ValueError( f"Expected y_pred and y to have the same shape. Got y_pred: {y_pred.shape} and y: {y.shape}." ) @reinit__is_reduced def reset(self) -> None: self._sum_of_batchwise_psnr = torch.tensor(0.0, dtype=torch.float64, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_shape_dtype(output) y_pred, y = output[0].detach(), output[1].detach() dim = tuple(range(1, y.ndim)) mse_error = torch.pow(y_pred.double() - y.view_as(y_pred).double(), 2).mean(dim=dim) self._sum_of_batchwise_psnr += torch.sum(10.0 * torch.log10(self.data_range**2 / (mse_error + 1e-10))).to( device=self._device ) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_batchwise_psnr", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("PSNR must have at least one example before it can be computed.") return (self._sum_of_batchwise_psnr / self._num_examples).item() ignite-0.5.1/ignite/metrics/recall.py000066400000000000000000000232131465426447700175610ustar00rootroot00000000000000from typing import Sequence import torch from ignite.metrics.metric import reinit__is_reduced from ignite.metrics.precision import _BasePrecisionRecall __all__ = ["Recall"] class Recall(_BasePrecisionRecall): r"""Calculates recall for binary, multiclass and multilabel data. .. math:: \text{Recall} = \frac{ TP }{ TP + FN } where :math:`\text{TP}` is true positives and :math:`\text{FN}` is false negatives. - ``update`` must receive output of the form ``(y_pred, y)``. - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...). - `y` must be in the following shape (batch_size, ...). Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. average: available options are False default option. For multicalss and multilabel inputs, per class and per label metric is returned respectively. None like `False` option except that per class metric is returned for binary data as well. For compatibility with Scikit-Learn api. 'micro' Metric is computed counting stats of classes/labels altogether. .. math:: \text{Micro Recall} = \frac{\sum_{k=1}^C TP_k}{\sum_{k=1}^C TP_k+FN_k} where :math:`C` is the number of classes/labels (2 in binary case). :math:`k` in :math:`TP_k` and :math:`FN_k`means that the measures are computed for class/label :math:`k` (in a one-vs-rest sense in multiclass case). For binary and multiclass inputs, this is equivalent with accuracy, so use :class:`~ignite.metrics.accuracy.Accuracy`. 'samples' for multilabel input, at first, recall is computed on a per sample basis and then average across samples is returned. .. math:: \text{Sample-averaged Recall} = \frac{\sum_{n=1}^N \frac{TP_n}{TP_n+FN_n}}{N} where :math:`N` is the number of samples. :math:`n` in :math:`TP_n` and :math:`FN_n` means that the measures are computed for sample :math:`n`, across labels. Incompatible with binary and multiclass inputs. 'weighted' like macro recall but considers class/label imbalance. For binary and multiclass input, it computes metric for each class then returns average of them weighted by support of classes (number of actual samples in each class). For multilabel input, it computes recall for each label then returns average of them weighted by support of labels (number of actual positive samples in each label). .. math:: Recall_k = \frac{TP_k}{TP_k+FN_k} .. math:: \text{Weighted Recall} = \frac{\sum_{k=1}^C P_k * Recall_k}{N} where :math:`C` is the number of classes (2 in binary case). :math:`P_k` is the number of samples belonged to class :math:`k` in binary and multiclass case, and the number of positive samples belonged to label :math:`k` in multilabel case. Note that for binary and multiclass data, weighted recall is equivalent with accuracy, so use :class:`~ignite.metrics.accuracy.Accuracy`. macro computes macro recall which is unweighted average of metric computed across classes or labels. .. math:: \text{Macro Recall} = \frac{\sum_{k=1}^C Recall_k}{C} where :math:`C` is the number of classes (2 in binary case). True like macro option. For backward compatibility. is_multilabel: flag to use in multilabel case. By default, value is False. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Binary case. In binary and multilabel cases, the elements of `y` and `y_pred` should have 0 or 1 values. .. testcode:: 1 metric = Recall() two_class_metric = Recall(average=None) # Returns recall for both classes metric.attach(default_evaluator, "recall") two_class_metric.attach(default_evaluator, "both classes recall") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) print(f"Recall: {state.metrics['recall']}") print(f"Recall for class 0 and class 1: {state.metrics['both classes recall']}") .. testoutput:: 1 Recall: 0.75 Recall for class 0 and class 1: tensor([0.5000, 0.7500], dtype=torch.float64) Multiclass case .. testcode:: 2 metric = Recall() macro_metric = Recall(average=True) metric.attach(default_evaluator, "recall") macro_metric.attach(default_evaluator, "macro recall") y_true = torch.tensor([2, 0, 2, 1, 0]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288] ]) state = default_evaluator.run([[y_pred, y_true]]) print(f"Recall: {state.metrics['recall']}") print(f"Macro Recall: {state.metrics['macro recall']}") .. testoutput:: 2 Recall: tensor([0.5000, 0.0000, 0.5000], dtype=torch.float64) Macro Recall: 0.3333333333333333 Multilabel case, the shapes must be (batch_size, num_categories, ...) .. testcode:: 3 metric = Recall(is_multilabel=True) micro_metric = Recall(is_multilabel=True, average='micro') macro_metric = Recall(is_multilabel=True, average=True) samples_metric = Recall(is_multilabel=True, average='samples') metric.attach(default_evaluator, "recall") micro_metric.attach(default_evaluator, "micro recall") macro_metric.attach(default_evaluator, "macro recall") samples_metric.attach(default_evaluator, "samples recall") y_true = torch.tensor([ [0, 0, 1], [0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 1], ]) y_pred = torch.tensor([ [1, 1, 0], [1, 0, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], ]) state = default_evaluator.run([[y_pred, y_true]]) print(f"Recall: {state.metrics['recall']}") print(f"Micro Recall: {state.metrics['micro recall']}") print(f"Macro Recall: {state.metrics['macro recall']}") print(f"Samples Recall: {state.metrics['samples recall']}") .. testoutput:: 3 Recall: tensor([1., 1., 0.], dtype=torch.float64) Micro Recall: 0.5 Macro Recall: 0.6666666666666666 Samples Recall: 0.3 Thresholding of predictions can be done as below: .. testcode:: 4 def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y metric = Recall(output_transform=thresholded_output_transform) metric.attach(default_evaluator, "recall") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['recall']) .. testoutput:: 4 0.75 .. versionchanged:: 0.4.10 Some new options were added to `average` parameter. .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: self._check_shape(output) self._check_type(output) _, y, correct = self._prepare_output(output) if self._average == "samples": actual_positives = y.sum(dim=1) true_positives = correct.sum(dim=1) self._numerator += torch.sum(true_positives / (actual_positives + self.eps)) self._denominator += y.size(0) elif self._average == "micro": self._denominator += y.sum() self._numerator += correct.sum() else: # _average in [False, 'macro', 'weighted'] self._denominator += y.sum(dim=0) self._numerator += correct.sum(dim=0) if self._average == "weighted": self._weight += y.sum(dim=0) self._updated = True ignite-0.5.1/ignite/metrics/regression/000077500000000000000000000000001465426447700201245ustar00rootroot00000000000000ignite-0.5.1/ignite/metrics/regression/__init__.py000066400000000000000000000024341465426447700222400ustar00rootroot00000000000000from ignite.metrics.regression.canberra_metric import CanberraMetric from ignite.metrics.regression.fractional_absolute_error import FractionalAbsoluteError from ignite.metrics.regression.fractional_bias import FractionalBias from ignite.metrics.regression.geometric_mean_absolute_error import GeometricMeanAbsoluteError from ignite.metrics.regression.geometric_mean_relative_absolute_error import GeometricMeanRelativeAbsoluteError from ignite.metrics.regression.manhattan_distance import ManhattanDistance from ignite.metrics.regression.maximum_absolute_error import MaximumAbsoluteError from ignite.metrics.regression.mean_absolute_relative_error import MeanAbsoluteRelativeError from ignite.metrics.regression.mean_error import MeanError from ignite.metrics.regression.mean_normalized_bias import MeanNormalizedBias from ignite.metrics.regression.median_absolute_error import MedianAbsoluteError from ignite.metrics.regression.median_absolute_percentage_error import MedianAbsolutePercentageError from ignite.metrics.regression.median_relative_absolute_error import MedianRelativeAbsoluteError from ignite.metrics.regression.pearson_correlation import PearsonCorrelation from ignite.metrics.regression.r2_score import R2Score from ignite.metrics.regression.wave_hedges_distance import WaveHedgesDistance ignite-0.5.1/ignite/metrics/regression/_base.py000066400000000000000000000043021465426447700215460ustar00rootroot00000000000000from abc import abstractmethod from typing import Tuple import torch from ignite.metrics.metric import Metric, reinit__is_reduced def _check_output_shapes(output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output c1 = y_pred.ndimension() == 2 and y_pred.shape[1] == 1 if not (y_pred.ndimension() == 1 or c1): raise ValueError(f"Input y_pred should have shape (N,) or (N, 1), but given {y_pred.shape}") c2 = y.ndimension() == 2 and y.shape[1] == 1 if not (y.ndimension() == 1 or c2): raise ValueError(f"Input y should have shape (N,) or (N, 1), but given {y.shape}") if y_pred.shape != y.shape: raise ValueError(f"Input data shapes should be the same, but given {y_pred.shape} and {y.shape}") def _check_output_types(output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output if y_pred.dtype not in (torch.float16, torch.float32, torch.float64): raise TypeError(f"Input y_pred dtype should be float 16, 32 or 64, but given {y_pred.dtype}") if y.dtype not in (torch.float16, torch.float32, torch.float64): raise TypeError(f"Input y dtype should be float 16, 32 or 64, but given {y.dtype}") def _torch_median(output: torch.Tensor) -> float: output = output.view(-1) len_ = len(output) if len_ % 2 == 0: return float((torch.kthvalue(output, len_ // 2)[0] + torch.kthvalue(output, len_ // 2 + 1)[0]) / 2) else: return float(torch.kthvalue(output, len_ // 2 + 1)[0]) class _BaseRegression(Metric): # Base class for all regression metrics # `update` method check the shapes and call internal overloaded # method `_update`. @reinit__is_reduced def update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: _check_output_shapes(output) _check_output_types(output) y_pred, y = output[0].detach(), output[1].detach() if y_pred.ndimension() == 2 and y_pred.shape[1] == 1: y_pred = y_pred.squeeze(dim=-1) if y.ndimension() == 2 and y.shape[1] == 1: y = y.squeeze(dim=-1) self._update((y_pred, y)) @abstractmethod def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: pass ignite-0.5.1/ignite/metrics/regression/canberra_metric.py000066400000000000000000000060051465426447700236170ustar00rootroot00000000000000from typing import Tuple import torch from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class CanberraMetric(_BaseRegression): r"""Calculates the Canberra Metric. .. math:: \text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{|A_j| + |P_j|} where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_ - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. .. _scikit-learn distance metrics: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.DistanceMetric.html Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. .. _`Botchkarev 2018`: https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = CanberraMetric() metric.attach(default_evaluator, 'canberra') y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]]) y_true = y_pred * 1.5 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['canberra']) .. testoutput:: 0.8000... .. versionchanged:: 0.4.3 - Fixed implementation: ``abs`` in denominator. - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors",) @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = torch.abs(y - y_pred) / (torch.abs(y_pred) + torch.abs(y) + 1e-15) self._sum_of_errors += torch.sum(errors).to(self._device) @sync_all_reduce("_sum_of_errors") def compute(self) -> float: return self._sum_of_errors.item() ignite-0.5.1/ignite/metrics/regression/fractional_absolute_error.py000066400000000000000000000062731465426447700257370ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class FractionalAbsoluteError(_BaseRegression): r"""Calculates the Fractional Absolute Error. .. math:: \text{FAE} = \frac{1}{n}\sum_{j=1}^n\frac{2 |A_j - P_j|}{|A_j| + |P_j|} where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = FractionalAbsoluteError() metric.attach(default_evaluator, 'fractional_abs_error') y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]]) y_true = y_pred * 0.8 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['fractional_abs_error']) .. testoutput:: 0.2222... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = 2 * torch.abs(y.view_as(y_pred) - y_pred) / (torch.abs(y_pred) + torch.abs(y.view_as(y_pred))) self._sum_of_errors += torch.sum(errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_num_examples", "_sum_of_errors") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "FractionalAbsoluteError must have at least one example before it can be computed." ) return self._sum_of_errors.item() / self._num_examples ignite-0.5.1/ignite/metrics/regression/fractional_bias.py000066400000000000000000000061441465426447700236230ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class FractionalBias(_BaseRegression): r"""Calculates the Fractional Bias. .. math:: \text{FB} = \frac{1}{n}\sum_{j=1}^n\frac{2 (A_j - P_j)}{A_j + P_j} where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = FractionalBias() metric.attach(default_evaluator, 'fractional_bias') y_pred = torch.tensor([[3.8], [9.9], [5.4], [2.1]]) y_true = y_pred * 1.5 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['fractional_bias']) .. testoutput:: 0.4000... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, dtype=torch.double, device=self._device) self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = 2 * (y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred) + 1e-30) self._sum_of_errors += torch.sum(errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_errors", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("FractionalBias must have at least one example before it can be computed.") return self._sum_of_errors.item() / self._num_examples ignite-0.5.1/ignite/metrics/regression/geometric_mean_absolute_error.py000066400000000000000000000061731465426447700265720ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class GeometricMeanAbsoluteError(_BaseRegression): r"""Calculates the Geometric Mean Absolute Error. .. math:: \text{GMAE} = \exp(\frac{1}{n}\sum_{j=1}^n\ln(|A_j - P_j|)) where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = GeometricMeanAbsoluteError() metric.attach(default_evaluator, 'gmae') y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]]) y_true = y_pred * 1.5 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['gmae']) .. testoutput:: 2.2723... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = torch.log(torch.abs(y.view_as(y_pred) - y_pred)) self._sum_of_errors += torch.sum(errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_errors", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "GeometricMeanAbsoluteError must have at least one example before it can be computed." ) return torch.exp((self._sum_of_errors) / self._num_examples).item() ignite-0.5.1/ignite/metrics/regression/geometric_mean_relative_absolute_error.py000066400000000000000000000102511465426447700304550ustar00rootroot00000000000000from typing import cast, List, Tuple import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced from ignite.metrics.regression._base import _BaseRegression class GeometricMeanRelativeAbsoluteError(_BaseRegression): r"""Calculates the Geometric Mean Relative Absolute Error. .. math:: \text{GMRAE} = \exp(\frac{1}{n}\sum_{j=1}^n \ln\frac{|A_j - P_j|}{|A_j - \bar{A}|}) where :math:`A_j` is the ground truth, :math:`P_j` is the predicted value and :math: `bar{A}` is the mean of the ground truth. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. .. warning:: Current implementation of GMRAE stores all input data (output and target) as tensors before computing the metric. This can potentially lead to a memory error if the input data is larger than available RAM. In distributed configuration, all stored data (output and target) is mutually collected across all processes using all gather collective operation. This can potentially lead to a memory error. Compute method compute the metric on zero rank process only and final result is broadcasted to all processes. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = GeometricMeanRelativeAbsoluteError() metric.attach(default_evaluator, 'gmare') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['gmare']) .. testoutput:: 0.0... """ _state_dict_all_req_keys = ("_predictions", "_targets") @reinit__is_reduced def reset(self) -> None: self._predictions: List[torch.Tensor] = [] self._targets: List[torch.Tensor] = [] def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() y_pred = y_pred.clone().to(self._device) y = y.clone().to(self._device) self._predictions.append(y_pred) self._targets.append(y) def compute(self) -> float: if len(self._predictions) < 1 or len(self._targets) < 1: raise NotComputableError( "GeometricMeanRelativeAbsoluteError must have at least one example before it can be computed." ) _prediction_tensor = torch.cat(self._predictions, dim=0) _target_tensor = torch.cat(self._targets, dim=0) # All gather across all processes _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor)) _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor)) result = torch.exp( torch.log( torch.abs(_target_tensor - _prediction_tensor) / torch.abs(_target_tensor - _target_tensor.mean()) ).mean() ).item() return result ignite-0.5.1/ignite/metrics/regression/manhattan_distance.py000066400000000000000000000053441465426447700243310ustar00rootroot00000000000000from typing import Tuple import torch from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class ManhattanDistance(_BaseRegression): r"""Calculates the Manhattan Distance. .. math:: \text{MD} = \sum_{j=1}^n |A_j - P_j| where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `scikit-learn distance metrics`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://scikit-learn.org/stable/modules/generated/sklearn.metrics.DistanceMetric.html Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = ManhattanDistance() metric.attach(default_evaluator, 'manhattan') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['manhattan']) .. testoutput:: 3.75... .. versionchanged:: 0.4.3 - Fixed sklearn compatibility. - Workes with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors",) @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = torch.abs(y - y_pred) self._sum_of_errors += torch.sum(errors).to(self._device) @sync_all_reduce("_sum_of_errors") def compute(self) -> float: return self._sum_of_errors.item() ignite-0.5.1/ignite/metrics/regression/maximum_absolute_error.py000066400000000000000000000057011465426447700252650ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class MaximumAbsoluteError(_BaseRegression): r"""Calculates the Maximum Absolute Error. .. math:: \text{MaxAE} = \max_{j=1,n} \left( \lvert A_j-P_j \rvert \right) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MaximumAbsoluteError() metric.attach(default_evaluator, 'mae') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mae']) .. testoutput:: 1.25... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_max_of_absolute_errors",) @reinit__is_reduced def reset(self) -> None: self._max_of_absolute_errors: float = -1 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() mae = torch.abs(y_pred - y.view_as(y_pred)).max().item() if self._max_of_absolute_errors < mae: self._max_of_absolute_errors = mae @sync_all_reduce("_max_of_absolute_errors:MAX") def compute(self) -> float: if self._max_of_absolute_errors < 0: raise NotComputableError("MaximumAbsoluteError must have at least one example before it can be computed.") return self._max_of_absolute_errors ignite-0.5.1/ignite/metrics/regression/mean_absolute_relative_error.py000066400000000000000000000066101465426447700264230ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class MeanAbsoluteRelativeError(_BaseRegression): r"""Calculate Mean Absolute Relative Error (MARE), also known as Mean Absolute Percentage Error (MAPE). .. math:: \text{MARE} = \frac{1}{n}\sum_{j=1}^n\frac{\left|A_j-P_j\right|}{\left|A_j\right|} where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in the reference `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanAbsoluteRelativeError() metric.attach(default_evaluator, 'mare') y_true = torch.tensor([1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mare']) .. testoutput:: 0.25... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_absolute_relative_errors", "_num_samples") @reinit__is_reduced def reset(self) -> None: self._sum_of_absolute_relative_errors = torch.tensor(0.0, device=self._device) self._num_samples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if (y == 0).any(): raise NotComputableError("The ground truth has 0.") absolute_error = torch.abs(y_pred - y.view_as(y_pred)) / torch.abs(y.view_as(y_pred)) self._sum_of_absolute_relative_errors += torch.sum(absolute_error).to(self._device) self._num_samples += y.size()[0] @sync_all_reduce("_sum_of_absolute_relative_errors", "_num_samples") def compute(self) -> float: if self._num_samples == 0: raise NotComputableError( "MeanAbsoluteRelativeError must have at least one sample before it can be computed." ) return self._sum_of_absolute_relative_errors.item() / self._num_samples ignite-0.5.1/ignite/metrics/regression/mean_error.py000066400000000000000000000056151465426447700226360ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class MeanError(_BaseRegression): r"""Calculates the Mean Error. .. math:: \text{ME} = \frac{1}{n}\sum_{j=1}^n (A_j - P_j) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in the reference `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanError() metric.attach(default_evaluator, 'me') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['me']) .. testoutput:: 0.625... """ _state_dict_all_req_keys = ("_sum_of_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = y.view_as(y_pred) - y_pred self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_errors", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("MeanError must have at least one example before it can be computed.") return self._sum_of_errors.item() / self._num_examples ignite-0.5.1/ignite/metrics/regression/mean_normalized_bias.py000066400000000000000000000061371465426447700246470ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class MeanNormalizedBias(_BaseRegression): r"""Calculates the Mean Normalized Bias. .. math:: \text{MNB} = \frac{1}{n}\sum_{j=1}^n\frac{A_j - P_j}{A_j} where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in the reference `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanNormalizedBias() metric.attach(default_evaluator, 'mnb') y_true = torch.tensor([1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mnb']) .. testoutput:: 0.25... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors", "_num_examples") @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if (y == 0).any(): raise NotComputableError("The ground truth has 0.") errors = (y.view_as(y_pred) - y_pred) / y self._sum_of_errors += torch.sum(errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_errors", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("MeanNormalizedBias must have at least one example before it can be computed.") return self._sum_of_errors.item() / self._num_examples ignite-0.5.1/ignite/metrics/regression/median_absolute_error.py000066400000000000000000000050731465426447700250470ustar00rootroot00000000000000from typing import Callable, Union import torch from ignite.metrics.epoch_metric import EpochMetric from ignite.metrics.regression._base import _torch_median def median_absolute_error_compute_fn(y_pred: torch.Tensor, y: torch.Tensor) -> float: e = torch.abs(y.view_as(y_pred) - y_pred) return _torch_median(e) class MedianAbsoluteError(EpochMetric): r"""Calculates the Median Absolute Error. .. math:: \text{MdAE} = \text{MD}_{j=1,n} \left( |A_j - P_j| \right) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`. .. warning:: Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM. __ https://arxiv.org/abs/1809.03006 Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: optional device specification for internal storage. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MedianAbsoluteError() metric.attach(default_evaluator, 'mae') y_true = torch.tensor([0, 1, 2, 3, 4, 5]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mae']) .. testoutput:: 0.625 """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): super(MedianAbsoluteError, self).__init__( median_absolute_error_compute_fn, output_transform=output_transform, device=device ) ignite-0.5.1/ignite/metrics/regression/median_absolute_percentage_error.py000066400000000000000000000052711465426447700272440ustar00rootroot00000000000000from typing import Callable, Union import torch from ignite.metrics.epoch_metric import EpochMetric from ignite.metrics.regression._base import _torch_median def median_absolute_percentage_error_compute_fn(y_pred: torch.Tensor, y: torch.Tensor) -> float: e = torch.abs(y.view_as(y_pred) - y_pred) / torch.abs(y.view_as(y_pred)) return 100.0 * _torch_median(e) class MedianAbsolutePercentageError(EpochMetric): r"""Calculates the Median Absolute Percentage Error. .. math:: \text{MdAPE} = 100 \cdot \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_j|} \right) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`. .. warning:: Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM. __ https://arxiv.org/abs/1809.03006 Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: optional device specification for internal storage. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MedianAbsolutePercentageError() metric.attach(default_evaluator, 'mape') y_true = torch.tensor([1, 2, 3, 4, 5]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mape']) .. testoutput:: 25.0... """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): super(MedianAbsolutePercentageError, self).__init__( median_absolute_percentage_error_compute_fn, output_transform=output_transform, device=device ) ignite-0.5.1/ignite/metrics/regression/median_relative_absolute_error.py000066400000000000000000000052751465426447700267460ustar00rootroot00000000000000from typing import Callable, Union import torch from ignite.metrics.epoch_metric import EpochMetric from ignite.metrics.regression._base import _torch_median def median_relative_absolute_error_compute_fn(y_pred: torch.Tensor, y: torch.Tensor) -> float: e = torch.abs(y.view_as(y_pred) - y_pred) / torch.abs(y.view_as(y_pred) - torch.mean(y)) return _torch_median(e) class MedianRelativeAbsoluteError(EpochMetric): r"""Calculates the Median Relative Absolute Error. .. math:: \text{MdRAE} = \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_j - \bar{A}|} \right) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`. .. warning:: Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM. __ https://arxiv.org/abs/1809.03006 Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: optional device specification for internal storage. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MedianRelativeAbsoluteError() metric.attach(default_evaluator, 'mrae') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mrae']) .. testoutput:: 0.5... """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): super(MedianRelativeAbsoluteError, self).__init__( median_relative_absolute_error_compute_fn, output_transform=output_transform, device=device ) ignite-0.5.1/ignite/metrics/regression/pearson_correlation.py000066400000000000000000000114111465426447700245440ustar00rootroot00000000000000from typing import Callable, Tuple, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class PearsonCorrelation(_BaseRegression): r"""Calculates the `Pearson correlation coefficient `_. .. math:: r = \frac{\sum_{j=1}^n (P_j-\bar{P})(A_j-\bar{A})} {\max (\sqrt{\sum_{j=1}^n (P_j-\bar{P})^2 \sum_{j=1}^n (A_j-\bar{A})^2}, \epsilon)}, \quad \bar{P}=\frac{1}{n}\sum_{j=1}^n P_j, \quad \bar{A}=\frac{1}{n}\sum_{j=1}^n A_j where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. Parameters are inherited from ``Metric.__init__``. Args: eps: a small value to avoid division by zero. Default: 1e-8 output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = PearsonCorrelation() metric.attach(default_evaluator, 'corr') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = torch.tensor([0.5, 1.3, 1.9, 2.8, 4.1, 6.0]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['corr']) .. testoutput:: 0.9768688678741455 """ def __init__( self, eps: float = 1e-8, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super().__init__(output_transform, device) self.eps = eps _state_dict_all_req_keys = ( "_sum_of_y_preds", "_sum_of_ys", "_sum_of_y_pred_squares", "_sum_of_y_squares", "_sum_of_products", "_num_examples", ) @reinit__is_reduced def reset(self) -> None: self._sum_of_y_preds = torch.tensor(0.0, device=self._device) self._sum_of_ys = torch.tensor(0.0, device=self._device) self._sum_of_y_pred_squares = torch.tensor(0.0, device=self._device) self._sum_of_y_squares = torch.tensor(0.0, device=self._device) self._sum_of_products = torch.tensor(0.0, device=self._device) self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() self._sum_of_y_preds += y_pred.sum().to(self._device) self._sum_of_ys += y.sum().to(self._device) self._sum_of_y_pred_squares += y_pred.square().sum().to(self._device) self._sum_of_y_squares += y.square().sum().to(self._device) self._sum_of_products += (y_pred * y).sum().to(self._device) self._num_examples += y.shape[0] @sync_all_reduce( "_sum_of_y_preds", "_sum_of_ys", "_sum_of_y_pred_squares", "_sum_of_y_squares", "_sum_of_products", "_num_examples", ) def compute(self) -> float: n = self._num_examples if n == 0: raise NotComputableError("PearsonCorrelation must have at least one example before it can be computed.") # cov = E[xy] - E[x]*E[y] cov = self._sum_of_products / n - self._sum_of_y_preds * self._sum_of_ys / (n * n) # var = E[x^2] - E[x]^2 y_pred_mean = self._sum_of_y_preds / n y_pred_var = self._sum_of_y_pred_squares / n - y_pred_mean * y_pred_mean y_pred_var = torch.clamp(y_pred_var, min=0.0) y_mean = self._sum_of_ys / n y_var = self._sum_of_y_squares / n - y_mean * y_mean y_var = torch.clamp(y_var, min=0.0) r = cov / torch.clamp(torch.sqrt(y_pred_var * y_var), min=self.eps) return float(r.item()) ignite-0.5.1/ignite/metrics/regression/r2_score.py000066400000000000000000000065141465426447700222220ustar00rootroot00000000000000from typing import Tuple import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class R2Score(_BaseRegression): r"""Calculates the R-Squared, the `coefficient of determination `_. .. math:: R^2 = 1 - \frac{\sum_{j=1}^n(A_j - P_j)^2}{\sum_{j=1}^n(A_j - \bar{A})^2} where :math:`A_j` is the ground truth, :math:`P_j` is the predicted value and :math:`\bar{A}` is the mean of the ground truth. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`. Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = R2Score() metric.attach(default_evaluator, 'r2') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['r2']) .. testoutput:: 0.8035... .. versionchanged:: 0.4.3 Works with DDP. """ _state_dict_all_req_keys = ("_num_examples", "_sum_of_errors", "_y_sq_sum", "_y_sum") @reinit__is_reduced def reset(self) -> None: self._num_examples = 0 self._sum_of_errors = torch.tensor(0.0, device=self._device) self._y_sq_sum = torch.tensor(0.0, device=self._device) self._y_sum = torch.tensor(0.0, device=self._device) def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output self._num_examples += y.shape[0] self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).to(self._device) self._y_sum += torch.sum(y).to(self._device) self._y_sq_sum += torch.sum(torch.pow(y, 2)).to(self._device) @sync_all_reduce("_num_examples", "_sum_of_errors", "_y_sq_sum", "_y_sum") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("R2Score must have at least one example before it can be computed.") return 1 - self._sum_of_errors.item() / (self._y_sq_sum.item() - (self._y_sum.item() ** 2) / self._num_examples) ignite-0.5.1/ignite/metrics/regression/wave_hedges_distance.py000066400000000000000000000053501465426447700246340ustar00rootroot00000000000000from typing import Tuple import torch from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce from ignite.metrics.regression._base import _BaseRegression class WaveHedgesDistance(_BaseRegression): r"""Calculates the Wave Hedges Distance. .. math:: \text{WHD} = \sum_{j=1}^n\frac{|A_j - P_j|}{max(A_j, P_j)} where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = WaveHedgesDistance() metric.attach(default_evaluator, 'whd') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['whd']) .. testoutput:: 1.25... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors",) @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = torch.abs(y.view_as(y_pred) - y_pred) / (torch.max(y_pred, y.view_as(y_pred)) + 1e-30) self._sum_of_errors += torch.sum(errors).to(self._device) @sync_all_reduce("_sum_of_errors") def compute(self) -> float: return self._sum_of_errors.item() ignite-0.5.1/ignite/metrics/roc_auc.py000066400000000000000000000216511465426447700177360ustar00rootroot00000000000000from typing import Any, Callable, cast, Tuple, Union import torch from ignite import distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.epoch_metric import EpochMetric def roc_auc_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: from sklearn.metrics import roc_auc_score y_true = y_targets.cpu().numpy() y_pred = y_preds.cpu().numpy() return roc_auc_score(y_true, y_pred) def roc_auc_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> Tuple[Any, Any, Any]: from sklearn.metrics import roc_curve y_true = y_targets.cpu().numpy() y_pred = y_preds.cpu().numpy() return roc_curve(y_true, y_pred) class ROC_AUC(EpochMetric): """Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.roc_auc_score `_ . Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. check_compute_fn: Default False. If True, `roc_curve `_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. device: optional device specification for internal storage. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Note: ROC_AUC expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below: .. code-block:: python def sigmoid_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y avg_precision = ROC_AUC(sigmoid_output_transform) Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: roc_auc = ROC_AUC() #The ``output_transform`` arg of the metric can be used to perform a sigmoid on the ``y_pred``. roc_auc.attach(default_evaluator, 'roc_auc') y_pred = torch.tensor([[0.0474], [0.5987], [0.7109], [0.9997]]) y_true = torch.tensor([[0], [0], [1], [0]]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['roc_auc']) .. testoutput:: 0.6666... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): try: from sklearn.metrics import roc_auc_score # noqa: F401 except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") super(ROC_AUC, self).__init__( roc_auc_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, skip_unrolling=skip_unrolling, ) class RocCurve(EpochMetric): """Compute Receiver operating characteristic (ROC) for binary classification task by accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.roc_curve `_ . Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. check_compute_fn: Default False. If True, `sklearn.metrics.roc_curve `_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. device: optional device specification for internal storage. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Note: RocCurve expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below: .. code-block:: python def sigmoid_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y avg_precision = RocCurve(sigmoid_output_transform) Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: roc_auc = RocCurve() #The ``output_transform`` arg of the metric can be used to perform a sigmoid on the ``y_pred``. roc_auc.attach(default_evaluator, 'roc_auc') y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997]) y_true = torch.tensor([0, 0, 1, 0]) state = default_evaluator.run([[y_pred, y_true]]) print("FPR", [round(i, 3) for i in state.metrics['roc_auc'][0].tolist()]) print("TPR", [round(i, 3) for i in state.metrics['roc_auc'][1].tolist()]) print("Thresholds", [round(i, 3) for i in state.metrics['roc_auc'][2].tolist()]) .. testoutput:: FPR [0.0, 0.333, 0.333, 1.0] TPR [0.0, 0.0, 1.0, 1.0] Thresholds [inf, 1.0, 0.711, 0.047] .. versionchanged:: 0.4.11 added `device` argument .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ) -> None: try: from sklearn.metrics import roc_curve # noqa: F401 except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") super(RocCurve, self).__init__( roc_auc_curve_compute_fn, # type: ignore[arg-type] output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, skip_unrolling=skip_unrolling, ) def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: ignore[override] if len(self._predictions) < 1 or len(self._targets) < 1: raise NotComputableError("RocCurve must have at least one example before it can be computed.") _prediction_tensor = torch.cat(self._predictions, dim=0) _target_tensor = torch.cat(self._targets, dim=0) ws = idist.get_world_size() if ws > 1: # All gather across all processes _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor)) _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor)) if idist.get_rank() == 0: # Run compute_fn on zero rank only fpr, tpr, thresholds = cast(Tuple, self.compute_fn(_prediction_tensor, _target_tensor)) fpr = torch.tensor(fpr, device=_prediction_tensor.device) tpr = torch.tensor(tpr, device=_prediction_tensor.device) thresholds = torch.tensor(thresholds, device=_prediction_tensor.device) else: fpr, tpr, thresholds = None, None, None if ws > 1: # broadcast result to all processes fpr = idist.broadcast(fpr, src=0, safe_mode=True) tpr = idist.broadcast(tpr, src=0, safe_mode=True) thresholds = idist.broadcast(thresholds, src=0, safe_mode=True) return fpr, tpr, thresholds ignite-0.5.1/ignite/metrics/root_mean_squared_error.py000066400000000000000000000055271465426447700232470ustar00rootroot00000000000000import math from typing import Union import torch from ignite.metrics.mean_squared_error import MeanSquaredError __all__ = ["RootMeanSquaredError"] class RootMeanSquaredError(MeanSquaredError): r"""Calculates the `root mean squared error `_. .. math:: \text{RMSE} = \sqrt{ \frac{1}{N} \sum_{i=1}^N \|y_{i} - x_{i} \|^2 } where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor. - ``update`` must receive output of the form ``(y_pred, y)``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = RootMeanSquaredError() metric.attach(default_evaluator, 'rmse') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['rmse']) .. testoutput:: 1.956559480312316 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ def compute(self) -> Union[torch.Tensor, float]: mse = super(RootMeanSquaredError, self).compute() return math.sqrt(mse) ignite-0.5.1/ignite/metrics/running_average.py000066400000000000000000000261051465426447700214740ustar00rootroot00000000000000import warnings from typing import Any, Callable, cast, Optional, Union import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.metrics.metric import Metric, MetricUsage, reinit__is_reduced, RunningBatchWise, SingleEpochRunningBatchWise __all__ = ["RunningAverage"] class RunningAverage(Metric): """Compute running average of a metric or the output of process function. Args: src: input source: an instance of :class:`~ignite.metrics.metric.Metric` or None. The latter corresponds to `engine.state.output` which holds the output of process function. alpha: running average decay factor, default 0.98 output_transform: a function to use to transform the output if `src` is None and corresponds the output of process function. Otherwise it should be None. epoch_bound: whether the running average should be reset after each epoch. It is depracated in favor of ``usage`` argument in :meth:`attach` method. Setting ``epoch_bound`` to ``False`` is equivalent to ``usage=SingleEpochRunningBatchWise()`` and setting it to ``True`` is equivalent to ``usage=RunningBatchWise()`` in the :meth:`attach` method. Default None. device: specifies which device updates are accumulated on. Should be None when ``src`` is an instance of :class:`~ignite.metrics.metric.Metric`, as the running average will use the ``src``'s device. Otherwise, defaults to CPU. Only applicable when the computed value from the metric is a tensor. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: 1 default_trainer = get_default_trainer() accuracy = Accuracy() metric = RunningAverage(accuracy) metric.attach(default_trainer, 'running_avg_accuracy') @default_trainer.on(Events.ITERATION_COMPLETED) def log_running_avg_metrics(): print(default_trainer.state.metrics['running_avg_accuracy']) y_true = [torch.tensor(y) for y in [[0], [1], [0], [1], [0], [1]]] y_pred = [torch.tensor(y) for y in [[0], [0], [0], [1], [1], [1]]] state = default_trainer.run(zip(y_pred, y_true)) .. testoutput:: 1 1.0 0.98 0.98039... 0.98079... 0.96117... 0.96195... .. testcode:: 2 default_trainer = get_default_trainer() metric = RunningAverage(output_transform=lambda x: x.item()) metric.attach(default_trainer, 'running_avg_accuracy') @default_trainer.on(Events.ITERATION_COMPLETED) def log_running_avg_metrics(): print(default_trainer.state.metrics['running_avg_accuracy']) y = [torch.tensor(y) for y in [[0], [1], [0], [1], [0], [1]]] state = default_trainer.run(y) .. testoutput:: 2 0.0 0.020000... 0.019600... 0.039208... 0.038423... 0.057655... .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ required_output_keys = None _state_dict_all_req_keys = ("_value", "src") def __init__( self, src: Optional[Metric] = None, alpha: float = 0.98, output_transform: Optional[Callable] = None, epoch_bound: Optional[bool] = None, device: Optional[Union[str, torch.device]] = None, skip_unrolling: bool = False, ): if not (isinstance(src, Metric) or src is None): raise TypeError("Argument src should be a Metric or None.") if not (0.0 < alpha <= 1.0): raise ValueError("Argument alpha should be a float between 0.0 and 1.0.") if isinstance(src, Metric): if output_transform is not None: raise ValueError("Argument output_transform should be None if src is a Metric.") def output_transform(x: Any) -> Any: return x if device is not None: raise ValueError("Argument device should be None if src is a Metric.") self.src: Union[Metric, None] = src device = src._device else: if output_transform is None: raise ValueError( "Argument output_transform should not be None if src corresponds " "to the output of process function." ) self.src = None if device is None: device = torch.device("cpu") if epoch_bound is not None: warnings.warn( "`epoch_bound` is deprecated and will be removed in the future. Consider using `usage` argument of" "`attach` method instead. `epoch_bound=True` is equivalent with `usage=SingleEpochRunningBatchWise()`" " and `epoch_bound=False` is equivalent with `usage=RunningBatchWise()`." ) self.epoch_bound = epoch_bound self.alpha = alpha super(RunningAverage, self).__init__( output_transform=output_transform, device=device, skip_unrolling=skip_unrolling ) @reinit__is_reduced def reset(self) -> None: self._value: Optional[Union[float, torch.Tensor]] = None if isinstance(self.src, Metric): self.src.reset() @reinit__is_reduced def update(self, output: Union[torch.Tensor, float]) -> None: if self.src is None: output = output.detach().to(self._device, copy=True) if isinstance(output, torch.Tensor) else output value = idist.all_reduce(output) / idist.get_world_size() else: value = self.src.compute() self.src.reset() if self._value is None: self._value = value else: self._value = self._value * self.alpha + (1.0 - self.alpha) * value def compute(self) -> Union[torch.Tensor, float]: return cast(Union[torch.Tensor, float], self._value) def attach(self, engine: Engine, name: str, usage: Union[str, MetricUsage] = RunningBatchWise()) -> None: r""" Attach the metric to the ``engine`` using the events determined by the ``usage``. Args: engine: the engine to get attached to. name: by which, the metric is inserted into ``engine.state.metrics`` dictionary. usage: the usage determining on which events the metric is reset, updated and computed. It should be an instance of the :class:`~ignite.metrics.metric.MetricUsage`\ s in the following table. ======================================================= =========================================== ``usage`` **class** **Description** ======================================================= =========================================== :class:`~.metrics.metric.RunningBatchWise` Running average of the ``src`` metric or ``engine.state.output`` is computed across batches. In the former case, on each batch, ``src`` is reset, updated and computed then its value is retrieved. Default. :class:`~.metrics.metric.SingleEpochRunningBatchWise` Same as above but the running average is computed across batches in an epoch so it is reset at the end of the epoch. :class:`~.metrics.metric.RunningEpochWise` Running average of the ``src`` metric or ``engine.state.output`` is computed across epochs. In the former case, ``src`` works as if it was attached in a :class:`~ignite.metrics.metric.EpochWise` manner and its computed value is retrieved at the end of the epoch. The latter case doesn't make much sense for this usage as the ``engine.state.output`` of the last batch is retrieved then. ======================================================= =========================================== ``RunningAverage`` retrieves ``engine.state.output`` at ``usage.ITERATION_COMPLETED`` if the ``src`` is not given and it's computed and updated using ``src``, by manually calling its ``compute`` method, or ``engine.state.output`` at ``usage.COMPLETED`` event. Also if ``src`` is given, it is updated at ``usage.ITERATION_COMPLETED``, but its reset event is determined by ``usage`` type. If ``isinstance(usage, BatchWise)`` holds true, ``src`` is reset on ``BatchWise().STARTED``, otherwise on ``EpochWise().STARTED`` if ``isinstance(usage, EpochWise)``. .. versionchanged:: 0.5.1 Added `usage` argument """ usage = self._check_usage(usage) if self.epoch_bound is not None: usage = SingleEpochRunningBatchWise() if self.epoch_bound else RunningBatchWise() if isinstance(self.src, Metric) and not engine.has_event_handler( self.src.iteration_completed, Events.ITERATION_COMPLETED ): engine.add_event_handler(Events.ITERATION_COMPLETED, self.src.iteration_completed) super().attach(engine, name, usage) def detach(self, engine: Engine, usage: Union[str, MetricUsage] = RunningBatchWise()) -> None: usage = self._check_usage(usage) if self.epoch_bound is not None: usage = SingleEpochRunningBatchWise() if self.epoch_bound else RunningBatchWise() if isinstance(self.src, Metric) and engine.has_event_handler( self.src.iteration_completed, Events.ITERATION_COMPLETED ): engine.remove_event_handler(self.src.iteration_completed, Events.ITERATION_COMPLETED) super().detach(engine, usage) ignite-0.5.1/ignite/metrics/ssim.py000066400000000000000000000230441465426447700172740ustar00rootroot00000000000000import warnings from typing import Callable, Optional, Sequence, Union import torch import torch.nn.functional as F from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["SSIM"] class SSIM(Metric): """ Computes Structural Similarity Index Measure - ``update`` must receive output of the form ``(y_pred, y)``. They have to be of the same type. Valid :class:`torch.dtype` are the following: - on CPU: `torch.float32`, `torch.float64`. - on CUDA: `torch.float16`, `torch.bfloat16`, `torch.float32`, `torch.float64`. Args: data_range: Range of the image. Typically, ``1.0`` or ``255``. kernel_size: Size of the kernel. Default: (11, 11) sigma: Standard deviation of the gaussian kernel. Argument is used if ``gaussian=True``. Default: (1.5, 1.5) k1: Parameter of SSIM. Default: 0.01 k2: Parameter of SSIM. Default: 0.03 gaussian: ``True`` to use gaussian kernel, ``False`` to use uniform kernel output_transform: A callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` can be un-normalized or normalized image tensors. Depending on that, the user might need to adjust ``data_range``. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = SSIM(data_range=1.0) metric.attach(default_evaluator, 'ssim') preds = torch.rand([4, 3, 16, 16]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['ssim']) .. testoutput:: 0.9218971... .. versionadded:: 0.4.2 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_sum_of_ssim", "_num_examples", "_kernel") def __init__( self, data_range: Union[int, float], kernel_size: Union[int, Sequence[int]] = (11, 11), sigma: Union[float, Sequence[float]] = (1.5, 1.5), k1: float = 0.01, k2: float = 0.03, gaussian: bool = True, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): if isinstance(kernel_size, int): self.kernel_size: Sequence[int] = [kernel_size, kernel_size] elif isinstance(kernel_size, Sequence): self.kernel_size = kernel_size else: raise ValueError("Argument kernel_size should be either int or a sequence of int.") if isinstance(sigma, float): self.sigma: Sequence[float] = [sigma, sigma] elif isinstance(sigma, Sequence): self.sigma = sigma else: raise ValueError("Argument sigma should be either float or a sequence of float.") if any(x % 2 == 0 or x <= 0 for x in self.kernel_size): raise ValueError(f"Expected kernel_size to have odd positive number. Got {kernel_size}.") if any(y <= 0 for y in self.sigma): raise ValueError(f"Expected sigma to have positive number. Got {sigma}.") super(SSIM, self).__init__(output_transform=output_transform, device=device, skip_unrolling=skip_unrolling) self.gaussian = gaussian self.data_range = data_range self.c1 = (k1 * data_range) ** 2 self.c2 = (k2 * data_range) ** 2 self.pad_h = (self.kernel_size[0] - 1) // 2 self.pad_w = (self.kernel_size[1] - 1) // 2 self._kernel_2d = self._gaussian_or_uniform_kernel(kernel_size=self.kernel_size, sigma=self.sigma) self._kernel: Optional[torch.Tensor] = None @reinit__is_reduced def reset(self) -> None: self._sum_of_ssim = torch.tensor(0.0, dtype=torch.float64, device=self._device) self._num_examples = 0 def _uniform(self, kernel_size: int) -> torch.Tensor: kernel = torch.zeros(kernel_size) start_uniform_index = max(kernel_size // 2 - 2, 0) end_uniform_index = min(kernel_size // 2 + 3, kernel_size) min_, max_ = -2.5, 2.5 kernel[start_uniform_index:end_uniform_index] = 1 / (max_ - min_) return kernel.unsqueeze(dim=0) # (1, kernel_size) def _gaussian(self, kernel_size: int, sigma: float) -> torch.Tensor: ksize_half = (kernel_size - 1) * 0.5 kernel = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, device=self._device) gauss = torch.exp(-0.5 * (kernel / sigma).pow(2)) return (gauss / gauss.sum()).unsqueeze(dim=0) # (1, kernel_size) def _gaussian_or_uniform_kernel(self, kernel_size: Sequence[int], sigma: Sequence[float]) -> torch.Tensor: if self.gaussian: kernel_x = self._gaussian(kernel_size[0], sigma[0]) kernel_y = self._gaussian(kernel_size[1], sigma[1]) else: kernel_x = self._uniform(kernel_size[0]) kernel_y = self._uniform(kernel_size[1]) return torch.matmul(kernel_x.t(), kernel_y) # (kernel_size, 1) * (1, kernel_size) @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if y_pred.dtype != y.dtype: raise TypeError( f"Expected y_pred and y to have the same data type. Got y_pred: {y_pred.dtype} and y: {y.dtype}." ) if y_pred.shape != y.shape: raise ValueError( f"Expected y_pred and y to have the same shape. Got y_pred: {y_pred.shape} and y: {y.shape}." ) if len(y_pred.shape) != 4 or len(y.shape) != 4: raise ValueError( f"Expected y_pred and y to have BxCxHxW shape. Got y_pred: {y_pred.shape} and y: {y.shape}." ) # converts potential integer tensor to fp if not y.is_floating_point(): y = y.float() if not y_pred.is_floating_point(): y_pred = y_pred.float() nb_channel = y_pred.size(1) if self._kernel is None or self._kernel.shape[0] != nb_channel: self._kernel = self._kernel_2d.expand(nb_channel, 1, -1, -1) if y_pred.device != self._kernel.device: if self._kernel.device == torch.device("cpu"): self._kernel = self._kernel.to(device=y_pred.device) elif y_pred.device == torch.device("cpu"): warnings.warn( "y_pred tensor is on cpu device but previous computation was on another device: " f"{self._kernel.device}. To avoid having a performance hit, please ensure that all " "y and y_pred tensors are on the same device.", ) y_pred = y_pred.to(device=self._kernel.device) y = y.to(device=self._kernel.device) y_pred = F.pad(y_pred, [self.pad_w, self.pad_w, self.pad_h, self.pad_h], mode="reflect") y = F.pad(y, [self.pad_w, self.pad_w, self.pad_h, self.pad_h], mode="reflect") if y_pred.dtype != self._kernel.dtype: self._kernel = self._kernel.to(dtype=y_pred.dtype) input_list = [y_pred, y, y_pred * y_pred, y * y, y_pred * y] outputs = F.conv2d(torch.cat(input_list), self._kernel, groups=nb_channel) batch_size = y_pred.size(0) output_list = [outputs[x * batch_size : (x + 1) * batch_size] for x in range(len(input_list))] mu_pred_sq = output_list[0].pow(2) mu_target_sq = output_list[1].pow(2) mu_pred_target = output_list[0] * output_list[1] sigma_pred_sq = output_list[2] - mu_pred_sq sigma_target_sq = output_list[3] - mu_target_sq sigma_pred_target = output_list[4] - mu_pred_target a1 = 2 * mu_pred_target + self.c1 a2 = 2 * sigma_pred_target + self.c2 b1 = mu_pred_sq + mu_target_sq + self.c1 b2 = sigma_pred_sq + sigma_target_sq + self.c2 ssim_idx = (a1 * a2) / (b1 * b2) self._sum_of_ssim += torch.mean(ssim_idx, (1, 2, 3), dtype=torch.float64).sum().to(device=self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_ssim", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("SSIM must have at least one example before it can be computed.") return (self._sum_of_ssim / self._num_examples).item() ignite-0.5.1/ignite/metrics/top_k_categorical_accuracy.py000066400000000000000000000111421465426447700236400ustar00rootroot00000000000000from typing import Callable, Sequence, Union import torch from ignite.exceptions import NotComputableError from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce __all__ = ["TopKCategoricalAccuracy"] class TopKCategoricalAccuracy(Metric): """ Calculates the top-k categorical accuracy. - ``update`` must receive output of the form ``(y_pred, y)``. Args: k: the k in β€œtop-k”. output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)`` Alternatively, ``output_transform`` can be used to handle this. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: def process_function(engine, batch): y_pred, y = batch return y_pred, y def one_hot_to_binary_output_transform(output): y_pred, y = output y = torch.argmax(y, dim=1) # one-hot vector to label index vector return y_pred, y engine = Engine(process_function) metric = TopKCategoricalAccuracy( k=2, output_transform=one_hot_to_binary_output_transform) metric.attach(engine, 'top_k_accuracy') preds = torch.tensor([ [0.7, 0.2, 0.05, 0.05], # 1 is in the top 2 [0.2, 0.3, 0.4, 0.1], # 0 is not in the top 2 [0.4, 0.4, 0.1, 0.1], # 0 is in the top 2 [0.7, 0.05, 0.2, 0.05] # 2 is in the top 2 ]) target = torch.tensor([ # targets as one-hot vectors [0, 1, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0] ]) state = engine.run([[preds, target]]) print(state.metrics['top_k_accuracy']) .. testoutput:: 0.75 .. versionchanged:: 0.5.1 ``skip_unrolling`` argument is added. """ _state_dict_all_req_keys = ("_num_correct", "_num_examples") def __init__( self, k: int = 5, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ) -> None: super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device, skip_unrolling=skip_unrolling) self._k = k @reinit__is_reduced def reset(self) -> None: self._num_correct = torch.tensor(0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() sorted_indices = torch.topk(y_pred, self._k, dim=1)[1] expanded_y = y.view(-1, 1).expand(-1, self._k) correct = torch.sum(torch.eq(sorted_indices, expanded_y), dim=1) self._num_correct += torch.sum(correct).to(self._device) self._num_examples += correct.shape[0] @sync_all_reduce("_num_correct", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError( "TopKCategoricalAccuracy must have at least one example before it can be computed." ) return self._num_correct.item() / self._num_examples ignite-0.5.1/ignite/py.typed000066400000000000000000000000001465426447700157630ustar00rootroot00000000000000ignite-0.5.1/ignite/utils.py000066400000000000000000000413251465426447700160150ustar00rootroot00000000000000import collections.abc as collections import functools import hashlib import logging import numbers import random import shutil import warnings from pathlib import Path from typing import Any, Callable, cast, Dict, List, Optional, TextIO, Tuple, Type, TypeVar, Union import torch __all__ = [ "convert_tensor", "apply_to_tensor", "apply_to_type", "_to_str_list", "to_onehot", "setup_logger", "manual_seed", "hash_checkpoint", ] def convert_tensor( x: Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, ) -> Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes]: """Move tensors to relevant device. Args: x: input tensor or mapping, or sequence of tensors. device: device type to move ``x``. non_blocking: convert a CPU Tensor with pinned memory to a CUDA Tensor asynchronously with respect to the host if possible """ def _func(tensor: torch.Tensor) -> torch.Tensor: return tensor.to(device=device, non_blocking=non_blocking) if device is not None else tensor return apply_to_tensor(x, _func) def apply_to_tensor( x: Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes], func: Callable ) -> Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes]: """Apply a function on a tensor or mapping, or sequence of tensors. Args: x: input tensor or mapping, or sequence of tensors. func: the function to apply on ``x``. """ return apply_to_type(x, torch.Tensor, func) def apply_to_type( x: Union[Any, collections.Sequence, collections.Mapping, str, bytes], input_type: Union[Type, Tuple[Type[Any], Any]], func: Callable, ) -> Union[Any, collections.Sequence, collections.Mapping, str, bytes]: """Apply a function on an object of `input_type` or mapping, or sequence of objects of `input_type`. Args: x: object or mapping or sequence. input_type: data type of ``x``. func: the function to apply on ``x``. """ if isinstance(x, input_type): return func(x) if isinstance(x, (str, bytes)): return x if isinstance(x, collections.Mapping): return cast(Callable, type(x))({k: apply_to_type(sample, input_type, func) for k, sample in x.items()}) if isinstance(x, tuple) and hasattr(x, "_fields"): # namedtuple return cast(Callable, type(x))(*(apply_to_type(sample, input_type, func) for sample in x)) if isinstance(x, collections.Sequence): return cast(Callable, type(x))([apply_to_type(sample, input_type, func) for sample in x]) raise TypeError((f"x must contain {input_type}, dicts or lists; found {type(x)}")) def _tree_map( func: Callable, x: Union[Any, collections.Sequence, collections.Mapping], key: Optional[Union[int, str]] = None ) -> Union[Any, collections.Sequence, collections.Mapping]: if isinstance(x, collections.Mapping): return cast(Callable, type(x))({k: _tree_map(func, sample, key=k) for k, sample in x.items()}) if isinstance(x, tuple) and hasattr(x, "_fields"): # namedtuple return cast(Callable, type(x))(*(_tree_map(func, sample) for sample in x)) if isinstance(x, collections.Sequence): return cast(Callable, type(x))([_tree_map(func, sample, key=i) for i, sample in enumerate(x)]) return func(x, key=key) def _to_str_list(data: Any) -> List[str]: """ Recursively flattens and formats complex data structures, including keys for dictionaries, into a list of human-readable strings. This function processes nested dictionaries, lists, tuples, numbers, and PyTorch tensors, formatting numbers to four decimal places and handling tensors with special formatting rules. It's particularly useful for logging, debugging, or any scenario where a human-readable representation of complex, nested data structures is required. The function handles the following types: - Numbers: Formatted to four decimal places. - PyTorch tensors: - Scalars are formatted to four decimal places. - 1D tensors with more than 10 elements show the first 10 elements followed by an ellipsis. - 1D tensors with 10 or fewer elements are fully listed. - Multi-dimensional tensors display their shape. - Dictionaries: Each key-value pair is included in the output with the key as a prefix. - Lists and tuples: Flattened and included in the output. Empty lists/tuples are represented by an empty string. - None values: Represented by an empty string. Args: data: The input data to be flattened and formatted. It can be a nested combination of dictionaries, lists, tuples, numbers, and PyTorch tensors. Returns: A list of formatted strings, each representing a part of the input data structure. """ formatted_items: List[str] = [] def format_item(item: Any, prefix: str = "") -> Optional[str]: if isinstance(item, numbers.Number): return f"{prefix}{item:.4f}" elif torch.is_tensor(item): if item.dim() == 0: return f"{prefix}{item.item():.4f}" # Format scalar tensor without brackets elif item.dim() == 1 and item.size(0) > 10: return f"{prefix}[" + ", ".join(f"{x.item():.4f}" for x in item[:10]) + ", ...]" elif item.dim() == 1: return f"{prefix}[" + ", ".join(f"{x.item():.4f}" for x in item) + "]" else: return f"{prefix}Shape{list(item.shape)}" elif isinstance(item, dict): for key, value in item.items(): formatted_value = format_item(value, f"{key}: ") if formatted_value is not None: formatted_items.append(formatted_value) elif isinstance(item, (list, tuple)): if not item: if prefix: formatted_items.append(f"{prefix}") else: values = [format_item(x) for x in item] values_str = [v for v in values if v is not None] if values_str: formatted_items.append(f"{prefix}" + ", ".join(values_str)) elif item is None: if prefix: formatted_items.append(f"{prefix}") return None # Directly handle single numeric values if isinstance(data, numbers.Number): return [f"{data:.4f}"] format_item(data) return formatted_items class _CollectionItem: types_as_collection_item: Tuple = (int, float, torch.Tensor) def __init__(self, collection: Union[Dict, List], key: Union[int, str]) -> None: if not isinstance(collection, (dict, list)): raise TypeError( f"Input type is expected to be a mapping or list, but got {type(collection)} " f"for input key '{key}'." ) if isinstance(collection, list) and isinstance(key, str): raise ValueError("Key should be int for collection of type list") self.collection = collection self.key = key def load_value(self, value: Any) -> None: self.collection[self.key] = value # type: ignore[index] def value(self) -> Any: return self.collection[self.key] # type: ignore[index] @staticmethod def wrap(object: Union[Dict, List], key: Union[int, str], value: Any) -> Union[Any, "_CollectionItem"]: return ( _CollectionItem(object, key) if value is None or isinstance(value, _CollectionItem.types_as_collection_item) else value ) def _tree_apply2( func: Callable, x: Union[Any, List, Dict], y: Union[Any, collections.Sequence, collections.Mapping], ) -> None: if isinstance(x, dict) and isinstance(y, collections.Mapping): for k, v in x.items(): if k not in y: raise ValueError(f"Key '{k}' from x is not found in y: {y.keys()}") _tree_apply2(func, _CollectionItem.wrap(x, k, v), y[k]) elif isinstance(x, list) and isinstance(y, collections.Sequence): if len(x) != len(y): raise ValueError(f"Size of y: {len(y)} does not match the size of x: '{len(x)}'") for i, (v1, v2) in enumerate(zip(x, y)): _tree_apply2(func, _CollectionItem.wrap(x, i, v1), v2) else: return func(x, y) def to_onehot(indices: torch.Tensor, num_classes: int) -> torch.Tensor: """Convert a tensor of indices of any shape `(N, ...)` to a tensor of one-hot indicators of shape `(N, num_classes, ...)` and of type uint8. Output's device is equal to the input's device`. Args: indices: input tensor to convert. num_classes: number of classes for one-hot tensor. .. versionchanged:: 0.4.3 This functions is now torchscriptable. """ new_shape = (indices.shape[0], num_classes) + indices.shape[1:] onehot = torch.zeros(new_shape, dtype=torch.uint8, device=indices.device) return onehot.scatter_(1, indices.unsqueeze(1), 1) def setup_logger( name: Optional[str] = "ignite", level: int = logging.INFO, stream: Optional[TextIO] = None, format: str = "%(asctime)s %(name)s %(levelname)s: %(message)s", filepath: Optional[str] = None, distributed_rank: Optional[int] = None, reset: bool = False, encoding: Optional[str] = "utf-8", ) -> logging.Logger: """Setups logger: name, level, format etc. Args: name: new name for the logger. If None, the standard logger is used. level: logging level, e.g. CRITICAL, ERROR, WARNING, INFO, DEBUG. stream: logging stream. If None, the standard stream is used (sys.stderr). format: logging format. By default, `%(asctime)s %(name)s %(levelname)s: %(message)s`. filepath: Optional logging file path. If not None, logs are written to the file. distributed_rank: Optional, rank in distributed configuration to avoid logger setup for workers. If None, distributed_rank is initialized to the rank of process. reset: if True, reset an existing logger rather than keep format, handlers, and level. encoding: open the file with the encoding. By default, 'utf-8'. Returns: logging.Logger Examples: Improve logs readability when training with a trainer and evaluator: .. code-block:: python from ignite.utils import setup_logger trainer = ... evaluator = ... trainer.logger = setup_logger("trainer") evaluator.logger = setup_logger("evaluator") trainer.run(data, max_epochs=10) # Logs will look like # 2020-01-21 12:46:07,356 trainer INFO: Engine run starting with max_epochs=5. # 2020-01-21 12:46:07,358 trainer INFO: Epoch[1] Complete. Time taken: 00:5:23 # 2020-01-21 12:46:07,358 evaluator INFO: Engine run starting with max_epochs=1. # 2020-01-21 12:46:07,358 evaluator INFO: Epoch[1] Complete. Time taken: 00:01:02 # ... Every existing logger can be reset if needed .. code-block:: python logger = setup_logger(name="my-logger", format="=== %(name)s %(message)s") logger.info("first message") setup_logger(name="my-logger", format="+++ %(name)s %(message)s", reset=True) logger.info("second message") # Logs will look like # === my-logger first message # +++ my-logger second message Change the level of an existing internal logger .. code-block:: python setup_logger( name="ignite.distributed.launcher.Parallel", level=logging.WARNING ) .. versionchanged:: 0.4.3 Added ``stream`` parameter. .. versionchanged:: 0.4.5 Added ``reset`` parameter. .. versionchanged:: 0.5.1 Argument ``encoding`` added to correctly handle special characters in the file, default "utf-8". """ # check if the logger already exists existing = name is None or name in logging.root.manager.loggerDict # if existing, get the logger otherwise create a new one logger = logging.getLogger(name) if distributed_rank is None: import ignite.distributed as idist distributed_rank = idist.get_rank() # Remove previous handlers if distributed_rank > 0 or reset: if logger.hasHandlers(): for h in list(logger.handlers): logger.removeHandler(h) if distributed_rank > 0: # Add null handler to avoid multiple parallel messages logger.addHandler(logging.NullHandler()) # Keep the existing configuration if not reset if existing and not reset: return logger if distributed_rank == 0: logger.setLevel(level) formatter = logging.Formatter(format) ch = logging.StreamHandler(stream=stream) ch.setLevel(level) ch.setFormatter(formatter) logger.addHandler(ch) if filepath is not None: fh = logging.FileHandler(filepath, encoding=encoding) fh.setLevel(level) fh.setFormatter(formatter) logger.addHandler(fh) # don't propagate to ancestors # the problem here is to attach handlers to loggers # should we provide a default configuration less open ? if name is not None: logger.propagate = False return logger def manual_seed(seed: int) -> None: """Setup random state from a seed for `torch`, `random` and optionally `numpy` (if can be imported). Args: seed: Random state seed .. versionchanged:: 0.4.3 Added ``torch.cuda.manual_seed_all(seed)``. .. versionchanged:: 0.4.5 Added ``torch_xla.core.xla_model.set_rng_state(seed)``. """ random.seed(seed) torch.manual_seed(seed) try: import torch_xla.core.xla_model as xm xm.set_rng_state(seed) except ImportError: pass try: import numpy as np np.random.seed(seed) except ImportError: pass def deprecated( deprecated_in: str, removed_in: str = "", reasons: Tuple[str, ...] = (), raise_exception: bool = False ) -> Callable: F = TypeVar("F", bound=Callable[..., Any]) def decorator(func: F) -> F: func_doc = func.__doc__ if func.__doc__ else "" deprecation_warning = ( f"This function has been deprecated since version {deprecated_in}" + (f" and will be removed in version {removed_in}" if removed_in else "") + ".\n Please refer to the documentation for more details." ) @functools.wraps(func) def wrapper(*args: Any, **kwargs: Dict[str, Any]) -> Callable: if raise_exception: raise DeprecationWarning(deprecation_warning) warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2) return func(*args, **kwargs) appended_doc = f".. deprecated:: {deprecated_in}" + ("\n\n\t" if len(reasons) > 0 else "") for reason in reasons: appended_doc += "\n\t- " + reason wrapper.__doc__ = f"**Deprecated function**.\n\n {func_doc}{appended_doc}" return cast(F, wrapper) return decorator def hash_checkpoint(checkpoint_path: Union[str, Path], output_dir: Union[str, Path]) -> Tuple[Path, str]: """ Hash the checkpoint file in the format of ``-.`` to be used with ``check_hash`` of :func:`torch.hub.load_state_dict_from_url`. Args: checkpoint_path: Path to the checkpoint file. output_dir: Output directory to store the hashed checkpoint file (will be created if not exist). Returns: Path to the hashed checkpoint file, the first 8 digits of SHA256 hash. .. versionadded:: 0.4.8 """ if isinstance(checkpoint_path, str): checkpoint_path = Path(checkpoint_path) if not checkpoint_path.exists(): raise FileNotFoundError(f"{checkpoint_path.name} does not exist in {checkpoint_path.parent}.") if isinstance(output_dir, str): output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) hash_obj = hashlib.sha256() # taken from https://github.com/pytorch/vision/blob/main/references/classification/utils.py with checkpoint_path.open("rb") as f: # Read and update hash string value in blocks of 4KB for byte_block in iter(lambda: f.read(4096), b""): hash_obj.update(byte_block) sha_hash = hash_obj.hexdigest() old_filename = checkpoint_path.stem new_filename = "-".join((old_filename, sha_hash[:8])) + ".pt" hash_checkpoint_path = output_dir / new_filename shutil.move(str(checkpoint_path), hash_checkpoint_path) return hash_checkpoint_path, sha_hash ignite-0.5.1/mypy.ini000066400000000000000000000032101465426447700145120ustar00rootroot00000000000000[mypy] files = ignite pretty = True show_error_codes = True check_untyped_defs = True ; a lot of work needed to fix issues disallow_any_generics = False disallow_incomplete_defs = True disallow_subclassing_any = True ; due to missing types in pytorch set to False disallow_untyped_calls = False disallow_untyped_decorators = True disallow_untyped_defs = True no_implicit_optional = True ; would need a more precise import of pytorch classes and methods, which is not possible, therefore set to False no_implicit_reexport = False strict_equality = True warn_redundant_casts = True ; due to missing types in multiple libs set to False warn_return_any = False ; results in too many false positives, therefore set to False warn_unreachable = False warn_unused_configs = True warn_unused_ignores = True [mypy-apex.*] ignore_missing_imports = True [mypy-clearml.*] ignore_missing_imports = True [mypy-horovod.*] ignore_missing_imports = True [mypy-matplotlib.*] ignore_missing_imports = True [mypy-mlflow.*] ignore_missing_imports = True [mypy-neptune.*] ignore_missing_imports = True [mypy-numpy.*] ignore_missing_imports = True [mypy-pandas.*] ignore_missing_imports = True [mypy-sklearn.*] ignore_missing_imports = True [mypy-polyaxon.*] ignore_missing_imports = True [mypy-polyaxon_client.*] ignore_missing_imports = True [mypy-pynvml.*] ignore_missing_imports = True [mypy-tensorboardX.*] ignore_missing_imports = True [mypy-torch_xla.*] ignore_missing_imports = True [mypy-trains.*] ignore_missing_imports = True [mypy-tqdm.*] ignore_missing_imports = True [mypy-scipy.*] ignore_missing_imports = True [mypy-torchvision.*] ignore_missing_imports = True ignite-0.5.1/pyproject.toml000066400000000000000000000013721465426447700157360ustar00rootroot00000000000000[tool.black] line-length = 120 target-version = ['py38', 'py39'] include = '\.pyi?$' exclude = ''' ( /( \.eggs # exclude a few common directories in the | \.git # root of the project | \.hg | \.mypy_cache | \.tox | \.venv | _build | buck-out | build | dist | assets )/ | foo.py # also separately exclude a file named foo.py in # the root of the project ) ''' [tool.usort.known] first_party = [ "ignite", ] third_party = [ "clearml", "dill", "matplotlib", "numpy", "pkg_resources", "pytest", "requests", "setuptools", "skimage", "sklearn", "torch", "torchvision", ] [tool.ufmt] excludes = [ "assets/", ] ignite-0.5.1/requirements-dev.txt000066400000000000000000000007771465426447700170720ustar00rootroot00000000000000# Tests numpy pytest pytest-cov pytest-xdist pytest-timeout dill filelock setuptools # Test contrib dependencies scipy pytorch_fid tqdm scikit-learn matplotlib tensorboardX visdom polyaxon wandb mlflow neptune-client>=0.16.17 tensorboard torchvision pynvml clearml scikit-image py-rouge # temporary fix for python=3.12 and v3.8.1 # nltk git+https://github.com/nltk/nltk@aba99c8 # Examples dependencies pandas gymnasium # temporary fix: E AttributeError: module 'mpmath' has no attribute 'rational' mpmath<1.4 ignite-0.5.1/setup.cfg000066400000000000000000000014021465426447700146350ustar00rootroot00000000000000[metadata] license_files = LICENSE [pycodestyle] exclude = .eggs,*.egg,build,docs/*,.git,versioneer.py,*/conf.py ignore = E402, E721 max_line_length = 120 [isort] known_third_party=clearml,dill,matplotlib,numpy,pkg_resources,pytest,requests,setuptools,skimage,sklearn,torch,torchvision multi_line_output=3 include_trailing_comma=True force_grid_wrap=0 use_parentheses=True line_length=120 skip_glob=docs/** filter_files=True profile=black [flake8] max-line-length = 120 ignore = E722,E203,E231,F841,W503,F403,E402 per-file-ignores = __init__.py: F401 [tool:pytest] markers = distributed: mark a test with distributed option multinode_distributed: mark a test with multi-node distributed option tpu: mark a test as requiring XLA addopts = --color=yes ignite-0.5.1/setup.py000066400000000000000000000024361465426447700145360ustar00rootroot00000000000000import io import os import re from setuptools import find_packages, setup def read(*names, **kwargs): with io.open(os.path.join(os.path.dirname(__file__), *names), encoding=kwargs.get("encoding", "utf8")) 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 version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") readme = read("README.md").replace( 'src="assets/', 'src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/' ) VERSION = find_version("ignite", "__init__.py") requirements = ["torch>=1.3,<3", "packaging"] setup( # Metadata name="pytorch-ignite", version=VERSION, author="PyTorch-Ignite Team", author_email="contact@pytorch-ignite.ai", url="https://github.com/pytorch/ignite", description="A lightweight library to help with training neural networks in PyTorch.", long_description_content_type="text/markdown", long_description=readme, license="BSD", # Package info packages=find_packages(exclude=("tests", "tests.*")), package_data={"ignite": ["py.typed"]}, zip_safe=False, install_requires=requirements, ) ignite-0.5.1/tests/000077500000000000000000000000001465426447700141615ustar00rootroot00000000000000ignite-0.5.1/tests/__init__.py000066400000000000000000000000421465426447700162660ustar00rootroot00000000000000# Needed to collect coverage data ignite-0.5.1/tests/common_test_functionality.sh000066400000000000000000000061531465426447700220210ustar00rootroot00000000000000#!/bin/bash # Will catch exit code 5 when tests are deselected from previous passing run # (relevent for --last-failed-no-failures none) last_failed_no_failures_code=5 # functions shared across test files run_tests() { # Set defaults local core_args="-vvv tests/ignite" local cache_dir=".unknown-cache" local skip_distrib_tests=1 local match_tests_expression="" local trap_deselected_exit_code=1 local use_last_failed=0 local use_coverage=0 local world_size=0 # Always clean up pytest.ini trap 'rm -f pytest.ini' RETURN # Parse arguments while [[ $# -gt 0 ]] do key="$1" case $key in --core_args) core_args="$2" shift shift ;; --cache_dir) cache_dir="$2" shift shift ;; --skip_distrib_tests) skip_distrib_tests="$2" shift shift ;; --match_tests_expression) match_tests_expression="$2" shift shift ;; --trap_deselected_exit_code) trap_deselected_exit_code="$2" shift shift ;; --use_last_failed) use_last_failed="$2" shift shift ;; --use_coverage) use_coverage="$2" shift shift ;; --world_size) world_size="$2" shift shift ;; *) echo "Error: Unknown argument $key" exit 1 shift ;; esac done if ! command -v pytest &> /dev/null then echo "pytest could not be found" echo "The path is: ${PATH}" exit 1 fi if [ "${skip_distrib_tests}" -eq "1" ]; then # can be overwritten by core_args skip_distrib_opt="-m 'not distributed and not tpu and not multinode_distributed'" else skip_distrib_opt="" fi echo [pytest] > pytest.ini ; echo "cache_dir=${cache_dir}" >> pytest.ini # Assemble options for the pytest command pytest_args="${skip_distrib_opt} ${core_args} --treat-unrun-as-failed -k '${match_tests_expression}'" if [ "${use_last_failed:-0}" -eq "1" ] && [ -d "${cache_dir}" ]; then pytest_args="--last-failed --last-failed-no-failures none ${pytest_args}" fi if [ "${use_coverage}" -eq "1" ]; then pytest_args="--cov ignite --cov-append --cov-report term-missing --cov-report xml ${pytest_args}" fi if [ ! "${world_size}" -eq "0" ]; then export WORLD_SIZE="${world_size}" pytest_args="--dist=each --tx ${WORLD_SIZE}*popen//python=python ${pytest_args}" fi # Run the command if [ "$trap_deselected_exit_code" -eq "1" ]; then CUDA_VISIBLE_DEVICES="" eval "pytest ${pytest_args}" || { exit_code=$?; if [ "$exit_code" -eq ${last_failed_no_failures_code} ]; then echo "All tests deselected"; else exit $exit_code; fi; } else CUDA_VISIBLE_DEVICES="" eval "pytest ${pytest_args}" fi } ignite-0.5.1/tests/ignite/000077500000000000000000000000001465426447700154405ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/__init__.py000066400000000000000000000006141465426447700175520ustar00rootroot00000000000000import torch def cpu_and_maybe_cuda(): return ("cpu",) + (("cuda",) if torch.cuda.is_available() else ()) def is_mps_available_and_functional(): if not torch.backends.mps.is_available(): return False try: # Try to allocate a small tensor on the MPS device torch.tensor([1.0], device="mps") return True except RuntimeError: return False ignite-0.5.1/tests/ignite/base/000077500000000000000000000000001465426447700163525ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/base/__init__.py000066400000000000000000000000001465426447700204510ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/base/test_mixins.py000066400000000000000000000003671465426447700213000ustar00rootroot00000000000000import pytest from ignite.base import Serializable def test_state_dict(): s = Serializable() with pytest.raises(NotImplementedError): s.state_dict() def test_load_state_dict(): s = Serializable() s.load_state_dict({}) ignite-0.5.1/tests/ignite/conftest.py000066400000000000000000000454511465426447700176500ustar00rootroot00000000000000import functools import os import shutil import signal import sys import tempfile import threading import time from pathlib import Path import pytest import torch import torch.distributed as dist import ignite.distributed as idist def pytest_addoption(parser): """ Add custom command line options for the ignite test suite here. See: This function is a pytest hook (due to its name) and is *"automatically" executed at the start of a test run https://docs.pytest.org/en/latest/reference/reference.html#initialization-hooks * "automatically" is true provided this conftest.py file is the root directory. See: https://docs.pytest.org/en/latest/reference/customize.html#initialization-determining-rootdir-and-configfile """ parser.addoption( "--treat-unrun-as-failed", action="store_true", help=""" If a session is interrupted, treat the unrun tests as failed so that a rerun with --last-failed runs any tests that have not passed or been skipped. Note that if all tests in a module have been skipped, the module will be skipped for all subsequent runs. """, ) def pytest_configure(config): """ This function is a pytest hook (due to its name) and is run after command line parsing is complete in order to configure the test session. """ config.addinivalue_line("markers", "distributed: run distributed") config.addinivalue_line("markers", "multinode_distributed: distributed") config.addinivalue_line("markers", "tpu: run on tpu") if config.option.treat_unrun_as_failed: unrun_tracker = UnrunTracker() config.pluginmanager.register(unrun_tracker, "unrun_tracker_plugin") @pytest.fixture(scope="session", autouse=True) def term_handler(): """ This allows the pytest session to be terminated upon retries on CI. It may be worth using this fixture solely in that context. For a discussion on whether sigterm should be ignored and why pytest usually ignores it see: https://github.com/pytest-dev/pytest/issues/5243 """ if threading.current_thread() is threading.main_thread() and hasattr(signal, "SIGTERM"): orig = signal.signal(signal.SIGTERM, signal.getsignal(signal.SIGINT)) yield signal.signal(signal.SIGTERM, orig) else: yield # Just pass through if SIGTERM isn't supported or we are not in the main thread @pytest.fixture( params=[ "cpu", pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no CUDA support")), ] ) def available_device(request): return request.param @pytest.fixture() def dirname(): path = Path(tempfile.mkdtemp()) yield path shutil.rmtree(path) @pytest.fixture() def get_fixed_dirname(worker_id): # multi-proc friendly fixed tmp dirname path = "/tmp/fixed_tmp_dirname_" lrank = int(worker_id.replace("gw", "")) if "gw" in worker_id else 0 def getter(name="test"): nonlocal path path += name time.sleep(0.5 * lrank) os.makedirs(path, exist_ok=True) return path yield getter time.sleep(1.0 * lrank + 1.0) if Path(path).exists(): shutil.rmtree(path) # sort of sync time.sleep(1.0) @pytest.fixture() def get_rank_zero_dirname(dirname): def func(): import ignite.distributed as idist zero_rank_dirname = Path(idist.all_gather(str(dirname))[0]) return zero_rank_dirname yield func @pytest.fixture(scope="module") def local_rank(worker_id): """use a different account in each xdist worker""" if "gw" in worker_id: lrank = int(worker_id.replace("gw", "")) elif "master" == worker_id: lrank = 0 else: raise RuntimeError(f"Can not get rank from worker_id={worker_id}") os.environ["LOCAL_RANK"] = f"{lrank}" yield lrank del os.environ["LOCAL_RANK"] @pytest.fixture(scope="module") def world_size(): remove_env_var = False if "WORLD_SIZE" not in os.environ: os.environ["WORLD_SIZE"] = "1" remove_env_var = True yield int(os.environ["WORLD_SIZE"]) if remove_env_var: del os.environ["WORLD_SIZE"] @pytest.fixture() def clean_env(): for k in ["RANK", "LOCAL_RANK", "WORLD_SIZE"]: if k in os.environ: del os.environ[k] def _create_dist_context(dist_info, lrank): dist.init_process_group(**dist_info) dist.barrier() if torch.cuda.is_available(): torch.cuda.set_device(lrank) return {"local_rank": lrank, "world_size": dist_info["world_size"], "rank": dist_info["rank"]} def _destroy_dist_context(): if dist.get_rank() == 0: # To support Python 3.7; Otherwise we could do `.unlink(missing_ok=True)` try: Path("/tmp/free_port").unlink() except FileNotFoundError: pass dist.barrier() dist.destroy_process_group() from ignite.distributed.utils import _SerialModel, _set_model # We need to set synced model to initial state _set_model(_SerialModel()) def _find_free_port(): # Taken from https://github.com/facebookresearch/detectron2/blob/master/detectron2/engine/launch.py import socket sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.bind(("", 0)) port = sock.getsockname()[1] sock.close() return port def _setup_free_port(local_rank): port_file = "/tmp/free_port" if local_rank == 0: port = _find_free_port() with open(port_file, "w") as h: h.write(str(port)) return port else: counter = 10 while counter > 0: counter -= 1 time.sleep(1) if not Path(port_file).exists(): continue with open(port_file, "r") as h: port = h.readline() return int(port) raise RuntimeError(f"Failed to fetch free port on local rank {local_rank}") @pytest.fixture() def distributed_context_single_node_nccl(local_rank, world_size): free_port = _setup_free_port(local_rank) dist_info = { "backend": "nccl", "world_size": world_size, "rank": local_rank, "init_method": f"tcp://localhost:{free_port}", } yield _create_dist_context(dist_info, local_rank) _destroy_dist_context() @pytest.fixture() def distributed_context_single_node_gloo(local_rank, world_size): from datetime import timedelta if sys.platform.startswith("win"): temp_file = tempfile.NamedTemporaryFile(delete=False) # can't use backslashes in f-strings backslash = "\\" init_method = f'file:///{temp_file.name.replace(backslash, "/")}' else: free_port = _setup_free_port(local_rank) init_method = f"tcp://localhost:{free_port}" temp_file = None dist_info = { "backend": "gloo", "world_size": world_size, "rank": local_rank, "init_method": init_method, "timeout": timedelta(seconds=30), } yield _create_dist_context(dist_info, local_rank) _destroy_dist_context() if temp_file: temp_file.close() @pytest.fixture() def multi_node_conf(local_rank): assert "node_id" in os.environ assert "nnodes" in os.environ assert "nproc_per_node" in os.environ node_id = int(os.environ["node_id"]) nnodes = int(os.environ["nnodes"]) nproc_per_node = int(os.environ["nproc_per_node"]) out = { "world_size": nnodes * nproc_per_node, "rank": local_rank + node_id * nproc_per_node, "local_rank": local_rank, } return out def _create_mnodes_dist_context(dist_info, mnodes_conf): dist.init_process_group(**dist_info) dist.barrier() if torch.cuda.is_available(): torch.cuda.device(mnodes_conf["local_rank"]) return mnodes_conf def _destroy_mnodes_dist_context(): dist.barrier() dist.destroy_process_group() from ignite.distributed.utils import _SerialModel, _set_model # We need to set synced model to initial state _set_model(_SerialModel()) @pytest.fixture() def distributed_context_multi_node_gloo(multi_node_conf): assert "MASTER_ADDR" in os.environ assert "MASTER_PORT" in os.environ dist_info = { "backend": "gloo", "init_method": "env://", "world_size": multi_node_conf["world_size"], "rank": multi_node_conf["rank"], } yield _create_mnodes_dist_context(dist_info, multi_node_conf) _destroy_mnodes_dist_context() @pytest.fixture() def distributed_context_multi_node_nccl(multi_node_conf): assert "MASTER_ADDR" in os.environ assert "MASTER_PORT" in os.environ os.environ["MASTER_PORT"] = str(int(os.getenv("MASTER_PORT")) + 1) dist_info = { "backend": "nccl", "init_method": "env://", "world_size": multi_node_conf["world_size"], "rank": multi_node_conf["rank"], } yield _create_mnodes_dist_context(dist_info, multi_node_conf) _destroy_mnodes_dist_context() def _xla_template_worker_task(index, fn, args): import torch_xla.core.xla_model as xm xm.rendezvous("init") fn(index, *args) def _xla_execute(fn, args, nprocs): import torch_xla.distributed.xla_multiprocessing as xmp spawn_kwargs = {} if "COLAB_TPU_ADDR" in os.environ: spawn_kwargs["start_method"] = "fork" try: xmp.spawn(_xla_template_worker_task, args=(fn, args), nprocs=nprocs, **spawn_kwargs) except SystemExit as ex_: assert ex_.code == 0, "Didn't successfully exit in XLA test" @pytest.fixture() def xmp_executor(): yield _xla_execute @pytest.fixture() def mock_gpu_is_not_available(): from unittest.mock import patch with patch("torch.cuda") as mock_cuda: mock_cuda.is_available.return_value = False yield mock_cuda def _hvd_task_with_init(func, args): import horovod.torch as hvd hvd.init() lrank = hvd.local_rank() if torch.cuda.is_available(): torch.cuda.set_device(lrank) func(*args) # Added a sleep to avoid flaky failures on circle ci # Sometimes a rank is terminated before final collective # op is finished. # https://github.com/pytorch/ignite/pull/2357 time.sleep(2) hvd.shutdown() def _gloo_hvd_execute(func, args, np=1, do_init=False): try: # old API from horovod.run.runner import run except ImportError: # new API: https://github.com/horovod/horovod/pull/2099 from horovod import run kwargs = dict(use_gloo=True, num_proc=np) if do_init: return run(_hvd_task_with_init, args=(func, args), **kwargs) return run(func, args=args, **kwargs) @pytest.fixture() def gloo_hvd_executor(): yield _gloo_hvd_execute skip_if_no_gpu = pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") skip_if_has_not_native_dist_support = pytest.mark.skipif( not idist.has_native_dist_support, reason="Skip if no native dist support" ) skip_if_has_not_xla_support = pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") skip_if_has_not_horovod_support = pytest.mark.skipif( not idist.has_hvd_support, reason="Skip if no Horovod dist support" ) # Unlike other backends, Horovod and multi-process XLA run user code by # providing a utility function which accepts user code as a callable argument. # To keep distributed tests backend-agnostic, we mark Horovod and multi-process XLA # tests during fixture preparation and replace their function with the proper one # just before running the test. PyTest stash is a safe way to share state between # different stages of tool runtime and we use it to mark the tests. is_horovod_stash_key = pytest.StashKey[bool]() is_xla_stash_key = pytest.StashKey[bool]() is_xla_single_device_stash_key = pytest.StashKey[bool]() @pytest.fixture( params=[ pytest.param("nccl", marks=[pytest.mark.distributed, skip_if_has_not_native_dist_support, skip_if_no_gpu]), pytest.param("gloo_cpu", marks=[pytest.mark.distributed, skip_if_has_not_native_dist_support]), pytest.param("gloo", marks=[pytest.mark.distributed, skip_if_has_not_native_dist_support, skip_if_no_gpu]), pytest.param( "horovod", marks=[ pytest.mark.distributed, skip_if_has_not_horovod_support, pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc"), ], ), pytest.param( "single_device_xla", marks=[ pytest.mark.tpu, skip_if_has_not_xla_support, pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars"), ], ), pytest.param( "xla_nprocs", marks=[ pytest.mark.tpu, skip_if_has_not_xla_support, pytest.mark.skipif( "NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars" ), ], ), ], scope="class", ) def distributed(request, local_rank, world_size): if request.param in ("nccl", "gloo_cpu", "gloo"): if "gloo" in request.param and sys.platform.startswith("win"): temp_file = tempfile.NamedTemporaryFile(delete=False) # can't use backslashes in f-strings backslash = "\\" init_method = f'file:///{temp_file.name.replace(backslash, "/")}' else: temp_file = None free_port = _setup_free_port(local_rank) init_method = f"tcp://localhost:{free_port}" dist_info = { "world_size": world_size, "rank": local_rank, "init_method": init_method, } if request.param == "nccl": dist_info["backend"] = "nccl" else: dist_info["backend"] = "gloo" from datetime import timedelta dist_info["timeout"] = timedelta(seconds=30) yield _create_dist_context(dist_info, local_rank) _destroy_dist_context() if temp_file: temp_file.close() elif request.param == "horovod": request.node.stash[is_horovod_stash_key] = True yield None elif request.param in ("single_device_xla", "xla_nprocs"): request.node.stash[is_xla_stash_key] = True request.node.stash[is_xla_single_device_stash_key] = request.param == "single_device_xla" yield {"xla_index": -1} if request.param == "xla_nprocs" else None else: raise RuntimeError(f"Invalid parameter value for `distributed` fixture, given {request.param}") class UnrunTracker: """ Keeps track of unrun tests to improve the user experience when using the "--last-failed" pytest option and a test session is interrupted. This is particularly useful on CI when rerunning "failing" tests where the failure was due to a deadlock and many tests weren't actually run so they didn't actually fail. This is a pytest plugin that implements some standard hooks to modify the test session. Its functionality can be added to a test session by registering it with the pytest plugin manager. """ def __init__(self): self.unrun_tests = [] def pytest_collection_finish(self, session): # At the end of the collection, add all items to the unrun_tests list self.unrun_tests.extend(session.items) def pytest_runtest_teardown(self, item): if item in self.unrun_tests: self.unrun_tests.remove(item) def record_unrun_as_failed(self, session, exitstatus): # Get current lastfailed entries (if any) lastfailed = session.config.cache.get("cache/lastfailed", {}) # Add unrun tests to lastfailed for test in self.unrun_tests: lastfailed[test.nodeid] = True # Update the cache with the new lastfailed session.config.cache.set("cache/lastfailed", lastfailed) @pytest.hookimpl def pytest_pyfunc_call(pyfuncitem: pytest.Function) -> None: if any(fx in pyfuncitem.fixturenames for fx in ["distributed", "multinode_distributed"]): # Run distributed tests on a single worker to avoid RACE conditions # This requires that the --dist=loadgroup option be passed to pytest. pyfuncitem.add_marker(pytest.mark.xdist_group("distributed")) # Add timeouts to prevent hanging if "tpu" in pyfuncitem.fixturenames: pyfuncitem.add_marker(pytest.mark.timeout(60)) else: pyfuncitem.add_marker(pytest.mark.timeout(45)) if pyfuncitem.stash.get(is_horovod_stash_key, False): def testfunc_wrapper(test_func, **kwargs): def hvd_worker(): import horovod.torch as hvd hvd.init() lrank = hvd.local_rank() if torch.cuda.is_available(): torch.cuda.set_device(lrank) test_func(**kwargs) hvd.shutdown() try: # old API from horovod.run.runner import run except ImportError: # new API: https://github.com/horovod/horovod/pull/2099 from horovod import run nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() hvd_kwargs = dict(use_gloo=True, num_proc=nproc) run(hvd_worker, **hvd_kwargs) pyfuncitem.obj = functools.partial(testfunc_wrapper, pyfuncitem.obj) elif pyfuncitem.stash.get(is_xla_stash_key, False) and not pyfuncitem.stash[is_xla_single_device_stash_key]: def testfunc_wrapper(testfunc, **kwargs): def xla_worker(index, fn): import torch_xla.core.xla_model as xm kwargs["distributed"]["xla_index"] = index xm.rendezvous("init") fn(**kwargs) import torch_xla.distributed.xla_multiprocessing as xmp spawn_kwargs = {"nprocs": int(os.environ["NUM_TPU_WORKERS"])} if "COLAB_TPU_ADDR" in os.environ: spawn_kwargs["start_method"] = "fork" try: xmp.spawn(xla_worker, args=(testfunc,), **spawn_kwargs) except SystemExit as ex_: assert ex_.code == 0, "Didn't successfully exit in XLA test" pyfuncitem.obj = functools.partial(testfunc_wrapper, pyfuncitem.obj) def pytest_sessionfinish(session, exitstatus): """ Any functionality that should be run at the end of the session should be added here. This is a pytest hook (due to its name) and is called after the whole test run finished, right before returning the exit status to the system. """ # If requested by the user, track all unrun tests and add them to the lastfailed cache if session.config.option.treat_unrun_as_failed: unrun_tracker = session.config.pluginmanager.get_plugin("unrun_tracker_plugin") unrun_tracker.record_unrun_as_failed(session, exitstatus) ignite-0.5.1/tests/ignite/contrib/000077500000000000000000000000001465426447700171005ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/contrib/__init__.py000066400000000000000000000000201465426447700212010ustar00rootroot00000000000000# coding: utf-8 ignite-0.5.1/tests/ignite/contrib/conftest.py000066400000000000000000000002011465426447700212700ustar00rootroot00000000000000import pytest @pytest.fixture() def visdom_offline_logfile(dirname): log_file = dirname / "logs.visdom" yield log_file ignite-0.5.1/tests/ignite/contrib/engines/000077500000000000000000000000001465426447700205305ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/contrib/engines/__init__.py000066400000000000000000000000001465426447700226270ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/contrib/engines/test_common.py000066400000000000000000000602261465426447700234370ustar00rootroot00000000000000import os import sys from unittest.mock import call, MagicMock import pytest import torch import torch.nn as nn from torch.utils.data.distributed import DistributedSampler import ignite.distributed as idist import ignite.handlers as handlers from ignite.contrib.engines.common import ( _setup_logging, add_early_stopping_by_val_score, gen_save_best_models_by_val_score, save_best_model_by_val_score, setup_any_logging, setup_clearml_logging, setup_common_training_handlers, setup_mlflow_logging, setup_neptune_logging, setup_plx_logging, setup_tb_logging, setup_trains_logging, setup_visdom_logging, setup_wandb_logging, ) from ignite.engine import Engine, Events from ignite.handlers import DiskSaver, TerminateOnNan class DummyModel(nn.Module): def __init__(self): super(DummyModel, self).__init__() self.net = nn.Linear(1, 1) def forward(self, x): return self.net(x) def _test_setup_common_training_handlers( dirname, device, rank=0, local_rank=0, distributed=False, lr_scheduler=None, save_handler=None, output_transform=lambda loss: loss, ): lr = 0.01 step_size = 100 gamma = 0.5 num_iters = 100 num_epochs = 10 model = DummyModel().to(device) if distributed and "cuda" in torch.device(device).type: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank) optimizer = torch.optim.SGD(model.parameters(), lr=lr) if lr_scheduler is None: lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) elif isinstance(lr_scheduler, str) and lr_scheduler == "ignite|LRScheduler": from ignite.handlers import LRScheduler lr_scheduler = LRScheduler(torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)) elif isinstance(lr_scheduler, str) and lr_scheduler == "ignite": from ignite.handlers import PiecewiseLinear milestones_values = [(0, 0.0), (step_size, lr), (num_iters * (num_epochs - 1), 0.0)] lr_scheduler = PiecewiseLinear(optimizer, param_name="lr", milestones_values=milestones_values) else: raise ValueError(f"Unknown lr_scheduler: {lr_scheduler}") def update_fn(engine, batch): optimizer.zero_grad() x = torch.tensor([batch], requires_grad=True, device=device) y_pred = model(x) loss = y_pred.mean() loss.backward() optimizer.step() return output_transform(loss) train_sampler = None if distributed and idist.get_world_size() > 1: train_sampler = MagicMock(spec=DistributedSampler) train_sampler.set_epoch = MagicMock() trainer = Engine(update_fn) setup_common_training_handlers( trainer, train_sampler=train_sampler, to_save={"model": model, "optimizer": optimizer}, save_every_iters=75, output_path=dirname, save_handler=save_handler, lr_scheduler=lr_scheduler, with_gpu_stats=False, output_names=["batch_loss"], with_pbars=True, with_pbar_on_iters=True, log_every_iters=50, ) data = [i * 0.1 for i in range(num_iters)] trainer.run(data, max_epochs=num_epochs) # check handlers handlers = trainer._event_handlers[Events.ITERATION_COMPLETED] for cls in [ TerminateOnNan, ]: assert any([isinstance(h[0], cls) for h in handlers]), f"{handlers}" assert "batch_loss" in trainer.state.metrics # Check saved checkpoint if rank == 0: if save_handler is not None: dirname = save_handler.dirname checkpoints = list(os.listdir(dirname)) assert len(checkpoints) == 1 for v in [ "training_checkpoint", ]: assert any([v in c for c in checkpoints]) # Check LR scheduling assert optimizer.param_groups[0]["lr"] <= lr * gamma ** ( (num_iters * num_epochs - 1) // step_size ), f"{optimizer.param_groups[0]['lr']} vs {lr * gamma ** ((num_iters * num_epochs - 1) // step_size)}" def test_asserts_setup_common_training_handlers(): trainer = Engine(lambda e, b: None) with pytest.raises( ValueError, match=r"If to_save argument is provided then output_path or save_handler arguments should be also defined", ): setup_common_training_handlers(trainer, to_save={}) with pytest.raises(ValueError, match=r"Arguments output_path and save_handler are mutually exclusive"): setup_common_training_handlers(trainer, to_save={}, output_path="abc", save_handler=lambda c, f, m: None) with pytest.warns(UserWarning, match=r"Argument train_sampler is a distributed sampler"): train_sampler = MagicMock(spec=DistributedSampler) setup_common_training_handlers(trainer, train_sampler=train_sampler) if not torch.cuda.is_available(): with pytest.raises(RuntimeError, match=r"This contrib module requires available GPU"): setup_common_training_handlers(trainer, with_gpu_stats=True) with pytest.raises(TypeError, match=r"Unhandled type of update_function's output."): trainer = Engine(lambda e, b: None) setup_common_training_handlers( trainer, output_names=["loss"], with_pbar_on_iters=False, with_pbars=False, with_gpu_stats=False, stop_on_nan=False, clear_cuda_cache=False, ) trainer.run([1]) def test_no_warning_with_train_sampler(recwarn): from torch.utils.data import RandomSampler trainer = Engine(lambda e, b: None) train_sampler = RandomSampler([0, 1, 2]) setup_common_training_handlers(trainer, train_sampler=train_sampler) assert len(recwarn) == 0, recwarn.pop() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" not in os.environ, reason="Should have more than 1 worker") def test_assert_setup_common_training_handlers_wrong_train_sampler(distributed_context_single_node_gloo): trainer = Engine(lambda e, b: None) from torch.utils.data.sampler import RandomSampler with pytest.raises(TypeError, match=r"Train sampler should be torch DistributedSampler"): train_sampler = RandomSampler([0, 1, 2, 3]) setup_common_training_handlers(trainer, train_sampler) def test_setup_common_training_handlers(dirname, capsys): _test_setup_common_training_handlers(dirname, device="cpu") # Check epoch-wise pbar captured = capsys.readouterr() out = captured.err.split("\r") out = list(map(lambda x: x.strip(), out)) out = list(filter(None, out)) assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}" _test_setup_common_training_handlers(dirname, device="cpu", output_transform=lambda loss: [loss]) # Check epoch-wise pbar captured = capsys.readouterr() out = captured.err.split("\r") out = list(map(lambda x: x.strip(), out)) out = list(filter(None, out)) assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}" _test_setup_common_training_handlers(dirname, device="cpu", output_transform=lambda loss: {"batch_loss": loss}) # Check epoch-wise pbar captured = capsys.readouterr() out = captured.err.split("\r") out = list(map(lambda x: x.strip(), out)) out = list(filter(None, out)) assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}" def test_setup_common_training_handlers_using_save_handler(dirname, capsys): save_handler = DiskSaver(dirname=dirname, require_empty=False) _test_setup_common_training_handlers(dirname=None, device="cpu", save_handler=save_handler) # Check epoch-wise pbar captured = capsys.readouterr() out = captured.err.split("\r") out = list(map(lambda x: x.strip(), out)) out = list(filter(None, out)) assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}" def test_save_best_model_by_val_score(dirname): acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.5, 0.6, 0.61, 0.7, 0.5] def setup_trainer(): trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) model = DummyModel() @trainer.on(Events.EPOCH_COMPLETED) def validate(engine): evaluator.run([0, 1]) @evaluator.on(Events.EPOCH_COMPLETED) def set_eval_metric(engine): acc = acc_scores[trainer.state.epoch - 1] engine.state.metrics = {"acc": acc, "loss": 1 - acc} return trainer, evaluator, model trainer, evaluator, model = setup_trainer() save_best_model_by_val_score(dirname, evaluator, model, metric_name="acc", n_saved=2, trainer=trainer) trainer.run([0, 1], max_epochs=len(acc_scores)) assert set(os.listdir(dirname)) == {"best_model_8_val_acc=0.6100.pt", "best_model_9_val_acc=0.7000.pt"} for fname in os.listdir(dirname): os.unlink(f"{dirname}/{fname}") trainer, evaluator, model = setup_trainer() save_best_model_by_val_score( dirname, evaluator, model, metric_name="loss", n_saved=2, trainer=trainer, score_sign=-1.0 ) trainer.run([0, 1], max_epochs=len(acc_scores)) assert set(os.listdir(dirname)) == {"best_model_8_val_loss=-0.3900.pt", "best_model_9_val_loss=-0.3000.pt"} def test_gen_save_best_models_by_val_score(): acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.5, 0.6, 0.61, 0.7, 0.5] loss_scores = [0.9, 0.8, 0.7, 0.6, 0.7, 0.5, 0.4, 0.39, 0.3, 0.5] def setup_trainer(): trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) model = DummyModel() @trainer.on(Events.EPOCH_COMPLETED) def validate(engine): evaluator.run([0, 1]) @evaluator.on(Events.EPOCH_COMPLETED) def set_eval_metric(engine): acc = acc_scores[trainer.state.epoch - 1] loss = loss_scores[trainer.state.epoch - 1] engine.state.metrics = {"acc": acc, "loss": loss} return trainer, evaluator, model trainer, evaluator, model = setup_trainer() save_handler = MagicMock() gen_save_best_models_by_val_score( save_handler, evaluator, {"a": model, "b": model}, metric_name="acc", n_saved=2, trainer=trainer ) trainer.run([0, 1], max_epochs=len(acc_scores)) assert save_handler.call_count == len(acc_scores) - 2 # 2 score values (0.3 and 0.5) are not the best obj_to_save = {"a": model.state_dict(), "b": model.state_dict()} save_handler.assert_has_calls( [ call( obj_to_save, f"best_checkpoint_{e}_val_acc={p:.4f}.pt", dict([("basename", "best_checkpoint"), ("score_name", "val_acc"), ("priority", p)]), ) for e, p in zip([1, 2, 3, 4, 6, 7, 8, 9], [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.61, 0.7]) ], any_order=True, ) trainer, evaluator, model = setup_trainer() save_handler = MagicMock() gen_save_best_models_by_val_score( save_handler, evaluator, {"a": model, "b": model}, metric_name="loss", n_saved=2, trainer=trainer, score_sign=-1.0, ) trainer.run([0, 1], max_epochs=len(acc_scores)) assert save_handler.call_count == len(acc_scores) - 2 # 2 score values (-0.7 and -0.5) are not the best obj_to_save = {"a": model.state_dict(), "b": model.state_dict()} save_handler.assert_has_calls( [ call( obj_to_save, f"best_checkpoint_{e}_val_loss={p:.4f}.pt", dict([("basename", "best_checkpoint"), ("score_name", "val_loss"), ("priority", p)]), ) for e, p in zip([1, 2, 3, 4, 6, 7, 8, 9], [-0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.39, -0.3]) ], any_order=True, ) def test_add_early_stopping_by_val_score(): acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.3, 0.2, 0.1, 0.1, 0.0] def setup_trainer(): trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) @trainer.on(Events.EPOCH_COMPLETED) def validate(engine): evaluator.run([0, 1]) @evaluator.on(Events.EPOCH_COMPLETED) def set_eval_metric(engine): acc = acc_scores[trainer.state.epoch - 1] engine.state.metrics = {"acc": acc, "loss": 1 - acc} return trainer, evaluator trainer, evaluator = setup_trainer() add_early_stopping_by_val_score(patience=3, evaluator=evaluator, trainer=trainer, metric_name="acc") state = trainer.run([0, 1], max_epochs=len(acc_scores)) assert state.epoch == 7 trainer, evaluator = setup_trainer() add_early_stopping_by_val_score( patience=3, evaluator=evaluator, trainer=trainer, metric_name="loss", score_sign=-1.0 ) state = trainer.run([0, 1], max_epochs=len(acc_scores)) assert state.epoch == 7 def test_deprecated_setup_any_logging(): with pytest.raises(DeprecationWarning, match=r"deprecated since version 0.4.0"): setup_any_logging(None, None, None, None, None, None) def test__setup_logging_wrong_args(): with pytest.raises(TypeError, match=r"Argument optimizers should be either a single optimizer or"): _setup_logging(MagicMock(), MagicMock(), "abc", MagicMock(), 1) with pytest.raises(TypeError, match=r"Argument evaluators should be either a single engine or"): _setup_logging(MagicMock(), MagicMock(), MagicMock(spec=torch.optim.SGD), "abc", 1) def _test_setup_logging( setup_logging_fn, kwargs_dict, output_handler_cls, opt_params_handler_cls, with_eval=True, with_optim=True, as_class=False, log_every_iters=1, ): trainer = Engine(lambda e, b: b) evaluators = None optimizers = None if with_eval: evaluator = Engine(lambda e, b: None) acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.3, 0.2, 0.1, 0.1, 0.0] @trainer.on(Events.EPOCH_COMPLETED) def validate(engine): evaluator.run([0, 1]) @evaluator.on(Events.EPOCH_COMPLETED) def set_eval_metric(engine): engine.state.metrics = {"acc": acc_scores[trainer.state.epoch - 1]} evaluators = {"validation": evaluator} if as_class: evaluators = evaluators["validation"] if with_optim: t = torch.tensor([0]) optimizers = {"optimizer": torch.optim.SGD([t], lr=0.01)} if as_class: optimizers = optimizers["optimizer"] kwargs_dict["trainer"] = trainer kwargs_dict["optimizers"] = optimizers kwargs_dict["evaluators"] = evaluators kwargs_dict["log_every_iters"] = log_every_iters x_logger = setup_logging_fn(**kwargs_dict) handlers = trainer._event_handlers[Events.ITERATION_COMPLETED] for cls in [ output_handler_cls, ]: assert any([isinstance(h[0], cls) for h in handlers]), f"{handlers}" if with_optim: handlers = trainer._event_handlers[Events.ITERATION_STARTED] for cls in [ opt_params_handler_cls, ]: assert any([isinstance(h[0], cls) for h in handlers]), f"{handlers}" if with_eval: handlers = evaluator._event_handlers[Events.COMPLETED] for cls in [ output_handler_cls, ]: assert any([isinstance(h[0], cls) for h in handlers]), f"{handlers}" data = [0, 1, 2] trainer.run(data, max_epochs=10) if "output_path" in kwargs_dict: tb_files = list(os.listdir(kwargs_dict["output_path"])) assert len(tb_files) == 1 for v in [ "events", ]: assert any([v in c for c in tb_files]), f"{tb_files}" return x_logger def test_setup_tb_logging(dirname): tb_logger = _test_setup_logging( setup_logging_fn=setup_tb_logging, kwargs_dict={"output_path": dirname / "t1"}, output_handler_cls=handlers.tensorboard_logger.OutputHandler, opt_params_handler_cls=handlers.tensorboard_logger.OptimizerParamsHandler, with_eval=False, with_optim=False, ) tb_logger.close() tb_logger = _test_setup_logging( setup_logging_fn=setup_tb_logging, kwargs_dict={"output_path": dirname / "t2"}, output_handler_cls=handlers.tensorboard_logger.OutputHandler, opt_params_handler_cls=handlers.tensorboard_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) tb_logger.close() tb_logger = _test_setup_logging( setup_logging_fn=setup_tb_logging, kwargs_dict={"output_path": dirname / "t3"}, output_handler_cls=handlers.tensorboard_logger.OutputHandler, opt_params_handler_cls=handlers.tensorboard_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, as_class=True, log_every_iters=None, ) tb_logger.close() @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_setup_visdom_logging(visdom_offline_logfile): vis_logger = _test_setup_logging( setup_logging_fn=setup_visdom_logging, kwargs_dict={"offline": True, "log_to_filename": visdom_offline_logfile}, output_handler_cls=handlers.visdom_logger.OutputHandler, opt_params_handler_cls=handlers.visdom_logger.OptimizerParamsHandler, with_eval=False, with_optim=False, ) vis_logger.close() vis_logger = _test_setup_logging( setup_logging_fn=setup_visdom_logging, kwargs_dict={"offline": True, "log_to_filename": visdom_offline_logfile}, output_handler_cls=handlers.visdom_logger.OutputHandler, opt_params_handler_cls=handlers.visdom_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) vis_logger.close() def test_setup_plx_logging(): os.environ["POLYAXON_NO_OP"] = "1" _test_setup_logging( setup_logging_fn=setup_plx_logging, kwargs_dict={}, output_handler_cls=handlers.polyaxon_logger.OutputHandler, opt_params_handler_cls=handlers.polyaxon_logger.OptimizerParamsHandler, with_eval=False, with_optim=False, ) _test_setup_logging( setup_logging_fn=setup_plx_logging, kwargs_dict={}, output_handler_cls=handlers.polyaxon_logger.OutputHandler, opt_params_handler_cls=handlers.polyaxon_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_setup_mlflow_logging(dirname): mlf_logger = _test_setup_logging( setup_logging_fn=setup_mlflow_logging, kwargs_dict={"tracking_uri": str(dirname / "p1")}, output_handler_cls=handlers.mlflow_logger.OutputHandler, opt_params_handler_cls=handlers.mlflow_logger.OptimizerParamsHandler, with_eval=False, with_optim=False, ) mlf_logger.close() mlf_logger = _test_setup_logging( setup_logging_fn=setup_mlflow_logging, kwargs_dict={"tracking_uri": str(dirname / "p2")}, output_handler_cls=handlers.mlflow_logger.OutputHandler, opt_params_handler_cls=handlers.mlflow_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) mlf_logger.close() def test_setup_wandb_logging(dirname): from unittest.mock import patch with patch("ignite.contrib.engines.common.WandBLogger") as _: setup_wandb_logging(MagicMock()) def test_setup_clearml_logging(): handlers.clearml_logger.ClearMLLogger.set_bypass_mode(True) with pytest.warns(UserWarning, match=r"running in bypass mode"): clearml_logger = _test_setup_logging( setup_logging_fn=setup_clearml_logging, kwargs_dict={}, output_handler_cls=handlers.clearml_logger.OutputHandler, opt_params_handler_cls=handlers.clearml_logger.OptimizerParamsHandler, with_eval=False, with_optim=False, ) clearml_logger.close() clearml_logger = _test_setup_logging( setup_logging_fn=setup_clearml_logging, kwargs_dict={}, output_handler_cls=handlers.clearml_logger.OutputHandler, opt_params_handler_cls=handlers.clearml_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) clearml_logger.close() clearml_logger = _test_setup_logging( setup_logging_fn=setup_trains_logging, kwargs_dict={}, output_handler_cls=handlers.clearml_logger.OutputHandler, opt_params_handler_cls=handlers.clearml_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) clearml_logger.close() with pytest.warns(UserWarning, match="setup_trains_logging was renamed to setup_clearml_logging"): clearml_logger = _test_setup_logging( setup_logging_fn=setup_trains_logging, kwargs_dict={}, output_handler_cls=handlers.clearml_logger.OutputHandler, opt_params_handler_cls=handlers.clearml_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) clearml_logger.close() def test_setup_neptune_logging(dirname): npt_logger = _test_setup_logging( setup_logging_fn=setup_neptune_logging, kwargs_dict={"mode": "offline"}, output_handler_cls=handlers.neptune_logger.OutputHandler, opt_params_handler_cls=handlers.neptune_logger.OptimizerParamsHandler, with_eval=False, with_optim=False, ) npt_logger.close() npt_logger = _test_setup_logging( setup_logging_fn=setup_neptune_logging, kwargs_dict={"mode": "offline"}, output_handler_cls=handlers.neptune_logger.OutputHandler, opt_params_handler_cls=handlers.neptune_logger.OptimizerParamsHandler, with_eval=True, with_optim=True, ) npt_logger.close() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(dirname, distributed_context_single_node_nccl): local_rank = distributed_context_single_node_nccl["local_rank"] device = idist.device() _test_setup_common_training_handlers(dirname, device, rank=local_rank, local_rank=local_rank, distributed=True) test_add_early_stopping_by_val_score() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(dirname, distributed_context_single_node_gloo): device = idist.device() local_rank = distributed_context_single_node_gloo["local_rank"] _test_setup_common_training_handlers(dirname, device, rank=local_rank, local_rank=local_rank, distributed=True) _test_setup_common_training_handlers( dirname, device, rank=local_rank, local_rank=local_rank, distributed=True, lr_scheduler="ignite|LRScheduler" ) _test_setup_common_training_handlers( dirname, device, rank=local_rank, local_rank=local_rank, distributed=True, lr_scheduler="ignite" ) test_add_early_stopping_by_val_score() @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(dirname, distributed_context_multi_node_gloo): device = idist.device() rank = distributed_context_multi_node_gloo["rank"] _test_setup_common_training_handlers(dirname, device, rank=rank) test_add_early_stopping_by_val_score() @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(dirname, distributed_context_multi_node_nccl): local_rank = distributed_context_multi_node_nccl["local_rank"] rank = distributed_context_multi_node_nccl["rank"] device = idist.device() _test_setup_common_training_handlers(dirname, device, rank=rank, local_rank=local_rank, distributed=True) test_add_early_stopping_by_val_score() ignite-0.5.1/tests/ignite/contrib/engines/test_tbptt.py000066400000000000000000000074651465426447700233120ustar00rootroot00000000000000# coding: utf-8 import unittest.mock as mock import pytest import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from ignite.contrib.engines import create_supervised_tbptt_trainer, Tbptt_Events from ignite.contrib.engines.tbptt import _detach_hidden def test_detach_hidden_RNN(): # Create hidden vector (in tuple) X = torch.ones(2, 3, 4) model = nn.RNN(4, 1) _, hidden = model(X) # Function to test hidden_ = _detach_hidden(hidden) assert hidden_.grad_fn is None # properly detached assert (hidden == hidden_).all().item() == 1 # Equal values def test_detach_hidden_LSTM(): # Create hidden vector (in tuple) X = torch.ones(2, 3, 4) model = nn.LSTM(4, 1) _, hidden = model(X) # Function to test hidden_ = _detach_hidden(hidden) for h, h_ in zip(hidden, hidden_): assert h_.grad_fn is None # properly detached assert (h == h_).all().item() == 1 # Equal values def test_detach_hidden_raise(): with pytest.raises(TypeError): _detach_hidden(0) @mock.patch("ignite.contrib.engines.tbptt._detach_hidden") def test_create_supervised_tbptt_trainer_callcounts(mock_detach_hidden): # Mocking objects model = mock.MagicMock() # Necessary to unpack output model.return_value = (1, 1) optimizer = mock.MagicMock() loss = mock.MagicMock() trainer = create_supervised_tbptt_trainer(model, optimizer, loss, tbtt_step=2) # Adding two mock handles to the trainer to monitor that TBPTT events are # called correctly handle_started = mock.MagicMock() trainer.add_event_handler(Tbptt_Events.TIME_ITERATION_STARTED, handle_started) handle_completed = mock.MagicMock() trainer.add_event_handler(Tbptt_Events.TIME_ITERATION_COMPLETED, handle_completed) # Fake data X = torch.ones(6, 2, 1) y = torch.ones(6, 2, 1) data = [(X, y)] # Running trainer trainer.run(data) # Verifications assert handle_started.call_count == 3 assert handle_completed.call_count == 3 assert mock_detach_hidden.call_count == 2 assert model.call_count == 3 assert loss.call_count == 3 assert optimizer.zero_grad.call_count == 3 assert optimizer.step.call_count == 3 n_args_tuple = tuple(len(args) for args, kwargs in model.call_args_list) assert n_args_tuple == (1, 2, 2) def _test_create_supervised_tbptt_trainer(device): # Defining dummy recurrent model with zero weights model = nn.RNN(1, 1, bias=False) model.to(device) # Move model before creating optimizer for p in model.parameters(): p.data.zero_() # Set some mock on forward to monitor forward_mock = mock.MagicMock() forward_mock.return_value = None model.register_forward_hook(forward_mock) # Defning optimizer and trainer optimizer = optim.SGD(model.parameters(), 1) trainer = create_supervised_tbptt_trainer(model, optimizer, F.mse_loss, tbtt_step=2, device=device) # Fake data X = torch.ones(6, 2, 1) y = torch.ones(6, 2, 1) data = [(X, y)] # Running trainer trainer.run(data) # If tbptt is not use (one gradient update), the hidden to hidden weight # should stay zero assert not model.weight_hh_l0.item() == pytest.approx(0) # Cheking forward calls assert forward_mock.call_count == 3 for i in range(3): inputs = forward_mock.call_args_list[i][0][1] if i == 0: assert len(inputs) == 1 else: assert len(inputs) == 2 x, h = inputs assert h.is_leaf def test_create_supervised_tbptt_trainer_with_cpu(): _test_create_supervised_tbptt_trainer("cpu") @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_tbptt_trainer_on_cuda(): _test_create_supervised_tbptt_trainer("cuda") ignite-0.5.1/tests/ignite/contrib/handlers/000077500000000000000000000000001465426447700207005ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/contrib/handlers/test_warnings_of_deprecation_of_handlers.py000066400000000000000000000055661465426447700315620ustar00rootroot00000000000000from importlib import __import__ import pytest @pytest.mark.parametrize( "log_module,fromlist", [ ("mlflow_logger", ["MLflowLogger", "OptimizerParamsHandler", "OutputHandler"]), ("polyaxon_logger", ["PolyaxonLogger", "OutputHandler", "OptimizerParamsHandler"]), ("wandb_logger", ["WandBLogger", "OutputHandler", "OptimizerParamsHandler"]), ("lr_finder", ["FastaiLRFinder"]), ("tqdm_logger", ["ProgressBar"]), ( "clearml_logger", [ "ClearMLLogger", "ClearMLSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "WeightsHistHandler", "GradsScalarHandler", "GradsHistHandler", ], ), ( "tensorboard_logger", [ "TensorboardLogger", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "WeightsHistHandler", "GradsScalarHandler", "GradsHistHandler", ], ), ( "visdom_logger", [ "VisdomLogger", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", ], ), ( "neptune_logger", [ "NeptuneLogger", "NeptuneSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", ], ), ( "base_logger", [ "BaseHandler", "BaseWeightsHandler", "BaseOptimizerParamsHandler", "BaseOutputHandler", "BaseWeightsScalarHandler", "BaseLogger", ], ), ( "time_profilers", [ "BasicTimeProfiler", "HandlersTimeProfiler", ], ), ( "param_scheduler", [ "ConcatScheduler", "CosineAnnealingScheduler", "LinearCyclicalScheduler", "LRScheduler", "ParamGroupScheduler", "ParamScheduler", "PiecewiseLinear", "CyclicalScheduler", "create_lr_scheduler_with_warmup", ], ), ], ) def test_imports(log_module, fromlist): with pytest.warns(DeprecationWarning, match="will be removed in version 0.6.0"): imported = __import__(f"ignite.contrib.handlers.{log_module}", globals(), locals(), fromlist) for attr in fromlist: getattr(imported, attr) ignite-0.5.1/tests/ignite/contrib/metrics/000077500000000000000000000000001465426447700205465ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/contrib/metrics/test_warnings_of_deprecation_of_metrics.py000066400000000000000000000033171465426447700312660ustar00rootroot00000000000000from importlib import __import__ import pytest @pytest.mark.parametrize( "log_module,fromlist", [ ("average_precision", ["AveragePrecision"]), ("cohen_kappa", ["CohenKappa"]), ("gpu_info", ["GpuInfo"]), ("precision_recall_curve", ["PrecisionRecallCurve"]), ("roc_auc", ["ROC_AUC", "RocCurve"]), ("regression.canberra_metric", ["CanberraMetric"]), ("regression.fractional_absolute_error", ["FractionalAbsoluteError"]), ("regression.fractional_bias", ["FractionalBias"]), ("regression.geometric_mean_absolute_error", ["GeometricMeanAbsoluteError"]), ("regression.geometric_mean_relative_absolute_error", ["GeometricMeanRelativeAbsoluteError"]), ("regression.manhattan_distance", ["ManhattanDistance"]), ("regression.maximum_absolute_error", ["MaximumAbsoluteError"]), ("regression.mean_absolute_relative_error", ["MeanAbsoluteRelativeError"]), ("regression.mean_error", ["MeanError"]), ("regression.mean_normalized_bias", ["MeanNormalizedBias"]), ("regression.median_absolute_error", ["MedianAbsoluteError"]), ("regression.median_absolute_percentage_error", ["MedianAbsolutePercentageError"]), ("regression.median_relative_absolute_error", ["MedianRelativeAbsoluteError"]), ("regression.r2_score", ["R2Score"]), ("regression.wave_hedges_distance", ["WaveHedgesDistance"]), ], ) def test_imports(log_module, fromlist): with pytest.warns(DeprecationWarning, match="will be removed in version 0.6.0"): imported = __import__(f"ignite.contrib.metrics.{log_module}", globals(), locals(), fromlist) for attr in fromlist: getattr(imported, attr) ignite-0.5.1/tests/ignite/distributed/000077500000000000000000000000001465426447700177625ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/distributed/__init__.py000066400000000000000000000000001465426447700220610ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/distributed/check_idist_parallel.py000066400000000000000000000072661465426447700244740ustar00rootroot00000000000000import argparse import torch import ignite.distributed as idist def training(local_rank, config, **kwargs): import time time.sleep(idist.get_rank() * 0.1) print(idist.get_rank(), ": run with config:", config, "- kwargs:", kwargs, f"- backend={idist.backend()}") t = torch.tensor([idist.get_rank()], device=idist.device()) t = idist.all_reduce(t) t = t.item() ws = idist.get_world_size() assert t == ws * (ws - 1) / 2, f"{t} vs {ws}" assert local_rank == idist.get_local_rank() # Test init method: if idist.model_name() == "native-dist": from ignite.distributed.utils import _model true_init_method = config.get("true_init_method", None) assert true_init_method is not None, true_init_method assert _model._init_method == true_init_method if __name__ == "__main__": """ Usage: - No distributed configuration: ``` python tests/ignite/distributed/check_idist_parallel.py ``` - Launch 4 procs using gloo backend with `torchrun`: ``` torchrun --nproc_per_node=4 tests/ignite/distributed/check_idist_parallel.py --backend=gloo ``` - Launch 2 procs in 2 nodes using gloo backend with `torchrun` or `torch.distributed.launch`: ``` bash -c "torchrun --nnodes=2 --node_rank=0 \ --master_addr=localhost --master_port=3344 --nproc_per_node=2 \ tests/ignite/distributed/check_idist_parallel.py --backend=gloo &" \ && bash -c "torchrun --nnodes=2 --node_rank=1 \ --master_addr=localhost --master_port=3344 --nproc_per_node=2 \ tests/ignite/distributed/check_idist_parallel.py --backend=gloo &" ``` - Spawn 4 procs in single node using gloo backend: ``` python tests/ignite/distributed/check_idist_parallel.py --backend=gloo --nproc_per_node=4 ``` - Spawn 2 procs in 2 nodes using gloo backend: ``` bash -c "python tests/ignite/distributed/check_idist_parallel.py --backend=gloo \ --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=localhost --master_port=3344 &" \ && bash -c "python tests/ignite/distributed/check_idist_parallel.py --backend=gloo \ --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=localhost --master_port=3344 &" ``` - Spawn 8 procs in single node using xla-tpu backend: ``` python tests/ignite/distributed/check_idist_parallel.py --backend=xla-tpu --nproc_per_node=8 ``` """ parser = argparse.ArgumentParser("Check idist.Parallel") parser.add_argument("--backend", type=str, default=None) parser.add_argument("--nproc_per_node", type=int, default=None) parser.add_argument("--nnodes", type=int, default=None) parser.add_argument("--node_rank", type=int, default=None) parser.add_argument("--master_addr", type=str, default=None) parser.add_argument("--master_port", type=str, default=None) parser.add_argument("--init_method", type=str, default=None) args = parser.parse_args() config = { "model": "resnet18", "lr": 0.01, } if args.backend in ["gloo", "nccl"]: config["true_init_method"] = args.init_method if args.init_method is not None else "env://" dist_config = dict( nproc_per_node=args.nproc_per_node, nnodes=args.nnodes, node_rank=args.node_rank, master_addr=args.master_addr, master_port=args.master_port, ) if args.init_method is not None: dist_config["init_method"] = args.init_method with idist.Parallel(backend=args.backend, **dist_config) as parallel: parallel.run(training, config, a=1, b=2) ignite-0.5.1/tests/ignite/distributed/comp_models/000077500000000000000000000000001465426447700222635ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/distributed/comp_models/__init__.py000066400000000000000000000000001465426447700243620ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/distributed/comp_models/test_base.py000066400000000000000000000116561465426447700246170ustar00rootroot00000000000000import pytest import torch from ignite.distributed.comp_models.base import _SerialModel, _torch_version_gt_112, ComputationModel def test_serial_model(): _SerialModel.create_from_backend() model = _SerialModel.create_from_context() assert model.get_local_rank() == 0 assert model.get_rank() == 0 assert model.get_world_size() == 1 assert model.get_nproc_per_node() == 1 assert model.get_nnodes() == 1 assert model.get_node_rank() == 0 if torch.cuda.is_available(): assert model.device().type == "cuda" elif _torch_version_gt_112 and torch.backends.mps.is_available(): assert model.device().type == "mps" else: assert model.device().type == "cpu" assert model.backend() is None model.finalize() with pytest.raises(NotImplementedError, match=r"Serial computation model does not implement spawn method"): model.spawn() model.all_reduce(1) model.all_gather(1) model.broadcast(1) assert model._do_all_reduce(torch.tensor(1)) == torch.tensor(1) assert model._do_all_gather(torch.tensor(1)) == torch.tensor(1) assert model._do_broadcast(torch.tensor(1), src=0) == torch.tensor(1) model.barrier() def test__encode_str__decode_str(): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") s = "test-abcedfg" encoded_s = ComputationModel._encode_str(s, device, 1024) assert isinstance(encoded_s, torch.Tensor) and encoded_s.shape == (1, 1025) decoded_s = ComputationModel._decode_str(encoded_s) assert isinstance(decoded_s, list) and len(decoded_s) == 1 assert decoded_s[0] == s def test__encode_input_data(): encoded_msg = ComputationModel._encode_input_data(None, is_src=True) assert encoded_msg == [-1] * 512 encoded_msg = ComputationModel._encode_input_data(12.0, is_src=True) assert encoded_msg == [1] + [-1] * 511 encoded_msg = ComputationModel._encode_input_data("abc", is_src=True) assert encoded_msg == [2] + [-1] * 511 t = torch.rand(2, 512, 32, 32, 64) encoded_msg = ComputationModel._encode_input_data(t, is_src=True) dtype_str = str(t.dtype) true_msg = [0, 5, 2, 512, 32, 32, 64, len(dtype_str), *list(bytearray(dtype_str, "utf-8"))] assert encoded_msg == true_msg + [-1] * (512 - len(true_msg)) t = torch.randint(-1235, 1233, size=(2, 512, 32, 32, 64)) encoded_msg = ComputationModel._encode_input_data(t, is_src=True) dtype_str = str(t.dtype) true_msg = [0, 5, 2, 512, 32, 32, 64, len(dtype_str), *list(bytearray(dtype_str, "utf-8"))] assert encoded_msg == true_msg + [-1] * (512 - len(true_msg)) t = torch.tensor(12) encoded_msg = ComputationModel._encode_input_data(t, is_src=True) dtype_str = str(t.dtype) true_msg = [0, 0, len(dtype_str), *list(bytearray(dtype_str, "utf-8"))] assert encoded_msg == true_msg + [-1] * (512 - len(true_msg)) for t in [None, "abc", torch.rand(2, 512, 32, 32, 64), 12.34, object()]: encoded_msg = ComputationModel._encode_input_data(t, is_src=False) assert encoded_msg == [-1] * 512 def test__decode_as_placeholder(): device = torch.device("cpu") encoded_msg = [-1] * 512 encoded_msg[0] = 1 res = ComputationModel._decode_as_placeholder(encoded_msg, device) assert isinstance(res, float) and res == 0.0 encoded_msg = [-1] * 512 encoded_msg[0] = 2 res = ComputationModel._decode_as_placeholder(encoded_msg, device) assert isinstance(res, str) and res == "" encoded_msg = [-1] * 512 encoded_msg[0] = 0 encoded_msg[1 : 1 + 7] = [6, 2, 3, 4, 5, 6, 7] dtype_str = "torch.int64" payload = [len(dtype_str), *list(bytearray(dtype_str, "utf-8"))] encoded_msg[1 + 7 : 1 + 7 + len(payload)] = payload res = ComputationModel._decode_as_placeholder(encoded_msg, device) assert isinstance(res, torch.Tensor) and res.dtype == torch.int64 and res.shape == (2, 3, 4, 5, 6, 7) encoded_msg = [-1] * 512 with pytest.raises(RuntimeError, match="Internal error: unhandled dtype"): ComputationModel._decode_as_placeholder(encoded_msg, device) t = torch.rand(2, 512, 32, 32, 64) encoded_msg = ComputationModel._encode_input_data(t, True) res = ComputationModel._decode_as_placeholder(encoded_msg, device) assert isinstance(res, torch.Tensor) and res.dtype == t.dtype and res.shape == t.shape t = torch.tensor(12) encoded_msg = ComputationModel._encode_input_data(t, True) res = ComputationModel._decode_as_placeholder(encoded_msg, device) assert isinstance(res, torch.Tensor) and res.dtype == t.dtype and res.shape == t.shape def test__setup_placeholder(): device = torch.device("cpu") from ignite.distributed.utils import _model for t in [torch.rand(2, 3, 4), "abc", 123.45]: data = _model._setup_placeholder(t, device, True) assert isinstance(data, type(t)) if isinstance(data, torch.Tensor): assert (data == t).all() else: assert data == t ignite-0.5.1/tests/ignite/distributed/comp_models/test_horovod.py000066400000000000000000000165241465426447700253640ustar00rootroot00000000000000import pytest import torch from ignite.distributed.comp_models import has_hvd_support if not has_hvd_support: pytest.skip("Skip if no Horovod package", allow_module_level=True) else: import horovod.torch as hvd from ignite.distributed.comp_models.horovod import _HorovodDistModel @pytest.mark.distributed def test__hvd_dist_model(): with pytest.raises(ValueError, match=r"Backend should be one of"): _HorovodDistModel.create_from_backend("abc") def _assert_model(model, true_conf): if "cuda" in true_conf["device"]: assert model.device() == torch.device(f"{true_conf['device']}:{true_conf['local_rank']}") else: assert model.device() == torch.device(true_conf["device"]) assert model.get_local_rank() == true_conf["local_rank"] assert model.get_rank() == true_conf["rank"] assert model.get_world_size() == true_conf["world_size"] assert model.get_node_rank() == true_conf["node_index"] assert model.get_nnodes() == true_conf["nnodes"] assert model.get_nproc_per_node() == true_conf["nproc_per_node"] def _test__hvd_dist_model_create_from_backend_no_dist(backend, true_device): model = _HorovodDistModel.create_from_backend(backend=backend) assert hvd.rank() > -1 _assert_model( model, { "device": true_device, "local_rank": 0, "rank": 0, "world_size": 1, "node_index": 0, "nnodes": 1, "nproc_per_node": 1, }, ) model.finalize() def _test__hvd_dist_model_create_from_backend_dist(backend, true_device): model = _HorovodDistModel.create_from_backend(backend=backend) assert hvd.rank() > -1 with pytest.raises(RuntimeError, match=r"Can not re-initialize Horovod if it is already initialized"): _HorovodDistModel.create_from_backend(backend=backend) _assert_model( model, { "device": true_device, "local_rank": hvd.local_rank(), "rank": hvd.rank(), "world_size": hvd.size(), "node_index": 0, "nnodes": 1, "nproc_per_node": hvd.local_size(), }, ) model.finalize() def _test__hvd_dist_model_create_from_context_no_dist(true_backend, true_device): with pytest.raises(ValueError, match=r"Horovod has not been initialized"): hvd.rank() assert _HorovodDistModel.create_from_context() is None hvd.init() true_conf = { "device": true_device, "local_rank": 0, "rank": 0, "world_size": 1, "node_index": 0, "nnodes": 1, "nproc_per_node": 1, } model = _HorovodDistModel.create_from_context() assert model.backend() == true_backend _assert_model(model, true_conf) hvd.shutdown() def _test__hvd_dist_model_create_from_context_dist(true_backend, true_device): assert _HorovodDistModel.create_from_context() is None hvd.init() lrank = hvd.local_rank() if torch.cuda.is_available(): torch.cuda.set_device(lrank) true_conf = { "device": true_device, "local_rank": lrank, "rank": hvd.rank(), "world_size": hvd.size(), "node_index": 0, "nnodes": 1, "nproc_per_node": hvd.local_size(), } model = _HorovodDistModel.create_from_context() assert model.backend() == true_backend _assert_model(model, true_conf) hvd.shutdown() @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() > 0, reason="Skip if has GPU") def test__hvd_dist_model_create_no_dist(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_create_from_backend_no_dist, ("horovod", "cpu"), np=1) gloo_hvd_executor(_test__hvd_dist_model_create_from_context_no_dist, ("horovod", "cpu"), np=1) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__hvd_dist_model_create_no_dist_cuda(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_create_from_backend_no_dist, ("horovod", "cuda"), np=1) gloo_hvd_executor(_test__hvd_dist_model_create_from_context_no_dist, ("horovod", "cuda"), np=1) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() > 0, reason="Skip if has GPU") def test__hvd_dist_model_create_dist_1(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_create_from_backend_dist, ("horovod", "cpu"), np=4) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() > 0, reason="Skip if has GPU") def test__hvd_dist_model_create_dist_2(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_create_from_context_dist, ("horovod", "cpu"), np=4) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__hvd_dist_model_create_dist_cuda_1(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_create_from_backend_dist, ("horovod", "cuda"), np=torch.cuda.device_count()) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__hvd_dist_model_create_dist_cuda_2(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_create_from_context_dist, ("horovod", "cuda"), np=torch.cuda.device_count()) def _test__hvd_dist_model_warning_index_less_localrank(): assert torch.cuda.is_available() assert _HorovodDistModel.create_from_context() is None hvd.init() # We deliberately incorrectly set cuda device to 0 torch.cuda.set_device(0) model = _HorovodDistModel.create_from_context() assert isinstance(model, _HorovodDistModel), f"{type(model)} vs _HorovodDistModel" if hvd.local_rank() == 1: with pytest.warns(UserWarning, match=r"Current device index is less than current local rank."): model.device() hvd.shutdown() @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Skip if less than 2 GPUs") def test__hvd_dist_model_warning_index_less_localrank(gloo_hvd_executor): gloo_hvd_executor(_test__hvd_dist_model_warning_index_less_localrank, (), np=torch.cuda.device_count()) def _test_dist_spawn_fn(local_rank, backend, world_size, device): from ignite.distributed.utils import _model assert hvd.rank() > -1 assert isinstance(_model, _HorovodDistModel), f"{type(_model)} vs _HorovodDistModel" assert _model.get_local_rank() == local_rank assert _model.get_world_size() == world_size assert _model.backend() == backend if "cuda" in device: assert _model.device() == torch.device(f"{device}:{local_rank}") else: assert _model.device() == torch.device(device) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() > 0, reason="Skip if has GPU") def test__hvd_dist_model_spawn(): num_workers_per_machine = 4 _HorovodDistModel.spawn( _test_dist_spawn_fn, args=("horovod", num_workers_per_machine, "cpu"), kwargs_dict={}, nproc_per_node=num_workers_per_machine, use_gloo=True, ) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__hvd_dist_model_spawn_cuda(): num_workers_per_machine = torch.cuda.device_count() _HorovodDistModel.spawn( _test_dist_spawn_fn, args=("horovod", num_workers_per_machine, "cuda"), kwargs_dict={}, nproc_per_node=num_workers_per_machine, use_gloo=True, ) ignite-0.5.1/tests/ignite/distributed/comp_models/test_native.py000066400000000000000000000624101465426447700251650ustar00rootroot00000000000000import os import pytest import torch import torch.distributed as dist from ignite.distributed.comp_models import has_native_dist_support if not has_native_dist_support: pytest.skip("Skip if no native dist support", allow_module_level=True) else: from ignite.distributed.comp_models.native import _expand_hostlist, _NativeDistModel, _setup_ddp_vars_from_slurm_env # tests from https://github.com/LLNL/py-hostlist/blob/master/hostlist/unittest_hostlist.py @pytest.mark.parametrize( "hostlist, expected", [ ("localhost", "localhost"), ("compute!:b24_[1-2].r", "compute!:b24_1.r,compute!:b24_2.r"), ("quartz[4-8]", "quartz4,quartz5,quartz6,quartz7,quartz8"), ("c1001a-[11,17]", "c1001a-11,c1001a-17"), ("c1001a-s[11,17]", "c1001a-s11,c1001a-s17"), ("c1009a-s17,c1010a-s11", "c1009a-s17,c1010a-s11"), ( "gpu-compute-on-demand-dy-g4dnxlarge-[1-4]", "gpu-compute-on-demand-dy-g4dnxlarge-1," "gpu-compute-on-demand-dy-g4dnxlarge-2," "gpu-compute-on-demand-dy-g4dnxlarge-3," "gpu-compute-on-demand-dy-g4dnxlarge-4", ), ( "node[18-19,1-16,21-22]", "node1,node2,node3,node4,node5," "node6,node7,node8,node9,node10," "node11,node12,node13,node14,node15," "node16,node18,node19,node21,node22", ), ( "node[4-8,12,16-20,22,24-26]", "node4,node5,node6,node7,node8," "node12,node16,node17,node18," "node19,node20,node22,node24," "node25,node26", ), ("machine2-[02-4]vm1", "machine2-02vm1,machine2-03vm1,machine2-04vm1"), ( "machine2-[02-3]vm1, machine4-[0003-5].vml2", "machine2-02vm1,machine2-03vm1,machine4-0003.vml2,machine4-0004.vml2,machine4-0005.vml2", ), ("machine2-[009-11]vm1", "machine2-009vm1,machine2-010vm1,machine2-011vm1"), ("node[1,2,3]", "node1,node2,node3"), ( "compute-b24-[1-3,5-9], compute-b25-[1,4,8],compute-b25-[2-9,13]", "compute-b24-1,compute-b24-2,compute-b24-3,compute-b24-5,compute-b24-6," "compute-b24-7,compute-b24-8,compute-b24-9,compute-b25-1,compute-b25-4," "compute-b25-8,compute-b25-2,compute-b25-3,compute-b25-4,compute-b25-5," "compute-b25-6,compute-b25-7,compute-b25-8,compute-b25-9,compute-b25-13", ), ], ) def test_expand_hostlist(hostlist, expected): assert _expand_hostlist(hostlist) == expected.split(",") def test_expand_hostlist_invalid(): with pytest.raises(ValueError, match=r"hostlist invalid"): _expand_hostlist("invalid[]") @pytest.mark.distributed def test__native_dist_model(): available_backends = _NativeDistModel.available_backends if dist.is_nccl_available(): assert "nccl" in available_backends else: assert "nccl" not in available_backends if dist.is_gloo_available(): assert "gloo" in available_backends else: assert "gloo" not in available_backends if dist.is_mpi_available(): assert "mpi" in available_backends else: assert "mpi" not in available_backends with pytest.raises(ValueError, match=r"Backend should be one of"): _NativeDistModel.create_from_backend("abc") @pytest.mark.distributed @pytest.mark.skipif(not dist.is_nccl_available(), reason="Skip if nccl not available") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test__native_nccl_but_no_gpu(mock_gpu_is_not_available): with pytest.raises(RuntimeError, match=r"Nccl backend is required but no cuda capable devices"): _NativeDistModel(backend="nccl") @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test__native_dist_model_create_from_backend_bad_config(): import os from datetime import timedelta os.environ["RANK"] = "1" with pytest.raises(RuntimeError, match=r"PyTorch distributed configuration should define env variables"): _NativeDistModel.create_from_backend(backend="gloo", timeout=timedelta(seconds=10)) del os.environ["RANK"] @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test__native_dist_model_create_from_backend_bad_slurm_config(): import os from datetime import timedelta os.environ["SLURM_JOB_ID"] = "1" with pytest.raises(RuntimeError, match=r"SLURM distributed configuration is missing"): _NativeDistModel.create_from_backend(backend="gloo", timeout=timedelta(seconds=10)) with pytest.raises(ValueError, match=r"Arguments rank and world_size should not be specified with SLURM"): _NativeDistModel.create_from_backend( backend="gloo", timeout=timedelta(seconds=10), rank=1, init_method="", world_size=1 ) os.environ["SLURM_PROCID"] = "0" os.environ["SLURM_LOCALID"] = "0" os.environ["SLURM_NTASKS"] = "1" os.environ["SLURM_JOB_NODELIST"] = "localhost" os.environ["SLURM_JOB_NUM_NODES"] = "1" os.environ["RANK"] = "1" with pytest.warns(UserWarning, match=r"We detected the following env variables"): model = _NativeDistModel.create_from_backend(backend="gloo", timeout=timedelta(seconds=10)) model.finalize() del os.environ["SLURM_JOB_ID"] del os.environ["SLURM_PROCID"] del os.environ["SLURM_LOCALID"] del os.environ["SLURM_NTASKS"] del os.environ["SLURM_JOB_NODELIST"] del os.environ["SLURM_JOB_NUM_NODES"] del os.environ["RANK"] def _assert_model(model, true_conf): assert model.device() == torch.device(true_conf["device"]) assert model.get_local_rank() == true_conf["local_rank"] assert model.get_rank() == true_conf["rank"] assert model.get_world_size() == true_conf["world_size"] assert model.get_node_rank() == true_conf["node_index"] assert model.get_nnodes() == true_conf["nnodes"] assert model.get_nproc_per_node() == true_conf["nproc_per_node"] def _test__native_dist_model_create_from_backend_no_dist(backend, true_device): from datetime import timedelta model = _NativeDistModel.create_from_backend(backend=backend, timeout=timedelta(seconds=20)) assert dist.is_available() and dist.is_initialized() assert dist.get_backend() == backend _assert_model( model, { "device": true_device, "local_rank": 0, "rank": 0, "world_size": 1, "node_index": 0, "nnodes": 1, "nproc_per_node": 1, }, ) model.finalize() def _test__native_dist_model_create_from_backend_dist(init_method, local_rank, rank, world_size, backend, true_device): import os from datetime import timedelta timeout = timedelta(seconds=20) os.environ["RANK"] = f"{rank}" assert "MASTER_ADDR" not in os.environ assert "MASTER_PORT" not in os.environ model = _NativeDistModel.create_from_backend(backend=backend, timeout=timeout, init_method=init_method) assert dist.is_available() and dist.is_initialized() assert dist.get_backend() == backend with pytest.raises(RuntimeError, match=r"Can not create new distributed process group if default one is"): _NativeDistModel.create_from_backend(backend=backend, timeout=timeout) _assert_model( model, { "device": true_device, "local_rank": local_rank, "rank": rank, "world_size": world_size, "node_index": 0, "nnodes": 1, "nproc_per_node": world_size, }, ) if init_method is None: assert model._init_method == "env://" else: assert model._init_method == init_method model.finalize() del os.environ["RANK"] assert "MASTER_ADDR" not in os.environ assert "MASTER_PORT" not in os.environ assert "RANK" not in os.environ def _test__native_dist_model_create_from_backend_slurm(local_rank, rank, world_size, backend, true_device): import os from datetime import timedelta timeout = timedelta(seconds=20) assert "MASTER_ADDR" not in os.environ assert "MASTER_PORT" not in os.environ del os.environ["WORLD_SIZE"] del os.environ["LOCAL_RANK"] os.environ["SLURM_JOB_ID"] = "15000" os.environ["SLURM_PROCID"] = str(rank) os.environ["SLURM_LOCALID"] = str(local_rank) os.environ["SLURM_NTASKS"] = str(world_size) os.environ["SLURM_JOB_NODELIST"] = "localhost" os.environ["SLURM_JOB_NUM_NODES"] = "1" model = _NativeDistModel.create_from_backend(backend=backend, timeout=timeout) assert dist.is_available() and dist.is_initialized() assert dist.get_backend() == backend with pytest.raises(RuntimeError, match=r"Can not create new distributed process group if default one is"): _NativeDistModel.create_from_backend(backend=backend, timeout=timeout) _assert_model( model, { "device": true_device, "local_rank": local_rank, "rank": rank, "world_size": world_size, "node_index": 0, "nnodes": 1, "nproc_per_node": world_size, }, ) model.finalize() del os.environ["SLURM_JOB_ID"] del os.environ["SLURM_PROCID"] del os.environ["SLURM_LOCALID"] del os.environ["SLURM_NTASKS"] del os.environ["SLURM_JOB_NODELIST"] del os.environ["SLURM_JOB_NUM_NODES"] assert "MASTER_ADDR" not in os.environ assert "MASTER_PORT" not in os.environ assert "RANK" not in os.environ os.environ["WORLD_SIZE"] = str(world_size) os.environ["LOCAL_RANK"] = str(local_rank) def _test__native_dist_model_create_from_context_no_local_rank(): if "LOCAL_RANK" in os.environ: del os.environ["LOCAL_RANK"] from ignite.distributed.comp_models.base import ComputationModel if ComputationModel._ext_local_rank is not None: ComputationModel._ext_local_rank = None with pytest.warns(UserWarning, match=r"Local rank information for native distributed setting will be initialized"): _NativeDistModel.create_from_context() def _test__native_dist_model_create_from_context_env_local_rank(true_conf): import os remove_lrank = False if "LOCAL_RANK" not in os.environ: os.environ["LOCAL_RANK"] = str(true_conf["local_rank"]) remove_lrank = True model = _NativeDistModel.create_from_context() _assert_model(model, true_conf) if remove_lrank: del os.environ["LOCAL_RANK"] def _test__native_dist_model_create_from_context_set_local_rank(true_conf): from ignite.distributed.comp_models.base import ComputationModel lrank = None if "LOCAL_RANK" in os.environ: lrank = os.environ["LOCAL_RANK"] del os.environ["LOCAL_RANK"] ComputationModel._ext_local_rank = true_conf["local_rank"] model = _NativeDistModel.create_from_context() _assert_model(model, true_conf) ComputationModel._ext_local_rank = None if lrank is not None: os.environ["LOCAL_RANK"] = lrank def _test__native_dist_model_create_from_context_no_dist(true_backend, true_device): assert _NativeDistModel.create_from_context() is None dist.init_process_group(true_backend, "tcp://0.0.0.0:2222", world_size=1, rank=0) dist.barrier() _test__native_dist_model_create_from_context_no_local_rank() true_conf = { "device": true_device, "local_rank": 0, "rank": 0, "world_size": 1, "node_index": 0, "nnodes": 1, "nproc_per_node": 1, } _test__native_dist_model_create_from_context_env_local_rank(true_conf) _test__native_dist_model_create_from_context_set_local_rank(true_conf) dist.destroy_process_group() def _test__native_dist_model_create_from_context_dist(local_rank, rank, world_size, true_backend, true_device): assert _NativeDistModel.create_from_context() is None dist.init_process_group(true_backend, "tcp://0.0.0.0:2222", world_size=world_size, rank=rank) dist.barrier() if torch.cuda.is_available(): torch.cuda.set_device(local_rank) true_conf = { "device": true_device, "local_rank": local_rank, "rank": rank, "world_size": world_size, "node_index": 0, "nnodes": 1, "nproc_per_node": world_size, } _test__native_dist_model_create_from_context_env_local_rank(true_conf) _test__native_dist_model_create_from_context_set_local_rank(true_conf) dist.destroy_process_group() @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Should be no-dist config") def test__native_dist_model_create_no_dist_gloo(clean_env): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") _test__native_dist_model_create_from_backend_no_dist("gloo", device) _test__native_dist_model_create_from_context_no_dist("gloo", device) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Should be no-dist config") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__native_dist_model_create_no_dist_nccl(clean_env): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") _test__native_dist_model_create_from_backend_no_dist("nccl", device) _test__native_dist_model_create_from_context_no_dist("nccl", device) @pytest.mark.distributed @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:22334", "FILE"]) def test__native_dist_model_create_dist_gloo_1(init_method, get_fixed_dirname, local_rank, world_size): if init_method == "FILE": init_method = f"file://{get_fixed_dirname('native_dist_model_create_dist_gloo_1')}/shared" device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") _test__native_dist_model_create_from_backend_dist(init_method, local_rank, local_rank, world_size, "gloo", device) if init_method is None: _test__native_dist_model_create_from_backend_slurm(local_rank, local_rank, world_size, "gloo", device) @pytest.mark.distributed def test__native_dist_model_create_dist_gloo_2(local_rank, world_size): device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") _test__native_dist_model_create_from_context_dist(local_rank, local_rank, world_size, "gloo", device) _test__native_dist_model_create_from_backend_slurm(local_rank, local_rank, world_size, "gloo", device) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:22334", "FILE"]) def test__native_dist_model_create_dist_nccl_1(init_method, get_fixed_dirname, local_rank, world_size): if init_method == "FILE": init_method = f"file://{get_fixed_dirname('native_dist_model_create_dist_nccl_1')}/shared" _test__native_dist_model_create_from_backend_dist( init_method, local_rank, local_rank, world_size, "nccl", f"cuda:{local_rank}" ) if init_method is None: _test__native_dist_model_create_from_backend_slurm( local_rank, local_rank, world_size, "nccl", f"cuda:{local_rank}" ) @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__native_dist_model_create_dist_nccl_2(local_rank, world_size): _test__native_dist_model_create_from_context_dist(local_rank, local_rank, world_size, "nccl", f"cuda:{local_rank}") @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Skip if less than 2 GPUs") def test__native_dist_model_warning_index_less_localrank(local_rank, world_size): assert _NativeDistModel.create_from_context() is None dist.init_process_group("nccl", "tcp://0.0.0.0:2222", world_size=world_size, rank=local_rank) dist.barrier() # We deliberately incorrectly set cuda device to 0 torch.cuda.set_device(0) model = _NativeDistModel.create_from_context() assert isinstance(model, _NativeDistModel), f"{type(model)} vs _NativeDistModel" if local_rank == 1: with pytest.warns(UserWarning, match=r"Current device index is less than current local rank."): model.device() dist.destroy_process_group() def _test_dist_spawn_fn(local_rank, backend, world_size, device, **kwargs): from ignite.distributed.utils import _model assert dist.is_available() and dist.is_initialized() assert dist.get_backend() == backend assert isinstance(_model, _NativeDistModel), f"{type(_model)} vs _NativeDistModel" assert _model.get_local_rank() == local_rank assert _model.get_world_size() == world_size assert _model.device().type == torch.device(device).type if "master_addr" in kwargs: assert os.environ["MASTER_ADDR"] == kwargs["master_addr"] if "master_port" in kwargs: assert os.environ["MASTER_PORT"] == str(kwargs["master_port"]) def _test__native_dist_model_spawn(backend, num_workers_per_machine, device, init_method=None, **spawn_kwargs): kwargs_dict = {} for key in ["master_addr", "master_port"]: if key in spawn_kwargs: kwargs_dict[key] = spawn_kwargs[key] _NativeDistModel.spawn( _test_dist_spawn_fn, args=(backend, num_workers_per_machine, device), kwargs_dict=kwargs_dict, backend=backend, nproc_per_node=num_workers_per_machine, init_method=init_method, **spawn_kwargs, ) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.parametrize("init_method", [None, "CUSTOM_ADDR_PORT", "env://", "tcp://0.0.0.0:22334", "FILE"]) def test__native_dist_model_spawn_gloo(init_method, dirname): spawn_kwargs = {} if init_method == "FILE": init_method = f"file://{dirname}/shared" elif init_method == "CUSTOM_ADDR_PORT": init_method = None spawn_kwargs["master_addr"] = "0.0.0.0" spawn_kwargs["master_port"] = 2345 nproc = torch.cuda.device_count() if torch.cuda.is_available() else 4 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") _test__native_dist_model_spawn( "gloo", num_workers_per_machine=nproc, device=device, init_method=init_method, **spawn_kwargs ) if device.type == "cpu": spawn_kwargs["start_method"] = "fork" _test__native_dist_model_spawn( "gloo", num_workers_per_machine=nproc, device=device, init_method=init_method, **spawn_kwargs ) if init_method not in [None, "env://"]: with pytest.raises(ValueError, match=r"master_addr should be None if init_method is provided"): _test__native_dist_model_spawn( "gloo", num_workers_per_machine=nproc, device=device, init_method=init_method, master_addr="abc" ) with pytest.raises(ValueError, match=r"master_port should be None if init_method is provided"): _test__native_dist_model_spawn( "gloo", num_workers_per_machine=nproc, device=device, init_method=init_method, master_port=123 ) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.parametrize("init_method", [None, "CUSTOM_ADDR_PORT", "tcp://0.0.0.0:22334", "FILE"]) def test__native_dist_model_spawn_nccl(init_method, dirname): spawn_kwargs = {} if init_method == "FILE": init_method = f"file://{dirname}/shared" elif init_method == "CUSTOM_ADDR_PORT": init_method = None spawn_kwargs["master_addr"] = "0.0.0.0" spawn_kwargs["master_port"] = 2345 nproc = torch.cuda.device_count() _test__native_dist_model_spawn( "nccl", num_workers_per_machine=nproc, device="cuda", init_method=init_method, **spawn_kwargs ) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test__native_dist_model_init_method_is_none(world_size): with pytest.raises(ValueError, match=r"Arguments rank and world_size should be None"): _NativeDistModel.create_from_backend(backend="gloo", world_size=world_size) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test__native_dist_model_init_method_is_not_none(world_size, local_rank, get_fixed_dirname): init_method = f"file://{get_fixed_dirname('native_dist_model_init_method_is_not_none')}/shared" with pytest.raises(ValueError, match=r"Both rank and world_size should be provided"): _NativeDistModel.create_from_backend(backend="gloo", world_size=world_size, init_method=init_method) with pytest.raises(ValueError, match=r"Both rank and world_size should be provided"): _NativeDistModel.create_from_backend(backend="gloo", rank=local_rank, init_method=init_method) @pytest.mark.parametrize( "environ, expected", [ # fmt: off # usual SLURM env ( { "SLURM_PROCID": "1", "SLURM_LOCALID": "1", "SLURM_NTASKS": "2", "SLURM_JOB_NUM_NODES": "1", "SLURM_JOB_NODELIST": "c1", "SLURM_JOB_ID": "12345", }, [1, 1, 2, "c1", 17345] ), # usual SLURM env mnode ( { "SLURM_PROCID": "5", "SLURM_LOCALID": "1", "SLURM_NTASKS": "8", "SLURM_JOB_NUM_NODES": "2", "SLURM_JOB_NODELIST": "c1, c2", "SLURM_JOB_ID": "12345", }, [5, 1, 8, "c1", 17345] ), # usual SLURM env 1 node, 1 task + torch.distributed.launch ( { "SLURM_PROCID": "0", "SLURM_LOCALID": "0", "SLURM_NTASKS": "1", "SLURM_JOB_NUM_NODES": "1", "SLURM_JOB_NODELIST": "c1", "SLURM_JOB_ID": "12345", "MASTER_ADDR": "127.0.0.1", "MASTER_PORT": "2233", "RANK": "2", "LOCAL_RANK": "2", "WORLD_SIZE": "8", }, [2, 2, 8, "127.0.0.1", 2233] ), # usual SLURM env + enroot's pytorch hook ( { "SLURM_PROCID": "3", "SLURM_LOCALID": "3", "SLURM_NTASKS": "4", "SLURM_JOB_NUM_NODES": "1", "SLURM_JOB_NODELIST": "c1", "SLURM_JOB_ID": "12345", "MASTER_ADDR": "c1", "MASTER_PORT": "12233", "RANK": "3", "LOCAL_RANK": "3", "WORLD_SIZE": "4", }, [3, 3, 4, "c1", 12233] ), # usual SLURM env mnode + enroot's pytorch hook ( { "SLURM_PROCID": "3", "SLURM_LOCALID": "1", "SLURM_NTASKS": "4", "SLURM_JOB_NUM_NODES": "2", "SLURM_JOB_NODELIST": "c1, c2", "SLURM_JOB_ID": "12345", "MASTER_ADDR": "c1", "MASTER_PORT": "12233", "RANK": "3", "LOCAL_RANK": "1", "WORLD_SIZE": "4" }, [3, 1, 4, "c1", 12233] ), # fmt: on ], ) def test__setup_ddp_vars_from_slurm_env(environ, expected): ddp_keys = ["RANK", "LOCAL_RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT"] ddp_vars = _setup_ddp_vars_from_slurm_env(environ) for key, value in zip(ddp_keys, expected): assert key in ddp_vars assert ddp_vars[key] == value def test__setup_ddp_vars_from_slurm_env_bad_configs(): with pytest.raises( RuntimeError, match=r"Environment variable defined for PyTorch Distributed context is inconsistent" ): environ = { "SLURM_PROCID": "3", "SLURM_LOCALID": "1", "SLURM_NTASKS": "4", "SLURM_JOB_NUM_NODES": "2", "SLURM_JOB_NODELIST": "c1, c2", "SLURM_JOB_ID": "12345", "MASTER_ADDR": "another-addr", "MASTER_PORT": "12233", "RANK": "1", "LOCAL_RANK": "1", "WORLD_SIZE": "2", } _setup_ddp_vars_from_slurm_env(environ) with pytest.raises( RuntimeError, match=r"Environment variable defined for PyTorch Distributed context is inconsistent" ): environ = { "SLURM_PROCID": "1", "SLURM_LOCALID": "1", "SLURM_NTASKS": "4", "SLURM_JOB_NUM_NODES": "1", "SLURM_JOB_NODELIST": "c1", "SLURM_JOB_ID": "12345", "MASTER_ADDR": "another-addr", "MASTER_PORT": "12233", "RANK": "1", "LOCAL_RANK": "1", "WORLD_SIZE": "2", } _setup_ddp_vars_from_slurm_env(environ) with pytest.warns(UserWarning, match=r"We detected the following env variables"): environ = { "SLURM_PROCID": "3", "SLURM_LOCALID": "1", "SLURM_NTASKS": "4", "SLURM_JOB_NUM_NODES": "2", "SLURM_JOB_NODELIST": "c1, c2", "SLURM_JOB_ID": "12345", "RANK": "1", "LOCAL_RANK": "1", "WORLD_SIZE": "2", } _setup_ddp_vars_from_slurm_env(environ) with pytest.raises(RuntimeError, match=r"No hostname detected in SLURM_JOB_NODELIST by ignite"): environ = { "SLURM_PROCID": "1", "SLURM_LOCALID": "1", "SLURM_NTASKS": "4", "SLURM_JOB_NUM_NODES": "1", "SLURM_JOB_NODELIST": "[]", "SLURM_JOB_ID": "12345", } _setup_ddp_vars_from_slurm_env(environ) ignite-0.5.1/tests/ignite/distributed/comp_models/test_xla.py000066400000000000000000000144301465426447700244620ustar00rootroot00000000000000import os import pytest import torch from ignite.distributed.comp_models import has_xla_support if not has_xla_support: pytest.skip("Skip if no XLA support", allow_module_level=True) else: from ignite.distributed.comp_models.xla import _XlaDistModel @pytest.mark.tpu @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_model(): available_backends = _XlaDistModel.available_backends assert "xla-tpu" in available_backends with pytest.raises(ValueError, match=r"Backend should be one of"): _XlaDistModel.create_from_backend("abc") def _test_xla_spawn_fn(local_rank, world_size, device): from ignite.distributed.utils import _model assert isinstance(_model, _XlaDistModel), f"{type(_model)} vs _XlaDistModel" assert _model.get_local_rank() == local_rank assert _model.get_world_size() == world_size d = _model.device() assert isinstance(d, torch.device) and d.type == device assert _model.get_rank() == local_rank assert _model.get_nproc_per_node() == world_size assert _model.get_node_rank() == 0 assert _model.get_nnodes() == 1 @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_dist_model_spawn_one_proc(): try: _XlaDistModel.spawn(_test_xla_spawn_fn, args=(1, "xla"), kwargs_dict={}, nproc_per_node=1) except SystemExit: pass @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_dist_model_spawn_n_procs(): n = int(os.environ["NUM_TPU_WORKERS"]) try: _XlaDistModel.spawn(_test_xla_spawn_fn, args=(n, "xla"), kwargs_dict={}, nproc_per_node=n) except SystemExit: pass def _assert_model(model, true_conf): assert model.device() == true_conf["device"] assert model.get_local_rank() == true_conf["local_rank"] assert model.get_rank() == true_conf["rank"] assert model.get_world_size() == true_conf["world_size"] assert model.get_node_rank() == true_conf["node_index"] assert model.get_nnodes() == true_conf["nnodes"] assert model.get_nproc_per_node() == true_conf["nproc_per_node"] @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_dist_model_create_from_backend(): # without spawn model = _XlaDistModel.create_from_backend("xla-tpu") import torch_xla.core.xla_model as xm _assert_model( model, { "device": xm.xla_device(), "local_rank": 0, "rank": 0, "world_size": 1, "node_index": 0, "nnodes": 1, "nproc_per_node": 1, }, ) model.finalize() @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_dist_model_create_from_context(): # without spawn model = _XlaDistModel.create_from_context() assert model.backend() == "xla-tpu" import torch_xla.core.xla_model as xm _assert_model( model, { "device": xm.xla_device(), "local_rank": 0, "rank": 0, "world_size": 1, "node_index": 0, "nnodes": 1, "nproc_per_node": 1, }, ) def _test__xla_dist_model_create_from_context_in_child_proc(index): model = _XlaDistModel.create_from_context() assert model.backend() == "xla-tpu" import torch_xla.core.xla_model as xm _assert_model( model, { "device": xm.xla_device(), "local_rank": index, "rank": xm.get_ordinal(), "world_size": xm.xrt_world_size(), "node_index": 0, "nnodes": 1, "nproc_per_node": xm.xrt_world_size(), }, ) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_dist_model_create_from_context_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test__xla_dist_model_create_from_context_in_child_proc, args=(), nprocs=n) def main_fold(fold): import time import torch.nn as nn import torch.optim as optim import torch_xla.core.xla_model as xm from ignite.engine import Engine device = xm.xla_device(fold) comp_model = _XlaDistModel.create_from_context() assert comp_model.device() == device model = nn.Linear(100, 10) model.to(device) # Move model before creating optimizer optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) def training_step(engine, _): data = torch.rand(4, 100, device=device) model.train() data = data.to(device) optimizer.zero_grad() output = model(data) loss = output.sum() loss.backward() xm.optimizer_step(optimizer, barrier=True) return loss.item() trainer = Engine(training_step) # THIS CAN BE A CAUSE OF CRASH if DEVICE is OTHER THAN device tensor = torch.tensor([fold + 1.0], dtype=torch.float).to(comp_model.device()) xm.all_reduce("max", [tensor]) time.sleep(0.01 * fold) trainer.run([0] * 100, max_epochs=2) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test__xla_dist_model_run_parallel_n_threads_without_sync(): # tests issue : https://github.com/pytorch/ignite/issues/1096 import torch_xla.core.xla_model as xm from joblib import delayed, Parallel devices = xm.get_xla_supported_devices() folds = 1 d = 0 if len(devices) > 5: folds = 5 d = 1 Parallel(n_jobs=folds, backend="threading")(delayed(main_fold)(i + d) for i in range(folds)) ignite-0.5.1/tests/ignite/distributed/test_auto.py000066400000000000000000000333721465426447700223530ustar00rootroot00000000000000import os import pytest import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torch.utils.data.dataloader import _InfiniteConstantSampler from torch.utils.data.dataset import Dataset, IterableDataset from torch.utils.data.distributed import DistributedSampler from torch.utils.data.sampler import BatchSampler, RandomSampler, Sampler, SequentialSampler, WeightedRandomSampler import ignite.distributed as idist from ignite.distributed.auto import auto_dataloader, auto_model, auto_optim, DistributedProxySampler from ignite.distributed.comp_models.base import _torch_version_gt_112 from tests.ignite import is_mps_available_and_functional class DummyDS(Dataset): def __init__(self, length=10): self.length = length def __len__(self): return self.length def __getitem__(self, index): return index class DummyIterableDataset(IterableDataset): def __init__(self, start, end): super(DummyIterableDataset).__init__() self.start = start self.end = end def __iter__(self): return iter(range(self.start, self.end)) def __len__(self): return self.end - self.start @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" not in os.environ, reason="Skip if WORLD_SIZE not in env vars") def test_auto_dataloader_warning(distributed_context_single_node_gloo): with pytest.warns(UserWarning, match=r"Found batch_sampler in provided kwargs"): auto_dataloader( DummyDS(), batch_sampler=BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False) ) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" not in os.environ, reason="Skip if WORLD_SIZE not in env vars") def test_auto_dataloader_warning_distributed_sampler(distributed_context_single_node_gloo): dataset = DummyDS() rank = idist.get_rank() world_size = idist.get_world_size() auto_dataloader(dataset, sampler=DistributedSampler(dataset, num_replicas=world_size, rank=rank)) if world_size > 1: wrong_rank = (rank + 1) % world_size expected_warning = f"Found distributed sampler with rank={wrong_rank}, but process rank is {rank}" with pytest.warns(UserWarning, match=expected_warning): auto_dataloader(dataset, sampler=DistributedSampler(dataset, num_replicas=world_size, rank=wrong_rank)) expected_warning = f"Found distributed sampler with num_replicas={world_size + 1}, but world size is {world_size}" with pytest.warns(UserWarning, match=expected_warning): auto_dataloader(dataset, sampler=DistributedSampler(dataset, num_replicas=world_size + 1, rank=rank)) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_auto_dataloader_warning_tpu(): with pytest.warns(UserWarning, match=r"Found incompatible options: xla support and pin_memory"): auto_dataloader(DummyDS(), pin_memory=True) def _test_auto_dataloader(ws, nproc, batch_size, num_workers=1, sampler_name=None, dl_type=DataLoader): def _test(data): if sampler_name is None: sampler = None elif sampler_name == "WeightedRandomSampler": sampler = WeightedRandomSampler(weights=torch.ones(100), num_samples=100) elif sampler_name == "DistributedSampler": sampler = DistributedSampler(data, num_replicas=ws, rank=idist.get_rank()) else: raise RuntimeError(f"Unknown sampler name: {sampler_name}") # Test auto_dataloader assert idist.get_world_size() == ws, f"{idist.get_world_size()} vs {ws}" shuffle = sampler is None if not isinstance(data, IterableDataset) else False dataloader = auto_dataloader( data, batch_size=batch_size, num_workers=num_workers, sampler=sampler, shuffle=shuffle ) assert isinstance(dataloader, dl_type) if hasattr(dataloader, "_loader"): dataloader = dataloader._loader if ws < batch_size: assert dataloader.batch_size == batch_size // ws else: assert dataloader.batch_size == batch_size if ws <= num_workers: assert dataloader.num_workers == (num_workers + nproc - 1) // nproc else: assert dataloader.num_workers == num_workers if isinstance(data, IterableDataset): sampler_type = _InfiniteConstantSampler elif ws > 1: if sampler is None or isinstance(sampler, DistributedSampler): sampler_type = DistributedSampler else: sampler_type = DistributedProxySampler else: sampler_type = RandomSampler if sampler is None else type(sampler) assert isinstance(dataloader.sampler, sampler_type) if isinstance(dataloader, DataLoader): assert dataloader.pin_memory == ("cuda" in idist.device().type) data = torch.rand(100, 3, 12, 12) _test(data) if sampler_name is None: data = DummyIterableDataset(0, 100) _test(data) def _test_auto_model(model, ws, device, sync_bn=False, **kwargs): model = auto_model(model, sync_bn=sync_bn, **kwargs) bnd = idist.backend() if ws > 1 and torch.device(device).type in ("cuda", "cpu"): if idist.has_native_dist_support and bnd in ("nccl", "gloo"): assert isinstance(model, nn.parallel.DistributedDataParallel) if sync_bn: assert any([isinstance(m, nn.SyncBatchNorm) for m in model.modules()]) if "find_unused_parameters" in kwargs: assert model.find_unused_parameters == kwargs["find_unused_parameters"] elif idist.has_hvd_support and bnd in ("horovod",): assert isinstance(model, nn.Module) elif device != "cpu" and torch.cuda.is_available() and torch.cuda.device_count() > 1: assert isinstance(model, nn.parallel.DataParallel) else: assert isinstance(model, nn.Module) assert all( [p.device.type == torch.device(device).type for p in model.parameters()] ), f"{[p.device.type for p in model.parameters()]} vs {torch.device(device).type}" def _test_auto_model_optimizer(ws, device): # Test auto_model model = nn.Linear(10, 10) _test_auto_model(model, ws, device) model = nn.Sequential(nn.Linear(20, 100), nn.BatchNorm1d(100)) _test_auto_model(model, ws, device, sync_bn="cuda" in torch.device(device).type) if ws > 1: _test_auto_model(model, ws, device, find_unused_parameters=True) _test_auto_model(model, ws, device, find_unused_parameters=False) # Test auto_optim bnd = idist.backend() optimizer = optim.SGD(model.parameters(), lr=0.01) optimizer = auto_optim(optimizer) if idist.has_xla_support and "xla" in device: assert isinstance(optimizer, optim.SGD) and hasattr(optimizer, "wrapped_optimizer") elif idist.has_hvd_support and bnd in ("horovod",): assert isinstance(optimizer, optim.SGD) and hasattr(optimizer, "_allreduce_grad_async") else: assert isinstance(optimizer, optim.SGD) and not hasattr(optimizer, "wrapped_optimizer") if idist.has_hvd_support and bnd in ("horovod",): backward_passes_per_step = 2 optimizer = optim.SGD(model.parameters(), lr=0.01) optimizer = auto_optim(optimizer, backward_passes_per_step=backward_passes_per_step) assert isinstance(optimizer, optim.SGD) and hasattr(optimizer, "backward_passes_per_step") assert optimizer.backward_passes_per_step == backward_passes_per_step @pytest.mark.skipif( (not _torch_version_gt_112) or (torch.backends.mps.is_available() and not is_mps_available_and_functional()), reason="Skip if MPS not functional", ) def test_auto_methods_no_dist(): _test_auto_dataloader(1, 1, batch_size=1) _test_auto_dataloader(1, 1, batch_size=10, num_workers=2) _test_auto_dataloader(1, 1, batch_size=10, sampler_name="WeightedRandomSampler") _test_auto_dataloader(1, 1, batch_size=10, sampler_name="DistributedSampler") device = idist.device() _test_auto_model_optimizer(1, device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_auto_methods_gloo(distributed_context_single_node_gloo): ws = distributed_context_single_node_gloo["world_size"] _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=10, num_workers=2) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=10, sampler_name="WeightedRandomSampler") _test_auto_dataloader(ws=ws, nproc=ws, batch_size=10, sampler_name="DistributedSampler") device = idist.device() _test_auto_model_optimizer(ws, device) if ws > 1 and device.type == "cpu": # Pytorch <= 1.9.0 => AssertionError # Pytorch > 1.9 => ValueError # https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py#L1498 with pytest.raises( (AssertionError, ValueError), match=r"SyncBatchNorm layers only work with (GPU|CUDA) modules" ): model = nn.Sequential(nn.Linear(20, 100), nn.BatchNorm1d(100)) auto_model(model, sync_bn=True) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_auto_methods_nccl(distributed_context_single_node_nccl): ws = distributed_context_single_node_nccl["world_size"] _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=10, num_workers=10) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1, sampler_name="WeightedRandomSampler") _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1, sampler_name="DistributedSampler") device = idist.device() _test_auto_model_optimizer(ws, device) if ws > 1: with pytest.raises(ValueError, match=r"Argument kwargs should not contain 'device_ids'"): auto_model(nn.Linear(1, 1), device_ids=[0]) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_auto_methods_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_auto_dataloader, args=(np, np, 1), np=np, do_init=True) gloo_hvd_executor(_test_auto_dataloader, args=(np, np, 10, 10), np=np, do_init=True) gloo_hvd_executor(_test_auto_dataloader, args=(np, np, 1, 1, "WeightedRandomSampler"), np=np, do_init=True) gloo_hvd_executor(_test_auto_dataloader, args=(np, np, 1, 1, "DistributedSampler"), np=np, do_init=True) gloo_hvd_executor(_test_auto_model_optimizer, args=(np, device), np=np, do_init=True) def _test_auto_methods_xla(index, ws): dl_type = DataLoader if ws > 1: from ignite.distributed.auto import _MpDeviceLoader dl_type = _MpDeviceLoader try: from torch_xla.distributed.parallel_loader import MpDeviceLoader dl_type = MpDeviceLoader except ImportError: pass _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1, dl_type=dl_type) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=10, num_workers=2, dl_type=dl_type) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1, sampler_name="WeightedRandomSampler", dl_type=dl_type) _test_auto_dataloader(ws=ws, nproc=ws, batch_size=1, sampler_name="DistributedSampler", dl_type=dl_type) device = "xla" _test_auto_model_optimizer(ws, device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_auto_methods_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_auto_methods_xla, args=(n,), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_auto_methods_xla(): _test_auto_methods_xla(index=0, ws=1) def test_dist_proxy_sampler(): weights = torch.ones(100) weights[:50] += 1 num_samples = 200 sampler = WeightedRandomSampler(weights, num_samples) num_replicas = 8 dist_samplers = [DistributedProxySampler(sampler, num_replicas=num_replicas, rank=i) for i in range(num_replicas)] for seed in range(100): torch.manual_seed(seed) true_indices = list(sampler) indices_per_rank = [] for s in dist_samplers: s.set_epoch(seed) indices_per_rank += list(s) set_indices_per_rank = set(indices_per_rank) set_true_indices = set(true_indices) assert ( set_indices_per_rank == set_true_indices ), f"{set_true_indices - set_indices_per_rank} | {set_indices_per_rank - set_true_indices}" with pytest.raises(TypeError, match=r"Argument sampler should be instance of torch Sampler"): DistributedProxySampler(None) with pytest.raises(TypeError, match=r"Argument sampler should have length"): DistributedProxySampler(Sampler([1])) with pytest.raises(TypeError, match=r"Argument sampler must not be a distributed sampler already"): DistributedProxySampler(DistributedSampler(sampler, num_replicas=num_replicas, rank=0)) ignite-0.5.1/tests/ignite/distributed/test_launcher.py000066400000000000000000000265101465426447700232000ustar00rootroot00000000000000import os import subprocess import sys from pathlib import Path import pytest import torch from packaging.version import Version import ignite.distributed as idist from ignite.distributed.comp_models.base import _torch_version_gt_112 from ignite.distributed.utils import has_hvd_support, has_native_dist_support, has_xla_support from tests.ignite import is_mps_available_and_functional def test_parallel_wrong_inputs(): with pytest.raises(ValueError, match=r"Unknown backend 'abc'. Available backends:"): idist.Parallel(backend="abc") with pytest.raises(ValueError, match=r"If backend is None, argument 'nnodes' should be also None"): idist.Parallel(nnodes=2) with pytest.raises(ValueError, match=r"Argument nproc_per_node should positive"): idist.Parallel(backend="gloo", nproc_per_node=-1) with pytest.raises(ValueError, match=r"Argument nnodes should positive"): idist.Parallel(backend="gloo", nproc_per_node=1, nnodes=-1) with pytest.raises(ValueError, match=r"If number of nodes larger than one"): idist.Parallel(backend="gloo", nproc_per_node=1, nnodes=2) with pytest.raises(ValueError, match=r"Argument node_rank should be between 0 and"): idist.Parallel(backend="gloo", nproc_per_node=1, nnodes=2, node_rank=2) with pytest.raises(ValueError, match=r"If number of nodes larger than one, arguments master_addr and master_port"): idist.Parallel(backend="gloo", nproc_per_node=1, nnodes=2, node_rank=1) @pytest.fixture() def exec_filepath(): fp = Path(__file__).parent / "check_idist_parallel.py" assert fp.exists() yield fp.as_posix() def execute(cmd, env=None): import ignite env = dict(os.environ) if env is None else env env["PYTHONPATH"] = f"{os.path.dirname(ignite.__path__[0])}" process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) process.wait() if process.returncode != 0: print(str(process.stdout.read()) + str(process.stderr.read())) raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd, stderr=process.stderr.read()) return str(process.stdout.read()) + str(process.stderr.read()) @pytest.mark.skipif( (not _torch_version_gt_112) or (torch.backends.mps.is_available() and not is_mps_available_and_functional()), reason="Skip if MPS not functional", ) def test_check_idist_parallel_no_dist(exec_filepath): cmd = [sys.executable, "-u", exec_filepath] out = execute(cmd) assert "backend=None" in out assert "in 1 processes" in out assert "End of run" in out def _test_check_idist_parallel_torch_launch(init_method, fp, backend, nprocs): # torchrun --nproc_per_node=nprocs tests/ignite/distributed/check_idist_parallel.py --backend=backend cmd = [] if Version(torch.__version__) >= Version("1.10.0"): cmd += ["torchrun"] else: cmd += [ sys.executable, "-m", "torch.distributed.launch", "--use_env", ] cmd += [ f"--nproc_per_node={nprocs}", fp, f"--backend={backend}", ] if init_method is not None: cmd.append(f"--init_method={init_method}") out = execute(cmd) assert f"backend={backend}" in out assert f"in {nprocs} processes" in out assert "End of run" in out @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip because test uses torch launch") @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:29500", "FILE"]) @pytest.mark.parametrize( "backend", ["gloo", pytest.param("nccl", marks=pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU"))], ) def test_check_idist_parallel_torch_launch_n_procs_native(init_method, dirname, exec_filepath, backend): if init_method == "FILE": init_method = f"file://{dirname}/shared" np = torch.cuda.device_count() if torch.cuda.is_available() else 4 _test_check_idist_parallel_torch_launch(init_method, exec_filepath, backend, np) def _test_check_idist_parallel_hvdrun(fp, backend, nprocs): # horovodrun -np=nprocs python tests/ignite/distributed/check_idist_parallel.py --backend=backend cmd = [ "horovodrun", "-np", f"{nprocs}", sys.executable, fp, f"--backend={backend}", ] out = execute(cmd) assert f"backend={backend}" in out assert f"in {nprocs} processes" in out assert "End of run" in out @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip because test uses horovodrun") def test_check_idist_parallel_hvdrun_launch_n_procs(exec_filepath): np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() _test_check_idist_parallel_hvdrun(exec_filepath, "horovod", np) def _test_check_idist_parallel_spawn(fp, backend, nprocs): # python tests/ignite/distributed/check_idist_parallel.py --backend=backend --nproc_per_node=nprocs cmd = [sys.executable, fp, f"--backend={backend}", f"--nproc_per_node={nprocs}"] out = execute(cmd) assert f"backend={backend}" in out assert "Spawn function" in out assert f"in {nprocs} processes" in out if "xla" not in backend: assert "End of run" in out @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.parametrize( "backend", ["gloo", pytest.param("nccl", marks=pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU"))], ) def test_check_idist_parallel_spawn_n_procs_native(exec_filepath, backend): np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() _test_check_idist_parallel_spawn(exec_filepath, backend, np) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_smoke_test_check_idist_parallel_spawn_multinode_n_procs_gloo(exec_filepath): # Just a smoke test from check_idist_parallel.py for an emulated multi-node configuration cmd1 = "export CUDA_VISIBLE_DEVICES= && " cmd1 += f'bash -c "{sys.executable} {exec_filepath} --backend=gloo --nproc_per_node=2 ' cmd1 += '--nnodes=2 --node_rank=0 --master_addr=localhost --master_port=3344 &"' os.system(cmd1) cmd2 = [ sys.executable, exec_filepath, "--backend=gloo", "--nproc_per_node=2", "--nnodes=2", "--node_rank=1", "--master_addr=localhost", "--master_port=3344", ] env = dict(os.environ) env["CUDA_VISIBLE_DEVICES"] = "" out = execute(cmd2, env=env) assert "backend=gloo" in out assert "nproc_per_node: 2" in out assert "nnodes: 2" in out assert "master_addr: localhost" in out assert "master_port: 3344" in out assert "End of run" in out @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_check_idist_parallel_spawn_n_procs_xla(exec_filepath): n = int(os.environ["NUM_TPU_WORKERS"]) if n > 1: _test_check_idist_parallel_spawn(exec_filepath, "xla-tpu", n) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_check_idist_parallel_spawn_n_procs_hvd(exec_filepath): np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() _test_check_idist_parallel_spawn(exec_filepath, "horovod", np) def _test_func(index, ws, device, backend, true_init_method): assert 0 <= index < ws assert index == idist.get_local_rank() assert ws == idist.get_world_size() assert torch.device(device).type == idist.device().type assert backend == idist.backend() if idist.model_name() == "native-dist": from ignite.distributed.utils import _model assert _model._init_method == true_init_method @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.parametrize("init_method", ["env://", "tcp://0.0.0.0:29500", "FILE"]) @pytest.mark.parametrize( "backend", ["gloo", pytest.param("nccl", marks=pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU"))], ) def test_idist_parallel_spawn_n_procs_native(init_method, backend, dirname): if init_method == "FILE": init_method = f"file://{dirname}/shared" nproc_per_node = torch.cuda.device_count() if torch.cuda.is_available() else 4 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node, init_method=init_method) as parallel: parallel.run(_test_func, ws=nproc_per_node, device=device, backend=backend, true_init_method=init_method) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" not in os.environ, reason="Skip if not launched as multiproc") @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.parametrize("init_method", ["env://", "tcp://0.0.0.0:29500", "FILE"]) @pytest.mark.parametrize( "backend", ["gloo", pytest.param("nccl", marks=pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU"))], ) def test_idist_parallel_n_procs_native(init_method, backend, get_fixed_dirname, local_rank, world_size): if init_method == "FILE": init_method = f"file://{get_fixed_dirname('idist_parallel_n_procs_native')}/shared" os.environ["RANK"] = str(local_rank) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") with idist.Parallel(backend=backend, init_method=init_method) as parallel: parallel.run(_test_func, ws=world_size, device=device, backend=backend, true_init_method=init_method) @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_parallel_no_dist(): device = idist.device() with idist.Parallel(backend=None) as parallel: parallel.run(_test_func, ws=1, device=device, backend=None, true_init_method=None) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_parallel_spawn_params_xla(): res = idist.Parallel._setup_spawn_params( nproc_per_node=8, nnodes=None, node_rank=None, master_addr=None, master_port=None, start_method="fork" ) assert "nproc_per_node" in res and res["nproc_per_node"] == 8 assert "start_method" in res and res["start_method"] == "fork" with idist.Parallel(backend="xla-tpu", nproc_per_node=8, start_method="fork") as parallel: assert parallel.backend == "xla-tpu" res = parallel._spawn_params assert "nproc_per_node" in res and res["nproc_per_node"] == 8 assert "start_method" in res and res["start_method"] == "fork" ignite-0.5.1/tests/ignite/distributed/utils/000077500000000000000000000000001465426447700211225ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/distributed/utils/__init__.py000066400000000000000000000401361465426447700232370ustar00rootroot00000000000000import pytest import torch import torch.distributed as dist import ignite.distributed as idist from ignite.distributed.utils import sync from ignite.engine import Engine, Events def _sanity_check(): from ignite.distributed.utils import _model assert _model.get_world_size() == _model.get_nnodes() * _model.get_nproc_per_node() assert _model.get_local_rank() < _model.get_nproc_per_node() assert _model.get_rank() < _model.get_world_size() assert _model.get_node_rank() < _model.get_nnodes() def _test_distrib_config(local_rank, backend, ws, true_device, rank=None, true_init_method=None): assert idist.backend() == backend, f"{idist.backend()} vs {backend}" this_device = idist.device() assert isinstance(this_device, torch.device) if backend in ("nccl", "gloo", "horovod") and "cuda" in this_device.type: assert this_device.type == torch.device(true_device).type, f"{this_device} vs {true_device}" elif backend in ("gloo", "horovod"): assert this_device.type == torch.device(true_device).type elif backend == "xla-tpu": assert true_device in this_device.type if rank is None: if idist.model_name() == "native-dist": rank = dist.get_rank() if rank is not None: assert idist.get_rank() == rank assert idist.get_world_size() == ws assert idist.get_local_rank() == local_rank assert idist.model_name() in ("native-dist", "xla-dist", "horovod-dist") _sanity_check() if idist.model_name() == "native-dist": from ignite.distributed.utils import _model if true_init_method is not None: assert _model._init_method == true_init_method def _test_sync(cls): from ignite.distributed.utils import _SerialModel, _set_model _set_model(_SerialModel()) sync() from ignite.distributed.utils import _model assert isinstance(_model, cls), f"{type(_model)} vs {cls}" def _test_distrib__get_max_length(device): ws = idist.get_world_size() x = "_test_distrib__get_max_length" * (idist.get_rank() + 2) from ignite.distributed.utils import _model res = _model._get_max_length(x, device) assert res == len("_test_distrib__get_max_length" * (ws + 1)) def _test_distrib_all_reduce(device): res = idist.all_reduce(10) assert res == 10 * idist.get_world_size() t = torch.tensor(10, device=device) res = idist.all_reduce(t) assert res.item() == 10 * idist.get_world_size() rank = idist.get_rank() t = torch.tensor(rank * 2.0 + 1.0, device=device) res = idist.all_reduce(t) assert res.item() == sum([i * 2.0 + 1.0 for i in range(idist.get_world_size())]) t = torch.tensor(rank * 2.0 + 1.0, device=device) res = idist.all_reduce(t, "MIN").item() true_val = min([i * 2 + 1 for i in range(idist.get_world_size())]) assert res == true_val, f"{res} vs {true_val}" t = torch.tensor(rank * 2.0 + 1.0, device=device) res = idist.all_reduce(t, "MAX").item() true_val = max([i * 2.0 + 1.0 for i in range(idist.get_world_size())]) assert res == true_val, f"{res} vs {true_val}" t = torch.ones(4, 4, device=device) * (rank * 2.0 + 1.0) res = idist.all_reduce(t, "MAX") true_val = torch.ones(4, 4, device=device) * ((idist.get_world_size() - 1) * 2.0 + 1.0) assert res.equal(true_val), f"{res} vs {true_val}" t = torch.tensor(rank * 2.0 + 1.0, device=device) res = idist.all_reduce(t, "PRODUCT").item() true_val = 1 for v in [i * 2.0 + 1.0 for i in range(idist.get_world_size())]: true_val *= v assert res == true_val, f"{res} vs {true_val}" if idist.get_world_size() > 1: with pytest.raises(TypeError, match=r"Unhandled input type"): idist.all_reduce("abc") with pytest.raises(ValueError, match=r"Unsupported reduction operation"): idist.all_reduce(10, op="ABC") t = torch.tensor([0, 1, 2]) res = idist.all_reduce(t) assert res.device == t.device, f"{res.device} vs {t.device}" def _test_distrib_all_reduce_group(device): if idist.get_world_size() > 1 and idist.backend() is not None: ranks = [0, 1] rank = idist.get_rank() t = torch.tensor([rank], device=device) bnd = idist.backend() group = idist.new_group(ranks) if bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_reduce with group for horovod is not implemented"): res = idist.all_reduce(t, group=group) else: res = idist.all_reduce(t, group=group) assert res == torch.tensor([sum(ranks)], device=device) t = torch.tensor([rank], device=device) if bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_reduce with group for horovod is not implemented"): res = idist.all_reduce(t, group=ranks) else: res = idist.all_reduce(t, group=ranks) assert res == torch.tensor([sum(ranks)], device=device) ranks = "abc" if bnd in ("nccl", "gloo", "mpi"): with pytest.raises(ValueError, match=r"Argument group should be list of int or ProcessGroup"): res = idist.all_reduce(t, group="abc") elif bnd in ("xla-tpu"): with pytest.raises(ValueError, match=r"Argument group should be list of int"): res = idist.all_reduce(t, group="abc") elif bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_reduce with group for horovod is not implemented"): res = idist.all_reduce(t, group="abc") def _test_distrib_all_gather(device): rank = idist.get_rank() ws = idist.get_world_size() res = torch.tensor(idist.all_gather(10), device=device) true_res = torch.tensor([10] * ws, device=device) assert (res == true_res).all() t = torch.tensor(rank, device=device) res = idist.all_gather(t) true_res = torch.tensor([i for i in range(ws)], device=device) assert (res == true_res).all() x = "test-test" if rank == 0: x = "abc" res = idist.all_gather(x) true_res = ["abc"] + ["test-test"] * (ws - 1) assert res == true_res base_x = "tests/ignite/distributed/utils/test_native.py" * 2000 x = base_x if rank == 0: x = "abc" res = idist.all_gather(x) true_res = ["abc"] + [base_x] * (ws - 1) assert res == true_res t = torch.arange(100, device=device).reshape(4, 25) * (rank + 1) in_dtype = t.dtype res = idist.all_gather(t) assert res.shape == (ws * 4, 25) assert res.dtype == in_dtype true_res = torch.zeros(ws * 4, 25, device=device) for i in range(ws): true_res[i * 4 : (i + 1) * 4, ...] = torch.arange(100, device=device).reshape(4, 25) * (i + 1) assert (res == true_res).all() if ws > 1 and idist.backend() != "xla-tpu": t = { "a": [rank + 1, rank + 2, torch.tensor(rank + 3, device=device)], "b": torch.tensor([[rank + 1, rank + 2, rank + 3]], device=device), "c": {"abcd": rank, "cdfg": torch.tensor(rank, dtype=torch.uint8, device=device)}, } res = idist.all_gather(t) assert isinstance(res, list) and len(res) == ws for i, obj in enumerate(res): assert isinstance(obj, dict) assert list(obj.keys()) == ["a", "b", "c"], obj expected_device = ( device if torch.device(device).type == "cpu" else torch.device(f"{torch.device(device).type}:{i}") ) expected = { "a": [i + 1, i + 2, torch.tensor(i + 3, device=expected_device)], "b": torch.tensor([[i + 1, i + 2, i + 3]], device=expected_device), "c": {"abcd": i, "cdfg": torch.tensor(i, dtype=torch.uint8, device=expected_device)}, } assert obj["a"] == expected["a"] assert (obj["b"] == expected["b"]).all() assert obj["c"] == expected["c"] def _test_distrib_all_gather_group(device): if idist.get_world_size() > 1: ranks = list(range(idist.get_world_size() - 1, 0, -1)) # [0, 1, 2, 3] -> [3, 2, 1] rank = idist.get_rank() bnd = idist.backend() t = torch.tensor([rank], device=device) group = idist.new_group(ranks) if bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_gather with group for horovod is not implemented"): res = idist.all_gather(t, group=group) else: res = idist.all_gather(t, group=group) if rank in ranks: assert torch.equal(res, torch.tensor(ranks, device=device)) else: assert res == t t = torch.tensor([rank], device=device) if bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_gather with group for horovod is not implemented"): res = idist.all_gather(t, group=ranks) else: res = idist.all_gather(t, group=ranks) if rank in ranks: assert torch.equal(res, torch.tensor(ranks, device=device)) else: assert res == t t = { "a": [rank + 1, rank + 2, torch.tensor(rank + 3, device=device)], "b": torch.tensor([[rank + 1, rank + 2, rank + 3]], device=device), "c": {"abcd": rank, "cdfg": torch.tensor(rank, dtype=torch.uint8, device=device)}, } if bnd in ("xla-tpu"): with pytest.raises(NotImplementedError, match=r"all_gather on object is not implemented for xla"): res = idist.all_gather(t, group=ranks) elif bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_gather with group for horovod is not implemented"): res = idist.all_gather(t, group=ranks) else: res = idist.all_gather(t, group=ranks) if rank in ranks: assert isinstance(res, list) and len(res) == len(ranks) for i, obj in zip(ranks, res): assert isinstance(obj, dict) assert list(obj.keys()) == ["a", "b", "c"], obj expected_device = ( device if torch.device(device).type == "cpu" else torch.device(f"{torch.device(device).type}:{i}") ) expected = { "a": [i + 1, i + 2, torch.tensor(i + 3, device=expected_device)], "b": torch.tensor([[i + 1, i + 2, i + 3]], device=expected_device), "c": {"abcd": i, "cdfg": torch.tensor(i, dtype=torch.uint8, device=expected_device)}, } assert obj["a"] == expected["a"], (obj, expected) assert (obj["b"] == expected["b"]).all(), (obj, expected) assert obj["c"] == expected["c"], (obj, expected) else: assert res == t if bnd in ("nccl", "gloo", "mpi"): with pytest.raises(ValueError, match=r"Argument group should be list of int or ProcessGroup"): res = idist.all_gather(t, group="abc") elif bnd in ("xla-tpu"): with pytest.raises(ValueError, match=r"Argument group should be list of int"): res = idist.all_gather(t, group="abc") elif bnd in ("horovod"): with pytest.raises(NotImplementedError, match=r"all_gather with group for horovod is not implemented"): res = idist.all_gather(t, group="abc") def _test_distrib_broadcast(device): rank = idist.get_rank() ws = idist.get_world_size() def _test(data_src, data_others, safe_mode): for src in range(ws): data = data_src if rank == src else data_others res = idist.broadcast(data, src=src, safe_mode=safe_mode) if isinstance(res, torch.Tensor): assert (res == data_src).all(), f"{res} vs {data_src}" assert data_src.dtype == res.dtype else: assert res == data_src, f"{res} vs {data_src}" _test(10, 0, safe_mode=False) _test(10, None, safe_mode=True) t = torch.tensor([1.2345, 2.3456], dtype=torch.float, device=device) _test(t, torch.empty_like(t), safe_mode=False) _test(t, None, safe_mode=True) _test(t, "abc", safe_mode=True) _test("test-abcdefg", "", safe_mode=False) _test("test-abcdefg", None, safe_mode=True) _test("test-abcdefg", 1.2, safe_mode=True) s = "tests/ignite/distributed/utils/test_horovod.py::test_idist_broadcast_hvd" * 200 _test(s, "", safe_mode=False) _test(s, None, safe_mode=True) _test(s, 123.0, safe_mode=True) t = torch.arange(100, device=device).reshape(4, 25) * 2 _test(t, torch.empty_like(t), safe_mode=False) _test(t, None, safe_mode=True) _test(t, "None", safe_mode=True) t = torch.tensor(12) _test(t, torch.empty_like(t), safe_mode=False) _test(t, None, safe_mode=True) _test(t, 123.4, safe_mode=True) if idist.get_world_size() > 1: with pytest.raises(TypeError, match=r"Unhandled input type"): idist.broadcast([0, 1, 2], src=0) if idist.get_world_size() > 1: msg = "Source data can not be None" if rank == 0 else "Argument safe_mode should be True" with pytest.raises(ValueError, match=msg): idist.broadcast(None, src=0) def _test_distrib_barrier(device): t = torch.tensor([idist.get_rank()], device=device, dtype=torch.float) true_res = sum([i for i in range(idist.get_world_size())]) if idist.get_rank() == 0: t += 10.0 idist.barrier() tt = idist.all_reduce(t) assert tt.item() == true_res + 10.0 def _test_distrib_new_group(device): if idist.get_world_size() > 1 and idist.backend() is not None: bnd = idist.backend() ranks = [0, 1] if idist.has_native_dist_support and bnd in ("nccl", "gloo", "mpi"): g1 = idist.new_group(ranks) g2 = dist.new_group(ranks) rank = idist.get_rank() if rank in ranks: assert g1.rank() == g2.rank() elif idist.has_xla_support and bnd in ("xla-tpu"): assert idist.new_group(ranks) == [ranks] elif idist.has_hvd_support and bnd in ("horovod"): from horovod.common.process_sets import ProcessSet g1 = idist.new_group(ranks) g2 = ProcessSet(ranks) rank = idist.get_rank() if rank in ranks: assert g1.ranks == g2.ranks elif idist.backend() is None: ranks = [0, 1] assert idist.new_group(ranks) == ranks with pytest.raises(ValueError, match="Argument ranks should be list of int"): ranks = ["a", "b", "c"] idist.new_group(ranks) with pytest.raises(ValueError, match="Argument ranks should be list of int"): ranks = 1 idist.new_group(ranks) def _test_distrib_one_rank_only(device): def _test(barrier): # last rank rank = idist.get_world_size() - 1 value = torch.tensor(0).to(device) @idist.one_rank_only(rank=rank, with_barrier=barrier) def initialize(): value.add_(torch.tensor(100).to(device)) initialize() value_list = idist.all_gather(tensor=value) for r in range(idist.get_world_size()): if r == rank: assert value_list[r].item() == 100 else: assert value_list[r].item() == 0 _test(barrier=True) _test(barrier=False) def _test_distrib_one_rank_only_with_engine(device): def _test(barrier): engine = Engine(lambda e, b: b) batch_sum = torch.tensor(0).to(device) @engine.on(Events.ITERATION_COMPLETED) @idist.one_rank_only(with_barrier=barrier) # ie rank == 0 def _(_): batch_sum.data += torch.tensor(engine.state.batch).to(device) engine.run([1, 2, 3], max_epochs=2) value_list = idist.all_gather(tensor=batch_sum) for r in range(idist.get_world_size()): if r == 0: assert value_list[r].item() == 12 else: assert value_list[r].item() == 0 _test(barrier=True) _test(barrier=False) ignite-0.5.1/tests/ignite/distributed/utils/test_horovod.py000066400000000000000000000232761465426447700242250ustar00rootroot00000000000000import os import pytest import torch import ignite.distributed as idist from ignite.distributed.utils import has_hvd_support from tests.ignite.distributed.utils import ( _test_distrib__get_max_length, _test_distrib_all_gather, _test_distrib_all_gather_group, _test_distrib_all_reduce, _test_distrib_all_reduce_group, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_config, _test_distrib_new_group, _test_distrib_one_rank_only, _test_distrib_one_rank_only_with_engine, _test_sync, ) @pytest.mark.skipif(has_hvd_support, reason="Skip if has Horovod package") def test_hvd_distrib_spawn_no_hvd_support(): with pytest.raises(ValueError, match=r"Backend should be one of"): idist.spawn("horovod", _test_distrib_config, args=("horovod", 1, "cpu"), nproc_per_node=1) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") def test_hvd_distrib_single_node_single_device(): import horovod.torch as hvd idist.initialize("horovod") device = "cpu" if torch.cuda.device_count() < 1 else "cuda" local_rank = hvd.local_rank() world_size = hvd.size() rank = hvd.rank() _test_distrib_config(local_rank, "horovod", world_size, device, rank) idist.finalize() @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(torch.cuda.device_count() > 0, reason="Skip if has GPU") def test_hvd_distrib_single_node_spawn(): world_size = 4 idist.spawn("horovod", _test_distrib_config, args=("horovod", world_size, "cpu"), nproc_per_node=world_size) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_hvd_distrib_multi_node_spawn_raise_error(): world_size = 4 with pytest.raises(RuntimeError, match=r"For multi-node configuration, please set 'hosts' argument instead"): idist.spawn( "horovod", _test_distrib_config, args=("horovod", world_size, "cpu"), nproc_per_node=world_size, nnodes=2 ) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_hvd_distrib_single_node_spawn_cuda(): world_size = torch.cuda.device_count() idist.spawn("horovod", _test_distrib_config, args=("horovod", world_size, "cuda"), nproc_per_node=world_size) def _test_sync_as_hvd(): import horovod.torch as hvd from ignite.distributed.comp_models.horovod import _HorovodDistModel hvd.init() lrank = hvd.local_rank() if torch.cuda.is_available(): torch.cuda.set_device(lrank) _test_sync(_HorovodDistModel) hvd.shutdown() @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif(os.getenv("HOROVOD_RANK", -1) == -1, reason="Skip as controller is not Gloo") def test_sync_as_hvd(): _test_sync_as_hvd() @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_sync_as_hvd_inside_gloo_executor(gloo_hvd_executor): np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_sync_as_hvd, (), np=np) def _test_idist_methods_in_hvd_context(backend, device): # We explicitly set _model as _SerialModel # then call idist.* methods and check that they give correct values import horovod.torch as hvd from ignite.distributed.utils import _SerialModel, _set_model hvd.init() _set_model(_SerialModel()) ws = hvd.size() rank = hvd.rank() local_rank = hvd.local_rank() if torch.cuda.is_available(): torch.cuda.set_device(local_rank) _test_distrib_config(local_rank, backend=backend, ws=ws, true_device=device, rank=rank) hvd.shutdown() @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_methods_in_hvd_context(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_idist_methods_in_hvd_context, ("horovod", device), np=np) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_all_reduce_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_all_reduce, (device,), np=np, do_init=True) gloo_hvd_executor(_test_distrib_all_reduce_group, (device,), np=np, do_init=True) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist__model_methods_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib__get_max_length, (device,), np=np, do_init=True) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_all_gather_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_all_gather, (device,), np=np, do_init=True) gloo_hvd_executor(_test_distrib_all_gather_group, (device,), np=np, do_init=True) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_broadcast_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_broadcast, (device,), np=np, do_init=True) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_barrier_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_barrier, (device,), np=np, do_init=True) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_new_group_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_new_group, (device,), np=np, do_init=True) def _test_idist_methods_overhead(ok_factor, sync_model): import time import horovod.torch as hvd if sync_model: idist.sync() from ignite.distributed.comp_models.horovod import _HorovodDistModel from ignite.distributed.utils import _model assert isinstance(_model, _HorovodDistModel) n = 100000 m = 5 t2 = 0.0 t1 = 0.0 for _ in range(m): start = time.time() for _ in range(n): _ = hvd.size() _ = hvd.rank() elapsed = time.time() - start t2 += elapsed / n / m start = time.time() for _ in range(n): _ = idist.get_world_size() _ = idist.get_rank() elapsed = time.time() - start t1 += elapsed / n / m overhead_factor = t1 / t2 assert overhead_factor < ok_factor, f"{overhead_factor} vs {ok_factor} | {t2} vs {t1}" @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_methods_overhead_hvd(gloo_hvd_executor): np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() ok_factor = 6.0 sync_model = False gloo_hvd_executor(_test_idist_methods_overhead, (ok_factor, sync_model), np=np, do_init=True) ok_factor = 2.5 sync_model = True gloo_hvd_executor(_test_idist_methods_overhead, (ok_factor, sync_model), np=np, do_init=True) @pytest.mark.distributed @pytest.mark.skipif(not has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_one_rank_only(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" np = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_one_rank_only, (device,), np=np, do_init=True) gloo_hvd_executor(_test_distrib_one_rank_only_with_engine, (device,), np=np, do_init=True) ignite-0.5.1/tests/ignite/distributed/utils/test_native.py000066400000000000000000000357671465426447700240430ustar00rootroot00000000000000import os import pytest import torch import torch.distributed as dist from packaging.version import Version import ignite.distributed as idist from ignite.distributed.utils import has_native_dist_support from tests.ignite.distributed.utils import ( _test_distrib__get_max_length, _test_distrib_all_gather, _test_distrib_all_gather_group, _test_distrib_all_reduce, _test_distrib_all_reduce_group, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_config, _test_distrib_new_group, _test_distrib_one_rank_only, _test_distrib_one_rank_only_with_engine, _test_sync, ) def _test_native_distrib_single_node_launch_tool(backend, device, local_rank, world_size, init_method=None, **kwargs): import os rank = local_rank os.environ["RANK"] = f"{rank}" idist.initialize(backend, init_method=init_method, **kwargs) _test_distrib_config(local_rank, backend, world_size, device, rank, true_init_method=init_method) idist.finalize() @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:22334", "FILE"]) def test_native_distrib_single_node_launch_tool_gloo(init_method, get_fixed_dirname, local_rank, world_size): from datetime import timedelta timeout = timedelta(seconds=20) if init_method == "FILE": init_method = f"file://{get_fixed_dirname('native_distrib_single_node_launch_tool_gloo')}/shared" device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") _test_native_distrib_single_node_launch_tool( "gloo", device, local_rank, world_size, timeout=timeout, init_method=init_method ) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:22334", "FILE"]) def test_native_distrib_single_node_launch_tool_nccl(init_method, get_fixed_dirname, local_rank, world_size): if init_method == "FILE": init_method = f"file://{get_fixed_dirname('native_distrib_single_node_launch_tool_nccl')}/shared" device = torch.device(f"cuda:{local_rank}") _test_native_distrib_single_node_launch_tool("nccl", device, local_rank, world_size, init_method=init_method) def _test_native_distrib_single_node_spawn(init_method, backend, device, **kwargs): world_size = 4 if torch.device(device).type == "cpu" else torch.cuda.device_count() idist.spawn( backend, _test_distrib_config, args=(backend, world_size, device), nproc_per_node=world_size, init_method=init_method, **kwargs, ) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:22334", "FILE"]) def test_native_distrib_single_node_spawn_gloo(init_method, dirname): from datetime import timedelta timeout = timedelta(seconds=20) if init_method == "FILE": init_method = f"file://{dirname}/shared" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") _test_native_distrib_single_node_spawn(init_method, "gloo", device, timeout=timeout) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.parametrize("init_method", [None, "tcp://0.0.0.0:22334", "FILE"]) def test_native_distrib_single_node_spawn_nccl(init_method, dirname): if init_method == "FILE": init_method = f"file://{dirname}/shared" device = torch.device("cuda") _test_native_distrib_single_node_spawn(init_method, "nccl", device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_sync_as_native_gloo(distributed_context_single_node_gloo): from ignite.distributed.comp_models.native import _NativeDistModel _test_sync(_NativeDistModel) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_sync_as_native_nccl(distributed_context_single_node_nccl): from ignite.distributed.comp_models.native import _NativeDistModel _test_sync(_NativeDistModel) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_new_group_native_nccl(distributed_context_single_node_nccl): device = idist.device() _test_distrib_new_group(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_new_group_native_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib_new_group(device) def _test_idist_methods_in_native_context(backend, device, local_rank): # We explicitly set _model as _SerialModel # then call idist.* methods and check that they give correct values from ignite.distributed.utils import _SerialModel, _set_model _set_model(_SerialModel()) ws = dist.get_world_size() rank = dist.get_rank() _test_distrib_config(local_rank, backend=backend, ws=ws, true_device=device, rank=rank) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_methods_in_native_gloo_context(distributed_context_single_node_gloo): local_rank = distributed_context_single_node_gloo["local_rank"] device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") _test_idist_methods_in_native_context("gloo", device, local_rank) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_methods_in_native_nccl_context(distributed_context_single_node_nccl): local_rank = distributed_context_single_node_nccl["local_rank"] device = torch.device(f"cuda:{local_rank}") _test_idist_methods_in_native_context("nccl", device, local_rank) def _test_idist_methods_in_native_context_set_local_rank(backend, device, local_rank): # We explicitly set _model as _SerialModel # then call idist.* methods and check that they give correct values from ignite.distributed.utils import _SerialModel, _set_model _set_model(_SerialModel()) lrank = int(os.environ["LOCAL_RANK"]) del os.environ["LOCAL_RANK"] ws = dist.get_world_size() rank = dist.get_rank() idist.set_local_rank(local_rank) _test_distrib_config(local_rank=local_rank, backend=backend, ws=ws, true_device=device, rank=rank) os.environ["LOCAL_RANK"] = str(lrank) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_methods_in_native_gloo_context_set_local_rank(distributed_context_single_node_gloo): local_rank = distributed_context_single_node_gloo["local_rank"] device = idist.device() _test_idist_methods_in_native_context_set_local_rank("gloo", device, local_rank) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_methods_in_native_nccl_context_set_local_rank(distributed_context_single_node_nccl): local_rank = distributed_context_single_node_nccl["local_rank"] device = idist.device() _test_idist_methods_in_native_context_set_local_rank("nccl", device, local_rank) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist__model_methods_nccl(distributed_context_single_node_nccl): device = idist.device() _test_distrib__get_max_length(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist__model_methods_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib__get_max_length(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_all_reduce_nccl(distributed_context_single_node_nccl): device = idist.device() _test_distrib_all_reduce(device) _test_distrib_all_reduce_group(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_all_reduce_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib_all_reduce(device) _test_distrib_all_reduce_group(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="dist.all_gather_object is not implemented") def test_idist_all_gather_nccl(distributed_context_single_node_nccl): device = idist.device() _test_distrib_all_gather(device) _test_distrib_all_gather_group(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="dist.all_gather_object is not implemented") def test_idist_all_gather_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib_all_gather(device) _test_distrib_all_gather_group(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_broadcast_nccl(distributed_context_single_node_nccl): device = idist.device() _test_distrib_broadcast(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_broadcast_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib_broadcast(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_barrier_nccl(distributed_context_single_node_nccl): device = idist.device() _test_distrib_barrier(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_barrier_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib_barrier(device) def _test_idist_methods_overhead(ok_factor): import time n = 100000 m = 5 t2 = 0.0 t1 = 0.0 for _ in range(m): start = time.time() for _ in range(n): _ = dist.get_world_size() _ = dist.get_rank() elapsed = time.time() - start t2 += elapsed / n / m start = time.time() for _ in range(n): _ = idist.get_world_size() _ = idist.get_rank() elapsed = time.time() - start t1 += elapsed / n / m overhead_factor = t1 / t2 assert overhead_factor < ok_factor, f"{overhead_factor} vs {ok_factor} | {t2} vs {t1}" @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif( not torch.cuda.is_available(), reason="Do not want to run this test on Github or Travis, but CircleCI" ) def test_idist_methods_overhead_gloo(distributed_context_single_node_gloo): _test_idist_methods_overhead(2.5) idist.sync() from ignite.distributed.comp_models.native import _NativeDistModel from ignite.distributed.utils import _model assert isinstance(_model, _NativeDistModel) _test_idist_methods_overhead(1.7) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_methods_overhead_nccl(distributed_context_single_node_nccl): _test_idist_methods_overhead(2.5) idist.sync() from ignite.distributed.comp_models.native import _NativeDistModel from ignite.distributed.utils import _model assert isinstance(_model, _NativeDistModel) _test_idist_methods_overhead(1.7) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_one_rank_only_gloo(distributed_context_single_node_gloo): device = idist.device() _test_distrib_one_rank_only(device=device) _test_distrib_one_rank_only_with_engine(device=device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_one_rank_only_nccl(local_rank, distributed_context_single_node_nccl): device = idist.device() _test_distrib_one_rank_only(device=device) _test_distrib_one_rank_only_with_engine(device=device) @pytest.mark.distributed @pytest.mark.parametrize("rank", range(int(os.environ.get("WORLD_SIZE", 1)))) @pytest.mark.parametrize("local", [True, False]) def test_one_rank_first(distributed, get_rank_zero_dirname, rank, local): def get_ds(file_path): rank = idist.get_local_rank() if local else idist.get_rank() if not file_path.exists(): with open(file_path, "w") as f: f.write("readed") return f"{rank} not readed" else: return f"{rank} readed" folder = get_rank_zero_dirname() file_path = folder / "res.txt" with idist.one_rank_first(rank, local=local): x = get_ds(file_path) output = idist.all_gather(x) if local: expected = [ f"{x} not readed" if x == rank else f"{x} readed" for x in range(idist.get_nproc_per_node()) ] * idist.get_nnodes() else: expected = [f"{x} not readed" if x == rank else f"{x} readed" for x in range(idist.get_world_size())] print("expected:", expected, idist.get_nnodes()) assert set(expected) == set(output) @pytest.mark.distributed def test_one_rank_first_asserts(): rank = 100 with pytest.raises( ValueError, match=f"rank should be between 0 and {idist.get_world_size() - 1}, but given {rank}" ): with idist.one_rank_first(rank): pass ignite-0.5.1/tests/ignite/distributed/utils/test_serial.py000066400000000000000000000052261465426447700240170ustar00rootroot00000000000000import torch import ignite.distributed as idist from ignite.distributed.comp_models.base import _torch_version_gt_112 from tests.ignite.distributed.utils import ( _sanity_check, _test_distrib__get_max_length, _test_distrib_all_gather, _test_distrib_all_reduce, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_new_group, _test_sync, ) def test_no_distrib(capsys): assert idist.backend() is None if torch.cuda.is_available(): assert idist.device().type == "cuda" elif _torch_version_gt_112 and torch.backends.mps.is_available(): assert idist.device().type == "mps" else: assert idist.device().type == "cpu" assert idist.get_rank() == 0 assert idist.get_world_size() == 1 assert idist.get_local_rank() == 0 assert idist.model_name() == "serial" from ignite.distributed.utils import _model, _SerialModel _sanity_check() assert isinstance(_model, _SerialModel) idist.show_config() captured = capsys.readouterr() out = captured.err.split("\r") out = list(map(lambda x: x.strip(), out)) out = list(filter(None, out)) assert "ignite.distributed.utils INFO: distributed configuration: serial" in out[-1] assert "ignite.distributed.utils INFO: backend: None" in out[-1] if torch.cuda.is_available(): assert "ignite.distributed.utils INFO: device: cuda" in out[-1] elif _torch_version_gt_112 and torch.backends.mps.is_available(): assert "ignite.distributed.utils INFO: device: mps" in out[-1] else: assert "ignite.distributed.utils INFO: device: cpu" in out[-1] assert "ignite.distributed.utils INFO: rank: 0" in out[-1] assert "ignite.distributed.utils INFO: local rank: 0" in out[-1] assert "ignite.distributed.utils INFO: world size: 1" in out[-1] def test_sync_no_dist(): from ignite.distributed.comp_models import _SerialModel _test_sync(_SerialModel) def test_idist_methods_no_dist(): assert idist.get_world_size() < 2 assert idist.backend() is None, f"{idist.backend()}" def test_idist__model_methods_no_dist(): _test_distrib__get_max_length("cpu") if torch.cuda.device_count() > 1: _test_distrib__get_max_length("cuda") def test_idist_collective_ops_no_dist(): _test_distrib_all_reduce("cpu") _test_distrib_all_gather("cpu") _test_distrib_barrier("cpu") _test_distrib_broadcast("cpu") _test_distrib_new_group("cpu") if torch.cuda.device_count() > 1: _test_distrib_all_reduce("cuda") _test_distrib_all_gather("cuda") _test_distrib_barrier("cuda") _test_distrib_broadcast("cuda") _test_distrib_new_group("cuda") ignite-0.5.1/tests/ignite/distributed/utils/test_xla.py000066400000000000000000000212741465426447700233250ustar00rootroot00000000000000import os import pytest import ignite.distributed as idist from ignite.distributed.utils import has_xla_support from tests.ignite.distributed.utils import ( _test_distrib_all_gather, _test_distrib_all_gather_group, _test_distrib_all_reduce, _test_distrib_all_reduce_group, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_config, _test_distrib_new_group, _test_distrib_one_rank_only, _test_distrib_one_rank_only_with_engine, _test_sync, ) @pytest.mark.skipif(has_xla_support, reason="Skip if has PyTorch XLA package") def test_xla_distrib_spawn_no_xla_support(): with pytest.raises(ValueError, match=r"Backend should be one of"): idist.spawn("xla-tpu", _test_distrib_config, args=("xla-tpu", 1, "xla"), nproc_per_node=1) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_xla_distrib_single_node_no_spawn(): idist.initialize("xla-tpu") _test_distrib_config(local_rank=0, backend="xla-tpu", ws=1, true_device="xla") idist.finalize() @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_xla_distrib_single_node_spawn_one_proc(): try: idist.spawn("xla-tpu", _test_distrib_config, args=("xla-tpu", 1, "xla"), nproc_per_node=1) except SystemExit: pass @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_xla_distrib_single_node_spawn_n_procs(): n = int(os.environ["NUM_TPU_WORKERS"]) try: idist.spawn("xla-tpu", _test_distrib_config, args=("xla-tpu", n, "xla"), nproc_per_node=n) except SystemExit: pass @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_sync_as_xla(): from ignite.distributed.comp_models.xla import _XlaDistModel _test_sync(_XlaDistModel) def _test_sync_as_xla_in_child_proc(index): from ignite.distributed.comp_models.xla import _XlaDistModel _test_sync(_XlaDistModel) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_sync_as_xla_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_sync_as_xla_in_child_proc, args=(), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_methods_in_xla_context(): # We explicitly set _model as _SerialModel # then call idist.* methods and check that they give correct values from ignite.distributed.utils import _SerialModel, _set_model _set_model(_SerialModel()) _test_distrib_config(local_rank=0, backend="xla-tpu", ws=1, true_device="xla", rank=0) def _test_idist_methods_in_xla_context_in_child_proc(index): # We explicitly set _model as _SerialModel # then call idist.* methods and check that they give correct values from ignite.distributed.utils import _SerialModel, _set_model _set_model(_SerialModel()) import torch_xla.core.xla_model as xm _test_distrib_config( local_rank=index, backend="xla-tpu", ws=xm.xrt_world_size(), true_device="xla", rank=xm.get_ordinal() ) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_methods_in_xla_context_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_idist_methods_in_xla_context_in_child_proc, args=(), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_all_reduce_xla(): device = idist.device() _test_distrib_all_reduce(device) _test_distrib_all_reduce_group(device) def _test_idist_all_reduce_xla_in_child_proc(index): device = idist.device() _test_distrib_all_reduce(device) _test_distrib_all_reduce_group(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_all_reduce_xla_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_idist_all_reduce_xla_in_child_proc, args=(), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_new_group_xla(): device = idist.device() _test_distrib_new_group(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_all_gather_xla(): device = idist.device() _test_distrib_all_gather(device) _test_distrib_all_gather_group(device) def _test_idist_all_gather_xla_in_child_proc(index): device = idist.device() _test_distrib_all_gather(device) _test_distrib_all_gather_group(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_all_gather_xla_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_idist_all_gather_xla_in_child_proc, args=(), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_broadcast_xla(): device = idist.device() _test_distrib_broadcast(device) def _test_idist_broadcast_xla_in_child_proc(index): device = idist.device() _test_distrib_broadcast(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_broadcast_xla_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_idist_broadcast_xla_in_child_proc, args=(), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_barrier_xla(): device = idist.device() _test_distrib_barrier(device) def _test_idist_barrier_xla_in_child_proc(index): device = idist.device() _test_distrib_barrier(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_barrier_xla_in_child_proc(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_idist_barrier_xla_in_child_proc, args=(), nprocs=n) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_one_rank_only_xla(): device = idist.device() _test_distrib_one_rank_only(device=device) _test_distrib_one_rank_only_with_engine(device=device) def _test_idist_one_rank_only_xla_nprocs(index): device = idist.device() _test_distrib_one_rank_only(device=device) _test_distrib_one_rank_only_with_engine(device=device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_one_rank_only_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_idist_one_rank_only_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/engine/000077500000000000000000000000001465426447700167055ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/engine/__init__.py000066400000000000000000000045661465426447700210310ustar00rootroot00000000000000import torch try: from torch.utils.data import IterableDataset except ImportError: class IterableDataset: pass class BatchChecker: def __init__(self, data, init_counter=0): self.counter = init_counter self.data = data self.true_batch = None def check(self, batch): self.true_batch = self.data[self.counter % len(self.data)] self.counter += 1 res = self.true_batch == batch return res.all() if not isinstance(res, bool) else res class IterationCounter: def __init__(self, start_value=1): self.current_iteration_count = start_value def __call__(self, engine): assert engine.state.iteration == self.current_iteration_count self.current_iteration_count += 1 class EpochCounter: def __init__(self, start_value=1): self.current_epoch_count = start_value def __call__(self, engine): assert engine.state.epoch == self.current_epoch_count self.current_epoch_count += 1 def setup_sampler(sampler_type, num_iters, batch_size): if sampler_type is None: return None, batch_size if sampler_type == "weighted": from torch.utils.data.sampler import WeightedRandomSampler w = torch.ones(num_iters * batch_size, dtype=torch.float) for i in range(num_iters): w[batch_size * i : batch_size * (i + 1)] += i * 1.0 return WeightedRandomSampler(w, num_samples=num_iters * batch_size, replacement=True), batch_size if sampler_type == "distributed": import torch.distributed as dist from torch.utils.data.distributed import DistributedSampler num_replicas = 1 rank = 0 if dist.is_available() and dist.is_initialized(): num_replicas = dist.get_world_size() rank = dist.get_rank() dataset = torch.zeros(num_iters * batch_size) return DistributedSampler(dataset, num_replicas=num_replicas, rank=rank), batch_size // num_replicas class MyIterableDataset(IterableDataset): def __init__(self, start, end): super(MyIterableDataset).__init__() assert end > start, "this example code only works with end >= start" self.start = start self.end = end def __iter__(self): return iter(range(self.start, self.end)) def get_iterable_dataset(*args, **kwargs): return MyIterableDataset(*args, **kwargs) ignite-0.5.1/tests/ignite/engine/test_create_supervised.py000066400000000000000000000705521465426447700240430ustar00rootroot00000000000000import os from importlib.util import find_spec from typing import Optional, Union from unittest import mock from unittest.mock import MagicMock, patch import pytest import torch from packaging.version import Version from pytest import approx from torch.nn.functional import mse_loss from torch.optim import SGD import ignite.distributed as idist from ignite.distributed.comp_models.base import _torch_version_gt_112 from ignite.engine import ( _check_arg, create_supervised_evaluator, create_supervised_trainer, Engine, Events, supervised_evaluation_step, supervised_evaluation_step_amp, supervised_training_step_tpu, ) from ignite.metrics import MeanSquaredError from tests.ignite import is_mps_available_and_functional class DummyModel(torch.nn.Module): def __init__(self, output_as_list=False): super(DummyModel, self).__init__() self.output_as_list = output_as_list self.fc = torch.nn.Linear(1, 1, bias=False) def forward(self, x, bias=None): if bias is None: bias = 0.0 if self.output_as_list: return self.fc(x) + bias, self.fc(x) + bias return self.fc(x) + bias def _default_create_supervised_trainer( gradient_accumulation_steps: int = 1, model_device: Optional[str] = None, trainer_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False, with_model_transform: bool = False, with_model_fn: bool = False, ): if with_model_transform: def get_first_element(output): return output[0] model = DummyModel(output_as_list=True) model_transform = get_first_element else: model = DummyModel() model_transform = None if model_device: model.to(model_device) model.fc.weight.data.zero_() optimizer = SGD(model.parameters(), 0.1) if trace: example_inputs = (torch.randn(1), torch.randn(1)) if with_model_fn else torch.randn(1) model = torch.jit.trace(model, example_inputs) if amp_mode == "apex" and model_device == trainer_device == "cuda": from apex import amp model, optimizer = amp.initialize(model, optimizer, opt_level="O2") trainer = create_supervised_trainer( model, optimizer, mse_loss, device=trainer_device, output_transform=lambda x, y, y_pred, loss: (y_pred, loss.item()), amp_mode=amp_mode, scaler=scaler, gradient_accumulation_steps=gradient_accumulation_steps, model_transform=model_transform if model_transform is not None else lambda x: x, model_fn=( (lambda model, x: model(x, torch.tensor([0.01], device=model_device))) if with_model_fn else (lambda model, x: model(x)) ), ) assert model.fc.weight.data[0, 0].item() == approx(0.0) return trainer, model def _test_create_supervised_trainer( gradient_accumulation_steps: int = 1, model_device: Optional[str] = None, trainer_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False, with_model_transform: bool = False, with_model_fn: bool = False, ): trainer, model = _default_create_supervised_trainer( gradient_accumulation_steps=gradient_accumulation_steps, model_device=model_device, trainer_device=trainer_device, trace=trace, amp_mode=amp_mode, scaler=scaler, with_model_transform=with_model_transform, with_model_fn=with_model_fn, ) x = torch.tensor([[0.01], [0.02], [0.03], [0.04], [0.05]]) y = torch.tensor([[0.015], [0.025], [0.035], [0.045], [0.055]]) if with_model_fn: y += 0.01 data = [(_x, _y) for _x, _y in zip(x, y)] theta = [0.0] accumulation = [0.0] loss = [0.0] @trainer.on(Events.ITERATION_COMPLETED) def _(): assert model.fc.weight.grad != 0 _x, _y = trainer.state.batch _x, _y = _x.to(model_device), _y.to(model_device) bias = 0.01 if with_model_fn else 0.0 accumulation[0] += 0.2 * _x.item() * (theta[0] * _x.item() - (_y.item() - bias)) # value of loss should not be accumulated _y_pred = model(_x, torch.tensor([bias], device=model_device)) if with_model_fn else model(_x) if with_model_transform: _y_pred = _y_pred[0] loss[0] = mse_loss(_y_pred, _y).item() @trainer.on(Events.ITERATION_COMPLETED(every=gradient_accumulation_steps)) def _(): theta[0] -= accumulation[0] / gradient_accumulation_steps assert pytest.approx(model.fc.weight.data[0, 0].item(), abs=1.0e-5) == theta[0] assert pytest.approx(trainer.state.output[-1], abs=1e-5) == loss[0] accumulation[0] = loss[0] = 0.0 if model_device == trainer_device or ((model_device == "cpu") ^ (trainer_device == "cpu")): state = trainer.run(data) if amp_mode == "amp": assert state.output[0].dtype is torch.half if scaler and isinstance(scaler, bool): assert hasattr(state, "scaler") else: assert not hasattr(state, "scaler") else: if Version(torch.__version__) >= Version("1.7.0"): # This is broken in 1.6.0 but will be probably fixed with 1.7.0 with pytest.raises(RuntimeError, match=r"Expected all tensors to be on the same device"): trainer.run(data) @pytest.mark.skipif(Version(torch.__version__) < Version("1.6.0"), reason="Skip if < 1.6.0") def test_create_supervised_training_scalar_assignment(): with mock.patch("ignite.engine._check_arg") as check_arg_mock: check_arg_mock.return_value = None, torch.cuda.amp.GradScaler(enabled=False) trainer, _ = _default_create_supervised_trainer(model_device="cpu", trainer_device="cpu", scaler=True) assert hasattr(trainer.state, "scaler") assert isinstance(trainer.state.scaler, torch.cuda.amp.GradScaler) def _test_create_mocked_supervised_trainer( model_device: Optional[str] = None, trainer_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False, ): with mock.patch("ignite.engine.supervised_training_step_amp") as training_step_amp_mock: with mock.patch("ignite.engine.supervised_training_step_apex") as training_step_apex_mock: with mock.patch("ignite.engine.supervised_training_step_tpu") as training_step_tpu_mock: with mock.patch("ignite.engine.supervised_training_step") as training_step_mock: trainer, _ = _default_create_supervised_trainer( model_device=model_device, trainer_device=trainer_device, trace=trace, amp_mode=amp_mode, scaler=scaler, ) x = torch.tensor([[0.1], [0.2]]) y = torch.tensor([[0.3], [0.5]]) data = [(x, y)] on_tpu = "xla" in trainer_device if trainer_device is not None else False on_mps = "mps" in trainer_device if trainer_device is not None else False mode, _ = _check_arg(on_tpu, on_mps, amp_mode, scaler) if model_device == trainer_device or ((model_device == "cpu") ^ (trainer_device == "cpu")): trainer.run(data) if mode == "amp": assert training_step_amp_mock.called elif mode == "apex": assert training_step_apex_mock.called elif mode == "tpu": assert training_step_tpu_mock.called else: assert training_step_mock.called def _test_create_supervised_trainer_wrong_accumulation( model_device=None, trainer_device=None, amp_mode=None, trace=False ): with pytest.raises(ValueError, match="Gradient_accumulation_steps must be strictly positive."): _default_create_supervised_trainer( gradient_accumulation_steps=0, model_device=model_device, trainer_device=trainer_device, amp_mode=amp_mode, trace=trace, ) def _default_create_supervised_evaluator( model_device: Optional[str] = None, evaluator_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, with_model_transform: bool = False, with_model_fn: bool = False, ): if with_model_transform: def get_first_element(output): return output[0] model = DummyModel(output_as_list=True) model_transform = get_first_element else: model = DummyModel() model_transform = None if model_device: model.to(model_device) model.fc.weight.data.zero_() if trace: example_inputs = (torch.randn(1), torch.randn(1)) if with_model_fn else torch.randn(1) model = torch.jit.trace(model, example_inputs) evaluator = create_supervised_evaluator( model, device=evaluator_device, amp_mode=amp_mode, model_transform=model_transform if model_transform is not None else lambda x: x, model_fn=( (lambda model, x: model(x, torch.tensor([0.01], device=model_device))) if with_model_fn else (lambda model, x: model(x)) ), ) assert model.fc.weight.data[0, 0].item() == approx(0.0) return model, evaluator def _test_create_supervised_evaluator( model_device: Optional[str] = None, evaluator_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, with_model_transform: bool = False, with_model_fn: bool = False, ): model, evaluator = _default_create_supervised_evaluator( model_device=model_device, evaluator_device=evaluator_device, trace=trace, amp_mode=amp_mode, with_model_transform=with_model_transform, with_model_fn=with_model_fn, ) x = torch.tensor([[1.0], [2.0]]) y = torch.tensor([[3.0], [5.0]]) if with_model_fn: y += 0.01 data = [(x, y)] if model_device == evaluator_device or ((model_device == "cpu") ^ (evaluator_device == "cpu")): state = evaluator.run(data) y_pred, y = state.output if with_model_fn: y_pred -= 0.01 y -= 0.01 assert y_pred[0, 0].item() == approx(0.0) assert y_pred[1, 0].item() == approx(0.0) assert y[0, 0].item() == approx(3.0) assert y[1, 0].item() == approx(5.0) assert model.fc.weight.data[0, 0].item() == approx(0.0) else: if Version(torch.__version__) >= Version("1.7.0"): # This is broken in 1.6.0 but will be probably fixed with 1.7.0 err_msg_1 = "Expected all tensors to be on the same device" err_msg_2 = "Placeholder storage has not been allocated on MPS device" err_msg_3 = "Tensor for argument weight is on cpu but expected on mps" with pytest.raises(RuntimeError, match=f"({err_msg_1}|{err_msg_2}|{err_msg_3})"): evaluator.run(data) def _test_mocked_supervised_evaluator( model_device: Optional[str] = None, evaluator_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, ): with mock.patch("ignite.engine.supervised_evaluation_step") as evaluation_step: with mock.patch("ignite.engine.supervised_evaluation_step_amp") as evaluation_step_amp: _, evaluator = _default_create_supervised_evaluator( model_device=model_device, evaluator_device=evaluator_device, trace=trace, amp_mode=amp_mode ) x = torch.tensor([[1.0], [2.0]]) y = torch.tensor([[3.0], [5.0]]) data = [(x, y)] if model_device == evaluator_device or ((model_device == "cpu") ^ (evaluator_device == "cpu")): evaluator.run(data) if amp_mode == "amp": assert evaluation_step_amp.called assert not evaluation_step.called else: assert evaluation_step.called assert not evaluation_step_amp.called def _test_create_evaluation_step_amp( autocast_mock, model_device: Optional[str] = None, evaluator_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, ): output_transform_mock = MagicMock() model = DummyModel() if model_device: model.to(model_device) model.fc.weight.data.zero_() if trace: example_input = torch.randn(1, 1) model = torch.jit.trace(model, example_input) device_type = evaluator_device.type if isinstance(evaluator_device, torch.device) else evaluator_device on_tpu = "xla" in device_type if device_type is not None else False on_mps = "mps" in device_type if device_type is not None else False mode, _ = _check_arg(on_tpu, on_mps, amp_mode, None) evaluate_step = supervised_evaluation_step_amp(model, evaluator_device, output_transform=output_transform_mock) x = torch.tensor([[1.0], [2.0]]) y = torch.tensor([[3.0], [5.0]]) data = [(x, y)] evaluator = Engine(evaluate_step) evaluator.run(data) assert autocast_mock.called assert output_transform_mock.called def _test_create_evaluation_step( mock_torch_cuda_amp_module, model_device: Optional[str] = None, evaluator_device: Optional[str] = None, trace: bool = False, amp_mode: str = None, ): output_transform_mock = MagicMock() model = DummyModel() if model_device: model.to(model_device) model.fc.weight.data.zero_() if trace: example_input = torch.randn(1, 1) model = torch.jit.trace(model, example_input) device_type = evaluator_device.type if isinstance(evaluator_device, torch.device) else evaluator_device on_tpu = "xla" in device_type if device_type is not None else False on_mps = "mps" in device_type if device_type is not None else False mode, _ = _check_arg(on_tpu, on_mps, amp_mode, None) evaluate_step = supervised_evaluation_step(model, evaluator_device, output_transform=output_transform_mock) x = torch.tensor([[1.0], [2.0]]) y = torch.tensor([[3.0], [5.0]]) data = [(x, y)] evaluator = Engine(evaluate_step) evaluator.run(data) assert not mock_torch_cuda_amp_module.called assert output_transform_mock.called @pytest.mark.parametrize("trainer_device", [None, "cpu"]) @pytest.mark.parametrize("trace", [False, True]) def test_create_supervised_trainer(trainer_device, trace): _test_create_supervised_trainer_wrong_accumulation(trainer_device=trainer_device, trace=trace) _test_create_supervised_trainer(gradient_accumulation_steps=1, trainer_device=trainer_device, trace=trace) _test_create_supervised_trainer(gradient_accumulation_steps=3, trainer_device=trainer_device, trace=trace) _test_create_supervised_trainer(with_model_transform=True, trainer_device=trainer_device, trace=trace) _test_create_supervised_trainer(with_model_fn=True, trainer_device=trainer_device, trace=trace) _test_create_mocked_supervised_trainer(trainer_device=trainer_device, trace=trace) @pytest.mark.skipif(find_spec("apex"), reason="Skip if APEX") def test_create_supervised_trainer_apex_error(): with pytest.raises( ModuleNotFoundError, match="Please install apex from https://github.com/nvidia/apex to use amp_mode='apex'." ): _test_create_supervised_trainer_wrong_accumulation(trainer_device="cpu", amp_mode="apex") with pytest.raises( ModuleNotFoundError, match="Please install apex from https://github.com/nvidia/apex to use amp_mode='apex'." ): _test_create_supervised_trainer(amp_mode="apex") @pytest.fixture def mock_torch_cuda_amp_module(): with patch.dict( "sys.modules", {"torch.cuda.amp": None, "torch.cuda.amp.grad_scaler": None, "torch.cuda.amp.autocast_mode": None}, ): yield torch def test_create_supervised_trainer_amp_error(mock_torch_cuda_amp_module): with pytest.raises(ImportError, match="Please install torch>=1.6.0 to use amp_mode='amp'."): _test_create_supervised_trainer_wrong_accumulation(trainer_device="cpu", amp_mode="amp") with pytest.raises(ImportError, match="Please install torch>=1.6.0 to use amp_mode='amp'."): _test_create_supervised_trainer(amp_mode="amp") with pytest.raises(ImportError, match="Please install torch>=1.6.0 to use scaler argument."): _test_create_supervised_trainer(amp_mode="amp", scaler=True) @pytest.mark.skipif(Version(torch.__version__) < Version("1.5.0"), reason="Skip if < 1.5.0") def test_create_supervised_trainer_scaler_not_amp(): scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available()) with pytest.raises(ValueError, match=f"scaler argument is {scaler}, but amp_mode is None."): _test_create_supervised_trainer(amp_mode=None, scaler=scaler) with pytest.raises(ValueError, match="scaler argument is True, but amp_mode is None."): _test_create_supervised_trainer(amp_mode=None, scaler=True) with pytest.raises(ValueError, match="scaler argument is True, but amp_mode is apex."): _test_create_supervised_trainer(amp_mode="apex", scaler=True) with pytest.raises(ValueError, match=f"scaler argument is {scaler}, but amp_mode is apex."): _test_create_supervised_trainer(amp_mode="apex", scaler=scaler) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_trainer_on_cuda(): model_device = trainer_device = "cuda" _test_create_supervised_trainer_wrong_accumulation(model_device=model_device, trainer_device=trainer_device) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device ) _test_create_mocked_supervised_trainer(model_device=model_device, trainer_device=trainer_device) @pytest.mark.skipif(not (_torch_version_gt_112 and is_mps_available_and_functional()), reason="Skip if no MPS") def test_create_supervised_trainer_on_mps(): model_device = trainer_device = "mps" _test_create_supervised_trainer_wrong_accumulation(model_device=model_device, trainer_device=trainer_device) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device ) _test_create_mocked_supervised_trainer(model_device=model_device, trainer_device=trainer_device) @pytest.mark.skipif(Version(torch.__version__) < Version("1.6.0"), reason="Skip if < 1.6.0") @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_trainer_on_cuda_amp(): model_device = trainer_device = "cuda" _test_create_supervised_trainer_wrong_accumulation( model_device=model_device, trainer_device=trainer_device, amp_mode="amp" ) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device, amp_mode="amp" ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device, amp_mode="amp" ) _test_create_mocked_supervised_trainer(model_device=model_device, trainer_device=trainer_device, amp_mode="amp") @pytest.mark.skipif(Version(torch.__version__) < Version("1.6.0"), reason="Skip if < 1.6.0") @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_trainer_on_cuda_amp_scaler(): model_device = trainer_device = "cuda" _test_create_supervised_trainer_wrong_accumulation( model_device=model_device, trainer_device=trainer_device, amp_mode="amp" ) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=True, ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=True, ) _test_create_mocked_supervised_trainer( model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=True ) scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available()) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=scaler, ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=scaler, ) _test_create_mocked_supervised_trainer( model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=scaler ) # @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") # @pytest.mark.skipif(not find_spec("apex"), reason="Skip if no APEX") @pytest.mark.skip(reason="Temporarily disabled, as it fails because of an issue from apex side") def test_create_supervised_trainer_on_cuda_apex(): model_device = trainer_device = "cuda" _test_create_supervised_trainer_wrong_accumulation( model_device=model_device, trainer_device=trainer_device, amp_mode="apex" ) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device, amp_mode="apex" ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device, amp_mode="apex" ) _test_create_mocked_supervised_trainer(model_device=model_device, trainer_device=trainer_device, amp_mode="apex") @pytest.mark.skipif(idist.has_xla_support, reason="Skip if has PyTorch XLA package") def test_supervised_training_step_tpu_no_xla(): with pytest.raises(ModuleNotFoundError, match="torch_xla cannot be imported, please install PyTorch XLA."): supervised_training_step_tpu(model=None, optimizer=None, loss_fn=None) @pytest.mark.skipif(idist.has_xla_support, reason="Skip if has PyTorch XLA package") def test_create_supervised_trainer_on_tpu_no_xla(): model_device = "cpu" trainer_device = "xla" with pytest.raises(RuntimeError, match=r"In order to run on TPU, please install PyTorch XLA"): _test_create_supervised_trainer(model_device=model_device, trainer_device=trainer_device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_create_supervised_trainer_on_tpu(): model_device = trainer_device = "xla" _test_create_supervised_trainer_wrong_accumulation(model_device=model_device, trainer_device=trainer_device) _test_create_supervised_trainer( gradient_accumulation_steps=1, model_device=model_device, trainer_device=trainer_device ) _test_create_supervised_trainer( gradient_accumulation_steps=3, model_device=model_device, trainer_device=trainer_device ) _test_create_mocked_supervised_trainer(model_device=model_device, trainer_device=trainer_device) @pytest.mark.tpu @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_create_supervised_trainer_on_tpu_amp(): model_device = trainer_device = "xla" with pytest.raises(ValueError, match="amp_mode cannot be used with xla device."): _test_create_supervised_trainer(model_device=model_device, trainer_device=trainer_device, amp_mode="amp") @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_trainer_on_cuda_with_model_on_cpu(): _test_create_supervised_trainer_wrong_accumulation(trainer_device="cuda") _test_create_supervised_trainer(gradient_accumulation_steps=1, trainer_device="cuda") _test_create_supervised_trainer(gradient_accumulation_steps=3, trainer_device="cuda") _test_create_mocked_supervised_trainer(trainer_device="cuda") def test_create_supervised_evaluator(): _test_create_supervised_evaluator() _test_create_supervised_evaluator(with_model_transform=True) _test_create_supervised_evaluator(with_model_fn=True) _test_mocked_supervised_evaluator() # older versions didn't have the autocast method so we skip the test for older builds if Version(torch.__version__) >= Version("1.6.0"): with mock.patch("torch.cuda.amp.autocast") as mock_torch_cuda_amp_module: _test_create_evaluation_step_amp(mock_torch_cuda_amp_module) def test_create_supervised_evaluator_on_cpu(): _test_create_supervised_evaluator(evaluator_device="cpu") _test_mocked_supervised_evaluator(evaluator_device="cpu") # older versions didn't have the autocast method so we skip the test for older builds if Version(torch.__version__) >= Version("1.6.0"): with mock.patch("torch.cuda.amp.autocast") as mock_torch_cuda_amp_module: _test_create_evaluation_step(mock_torch_cuda_amp_module, evaluator_device="cpu") _test_create_evaluation_step_amp(mock_torch_cuda_amp_module, evaluator_device="cpu") def test_create_supervised_evaluator_traced_on_cpu(): _test_create_supervised_evaluator(evaluator_device="cpu", trace=True) _test_mocked_supervised_evaluator(evaluator_device="cpu", trace=True) # older versions didn't have the autocast method so we skip the test for older builds if Version(torch.__version__) >= Version("1.6.0"): with mock.patch("torch.cuda.amp.autocast") as mock_torch_cuda_amp_module: _test_create_evaluation_step(mock_torch_cuda_amp_module, evaluator_device="cpu", trace=True) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_evaluator_on_cuda(): model_device = evaluator_device = "cuda" _test_create_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device) _test_mocked_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_evaluator_on_cuda_with_model_on_cpu(): _test_create_supervised_evaluator(evaluator_device="cuda") _test_mocked_supervised_evaluator(evaluator_device="cuda") @pytest.mark.skipif(not (_torch_version_gt_112 and is_mps_available_and_functional()), reason="Skip if no MPS") def test_create_supervised_evaluator_on_mps(): model_device = evaluator_device = "mps" _test_create_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device) _test_mocked_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device) @pytest.mark.skipif(not (_torch_version_gt_112 and is_mps_available_and_functional()), reason="Skip if no MPS") def test_create_supervised_evaluator_on_mps_with_model_on_cpu(): _test_create_supervised_evaluator(evaluator_device="mps") _test_mocked_supervised_evaluator(evaluator_device="mps") @pytest.mark.skipif(Version(torch.__version__) < Version("1.6.0"), reason="Skip if < 1.6.0") @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_create_supervised_evaluator_on_cuda_amp(): model_device = evaluator_device = "cuda" _test_create_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device, amp_mode="amp") _test_mocked_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device, amp_mode="amp") def test_create_supervised_evaluator_amp_error(mock_torch_cuda_amp_module): with pytest.raises(ImportError, match="Please install torch>=1.6.0 to use amp_mode='amp'."): _test_create_supervised_evaluator(amp_mode="amp") def test_create_supervised_evaluator_with_metrics(): model = DummyModel() model.fc.weight.data.zero_() evaluator = create_supervised_evaluator(model, metrics={"mse": MeanSquaredError()}) x = torch.tensor([[1.0], [2.0]]) y = torch.tensor([[3.0], [4.0]]) data = [(x, y)] state = evaluator.run(data) assert state.metrics["mse"] == 12.5 ignite-0.5.1/tests/ignite/engine/test_custom_events.py000066400000000000000000000540351465426447700232230ustar00rootroot00000000000000from enum import Enum from unittest.mock import MagicMock import pytest import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.engine.events import CallableEventWithFilter, EventEnum, EventsList def test_custom_events(): class CustomEvents(EventEnum): TEST_EVENT = "test_event" # Dummy engine engine = Engine(lambda engine, batch: 0) engine.register_events(*CustomEvents) engine.register_events("a", "b", "c") evs = [CustomEvents.TEST_EVENT, "a", "b", "c"] # Handle is never called handlers = [(e, MagicMock()) for e in evs] for e, h in handlers: engine.add_event_handler(e, h) engine.run(range(1)) for _, h in handlers: assert not h.called # Advanced engine def process_func(engine, batch): for e, _ in handlers: engine.fire_event(e) engine = Engine(process_func) engine.register_events(*CustomEvents) engine.register_events("a", "b", "c") # Handle should be called handlers = [(e, MagicMock()) for e in evs] for e, h in handlers: engine.add_event_handler(e, h) engine.run(range(1)) for _, h in handlers: assert h.called def test_custom_events_asserts(): # Dummy engine engine = Engine(lambda engine, batch: 0) class A: pass with pytest.raises(TypeError, match=r"Value at \d of event_names should be a str or EventEnum"): engine.register_events(None) with pytest.raises(TypeError, match=r"Value at \d of event_names should be a str or EventEnum"): engine.register_events("str", None) with pytest.raises(TypeError, match=r"Value at \d of event_names should be a str or EventEnum"): engine.register_events(1) with pytest.raises(TypeError, match=r"Value at \d of event_names should be a str or EventEnum"): engine.register_events(A()) assert Events.EPOCH_COMPLETED != 1 assert Events.EPOCH_COMPLETED != "abc" assert Events.ITERATION_COMPLETED != Events.EPOCH_COMPLETED assert Events.ITERATION_COMPLETED != Events.EPOCH_COMPLETED(every=2) # In current implementation, EPOCH_COMPLETED and EPOCH_COMPLETED with event filter are the same assert Events.EPOCH_COMPLETED == Events.EPOCH_COMPLETED(every=2) assert Events.ITERATION_COMPLETED == Events.ITERATION_COMPLETED(every=2) def test_custom_events_with_event_to_attr(): class CustomEvents(EventEnum): TEST_EVENT = "test_event" custom_event_to_attr = {CustomEvents.TEST_EVENT: "test_event"} # Dummy engine engine = Engine(lambda engine, batch: 0) engine.register_events(*CustomEvents, event_to_attr=custom_event_to_attr) # Handle is never called handle = MagicMock() engine.add_event_handler(CustomEvents.TEST_EVENT, handle) engine.run(range(1)) assert hasattr(engine.state, "test_event") assert engine.state.test_event == 0 # Advanced engine def process_func(engine, batch): engine.fire_event(CustomEvents.TEST_EVENT) engine = Engine(process_func) engine.register_events(*CustomEvents, event_to_attr=custom_event_to_attr) def handle(engine): engine.state.test_event += 1 engine.add_event_handler(CustomEvents.TEST_EVENT, handle) engine.run(range(25)) assert engine.state.test_event == 25 custom_event_to_attr = "a" engine = Engine(lambda engine, batch: 0) with pytest.raises(ValueError): engine.register_events(*CustomEvents, event_to_attr=custom_event_to_attr) def test_custom_events_with_events_list(): class CustomEvents(EventEnum): TEST_EVENT = "test_event" def process_func(engine, batch): engine.fire_event(CustomEvents.TEST_EVENT) engine = Engine(process_func) engine.register_events(*CustomEvents) # Handle should be called handle = MagicMock() engine.add_event_handler(CustomEvents.TEST_EVENT | Events.STARTED, handle) engine.run(range(1)) assert handle.called def test_callable_events_with_wrong_inputs(): def ef(e, i): return 1 expected_raise = { # event_filter, every, once, before, after (None, None, None, None, None): True, # raises ValueError (ef, None, None, None, None): False, (None, 2, None, None, None): False, (ef, 2, None, None, None): True, (None, None, 2, None, None): False, (ef, None, 2, None, None): True, (None, 2, 2, None, None): True, (ef, 2, 2, None, None): True, (None, None, None, 30, None): False, (ef, None, None, 30, None): True, (None, 2, None, 30, None): False, (ef, 2, None, 30, None): True, (None, None, 2, 30, None): True, (ef, None, 2, 30, None): True, (None, 2, 2, 30, None): True, (ef, 2, 2, 30, None): True, # event_filter, every, once, before, after (None, None, None, None, 10): False, (ef, None, None, None, 10): True, (None, 2, None, None, 10): False, (ef, 2, None, None, 10): True, (None, None, 2, None, 10): True, (ef, None, 2, None, 10): True, (None, 2, 2, None, 10): True, (ef, 2, 2, None, 10): True, (None, None, None, 25, 8): False, (ef, None, None, 25, 8): True, (None, 2, None, 25, 8): False, (ef, 2, None, 25, 8): True, (None, None, 2, 25, 8): True, (ef, None, 2, 25, 8): True, (None, 2, 2, 25, 8): True, (ef, 2, 2, 25, 8): True, } for event_filter in [None, ef]: for every in [None, 2]: for once in [None, 2]: for before, after in [(None, None), (None, 10), (30, None), (25, 8)]: if expected_raise[(event_filter, every, once, before, after)]: with pytest.raises( ValueError, match=r"Only one of the input arguments should be specified, " "except before, after and every", ): Events.ITERATION_STARTED( event_filter=event_filter, once=once, every=every, before=before, after=after ) else: Events.ITERATION_STARTED( event_filter=event_filter, once=once, every=every, before=before, after=after ) with pytest.raises(TypeError, match=r"Argument event_filter should be a callable"): Events.ITERATION_STARTED(event_filter="123") with pytest.raises(ValueError, match=r"Argument every should be integer and greater than zero"): Events.ITERATION_STARTED(every=-1) with pytest.raises( ValueError, match=r"Argument once should either be a positive integer or a list of positive integers, got .+" ): Events.ITERATION_STARTED(once=-1) with pytest.raises( ValueError, match=r"Argument once should either be a positive integer or a list of positive integers, got .+" ): Events.ITERATION_STARTED(once=[1, 10.0, "pytorch"]) with pytest.raises( ValueError, match=r"Argument once should either be a positive integer or a list of positive integers, got .+" ): Events.ITERATION_STARTED(once=[]) with pytest.raises(ValueError, match=r"Argument before should be integer and greater or equal to zero"): Events.ITERATION_STARTED(before=-1) with pytest.raises(ValueError, match=r"Argument after should be integer and greater or equal to zero"): Events.ITERATION_STARTED(after=-1) with pytest.raises(ValueError, match=r"but will be called with"): Events.ITERATION_STARTED(event_filter=lambda x: x) with pytest.warns(UserWarning, match=r"default_event_filter is deprecated and will be removed"): Events.default_event_filter(None, None) @pytest.mark.parametrize( "event", [ Events.ITERATION_STARTED, Events.ITERATION_COMPLETED, Events.EPOCH_STARTED, Events.EPOCH_COMPLETED, Events.GET_BATCH_STARTED, Events.GET_BATCH_COMPLETED, Events.STARTED, Events.COMPLETED, ], ) def test_callable_events(event): assert isinstance(event.value, str) def foo(engine, _): return True ret = event(event_filter=foo) assert isinstance(ret, CallableEventWithFilter) assert ret == event assert ret.filter == foo assert event.name in f"{ret}" ret = event(every=10) assert isinstance(ret, CallableEventWithFilter) assert ret == event assert ret.filter is not None assert event.name in f"{ret}" ret = event(once=10) assert isinstance(ret, CallableEventWithFilter) assert ret == event assert ret.filter is not None assert event.name in f"{ret}" ret = event(once=[1, 10]) assert isinstance(ret, CallableEventWithFilter) assert ret == event assert ret.filter is not None assert event.name in f"{ret}" ret = event assert isinstance(ret, CallableEventWithFilter) assert ret.filter is None assert event.name in f"{ret}" def test_callable_events_every_eq_one(): e = Events.ITERATION_STARTED(every=1) assert isinstance(e, CallableEventWithFilter) def test_has_handler_on_callable_events(): engine = Engine(lambda e, b: 1) def foo(e): pass assert not engine.has_event_handler(foo) engine.add_event_handler(Events.EPOCH_STARTED, foo) assert engine.has_event_handler(foo) def bar(e): pass engine.add_event_handler(Events.EPOCH_COMPLETED(every=3), bar) assert engine.has_event_handler(bar) assert engine.has_event_handler(bar, Events.EPOCH_COMPLETED) assert engine.has_event_handler(bar, Events.EPOCH_COMPLETED(every=3)) def test_remove_event_handler_on_callable_events(): engine = Engine(lambda e, b: 1) def foo(e): pass assert not engine.has_event_handler(foo) engine.add_event_handler(Events.EPOCH_STARTED, foo) assert engine.has_event_handler(foo) engine.remove_event_handler(foo, Events.EPOCH_STARTED) assert not engine.has_event_handler(foo) def bar(e): pass engine.add_event_handler(Events.EPOCH_COMPLETED(every=3), bar) assert engine.has_event_handler(bar) engine.remove_event_handler(bar, Events.EPOCH_COMPLETED) assert not engine.has_event_handler(bar) engine.add_event_handler(Events.EPOCH_COMPLETED(every=3), bar) assert engine.has_event_handler(bar) engine.remove_event_handler(bar, Events.EPOCH_COMPLETED(every=3)) assert not engine.has_event_handler(bar) def _test_every_event_filter_with_engine(device="cpu"): data = torch.rand(100, 4, device=device) def _test(event_name, event_attr, every, true_num_calls): engine = Engine(lambda e, b: b) counter = [0] counter_every = [0] num_calls = [0] @engine.on(event_name(every=every)) def assert_every(engine): counter_every[0] += every assert getattr(engine.state, event_attr) % every == 0 assert counter_every[0] == getattr(engine.state, event_attr) num_calls[0] += 1 @engine.on(event_name(every=every)) def assert_every_no_engine(): assert getattr(engine.state, event_attr) % every == 0 assert counter_every[0] == getattr(engine.state, event_attr) @engine.on(event_name) def assert_(engine): counter[0] += 1 assert getattr(engine.state, event_attr) == counter[0] @engine.on(event_name) def assert_no_engine(): assert getattr(engine.state, event_attr) == counter[0] engine.run(data, max_epochs=5) assert num_calls[0] == true_num_calls _test(Events.ITERATION_STARTED, "iteration", 10, 100 * 5 // 10) _test(Events.ITERATION_COMPLETED, "iteration", 10, 100 * 5 // 10) _test(Events.EPOCH_STARTED, "epoch", 2, 5 // 2) _test(Events.EPOCH_COMPLETED, "epoch", 2, 5 // 2) def test_every_event_filter_with_engine(): _test_every_event_filter_with_engine() @pytest.mark.parametrize( "event_name, event_attr, before, expect_calls", [ (Events.ITERATION_COMPLETED, "iteration", 0, 0), (Events.ITERATION_COMPLETED, "iteration", 300, 299), (Events.ITERATION_COMPLETED, "iteration", 501, 500), (Events.EPOCH_COMPLETED, "epoch", 0, 0), (Events.EPOCH_COMPLETED, "epoch", 3, 2), (Events.EPOCH_COMPLETED, "epoch", 6, 5), ], ) def test_before_event_filter_with_engine(event_name, event_attr, before, expect_calls): data = range(100) engine = Engine(lambda e, b: 1) num_calls = 0 @engine.on(event_name(before=before)) def _before_event(): nonlocal num_calls num_calls += 1 assert getattr(engine.state, event_attr) < before engine.run(data, max_epochs=5) assert num_calls == expect_calls @pytest.mark.parametrize( "event_name, event_attr, after, expect_calls", [ (Events.ITERATION_STARTED, "iteration", 0, 500), (Events.ITERATION_COMPLETED, "iteration", 300, 200), (Events.ITERATION_COMPLETED, "iteration", 500, 0), (Events.EPOCH_STARTED, "epoch", 0, 5), (Events.EPOCH_COMPLETED, "epoch", 3, 2), (Events.EPOCH_COMPLETED, "epoch", 5, 0), ], ) def test_after_event_filter_with_engine(event_name, event_attr, after, expect_calls): data = range(100) engine = Engine(lambda e, b: 1) num_calls = 0 @engine.on(event_name(after=after)) def _after_event(): nonlocal num_calls num_calls += 1 assert getattr(engine.state, event_attr) > after engine.run(data, max_epochs=5) assert num_calls == expect_calls @pytest.mark.parametrize( "event_name, event_attr, before, after, expect_calls", [(Events.ITERATION_STARTED, "iteration", 300, 100, 199), (Events.EPOCH_COMPLETED, "epoch", 4, 1, 2)], ) def test_before_and_after_event_filter_with_engine(event_name, event_attr, before, after, expect_calls): data = range(100) engine = Engine(lambda e, b: 1) num_calls = 0 @engine.on(event_name(before=before, after=after)) def _before_and_after_event(): nonlocal num_calls num_calls += 1 assert getattr(engine.state, event_attr) > after engine.run(data, max_epochs=5) assert num_calls == expect_calls @pytest.mark.parametrize( "event_name, event_attr, every, before, after, expect_calls", [(Events.ITERATION_STARTED, "iteration", 5, 25, 8, 4), (Events.EPOCH_COMPLETED, "epoch", 2, 5, 1, 2)], ) def test_every_before_and_after_event_filter_with_engine(event_name, event_attr, every, before, after, expect_calls): data = range(100) engine = Engine(lambda e, b: 1) num_calls = 0 @engine.on(event_name(every=every, before=before, after=after)) def _every_before_and_after_event(): assert getattr(engine.state, event_attr) > after assert getattr(engine.state, event_attr) < before assert ((getattr(engine.state, event_attr) - after - 1) % every) == 0 nonlocal num_calls num_calls += 1 engine.run(data, max_epochs=5) assert num_calls == expect_calls @pytest.mark.parametrize( "event_name, event_attr, once, expect_calls", [ (Events.ITERATION_STARTED, "iteration", 2, 1), (Events.ITERATION_COMPLETED, "iteration", 2, 1), (Events.EPOCH_STARTED, "epoch", 2, 1), (Events.EPOCH_COMPLETED, "epoch", 2, 1), (Events.ITERATION_STARTED, "iteration", [1, 5], 2), (Events.ITERATION_COMPLETED, "iteration", [1, 5], 2), (Events.EPOCH_STARTED, "epoch", [1, 5], 2), (Events.EPOCH_COMPLETED, "epoch", [1, 5], 2), ], ) def test_once_event_filter(event_name, event_attr, once, expect_calls): data = list(range(100)) engine = Engine(lambda e, b: b) num_calls = [0] counter = [0] test_once = [once] if isinstance(once, int) else once @engine.on(event_name(once=once)) def assert_once(engine): assert getattr(engine.state, event_attr) in test_once num_calls[0] += 1 @engine.on(event_name) def assert_(engine): counter[0] += 1 assert getattr(engine.state, event_attr) == counter[0] engine.run(data, max_epochs=10) assert num_calls[0] == expect_calls def test_custom_event_filter_with_engine(): special_events = [1, 2, 5, 7, 17, 20] def custom_event_filter(engine, event): if event in special_events: return True return False def _test(event_name, event_attr, true_num_calls): engine = Engine(lambda e, b: b) num_calls = [0] @engine.on(event_name(event_filter=custom_event_filter)) def assert_on_special_event(engine): assert getattr(engine.state, event_attr) == special_events.pop(0) num_calls[0] += 1 d = list(range(50)) engine.run(d, max_epochs=25) assert num_calls[0] == true_num_calls _test(Events.ITERATION_STARTED, "iteration", len(special_events)) _test(Events.ITERATION_COMPLETED, "iteration", len(special_events)) _test(Events.EPOCH_STARTED, "epoch", len(special_events)) _test(Events.EPOCH_COMPLETED, "epoch", len(special_events)) def test_callable_event_bad_behaviour(): special_events = [1, 2, 5, 7, 17, 20] def custom_event_filter(engine, event): if event in special_events: return True return False # Check bad behaviour engine = Engine(lambda e, b: b) counter = [0] # Modify events Events.ITERATION_STARTED(event_filter=custom_event_filter) @engine.on(Events.ITERATION_STARTED) def assert_all_iters(engine): counter[0] += 1 assert engine.state.iteration == counter[0] d = list(range(50)) engine.run(d, max_epochs=25) assert counter[0] == engine.state.iteration def test_custom_callable_events(): class CustomEvents(Enum): TEST_EVENT = "test_event" with pytest.raises(TypeError, match=r"object is not callable"): CustomEvents.TEST_EVENT(every=10) class CustomEvents2(EventEnum): TEST_EVENT = "test_event" CustomEvents2.TEST_EVENT(every=10) def test_custom_callable_events_with_engine(): class CustomEvents(EventEnum): TEST_EVENT = "test_event" event_to_attr = {CustomEvents.TEST_EVENT: "test_event"} special_events = [1, 2, 5, 7, 17, 20] def custom_event_filter(engine, event): if event in special_events: return True return False def _test(event_name, event_attr, true_num_calls): def update_fn(engine, batch): engine.state.test_event = engine.state.iteration engine.fire_event(CustomEvents.TEST_EVENT) engine = Engine(update_fn) engine.register_events(*CustomEvents, event_to_attr=event_to_attr) num_calls = [0] @engine.on(event_name(event_filter=custom_event_filter)) def assert_on_special_event(engine): assert getattr(engine.state, event_attr) == special_events.pop(0) num_calls[0] += 1 d = list(range(50)) engine.run(d, max_epochs=25) assert num_calls[0] == true_num_calls _test(CustomEvents.TEST_EVENT, "test_event", len(special_events)) def _test_every_event_filter_with_engine_with_dataloader(device): def _test(num_workers): max_epochs = 3 batch_size = 4 num_iters = 21 data = torch.randint(0, 1000, size=(num_iters * batch_size,)) dataloader = torch.utils.data.DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory="cuda" in torch.device(device).type, drop_last=True, shuffle=True, ) seen_batchs = [] def update_fn(_, batch): batch_to_device = batch.to(device) seen_batchs.append(batch) engine = Engine(update_fn) def foo(_): pass engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo) engine.run(dataloader, max_epochs=max_epochs) engine = None import gc gc.collect() assert len(gc.garbage) == 0 _test(num_workers=0) _test(num_workers=1) def test_every_event_filter_with_engine_with_dataloader(): _test_every_event_filter_with_engine_with_dataloader("cpu") @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_every_event_filter_with_engine(device) _test_every_event_filter_with_engine_with_dataloader(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_every_event_filter_with_engine(device) _test_every_event_filter_with_engine_with_dataloader(device) def test_event_list(): e1 = Events.ITERATION_STARTED(once=1) e2 = Events.ITERATION_STARTED(every=3) e3 = Events.COMPLETED event_list = e1 | e2 | e3 assert isinstance(event_list, EventsList) assert len(event_list) == 3 assert event_list[0] == e1 assert event_list[1] == e2 assert event_list[2] == e3 def test_list_of_events(): def _test(event_list, true_iterations): engine = Engine(lambda e, b: b) iterations = [] num_calls = [0] @engine.on(event_list) def execute_some_handler(e): iterations.append(e.state.iteration) num_calls[0] += 1 engine.run(range(3), max_epochs=5) assert iterations == true_iterations assert num_calls[0] == len(true_iterations) _test(Events.ITERATION_STARTED(once=1) | Events.ITERATION_STARTED(once=1), [1, 1]) _test(Events.ITERATION_STARTED(once=1) | Events.ITERATION_STARTED(once=10), [1, 10]) _test(Events.ITERATION_STARTED(once=1) | Events.ITERATION_STARTED(every=3), [1, 3, 6, 9, 12, 15]) _test(Events.ITERATION_STARTED(once=8) | Events.ITERATION_STARTED(before=3), [1, 2, 8]) _test(Events.ITERATION_STARTED(once=1) | Events.ITERATION_STARTED(after=12), [1, 13, 14, 15]) ignite-0.5.1/tests/ignite/engine/test_deterministic.py000066400000000000000000000745231465426447700231740ustar00rootroot00000000000000import os import random import sys from collections.abc import Mapping from unittest.mock import patch import numpy as np import pytest import torch import torch.nn as nn from torch.optim import SGD from torch.utils.data import BatchSampler, DataLoader, RandomSampler import ignite.distributed as idist from ignite.engine import Events from ignite.engine.deterministic import ( _set_rng_states, DeterministicEngine, keep_random_state, ReproducibleBatchSampler, update_dataloader, ) from ignite.utils import manual_seed from tests.ignite.engine import BatchChecker, setup_sampler def test_dengine_setup_seed_div_by_zero(): with pytest.raises(ValueError, match=r"iter_counter should be positive value"): DeterministicEngine(lambda e, b: None)._setup_seed(iter_counter=0) def test_update_dataloader(): def _test(sampler_type=None): num_epochs = 3 total_batch_size = 4 num_iters = 17 data = torch.randint(0, 1000, size=(num_iters * total_batch_size,)) num_workers = 2 sampler, batch_size = setup_sampler(sampler_type, num_iters, total_batch_size) dataloader = DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory=False, sampler=sampler, drop_last=True, shuffle=sampler is None, ) torch.manual_seed(12) seen_batches = [] for i in range(num_epochs): t = [] if sampler_type == "distributed": sampler.set_epoch(i) for b in dataloader: t.append(b) seen_batches.append(t) sampler, batch_size = setup_sampler(sampler_type, num_iters, total_batch_size) dataloader = DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory=False, sampler=sampler, drop_last=True, shuffle=sampler is None, ) batch_sampler = dataloader.batch_sampler new_dataloader = update_dataloader(dataloader, ReproducibleBatchSampler(batch_sampler)) torch.manual_seed(12) new_batches = [] for i in range(num_epochs): t = [] if sampler_type == "distributed": sampler.set_epoch(i) for b in new_dataloader: t.append(b) new_batches.append(t) for i in range(num_epochs): assert all([(b1 == b2).all() for b1, b2 in zip(seen_batches[i], new_batches[i])]) _test() _test("weighted") _test("distributed") def test_reproducible_batch_sampler_wrong_input(): with pytest.raises(TypeError, match=r"Argument batch_sampler should be torch.utils.data.sampler.BatchSampler"): ReproducibleBatchSampler("abc") def test_reproducible_batch_sampler(): data = list(range(100)) dataloader = DataLoader(data, batch_size=12, num_workers=0, shuffle=True, drop_last=True) torch.manual_seed(12 + 0) dataloader_ = update_dataloader(dataloader, ReproducibleBatchSampler(dataloader.batch_sampler)) seen_batches = [] num_epochs = 3 for i in range(num_epochs): t = [] for b in dataloader_: t.append(b) seen_batches.append(t) torch.manual_seed(12 + i + 1) for i in range(num_epochs - 1): for j in range(i + 1, num_epochs): assert not all([(b1 == b2).all() for b1, b2 in zip(seen_batches[i], seen_batches[j])]) for resume_epoch in range(num_epochs): torch.manual_seed(12 + resume_epoch) dataloader_ = update_dataloader(dataloader, ReproducibleBatchSampler(dataloader.batch_sampler)) resumed_seen_batches = [] for b in dataloader_: resumed_seen_batches.append(b) assert all([(b1 == b2).all() for b1, b2 in zip(seen_batches[resume_epoch], resumed_seen_batches)]) def _test_keep_random_state(with_numpy): manual_seed(54) true_values = [] for _ in range(5): t = [ torch.tensor([random.random()]), torch.rand(2), ] if with_numpy: t.append(torch.from_numpy(np.random.rand(2))) true_values.append(t) @keep_random_state def user_handler(): manual_seed(22) _ = [ random.random(), torch.rand(2), ] if with_numpy: _ = np.random.rand(2) manual_seed(54) res_values = [] for _ in range(5): r = [ torch.tensor([random.random()]), torch.rand(2), ] if with_numpy: r.append(torch.from_numpy(np.random.rand(2))) res_values.append(r) user_handler() for a, b in zip(true_values, res_values): for i, j in zip(a, b): assert (i == j).all() def test_keep_random_state(): _test_keep_random_state(with_numpy=True) def test_keep_random_state_without_numpy(): with patch.dict("sys.modules", {"numpy": None}): _test_keep_random_state(with_numpy=False) def test_strict_resume_from_iter(): def _test(epoch_length=None): max_epochs = 5 num_iters = 21 torch.manual_seed(0) data = torch.randint(0, 1000, size=(num_iters,)) if epoch_length is None: epoch_length = num_iters for resume_iteration in range(2, min(num_iters * max_epochs, epoch_length * max_epochs), 4): batch_checker = BatchChecker(data, init_counter=resume_iteration) def update_fn(_, batch): assert batch_checker.check( batch ), f"{resume_iteration} | {batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = DeterministicEngine(update_fn) @engine.on(Events.EPOCH_COMPLETED) def check_iteration(_): assert engine.state.iteration == batch_checker.counter resume_state_dict = dict( iteration=resume_iteration, max_epochs=max_epochs, epoch_length=epoch_length, rng_states=None ) engine.load_state_dict(resume_state_dict) engine.run(data) assert engine.state.epoch == max_epochs assert engine.state.iteration == epoch_length * max_epochs _test() _test(60) _test(15) def test_strict_resume_from_epoch(): def _test(epoch_length=None): max_epochs = 10 num_iters = 21 torch.manual_seed(0) data = torch.randint(0, 1000, size=(num_iters,)) if epoch_length is None: epoch_length = num_iters for resume_epoch in range(1, max_epochs): batch_checker = BatchChecker(data, init_counter=resume_epoch * epoch_length) def update_fn(_, batch): assert batch_checker.check( batch ), f"{resume_epoch} | {batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = DeterministicEngine(update_fn) resume_state_dict = dict( epoch=resume_epoch, max_epochs=max_epochs, epoch_length=epoch_length, rng_states=None ) engine.load_state_dict(resume_state_dict) engine.run(data) assert engine.state.epoch == max_epochs assert engine.state.iteration == epoch_length * max_epochs _test() _test(60) _test(15) def _test_resume_random_dataloader_from_epoch(device, _setup_sampler, sampler_type=None): def _test(epoch_length=None): max_epochs = 5 total_batch_size = 4 num_iters = 21 torch.manual_seed(0) data = torch.randint(0, 1000, size=(num_iters * total_batch_size,)) if epoch_length is None: epoch_length = num_iters for resume_epoch in range(1, max_epochs, 2): for num_workers in [0, 2]: sampler, batch_size = _setup_sampler(sampler_type, num_iters, total_batch_size) orig_dataloader = DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory="cuda" in torch.device(device).type, sampler=sampler, drop_last=True, shuffle=sampler is None, ) seen_batchs = [] def update_fn(_, batch): batch_to_device = batch.to(device) seen_batchs.append(batch) engine = DeterministicEngine(update_fn) if sampler_type == "distributed": @engine.on(Events.EPOCH_STARTED) def _(engine): sampler.set_epoch(engine.state.epoch - 1) torch.manual_seed(87) engine.run(orig_dataloader, max_epochs=max_epochs, epoch_length=epoch_length) batch_checker = BatchChecker(seen_batchs, init_counter=resume_epoch * epoch_length) sampler, batch_size = _setup_sampler(sampler_type, num_iters, total_batch_size) resume_dataloader = DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory="cuda" in torch.device(device).type, sampler=sampler, drop_last=True, shuffle=sampler is None, ) def update_fn(_, batch): batch_to_device = batch.to(device) assert batch_checker.check( batch ), f"{num_workers} {resume_epoch} | {batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = DeterministicEngine(update_fn) if sampler_type == "distributed": @engine.on(Events.EPOCH_STARTED) def _(engine): sampler.set_epoch(engine.state.epoch - 1) resume_state_dict = dict( epoch=resume_epoch, max_epochs=max_epochs, epoch_length=epoch_length, rng_states=None ) engine.load_state_dict(resume_state_dict) torch.manual_seed(87) engine.run(resume_dataloader) assert engine.state.epoch == max_epochs assert engine.state.iteration == epoch_length * max_epochs _test() if sampler_type != "distributed": _test(60) _test(15) @pytest.mark.skipif("win" in sys.platform, reason="Skip extremely slow test on Windows/MacOSX") def test_resume_random_dataloader_from_epoch(): _test_resume_random_dataloader_from_epoch("cpu", setup_sampler) _test_resume_random_dataloader_from_epoch("cpu", setup_sampler, sampler_type="weighted") _test_resume_random_dataloader_from_epoch("cpu", setup_sampler, sampler_type="distributed") class AugmentedData: def __init__(self, data, enabled=True): self.data = data self.enabled = enabled def __getitem__(self, i): dp = self.data[i] r = torch.randint_like(dp, -100, 100) if self.enabled else 0.0 return dp + r * 0.01 def __len__(self): return len(self.data) def _test_resume_random_dataloader_from_iter(device, _setup_sampler, sampler_type=None): def _test(epoch_length=None): max_epochs = 3 total_batch_size = 4 num_iters = 17 torch.manual_seed(0) data = torch.randint(0, 1000, size=(num_iters * total_batch_size,)) if epoch_length is None: epoch_length = num_iters for resume_iteration in range(2, min(num_iters * max_epochs, epoch_length * max_epochs), 13): for num_workers in [0, 2]: sampler, batch_size = _setup_sampler(sampler_type, num_iters, total_batch_size) orig_dataloader = DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory="cuda" in torch.device(device).type, sampler=sampler, drop_last=True, shuffle=sampler is None, ) seen_batchs = [] def update_fn(_, batch): batch_to_device = batch.to(device) seen_batchs.append(batch) engine = DeterministicEngine(update_fn) if sampler_type == "distributed": @engine.on(Events.EPOCH_STARTED) def _(engine): sampler.set_epoch(engine.state.epoch) torch.manual_seed(12) engine.run(orig_dataloader, max_epochs=max_epochs, epoch_length=epoch_length) batch_checker = BatchChecker(seen_batchs, init_counter=resume_iteration) sampler, batch_size = _setup_sampler(sampler_type, num_iters, total_batch_size) resume_dataloader = DataLoader( data, batch_size=batch_size, num_workers=num_workers, pin_memory="cuda" in torch.device(device).type, sampler=sampler, drop_last=True, shuffle=sampler is None, ) def update_fn(_, batch): batch_to_device = batch.to(device) cfg_msg = f"{num_workers} {resume_iteration}" assert batch_checker.check( batch ), f"{cfg_msg} | {batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = DeterministicEngine(update_fn) if sampler_type == "distributed": @engine.on(Events.EPOCH_STARTED) def _(engine): sampler.set_epoch(engine.state.epoch) resume_state_dict = dict( iteration=resume_iteration, max_epochs=max_epochs, epoch_length=epoch_length, rng_states=None ) engine.load_state_dict(resume_state_dict) torch.manual_seed(12) engine.run(resume_dataloader) assert engine.state.epoch == max_epochs assert ( engine.state.iteration == epoch_length * max_epochs ), f"{num_workers}, {resume_iteration} | {engine.state.iteration} vs {epoch_length * max_epochs}" _test() if sampler_type != "distributed": _test(40) _test(11) @pytest.mark.skipif("win" in sys.platform, reason="Skip extremely slow test on Windows/MacOSX") def test_resume_random_dataloader_from_iter(): _test_resume_random_dataloader_from_iter("cpu", setup_sampler) _test_resume_random_dataloader_from_iter("cpu", setup_sampler, sampler_type="weighted") _test_resume_random_dataloader_from_iter("cpu", setup_sampler, sampler_type="distributed") def _test_resume_random_data_iterator_from_epoch(device): def _test(epoch_length=None): max_epochs = 5 batch_size = 4 num_iters = 21 def infinite_data_iterator(): while True: for _ in range(num_iters): data = torch.randint(0, 1000, size=(batch_size,), device=device) yield data if epoch_length is None: epoch_length = num_iters for resume_epoch in range(1, max_epochs): seen_batchs = [] def update_fn(_, batch): # if there is a random op when using data batch etc, we can not resume correctly # torch.rand(1) seen_batchs.append(batch) engine = DeterministicEngine(update_fn) torch.manual_seed(121) engine.run(infinite_data_iterator(), max_epochs=max_epochs, epoch_length=epoch_length) batch_checker = BatchChecker(seen_batchs, init_counter=resume_epoch * epoch_length) def update_fn(_, batch): assert batch_checker.check( batch ), f"{resume_epoch} | {batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = DeterministicEngine(update_fn) resume_state_dict = dict( epoch=resume_epoch, max_epochs=max_epochs, epoch_length=epoch_length, rng_states=None ) engine.load_state_dict(resume_state_dict) torch.manual_seed(121) engine.run(infinite_data_iterator()) assert engine.state.epoch == max_epochs assert engine.state.iteration == epoch_length * max_epochs _test() _test(60) _test(15) def test_resume_random_data_iterator_from_epoch(): _test_resume_random_data_iterator_from_epoch("cpu") def _test_resume_random_data_iterator_from_iter(device): def _test(epoch_length=None): max_epochs = 3 batch_size = 4 num_iters = 17 def infinite_data_iterator(): while True: for _ in range(num_iters): data = torch.randint(0, 1000, size=(batch_size,), device=device) yield data if epoch_length is None: epoch_length = num_iters for resume_iteration in range(1, min(num_iters * max_epochs, epoch_length * max_epochs), 7): seen_batchs = [] def update_fn(_, batch): seen_batchs.append(batch) engine = DeterministicEngine(update_fn) torch.manual_seed(24) engine.run(infinite_data_iterator(), max_epochs=max_epochs, epoch_length=epoch_length) batch_checker = BatchChecker(seen_batchs, init_counter=resume_iteration) def update_fn(_, batch): assert batch_checker.check( batch ), f"{resume_iteration} | {batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = DeterministicEngine(update_fn) resume_state_dict = dict( iteration=resume_iteration, max_epochs=max_epochs, epoch_length=epoch_length, rng_states=None ) engine.load_state_dict(resume_state_dict) torch.manual_seed(24) engine.run(infinite_data_iterator()) assert engine.state.epoch == max_epochs assert ( engine.state.iteration == epoch_length * max_epochs ), f"{resume_iteration} | {engine.state.iteration} vs {epoch_length * max_epochs}" _test() _test(50) _test(11) def test_resume_random_data_iterator_from_iter(): _test_resume_random_data_iterator_from_iter("cpu") @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_resume_random_dataloader_from_iter(device, setup_sampler, sampler_type="distributed") _test_resume_random_dataloader_from_epoch(device, setup_sampler, sampler_type="distributed") @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_resume_random_dataloader_from_iter(device, setup_sampler, sampler_type="distributed") _test_resume_random_dataloader_from_epoch(device, setup_sampler, sampler_type="distributed") @pytest.mark.xfail @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_resume_random_dataloader_from_iter(device, setup_sampler, sampler_type="distributed") _test_resume_random_dataloader_from_epoch(device, setup_sampler, sampler_type="distributed") @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_resume_random_dataloader_from_iter(device, setup_sampler, sampler_type="distributed") _test_resume_random_dataloader_from_epoch(device, setup_sampler, sampler_type="distributed") def test_concepts_snippet_resume(): # Commented imports required in the snippet # import torch # from torch.utils.data import DataLoader # from ignite.engine import DeterministicEngine # from ignite.utils import manual_seed seen_batches = [] manual_seed(seed=15) def random_train_data_loader(size): data = torch.arange(0, size) return DataLoader(data, batch_size=4, shuffle=True) def print_train_data(engine, batch): i = engine.state.iteration e = engine.state.epoch print("train", e, i, batch.tolist()) seen_batches.append(batch) trainer = DeterministicEngine(print_train_data) print("Original Run") manual_seed(56) trainer.run(random_train_data_loader(40), max_epochs=2, epoch_length=5) original_batches = list(seen_batches) seen_batches = [] print("Resumed Run") trainer.load_state_dict({"epoch": 1, "epoch_length": 5, "max_epochs": 2, "rng_states": None}) manual_seed(56) trainer.run(random_train_data_loader(40)) resumed_batches = list(seen_batches) seen_batches = [] for b1, b2 in zip(original_batches[5:], resumed_batches): assert (b1 == b2).all() def test_concepts_snippet_warning(): def random_train_data_generator(): while True: yield torch.randint(0, 100, size=(1,)) def print_train_data(engine, batch): i = engine.state.iteration e = engine.state.epoch print("train", e, i, batch.tolist()) trainer = DeterministicEngine(print_train_data) @trainer.on(Events.ITERATION_COMPLETED(every=3)) def user_handler(_): # handler synchronizes the random state torch.manual_seed(12) a = torch.rand(1) trainer.run(random_train_data_generator(), max_epochs=3, epoch_length=5) def _test_gradients_on_resume( dirname, device, with_dropout=True, with_dataaugs=True, data_size=24, batch_size=4, save_iter=None, save_epoch=None ): debug = False def random_train_data_loader(size): d = AugmentedData(torch.rand(size, 3, 32, 32), enabled=with_dataaugs) return DataLoader(d, batch_size=batch_size, shuffle=True, num_workers=2) def _train(save_iter=None, save_epoch=None, sd=None): w_norms = [] grad_norms = [] data = [] chkpt = [] manual_seed(12) arch = [ nn.Conv2d(3, 10, 3), nn.ReLU(), nn.Conv2d(10, 10, 3), nn.ReLU(), nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 2), ] if with_dropout: arch.insert(2, nn.Dropout2d()) arch.insert(-2, nn.Dropout()) model = nn.Sequential(*arch).to(device) opt = SGD(model.parameters(), lr=0.001) def proc_fn(e, b): from ignite.engine.deterministic import _get_rng_states, _repr_rng_state s = _repr_rng_state(_get_rng_states()) model.train() opt.zero_grad() y = model(b.to(device)) y.sum().backward() opt.step() if debug: print( trainer.state.iteration, trainer.state.epoch, "proc_fn - b.shape", b.shape, torch.norm(y).item(), s ) trainer = DeterministicEngine(proc_fn) if save_iter is not None: ev = Events.ITERATION_COMPLETED(once=save_iter) elif save_epoch is not None: ev = Events.EPOCH_COMPLETED(once=save_epoch) save_iter = save_epoch * (data_size // batch_size) @trainer.on(ev) def save_chkpt(_): if debug: print(trainer.state.iteration, "save_chkpt") fp = dirname / "test.pt" from ignite.engine.deterministic import _repr_rng_state tsd = trainer.state_dict() if debug: print("->", _repr_rng_state(tsd["rng_states"])) torch.save([model.state_dict(), opt.state_dict(), tsd], fp) chkpt.append(fp) def log_event_filter(_, event): if (event // save_iter == 1) and 1 <= (event % save_iter) <= 5: return True return False @trainer.on(Events.ITERATION_COMPLETED(event_filter=log_event_filter)) def write_data_grads_weights(e): x = e.state.batch i = e.state.iteration data.append([i, x.mean().item(), x.std().item()]) total = [0.0, 0.0] out1 = [] out2 = [] for p in model.parameters(): n1 = torch.norm(p).item() n2 = torch.norm(p.grad).item() out1.append(n1) out2.append(n2) total[0] += n1 total[1] += n2 w_norms.append([i, total[0]] + out1) grad_norms.append([i, total[1]] + out2) if sd is not None: sd = torch.load(sd) model.load_state_dict(sd[0]) opt.load_state_dict(sd[1]) from ignite.engine.deterministic import _repr_rng_state if debug: print("-->", _repr_rng_state(sd[2]["rng_states"])) trainer.load_state_dict(sd[2]) manual_seed(32) trainer.run(random_train_data_loader(size=data_size), max_epochs=5) return {"sd": chkpt, "data": data, "grads": grad_norms, "weights": w_norms} out_original = _train(save_iter=save_iter, save_epoch=save_epoch) assert len(out_original["sd"]) > 0 out_resumed = _train(save_iter=save_iter, save_epoch=save_epoch, sd=out_original["sd"][0]) if debug: print("Original:") print(" data:", out_original["data"]) print("grads:", out_original["grads"]) print(" W:", out_original["weights"]) print("Resume:") print(" data:", out_resumed["data"]) print("grads:", out_resumed["grads"]) print(" W:", out_resumed["weights"]) # check data: for d1, d2 in zip(out_original["data"], out_resumed["data"]): assert d1 == d2 # check grads: for d1, d2 in zip(out_original["grads"], out_resumed["grads"]): assert d1 == d2 # check weights: for d1, d2 in zip(out_original["weights"], out_resumed["weights"]): assert d1 == d2 def test_gradients_on_resume_cpu(dirname): with pytest.raises(AssertionError): _test_gradients_on_resume(dirname, "cpu", with_dataaugs=True, save_iter=25) _test_gradients_on_resume(dirname, "cpu", with_dataaugs=False, save_iter=25) # resume from epoch _test_gradients_on_resume(dirname, "cpu", with_dataaugs=True, save_epoch=3) _test_gradients_on_resume(dirname, "cpu", with_dataaugs=False, save_epoch=3) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_gradients_on_resume_on_cuda(dirname): with pytest.raises(AssertionError): _test_gradients_on_resume(dirname, "cuda", with_dataaugs=True, save_iter=25) with pytest.raises(AssertionError): _test_gradients_on_resume(dirname, "cuda", with_dataaugs=False, save_iter=25) # resume from epoch _test_gradients_on_resume(dirname, "cuda", with_dataaugs=True, save_epoch=3) _test_gradients_on_resume(dirname, "cuda", with_dataaugs=False, save_epoch=3) def test_engine_with_dataloader_no_auto_batching(): # tests https://github.com/pytorch/ignite/issues/941 data = torch.rand(64, 4, 10) data_loader = DataLoader( data, batch_size=None, sampler=BatchSampler(RandomSampler(data), batch_size=8, drop_last=True) ) counter = [0] def foo(e, b): print(f"{e.state.epoch}-{e.state.iteration}: {b}") counter[0] += 1 engine = DeterministicEngine(foo) engine.run(data_loader, epoch_length=10, max_epochs=5) assert counter[0] == 50 def test_run_finite_iterator_no_epoch_length(): # FR: https://github.com/pytorch/ignite/issues/871 unknown_size = 11 def finite_unk_size_data_iter(): for i in range(unknown_size): yield i bc = BatchChecker(data=list(range(unknown_size))) engine = DeterministicEngine(lambda e, b: bc.check(b)) @engine.on(Events.DATALOADER_STOP_ITERATION) def restart_iter(): engine.state.dataloader = finite_unk_size_data_iter() data_iter = finite_unk_size_data_iter() engine.run(data_iter, max_epochs=5) assert engine.state.epoch == 5 assert engine.state.iteration == unknown_size * 5 class OldDataLoader(DataLoader): def __init__(self, dl, *args, **kwargs): self.dl = dl self.sampler = self.dl.sampler self.batch_sampler = self.dl.batch_sampler def __len__(self): return len(self.dl) def __iter__(self): return iter(self.dl) def test_dataloader_no_dataset_kind(): # tests issue : https://github.com/pytorch/ignite/issues/1022 engine = DeterministicEngine(lambda e, b: None) data = torch.randint(0, 1000, size=(100 * 4,)) dataloader = DataLoader(data, batch_size=4) dataloader = OldDataLoader(dataloader) engine.run(dataloader) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test__set_rng_states_cuda(): # Checks https://github.com/pytorch/ignite/issues/2076 rng_states = [random.getstate(), torch.get_rng_state().cuda(), np.random.get_state()] _set_rng_states(rng_states) assert rng_states[1].device.type == "cpu" def test_engine_no_data_asserts(): trainer = DeterministicEngine(lambda e, b: None) with pytest.raises(ValueError, match=r"Deterministic engine does not support the option of data=None"): trainer.run(max_epochs=10, epoch_length=10) def test_state_dict(): engine = DeterministicEngine(lambda e, b: 1) sd = engine.state_dict() assert isinstance(sd, Mapping) and len(sd) == 4 assert "iteration" in sd and sd["iteration"] == 0 assert "max_epochs" in sd and sd["max_epochs"] is None assert "epoch_length" in sd and sd["epoch_length"] is None assert "rng_states" in sd and sd["rng_states"] is not None ignite-0.5.1/tests/ignite/engine/test_engine.py000066400000000000000000001417361465426447700215770ustar00rootroot00000000000000import os import time from unittest.mock import call, MagicMock, Mock import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine, Events, State from ignite.engine.deterministic import keep_random_state from ignite.metrics import Average from tests.ignite.engine import BatchChecker, EpochCounter, IterationCounter class RecordedEngine(Engine): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.called_events = [] def _fire_event(self, event_name, *event_args, **event_kwargs): self.called_events.append((self.state.epoch, self.state.iteration, event_name.name)) return super()._fire_event(event_name, *event_args, **event_kwargs) def _create_mock_data_loader(epochs, batches_per_epoch): batches = [MagicMock()] * batches_per_epoch data_loader_manager = MagicMock() batch_iterators = [iter(batches) for _ in range(epochs)] data_loader_manager.__iter__.side_effect = batch_iterators data_loader_manager.__len__.return_value = batches_per_epoch return data_loader_manager @pytest.mark.parametrize("interrupt_resume_enabled", [False, True]) class TestEngine: @pytest.fixture(autouse=True) def set_interrupt_resume_enabled(self, interrupt_resume_enabled): Engine.interrupt_resume_enabled = interrupt_resume_enabled def test_terminate(self): engine = Engine(lambda e, b: 1) assert not engine.should_terminate engine.terminate() assert engine.should_terminate def test_invalid_process_raises_with_invalid_signature(self): with pytest.raises(ValueError, match=r"Engine must be given a processing function in order to run"): Engine(None) with pytest.raises(ValueError, match=r"Error adding .+ takes parameters .+ but will be called with"): Engine(lambda: None) with pytest.raises(ValueError, match=r"Error adding .+ takes parameters .+ but will be called with"): Engine(lambda batch: None) with pytest.raises(ValueError, match=r"Error adding .+ takes parameters .+ but will be called with"): Engine(lambda engine, batch, extra_arg: None) def test_invalid_input_data(self): engine = Engine(lambda e, b: None) def data(): pass with pytest.raises(TypeError, match=r"Argument data should be iterable"): engine.run(data) @pytest.mark.parametrize("data", [None, [1, 2]]) def test_current_epoch_counter_increases_every_epoch(self, data): engine = Engine(MagicMock(return_value=1)) max_epochs = 5 counter = EpochCounter() engine.add_event_handler(Events.EPOCH_STARTED, counter) state = engine.run(data, max_epochs=max_epochs, epoch_length=2) assert state.epoch == max_epochs counter.current_epoch_count = 1 state = engine.run(data, max_epochs=max_epochs, epoch_length=2) assert state.epoch == max_epochs @pytest.mark.parametrize("data", [None, [1, 2, 3]]) def test_current_iteration_counter_increases_every_iteration(self, data): engine = Engine(MagicMock(return_value=1)) max_epochs = 5 counter = IterationCounter() engine.add_event_handler(Events.ITERATION_STARTED, counter) epoch_length = 3 state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) assert state.iteration == max_epochs * epoch_length counter.current_iteration_count = 1 state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) assert state.iteration == max_epochs * epoch_length def test_stopping_criterion_is_max_epochs(self): engine = Engine(MagicMock(return_value=1)) max_epochs = 5 state = engine.run([1], max_epochs=max_epochs) assert state.epoch == max_epochs @pytest.mark.parametrize("data", [None, [1, 2]]) def test_terminate_at_end_of_epoch_stops_run(self, data): max_epochs = 5 last_epoch_to_run = 3 engine = Engine(MagicMock(return_value=1)) def end_of_epoch_handler(engine): if engine.state.epoch == last_epoch_to_run: engine.terminate() engine.add_event_handler(Events.EPOCH_COMPLETED, end_of_epoch_handler) assert not engine.should_terminate state = engine.run(data, max_epochs=max_epochs, epoch_length=2) assert state.epoch == last_epoch_to_run assert engine.should_terminate assert engine._dataloader_iter is None @pytest.mark.parametrize("data, epoch_length", [(None, 10), (range(10), None)]) def test_terminate_at_start_of_epoch(self, data, epoch_length): max_epochs = 5 epoch_to_terminate_on = 3 real_epoch_length = epoch_length if data is None else len(data) engine = Engine(MagicMock(return_value=1)) def start_of_epoch_handler(engine): if engine.state.epoch == epoch_to_terminate_on: engine.terminate() engine.add_event_handler(Events.EPOCH_STARTED, start_of_epoch_handler) assert not engine.should_terminate state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) # epoch is not completed so counter is not incremented assert state.epoch == epoch_to_terminate_on assert engine.should_terminate assert engine._dataloader_iter is None assert state.iteration == ((epoch_to_terminate_on - 1) * real_epoch_length) # Engine continue from epoch_to_terminate_on until max_epochs first_epoch_iter = [None, None] @engine.on(Events.STARTED) def check_iter_epoch(): assert engine.state.epoch == first_epoch_iter[0] assert engine.state.iteration == first_epoch_iter[1] if data is not None: expected_data_iter = iter(data) expected_iter = state.iteration @engine.on(Events.ITERATION_STARTED) def check_iter_and_data(): nonlocal expected_data_iter, expected_iter expected_iter += 1 assert engine.state.iteration == expected_iter try: assert engine.state.batch == next(expected_data_iter) except StopIteration: expected_data_iter = iter(data) assert engine.state.batch == next(expected_data_iter) first_epoch_iter[0], first_epoch_iter[1] = state.epoch, state.iteration state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) assert state.epoch == max_epochs assert not engine.should_terminate assert engine._dataloader_iter is None # As terminated epoch is skipped -> iterations are not incremented assert state.iteration == real_epoch_length * (max_epochs - 1) @pytest.mark.parametrize("data, epoch_length", [(None, 10), (range(10), None)]) def test_terminate_stops_run_mid_epoch(self, data, epoch_length): max_epochs = 5 iteration_to_stop = 13 real_epoch_length = epoch_length if data is None else len(data) engine = Engine(MagicMock(return_value=1)) def start_of_iteration_handler(engine): if engine.state.iteration == iteration_to_stop: engine.terminate() @engine.on(Events.EXCEPTION_RAISED) def assert_no_exceptions(ee): assert False, f"Engine should terminate without raising an exception, got '{type(ee)}'" engine.add_event_handler(Events.ITERATION_STARTED, start_of_iteration_handler) state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) # completes the iteration but doesn't increment counter (this happens just before a new iteration starts) assert state.iteration == iteration_to_stop assert state.epoch == np.ceil(iteration_to_stop / real_epoch_length) # it starts from 0 assert engine._dataloader_iter is None # Engine continue from epoch_to_terminate_on until max_epochs first_epoch_iter = [None, None] num_calls_check_iter_epoch = 0 @engine.on(Events.STARTED, first_epoch_iter) def check_iter_epoch(first_epoch_iter): nonlocal num_calls_check_iter_epoch assert engine.state.epoch == first_epoch_iter[0] assert engine.state.iteration == first_epoch_iter[1] num_calls_check_iter_epoch += 1 if data is not None: expected_iter = state.iteration @engine.on(Events.ITERATION_STARTED) def check_iter_and_data(): nonlocal expected_iter expected_iter += 1 assert engine.state.iteration == expected_iter assert engine.state.batch == data[(expected_iter - first_epoch_iter[1] - 1) % len(data)] first_epoch_iter[0], first_epoch_iter[1] = state.epoch, state.iteration state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) assert state.epoch == max_epochs assert not engine.should_terminate assert state.iteration == real_epoch_length * (max_epochs - 1) + (iteration_to_stop % real_epoch_length) assert num_calls_check_iter_epoch == 1 @pytest.mark.parametrize( "terminate_event, e, i", [ (Events.STARTED, 0, 0), (Events.EPOCH_STARTED(once=2), 2, None), (Events.EPOCH_COMPLETED(once=2), 2, None), (Events.GET_BATCH_STARTED(once=12), None, 12), (Events.GET_BATCH_COMPLETED(once=12), None, 12), (Events.ITERATION_STARTED(once=14), None, 14), (Events.ITERATION_COMPLETED(once=14), None, 14), ], ) def test_terminate_events_sequence(self, terminate_event, e, i): engine = RecordedEngine(MagicMock(return_value=1)) data = range(10) max_epochs = 5 @engine.on(terminate_event) def call_terminate(): engine.terminate() @engine.on(Events.EXCEPTION_RAISED) def assert_no_exceptions(ee): assert False, f"Engine should terminate without raising an exception, got '{type(ee)}'" engine.run(data, max_epochs=max_epochs) if i is None: if terminate_event == Events.EPOCH_STARTED: i = len(data) * (e - 1) else: i = len(data) * e if e is None: e = i // len(data) + 1 assert engine.called_events[0] == (0, 0, Events.STARTED) assert engine.called_events[-1] == (e, i, Events.COMPLETED) assert engine.called_events[-2] == (e, i, Events.TERMINATE) assert engine.called_events[-3] == (e, i, terminate_event) assert engine._dataloader_iter is None @pytest.mark.parametrize("data, epoch_length", [(None, 10), (range(10), None)]) def test_terminate_epoch_stops_mid_epoch(self, data, epoch_length): real_epoch_length = epoch_length if data is None else len(data) iteration_to_stop = real_epoch_length + 4 engine = Engine(MagicMock(return_value=1)) def start_of_iteration_handler(engine): if engine.state.iteration == iteration_to_stop: engine.terminate_epoch() max_epochs = 3 engine.add_event_handler(Events.ITERATION_STARTED, start_of_iteration_handler) state = engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) # completes the iteration but doesn't increment counter (this happens just before a new iteration starts) true_value = real_epoch_length * (max_epochs - 1) + iteration_to_stop % real_epoch_length assert state.iteration == true_value assert state.epoch == max_epochs @pytest.mark.parametrize( "terminate_epoch_event, i", [ (Events.GET_BATCH_STARTED(once=12), 12), (Events.GET_BATCH_COMPLETED(once=12), 12), (Events.ITERATION_STARTED(once=14), 14), (Events.ITERATION_COMPLETED(once=14), 14), ], ) def test_terminate_epoch_events_sequence(self, terminate_epoch_event, i): engine = RecordedEngine(MagicMock(return_value=1)) data = range(10) max_epochs = 3 # TODO: Bug: Events.GET_BATCH_STARTED(once=12) is called twice ! # prevent call_terminate_epoch to be called twice call_count = 0 @engine.on(terminate_epoch_event) def call_terminate_epoch(): nonlocal call_count if call_count < 1: engine.terminate_epoch() call_count += 1 @engine.on(Events.TERMINATE_SINGLE_EPOCH) def check_previous_events(iter_counter): e = i // len(data) + 1 assert engine.called_events[0] == (0, 0, Events.STARTED) assert engine.called_events[-2] == (e, i, terminate_epoch_event) assert engine.called_events[-1] == (e, i, Events.TERMINATE_SINGLE_EPOCH) @engine.on(Events.EPOCH_COMPLETED) def check_previous_events2(): e = i // len(data) + 1 if e == engine.state.epoch and i == engine.state.iteration: assert engine.called_events[-3] == (e, i, terminate_epoch_event) assert engine.called_events[-2] == (e, i, Events.TERMINATE_SINGLE_EPOCH) assert engine.called_events[-1] == (e, i, Events.EPOCH_COMPLETED) engine.run(data, max_epochs=max_epochs) assert engine.state.epoch == max_epochs assert (max_epochs - 1) * len(data) < engine.state.iteration < max_epochs * len(data) @pytest.mark.parametrize("data", [None, "mock_data_loader"]) def test_iteration_events_are_fired(self, data): max_epochs = 5 num_batches = epoch_length = 3 if isinstance(data, str) and data == "mock_data_loader": data = _create_mock_data_loader(max_epochs, num_batches) epoch_length = None engine = Engine(MagicMock(return_value=1)) mock_manager = Mock() iteration_started = Mock() engine.add_event_handler(Events.ITERATION_STARTED, iteration_started) iteration_complete = Mock() engine.add_event_handler(Events.ITERATION_COMPLETED, iteration_complete) mock_manager.attach_mock(iteration_started, "iteration_started") mock_manager.attach_mock(iteration_complete, "iteration_complete") engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) assert iteration_started.call_count == num_batches * max_epochs assert iteration_complete.call_count == num_batches * max_epochs expected_calls = [] for _ in range(max_epochs * num_batches): expected_calls.append(call.iteration_started(engine)) expected_calls.append(call.iteration_complete(engine)) assert mock_manager.mock_calls == expected_calls @pytest.mark.parametrize("data", [None, [1, 2]]) def test_last_event_name(self, data): engine = Engine(MagicMock(return_value=1)) assert engine.last_event_name is None @engine.on(Events.STARTED) def _(_engine): assert _engine.last_event_name == Events.STARTED @engine.on(Events.EPOCH_STARTED) def _(_engine): assert _engine.last_event_name == Events.EPOCH_STARTED @engine.on(Events.ITERATION_STARTED) def _(_engine): assert _engine.last_event_name == Events.ITERATION_STARTED @engine.on(Events.ITERATION_COMPLETED) def _(_engine): assert _engine.last_event_name == Events.ITERATION_COMPLETED @engine.on(Events.EPOCH_COMPLETED) def _(_engine): assert _engine.last_event_name == Events.EPOCH_COMPLETED epoch_length = 2 if data is None else None engine.run(data, epoch_length=epoch_length) assert engine.last_event_name == Events.COMPLETED def test_reset_should_terminate(self): def update_fn(engine, batch): pass engine = Engine(update_fn) @engine.on(Events.ITERATION_COMPLETED) def terminate_on_iteration_10(engine): if engine.state.iteration == 10: engine.terminate() engine.run([0] * 20) assert engine.state.iteration == 10 engine.run([0] * 20) assert engine.state.iteration == 10 def test_batch_values(self): def _test(data): # This test check the content passed to update function counter = [0] num_iters = len(data) def update_fn(_, batch): assert batch == data[counter[0] % num_iters] counter[0] += 1 engine = Engine(update_fn) engine.run(data, max_epochs=10) data = torch.randint(0, 1000, size=(256,)) _test(data) def test_state_repr(self): data = [0, 1, 2, 3, 4, 5] max_epochs = 1 metrics = {"accuracy": Mock()} state = State(dataloader=data, max_epochs=max_epochs, metrics=metrics) s = repr(state) assert "iteration" in s assert "epoch" in s assert "max_epochs: 1" in s assert "dataloader" in s assert "metrics" in s assert "output" in s assert "batch" in s def test_alter_batch(self): small_shape = (1, 2, 2) large_shape = (1, 3, 3) small_loader = torch.randint(0, 256, size=(30,) + small_shape) large_loader = torch.randint(0, 256, size=(20,) + large_shape) switch_iteration = 50 def should_take_large_img(i): return i >= switch_iteration def update_fn(engine, batch): i = engine.state.iteration if i < switch_iteration: assert batch.shape == small_shape assert (small_loader[(i - 1) % len(small_loader), ...] == batch).all() else: assert batch.shape == large_shape assert (large_loader[(i - switch_iteration) % len(large_loader), ...] == batch).all() trainer = Engine(update_fn) def cycle(seq): while True: for i in seq: yield i small_loader_iter = cycle(small_loader) large_loader_iter = cycle(large_loader) @trainer.on(Events.ITERATION_STARTED) def choose_batch(engine): i = engine.state.iteration if should_take_large_img(i): batch = next(large_loader_iter) else: batch = next(small_loader_iter) engine.state.batch = batch num_epochs = 5 num_iters = 25 data = range(num_iters) trainer.run(data, num_epochs) def test__is_done(self): state = State(iteration=10, epoch=1, max_epochs=100, epoch_length=100) assert not Engine._is_done(state) state = State(iteration=1000, max_epochs=10, epoch_length=100) assert Engine._is_done(state) def test__setup_engine(self): engine = Engine(lambda e, b: 1) engine.state = State(iteration=10, epoch=1, max_epochs=100, epoch_length=100) data = list(range(100)) engine.state.dataloader = data engine._setup_engine() assert engine._init_iter == 10 def test_run_asserts(self): engine = Engine(lambda e, b: 1) with pytest.raises(ValueError, match=r"Input data has zero size. Please provide non-empty data"): engine.run([]) def test_state_get_event_attrib_value(self): state = State() state.iteration = 10 state.epoch = 9 e = Events.ITERATION_STARTED assert state.get_event_attrib_value(e) == state.iteration e = Events.ITERATION_COMPLETED assert state.get_event_attrib_value(e) == state.iteration e = Events.EPOCH_STARTED assert state.get_event_attrib_value(e) == state.epoch e = Events.EPOCH_COMPLETED assert state.get_event_attrib_value(e) == state.epoch e = Events.STARTED assert state.get_event_attrib_value(e) == state.epoch e = Events.COMPLETED assert state.get_event_attrib_value(e) == state.epoch e = Events.ITERATION_STARTED(every=10) assert state.get_event_attrib_value(e) == state.iteration e = Events.ITERATION_COMPLETED(every=10) assert state.get_event_attrib_value(e) == state.iteration e = Events.EPOCH_STARTED(once=5) assert state.get_event_attrib_value(e) == state.epoch e = Events.EPOCH_COMPLETED(once=5) assert state.get_event_attrib_value(e) == state.epoch @pytest.mark.parametrize( "data, max_epochs, epoch_length", [(range(100), 2, 100), (range(200), 2, 100), (range(200), 5, 100)] ) def test_time_stored_in_state(self, data, max_epochs, epoch_length): sleep_time = 0.01 extra_sleep_time = 0.1 engine = Engine(lambda e, b: time.sleep(sleep_time)) @engine.on(Events.EPOCH_COMPLETED) def check_epoch_time(): assert engine.state.times[Events.EPOCH_COMPLETED.name] >= sleep_time * epoch_length time.sleep(extra_sleep_time) @engine.on(Events.COMPLETED) def check_completed_time(): assert ( engine.state.times[Events.COMPLETED.name] >= (sleep_time * epoch_length + extra_sleep_time) * max_epochs ) time.sleep(extra_sleep_time) engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) assert engine.state.times[Events.EPOCH_COMPLETED.name] >= sleep_time * epoch_length + extra_sleep_time assert ( engine.state.times[Events.COMPLETED.name] >= (sleep_time * epoch_length + extra_sleep_time) * max_epochs + extra_sleep_time ) def _test_check_triggered_events(self, data, max_epochs, epoch_length, exp_iter_stops=None): engine = Engine(lambda e, b: 1) events = [ Events.STARTED, Events.EPOCH_STARTED, Events.ITERATION_STARTED, Events.ITERATION_COMPLETED, Events.EPOCH_COMPLETED, Events.COMPLETED, Events.GET_BATCH_STARTED, Events.GET_BATCH_COMPLETED, Events.DATALOADER_STOP_ITERATION, ] handlers = {e: MagicMock() for e in events} for e, handler in handlers.items(): engine.add_event_handler(e, handler) engine.run(data, max_epochs=max_epochs, epoch_length=epoch_length) expected_num_calls = { Events.STARTED: 1, Events.COMPLETED: 1, Events.EPOCH_STARTED: max_epochs, Events.EPOCH_COMPLETED: max_epochs, Events.ITERATION_STARTED: max_epochs * epoch_length, Events.ITERATION_COMPLETED: max_epochs * epoch_length, Events.GET_BATCH_STARTED: max_epochs * epoch_length if data is not None else 0, Events.GET_BATCH_COMPLETED: max_epochs * epoch_length if data is not None else 0, Events.DATALOADER_STOP_ITERATION: (max_epochs - 1) if exp_iter_stops is None else exp_iter_stops, } for n, handler in handlers.items(): assert handler.call_count == expected_num_calls[n], f"{n}: {handler.call_count} vs {expected_num_calls[n]}" def _test_run_check_triggered_events(self): # tests issue https://github.com/pytorch/ignite/issues/818 self._test_check_triggered_events(list(range(10)), max_epochs=4, epoch_length=10) self._test_check_triggered_events(list(range(100)), max_epochs=5, epoch_length=100) self._test_check_triggered_events(list(range(100)), max_epochs=5, epoch_length=50, exp_iter_stops=50 * 5 // 100) self._test_check_triggered_events( list(range(100)), max_epochs=5, epoch_length=150, exp_iter_stops=150 * 5 // 100 ) self._test_check_triggered_events(None, max_epochs=5, epoch_length=150, exp_iter_stops=0) def test_run_check_triggered_events_list(self): self._test_run_check_triggered_events() def _test_run_check_triggered_events_on_iterator(self): def infinite_data_iterator(): while True: for i in range(100): yield i self._test_check_triggered_events(infinite_data_iterator(), max_epochs=5, epoch_length=100, exp_iter_stops=0) self._test_check_triggered_events(infinite_data_iterator(), max_epochs=5, epoch_length=50, exp_iter_stops=0) self._test_check_triggered_events(infinite_data_iterator(), max_epochs=5, epoch_length=150, exp_iter_stops=0) def limited_data_iterator(): for i in range(100): yield i self._test_check_triggered_events(limited_data_iterator(), max_epochs=1, epoch_length=100, exp_iter_stops=0) self._test_check_triggered_events(limited_data_iterator(), max_epochs=10, epoch_length=10, exp_iter_stops=0) # These tests should fail with pytest.raises(AssertionError): with pytest.warns(UserWarning, match=r"Data iterator can not provide data anymore"): self._test_check_triggered_events(limited_data_iterator(), max_epochs=3, epoch_length=100) with pytest.raises(AssertionError): with pytest.warns(UserWarning, match=r"Data iterator can not provide data anymore"): self._test_check_triggered_events(limited_data_iterator(), max_epochs=3, epoch_length=75) with pytest.raises(AssertionError): # Below test does not raise "Data iterator can not provide data anymore" warning as the last # epoch is equal max_epochs # with pytest.warns(UserWarning, match=r"Data iterator can not provide data anymore"): self._test_check_triggered_events(limited_data_iterator(), max_epochs=1, epoch_length=101) def test_run_check_triggered_events_on_iterator(self): self._test_run_check_triggered_events_on_iterator() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(self, distributed_context_single_node_nccl): self._test_run_check_triggered_events_on_iterator() self._test_run_check_triggered_events() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(self, distributed_context_single_node_gloo): self._test_run_check_triggered_events_on_iterator() self._test_run_check_triggered_events() @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(self, distributed_context_multi_node_gloo): self._test_run_check_triggered_events_on_iterator() self._test_run_check_triggered_events() @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(self, distributed_context_multi_node_nccl): self._test_run_check_triggered_events_on_iterator() self._test_run_check_triggered_events() def test_engine_random_state(self): def random_data_generator(): while True: yield torch.randint(0, 100, size=(5,)) def sum_data(_, batch): result = torch.sum(batch) return result def get_engine(): engine = Engine(sum_data) average = Average() average.attach(engine, "average") return engine torch.manual_seed(34) engine = get_engine() state1 = engine.run(random_data_generator(), max_epochs=2, epoch_length=2) torch.manual_seed(34) engine = get_engine() state2 = engine.run(random_data_generator(), max_epochs=2, epoch_length=2) torch.manual_seed(42) engine = get_engine() state3 = engine.run(random_data_generator(), max_epochs=2, epoch_length=2) assert state1.metrics["average"] == pytest.approx(state2.metrics["average"]) assert state1.metrics["average"] != pytest.approx(state3.metrics["average"]) assert state2.metrics["average"] != pytest.approx(state3.metrics["average"]) def test_altered_random_state(self): # tests issue https://github.com/pytorch/ignite/issues/795 size = 1 def random_train_data_generator(size): while True: yield torch.randint(0, 100, size=(size,)) def random_val_data_generator(size): while True: yield torch.randint(0, 100, size=(size,)) + 100 train_only_batches = [] def train_fn(_, batch): train_only_batches.append(batch[0].item()) torch.manual_seed(1) epoch_length = 6 trainer = Engine(train_fn) trainer.run(random_train_data_generator(size), max_epochs=4, epoch_length=epoch_length) def val_fn(_1, _2): pass evaluator = Engine(val_fn) train_batches = [] def train_fn2(_, batch): train_batches.append(batch[0].item()) trainer = Engine(train_fn2) @trainer.on(Events.EPOCH_COMPLETED) @keep_random_state def run_evaluation(_): evaluator.run(random_val_data_generator(size), epoch_length=4) torch.manual_seed(1) trainer.run(random_train_data_generator(size), max_epochs=4, epoch_length=epoch_length) for i in range(epoch_length): assert train_batches[epoch_length + i] != train_batches[2 * epoch_length + i] assert train_batches[i] == train_only_batches[i] def test_engine_with_dataloader_no_auto_batching(self): # tests https://github.com/pytorch/ignite/issues/941 from torch.utils.data import BatchSampler, DataLoader, RandomSampler data = torch.rand(64, 4, 10) data_loader = DataLoader( data, batch_size=None, sampler=BatchSampler(RandomSampler(data), batch_size=8, drop_last=True) ) counter = [0] def foo(e, b): counter[0] += 1 engine = Engine(foo) engine.run(data_loader, epoch_length=10, max_epochs=5) assert counter[0] == 50 def test_run_once_finite_iterator_no_epoch_length(self): # FR: https://github.com/pytorch/ignite/issues/871 unknown_size = 11 def finite_unk_size_data_iter(): for i in range(unknown_size): yield i bc = BatchChecker(data=list(range(unknown_size))) engine = Engine(lambda e, b: bc.check(b)) completed_handler = MagicMock() engine.add_event_handler(Events.COMPLETED, completed_handler) data_iter = finite_unk_size_data_iter() engine.run(data_iter) assert engine.state.epoch == 1 assert engine.state.iteration == unknown_size assert completed_handler.call_count == 1 def test_run_finite_iterator_no_epoch_length(self): # FR: https://github.com/pytorch/ignite/issues/871 unknown_size = 11 def finite_unk_size_data_iter(): for i in range(unknown_size): yield i bc = BatchChecker(data=list(range(unknown_size))) engine = Engine(lambda e, b: bc.check(b)) @engine.on(Events.DATALOADER_STOP_ITERATION) def restart_iter(): engine.state.dataloader = finite_unk_size_data_iter() data_iter = finite_unk_size_data_iter() engine.run(data_iter, max_epochs=5) assert engine.state.epoch == 5 assert engine.state.iteration == unknown_size * 5 def test_run_finite_iterator_no_epoch_length_2(self): # FR: https://github.com/pytorch/ignite/issues/871 known_size = 11 def finite_size_data_iter(size): for i in range(size): yield i bc = BatchChecker(data=list(range(known_size))) engine = Engine(lambda e, b: bc.check(b)) @engine.on(Events.ITERATION_COMPLETED(every=known_size)) def restart_iter(): engine.state.dataloader = finite_size_data_iter(known_size) data_iter = finite_size_data_iter(known_size) engine.run(data_iter, max_epochs=5) assert engine.state.epoch == 5 assert engine.state.iteration == known_size * 5 def test_faq_inf_iterator_with_epoch_length(self): # Code snippet from FAQ # import torch torch.manual_seed(12) def infinite_iterator(batch_size): while True: batch = torch.rand(batch_size, 3, 32, 32) yield batch def train_step(trainer, batch): # ... s = trainer.state print(f"{s.epoch}/{s.max_epochs} : {s.iteration} - {batch.norm():.3f}") trainer = Engine(train_step) # We need to specify epoch_length to define the epoch trainer.run(infinite_iterator(4), epoch_length=5, max_epochs=3) assert trainer.state.epoch == 3 assert trainer.state.iteration == 3 * 5 def test_faq_inf_iterator_no_epoch_length(self): # Code snippet from FAQ # import torch torch.manual_seed(12) def infinite_iterator(batch_size): while True: batch = torch.rand(batch_size, 3, 32, 32) yield batch def train_step(trainer, batch): # ... s = trainer.state print(f"{s.epoch}/{s.max_epochs} : {s.iteration} - {batch.norm():.3f}") trainer = Engine(train_step) @trainer.on(Events.ITERATION_COMPLETED(once=15)) def stop_training(): trainer.terminate() trainer.run(infinite_iterator(4)) assert trainer.state.epoch == 1 assert trainer.state.iteration == 15 def test_faq_fin_iterator_unknw_size(self): # Code snippet from FAQ # import torch torch.manual_seed(12) def finite_unk_size_data_iter(): for i in range(11): yield i def train_step(trainer, batch): # ... s = trainer.state print(f"{s.epoch}/{s.max_epochs} : {s.iteration} - {batch:.3f}") trainer = Engine(train_step) @trainer.on(Events.DATALOADER_STOP_ITERATION) def restart_iter(): trainer.state.dataloader = finite_unk_size_data_iter() data_iter = finite_unk_size_data_iter() trainer.run(data_iter, max_epochs=5) assert trainer.state.epoch == 5 assert trainer.state.iteration == 5 * 11 # Code snippet from FAQ # import torch torch.manual_seed(12) def finite_unk_size_data_iter(): for i in range(11): yield i def val_step(evaluator, batch): # ... s = evaluator.state print(f"{s.epoch}/{s.max_epochs} : {s.iteration} - {batch:.3f}") evaluator = Engine(val_step) data_iter = finite_unk_size_data_iter() evaluator.run(data_iter) assert evaluator.state.epoch == 1 assert evaluator.state.iteration == 1 * 11 def test_faq_fin_iterator(self): # Code snippet from FAQ # import torch torch.manual_seed(12) size = 11 def finite_size_data_iter(size): for i in range(size): yield i def train_step(trainer, batch): # ... s = trainer.state print(f"{s.epoch}/{s.max_epochs} : {s.iteration} - {batch:.3f}") trainer = Engine(train_step) @trainer.on(Events.ITERATION_COMPLETED(every=size)) def restart_iter(): trainer.state.dataloader = finite_size_data_iter(size) data_iter = finite_size_data_iter(size) trainer.run(data_iter, max_epochs=5) assert trainer.state.epoch == 5 assert trainer.state.iteration == 5 * size # Code snippet from FAQ # import torch torch.manual_seed(12) size = 11 def finite_size_data_iter(size): for i in range(size): yield i def val_step(evaluator, batch): # ... s = evaluator.state print(f"{s.epoch}/{s.max_epochs} : {s.iteration} - {batch:.3f}") evaluator = Engine(val_step) data_iter = finite_size_data_iter(size) evaluator.run(data_iter) assert evaluator.state.epoch == 1 assert evaluator.state.iteration == size def test_set_data(self): # tests FR https://github.com/pytorch/ignite/issues/833 from torch.utils.data import DataLoader num_iters1 = 10 num_iters2 = 20 batch_size = 4 torch.manual_seed(1) data1 = DataLoader(torch.rand(num_iters1 * batch_size, 11), batch_size=batch_size) data2 = DataLoader(torch.rand(num_iters2 * batch_size, 22), batch_size=batch_size) switch_iteration = 35 def train_fn(e, batch): if e.state.iteration <= switch_iteration: assert batch.shape[1] == 11, f"{e.state.iteration}: {batch.shape}" else: assert batch.shape[1] == 22, f"{e.state.iteration}: {batch.shape}" trainer = Engine(train_fn) @trainer.on(Events.ITERATION_COMPLETED(once=switch_iteration)) def switch_dataloader(): trainer.set_data(data2) trainer.run(data1, max_epochs=10) @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_batch_is_released_before_new_one_is_loaded_on_cuda(self): torch.cuda.empty_cache() engine = Engine(lambda e, b: None) def _test(): mem_consumption = [] def dataloader(): for _ in range(4): mem_consumption.append(torch.cuda.memory_allocated()) batch = torch.randn(10).cuda() mem_consumption.append(torch.cuda.memory_allocated()) yield batch engine.run(dataloader(), max_epochs=2, epoch_length=2) return mem_consumption mem_consumption1 = _test() # mem_consumption should look like [0, 512, 512, 512, 512, 512, 512, 512] assert len(set(mem_consumption1[1:])) == 1 mem_consumption2 = _test() assert len(set(mem_consumption2[1:])) == 1 assert mem_consumption1 == mem_consumption2 @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_output_is_released_before_new_one_is_assigned_on_cuda(self): torch.cuda.empty_cache() def _test(): mem_consumption = [] def update_fn(engine, batch): mem_consumption.append(torch.cuda.memory_allocated()) output = torch.rand(10).cuda() mem_consumption.append(torch.cuda.memory_allocated()) return output engine = Engine(update_fn) engine.run([0, 1], max_epochs=2) return mem_consumption mem_consumption1 = _test()[2:] # mem_consumption ~ [0, 512, 0, 512, 0, 512, 0, 512] assert len(set(mem_consumption1)) == 2 mem_consumption2 = _test()[2:] assert len(set(mem_consumption2)) == 2 assert mem_consumption1 == mem_consumption2 def test_engine_no_data_asserts(self): trainer = Engine(lambda e, b: None) with pytest.raises(ValueError, match=r"epoch_length should be provided if data is None"): trainer.run(max_epochs=10) def test_engine_no_data(self): def train_step(engine, batch): assert batch is None trainer = Engine(train_step) trainer.run(max_epochs=10, epoch_length=10) assert trainer.state.iteration == 10 * 10 assert trainer.state.epoch == 10 assert trainer.state.dataloader is None # continue trainer.run(max_epochs=20) assert trainer.state.iteration == 20 * 10 assert trainer.state.epoch == 20 assert trainer.state.dataloader is None def test_engine_no_data_events(self): # Reproduces the issue https://github.com/pytorch/ignite/issues/3190 max_epochs = 4 dataset = range(10) def training_step(engine, _): assert engine.state.dataloader is None trainer = Engine(training_step) trainer.state.dataiter = iter(dataset) @trainer.on(Events.DATALOADER_STOP_ITERATION) @trainer.on(Events.GET_BATCH_STARTED) @trainer.on(Events.GET_BATCH_COMPLETED) def should_not_be_called(): assert False, trainer.last_event_name trainer.run(max_epochs=max_epochs, epoch_length=4) @pytest.mark.parametrize("data, epoch_length", [(None, 10), (range(10), None)]) def test_engine_run_resume(self, data, epoch_length): # https://github.com/pytorch/ignite/wiki/Roadmap#runresume-logic-improvements engine = Engine(lambda e, b: None) real_epoch_length = len(data) if data is not None else epoch_length first_epoch_iter = [None, None] @engine.on(Events.STARTED, first_epoch_iter) def check_iter_epoch(first_epoch_iter): assert engine.state.epoch == first_epoch_iter[0] assert engine.state.iteration == first_epoch_iter[1] # (re)start from 0 to 5 first_epoch_iter[0], first_epoch_iter[1] = 0, 0 # Engine run starting with max_epochs=5 => state.epoch=5 engine.run(data, max_epochs=5, epoch_length=epoch_length) assert engine.state.epoch == 5 assert engine.state.iteration == 5 * real_epoch_length # continue from 5 to 7 first_epoch_iter[0], first_epoch_iter[1] = 5, 5 * real_epoch_length # Engine run resuming from iteration 50, epoch 5 until 7 epochs => state.epoch=7 engine.run(data, max_epochs=7, epoch_length=epoch_length) assert engine.state.epoch == 7 assert engine.state.iteration == 7 * real_epoch_length # error with pytest.raises(ValueError, match="Argument max_epochs should be greater than or equal to the start epoch"): engine.run(data, max_epochs=4, epoch_length=epoch_length) # restart from 0 to 7 (As state.epoch == max_epochs(=7), # this should be like that as we always do: evaluator.run(data) without any other instructions) first_epoch_iter[0], first_epoch_iter[1] = 0, 0 # Engine run starting with max_epochs=7 => state.epoch=7 engine.run(data, max_epochs=7, epoch_length=epoch_length) assert engine.state.epoch == 7 assert engine.state.iteration == 7 * real_epoch_length # forced restart from 0 to 5 engine.state.max_epochs = None first_epoch_iter[0], first_epoch_iter[1] = 0, 0 # Engine run starting with max_epochs=5 => state.epoch=5 engine.run(data, max_epochs=5, epoch_length=epoch_length) assert engine.state.epoch == 5 assert engine.state.iteration == 5 * real_epoch_length # forced restart from 0 to 9, instead of continue from state.epoch=5 engine.state.max_epochs = None first_epoch_iter[0], first_epoch_iter[1] = 0, 0 # Engine run starting with max_epochs=9 => state.epoch=9 engine.run(data, max_epochs=9, epoch_length=epoch_length) assert engine.state.epoch == 9 assert engine.state.iteration == 9 * real_epoch_length # continue from 9 until 10 first_epoch_iter[0], first_epoch_iter[1] = 9, 9 * real_epoch_length # Engine run resuming from iteration 90, epoch 9 until 10 epochs => state.epoch=10 engine.run(data, max_epochs=10, epoch_length=epoch_length) assert engine.state.epoch == 10 assert engine.state.iteration == 10 * real_epoch_length @pytest.mark.parametrize( "interrupt_event, e, i", [ (Events.EPOCH_STARTED(once=2), 2, None), (Events.EPOCH_COMPLETED(once=2), 2, None), (Events.GET_BATCH_STARTED(once=12), None, 12), (Events.GET_BATCH_COMPLETED(once=12), None, 12), (Events.ITERATION_STARTED(once=14), None, 14), (Events.ITERATION_COMPLETED(once=14), None, 14), ], ) def test_engine_run_interrupt_resume(interrupt_event, e, i): assert Engine.interrupt_resume_enabled data = range(10) max_epochs = 5 def check_input_data(e, b): i = (e.state.iteration - 1) % len(data) assert b == data[i] engine = RecordedEngine(check_input_data) engine.run(data, max_epochs=max_epochs) expected_called_events = list(engine.called_events) engine.called_events = [] @engine.on(interrupt_event) def call_interrupt(): engine.interrupt() state = engine.run(data, max_epochs=max_epochs) if i is None: if interrupt_event == Events.EPOCH_STARTED: i = len(data) * (e - 1) else: i = len(data) * e if e is None: e = i // len(data) + 1 # Check the last events assert engine.called_events[-1] == (e, i, Events.INTERRUPT) assert engine.called_events[-2] == (e, i, interrupt_event) assert state.epoch == e assert state.iteration == i assert not engine.should_interrupt # implementation detail check: assert engine._dataloader_iter is not None assert engine._internal_run_generator is not None le = len(engine.called_events) # We need to skip the last INTERRUPT event to compare assert expected_called_events[: le - 1] == engine.called_events[:-1] engine.called_events = [] @engine.on(Events.STARTED) def raise_error(): raise RuntimeError("Shouldn't be here") engine.run(data, max_epochs=max_epochs) assert expected_called_events[le - 1 :] == engine.called_events # implementation detail check: assert engine._dataloader_iter is None assert engine._internal_run_generator is None def test_engine_run_multiple_interrupt_resume(): assert Engine.interrupt_resume_enabled data = range(10) max_epochs = 3 def check_input_data(e, b): i = (e.state.iteration - 1) % len(data) assert b == data[i] engine = Engine(check_input_data) can_interrupt = True @engine.on(Events.ITERATION_COMPLETED(every=6)) def call_interrupt(): if can_interrupt: engine.interrupt() state = engine.run(data, max_epochs=max_epochs) assert state.iteration == 6 * 1 and state.epoch == 1 state = engine.run(data, max_epochs=max_epochs) assert state.iteration == 6 * 2 and state.epoch == 2 state = engine.run(data, max_epochs=max_epochs) assert state.iteration == 6 * 3 and state.epoch == 2 state = engine.run(data, max_epochs=max_epochs) assert state.iteration == 6 * 4 and state.epoch == 3 # We did an interruption on the last epoch assert state.epoch == max_epochs # Run remaining iterations without interruptions can_interrupt = False state = engine.run(data, max_epochs=max_epochs) assert state.iteration == max_epochs * len(data) and state.epoch == max_epochs # Check implementation details assert engine._dataloader_iter is None assert engine._internal_run_generator is None # Rerun the engine from start to end without interruptions num_calls_check_iter_epoch = 0 @engine.on(Events.STARTED) def check_iter_epoch(): nonlocal num_calls_check_iter_epoch assert engine.state.epoch == 0 assert engine.state.iteration == 0 num_calls_check_iter_epoch += 1 state = engine.run(data, max_epochs=max_epochs) assert state.iteration == max_epochs * len(data) and state.epoch == max_epochs assert num_calls_check_iter_epoch == 1 def test_engine_should_interrupt_error(): Engine.interrupt_resume_enabled = False engine = Engine(lambda e, b: None) with pytest.raises(RuntimeError, match="Engine 'interrupt/resume' feature is disabled"): engine.interrupt() Engine.interrupt_resume_enabled = True def test_engine_interrupt_restart(): assert Engine.interrupt_resume_enabled data = range(10) max_epochs = 3 def check_input_data(e, b): i = (e.state.iteration - 1) % len(data) assert b == data[i] engine = Engine(check_input_data) can_interrupt = True @engine.on(Events.ITERATION_COMPLETED(every=11)) def call_interrupt(): if can_interrupt: engine.interrupt() # Run and interrupt state = engine.run(data, max_epochs=max_epochs) assert state.iteration == 11 and state.epoch == 2 num_calls_check_iter_epoch = 0 @engine.on(Events.STARTED) def check_iter_epoch(): nonlocal num_calls_check_iter_epoch assert engine.state.epoch == 0 assert engine.state.iteration == 0 num_calls_check_iter_epoch += 1 # Reset and run with interruption state.max_epochs = None state = engine.run(data, max_epochs=max_epochs) assert state.iteration == 11 and state.epoch == 2 assert num_calls_check_iter_epoch == 1 can_interrupt = False num_calls_check_iter_epoch = 0 # Reset and run without interruption state.max_epochs = None state = engine.run(data, max_epochs=max_epochs) assert state.iteration == max_epochs * len(data) and state.epoch == max_epochs assert num_calls_check_iter_epoch == 1 ignite-0.5.1/tests/ignite/engine/test_engine_state_dict.py000066400000000000000000000234241465426447700237730ustar00rootroot00000000000000from collections.abc import Mapping import pytest import torch from ignite.engine import Engine, Events, State from tests.ignite.engine import BatchChecker, EpochCounter, IterationCounter def test_state_dict(): engine = Engine(lambda e, b: 1) sd = engine.state_dict() assert isinstance(sd, Mapping) and len(sd) == 3 assert "iteration" in sd and sd["iteration"] == 0 assert "max_epochs" in sd and sd["max_epochs"] is None assert "epoch_length" in sd and sd["epoch_length"] is None def _test(state): engine.state = state sd = engine.state_dict() assert isinstance(sd, Mapping) and len(sd) == len(engine._state_dict_all_req_keys) + 1 assert sd["iteration"] == engine.state.iteration assert sd["epoch_length"] == engine.state.epoch_length assert sd["max_epochs"] == engine.state.max_epochs _test(State(iteration=500, epoch_length=1000, max_epochs=100)) _test(State(epoch=5, epoch_length=1000, max_epochs=100)) def test_state_dict_with_user_keys(): engine = Engine(lambda e, b: 1) engine.state_dict_user_keys.append("alpha") engine.state_dict_user_keys.append("beta") def _test(state): engine.state = state sd = engine.state_dict() assert isinstance(sd, Mapping) and len(sd) == len(engine._state_dict_all_req_keys) + 1 + len( engine.state_dict_user_keys ) assert sd["iteration"] == engine.state.iteration assert sd["epoch_length"] == engine.state.epoch_length assert sd["max_epochs"] == engine.state.max_epochs assert sd["alpha"] == engine.state.alpha assert sd["beta"] == engine.state.beta _test(State(iteration=500, epoch_length=1000, max_epochs=100, alpha=0.01, beta="Good")) def test_state_dict_integration(): engine = Engine(lambda e, b: 1) data = range(100) engine.run(data, max_epochs=10) sd = engine.state_dict() assert isinstance(sd, Mapping) and len(sd) == len(engine._state_dict_all_req_keys) + 1 assert sd["iteration"] == engine.state.iteration == 10 * 100 assert sd["epoch_length"] == engine.state.epoch_length == 100 assert sd["max_epochs"] == engine.state.max_epochs == 10 def test_load_state_dict_asserts(): engine = Engine(lambda e, b: 1) with pytest.raises(TypeError, match=r"Argument state_dict should be a dictionary"): engine.load_state_dict("123") with pytest.raises(ValueError, match=r"is absent in provided state_dict"): engine.load_state_dict({}) with pytest.raises(ValueError, match=r"state_dict should contain only one of"): engine.load_state_dict({"max_epochs": 100, "epoch_length": 120}) with pytest.raises(ValueError, match=r"state_dict should contain only one of"): engine.load_state_dict({"max_epochs": 100, "epoch_length": 120, "iteration": 12, "epoch": 123}) engine = Engine(lambda e, b: 1) engine.state_dict_user_keys.append("alpha") with pytest.raises(ValueError, match=r"Required user state attribute"): engine.load_state_dict({"max_epochs": 100, "epoch_length": 120, "iteration": 12}) engine = Engine(lambda e, b: 1) with pytest.raises(ValueError, match=r"If epoch is provided in the state dict, epoch_length should not be None"): engine.load_state_dict({"max_epochs": 100, "epoch": 2, "epoch_length": None}) def test_load_state_dict(): engine = Engine(lambda e, b: 1) def _test(sd): engine.load_state_dict(sd) if "iteration" in sd: assert sd["iteration"] == engine.state.iteration elif "epoch" in sd: assert sd["epoch"] == engine.state.epoch assert sd["epoch_length"] == engine.state.epoch_length assert sd["max_epochs"] == engine.state.max_epochs _test({"max_epochs": 100, "epoch_length": 120, "iteration": 123}) _test({"max_epochs": 100, "epoch_length": 120, "epoch": 5}) def test_load_state_dict_with_user_keys(): engine = Engine(lambda e, b: 1) engine.state_dict_user_keys.append("alpha") engine.state_dict_user_keys.append("beta") def _test(sd): engine.load_state_dict(sd) if "iteration" in sd: assert sd["iteration"] == engine.state.iteration elif "epoch" in sd: assert sd["epoch"] == engine.state.epoch assert sd["epoch_length"] == engine.state.epoch_length assert sd["max_epochs"] == engine.state.max_epochs assert sd["alpha"] == engine.state.alpha assert sd["beta"] == engine.state.beta _test({"max_epochs": 100, "epoch_length": 120, "iteration": 123, "alpha": 0.1, "beta": "abc"}) def test_load_state_dict_integration(): engine = Engine(lambda e, b: 1) state_dict = {"max_epochs": 100, "epoch_length": 120, "epoch": 5} engine.load_state_dict(state_dict) engine.add_event_handler(Events.ITERATION_COMPLETED, IterationCounter(5 * 120 + 1)) engine.add_event_handler(Events.EPOCH_COMPLETED, EpochCounter(6)) data = range(120) engine.run(data) def test_load_state_dict_with_params_overriding_integration(): state_dict = {"max_epochs": 100, "epoch_length": 120, "epoch": 5} data = range(120) # Override max_epochs new_max_epochs = 10 engine = Engine(lambda e, b: 1) engine.load_state_dict(state_dict) state = engine.run(data, max_epochs=new_max_epochs) assert state.max_epochs == new_max_epochs assert state.iteration == state_dict["epoch_length"] * new_max_epochs assert state.epoch == new_max_epochs with pytest.raises(ValueError, match=r"Argument max_epochs should be greater than or equal to the start epoch"): engine.load_state_dict(state_dict) engine.run(data, max_epochs=3) # Override epoch_length with pytest.raises(ValueError, match=r"Argument epoch_length should be same as in the state"): engine.load_state_dict(state_dict) engine.run(data, epoch_length=90) def test_empty_state_dict_load_state_dict(): engine = Engine(lambda e, b: 1) sd = engine.state_dict() engine.load_state_dict(sd) def test_continue_training(): # Tests issue : https://github.com/pytorch/ignite/issues/993 max_epochs = 2 data = range(10) engine = Engine(lambda e, b: 1) state = engine.run(data, max_epochs=max_epochs) assert state.max_epochs == max_epochs assert state.iteration == len(data) * max_epochs assert state.epoch == max_epochs @engine.on(Events.STARTED) def assert_continue_training(): assert engine.state.epoch == max_epochs state = engine.run(data, max_epochs=max_epochs * 2) assert state.max_epochs == max_epochs * 2 assert state.iteration == len(data) * max_epochs * 2 assert state.epoch == max_epochs * 2 def test_state_dict_with_user_keys_integration(dirname): engine = Engine(lambda e, b: 1) engine.state_dict_user_keys.append("alpha") @engine.on(Events.STARTED) def init_user_values(_): engine.state.alpha = 0.1 fp = dirname / "engine.pt" @engine.on(Events.COMPLETED) def save_engine(_): state_dict = engine.state_dict() assert "alpha" in state_dict torch.save(state_dict, fp) engine.run([0, 1]) assert fp.exists() state_dict = torch.load(fp) assert "alpha" in state_dict and state_dict["alpha"] == 0.1 def test_epoch_length(): def _test(data, max_epochs, num_iters): batch_checker = BatchChecker(data) def update_fn(_, batch): assert batch_checker.check(batch), f"{batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = Engine(update_fn) engine.run(data, max_epochs=max_epochs, epoch_length=num_iters) if num_iters is None: num_iters = len(data) assert engine.state.iteration == num_iters * max_epochs assert engine.state.epoch == max_epochs def _test_as_iter(data, max_epochs, num_iters): batch_checker = BatchChecker(data) def update_fn(_, batch): assert batch_checker.check(batch), f"{batch_checker.counter}: {batch_checker.true_batch} vs {batch}" engine = Engine(update_fn) engine.run(iter(data), max_epochs=max_epochs, epoch_length=num_iters) if num_iters is None: num_iters = len(data) assert engine.state.iteration == num_iters * max_epochs assert engine.state.epoch == max_epochs max_epochs = 10 num_iters = 21 data = torch.randint(0, 1000, size=(num_iters,)) _test(data, max_epochs, num_iters=None) _test(data, max_epochs, num_iters) _test(data, max_epochs, num_iters // 2) _test(data, max_epochs, num_iters * 2) _test_as_iter(data, 1, num_iters) _test_as_iter(data, 2, num_iters // 2) def test_state_custom_attrs_init(): def _test(with_load_state_dict=False): engine = Engine(lambda e, b: None) engine.state.alpha = 0.0 engine.state.beta = 1.0 if with_load_state_dict: engine.load_state_dict({"iteration": 3, "max_epochs": 5, "epoch_length": 5}) @engine.on(Events.STARTED | Events.EPOCH_STARTED | Events.EPOCH_COMPLETED | Events.COMPLETED) def check_custom_attr(): assert hasattr(engine.state, "alpha") and engine.state.alpha == 0.0 assert hasattr(engine.state, "beta") and engine.state.beta == 1.0 engine.run([0, 1, 2, 3, 4], max_epochs=5) _test() _test(with_load_state_dict=True) def test_restart_training(): data = range(10) engine = Engine(lambda e, b: 1) state = engine.run(data, max_epochs=5) with pytest.raises( ValueError, match=r"Argument max_epochs should be greater than or equal to the start epoch defined in the state: 2 vs 5. " r"Please, .+ " r"before calling engine.run\(\) in order to restart the training from the beginning.", ): state = engine.run(data, max_epochs=2) state.max_epochs = None engine.run(data, max_epochs=2) ignite-0.5.1/tests/ignite/engine/test_event_handlers.py000066400000000000000000000422131465426447700233210ustar00rootroot00000000000000import functools import gc from unittest.mock import call, create_autospec, MagicMock import pytest from pytest import raises from ignite.engine import Engine, Events, State from ignite.engine.events import EventsList class DummyEngine(Engine): def __init__(self): super(DummyEngine, self).__init__(lambda e, b: 1) def run(self, num_times): self.state = State() for _ in range(num_times): self.fire_event(Events.STARTED) self.fire_event(Events.COMPLETED) return self.state def test_add_event_handler_raises_with_invalid_event(): engine = Engine(lambda e, b: 1) with pytest.raises(ValueError, match=r"is not a valid event for this Engine"): engine.add_event_handler("incorrect", lambda engine: None) def test_add_event_handler_raises_with_invalid_signature(): engine = Engine(MagicMock()) def handler(engine): pass engine.add_event_handler(Events.STARTED, handler) engine.add_event_handler(Events.STARTED, handler, 1) def handler_with_args(engine, a): pass engine.add_event_handler(Events.STARTED, handler_with_args, 1) with pytest.raises(ValueError): engine.add_event_handler(Events.STARTED, handler_with_args) def handler_with_kwargs(engine, b=42): pass engine.add_event_handler(Events.STARTED, handler_with_kwargs, b=2) with pytest.raises(ValueError): engine.add_event_handler(Events.STARTED, handler_with_kwargs, c=3) engine.add_event_handler(Events.STARTED, handler_with_kwargs, 1, b=2) def handler_with_args_and_kwargs(engine, a, b=42): pass engine.add_event_handler(Events.STARTED, handler_with_args_and_kwargs, 1, b=2) engine.add_event_handler(Events.STARTED, handler_with_args_and_kwargs, 1, 2, b=2) with pytest.raises(ValueError): engine.add_event_handler(Events.STARTED, handler_with_args_and_kwargs, 1, b=2, c=3) def test_add_event_handler(): engine = DummyEngine() class Counter(object): def __init__(self, count=0): self.count = count started_counter = Counter() def handle_iteration_started(engine, counter): counter.count += 1 engine.add_event_handler(Events.STARTED, handle_iteration_started, started_counter) completed_counter = Counter() def handle_iteration_completed(engine, counter): counter.count += 1 engine.add_event_handler(Events.COMPLETED, handle_iteration_completed, completed_counter) engine.run(15) assert started_counter.count == 15 assert completed_counter.count == 15 def test_add_event_handler_without_engine(): engine = DummyEngine() class Counter(object): def __init__(self, count=0): self.count = count started_counter = Counter() def handle_iteration_started(): started_counter.count += 1 engine.add_event_handler(Events.STARTED, handle_iteration_started) completed_counter = Counter() def handle_iteration_completed(counter): counter.count += 1 engine.add_event_handler(Events.COMPLETED, handle_iteration_completed, completed_counter) engine.run(15) assert started_counter.count == 15 assert completed_counter.count == 15 def test_adding_multiple_event_handlers(): mock_fn_1 = create_autospec(spec=lambda x: None) mock_fn_2 = create_autospec(spec=lambda x: None) engine = DummyEngine() handlers = [mock_fn_1, mock_fn_2] for handler in handlers: engine.add_event_handler(Events.STARTED, handler) engine.run(1) for handler in handlers: handler.assert_called_once_with(engine) @pytest.mark.parametrize( "event1, event2", [ (Events.STARTED, Events.COMPLETED), (Events.EPOCH_STARTED, Events.EPOCH_COMPLETED), (Events.ITERATION_STARTED, Events.ITERATION_COMPLETED), (Events.ITERATION_STARTED(every=2), Events.ITERATION_COMPLETED(every=2)), ], ) def test_event_removable_handle(event1, event2): # Removable handle removes event from engine. engine = Engine(lambda e, b: None) handler = create_autospec(spec=lambda x: None) assert not hasattr(handler, "_parent") removable_handle = engine.add_event_handler(event1, handler) assert engine.has_event_handler(handler, event1) engine.run([1, 2]) handler.assert_any_call(engine) num_calls = handler.call_count removable_handle.remove() assert not engine.has_event_handler(handler, event1) # Second engine pass does not fire handle again. engine.run([1, 2]) # Assert that handler wasn't call assert handler.call_count == num_calls # Removable handle can be used as a context manager handler = create_autospec(spec=lambda x: None) with engine.add_event_handler(event1, handler): assert engine.has_event_handler(handler, event1) engine.run([1, 2]) assert not engine.has_event_handler(handler, event1) handler.assert_any_call(engine) num_calls = handler.call_count engine.run([1, 2]) # Assert that handler wasn't call assert handler.call_count == num_calls # Removeable handle only effects a single event registration handler = MagicMock(spec_set=True) with engine.add_event_handler(event1, handler): with engine.add_event_handler(event2, handler): assert engine.has_event_handler(handler, event1) assert engine.has_event_handler(handler, event2) assert engine.has_event_handler(handler, event1) assert not engine.has_event_handler(handler, event2) assert not engine.has_event_handler(handler, event1) assert not engine.has_event_handler(handler, event2) # Removeable handle is re-enter and re-exitable handler = MagicMock(spec_set=True) remove = engine.add_event_handler(event1, handler) with remove: with remove: assert engine.has_event_handler(handler, event1) assert not engine.has_event_handler(handler, event1) assert not engine.has_event_handler(handler, event1) # Removeable handle is a weakref, does not keep engine or event alive def _add_in_closure(): _engine = Engine(lambda e, b: None) def _handler(_): pass _handle = _engine.add_event_handler(event1, _handler) assert _handle.engine() is _engine if event1.filter is None: assert _handle.handler() is _handler else: assert _handle.handler()._parent() is _handler return _handle removable_handle = _add_in_closure() # gc.collect, resolving reference cycles in engine/state # required to ensure object deletion in python2 gc.collect() assert removable_handle.engine() is None assert removable_handle.handler() is None def test_events_list_removable_handle(): # Removable handle removes event from engine. engine = DummyEngine() handler = create_autospec(spec=lambda x: None) assert not hasattr(handler, "_parent") events_list = Events.STARTED | Events.COMPLETED removable_handle = engine.add_event_handler(events_list, handler) for e in events_list: assert engine.has_event_handler(handler, e) engine.run(1) calls = [call(engine), call(engine)] handler.assert_has_calls(calls) assert handler.call_count == 2 removable_handle.remove() for e in events_list: assert not engine.has_event_handler(handler, e) # Second engine pass does not fire handle again. engine.run(1) handler.assert_has_calls(calls) assert handler.call_count == 2 # Removable handle can be used as a context manager handler = create_autospec(spec=lambda x: None) with engine.add_event_handler(events_list, handler): for e in events_list: assert engine.has_event_handler(handler, e) engine.run(1) for e in events_list: assert not engine.has_event_handler(handler, e) handler.assert_has_calls(calls) assert handler.call_count == 2 engine.run(1) handler.assert_has_calls(calls) assert handler.call_count == 2 # Removeable handle only effects a single event registration handler = create_autospec(spec=lambda x: None) other_events_list = Events.EPOCH_STARTED | Events.EPOCH_COMPLETED with engine.add_event_handler(events_list, handler): with engine.add_event_handler(other_events_list, handler): for e in events_list: assert engine.has_event_handler(handler, e) for e in other_events_list: assert engine.has_event_handler(handler, e) for e in events_list: assert engine.has_event_handler(handler, e) for e in other_events_list: assert not engine.has_event_handler(handler, e) for e in events_list: assert not engine.has_event_handler(handler, e) for e in other_events_list: assert not engine.has_event_handler(handler, e) # Removeable handle is re-enter and re-exitable handler = create_autospec(spec=lambda x: None) remove = engine.add_event_handler(events_list, handler) with remove: with remove: for e in events_list: assert engine.has_event_handler(handler, e) for e in events_list: assert not engine.has_event_handler(handler, e) for e in events_list: assert not engine.has_event_handler(handler, e) # Removeable handle is a weakref, does not keep engine or event alive def _add_in_closure(): _engine = DummyEngine() def _handler(_): pass _handle = _engine.add_event_handler(events_list, _handler) assert _handle.engine() is _engine assert _handle.handler() is _handler return _handle removable_handle = _add_in_closure() # gc.collect, resolving reference cycles in engine/state # required to ensure object deletion in python2 gc.collect() assert removable_handle.engine() is None assert removable_handle.handler() is None def test_eventslist__append_raises(): ev_list = EventsList() with pytest.raises(TypeError, match=r"Argument event should be Events or CallableEventWithFilter"): ev_list._append("abc") def test_has_event_handler(): engine = DummyEngine() handlers = [MagicMock(spec_set=True), MagicMock(spec_set=True)] m = MagicMock(spec_set=True) for handler in handlers: engine.add_event_handler(Events.STARTED, handler) engine.add_event_handler(Events.COMPLETED, m) for handler in handlers: assert engine.has_event_handler(handler, Events.STARTED) assert engine.has_event_handler(handler) assert not engine.has_event_handler(handler, Events.COMPLETED) assert not engine.has_event_handler(handler, Events.EPOCH_STARTED) assert not engine.has_event_handler(m, Events.STARTED) assert engine.has_event_handler(m, Events.COMPLETED) assert engine.has_event_handler(m) assert not engine.has_event_handler(m, Events.EPOCH_STARTED) def test_remove_event_handler(): engine = DummyEngine() with pytest.raises(ValueError, match=r"Input event name"): engine.remove_event_handler(lambda x: x, "an event") def on_started(engine): return 0 engine.add_event_handler(Events.STARTED, on_started) with pytest.raises(ValueError, match=r"Input handler"): engine.remove_event_handler(lambda x: x, Events.STARTED) h1 = MagicMock(spec_set=True) h2 = MagicMock(spec_set=True) handlers = [h1, h2] m = MagicMock(spec_set=True) for handler in handlers: engine.add_event_handler(Events.EPOCH_STARTED, handler) engine.add_event_handler(Events.EPOCH_COMPLETED, m) assert len(engine._event_handlers[Events.EPOCH_STARTED]) == 2 engine.remove_event_handler(h1, Events.EPOCH_STARTED) assert len(engine._event_handlers[Events.EPOCH_STARTED]) == 1 assert engine._event_handlers[Events.EPOCH_STARTED][0][0] == h2 assert len(engine._event_handlers[Events.EPOCH_COMPLETED]) == 1 engine.remove_event_handler(m, Events.EPOCH_COMPLETED) assert len(engine._event_handlers[Events.EPOCH_COMPLETED]) == 0 def test_args_and_kwargs_are_passed_to_event(): engine = DummyEngine() kwargs = {"a": "a", "b": "b"} args = (1, 2, 3) handlers = [] for event in [Events.STARTED, Events.COMPLETED]: handler = create_autospec(spec=lambda e, x1, x2, x3, a, b: None) engine.add_event_handler(event, handler, *args, **kwargs) handlers.append(handler) engine.run(1) called_handlers = [handle for handle in handlers if handle.called] assert len(called_handlers) == 2 for handler in called_handlers: handler_args, handler_kwargs = handler.call_args assert handler_args[0] == engine assert handler_args[1::] == args assert handler_kwargs == kwargs def test_on_decorator_raises_with_invalid_event(): engine = DummyEngine() with pytest.raises(ValueError): @engine.on("incorrect") def f(engine): pass def test_on_decorator(): engine = DummyEngine() class Counter(object): def __init__(self, count=0): self.count = count started_counter = Counter() @engine.on(Events.STARTED, started_counter) def handle_iteration_started(engine, started_counter): started_counter.count += 1 completed_counter = Counter() @engine.on(Events.COMPLETED, completed_counter) def handle_iteration_completed(engine, completed_counter): completed_counter.count += 1 engine.run(15) assert started_counter.count == 15 assert completed_counter.count == 15 def test_returns_state(): engine = Engine(MagicMock(return_value=1)) state = engine.run([0]) assert isinstance(state, State) def test_state_attributes(): dataloader = [1, 2, 3] engine = Engine(MagicMock(return_value=1)) state = engine.run(dataloader, max_epochs=3) assert state.iteration == 9 assert state.output == 1 assert state.batch == 3 assert state.dataloader == dataloader assert state.epoch == 3 assert state.max_epochs == 3 assert state.metrics == {} with pytest.raises(RuntimeError, match=r"Unknown event name"): state.get_event_attrib_value("abc") def test_default_exception_handler(): update_function = MagicMock(side_effect=ValueError()) engine = Engine(update_function) with raises(ValueError): engine.run([1]) def test_custom_exception_handler(): value_error = ValueError() update_function = MagicMock(side_effect=value_error) engine = Engine(update_function) class ExceptionCounter(object): def __init__(self): self.exceptions = [] def __call__(self, engine, e): self.exceptions.append(e) counter = ExceptionCounter() engine.add_event_handler(Events.EXCEPTION_RAISED, counter) engine.run([1]) # only one call from _run_once_over_data, since the exception is swallowed assert len(counter.exceptions) == 1 and counter.exceptions[0] == value_error def test_event_handlers_with_decoration(): engine = Engine(lambda e, b: b) def decorated(fun): @functools.wraps(fun) def wrapper(*args, **kwargs): return fun(*args, **kwargs) return wrapper values = [] def foo(): values.append("foo") @decorated def decorated_foo(): values.append("decorated_foo") engine.add_event_handler(Events.EPOCH_STARTED, foo) engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo) engine.add_event_handler(Events.EPOCH_STARTED, decorated_foo) engine.add_event_handler(Events.EPOCH_STARTED(every=2), decorated_foo) def foo_args(e): values.append("foo_args") values.append(e.state.iteration) @decorated def decorated_foo_args(e): values.append("decorated_foo_args") values.append(e.state.iteration) engine.add_event_handler(Events.EPOCH_STARTED, foo_args) engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo_args) engine.add_event_handler(Events.EPOCH_STARTED, decorated_foo_args) engine.add_event_handler(Events.EPOCH_STARTED(every=2), decorated_foo_args) class Foo: def __init__(self): self.values = [] def foo(self): self.values.append("foo") @decorated def decorated_foo(self): self.values.append("decorated_foo") def foo_args(self, e): self.values.append("foo_args") self.values.append(e.state.iteration) @decorated def decorated_foo_args(self, e): self.values.append("decorated_foo_args") self.values.append(e.state.iteration) foo = Foo() engine.add_event_handler(Events.EPOCH_STARTED, foo.foo) engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo.foo) engine.add_event_handler(Events.EPOCH_STARTED, foo.decorated_foo) engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo.decorated_foo) engine.add_event_handler(Events.EPOCH_STARTED, foo.foo_args) engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo.foo_args) engine.add_event_handler(Events.EPOCH_STARTED, foo.decorated_foo_args) engine.add_event_handler(Events.EPOCH_STARTED(every=2), foo.decorated_foo_args) engine.run([0], max_epochs=2) assert values == foo.values ignite-0.5.1/tests/ignite/handlers/000077500000000000000000000000001465426447700172405ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/handlers/__init__.py000066400000000000000000000006641465426447700213570ustar00rootroot00000000000000# Needed to collect coverage data class MockFP16DeepSpeedZeroOptimizer: def __init__(self, optimizer): self.optimizer = optimizer def step(self, closure=None): self.optimizer.step() def _get_param_groups(self): return self.optimizer.param_groups def _set_param_groups(self, value): self.optimizer.param_groups = value param_groups = property(_get_param_groups, _set_param_groups) ignite-0.5.1/tests/ignite/handlers/conftest.py000066400000000000000000000055231465426447700214440ustar00rootroot00000000000000import subprocess import time from pathlib import Path from unittest.mock import Mock import pytest import torch from visdom import Visdom from visdom.server.build import download_scripts @pytest.fixture(scope="session") def visdom_server(): # Start Visdom server once and stop it with visdom_server_stop vd_hostname = "localhost" if not (Path.home() / ".visdom").exists(): (Path.home() / ".visdom").mkdir(exist_ok=True) download_scripts() vis = None vd_port = 29777 vd_server_process = subprocess.Popen( ["python", "-m", "visdom.server", "--hostname", vd_hostname, "-port", str(vd_port)] ) time.sleep(2) for ii in range(5): try: time.sleep(1) vis = Visdom(server=vd_hostname, port=vd_port, raise_exceptions=True) break except ConnectionError: continue assert vis and vis.check_connection() yield (vd_hostname, vd_port) # Trying to clean up slows things down and sometimes causes hangs. # vis.close() # vd_server_process.kill() @pytest.fixture def no_site_packages(request): import sys modules = {} for k in sys.modules: if request.param in k: modules[k] = sys.modules[k] for k in modules: del sys.modules[k] prev_path = list(sys.path) sys.path = [p for p in sys.path if "site-packages" not in p] yield "no_site_packages" sys.path = prev_path for k in modules: sys.modules[k] = modules[k] @pytest.fixture() def norm_mock(): def norm(x: torch.Tensor): return x.norm() norm_mock = Mock(side_effect=norm, spec=norm) norm_mock.configure_mock(**{"__name__": "norm"}) norm_mock.reset_mock() return norm_mock @pytest.fixture() def dummy_model_factory(): class DummyModel(torch.nn.Module): def __init__(self): super(DummyModel, self).__init__() self.fc1 = torch.nn.Linear(10, 10) self.fc2 = torch.nn.Linear(12, 12) self.fc1.weight.data.zero_() self.fc1.bias.data.zero_() self.fc2.weight.data.fill_(1.0) self.fc2.bias.data.fill_(1.0) def get_dummy_model(with_grads=True, with_frozen_layer=False, with_buffer=False): model = DummyModel() if with_grads: model.fc2.weight.grad = torch.zeros_like(model.fc2.weight) model.fc2.bias.grad = torch.zeros_like(model.fc2.bias) if not with_frozen_layer: model.fc1.weight.grad = torch.zeros_like(model.fc1.weight) model.fc1.bias.grad = torch.zeros_like(model.fc1.bias) if with_frozen_layer: for param in model.fc1.parameters(): param.requires_grad = False if with_buffer: model.register_buffer("buffer1", torch.ones(1)) return model return get_dummy_model ignite-0.5.1/tests/ignite/handlers/test_base_logger.py000066400000000000000000000250101465426447700231200ustar00rootroot00000000000000from typing import Any, Union from unittest.mock import call, MagicMock import pytest import torch from ignite.engine import Engine, Events, EventsList, State from ignite.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHandler, BaseWeightsScalarHandler, ) from tests.ignite.handlers import MockFP16DeepSpeedZeroOptimizer class DummyOutputHandler(BaseOutputHandler): def __call__(self, *args, **kwargs): pass class DummyOptParamsHandler(BaseOptimizerParamsHandler): def __call__(self, engine, logger, event_name, **kwargs): tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } return params class DummyLogger(BaseLogger): def _create_output_handler(self, *args, **kwargs): return DummyOutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args, **kwargs): return DummyOptParamsHandler(*args, **kwargs) class DummyWeightsHandler(BaseWeightsHandler): def __call__(self, engine: Engine, logger: Any, event_name: Union[str, Events]) -> None: pass class DummyWeightsScalarHandler(BaseWeightsScalarHandler): def __call__(self, engine: Engine, logger: Any, event_name: Union[str, Events]) -> None: pass def test_base_output_handler_wrong_setup(): with pytest.raises(TypeError, match="metric_names should be either a list or equal 'all'"): DummyOutputHandler("tag", metric_names="abc", output_transform=None) with pytest.raises(TypeError, match="output_transform should be a function"): DummyOutputHandler("tag", metric_names=None, output_transform="abc") with pytest.raises(ValueError, match="Either metric_names, output_transform or state_attributes should be defined"): DummyOutputHandler("tag", None, None) with pytest.raises(TypeError, match="global_step_transform should be a function"): DummyOutputHandler("tag", metric_names=["loss"], global_step_transform="abc") with pytest.raises(TypeError, match=r"Argument optimizer should be torch.optim.Optimizer"): DummyOptParamsHandler({}, "lr") def test_base_output_handler_setup_output_metrics(): engine = Engine(lambda engine, batch: None) true_metrics = {"a": 0, "b": 1} engine.state = State(metrics=true_metrics) engine.state.output = 12345 # Only metric_names handler = DummyOutputHandler("tag", metric_names=["a", "b"], output_transform=None) metrics = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert metrics == {"tag/a": 0, "tag/b": 1} # Only metric_names with a warning handler = DummyOutputHandler("tag", metric_names=["a", "c"], output_transform=None) with pytest.warns(UserWarning): metrics = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert metrics == {"tag/a": 0} # Only output as "output" handler = DummyOutputHandler("tag", metric_names=None, output_transform=lambda x: x) metrics = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert metrics == {"tag/output": engine.state.output} # Only output as "loss" handler = DummyOutputHandler("tag", metric_names=None, output_transform=lambda x: {"loss": x}) metrics = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert metrics == {"tag/loss": engine.state.output} # Metrics and output handler = DummyOutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x}) metrics = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert metrics == {"tag/a": 0, "tag/b": 1, "tag/loss": engine.state.output} # All metrics handler = DummyOutputHandler("tag", metric_names="all", output_transform=None) metrics = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert metrics == {"tag/a": 0, "tag/b": 1} def test_base_output_handler_setup_output_state_attrs(): engine = Engine(lambda engine, batch: None) true_metrics = {"a": 0, "b": 1} engine.state = State(metrics=true_metrics) engine.state.alpha = 3.899 engine.state.beta = torch.tensor(5.499) engine.state.gamma = torch.tensor([2106.0, 6.0]) engine.state.output = 12345 # Only State Attributes handler = DummyOutputHandler( tag="tag", metric_names=None, output_transform=None, state_attributes=["alpha", "beta", "gamma"] ) state_attrs = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert state_attrs == { "tag/alpha": 3.899, "tag/beta": torch.tensor(5.499), "tag/gamma/0": 2106.0, "tag/gamma/1": 6.0, } # Metrics and Attributes handler = DummyOutputHandler( tag="tag", metric_names=["a", "b"], output_transform=None, state_attributes=["alpha", "beta", "gamma"] ) state_attrs = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert state_attrs == { "tag/a": 0, "tag/b": 1, "tag/alpha": 3.899, "tag/beta": torch.tensor(5.499), "tag/gamma/0": 2106.0, "tag/gamma/1": 6.0, } # Metrics, Attributes and output handler = DummyOutputHandler( tag="tag", metric_names="all", output_transform=lambda x: {"loss": x}, state_attributes=["alpha", "beta", "gamma"], ) state_attrs = handler._setup_output_metrics_state_attrs(engine=engine, key_tuple=False) assert state_attrs == { "tag/a": 0, "tag/b": 1, "tag/alpha": 3.899, "tag/beta": torch.tensor(5.499), "tag/gamma/0": 2106.0, "tag/gamma/1": 6.0, "tag/loss": engine.state.output, } def test_opt_params_handler_on_non_torch_optimizers(): tensor = torch.zeros([1], requires_grad=True) base_optimizer = torch.optim.SGD([tensor], lr=0.1234) optimizer = MockFP16DeepSpeedZeroOptimizer(base_optimizer) handler = DummyOptParamsHandler(optimizer=optimizer, param_name="lr") res = handler(engine=None, logger=None, event_name=None) assert isinstance(res, dict) assert "lr/group_0" in res and res["lr/group_0"] == 0.1234 @pytest.mark.parametrize( "event, n_calls, kwargs", [ (Events.ITERATION_STARTED, 50 * 5, {"a": 0}), (Events.ITERATION_COMPLETED, 50 * 5, {}), (Events.EPOCH_STARTED, 5, {}), (Events.EPOCH_COMPLETED, 5, {}), (Events.STARTED, 1, {}), (Events.COMPLETED, 1, {}), (Events.ITERATION_STARTED(every=10), 50 // 10 * 5, {}), (Events.STARTED | Events.COMPLETED, 2, {}), ], ) def test_attach(event, n_calls, kwargs): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) logger = DummyLogger() mock_log_handler = MagicMock() logger.attach(trainer, log_handler=mock_log_handler, event_name=event, **kwargs) trainer.run(data, max_epochs=n_epochs) if isinstance(event, EventsList): events = [e for e in event] else: events = [event] if len(kwargs) > 0: calls = [call(trainer, logger, e, **kwargs) for e in events] else: calls = [call(trainer, logger, e) for e in events] mock_log_handler.assert_has_calls(calls) assert mock_log_handler.call_count == n_calls def test_attach_wrong_event_name(): trainer = Engine(lambda b, e: None) logger = DummyLogger() mock_log_handler = MagicMock() with pytest.raises(RuntimeError, match="Unknown event name"): logger.attach(trainer, log_handler=mock_log_handler, event_name="unknown") events_list = EventsList() events_list._events = ["unknown"] with pytest.raises(RuntimeError, match="Unknown event name"): logger.attach(trainer, log_handler=mock_log_handler, event_name=events_list) def test_attach_on_custom_event(): n_epochs = 10 data = list(range(150)) def _test(event, n_calls, cpe): losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) cpe.attach(trainer) logger = DummyLogger() mock_log_handler = MagicMock() logger.attach(trainer, log_handler=mock_log_handler, event_name=event) trainer.run(data, max_epochs=n_epochs) mock_log_handler.assert_called_with(trainer, logger, event) assert mock_log_handler.call_count == n_calls @pytest.mark.parametrize( "event, n_calls", [ (Events.ITERATION_STARTED, 50 * 5), (Events.ITERATION_COMPLETED, 50 * 5), (Events.EPOCH_STARTED, 5), (Events.EPOCH_COMPLETED, 5), (Events.STARTED, 1), (Events.COMPLETED, 1), (Events.ITERATION_STARTED(every=10), 50 // 10 * 5), ], ) def test_as_context_manager(event, n_calls): n_epochs = 5 data = list(range(50)) class _DummyLogger(DummyLogger): def __init__(self, writer): self.writer = writer def close(self): self.writer.close() global close_counter close_counter = 0 losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) writer = MagicMock() writer.close = MagicMock() with _DummyLogger(writer) as logger: assert isinstance(logger, _DummyLogger) trainer = Engine(update_fn) mock_log_handler = MagicMock() logger.attach(trainer, log_handler=mock_log_handler, event_name=event) trainer.run(data, max_epochs=n_epochs) mock_log_handler.assert_called_with(trainer, logger, event) assert mock_log_handler.call_count == n_calls writer.close.assert_called_once_with() def test_base_weights_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): DummyWeightsHandler(None) def test_base_weights_scalar_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) with pytest.raises(TypeError, match="Argument reduction should be callable"): DummyWeightsScalarHandler(model, reduction=123) with pytest.raises(TypeError, match="Output of the reduction function should be a scalar"): DummyWeightsScalarHandler(model, reduction=lambda x: x) ignite-0.5.1/tests/ignite/handlers/test_checkpoint.py000066400000000000000000001744071465426447700230150ustar00rootroot00000000000000import os import stat import warnings from collections import OrderedDict from collections.abc import Mapping from pathlib import Path from unittest.mock import MagicMock import pytest import torch import torch.nn as nn from packaging.version import Version import ignite.distributed as idist from ignite.engine import Engine, Events, State from ignite.handlers import Checkpoint, DiskSaver, EarlyStopping, global_step_from_engine, ModelCheckpoint from ignite.handlers.checkpoint import BaseSaveHandler _PREFIX = "PREFIX" class DummyModel(nn.Module): def __init__(self): super(DummyModel, self).__init__() self.net = nn.Linear(1, 1) def forward(self, x): return self.net(x) model = DummyModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) class DummyPretrainedModel(nn.Module): def __init__(self): super(DummyPretrainedModel, self).__init__() self.features = nn.Linear(4, 2, bias=False) self.fc = nn.Linear(2, 1) def forward(self, x): x = self.features(x) x = self.fc(x) return x def test_checkpoint_wrong_input(): with pytest.raises(TypeError, match=r"Argument `to_save` should be a dictionary"): Checkpoint(12, lambda x: x, "prefix") with pytest.raises(TypeError, match=r"Argument `to_save` should be a dictionary"): Checkpoint([12], lambda x: x, "prefix") with pytest.raises(TypeError, match=r"should have `state_dict`"): Checkpoint({"model": {"abc": 12}}, lambda x: x, "prefix") to_save = {"model": model} with pytest.raises( TypeError, match=r"Argument `save_handler` should be a string or Path object or callable or inherit from BaseSaveHandler", ): Checkpoint(to_save, 12, "prefix") with pytest.raises(TypeError, match=r"global_step_transform should be a function."): Checkpoint(to_save, lambda x: x, score_function=lambda e: 123, score_name="acc", global_step_transform=123) with pytest.raises(ValueError, match=r"Cannot have key 'checkpointer' if `include_self` is True"): Checkpoint({"checkpointer": model}, lambda x: x, include_self=True) class ImmutableMapping(Mapping): def __init__(self, d): self._dict = d def __getitem__(self, key): return self._dict[key] def __iter__(self): return iter(self._dict) def __len__(self): return len(self._dict) with pytest.raises(TypeError, match="If `include_self` is True, then `to_save` must be mutable"): Checkpoint(ImmutableMapping(to_save), lambda x: x, include_self=True) checkpoint = Checkpoint(to_save, lambda x: x) with pytest.raises(AttributeError, match="Checkpoint's `save_handler` should be of type `DiskSaver`"): checkpoint.reload_objects(to_save) def test_save_handler_as_str(dirname): to_save = {"model": model} checkpointer = Checkpoint(to_save, save_handler=dirname) assert isinstance(checkpointer.save_handler, DiskSaver) def test_checkpoint_score_function_wrong_output(): to_save = {"model": model} checkpointer = Checkpoint(to_save, lambda x: x, score_function=lambda e: {"1": 1}, score_name="acc") trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) with pytest.raises(ValueError, match=r"Output of score_function should be a number"): checkpointer(trainer) @pytest.mark.parametrize( "to_save, obj, name", [ ({"model": model}, model.state_dict(), "model"), ( {"model": model, "optimizer": optimizer}, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint", ), ], ) def test_checkpoint_default(to_save, obj, name): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler) assert checkpointer.last_checkpoint is None trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpointer(trainer) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": None, "priority": 0} save_handler.assert_called_with(obj, f"{name}_0.pt", metadata) trainer.state.epoch = 12 trainer.state.iteration = 1234 checkpointer(trainer) assert save_handler.call_count == 2 metadata["priority"] = 1234 save_handler.assert_called_with(obj, f"{name}_1234.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_0.pt") assert checkpointer.last_checkpoint == f"{name}_1234.pt" @pytest.mark.parametrize( "to_save, obj, name", [ ({"model": model}, model.state_dict(), "model"), ( {"model": model, "optimizer": optimizer}, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint", ), ], ) def test_checkpoint_include_self_state_dict(to_save, obj, name): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler, include_self=True) assert checkpointer.last_checkpoint is None trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpointer(trainer) assert save_handler.call_count == 1 fname = f"{name}_0.pt" obj["checkpointer"] = OrderedDict([("_saved", [(0, fname)])]) metadata = {"basename": name, "score_name": None, "priority": 0} save_handler.assert_called_with(obj, fname, metadata) # Swap object, state should be maintained checkpointer2 = Checkpoint(to_save, save_handler=save_handler, include_self=True) checkpointer2.load_state_dict(checkpointer.state_dict()) assert checkpointer2.last_checkpoint == fname trainer.state.epoch = 12 trainer.state.iteration = 1234 checkpointer2(trainer) assert save_handler.call_count == 2 metadata["priority"] = 1234 # This delete only happens if state was restored correctly. save_handler.remove.assert_called_with(f"{name}_0.pt") fname = f"{name}_1234.pt" obj["checkpointer"] = OrderedDict([("_saved", [(1234, fname)])]) save_handler.assert_called_with(obj, fname, metadata) assert save_handler.remove.call_count == 1 assert checkpointer2.last_checkpoint == fname def test_checkpoint_with_dp(): dp_model = nn.DataParallel(model) to_save = {"model": dp_model} save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpointer(trainer) assert save_handler.call_count == 1 metadata = {"basename": "model", "score_name": None, "priority": 0} save_handler.assert_called_with(model.state_dict(), "model_0.pt", metadata) @pytest.mark.parametrize("filename_prefix", ["", "dummytask"]) @pytest.mark.parametrize( "to_save, obj, name", [ ({"model": model}, model.state_dict(), "model"), ( {"model": model, "optimizer": optimizer}, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint", ), ], ) def test_checkpoint_with_global_step_transform(filename_prefix, to_save, obj, name): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint( to_save, save_handler=save_handler, filename_prefix=filename_prefix, global_step_transform=lambda e, _: e.state.epoch, ) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=2, iteration=1) checkpointer(trainer) assert save_handler.call_count == 1 if len(filename_prefix) > 0: filename_prefix += "_" metadata = {"basename": f"{filename_prefix}{name}", "score_name": None, "priority": 2} save_handler.assert_called_with(obj, f"{filename_prefix}{name}_2.pt", metadata) trainer.state.epoch = 12 trainer.state.iteration = 1234 checkpointer(trainer) assert save_handler.call_count == 2 metadata["priority"] = 12 save_handler.assert_called_with(obj, f"{filename_prefix}{name}_12.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{filename_prefix}{name}_2.pt") assert checkpointer.last_checkpoint == f"{filename_prefix}{name}_12.pt" @pytest.mark.parametrize( "to_save, obj, name", [ ({"model": model}, model.state_dict(), "model"), ( {"model": model, "optimizer": optimizer}, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint", ), ], ) def test_checkpoint_with_score_function(to_save, obj, name): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler, score_function=lambda e: e.state.score) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=1, iteration=1, score=0.77) checkpointer(trainer) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": None, "priority": 0.77} save_handler.assert_called_with(obj, f"{name}_0.7700.pt", metadata) trainer.state.epoch = 12 trainer.state.iteration = 1234 trainer.state.score = 0.78 checkpointer(trainer) assert save_handler.call_count == 2 metadata["priority"] = 0.78 save_handler.assert_called_with(obj, f"{name}_0.7800.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_0.7700.pt") assert checkpointer.last_checkpoint == f"{name}_0.7800.pt" def test_checkpoint_with_score_name_only(): to_save = {"model": model} obj = model.state_dict() name = "model" save_handler = MagicMock(spec=BaseSaveHandler) trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) trainer.state = State(epoch=11, iteration=1) checkpointer = Checkpoint( to_save, save_handler=save_handler, global_step_transform=lambda _1, _2: trainer.state.epoch, score_name="val_acc", ) evaluator.state = State(epoch=1, iteration=1000, metrics={"val_acc": 0.77}) checkpointer(evaluator) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": "val_acc", "priority": 0.77} save_handler.assert_called_with(obj, f"{name}_11_val_acc=0.7700.pt", metadata) trainer.state.epoch = 12 evaluator.state.metrics["val_acc"] = 0.78 checkpointer(evaluator) assert save_handler.call_count == 2 metadata["priority"] = 0.78 save_handler.assert_called_with(obj, f"{name}_12_val_acc=0.7800.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_11_val_acc=0.7700.pt") assert checkpointer.last_checkpoint == f"{name}_12_val_acc=0.7800.pt" @pytest.mark.parametrize( "to_save, obj, name", [ ({"model": model}, model.state_dict(), "model"), ( {"model": model, "optimizer": optimizer}, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint", ), ], ) def test_checkpoint_with_score_name_and_function(to_save, obj, name): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint( to_save, save_handler=save_handler, score_name="loss", score_function=lambda e: e.state.score ) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=1, iteration=1, score=-0.77) checkpointer(trainer) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": "loss", "priority": -0.77} save_handler.assert_called_with(obj, f"{name}_loss=-0.7700.pt", metadata) trainer.state.epoch = 12 trainer.state.iteration = 1234 trainer.state.score = -0.76 checkpointer(trainer) assert save_handler.call_count == 2 metadata["priority"] = -0.76 save_handler.assert_called_with(obj, f"{name}_loss=-0.7600.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_loss=-0.7700.pt") assert checkpointer.last_checkpoint == f"{name}_loss=-0.7600.pt" def test_checkpoint_with_int_score(): def _test(to_save, obj, name, score_name=None): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint( to_save, save_handler=save_handler, score_name=score_name, score_function=lambda e: e.state.epoch ) if score_name is None: score_name = "" else: score_name += "=" trainer = Engine(lambda e, b: None) trainer.state = State(epoch=1, iteration=1) checkpointer(trainer) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": score_name[:-1] if len(score_name) > 0 else None, "priority": 1} save_handler.assert_called_with(obj, f"{name}_{score_name}1.pt", metadata) trainer.state.epoch = 12 trainer.state.iteration = 1234 checkpointer(trainer) assert save_handler.call_count == 2 metadata["priority"] = 12 save_handler.assert_called_with(obj, f"{name}_{score_name}12.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_{score_name}1.pt") assert checkpointer.last_checkpoint == f"{name}_{score_name}12.pt" model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") _test(to_save, model.state_dict(), "model", "epoch") model = DummyModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) to_save = {"model": model, "optimizer": optimizer} _test(to_save, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint") _test(to_save, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint", "epoch") def test_checkpoint_with_score_function_and_trainer_epoch(): to_save = {"model": model} obj = model.state_dict() name = "model" save_handler = MagicMock(spec=BaseSaveHandler) trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) trainer.state = State(epoch=11, iteration=1) checkpointer = Checkpoint( to_save, save_handler=save_handler, global_step_transform=lambda _1, _2: trainer.state.epoch, score_function=lambda e: e.state.metrics["val_acc"], ) evaluator.state = State(epoch=1, iteration=1000, metrics={"val_acc": 0.77}) checkpointer(evaluator) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": None, "priority": 0.77} save_handler.assert_called_with(obj, f"{name}_11_0.7700.pt", metadata) trainer.state.epoch = 12 evaluator.state.metrics["val_acc"] = 0.78 checkpointer(evaluator) assert save_handler.call_count == 2 metadata["priority"] = 0.78 save_handler.assert_called_with(obj, f"{name}_12_0.7800.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_11_0.7700.pt") assert checkpointer.last_checkpoint == f"{name}_12_0.7800.pt" def test_checkpoint_with_score_name_and_function_and_trainer_epoch(): to_save = {"model": model} obj = model.state_dict() name = "model" save_handler = MagicMock(spec=BaseSaveHandler) trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) trainer.state = State(epoch=11, iteration=1) checkpointer = Checkpoint( to_save, save_handler=save_handler, global_step_transform=lambda _1, _2: trainer.state.epoch, score_name="val_acc", score_function=lambda e: e.state.metrics["val_acc"], ) evaluator.state = State(epoch=1, iteration=1000, metrics={"val_acc": 0.77}) checkpointer(evaluator) assert save_handler.call_count == 1 metadata = {"basename": name, "score_name": "val_acc", "priority": 0.77} save_handler.assert_called_with(obj, f"{name}_11_val_acc=0.7700.pt", metadata) trainer.state.epoch = 12 evaluator.state.metrics["val_acc"] = 0.78 checkpointer(evaluator) assert save_handler.call_count == 2 metadata["priority"] = 0.78 save_handler.assert_called_with(obj, f"{name}_12_val_acc=0.7800.pt", metadata) assert save_handler.remove.call_count == 1 save_handler.remove.assert_called_with(f"{name}_11_val_acc=0.7700.pt") assert checkpointer.last_checkpoint == f"{name}_12_val_acc=0.7800.pt" def test_checkpoint_last_checkpoint(): save_handler = MagicMock(spec=BaseSaveHandler) to_save = {"model": DummyModel()} checkpointer = Checkpoint(to_save, save_handler=save_handler, n_saved=None) trainer = Engine(lambda e, b: None) for i in range(10): trainer.state = State(epoch=1, iteration=i) checkpointer(trainer) assert save_handler.call_count == 10 assert checkpointer.last_checkpoint == "model_9.pt" def test_checkpoint_last_checkpoint_on_score(): save_handler = MagicMock(spec=BaseSaveHandler) to_save = {"model": DummyModel()} checkpointer = Checkpoint( to_save, save_handler=save_handler, n_saved=None, score_name="val_acc", score_function=lambda e: e.state.metrics["val_acc"], ) trainer = Engine(lambda e, b: None) val_acc = 0.0 for i in range(10): val_acc = i * 0.1 trainer.state = State(epoch=1, iteration=i, metrics={"val_acc": val_acc}) checkpointer(trainer) assert save_handler.call_count == 10 assert checkpointer.last_checkpoint == "model_val_acc=0.9000.pt" def test_checkpoint_save_handler_callable(): def save_handler(c, f): assert f == "model_12.pt" to_save = {"model": DummyModel()} checkpointer = Checkpoint(to_save, save_handler=save_handler) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=1, iteration=12) checkpointer(trainer) def test_model_checkpoint_args_validation(dirname): existing = dirname / "existing_dir" nonempty = dirname / "nonempty" existing.mkdir(parents=True) nonempty.mkdir(parents=True) with open(nonempty / f"{_PREFIX}_name_0.pt", "w"): pass with pytest.raises(ValueError, match=r"with extension '.pt' are already present "): ModelCheckpoint(nonempty, _PREFIX) with pytest.raises(ValueError, match=r"Directory path '\S+' is not found"): ModelCheckpoint(dirname / "non_existing_dir", _PREFIX, create_dir=False) with pytest.raises(TypeError, match=r"global_step_transform should be a function"): ModelCheckpoint(existing, _PREFIX, create_dir=False, global_step_transform=1234) h = ModelCheckpoint(dirname, _PREFIX, create_dir=False) assert h.last_checkpoint is None with pytest.raises(RuntimeError, match=r"No objects to checkpoint found."): h(None, []) def test_model_checkpoint_simple_recovery(dirname): h = ModelCheckpoint(dirname, _PREFIX, create_dir=False) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=1) model = DummyModel() to_save = {"model": model} h(engine, to_save) fname = h.last_checkpoint assert isinstance(fname, Path) assert str(dirname / _PREFIX) in str(fname) assert fname.exists() loaded_objects = torch.load(fname) assert loaded_objects == model.state_dict() to_load = {"model": DummyModel()} h.reload_objects(to_load=to_load, global_step=1) assert to_load["model"].state_dict() == model.state_dict() @pytest.mark.parametrize("ext, require_empty", [(".txt", True), (".pt", False)]) def test_model_checkpoint_simple_recovery_from_existing_non_empty(ext, require_empty, dirname): previous_fname = dirname / f"{_PREFIX}_obj_{1}{ext}" with open(previous_fname, "w") as f: f.write("test") h = ModelCheckpoint(dirname, _PREFIX, create_dir=True, require_empty=require_empty) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=1) to_save = {"model": model} h(engine, to_save) fname = h.last_checkpoint ext = ".pt" assert isinstance(fname, Path) assert dirname / f"{_PREFIX}_model_{1}{ext}" == fname assert fname.exists() assert previous_fname.exists() loaded_objects = torch.load(fname) assert loaded_objects == model.state_dict() to_load = {"model": DummyModel()} h.reload_objects(to_load=to_load, global_step=1) assert to_load["model"].state_dict() == model.state_dict() fname.unlink() def test_model_checkpoint_invalid_save_handler(dirname): h = ModelCheckpoint(dirname, _PREFIX) to_save = {"model": DummyModel()} # Redefine save_handler h.save_handler = lambda x, y: None h(Engine(lambda x, y: None), to_save) with pytest.raises( RuntimeError, match=rf"Internal error, save_handler should be DiskSaver, but has {type(h.save_handler)}." ): h.last_checkpoint def test_disk_saver_atomic(dirname): model = DummyModel() to_save_serializable = {"model": model} to_save_non_serializable = {"model": lambda x: x} def _test_existence(atomic, _to_save, expected): saver = DiskSaver(dirname, atomic=atomic, create_dir=False, require_empty=False) fname = "test.pt" try: with warnings.catch_warnings(): # Ignore torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type # DummyModel. It won't be checked for correctness upon loading. warnings.simplefilter("ignore", category=UserWarning) saver(_to_save, fname) except Exception: pass fp = saver.dirname / fname assert fp.exists() == expected if expected: # related to https://github.com/pytorch/ignite/issues/1876 mode = stat.filemode(fp.stat().st_mode) assert [mode[1], mode[4], mode[7]] == ["r", "r", "r"], mode if expected: saver.remove(fname) _test_existence(atomic=False, _to_save=to_save_serializable, expected=True) _test_existence(atomic=False, _to_save=to_save_non_serializable, expected=True) _test_existence(atomic=True, _to_save=to_save_serializable, expected=True) _test_existence(atomic=True, _to_save=to_save_non_serializable, expected=False) @pytest.mark.skipif( Version(torch.__version__) < Version("1.4.0"), reason="Zipfile serialization was introduced in 1.4.0" ) def test_disk_saver_zipfile_serialization_keyword(dirname): model = DummyModel() to_save = {"model": model} saver = DiskSaver(dirname, create_dir=False, _use_new_zipfile_serialization=False) fname = "test.pt" saver(to_save, fname) fp = saver.dirname / fname assert fp.exists() saver.remove(fname) def test_disk_saver_unknown_keyword(dirname): model = DummyModel() to_save = {"model": model} saver = DiskSaver(dirname, create_dir=False, unknown_keyword="") fname = "test.pt" with pytest.raises(TypeError, match=r"got an unexpected keyword argument 'unknown_keyword'"): saver(to_save, fname) def test_last_k(dirname): h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=0) model = DummyModel() to_save = {"model": model} h(engine, to_save) for i in range(1, 9): engine.state.iteration = i h(engine, to_save) expected = [f"{_PREFIX}_model_{i}.pt" for i in [7, 8]] assert sorted(os.listdir(dirname)) == expected, f"{sorted(os.listdir(dirname))} vs {expected}" def test_disabled_n_saved(dirname): h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=None) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=0) model = DummyModel() to_save = {"model": model} num_iters = 100 for i in range(num_iters): engine.state.iteration = i h(engine, to_save) saved_files = sorted(os.listdir(dirname)) assert len(saved_files) == num_iters, f"{saved_files}" expected = sorted([f"{_PREFIX}_model_{i}.pt" for i in range(num_iters)]) assert saved_files == expected, f"{saved_files} vs {expected}" def test_best_k(dirname): scores = iter([1.2, -2.0, 3.1, -4.0]) def score_function(_): return next(scores) h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2, score_function=score_function) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=0) model = DummyModel() to_save = {"model": model} for _ in range(4): h(engine, to_save) expected = [f"{_PREFIX}_model_{i:.4f}.pt" for i in [1.2, 3.1]] assert sorted(os.listdir(dirname)) == expected def test_best_k_with_suffix(dirname): scores = [0.3456789, 0.1234, 0.4567, 0.134567] scores_iter = iter(scores) def score_function(engine): return next(scores_iter) h = ModelCheckpoint( dirname, _PREFIX, create_dir=False, n_saved=2, score_function=score_function, score_name="val_loss" ) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=0) model = DummyModel() to_save = {"model": model} for _ in range(4): engine.state.epoch += 1 h(engine, to_save) expected = [f"{_PREFIX}_model_val_loss={scores[e - 1]:.4}.pt" for e in [1, 3]] assert sorted(os.listdir(dirname)) == expected def test_removes_each_score_at_most_once(dirname): scores = [0, 1, 1, 2, 3] scores_iter = iter(scores) def score_function(_): return next(scores_iter) h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2, score_function=score_function) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=0) model = DummyModel() to_save = {"model": model} for _ in range(len(scores)): h(engine, to_save) # If a score was removed multiple times, the code above would have raise a # FileNotFoundError. So this just tests the absence of such a failure # without futher assertions. def test_with_engine(dirname): def update_fn(_1, _2): pass name = "model" engine = Engine(update_fn) handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2) model = DummyModel() to_save = {"model": model} engine.add_event_handler(Events.EPOCH_COMPLETED, handler, to_save) engine.run([0, 1], max_epochs=4) expected = sorted([f"{_PREFIX}_{name}_{i}.pt" for i in [3 * 2, 4 * 2]]) assert sorted(os.listdir(dirname)) == expected def test_with_state_dict(dirname): def update_fn(_1, _2): pass engine = Engine(update_fn) handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) model = DummyModel() to_save = {"model": model} engine.add_event_handler(Events.EPOCH_COMPLETED, handler, to_save) engine.run([0, 1, 2], max_epochs=4) saved_model = dirname / os.listdir(dirname)[0] load_model = torch.load(saved_model) assert not isinstance(load_model, DummyModel) assert isinstance(load_model, dict) model_state_dict = model.state_dict() loaded_model_state_dict = load_model for key in model_state_dict.keys(): assert key in loaded_model_state_dict model_value = model_state_dict[key] loaded_model_value = loaded_model_state_dict[key] assert model_value.numpy() == loaded_model_value.numpy() def test_valid_state_dict_save(dirname): model = DummyModel() h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=0) to_save = {"name": 42} with pytest.raises(TypeError, match=r"should have `state_dict` method"): h(engine, to_save) to_save = {"name": model} try: h(engine, to_save) except ValueError: pytest.fail("Unexpected ValueError") def _test_save_model_optimizer_lr_scheduler_with_state_dict(device, dirname, just_on_zero_rank=False): torch.manual_seed(23) model = DummyModel().to(device) optim = torch.optim.SGD(model.parameters(), lr=0.1) lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optim, gamma=0.5) def update_fn(engine, batch): x = torch.rand((4, 1)).to(device) optim.zero_grad() y = model(x) # Below code raises: RuntimeError: torch_xla/csrc/tensor_impl.cpp:144 : XLA tensors do not have storage # Probably related to https://github.com/pytorch/xla/issues/2576 # loss = y.pow(2.0).sum() loss = y.sum() loss.backward() if idist.has_xla_support: import torch_xla.core.xla_model as xm xm.optimizer_step(optim, barrier=True) else: optim.step() lr_scheduler.step() engine = Engine(update_fn) if (not just_on_zero_rank) or (just_on_zero_rank and idist.get_rank() == 0): handler = ModelCheckpoint(dirname, _PREFIX, create_dir=True, n_saved=1) engine.add_event_handler( Events.EPOCH_COMPLETED, handler, {"model": model, "optimizer": optim, "lr_scheduler": lr_scheduler} ) engine.run([0, 1, 2], max_epochs=4) idist.barrier() saved_objects = sorted(os.listdir(dirname)) # saved object is ['PREFIX_checkpoint_3.pt', ] saved_checkpoint = dirname / saved_objects[0] if idist.has_xla_support: device = "cpu" loaded_obj = torch.load(saved_checkpoint, map_location=device) for f in ["model", "optimizer", "lr_scheduler"]: assert f in loaded_obj loaded_model_state_dict = loaded_obj["model"] loaded_optimizer_state_dict = loaded_obj["optimizer"] loaded_lr_scheduler_state_dict = loaded_obj["lr_scheduler"] assert isinstance(loaded_model_state_dict, dict) assert isinstance(loaded_optimizer_state_dict, dict) assert isinstance(loaded_lr_scheduler_state_dict, dict) # Specifically move device to CPU first model_state_dict = model.cpu().state_dict() for key in model_state_dict.keys(): assert key in loaded_model_state_dict model_value = model_state_dict[key] loaded_model_value = loaded_model_state_dict[key] assert model_value.cpu().numpy() == loaded_model_value.cpu().numpy() optim_state_dict = optim.state_dict() for key in optim_state_dict.keys(): assert key in loaded_optimizer_state_dict optim_value = optim_state_dict[key] loaded_optim_value = loaded_optimizer_state_dict[key] if idist.get_rank() == 0: assert optim_value == loaded_optim_value lr_scheduler_state_dict = lr_scheduler.state_dict() for key in lr_scheduler_state_dict.keys(): assert key in loaded_lr_scheduler_state_dict lr_scheduler_value = lr_scheduler_state_dict[key] loaded_lr_scheduler_value = loaded_lr_scheduler_state_dict[key] assert lr_scheduler_value == loaded_lr_scheduler_value def test_save_model_optimizer_lr_scheduler_with_state_dict(dirname): _test_save_model_optimizer_lr_scheduler_with_state_dict("cpu", dirname) def _test_save_model_optimizer_lr_scheduler_with_validation(device, dirname, just_on_zero_rank=False): torch.manual_seed(23) def _build_objects(acc_list): model = DummyModel().to(device) optim = torch.optim.SGD(model.parameters(), lr=0.1) lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optim, gamma=0.5) def update_fn(engine, batch): x = torch.rand((4, 1)).to(device) optim.zero_grad() y = model(x) loss = y.pow(2.0).sum() loss.backward() if idist.has_xla_support: import torch_xla.core.xla_model as xm xm.optimizer_step(optim, barrier=True) else: optim.step() lr_scheduler.step() trainer = Engine(update_fn) evaluator = Engine(lambda e, b: None) acc_iter = iter(acc_list) @evaluator.on(Events.EPOCH_COMPLETED) def setup_result(): evaluator.state.metrics["accuracy"] = next(acc_iter) @trainer.on(Events.EPOCH_COMPLETED) def run_eval(): evaluator.run([0, 1, 2]) def score_function(engine): return engine.state.metrics["accuracy"] save_handler = DiskSaver(dirname, create_dir=True, require_empty=False) early_stop = EarlyStopping(score_function=score_function, patience=2, trainer=trainer) evaluator.add_event_handler(Events.COMPLETED, early_stop) checkpointer = Checkpoint( { "trainer": trainer, "model": model, "optim": optim, "lr_scheduler": lr_scheduler, "early_stop": early_stop, }, save_handler, include_self=True, global_step_transform=global_step_from_engine(trainer), ) evaluator.add_event_handler(Events.COMPLETED, checkpointer) return trainer, evaluator, model, optim, lr_scheduler, early_stop, checkpointer trainer, evaluator, model, optim, scheduler, early, checkpointer = _build_objects([0.2, 0.3, 0.2]) trainer.run([0, 1, 2], max_epochs=3) saved_objects = sorted(os.listdir(dirname)) saved_checkpoint = dirname / saved_objects[0] loaded_obj = torch.load(saved_checkpoint, map_location=device) for f in ["trainer", "model", "optim", "lr_scheduler", "early_stop", "checkpointer"]: assert f in loaded_obj trainer2, evaluator2, model2, optim2, scheduler2, early2, checkpointer2 = _build_objects([0.1, 0.1, 0.1]) Checkpoint.load_objects( { "trainer": trainer2, "model": model2, "optim": optim2, "lr_scheduler": scheduler2, "early_stop": early2, "checkpointer": checkpointer2, }, loaded_obj, ) assert checkpointer2.last_checkpoint == checkpointer.last_checkpoint model_state_dict = model.cpu().state_dict() loaded_model_state_dict = model2.cpu().state_dict() for key in model_state_dict.keys(): assert key in loaded_model_state_dict model_value = model_state_dict[key] loaded_model_value = loaded_model_state_dict[key] assert model_value.cpu().numpy() == loaded_model_value.cpu().numpy() optim_state_dict = optim.state_dict() loaded_optimizer_state_dict = optim2.state_dict() # "params" contains tensor IDs, which are different del optim_state_dict["param_groups"][0]["params"] del loaded_optimizer_state_dict["param_groups"][0]["params"] for key in optim_state_dict.keys(): assert key in loaded_optimizer_state_dict optim_value = optim_state_dict[key] loaded_optim_value = loaded_optimizer_state_dict[key] if idist.get_rank() == 0: assert optim_value == loaded_optim_value def _check_state_dict(original, loaded): original_state_dict = original.state_dict() loaded_state_dict = loaded.state_dict() for key in original_state_dict.keys(): assert key in loaded_state_dict original_value = original_state_dict[key] loaded_value = loaded_state_dict[key] assert original_value == loaded_value _check_state_dict(trainer, trainer2) _check_state_dict(scheduler, scheduler2) _check_state_dict(early, early2) _check_state_dict(checkpointer, checkpointer2) trainer2.run([0, 1, 2], max_epochs=6) # early stopping should have triggered assert trainer2.state.epoch == 4 # If Checkpoint's state was restored correctly, it should continue to respect n_saved # and delete old checkpoints, and have the correct last_checkpoint. assert os.listdir(dirname) == ["checkpoint_4.pt"] assert checkpointer2.last_checkpoint == dirname / "checkpoint_4.pt" def test_save_model_optimizer_lr_scheduler_with_validation(dirname): _test_save_model_optimizer_lr_scheduler_with_validation("cpu", dirname) def test_checkpoint_load_objects(): with pytest.raises(TypeError, match=r"Argument checkpoint should be a string or a dictionary"): Checkpoint.load_objects({}, []) with pytest.raises(TypeError, match=r"should have `load_state_dict` method"): Checkpoint.load_objects({"a": None}, {"a": None}) with pytest.raises(TypeError, match=r"should have `load_state_dict` method"): Checkpoint.load_objects({"a": {"b": None}}, {"a": {"b": None}}) model = DummyModel() to_load = {"model": model, "another_model": model} with pytest.raises(ValueError, match=r"Key 'model' from x is not found in y"): Checkpoint.load_objects(to_load, {}) model = DummyModel() to_load = {"model": model} model2 = DummyModel() chkpt = {"model": model2.state_dict()} Checkpoint.load_objects(to_load, chkpt) assert model.state_dict() == model2.state_dict() chkpt = {"models": [{"model1": {"abc": model.state_dict()}}, model.state_dict()]} to_load = {"models": [{"model1": {"abc": model}}, model]} Checkpoint.load_objects(to_load, chkpt) assert model.state_dict() == model2.state_dict() def test_checkpoint_load_objects_from_saved_file(dirname): def _get_single_obj_to_save(): model = DummyModel() to_save = {"model": model} return to_save def _get_multiple_objs_to_save(): model = DummyModel() optim = torch.optim.SGD(model.parameters(), lr=0.001) lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optim, gamma=0.5) to_save = { "model": model, "optimizer": optim, "lr_scheduler": lr_scheduler, } return to_save trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) # case: load from filepath handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) to_save = _get_multiple_objs_to_save() handler(trainer, to_save) fname = handler.last_checkpoint assert isinstance(fname, Path) assert str(dirname / _PREFIX) in str(fname) assert fname.exists() Checkpoint.load_objects(to_save, str(fname)) Checkpoint.load_objects(to_save, fname) fname.unlink() # case: multiple objects handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) to_save = _get_multiple_objs_to_save() handler(trainer, to_save) fname = handler.last_checkpoint assert isinstance(fname, Path) assert str(dirname / _PREFIX) in str(fname) assert fname.exists() loaded_objects = torch.load(fname) Checkpoint.load_objects(to_save, loaded_objects) fname.unlink() # case: saved multiple objects, loaded single object handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) to_save = _get_multiple_objs_to_save() handler(trainer, to_save) fname = handler.last_checkpoint assert isinstance(fname, Path) assert str(dirname / _PREFIX) in str(fname) assert fname.exists() loaded_objects = torch.load(fname) to_load = {"model": to_save["model"]} Checkpoint.load_objects(to_load, loaded_objects) fname.unlink() # case: single object handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) to_save = _get_single_obj_to_save() handler(trainer, to_save) fname = handler.last_checkpoint assert isinstance(fname, Path) assert str(dirname / _PREFIX) in str(fname) assert fname.exists() loaded_objects = torch.load(fname) Checkpoint.load_objects(to_save, loaded_objects) fname.unlink() def test_load_checkpoint_with_different_num_classes(dirname): model = DummyPretrainedModel() to_save_single_object = {"model": model} trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1) handler(trainer, to_save_single_object) fname = handler.last_checkpoint loaded_checkpoint = torch.load(fname) to_load_single_object = {"pretrained_features": model.features} with pytest.raises(RuntimeError): Checkpoint.load_objects(to_load_single_object, loaded_checkpoint) Checkpoint.load_objects(to_load_single_object, loaded_checkpoint, strict=False) loaded_weights = to_load_single_object["pretrained_features"].state_dict()["weight"] assert torch.all(model.state_dict()["features.weight"].eq(loaded_weights)) def test_disksaver_wrong_input(dirname): with pytest.raises(ValueError, match=r"Directory path '\S+' is not found"): DiskSaver("/tmp/non-existing-folder", create_dir=False) def _test(ext): previous_fname = dirname / f"{_PREFIX}_obj_{1}{ext}" with open(previous_fname, "w") as f: f.write("test") with pytest.raises(ValueError, match=r"with extension '.pt' are already present"): DiskSaver(dirname, require_empty=True) _test(".pt") def _test_checkpoint_with_ddp(device): torch.manual_seed(0) model = DummyModel().to(device) device_ids = None if "cpu" in device.type else [device] ddp_model = nn.parallel.DistributedDataParallel(model, device_ids=device_ids) to_save = {"model": ddp_model} save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpointer(trainer) assert save_handler.call_count == 1 metadata = {"basename": "model", "score_name": None, "priority": 0} save_handler.assert_called_with(model.state_dict(), "model_0.pt", metadata) def _test_checkpoint_load_objects_ddp(device): model = DummyModel().to(device) device_ids = None if "cpu" in device.type else [device] ddp_model = nn.parallel.DistributedDataParallel(model, device_ids=device_ids) opt = torch.optim.SGD(ddp_model.parameters(), lr=0.01) # single object: to_load = {"model": ddp_model} checkpoint = ddp_model.module.state_dict() Checkpoint.load_objects(to_load, checkpoint) # multiple objects: to_load = {"model": ddp_model, "opt": opt} checkpoint = {"model": ddp_model.module.state_dict(), "opt": opt.state_dict()} Checkpoint.load_objects(to_load, checkpoint) def _test_checkpoint_with_ZeRO(device, dirname, local_rank): from torch.distributed.optim import ZeroRedundancyOptimizer model = DummyModel().to(device) opt = ZeroRedundancyOptimizer(model.parameters(), torch.optim.SGD, lr=0.01) mocked_opt = MagicMock(ZeroRedundancyOptimizer, wraps=opt) # A `step` should be called to optimizer state get populated. out = model(torch.tensor([1.0], device=device)) out.backward() mocked_opt.step() to_save = {"model": model, "optim": mocked_opt} checkpointer = Checkpoint(to_save, dirname, save_on_rank=1) engine = Engine(lambda e, b: None) checkpointer(engine) mocked_opt.consolidate_state_dict.assert_called_once_with(to=1) if local_rank == 1: loaded_state_dict = torch.load(dirname / "checkpoint_0.pt", map_location=device)["optim"] state_dict = opt.state_dict() assert loaded_state_dict == state_dict @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo, dirname, get_rank_zero_dirname, local_rank): device = idist.device() rank_zero_dirname = get_rank_zero_dirname() _test_save_model_optimizer_lr_scheduler_with_state_dict(device, rank_zero_dirname / "1") _test_save_model_optimizer_lr_scheduler_with_state_dict(device, rank_zero_dirname / "2", just_on_zero_rank=True) _test_checkpoint_with_ddp(device) _test_checkpoint_load_objects_ddp(device) from ignite.handlers.checkpoint import HAVE_ZERO if HAVE_ZERO: _test_checkpoint_with_ZeRO(device, dirname, local_rank) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl, get_rank_zero_dirname): device = idist.device() dirname = get_rank_zero_dirname() _test_save_model_optimizer_lr_scheduler_with_state_dict(device, dirname / "1") _test_save_model_optimizer_lr_scheduler_with_state_dict("cpu", dirname / "2", just_on_zero_rank=True) _test_checkpoint_with_ddp(device=device) _test_checkpoint_load_objects_ddp(device=device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor, get_rank_zero_dirname): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() dirname = get_rank_zero_dirname() gloo_hvd_executor( _test_save_model_optimizer_lr_scheduler_with_state_dict, (device, dirname / "1"), np=nproc, do_init=True, ) gloo_hvd_executor( _test_save_model_optimizer_lr_scheduler_with_state_dict, ("cpu", dirname / "2", True), np=nproc, do_init=True, ) def _test_tpu_saves_to_cpu(device, dirname): torch.manual_seed(0) h = ModelCheckpoint(dirname, _PREFIX) engine = Engine(lambda e, b: None) engine.state = State(epoch=0, iteration=1) model = DummyModel().to(device) to_save = {"model": model} h(engine, to_save) idist.barrier() fname = h.last_checkpoint assert isinstance(fname, Path) assert str(dirname / _PREFIX) in str(fname) assert fname.exists() loaded_objects = torch.load(fname) assert loaded_objects == model.cpu().state_dict() @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device") def test_distrib_single_device_xla(dirname): assert "xla" in idist.device().type _test_tpu_saves_to_cpu(idist.device(), dirname / "1") _test_save_model_optimizer_lr_scheduler_with_state_dict(idist.device(), dirname / "2") def _test_tpu_saves_to_cpu_nprocs(index, dirname): device = idist.device() _test_tpu_saves_to_cpu(device, dirname / "1") _test_save_model_optimizer_lr_scheduler_with_state_dict(device, dirname / "2") import time # hack to have all proc properly sync: time.sleep(1) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device") def test_distrib_xla_nprocs(xmp_executor, dirname): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_tpu_saves_to_cpu_nprocs, args=(dirname,), nprocs=n) def _test_checkpoint_filename_pattern_helper( to_save, filename_prefix="", score_function=None, score_name=None, global_step_transform=None, filename_pattern=None, dirname=None, ): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint( to_save, save_handler=save_handler, filename_prefix=filename_prefix, score_function=score_function, score_name=score_name, global_step_transform=global_step_transform, filename_pattern=filename_pattern, ) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=12, iteration=203, score=0.9999) checkpointer(trainer) return checkpointer.last_checkpoint def _test_model_checkpoint_filename_pattern_helper( to_save, filename_prefix="", score_function=None, score_name=None, global_step_transform=None, filename_pattern=None, dirname=None, ): checkpointer = ModelCheckpoint( dirname=dirname, filename_prefix=filename_prefix, score_function=score_function, score_name=score_name, global_step_transform=global_step_transform, filename_pattern=filename_pattern, require_empty=False, ) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=12, iteration=203, score=0.9999) checkpointer(trainer, to_save) return Path(checkpointer.last_checkpoint).name @pytest.mark.parametrize("test_class", ["checkpoint", "model_checkpoint"]) def test_checkpoint_filename_pattern(test_class, dirname): if test_class == "checkpoint": _test = _test_checkpoint_filename_pattern_helper elif test_class == "model_checkpoint": _test = _test_model_checkpoint_filename_pattern_helper model = DummyModel() to_save = {"model": model} assert _test(to_save, dirname=dirname) == "model_203.pt" assert _test(to_save, "best", dirname=dirname) == "best_model_203.pt" assert _test(to_save, score_function=lambda e: e.state.score, dirname=dirname) == "model_0.9999.pt" res = _test( to_save, score_function=lambda e: e.state.score, global_step_transform=lambda e, _: e.state.epoch, dirname=dirname, ) assert res == "model_12_0.9999.pt" assert ( _test(to_save, score_function=lambda e: e.state.score, score_name="acc", dirname=dirname) == "model_acc=0.9999.pt" ) res = _test( to_save, score_function=lambda e: e.state.score, score_name="acc", global_step_transform=lambda e, _: e.state.epoch, dirname=dirname, ) assert res == "model_12_acc=0.9999.pt" assert _test(to_save, "best", score_function=lambda e: e.state.score, dirname=dirname) == "best_model_0.9999.pt" res = _test( to_save, "best", score_function=lambda e: e.state.score, global_step_transform=lambda e, _: e.state.epoch, dirname=dirname, ) assert res == "best_model_12_0.9999.pt" res = _test(to_save, "best", score_function=lambda e: e.state.score, score_name="acc", dirname=dirname) assert res == "best_model_acc=0.9999.pt" res = _test( to_save, "best", score_function=lambda e: e.state.score, score_name="acc", global_step_transform=lambda e, _: e.state.epoch, dirname=dirname, ) assert res == "best_model_12_acc=0.9999.pt" pattern = "{name}.{ext}" assert _test(to_save, filename_pattern=pattern, dirname=dirname) == "model.pt" pattern = "chk-{name}--{global_step}.{ext}" assert _test(to_save, to_save, filename_pattern=pattern, dirname=dirname) == "chk-model--203.pt" pattern = "chk-{filename_prefix}--{name}--{global_step}.{ext}" assert _test(to_save, "best", filename_pattern=pattern, dirname=dirname) == "chk-best--model--203.pt" pattern = "chk-{name}--{score}.{ext}" assert ( _test(to_save, score_function=lambda e: e.state.score, filename_pattern=pattern, dirname=dirname) == "chk-model--0.9999.pt" ) pattern = "{global_step}-{name}-{score}.chk.{ext}" res = _test( to_save, score_function=lambda e: e.state.score, global_step_transform=lambda e, _: e.state.epoch, filename_pattern=pattern, dirname=dirname, ) assert res == "12-model-0.9999.chk.pt" pattern = "chk-{name}--{score_name}--{score}.{ext}" res = _test( to_save, score_function=lambda e: e.state.score, score_name="acc", filename_pattern=pattern, dirname=dirname ) assert res == "chk-model--acc--0.9999.pt" pattern = "chk-{name}-{global_step}-{score_name}-{score}.{ext}" res = _test( to_save, score_function=lambda e: e.state.score, score_name="acc", global_step_transform=lambda e, _: e.state.epoch, filename_pattern=pattern, dirname=dirname, ) assert res == "chk-model-12-acc-0.9999.pt" pattern = "{filename_prefix}-{name}-{score}.chk" res = _test(to_save, "best", score_function=lambda e: e.state.score, filename_pattern=pattern, dirname=dirname) assert res == "best-model-0.9999.chk" pattern = "resnet-{filename_prefix}-{name}-{global_step}-{score}.chk" res = _test( to_save, "best", score_function=lambda e: e.state.score, global_step_transform=lambda e, _: e.state.epoch, filename_pattern=pattern, dirname=dirname, ) assert res == "resnet-best-model-12-0.9999.chk" pattern = "{filename_prefix}-{name}-{score_name}-{score}.chk" res = _test( to_save, "best", score_function=lambda e: e.state.score, score_name="acc", filename_pattern=pattern, dirname=dirname, ) assert res == "best-model-acc-0.9999.chk" pattern = "{global_step}-{filename_prefix}-{name}-{score_name}-{score}" res = _test( to_save, "best", score_function=lambda e: e.state.score, score_name="acc", global_step_transform=lambda e, _: e.state.epoch, filename_pattern=pattern, dirname=dirname, ) assert res == "12-best-model-acc-0.9999" pattern = "SAVE-{name}-{score_name}-{score}.pth" res = _test( to_save, "best", score_function=lambda e: e.state.score, score_name="acc", global_step_transform=lambda e, _: e.state.epoch, filename_pattern=pattern, dirname=dirname, ) assert res == "SAVE-model-acc-0.9999.pth" pattern = "{global_step}-chk-{filename_prefix}-{name}-{score_name}-{score}.{ext}" assert _test(to_save, filename_pattern=pattern, dirname=dirname) == "203-chk--model-None-None.pt" with pytest.raises(KeyError, match=r"random_key"): pattern = "SAVE-{random_key}.{ext}" _test(to_save, filename_pattern=pattern, dirname=dirname) def test_setup_filename_pattern(): # default filename pattern assert Checkpoint.setup_filename_pattern() == "{filename_prefix}_{name}_{global_step}_{score_name}={score}.{ext}" assert Checkpoint.setup_filename_pattern(False) == "{name}_{global_step}_{score_name}={score}.{ext}" assert Checkpoint.setup_filename_pattern(False, False, False) == "{name}_{global_step}.{ext}" assert Checkpoint.setup_filename_pattern(False, True, False) == "{name}_{global_step}_{score}.{ext}" assert Checkpoint.setup_filename_pattern(False, True, False, False) == "{name}_{score}.{ext}" assert Checkpoint.setup_filename_pattern(False, True, True, False) == "{name}_{score_name}={score}.{ext}" with pytest.raises(ValueError, match=r"At least one of with_score and with_global_step should be True."): Checkpoint.setup_filename_pattern(False, False, False, False) with pytest.raises(ValueError, match=r"If with_score_name is True, with_score should be also True"): Checkpoint.setup_filename_pattern(True, False, True, True) def _setup_checkpoint(): save_handler = MagicMock(spec=BaseSaveHandler) model = DummyModel() to_save = {"model": model} checkpointer = Checkpoint(to_save, save_handler=save_handler, n_saved=None) assert checkpointer.last_checkpoint is None trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpointer(trainer) trainer.state.iteration = 10 checkpointer(trainer) trainer.state.iteration = 20 checkpointer(trainer) assert save_handler.call_count == 3 return checkpointer def test_checkpoint_state_dict(): checkpointer = _setup_checkpoint() sd = checkpointer.state_dict() assert "_saved" in sd assert isinstance(sd["_saved"], list) and len(sd["_saved"]) == len(checkpointer._saved) for saved_item, true_item in zip(sd["_saved"], checkpointer._saved): assert saved_item[0] == true_item.priority assert saved_item[1] == true_item.filename def test_checkpoint_load_state_dict(): true_checkpointer = _setup_checkpoint() save_handler = MagicMock(spec=BaseSaveHandler) model = DummyModel() to_save = {"model": model} checkpointer = Checkpoint(to_save, save_handler=save_handler, n_saved=None) sd = {"_saved": [(0, "model_0.pt"), (10, "model_10.pt"), (20, "model_20.pt")]} checkpointer.load_state_dict(sd) assert checkpointer._saved == true_checkpointer._saved @pytest.mark.parametrize( "to_save", [ {"model": DummyModel()}, {"model": [DummyModel(), DummyModel()]}, {"model": {"a": {"b": DummyModel()}}}, ], ) def test_checkpoint__setup_checkpoint(to_save): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler, n_saved=2) checkpoint = checkpointer._setup_checkpoint() assert isinstance(checkpoint, dict) for k, obj in to_save.items(): assert k in checkpoint if isinstance(obj, torch.nn.Module): assert checkpoint[k] == obj.state_dict() elif isinstance(obj, list): for c2, obj2 in zip(checkpoint[k], obj): assert c2 == obj2.state_dict() elif isinstance(obj, dict): c2 = checkpoint[k] for k2, obj2 in obj.items(): if isinstance(obj2, torch.nn.Module): assert c2[k2] == obj2.state_dict() elif isinstance(obj2, dict): c3 = c2[k2] for k3, obj3 in obj2.items(): assert c3[k3] == obj3.state_dict() def test_checkpoint_fixed_filename(): model = DummyModel() to_save = {"model": model} def _test(n_saved): save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler, n_saved=n_saved, filename_pattern="{name}.{ext}") trainer = Engine(lambda e, b: None) for i in range(10): trainer.state = State(epoch=i, iteration=i) checkpointer(trainer) assert save_handler.call_count == i + 1 metadata = {"basename": "model", "score_name": None, "priority": i} save_handler.assert_called_with(model.state_dict(), "model.pt", metadata) _test(None) _test(1) _test(3) def test_checkpoint_reset(): model = DummyModel() to_save = {"model": model} save_handler = MagicMock(spec=BaseSaveHandler) checkpointer = Checkpoint(to_save, save_handler=save_handler, n_saved=2) assert checkpointer.last_checkpoint is None trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=123) checkpointer(trainer) trainer.state.iteration = 234 checkpointer(trainer) assert save_handler.call_count == 2 assert checkpointer.last_checkpoint == "model_234.pt" assert len(checkpointer._saved) == 2 assert sorted([item.filename for item in checkpointer._saved]) == sorted(["model_123.pt", "model_234.pt"]) checkpointer.reset() assert len(checkpointer._saved) == 0 trainer.state.iteration = 124 checkpointer(trainer) assert save_handler.call_count == 3 assert checkpointer.last_checkpoint == "model_124.pt" assert len(checkpointer._saved) == 1 assert sorted([item.filename for item in checkpointer._saved]) == sorted(["model_124.pt"]) def test_checkpoint_reset_with_engine(dirname): name = "model" engine = Engine(lambda e, b: None) handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2) model = DummyModel() to_save = {"model": model} engine.add_event_handler(Events.EPOCH_COMPLETED, handler, to_save) engine.run([0, 1], max_epochs=10) expected = sorted([f"{_PREFIX}_{name}_{i}.pt" for i in [9 * 2, 10 * 2]]) assert sorted(os.listdir(dirname)) == expected assert "PREFIX_model_20.pt" in str(handler.last_checkpoint) handler.reset() engine.state.max_epochs = None engine.run([0, 1], max_epochs=2) expected += [f"{_PREFIX}_{name}_{i}.pt" for i in [1 * 2, 2 * 2]] assert sorted(os.listdir(dirname)) == sorted(expected) assert "PREFIX_model_4.pt" in str(handler.last_checkpoint) def test_greater_or_equal(): scores = iter([1, 2, 2, 2]) def score_function(_): return next(scores) class Saver: def __init__(self): self.counter = 0 def __call__(self, c, f, m): if self.counter == 0: assert f == "model_1.pt" else: assert f == "model_2.pt" self.counter += 1 handler = Saver() checkpointer = Checkpoint( to_save={"model": DummyModel()}, save_handler=handler, score_function=score_function, n_saved=2, greater_or_equal=True, ) trainer = Engine(lambda e, b: None) for _ in range(4): checkpointer(trainer) assert handler.counter == 4 def test_greater_or_equal_model_checkpoint(dirname): scores = iter([1, 2, 2, 2]) def score_function(_): return next(scores) checkpointer = ModelCheckpoint( dirname, score_function=score_function, n_saved=2, greater_or_equal=True, ) trainer = Engine(lambda e, b: None) to_save = {"model": DummyModel()} for i in range(4): checkpointer(trainer, to_save) if i == 0: assert Path(checkpointer.last_checkpoint).name == "model_1.pt" else: assert Path(checkpointer.last_checkpoint).name == "model_2.pt" def test_get_default_score_fn(): with pytest.raises(ValueError, match=r"Argument score_sign should be 1 or -1"): Checkpoint.get_default_score_fn("acc", 2.0) engine = Engine(lambda e, b: None) engine.state.metrics["acc"] = 0.9 engine.state.metrics["loss"] = 0.123 score_fn = Checkpoint.get_default_score_fn("acc") score = score_fn(engine) assert score == 0.9 score_fn = Checkpoint.get_default_score_fn("loss", -1) score = score_fn(engine) assert score == -0.123 @pytest.mark.parametrize("obj_to_save", ["optim", "trainer"]) def test_load_single_object(obj_to_save, dirname): # Checks https://github.com/pytorch/ignite/issues/2479 trainer = Engine(lambda e, b: None) if obj_to_save == "optim": t = torch.tensor(0.0) optim = torch.optim.SGD([t], lr=0.1) to_save = {"optim": optim} elif obj_to_save == "trainer": to_save = {"trainer": trainer} c = Checkpoint(to_save, save_handler=dirname) c(trainer) checkpoint_fp = dirname / c.last_checkpoint Checkpoint.load_objects(to_load=to_save, checkpoint=str(checkpoint_fp)) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.parametrize("atomic", [False, True]) def test_disksaver_distrib(distributed_context_single_node_gloo, dirname, local_rank, atomic): saver = DiskSaver(dirname, atomic, save_on_rank=1) mocked_saver = MagicMock(wraps=saver) mocked_saver(checkpoint={}, filename="test_disksaver_distrib.pt") if local_rank == 1: assert (dirname / "test_disksaver_distrib.pt").exists() else: mocked_saver._save_func.assert_not_called() ignite-0.5.1/tests/ignite/handlers/test_clearml_logger.py000066400000000000000000001152071465426447700236350ustar00rootroot00000000000000import math import os from collections import defaultdict from unittest.mock import ANY, call, MagicMock, patch import clearml import pytest import torch from clearml.binding.frameworks import WeightsFileHandler from clearml.model import Framework import ignite.distributed as idist from ignite.engine import Engine, Events, State from ignite.handlers import Checkpoint from ignite.handlers.clearml_logger import ( ClearMLLogger, ClearMLSaver, global_step_from_engine, GradsHistHandler, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, WeightsHistHandler, WeightsScalarHandler, ) def test_no_clearml(): with patch.dict("sys.modules", {"clearml": None, "trains": None}): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires clearml to be installed."): ClearMLSaver() with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires clearml to be installed."): ClearMLLogger() with patch.dict("sys.modules", {"clearml.binding.frameworks.tensorflow_bind": None}): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires clearml to be installed."): ClearMLLogger() with patch.dict("sys.modules", {"clearml.binding.frameworks": None, "trains.binding.frameworks": None}): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires clearml to be installed."): ClearMLSaver.__call__(None, {}, "") def test_optimizer_params_handler_wrong_setup(): with pytest.raises(TypeError): OptimizerParamsHandler(optimizer=None) optimizer = MagicMock(spec=torch.optim.Optimizer) handler = OptimizerParamsHandler(optimizer=optimizer) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler OptimizerParamsHandler works only with ClearMLLogger"): handler(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_optimizer_params(): optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01) wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr") mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.clearml_logger.report_scalar.assert_called_once_with(iteration=123, series="0", title="lr", value=0.01) wrapper = OptimizerParamsHandler(optimizer, param_name="lr", tag="generator") mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.clearml_logger.report_scalar.assert_called_once_with( iteration=123, series="0", title="generator/lr", value=0.01 ) def test_output_handler_with_wrong_logger_type(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler OutputHandler works only with ClearMLLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_output_handler_output_transform(dirname): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.output = 12345 mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.clearml_logger.report_scalar.assert_called_once_with( iteration=123, series="output", title="tag", value=12345 ) wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.clearml_logger.report_scalar.assert_called_once_with( iteration=123, series="loss", title="another_tag", value=12345 ) def test_output_handler_metric_names(dirname): wrapper = OutputHandler("tag", metric_names=["a", "b"]) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 2 mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="tag", series="a", iteration=5, value=12.23), call(title="tag", series="b", iteration=5, value=23.45), ], any_order=True, ) wrapper = OutputHandler("tag", metric_names=["a", "c"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 55.56, "c": "Some text"}) mock_engine.state.iteration = 7 mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() with pytest.warns(UserWarning, match=r"Logger output_handler can not log metrics value type"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 1 mock_logger.clearml_logger.report_scalar.assert_has_calls( [call(title="tag", series="a", iteration=7, value=55.56)], any_order=True ) # all metrics wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 2 mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="tag", series="a", iteration=5, value=12.23), call(title="tag", series="b", iteration=5, value=23.45), ], any_order=True, ) # log a torch vector wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() vector = torch.tensor([0.1, 0.2, 0.1, 0.2, 0.33]) mock_engine.state = State(metrics={"vector": vector}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 5 mock_logger.clearml_logger.report_scalar.assert_has_calls( [call(title="tag/vector", series=str(i), iteration=5, value=vector[i].item()) for i in range(5)], any_order=True, ) # log a torch tensor (ndimension = 0) wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor(12.23), "b": torch.tensor(23.45), "c": torch.tensor(5.01)}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 3 mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="tag", series="a", iteration=5, value=torch.tensor(12.23).item()), call(title="tag", series="b", iteration=5, value=torch.tensor(23.45).item()), call(title="tag", series="c", iteration=5, value=torch.tensor(5.01).item()), ], any_order=True, ) def test_output_handler_both(dirname): wrapper = OutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 3 mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="tag", series="a", iteration=5, value=12.23), call(title="tag", series="b", iteration=5, value=23.45), call(title="tag", series="loss", iteration=5, value=12345), ], any_order=True, ) def test_output_handler_with_wrong_global_step_transform_output(): def global_step_transform(*args, **kwargs): return "a" wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 with pytest.raises(TypeError, match="global_step must be int"): wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) def test_output_handler_with_global_step_from_engine(): mock_another_engine = MagicMock() mock_another_engine.state = State() mock_another_engine.state.epoch = 10 mock_another_engine.state.output = 12.345 wrapper = OutputHandler( "tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_from_engine(mock_another_engine), ) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 1 mock_engine.state.output = 0.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 1 mock_logger.clearml_logger.report_scalar.assert_has_calls( [call(title="tag", series="loss", iteration=mock_another_engine.state.epoch, value=mock_engine.state.output)] ) mock_another_engine.state.epoch = 11 mock_engine.state.output = 1.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 2 mock_logger.clearml_logger.report_scalar.assert_has_calls( [call(title="tag", series="loss", iteration=mock_another_engine.state.epoch, value=mock_engine.state.output)] ) def test_output_handler_state_attrs(): wrapper = OutputHandler("tag", state_attributes=["alpha", "beta", "gamma"]) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 5 mock_engine.state.alpha = 3.899 mock_engine.state.beta = torch.tensor(12.0) mock_engine.state.gamma = torch.tensor([21.0, 6.0]) wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 4 mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="tag", series="alpha", iteration=5, value=3.899), call(title="tag", series="beta", iteration=5, value=12.0), call(title="tag/gamma", series="0", iteration=5, value=21.0), call(title="tag/gamma", series="1", iteration=5, value=6.0), ], any_order=True, ) def test_output_handler_with_global_step_transform(): def global_step_transform(*args, **kwargs): return 10 wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.clearml_logger.report_scalar.call_count == 1 mock_logger.clearml_logger.report_scalar.assert_has_calls( [call(title="tag", series="loss", iteration=10, value=12345)] ) def test_weights_scalar_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = WeightsScalarHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler WeightsScalarHandler works only with ClearMLLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_weights_scalar_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = WeightsScalarHandler(model, tag=tag) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert mock_logger.clearml_logger.report_scalar.call_count == 4 mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title=tag_prefix + "weights_norm/fc1", series="weight", iteration=5, value=0.0), call(title=tag_prefix + "weights_norm/fc1", series="bias", iteration=5, value=0.0), call(title=tag_prefix + "weights_norm/fc2", series="weight", iteration=5, value=12.0), call(title=tag_prefix + "weights_norm/fc2", series="bias", iteration=5, value=math.sqrt(12.0)), ], any_order=True, ) _test() _test(tag="tag") def test_weights_scalar_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = WeightsScalarHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.clearml_logger.report_scalar.assert_called_once_with( title="weights_norm/fc2", value=ANY, series="weight", iteration=mock_engine.state.epoch ) mock_logger.clearml_logger.report_scalar.reset_mock() wrapper = WeightsScalarHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="model/weights_norm/fc1", value=ANY, series="weight", iteration=mock_engine.state.epoch), call(title="model/weights_norm/fc1", value=ANY, series="bias", iteration=mock_engine.state.epoch), ], any_order=True, ) assert mock_logger.clearml_logger.report_scalar.call_count == 2 mock_logger.clearml_logger.report_scalar.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = WeightsScalarHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="model/weights_norm/fc1", value=ANY, series="bias", iteration=mock_engine.state.epoch), call(title="model/weights_norm/fc2", value=ANY, series="bias", iteration=mock_engine.state.epoch), ], any_order=True, ) assert mock_logger.clearml_logger.report_scalar.call_count == 2 def test_weights_hist_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = WeightsHistHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'WeightsHistHandler' works only with ClearMLLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_weights_hist_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = WeightsHistHandler(model, tag=tag) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.grad_helper = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert mock_logger.grad_helper.add_histogram.call_count == 4 mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title=tag_prefix + "weights_fc1", hist_data=ANY, series="weight", step=5), call(title=tag_prefix + "weights_fc1", hist_data=ANY, series="bias", step=5), call(title=tag_prefix + "weights_fc2", hist_data=ANY, series="weight", step=5), call(title=tag_prefix + "weights_fc2", hist_data=ANY, series="bias", step=5), ], any_order=True, ) _test() _test(tag="tag") def test_weights_hist_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = WeightsHistHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.grad_helper = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.grad_helper.add_histogram.assert_called_once_with( title="weights_fc2", hist_data=ANY, series="weight", step=5 ) mock_logger.grad_helper.add_histogram.reset_mock() wrapper = WeightsHistHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="model/weights_fc1", hist_data=ANY, series="weight", step=5), call(title="model/weights_fc1", hist_data=ANY, series="bias", step=5), ], any_order=True, ) assert mock_logger.grad_helper.add_histogram.call_count == 2 mock_logger.grad_helper.add_histogram.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = WeightsHistHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="model/weights_fc1", hist_data=ANY, series="bias", step=5), call(title="model/weights_fc2", hist_data=ANY, series="bias", step=5), ], any_order=True, ) assert mock_logger.grad_helper.add_histogram.call_count == 2 def test_grads_scalar_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = GradsScalarHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler GradsScalarHandler works only with ClearMLLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_grads_scalar_handler(dummy_model_factory, norm_mock): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = GradsScalarHandler(model, reduction=norm_mock, tag=tag) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 norm_mock.reset_mock() wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call( title=tag_prefix + "grads_norm/fc1", value=ANY, series="weight", iteration=mock_engine.state.epoch ), call(title=tag_prefix + "grads_norm/fc1", value=ANY, series="bias", iteration=mock_engine.state.epoch), call( title=tag_prefix + "grads_norm/fc2", value=ANY, series="weight", iteration=mock_engine.state.epoch ), call(title=tag_prefix + "grads_norm/fc2", value=ANY, series="bias", iteration=mock_engine.state.epoch), ], any_order=True, ) assert mock_logger.clearml_logger.report_scalar.call_count == 4 assert norm_mock.call_count == 4 _test() _test(tag="tag") def test_grads_scalar_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = GradsScalarHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.clearml_logger = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.clearml_logger.report_scalar.assert_called_once_with( title="grads_norm/fc2", value=ANY, series="weight", iteration=mock_engine.state.epoch ) mock_logger.clearml_logger.report_scalar.reset_mock() wrapper = GradsScalarHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="model/grads_norm/fc1", value=ANY, series="weight", iteration=mock_engine.state.epoch), call(title="model/grads_norm/fc1", value=ANY, series="bias", iteration=mock_engine.state.epoch), ], any_order=True, ) assert mock_logger.clearml_logger.report_scalar.call_count == 2 mock_logger.clearml_logger.report_scalar.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = GradsScalarHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="model/grads_norm/fc1", value=ANY, series="bias", iteration=mock_engine.state.epoch), call(title="model/grads_norm/fc2", value=ANY, series="bias", iteration=mock_engine.state.epoch), ], any_order=True, ) assert mock_logger.clearml_logger.report_scalar.call_count == 2 def test_grads_hist_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = GradsHistHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'GradsHistHandler' works only with ClearMLLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_grads_hist_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = GradsHistHandler(model, tag=tag) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.grad_helper = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert mock_logger.grad_helper.add_histogram.call_count == 4 mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title=tag_prefix + "grads_fc1", hist_data=ANY, series="weight", step=5), call(title=tag_prefix + "grads_fc1", hist_data=ANY, series="bias", step=5), call(title=tag_prefix + "grads_fc2", hist_data=ANY, series="weight", step=5), call(title=tag_prefix + "grads_fc2", hist_data=ANY, series="bias", step=5), ], any_order=True, ) _test() _test(tag="tag") def test_grads_hist_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = GradsHistHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=ClearMLLogger) mock_logger.grad_helper = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.grad_helper.add_histogram.assert_called_once_with( title="grads_fc2", hist_data=ANY, series="weight", step=5 ) mock_logger.grad_helper.reset_mock() wrapper = GradsHistHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="model/grads_fc1", hist_data=ANY, series="weight", step=5), call(title="model/grads_fc1", hist_data=ANY, series="bias", step=5), ], any_order=True, ) assert mock_logger.grad_helper.add_histogram.call_count == 2 mock_logger.grad_helper.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = GradsHistHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="model/grads_fc1", hist_data=ANY, series="bias", step=5), call(title="model/grads_fc2", hist_data=ANY, series="bias", step=5), ], any_order=True, ) assert mock_logger.grad_helper.add_histogram.call_count == 2 def test_integration(dirname): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) with pytest.warns(UserWarning, match="ClearMLSaver: running in bypass mode"): ClearMLLogger.set_bypass_mode(True) logger = ClearMLLogger(output_uri=dirname) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) test_value = 0.3 # example logger.clearml_logger.report_scalar(title="", series="", value=test_value, iteration=global_step) logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) logger.close() def test_integration_as_context_manager(dirname): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) with pytest.warns(UserWarning, match="ClearMLSaver: running in bypass mode"): ClearMLLogger.set_bypass_mode(True) with ClearMLLogger(output_uri=dirname) as clearml_logger: trainer = Engine(update_fn) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) test_value = 0.3 # example logger.clearml_logger.report_scalar(title="", series="", value=test_value, iteration=global_step) clearml_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) def test_clearml_logger_getattr_method(dirname): with pytest.warns(UserWarning, match="ClearMLSaver: running in bypass mode"): ClearMLLogger.set_bypass_mode(True) logger = ClearMLLogger(output_uri=dirname) # Create a mock clearml.Logger() object mock_logger = MagicMock() logger.clearml_logger = mock_logger # Test a method called by __getattr__ calls the corresponding method of the mock project. logger.report_single_value("accuracy", 0.72) mock_logger.report_single_value.assert_called_once_with("accuracy", 0.72) # Test a method called by __getattr__ calls the corresponding classmethod of the mock project's class. logger.current_logger() mock_logger.current_logger.assert_called_once() logger.close() def test_clearml_logger_get_task_bypass(dirname): with pytest.warns(UserWarning, match="ClearMLSaver: running in bypass mode"): ClearMLLogger.set_bypass_mode(True) with ClearMLLogger(output_uri=dirname) as clearml_logger: task = clearml_logger.get_task() assert isinstance(task, clearml.Task) assert task == clearml.Task.current_task() task.close() def test_clearml_disk_saver_integration(): model = torch.nn.Module() to_save_serializable = {"model": model} with pytest.warns(UserWarning, match="ClearMLSaver created a temporary checkpoints directory"): mock_logger = MagicMock(spec=ClearMLLogger) clearml.Task.current_task = MagicMock(spec=clearml.Task) clearml_saver = ClearMLSaver(mock_logger) clearml.binding.frameworks.WeightsFileHandler.create_output_model = MagicMock() checkpoint = Checkpoint(to_save=to_save_serializable, save_handler=clearml_saver, n_saved=1) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpoint(trainer) trainer.state.iteration = 1 checkpoint(trainer) if clearml_saver._atomic: assert clearml.binding.frameworks.WeightsFileHandler.create_output_model.call_count == 2 else: saved_files = list(os.listdir(clearml_saver.dirname)) assert len(saved_files) == 1 assert saved_files[0] == "model_1.pt" def test_clearml_disk_saver_integration_no_logger(): model = torch.nn.Module() to_save_serializable = {"model": model} with pytest.warns(UserWarning, match="ClearMLSaver created a temporary checkpoints directory"): clearml.Task.current_task = MagicMock(spec=clearml.Task) clearml.binding.frameworks.WeightsFileHandler.create_output_model = MagicMock() clearml_saver = ClearMLSaver() checkpoint = Checkpoint(to_save=to_save_serializable, save_handler=clearml_saver, n_saved=1) trainer = Engine(lambda e, b: None) trainer.state = State(epoch=0, iteration=0) checkpoint(trainer) trainer.state.iteration = 1 checkpoint(trainer) if clearml_saver._atomic: assert clearml.binding.frameworks.WeightsFileHandler.create_output_model.call_count == 2 else: saved_files = list(os.listdir(clearml_saver.dirname)) assert len(saved_files) == 1 assert saved_files[0] == "model_1.pt" def test_clearml_saver_callbacks(): mock_task = MagicMock(spec=clearml.Task) mock_task.name = "check-task" mock_model = MagicMock(spec=clearml.OutputModel) model_info = WeightsFileHandler.ModelInfo( model=mock_model, upload_filename="test.pt", local_model_path="", local_model_id="", framework=Framework.pytorch, task=mock_task, ) mock_model_info = MagicMock(spec_set=model_info) # Simulate 4 calls to save model and 2 to remove (n_saved=2) filenames = [ "best_model_5_val_acc=0.123.pt", "best_model_6_val_acc=0.234.pt", "best_model_7_val_acc=0.356.pt", "best_model_8_val_acc=0.456.pt", ] metadata_list = [ {"basename": "best_model", "score_name": "val_acc", "priority": 0.123}, {"basename": "best_model", "score_name": "val_acc", "priority": 0.234}, {"basename": "best_model", "score_name": "val_acc", "priority": 0.345}, {"basename": "best_model", "score_name": "val_acc", "priority": 0.456}, ] dirname = "/tmp/test" _checkpoint_slots = defaultdict(list) n_saved = 2 for i, (filename, metadata) in enumerate(zip(filenames, metadata_list)): mock_model_info.upload_filename = filename if i >= n_saved: # Remove filename_to_remove = filenames[i % n_saved] for slots in _checkpoint_slots.values(): try: slots[slots.index(filename_to_remove)] = None except ValueError: pass else: i = i % n_saved break basename = metadata["basename"] checkpoint_key = (dirname, basename) context = ClearMLSaver._CallbacksContext( callback_type=WeightsFileHandler.CallbackType, slots=_checkpoint_slots[checkpoint_key], checkpoint_key=str(checkpoint_key), filename=filename, basename=basename, metadata=metadata, ) output_model_info = context.pre_callback(str(WeightsFileHandler.CallbackType.save), mock_model_info) assert ( hasattr(output_model_info, "upload_filename") and f"{basename}_{i}.pt" in output_model_info.upload_filename ) assert hasattr(output_model_info, "local_model_id") and str(checkpoint_key) in output_model_info.local_model_id output_model_info = context.post_callback(str(WeightsFileHandler.CallbackType.save), mock_model_info) assert hasattr(output_model_info, "model") and hasattr(output_model_info.model, "name") assert hasattr(output_model_info, "model") and hasattr(output_model_info.model, "comment") assert isinstance(output_model_info.model.name, str) and filename in output_model_info.model.name assert ( isinstance(output_model_info.model.comment, str) and metadata["basename"] in output_model_info.model.comment and metadata["score_name"] in output_model_info.model.comment ) class DummyModel(torch.nn.Module): def __init__(self): super(DummyModel, self).__init__() self.net = torch.nn.Linear(2, 2) def forward(self, x): return self.net(x) def _test_save_model_optimizer_lr_scheduler_with_state_dict(device, on_zero_rank=False): if idist.get_rank() == 0: clearml.Task.current_task = MagicMock(spec=clearml.Task) clearml.binding.frameworks.WeightsFileHandler.create_output_model = MagicMock() torch.manual_seed(23) model = DummyModel().to(device) optim = torch.optim.SGD(model.parameters(), lr=0.1) lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optim, gamma=0.5) def update_fn(engine, batch): x = torch.rand((4, 2)).to(device) optim.zero_grad() y = model(x) # Below code raises: RuntimeError: torch_xla/csrc/tensor_impl.cpp:144 : XLA tensors do not have storage # Probably related to https://github.com/pytorch/xla/issues/2576 # loss = y.pow(2.0).sum() loss = y.sum() loss.backward() if idist.has_xla_support: import torch_xla.core.xla_model as xm xm.optimizer_step(optim, barrier=True) else: optim.step() lr_scheduler.step() engine = Engine(update_fn) to_save = {"model": model, "optimizer": optim, "lr_scheduler": lr_scheduler} with pytest.warns(UserWarning, match=r"ClearMLSaver created a temporary checkpoints directory"): clearml_saver = ClearMLSaver() if (not on_zero_rank) or (on_zero_rank and idist.get_rank() == 0): checkpoint = Checkpoint(to_save=to_save, save_handler=clearml_saver, n_saved=1) engine.add_event_handler(Events.EPOCH_COMPLETED, checkpoint) engine.run([0], max_epochs=4) idist.barrier() saved_objects = sorted(os.listdir(clearml_saver.dirname)) # saved object is ['PREFIX_checkpoint_3.pt', ] saved_checkpoint = clearml_saver.dirname / saved_objects[0] if idist.has_xla_support: device = "cpu" loaded_obj = torch.load(saved_checkpoint, map_location=device) for f in ["model", "optimizer", "lr_scheduler"]: assert f in loaded_obj loaded_model_state_dict = loaded_obj["model"] loaded_optimizer_state_dict = loaded_obj["optimizer"] loaded_lr_scheduler_state_dict = loaded_obj["lr_scheduler"] assert isinstance(loaded_model_state_dict, dict) assert isinstance(loaded_optimizer_state_dict, dict) assert isinstance(loaded_lr_scheduler_state_dict, dict) # Specifically move device to CPU first model_state_dict = model.cpu().state_dict() for key in model_state_dict.keys(): assert key in loaded_model_state_dict model_value = model_state_dict[key] loaded_model_value = loaded_model_state_dict[key] assert (model_value.cpu().numpy() == loaded_model_value.cpu().numpy()).all() optim_state_dict = optim.state_dict() for key in optim_state_dict.keys(): assert key in loaded_optimizer_state_dict optim_value = optim_state_dict[key] loaded_optim_value = loaded_optimizer_state_dict[key] if idist.get_rank() == 0: assert optim_value == loaded_optim_value lr_scheduler_state_dict = lr_scheduler.state_dict() for key in lr_scheduler_state_dict.keys(): assert key in loaded_lr_scheduler_state_dict lr_scheduler_value = lr_scheduler_state_dict[key] loaded_lr_scheduler_value = loaded_lr_scheduler_state_dict[key] assert lr_scheduler_value == loaded_lr_scheduler_value @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_save_model_optimizer_lr_scheduler_with_state_dict(device) _test_save_model_optimizer_lr_scheduler_with_state_dict(device, on_zero_rank=True) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_save_model_optimizer_lr_scheduler_with_state_dict(device) _test_save_model_optimizer_lr_scheduler_with_state_dict(device, on_zero_rank=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device") def test_distrib_single_device_xla(): device = idist.device() assert "xla" in device.type _test_save_model_optimizer_lr_scheduler_with_state_dict(device) def _test_save_model_optimizer_lr_scheduler_with_state_dict_xla_nprocs(index): device = idist.device() _test_save_model_optimizer_lr_scheduler_with_state_dict(device) import time # hack to have all proc properly sync: time.sleep(1) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device") def test_distrib_single_device_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_save_model_optimizer_lr_scheduler_with_state_dict_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/handlers/test_early_stopping.py000066400000000000000000000314401465426447700237120ustar00rootroot00000000000000import os import pytest import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers import EarlyStopping def do_nothing_update_fn(engine, batch): pass def test_args_validation(): trainer = Engine(do_nothing_update_fn) with pytest.raises(ValueError, match=r"Argument patience should be positive integer."): EarlyStopping(patience=-1, score_function=lambda engine: 0, trainer=trainer) with pytest.raises(ValueError, match=r"Argument min_delta should not be a negative number."): EarlyStopping(patience=2, min_delta=-0.1, score_function=lambda engine: 0, trainer=trainer) with pytest.raises(TypeError, match=r"Argument score_function should be a function."): EarlyStopping(patience=2, score_function=12345, trainer=trainer) with pytest.raises(TypeError, match=r"Argument trainer should be an instance of Engine."): EarlyStopping(patience=2, score_function=lambda engine: 0, trainer=None) def test_simple_early_stopping(): scores = iter([1.0, 0.8, 0.88]) def score_function(engine): return next(scores) trainer = Engine(do_nothing_update_fn) h = EarlyStopping(patience=2, score_function=score_function, trainer=trainer) # Call 3 times and check if stopped assert not trainer.should_terminate h(None) assert not trainer.should_terminate h(None) assert not trainer.should_terminate h(None) assert trainer.should_terminate def test_state_dict(): scores = iter([1.0, 0.8, 0.88]) def score_function(engine): return next(scores) trainer = Engine(do_nothing_update_fn) h = EarlyStopping(patience=2, score_function=score_function, trainer=trainer) # Call 3 times and check if stopped assert not trainer.should_terminate h(None) assert not trainer.should_terminate # Swap to new object, but maintain state h2 = EarlyStopping(patience=2, score_function=score_function, trainer=trainer) h2.load_state_dict(h.state_dict()) h2(None) assert not trainer.should_terminate h2(None) assert trainer.should_terminate def test_early_stopping_on_delta(): scores = iter([1.0, 2.0, 2.01, 3.0, 3.01, 3.02]) trainer = Engine(do_nothing_update_fn) h = EarlyStopping(patience=2, min_delta=0.1, score_function=lambda _: next(scores), trainer=trainer) assert not trainer.should_terminate h(None) # counter == 0 assert not trainer.should_terminate h(None) # delta == 1.0; counter == 0 assert not trainer.should_terminate h(None) # delta == 0.01; counter == 1 assert not trainer.should_terminate h(None) # delta == 0.99; counter == 0 assert not trainer.should_terminate h(None) # delta == 0.01; counter == 1 assert not trainer.should_terminate h(None) # delta == 0.01; counter == 2 assert trainer.should_terminate def test_early_stopping_on_last_event_delta(): scores = iter([0.0, 0.3, 0.6]) trainer = Engine(do_nothing_update_fn) h = EarlyStopping( patience=2, min_delta=0.4, cumulative_delta=False, score_function=lambda _: next(scores), trainer=trainer ) assert not trainer.should_terminate h(None) # counter == 0 assert not trainer.should_terminate h(None) # delta == 0.3; counter == 1 assert not trainer.should_terminate h(None) # delta == 0.3; counter == 2 assert trainer.should_terminate def test_early_stopping_on_cumulative_delta(): scores = iter([0.0, 0.3, 0.6]) trainer = Engine(do_nothing_update_fn) h = EarlyStopping( patience=2, min_delta=0.4, cumulative_delta=True, score_function=lambda _: next(scores), trainer=trainer ) assert not trainer.should_terminate h(None) # counter == 0 assert not trainer.should_terminate h(None) # delta == 0.3; counter == 1 assert not trainer.should_terminate h(None) # delta == 0.6; counter == 0 assert not trainer.should_terminate def test_simple_early_stopping_on_plateau(): def score_function(engine): return 42 trainer = Engine(do_nothing_update_fn) h = EarlyStopping(patience=1, score_function=score_function, trainer=trainer) # Call 2 times and check if stopped assert not trainer.should_terminate h(None) assert not trainer.should_terminate h(None) assert trainer.should_terminate def test_simple_no_early_stopping(): scores = iter([1.0, 0.8, 1.2]) def score_function(engine): return next(scores) trainer = Engine(do_nothing_update_fn) h = EarlyStopping(patience=2, score_function=score_function, trainer=trainer) # Call 3 times and check if not stopped assert not trainer.should_terminate h(None) h(None) h(None) assert not trainer.should_terminate def test_with_engine_early_stopping(): class Counter(object): def __init__(self, count=0): self.count = count n_epochs_counter = Counter() scores = iter([1.0, 0.8, 1.2, 1.5, 0.9, 1.0, 0.99, 1.1, 0.9]) def score_function(engine): return next(scores) trainer = Engine(do_nothing_update_fn) evaluator = Engine(do_nothing_update_fn) early_stopping = EarlyStopping(patience=3, score_function=score_function, trainer=trainer) @trainer.on(Events.EPOCH_COMPLETED) def evaluation(engine): evaluator.run([0]) n_epochs_counter.count += 1 evaluator.add_event_handler(Events.COMPLETED, early_stopping) trainer.run([0], max_epochs=10) assert n_epochs_counter.count == 7 assert trainer.state.epoch == 7 def test_with_engine_early_stopping_on_plateau(): class Counter(object): def __init__(self, count=0): self.count = count n_epochs_counter = Counter() def score_function(engine): return 0.047 trainer = Engine(do_nothing_update_fn) evaluator = Engine(do_nothing_update_fn) early_stopping = EarlyStopping(patience=4, score_function=score_function, trainer=trainer) @trainer.on(Events.EPOCH_COMPLETED) def evaluation(engine): evaluator.run([0]) n_epochs_counter.count += 1 evaluator.add_event_handler(Events.COMPLETED, early_stopping) trainer.run([0], max_epochs=10) assert n_epochs_counter.count == 5 assert trainer.state.epoch == 5 def test_with_engine_no_early_stopping(): class Counter(object): def __init__(self, count=0): self.count = count n_epochs_counter = Counter() scores = iter([1.0, 0.8, 1.2, 1.23, 0.9, 1.0, 1.1, 1.253, 1.26, 1.2]) def score_function(engine): return next(scores) trainer = Engine(do_nothing_update_fn) evaluator = Engine(do_nothing_update_fn) early_stopping = EarlyStopping(patience=5, score_function=score_function, trainer=trainer) @trainer.on(Events.EPOCH_COMPLETED) def evaluation(engine): evaluator.run([0]) n_epochs_counter.count += 1 evaluator.add_event_handler(Events.COMPLETED, early_stopping) trainer.run([0], max_epochs=10) assert n_epochs_counter.count == 10 assert trainer.state.epoch == 10 def _test_distrib_with_engine_early_stopping(device): if device is None: device = idist.device() if isinstance(device, str): device = torch.device(device) torch.manual_seed(12) class Counter(object): def __init__(self, count=0): self.count = count n_epochs_counter = Counter() scores = torch.tensor([1.0, 0.8, 1.2, 1.5, 0.9, 1.0, 0.99, 1.1, 0.9], requires_grad=False).to(device) def score_function(engine): i = trainer.state.epoch - 1 v = scores[i] idist.all_reduce(v) v /= idist.get_world_size() return v.item() trainer = Engine(do_nothing_update_fn) evaluator = Engine(do_nothing_update_fn) early_stopping = EarlyStopping(patience=3, score_function=score_function, trainer=trainer) @trainer.on(Events.EPOCH_COMPLETED) def evaluation(engine): evaluator.run([0]) n_epochs_counter.count += 1 evaluator.add_event_handler(Events.COMPLETED, early_stopping) trainer.run([0], max_epochs=10) assert trainer.state.epoch == 7 assert n_epochs_counter.count == 7 def _test_distrib_integration_engine_early_stopping(device): from ignite.metrics import Accuracy if device is None: device = idist.device() if isinstance(device, str): device = torch.device(device) metric_device = device if device.type == "xla": metric_device = "cpu" rank = idist.get_rank() ws = idist.get_world_size() torch.manual_seed(12) n_epochs = 10 n_iters = 20 y_preds = ( [torch.randint(0, 2, size=(n_iters, ws)).to(device)] + [torch.ones(n_iters, ws).to(device)] + [torch.randint(0, 2, size=(n_iters, ws)).to(device) for _ in range(n_epochs - 2)] ) y_true = ( [torch.randint(0, 2, size=(n_iters, ws)).to(device)] + [torch.ones(n_iters, ws).to(device)] + [torch.randint(0, 2, size=(n_iters, ws)).to(device) for _ in range(n_epochs - 2)] ) def update(engine, _): e = trainer.state.epoch - 1 i = engine.state.iteration - 1 return y_preds[e][i, rank], y_true[e][i, rank] evaluator = Engine(update) acc = Accuracy(device=metric_device) acc.attach(evaluator, "acc") def score_function(engine): return engine.state.metrics["acc"] trainer = Engine(lambda e, b: None) early_stopping = EarlyStopping(patience=3, score_function=score_function, trainer=trainer) @trainer.on(Events.EPOCH_COMPLETED) def evaluation(engine): data = list(range(n_iters)) evaluator.run(data=data) evaluator.add_event_handler(Events.COMPLETED, early_stopping) trainer.run([0], max_epochs=10) assert trainer.state.epoch == 5 @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_with_engine_early_stopping, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_engine_early_stopping, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/handlers/test_ema_handler.py000066400000000000000000000372561465426447700231250ustar00rootroot00000000000000import os from typing import Any, Callable, Union import pytest import torch import torch.nn as nn from torch.nn.parallel import DataParallel, DistributedDataParallel import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.handlers import EMAHandler def _get_dummy_model() -> nn.Module: model = nn.Linear(2, 1, bias=False) model.weight.data.fill_(1) return model def _unwrap_model(model): if isinstance(model, (DataParallel, DistributedDataParallel)): return model.module else: return model @pytest.fixture(scope="module") def get_dummy_model(): """Returns a function since the fixture is needed multiple times in a single test""" yield _get_dummy_model def _get_dummy_step_fn(model: Union[nn.Module, DataParallel, DistributedDataParallel]) -> Callable: """Get a dummy step function, given model is a (wrapper of) dummy model returned from _get_dummy_model""" def step_fn(engine, batch): """Increment the weight by 1 at each iteration""" _unwrap_model(model).weight.data.add_(1) return 0 return step_fn @pytest.mark.parametrize("momentum", [-1, 2]) def test_ema_invalid_momentum(get_dummy_model, momentum): with pytest.raises(ValueError, match="Invalid momentum"): EMAHandler(get_dummy_model(), momentum=momentum) def test_has_momentum_scheduler(get_dummy_model): """Test the handler has attribute `momentum_scheduler` and `_momentum_lambda_obj`""" momentum_warmup = 0.0 warmup_iters = 10 ema_handler = EMAHandler(get_dummy_model(), momentum_warmup=momentum_warmup, warmup_iters=warmup_iters) assert hasattr(ema_handler, "momentum_scheduler") assert hasattr(ema_handler, "_momentum_lambda_obj") def test_ema_warmup_func(get_dummy_model): """Test the built-in linear warmup function for the EMA momentum""" momentum = 0.5 momentum_warmup_1 = 0.0 momentum_warmup_2 = 1.0 warmup_iters = 5 def check_ema_momentum(engine: Engine, momentum_warmup, final_momentum, warmup_iters): if engine.state.iteration == 1: assert engine.state.ema_momentum == momentum_warmup elif engine.state.iteration >= 1 + warmup_iters: assert engine.state.ema_momentum == final_momentum else: min_momentum = min(momentum, momentum_warmup) max_momentum = max(momentum, momentum_warmup) assert min_momentum <= engine.state.ema_momentum <= max_momentum # momentum_warmup < momentum model_1 = get_dummy_model() engine_1 = Engine(_get_dummy_step_fn(model_1)) ema_handler_1 = EMAHandler(model_1, momentum, momentum_warmup_1, warmup_iters) ema_handler_1.attach(engine_1) engine_1.add_event_handler( Events.ITERATION_COMPLETED, check_ema_momentum, momentum_warmup_1, momentum, warmup_iters ) engine_1.run(range(10)) # momentum_warmup > momentum model_2 = get_dummy_model() engine_2 = Engine(_get_dummy_step_fn(model_2)) ema_handler_2 = EMAHandler(model_2, momentum, momentum_warmup_2, warmup_iters) ema_handler_2.attach(engine_2) engine_2.add_event_handler( Events.ITERATION_COMPLETED, check_ema_momentum, momentum_warmup_2, momentum, warmup_iters ) engine_2.run(range(10)) def test_ema_invalid_model(): with pytest.raises(ValueError, match="model should be an instance of nn.Module or its subclasses"): model = "Invalid Model" EMAHandler(model) # type: ignore @pytest.mark.distributed @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_ema_ema_model_on_cuda(get_dummy_model): """Test if ema_handler.ema_model is nn.Module or nn.DataParallel and under eval mode""" model = get_dummy_model().to(idist.device()) model = idist.auto_model(model) ema_handler = EMAHandler(model) ema_model = ema_handler.ema_model assert not ema_model.training if isinstance(model, DataParallel): assert isinstance(ema_model, DataParallel) else: assert ( isinstance(ema_model, nn.Module) and (not isinstance(ema_model, DataParallel)) and (not isinstance(ema_model, DistributedDataParallel)) ) def test_ema_load_state_dict(get_dummy_model): model_1 = get_dummy_model() model_1.weight.data.fill_(2) state_dict_1 = model_1.state_dict() model_2 = get_dummy_model() ema_handler = EMAHandler(model_2) ema_model = ema_handler.ema_model ema_model.load_state_dict(state_dict_1) assert ema_model.weight.data.allclose(model_1.weight.data) def test_ema_get_const_momentum(get_dummy_model): """Test if momentum retrieved from the engine is constant and equal to the handler's momentum""" model = get_dummy_model() step_fn = _get_dummy_step_fn(model) engine = Engine(step_fn) def assert_const_momentum(engine: Engine, const_momentum): assert engine.state.ema_momentum == const_momentum ema_handler = EMAHandler(model, momentum=0.002) ema_handler.attach(engine) engine.add_event_handler(Events.ITERATION_COMPLETED, assert_const_momentum, ema_handler.momentum) engine.run(range(10)) @pytest.mark.parametrize("handle_buffers", ["copy", "update", "ema_train", "invalid"]) def test_ema_buffer(handle_buffers): """Test if the tensors in buffer are also correctly updated""" model = nn.BatchNorm2d(2) model.running_mean.data.fill_(1.5) model.running_var.data.fill_(1.5) # manually register a buffer to test if it will be correctly updated model.register_buffer("dummy_buffer", tensor=torch.tensor(1.0, dtype=torch.float32)) if handle_buffers == "invalid": with pytest.raises(ValueError, match="handle_buffers can only"): _ = EMAHandler(model, momentum=0.5, handle_buffers=handle_buffers) else: ema_handler = EMAHandler(model, momentum=0.5, handle_buffers=handle_buffers) def _bn_step_fn(engine, batch): x = torch.rand(4, 2, 32, 32) _ = model(x) # manually increment the dummy_buffer at every step model.dummy_buffer += 1.0 return 1 engine = Engine(_bn_step_fn) ema_handler.attach(engine) ema_model = ema_handler.ema_model if handle_buffers == "ema_train": assert ema_model.training else: assert not ema_model.training @engine.on(Events.ITERATION_COMPLETED) def check_buffers(): if handle_buffers == "update": # the buffers with torch.int64 data type should be directly copied assert ema_model.num_batches_tracked.allclose(model.num_batches_tracked) # buffers with floating type will be updated rather than copied assert not ema_model.dummy_buffer.allclose(model.dummy_buffer) assert not ema_model.running_mean.allclose(model.running_mean) assert not ema_model.running_var.allclose(model.running_var) elif handle_buffers == "copy": # the buffers with torch.int64 data type should be directly copied assert ema_model.num_batches_tracked.allclose(model.num_batches_tracked) assert ema_model.dummy_buffer.allclose(model.dummy_buffer) assert ema_model.running_mean.allclose(model.running_mean) assert ema_model.running_var.allclose(model.running_var) else: # buffers will not be copied or EMA updated assert ema_model.num_batches_tracked.allclose(torch.tensor(0, dtype=torch.int64)) assert ema_model.dummy_buffer.allclose(torch.tensor(1.0, dtype=torch.float32)) # engine will run 4 iterations engine.run([0, 1], max_epochs=2) if handle_buffers == "update": assert ema_model.num_batches_tracked.allclose(model.num_batches_tracked) assert ema_model.dummy_buffer.allclose(torch.tensor(4.0625, dtype=torch.float32)) assert not ema_model.dummy_buffer.allclose(model.dummy_buffer) assert not ema_model.running_mean.allclose(model.running_mean) assert not ema_model.running_var.allclose(model.running_var) elif handle_buffers == "copy": assert ema_model.num_batches_tracked.allclose(model.num_batches_tracked) assert ema_model.dummy_buffer.allclose(model.dummy_buffer) assert ema_model.running_mean.allclose(model.running_mean) assert ema_model.running_var.allclose(model.running_var) else: # buffers will not be copied or EMA updated assert ema_model.num_batches_tracked.allclose(torch.tensor(0, dtype=torch.int64)) assert ema_model.dummy_buffer.allclose(torch.tensor(1.0, dtype=torch.float32)) def test_ema_two_handlers(get_dummy_model): """Test when two EMA handlers are attached to a trainer""" model_1 = get_dummy_model() ema_handler_1 = EMAHandler(model_1, momentum=0.5) model_2 = get_dummy_model() ema_handler_2 = EMAHandler(model_2, momentum=0.5) def _step_fn(engine: Engine, batch: Any): model_1.weight.data.add_(1) model_2.weight.data.add_(1) return 0 engine = Engine(_step_fn) assert not hasattr(engine.state, "ema_momentum_1") # handler_1 update EMA model of model_1 every 1 iteration ema_handler_1.attach(engine, "ema_momentum_1", event=Events.ITERATION_COMPLETED) assert hasattr(engine.state, "ema_momentum_1") # handler_2 update EMA model for model_2 every 2 iterations ema_handler_2.attach(engine, "ema_momentum_2", event=Events.ITERATION_COMPLETED(every=2)) assert hasattr(engine.state, "ema_momentum_2") # engine will run 4 iterations engine.run(range(2), max_epochs=2) # explicitly cast to float32 to avoid test failure on XLA devices ema_weight_1 = ema_handler_1.ema_model.weight.data.to(torch.float32) ema_weight_2 = ema_handler_2.ema_model.weight.data.to(torch.float32) assert ema_weight_1.allclose(ema_weight_1.new_full((1, 2), 4.0625)) assert ema_weight_2.allclose(ema_weight_2.new_full((1, 2), 3.5)) assert engine.state.ema_momentum_1 == 0.5 assert engine.state.ema_momentum_2 == 0.5 model_3 = get_dummy_model() ema_handler_3 = EMAHandler(model_3) with pytest.warns(UserWarning, match="Attribute 'ema_momentum_1' already exists"): ema_handler_3.attach(engine, name="ema_momentum_1") def _test_ema_final_weight(model, device=None, ddp=False, interval=1): """Test if final smoothed weights are correct""" if device is None: # let horovod decide the device device = idist.device() if isinstance(device, str): device = torch.device(device) model = model.to(device) if ddp: model = idist.auto_model(model) step_fn = _get_dummy_step_fn(model) engine = Engine(step_fn) ema_handler = EMAHandler(model, momentum=0.5) ema_handler.attach(engine, "model", event=Events.ITERATION_COMPLETED(every=interval)) # engine will run 4 iterations engine.run(range(2), max_epochs=2) # ema_model and model can be DP or DDP # explicitly cast to float32 to avoid test failure on XLA devices ema_weight = _unwrap_model(ema_handler.ema_model).weight.data.to(torch.float32) model_weight = _unwrap_model(model).weight.data.to(torch.float32) assert ema_weight.device == device assert model_weight.device == device if interval == 1: assert ema_weight.allclose(ema_weight.new_full((1, 2), 4.0625)) elif interval == 2: assert ema_weight.allclose(ema_weight.new_full((1, 2), 3.5)) else: pass assert model_weight.allclose(model_weight.new_full((1, 2), 5.0)) @pytest.mark.parametrize("interval", [1, 2]) def test_ema_final_weight_cpu(get_dummy_model, interval): device = torch.device("cpu") _test_ema_final_weight(get_dummy_model(), device=device, ddp=False, interval=interval) @pytest.mark.parametrize("interval", [1, 2]) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_ema_final_weight_cuda(get_dummy_model, interval): device = torch.device("cuda:0") _test_ema_final_weight(get_dummy_model(), device=device, ddp=False, interval=interval) @pytest.mark.distributed @pytest.mark.parametrize("interval", [1, 2]) @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_ema_final_weight_distrib_nccl_gpu(get_dummy_model, distributed_context_single_node_nccl, interval): device = idist.device() _test_ema_final_weight(get_dummy_model(), device=device, ddp=True, interval=interval) @pytest.mark.distributed @pytest.mark.parametrize("interval", [1, 2]) @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_ema_final_weight_distrib_gloo_cpu_or_gpu(get_dummy_model, distributed_context_single_node_gloo, interval): device = idist.device() _test_ema_final_weight(get_dummy_model(), device=device, ddp=True, interval=interval) @pytest.mark.distributed @pytest.mark.parametrize("interval", [1, 2]) @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_ema_final_weight_distrib_hvd(get_dummy_model, gloo_hvd_executor, interval): nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() # pass device = None to the executor. Different from other distributed tests where the processes are # already spawn in the context, the processes here will be explicitly spawn by the executor, so we # pass None to the function, and call idist.device() in side the function to get the corresponding device gloo_hvd_executor(_test_ema_final_weight, (get_dummy_model(), None, True, interval), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_ema_final_weight_distrib_single_device_xla(get_dummy_model): device = idist.device() _test_ema_final_weight(get_dummy_model(), device=device, ddp=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_ema_final_weight_distrib_xla_nprocs(get_dummy_model, xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) def _test_ema_final_weight_xla_nprocs(index): device = idist.device() _test_ema_final_weight(get_dummy_model(), device=device, ddp=True) xmp_executor(_test_ema_final_weight_xla_nprocs, args=(), nprocs=n) @pytest.mark.multinode_distributed @pytest.mark.parametrize("interval", [1, 2]) @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_ema_final_weight_distrib_multinode_gloo_cpu_or_gpu( get_dummy_model, distributed_context_multi_node_gloo, interval ): device = idist.device() _test_ema_final_weight(get_dummy_model(), device=device, ddp=True, interval=interval) @pytest.mark.multinode_distributed @pytest.mark.parametrize("interval", [1, 2]) @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_ema_final_weight_distrib_multinode_nccl_gpu(get_dummy_model, distributed_context_multi_node_nccl, interval): device = idist.device() _test_ema_final_weight(get_dummy_model(), device=device, ddp=True, interval=interval) ignite-0.5.1/tests/ignite/handlers/test_fbresearch_logger.py000066400000000000000000000071601465426447700243200ustar00rootroot00000000000000import logging import re from unittest.mock import MagicMock import pytest import torch import torch.nn as nn import torch.optim as optim from ignite.engine import create_supervised_trainer, Engine, Events from ignite.handlers.fbresearch_logger import FBResearchLogger from ignite.utils import setup_logger @pytest.fixture def mock_engine(): engine = Engine(lambda e, b: None) engine.state.epoch = 1 engine.state.max_epochs = 10 engine.state.epoch_length = 100 engine.state.iteration = 50 return engine @pytest.fixture def mock_logger(): return MagicMock(spec=logging.Logger) @pytest.fixture def fb_research_logger(mock_logger): yield FBResearchLogger(logger=mock_logger, show_output=True) def test_fbresearch_logger_initialization(mock_logger): logger = FBResearchLogger(logger=mock_logger, show_output=True) assert logger.logger == mock_logger assert logger.show_output is True def test_fbresearch_logger_attach(mock_engine, mock_logger): logger = FBResearchLogger(logger=mock_logger, show_output=True) logger.attach(mock_engine, name="Test", every=1) assert mock_engine.has_event_handler(logger.log_every, Events.ITERATION_COMPLETED) @pytest.mark.parametrize( "output,expected_pattern", [ ({"loss": 0.456, "accuracy": 0.789}, r"loss. *0.456.*accuracy. *0.789"), ((0.456, 0.789), r"0.456.*0.789"), ([0.456, 0.789], r"0.456.*0.789"), ], ) def test_output_formatting(mock_engine, fb_research_logger, output, expected_pattern): # Ensure the logger correctly formats and logs the output for each type mock_engine.state.output = output fb_research_logger.attach(mock_engine, name="Test", every=1) mock_engine.fire_event(Events.ITERATION_COMPLETED) actual_output = fb_research_logger.logger.info.call_args_list[0].args[0] assert re.search(expected_pattern, actual_output) def test_logger_type_support(): model = nn.Linear(10, 5) opt = optim.SGD(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() data = [(torch.rand(4, 10), torch.randint(0, 5, size=(4,))) for _ in range(100)] trainer = create_supervised_trainer(model, opt, criterion) logger = setup_logger("trainer", level=logging.INFO) logger = FBResearchLogger(logger=logger, show_output=True) logger.attach(trainer, name="Train", every=20, optimizer=opt) trainer.run(data, max_epochs=4) trainer.state.output = {"loss": 4.2} trainer.fire_event(Events.ITERATION_COMPLETED) trainer.state.output = "4.2" trainer.fire_event(Events.ITERATION_COMPLETED) trainer.state.output = [4.2, 4.2] trainer.fire_event(Events.ITERATION_COMPLETED) trainer.state.output = (4.2, 4.2) trainer.fire_event(Events.ITERATION_COMPLETED) def test_fbrlogger_with_output_transform(mock_logger): trainer = Engine(lambda e, b: 42) fbr = FBResearchLogger(logger=mock_logger, show_output=True) fbr.attach(trainer, "Training", output_transform=lambda x: {"loss": x}) trainer.run(data=[10], epoch_length=1, max_epochs=1) assert "loss: 42.0000" in fbr.logger.info.call_args_list[-2].args[0] def test_fbrlogger_with_state_attrs(mock_logger): trainer = Engine(lambda e, b: 42) fbr = FBResearchLogger(logger=mock_logger, show_output=True) fbr.attach(trainer, "Training", state_attributes=["alpha", "beta", "gamma"]) trainer.state.alpha = 3.899 trainer.state.beta = torch.tensor(12.21) trainer.state.gamma = torch.tensor([21.0, 6.0]) trainer.run(data=[10], epoch_length=1, max_epochs=1) attrs = "alpha: 3.8990 beta: 12.2100 gamma: [21.0000, 6.0000]" assert attrs in fbr.logger.info.call_args_list[-2].args[0] ignite-0.5.1/tests/ignite/handlers/test_handlers.py000066400000000000000000000010771465426447700224560ustar00rootroot00000000000000from unittest.mock import MagicMock from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine def test_global_step_from_engine(): iteration = 12 epoch = 23 trainer = Engine(lambda e, b: None) trainer.state.iteration = iteration trainer.state.epoch = epoch gst = global_step_from_engine(trainer) assert gst(MagicMock(), Events.EPOCH_COMPLETED) == epoch gst = global_step_from_engine(trainer, custom_event_name=Events.ITERATION_COMPLETED) assert gst(MagicMock(), Events.EPOCH_COMPLETED) == iteration ignite-0.5.1/tests/ignite/handlers/test_lr_finder.py000066400000000000000000000623641465426447700226300ustar00rootroot00000000000000import copy import os from pathlib import Path from unittest.mock import MagicMock import filelock import matplotlib import pytest import torch import torch.nn.functional as F from torch import nn from torch.optim import SGD import ignite.distributed as idist from ignite.engine import create_supervised_trainer, Engine, Events from ignite.handlers import FastaiLRFinder matplotlib.use("agg") @pytest.fixture def no_site_packages(): import sys matplotlib = sys.modules["matplotlib"] del sys.modules["matplotlib"] prev_path = list(sys.path) sys.path = [p for p in sys.path if "site-packages" not in p] yield "no_site_packages" sys.path = prev_path sys.modules["matplotlib"] = matplotlib class DummyModel(nn.Module): def __init__(self, n_channels=10, out_channels=1, flatten_input=False): super(DummyModel, self).__init__() self.net = nn.Sequential(nn.Flatten() if flatten_input else nn.Identity(), nn.Linear(n_channels, out_channels)) def forward(self, x): return self.net(x) class DummyModelMulipleParamGroups(nn.Module): def __init__(self): super(DummyModelMulipleParamGroups, self).__init__() self.fc1 = nn.Linear(10, 20) self.fc2 = nn.Linear(20, 10) self.fc3 = nn.Linear(10, 10) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) return self.fc3(x) @pytest.fixture def model(): model = DummyModel(out_channels=10) yield model @pytest.fixture def model_multiple_param_groups(): model_multiple_param_groups = DummyModelMulipleParamGroups() yield model_multiple_param_groups @pytest.fixture def mnist_model(): model = DummyModel(n_channels=784, out_channels=10, flatten_input=True) yield model @pytest.fixture def optimizer(model): yield SGD(model.parameters(), lr=1e-4, momentum=0.0) @pytest.fixture def optimizer_multiple_param_groups(model_multiple_param_groups): optimizer_multiple_param_groups = SGD( [ {"params": model_multiple_param_groups.fc1.parameters(), "lr": 4e-1}, {"params": model_multiple_param_groups.fc2.parameters(), "lr": 3e-2}, {"params": model_multiple_param_groups.fc3.parameters(), "lr": 3e-3}, ] ) yield optimizer_multiple_param_groups @pytest.fixture def mnist_optimizer(mnist_model): yield SGD(mnist_model.parameters(), lr=1e-4, momentum=0.0) @pytest.fixture def to_save(model, optimizer): yield {"model": model, "optimizer": optimizer} @pytest.fixture def mnist_to_save(mnist_model, mnist_optimizer): yield {"model": mnist_model, "optimizer": mnist_optimizer} @pytest.fixture def to_save_mulitple_param_groups(model_multiple_param_groups, optimizer_multiple_param_groups): yield {"model": model_multiple_param_groups, "optimizer": optimizer_multiple_param_groups} @pytest.fixture def lr_finder(): yield FastaiLRFinder() @pytest.fixture def dummy_engine(model, optimizer): engine = create_supervised_trainer(model, optimizer, nn.MSELoss()) yield engine @pytest.fixture def dummy_engine_mnist(mnist_model, mnist_optimizer): mnist_engine = create_supervised_trainer(mnist_model, mnist_optimizer, nn.CrossEntropyLoss()) yield mnist_engine @pytest.fixture def dummy_engine_mulitple_param_groups(model_multiple_param_groups, optimizer_multiple_param_groups): engine_multiple_param_groups = create_supervised_trainer( model_multiple_param_groups, optimizer_multiple_param_groups, nn.MSELoss() ) yield engine_multiple_param_groups @pytest.fixture def dataloader(): yield torch.rand(100, 2, 10) @pytest.fixture def dataloader_plot(): yield torch.rand(500, 2, 10) @pytest.fixture def mnist_dataloader(tmp_path_factory): from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) root_tmp_dir = tmp_path_factory.getbasetemp().parent while True: try: with filelock.FileLock(root_tmp_dir / "mnist_download.lock", timeout=0.2) as fn: fn.acquire() train_loader = DataLoader( MNIST(download=True, root="/tmp", transform=data_transform, train=True), batch_size=256, shuffle=True, ) fn.release() break except filelock._error.Timeout: pass yield train_loader def test_attach_incorrect_input_args(lr_finder, dummy_engine, model, optimizer, dataloader): with pytest.raises(TypeError, match=r"Argument to_save should be a mapping"): with lr_finder.attach(dummy_engine, to_save=123): pass with pytest.raises(TypeError, match=r"Object should have `state_dict` method"): with lr_finder.attach(dummy_engine, to_save={1: 2}): pass with pytest.raises(ValueError, match=r"Mapping to_save should contain 'optimizer' key"): with lr_finder.attach(dummy_engine, to_save={"model": model}): pass to_save = {"model": model, "optimizer": optimizer} with pytest.raises(ValueError, match=r"smooth_f is outside the range \[0, 1\]"): with lr_finder.attach(dummy_engine, to_save=to_save, smooth_f=234): pass with pytest.raises(ValueError, match=r"diverge_th should be larger than 1"): with lr_finder.attach(dummy_engine, to_save=to_save, diverge_th=0.0): pass with pytest.raises(TypeError, match=r"if provided, num_iter should be an integer"): with lr_finder.attach(dummy_engine, to_save=to_save, num_iter=0.0): pass with pytest.raises(ValueError, match=r"if provided, num_iter should be positive"): with lr_finder.attach(dummy_engine, to_save=to_save, num_iter=0): pass with pytest.raises(TypeError, match=r"Object to_save\['optimizer'] should be torch optimizer"): with lr_finder.attach(dummy_engine, {"model": to_save["model"], "optimizer": to_save["model"]}): pass with pytest.raises(ValueError, match=r"step_mode should be 'exp' or 'linear'"): with lr_finder.attach(dummy_engine, to_save=to_save, step_mode="abc"): pass with lr_finder.attach(dummy_engine, to_save) as trainer_with_finder: trainer_with_finder.run(dataloader) with pytest.raises(ValueError, match=r"skip_start cannot be negative"): lr_finder.plot(skip_start=-1) with pytest.raises(ValueError, match=r"skip_end cannot be negative"): lr_finder.plot(skip_end=-1) with pytest.raises(ValueError, match=r"Number of values of start_lr should be equal to optimizer values."): with lr_finder.attach(dummy_engine, to_save, start_lr=[0.1, 0.1]): pass with pytest.raises(ValueError, match=r"Number of values of end_lr should be equal to optimizer values."): with lr_finder.attach(dummy_engine, to_save, end_lr=[0.1, 0.1]): pass with pytest.raises(TypeError, match=r"start_lr should be a float or list of floats"): with lr_finder.attach(dummy_engine, to_save, start_lr=1): pass with pytest.raises(TypeError, match=r"end_lr should be a float or list of floats"): with lr_finder.attach(dummy_engine, to_save, end_lr=1): pass def test_attach_without_with(lr_finder, dummy_engine, to_save): _ = lr_finder.attach(dummy_engine, to_save=to_save) for event in dummy_engine._event_handlers: assert len(dummy_engine._event_handlers[event]) == 0 with lr_finder.attach(dummy_engine, to_save=to_save) as _: assert any([len(dummy_engine._event_handlers[event]) != 0 for event in dummy_engine._event_handlers]) with pytest.raises( RuntimeError, match=r"learning rate finder didn't run yet so lr_suggestion can't be returned" ): lr_finder.lr_suggestion() with pytest.raises(RuntimeError, match=r"learning rate finder didn't run yet so results can't be plotted"): lr_finder.plot() def test_with_attach(lr_finder, to_save, dummy_engine, dataloader): with lr_finder.attach(dummy_engine, to_save=to_save) as trainer_with_finder: trainer_with_finder.run(dataloader) assert lr_finder.get_results() is not None for event in dummy_engine._event_handlers: assert len(dummy_engine._event_handlers[event]) == 0 def test_wrong_values_start_lr_and_end_lr( lr_finder, dummy_engine, to_save, dummy_engine_mulitple_param_groups, to_save_mulitple_param_groups ): with pytest.raises(ValueError, match=r"start_lr must be less than end_lr"): with lr_finder.attach(dummy_engine, to_save=to_save, start_lr=10.0, end_lr=1.0): pass with pytest.raises(ValueError, match=r"start_lr must be less than end_lr"): with lr_finder.attach( dummy_engine_mulitple_param_groups, to_save=to_save_mulitple_param_groups, start_lr=[1.0, 10.0, 5.0], end_lr=[10.0, 10.0, 10.0], ): pass def test_model_optimizer_reset(lr_finder, to_save, dummy_engine, dataloader): optimizer = to_save["optimizer"] model = to_save["model"] init_optimizer_sd = copy.deepcopy(optimizer.state_dict()) init_model_sd = copy.deepcopy(model.state_dict()) init_trainer_sd = copy.deepcopy(dummy_engine.state_dict()) with pytest.warns(UserWarning, match=r"Run completed without loss diverging"): with lr_finder.attach(dummy_engine, to_save=to_save, diverge_th=float("inf")) as trainer_with_finder: trainer_with_finder.run(dataloader) assert init_optimizer_sd == optimizer.state_dict() for tensor1, tensor2 in zip(init_model_sd.values(), model.state_dict().values()): assert torch.all(torch.eq(tensor1, tensor2)) assert init_trainer_sd == dummy_engine.state_dict() def test_lr_policy(lr_finder, to_save, dummy_engine, dataloader): with lr_finder.attach(dummy_engine, to_save=to_save, step_mode="linear") as trainer_with_finder: trainer_with_finder.run(dataloader) lr = lr_finder.get_results()["lr"] assert all([lr[i - 1] < lr[i] for i in range(1, len(lr))]) with lr_finder.attach(dummy_engine, to_save=to_save, step_mode="exp") as trainer_with_finder: trainer_with_finder.run(dataloader) lr = lr_finder.get_results()["lr"] assert all([lr[i - 1] < lr[i] for i in range(1, len(lr))]) @pytest.mark.parametrize("step_mode", ["exp", "linear"]) def test_multiple_optimizers( lr_finder, dummy_engine_mulitple_param_groups, to_save_mulitple_param_groups, dataloader, step_mode ): start_lr = [0.1, 0.1, 0.01] end_lr = [1.0, 1.0, 1.0] with lr_finder.attach( dummy_engine_mulitple_param_groups, to_save_mulitple_param_groups, start_lr=start_lr, end_lr=end_lr, step_mode=step_mode, ) as trainer: trainer.run(dataloader) groups_lrs = lr_finder.get_results()["lr"] assert [all([group_lrs[i - 1] < group_lrs[i] for i in range(1, len(group_lrs))]) for group_lrs in groups_lrs] def assert_output_sizes(lr_finder, dummy_engine): iteration = dummy_engine.state.iteration lr_finder_results = lr_finder.get_results() lr, loss = lr_finder_results["lr"], lr_finder_results["loss"] assert len(lr) == len(loss) == iteration def test_num_iter_is_none(lr_finder, to_save, dummy_engine, dataloader): with pytest.warns(UserWarning, match=r"Run completed without loss diverging"): with lr_finder.attach(dummy_engine, to_save=to_save, diverge_th=float("inf")) as trainer_with_finder: trainer_with_finder.run(dataloader) assert_output_sizes(lr_finder, dummy_engine) assert dummy_engine.state.iteration == len(dataloader) def test_num_iter_is_enough(lr_finder, to_save, dummy_engine, dataloader): with pytest.warns(UserWarning, match=r"Run completed without loss diverging"): with lr_finder.attach( dummy_engine, to_save=to_save, num_iter=50, diverge_th=float("inf") ) as trainer_with_finder: trainer_with_finder.run(dataloader) assert_output_sizes(lr_finder, dummy_engine) # -1 because it terminates when state.iteration > num_iter assert dummy_engine.state.iteration - 1 == 50 def test_num_iter_is_not_enough(lr_finder, to_save, dummy_engine, dataloader): with lr_finder.attach(dummy_engine, to_save, num_iter=150, diverge_th=float("inf")) as trainer_with_finder: with pytest.warns(UserWarning): trainer_with_finder.run(dataloader) assert_output_sizes(lr_finder, dummy_engine) assert dummy_engine.state.iteration != len(dataloader) assert dummy_engine.state.iteration == 150 + 1 def test_detach_terminates(lr_finder, to_save, dummy_engine, dataloader): with lr_finder.attach(dummy_engine, to_save, end_lr=100.0, diverge_th=2) as trainer_with_finder: trainer_with_finder.run(dataloader) dummy_engine.run(dataloader, max_epochs=3) assert dummy_engine.state.epoch == 3 def test_different_num_iters(lr_finder, to_save, dummy_engine, dataloader): with pytest.warns(UserWarning, match=r"Run completed without loss diverging"): with lr_finder.attach(dummy_engine, to_save, num_iter=200, diverge_th=float("inf")) as trainer_with_finder: trainer_with_finder.run(dataloader) assert trainer_with_finder.state.iteration == 200 # num_iter with pytest.warns(UserWarning, match=r"Run completed without loss diverging"): with lr_finder.attach(dummy_engine, to_save, num_iter=1000, diverge_th=float("inf")) as trainer_with_finder: trainer_with_finder.run(dataloader) assert trainer_with_finder.state.iteration == 1000 # num_iter @pytest.mark.parametrize("step_mode", ["exp", "linear"]) def test_start_lr(lr_finder, to_save, dummy_engine, dataloader, step_mode): with lr_finder.attach( dummy_engine, to_save, start_lr=0.01, end_lr=10.0, num_iter=5, step_mode=step_mode, diverge_th=1 ) as trainer_with_finder: trainer_with_finder.run(dataloader) history = lr_finder.get_results() if step_mode == "exp": assert 0.01 < history["lr"][0] < 0.16 else: assert pytest.approx(history["lr"][0]) == 0.01 def test_engine_output_type(lr_finder, dummy_engine, optimizer): from ignite.handlers.param_scheduler import PiecewiseLinear dummy_engine.state.iteration = 1 dummy_engine.state.output = [10] with pytest.raises(TypeError, match=r"output of the engine should be of type float or 0d torch.Tensor"): lr_finder._log_lr_and_loss(dummy_engine, output_transform=lambda x: x, smooth_f=0, diverge_th=1) dummy_engine.state.output = (10, 5) with pytest.raises(TypeError, match=r"output of the engine should be of type float or 0d torch.Tensor"): lr_finder._log_lr_and_loss(dummy_engine, output_transform=lambda x: x, smooth_f=0, diverge_th=1) dummy_engine.state.output = torch.tensor([1, 2], dtype=torch.float32) with pytest.raises(ValueError, match=r"if output of the engine is torch.Tensor"): lr_finder._log_lr_and_loss(dummy_engine, output_transform=lambda x: x, smooth_f=0, diverge_th=1) lr_finder._lr_schedule = PiecewiseLinear( optimizer, param_name="lr", milestones_values=[(0, optimizer.param_groups[0]["lr"]), (100, 10)] ) dummy_engine.state.output = torch.tensor(10.0, dtype=torch.float32) lr_finder._history = {"lr": [], "loss": []} lr_finder._log_lr_and_loss(dummy_engine, output_transform=lambda x: x, smooth_f=0, diverge_th=1) loss = lr_finder._history["loss"][-1] assert type(loss) is float dummy_engine.state.output = torch.tensor([10.0], dtype=torch.float32) lr_finder._history = {"lr": [], "loss": []} lr_finder._log_lr_and_loss(dummy_engine, output_transform=lambda x: x, smooth_f=0, diverge_th=1) loss = lr_finder._history["loss"][-1] assert type(loss) is float def test_lr_suggestion_unexpected_curve(lr_finder, to_save, dummy_engine, dataloader): with lr_finder.attach(dummy_engine, to_save) as trainer_with_finder: trainer_with_finder.run(dataloader) lr_finder._history["loss"].insert(0, 0) with pytest.raises( RuntimeError, match=r"FastaiLRFinder got unexpected curve shape, the curve should be somehow U-shaped" ): lr_finder.lr_suggestion() def test_lr_suggestion_single_param_group(lr_finder): # , to_save, dummy_engine, dataloader): import numpy as np noise = 0.05 lr_finder._history["loss"] = np.linspace(-5.0, 5.0, num=100) ** 2 + noise lr_finder._history["lr"] = np.linspace(0.01, 10, num=100) # lr_finder.lr_suggestion() is supposed to return a value, but as # we assign loss and lr to tensors, instead of lists, it will return tensors suggested_lr = lr_finder.lr_suggestion() assert pytest.approx(suggested_lr.item()) == 0.110909089 def test_lr_suggestion_multiple_param_groups(lr_finder): import numpy as np noise = 0.06 lr_finder._history["loss"] = np.linspace(-5.0, 5, num=50) ** 2 + noise # 2 param_groups lr_finder._history["lr"] = np.linspace(0.01, 10, num=100).reshape(50, 2) # lr_finder.lr_suggestion() is supposed to return a list of values, # but as we assign loss and lr to tensors, instead of lists, it will return tensors suggested_lrs = lr_finder.lr_suggestion() assert pytest.approx(suggested_lrs[0].item()) == 0.21181818 assert pytest.approx(suggested_lrs[1].item()) == 0.31272727 def test_lr_suggestion_mnist(lr_finder, mnist_to_save, dummy_engine_mnist, mnist_dataloader): max_iters = 50 with lr_finder.attach(dummy_engine_mnist, mnist_to_save, diverge_th=2, step_mode="linear") as trainer_with_finder: with trainer_with_finder.add_event_handler( Events.ITERATION_COMPLETED(once=max_iters), lambda _: trainer_with_finder.terminate() ): trainer_with_finder.run(mnist_dataloader) assert 1e-4 <= lr_finder.lr_suggestion() <= 2 def test_apply_suggested_lr_unmatched_optimizers( lr_finder, mnist_to_save, dummy_engine_mnist, optimizer_multiple_param_groups, mnist_dataloader ): with lr_finder.attach(dummy_engine_mnist, mnist_to_save) as trainer_with_finder: trainer_with_finder.run(mnist_dataloader) sug_lr = lr_finder.lr_suggestion() with pytest.raises(RuntimeError, match=r"The number of parameter groups does not match"): lr_finder.apply_suggested_lr(optimizer_multiple_param_groups) def test_apply_suggested_lr_single_param_groups( lr_finder, mnist_to_save, dummy_engine_mnist, mnist_optimizer, mnist_dataloader ): with lr_finder.attach(dummy_engine_mnist, mnist_to_save) as trainer_with_finder: trainer_with_finder.run(mnist_dataloader) sug_lr = lr_finder.lr_suggestion() lr_finder.apply_suggested_lr(mnist_optimizer) assert mnist_optimizer.param_groups[0]["lr"] == sug_lr def test_apply_suggested_lr_multiple_param_groups( lr_finder, to_save_mulitple_param_groups, dummy_engine_mulitple_param_groups, optimizer_multiple_param_groups, dataloader_plot, ): with lr_finder.attach(dummy_engine_mulitple_param_groups, to_save_mulitple_param_groups) as trainer_with_finder: trainer_with_finder.run(dataloader_plot) sug_lr = lr_finder.lr_suggestion() lr_finder.apply_suggested_lr(optimizer_multiple_param_groups) for i in range(len(sug_lr)): assert optimizer_multiple_param_groups.param_groups[i]["lr"] == sug_lr[i] def test_no_matplotlib(no_site_packages, lr_finder): with pytest.raises(ModuleNotFoundError, match=r"This method requires matplotlib to be installed"): lr_finder.plot() def test_plot_single_param_group(dirname, lr_finder, mnist_to_save, dummy_engine_mnist, mnist_dataloader): with lr_finder.attach(dummy_engine_mnist, mnist_to_save, end_lr=20.0, smooth_f=0.04) as trainer_with_finder: trainer_with_finder.run(mnist_dataloader) def _test(ax): assert ax is not None assert ax.get_xscale() == "log" assert ax.get_xlabel() == "Learning rate" assert ax.get_ylabel() == "Loss" filepath = Path(dirname) / "dummy.jpg" ax.figure.savefig(filepath) assert filepath.exists() filepath.unlink() lr_finder.plot() ax = lr_finder.plot(skip_end=0) _test(ax) # Passing axes object from matplotlib import pyplot as plt _, ax = plt.subplots() lr_finder.plot(skip_end=0, ax=ax) _test(ax) def test_plot_multiple_param_groups( dirname, lr_finder, to_save_mulitple_param_groups, dummy_engine_mulitple_param_groups, dataloader_plot ): with lr_finder.attach( dummy_engine_mulitple_param_groups, to_save_mulitple_param_groups, end_lr=20.0, smooth_f=0.04 ) as trainer_with_finder: trainer_with_finder.run(dataloader_plot) def _test(ax): assert ax is not None assert ax.get_xscale() == "log" assert ax.get_xlabel() == "Learning rate" assert ax.get_ylabel() == "Loss" filepath = Path(dirname) / "dummy_muliple_param_groups.jpg" ax.figure.savefig(filepath) assert filepath.exists() filepath.unlink() ax = lr_finder.plot(skip_start=0, skip_end=0) _test(ax) # Passing axes object from matplotlib import pyplot as plt _, ax = plt.subplots() lr_finder.plot(skip_start=0, skip_end=0, ax=ax) _test(ax) def _test_distrib_log_lr_and_loss(device): from ignite.handlers import ParamScheduler lr_finder = FastaiLRFinder() _lr_schedule = MagicMock(spec=ParamScheduler) # minimal setup for lr_finder to make _log_lr_and_loss work rank = idist.get_rank() loss = 0.01 * (rank + 1) engine = Engine(lambda e, b: None) engine.state.output = loss engine.state.iteration = 1 lr_finder._lr_schedule = _lr_schedule lr_finder._history["loss"] = [] lr_finder._history["lr"] = [] lr_finder._log_lr_and_loss(engine, output_transform=lambda x: x, smooth_f=0.1, diverge_th=10.0) expected_loss = idist.all_reduce(loss) assert pytest.approx(lr_finder._history["loss"][-1]) == expected_loss def _test_distrib_integration_mnist(dirname, device): from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.transforms import Compose, Normalize, ToTensor data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader( MNIST(download=True, root="/tmp", transform=data_transform, train=True), batch_size=256, shuffle=True ) class DummyModel(nn.Module): def __init__(self, n_channels=10, out_channels=1, flatten_input=False): super(DummyModel, self).__init__() self.net = nn.Sequential( nn.Flatten() if flatten_input else nn.Identity(), nn.Linear(n_channels, out_channels) ) def forward(self, x): return self.net(x) model = DummyModel(n_channels=784, out_channels=10, flatten_input=True) model = model.to(device) optimizer = SGD(model.parameters(), lr=1e-4, momentum=0.0) to_save = {"model": model, "optimizer": optimizer} engine = create_supervised_trainer(model, optimizer, nn.CrossEntropyLoss(), device=device) lr_finder = FastaiLRFinder() with lr_finder.attach(engine, to_save) as trainer_with_finder: trainer_with_finder.run(train_loader) lr_finder.plot() if idist.get_rank() == 0: ax = lr_finder.plot(skip_end=0) filepath = Path(dirname) / "distrib_dummy.jpg" ax.figure.savefig(filepath) assert filepath.exists() sug_lr = lr_finder.lr_suggestion() assert 1e-3 <= sug_lr <= 1 lr_finder.apply_suggested_lr(optimizer) assert optimizer.param_groups[0]["lr"] == sug_lr @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(dirname, distributed_context_single_node_gloo): device = idist.device() _test_distrib_log_lr_and_loss(device) _test_distrib_integration_mnist(dirname, device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(dirname, distributed_context_single_node_nccl): device = idist.device() _test_distrib_log_lr_and_loss(device) _test_distrib_integration_mnist(dirname, device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device") def test_distrib_single_device_xla(dirname): device = idist.device() assert "xla" in device.type _test_distrib_log_lr_and_loss(device) _test_distrib_integration_mnist(dirname, device) def _test_distrib_log_lr_and_loss_xla_nprocs(index, dirname): device = idist.device() _test_distrib_log_lr_and_loss(device) _test_distrib_integration_mnist(dirname, device) import time # hack to have all proc properly sync: time.sleep(1) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Not on TPU device") def test_distrib_xla_nprocs(dirname, xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_log_lr_and_loss_xla_nprocs, args=(dirname,), nprocs=n) ignite-0.5.1/tests/ignite/handlers/test_mlflow_logger.py000066400000000000000000000273761465426447700235270ustar00rootroot00000000000000import sys from unittest.mock import call, MagicMock import pytest import torch from ignite.engine import Engine, Events, State from ignite.handlers.mlflow_logger import global_step_from_engine, MLflowLogger, OptimizerParamsHandler, OutputHandler def test_output_handler_with_wrong_logger_type(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(TypeError, match="Handler 'OutputHandler' works only with MLflowLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_output_handler_output_transform(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.output = 12345 mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with({"tag output": 12345}, step=123) wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with({"another_tag loss": 12345}, step=123) def test_output_handler_metric_names(): wrapper = OutputHandler("tag", metric_names=["a", "b", "c"]) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45, "c": torch.tensor(10.0)}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_called_once_with({"tag a": 12.23, "tag b": 23.45, "tag c": 10.0}, step=5) wrapper = OutputHandler("tag", metric_names=["a"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])}) mock_engine.state.iteration = 5 mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_has_calls( [call({"tag a 0": 0.0, "tag a 1": 1.0, "tag a 2": 2.0, "tag a 3": 3.0}, step=5)], any_order=True ) wrapper = OutputHandler("tag", metric_names=["a", "c"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 55.56, "c": "Some text"}) mock_engine.state.iteration = 7 mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() with pytest.warns(UserWarning): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_has_calls([call({"tag a": 55.56}, step=7)], any_order=True) def test_output_handler_both(): wrapper = OutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_called_once_with({"tag a": 12.23, "tag b": 23.45, "tag loss": 12345}, step=5) def test_output_handler_with_wrong_global_step_transform_output(): def global_step_transform(*args, **kwargs): return "a" wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 with pytest.raises(TypeError, match="global_step must be int"): wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) def test_output_handler_with_global_step_transform(): def global_step_transform(*args, **kwargs): return 10 wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.log_metrics.assert_called_once_with({"tag loss": 12345}, step=10) def test_output_handler_with_global_step_from_engine(): mock_another_engine = MagicMock() mock_another_engine.state = State() mock_another_engine.state.epoch = 10 mock_another_engine.state.output = 12.345 wrapper = OutputHandler( "tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_from_engine(mock_another_engine), ) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 1 mock_engine.state.output = 0.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_has_calls( [call({"tag loss": mock_engine.state.output}, step=mock_another_engine.state.epoch)] ) mock_another_engine.state.epoch = 11 mock_engine.state.output = 1.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.log_metrics.call_count == 2 mock_logger.log_metrics.assert_has_calls( [call({"tag loss": mock_engine.state.output}, step=mock_another_engine.state.epoch)] ) def test_output_handler_state_attrs(): wrapper = OutputHandler("tag", state_attributes=["alpha", "beta", "gamma"]) mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 5 mock_engine.state.alpha = 3.899 mock_engine.state.beta = torch.tensor(12.21) mock_engine.state.gamma = torch.tensor([21.0, 6.0]) wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with( {"tag alpha": 3.899, "tag beta": torch.tensor(12.21).item(), "tag gamma 0": 21.0, "tag gamma 1": 6.0}, step=5 ) def test_optimizer_params_handler_wrong_setup(): with pytest.raises(TypeError): OptimizerParamsHandler(optimizer=None) optimizer = MagicMock(spec=torch.optim.Optimizer) handler = OptimizerParamsHandler(optimizer=optimizer) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(TypeError, match="Handler OptimizerParamsHandler works only with MLflowLogger"): handler(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_optimizer_params(): optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01) wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr") mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with({"lr group_0": 0.01}, step=123) wrapper = OptimizerParamsHandler(optimizer, param_name="lr", tag="generator") mock_logger = MagicMock(spec=MLflowLogger) mock_logger.log_metrics = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with({"generator lr group_0": 0.01}, step=123) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_integration(dirname): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) mlflow_logger = MLflowLogger(tracking_uri=str(dirname / "mlruns")) true_values = [] def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) v = global_step * 0.1 true_values.append(v) logger.log_metrics({"test_value": v}, step=global_step) mlflow_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) import mlflow active_run = mlflow.active_run() trainer.run(data, max_epochs=n_epochs) mlflow_logger.close() from mlflow.tracking import MlflowClient client = MlflowClient(tracking_uri=str(dirname / "mlruns")) stored_values = client.get_metric_history(active_run.info.run_id, "test_value") for t, s in zip(true_values, stored_values): assert pytest.approx(t) == s.value @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_integration_as_context_manager(dirname): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) true_values = [] with MLflowLogger(str(dirname / "mlruns")) as mlflow_logger: trainer = Engine(update_fn) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) v = global_step * 0.1 true_values.append(v) logger.log_metrics({"test_value": v}, step=global_step) mlflow_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) import mlflow active_run = mlflow.active_run() trainer.run(data, max_epochs=n_epochs) from mlflow.tracking import MlflowClient client = MlflowClient(tracking_uri=str(dirname / "mlruns")) stored_values = client.get_metric_history(active_run.info.run_id, "test_value") for t, s in zip(true_values, stored_values): assert pytest.approx(t) == s.value @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_mlflow_bad_metric_name_handling(dirname): import mlflow true_values = [123.0, 23.4, 333.4] with MLflowLogger(str(dirname / "mlruns")) as mlflow_logger: active_run = mlflow.active_run() handler = OutputHandler(tag="training", metric_names="all") engine = Engine(lambda e, b: None) engine.state = State(metrics={"metric:0 in %": 123.0, "metric 0": 1000.0}) with pytest.warns(UserWarning, match=r"MLflowLogger output_handler encountered an invalid metric name"): engine.state.epoch = 1 handler(engine, mlflow_logger, event_name=Events.EPOCH_COMPLETED) for _, v in enumerate(true_values): engine.state.epoch += 1 engine.state.metrics["metric 0"] = v handler(engine, mlflow_logger, event_name=Events.EPOCH_COMPLETED) from mlflow.tracking import MlflowClient client = MlflowClient(tracking_uri=str(dirname / "mlruns")) stored_values = client.get_metric_history(active_run.info.run_id, "training metric 0") for t, s in zip([1000.0] + true_values, stored_values): assert t == s.value @pytest.mark.parametrize("no_site_packages", ["mlflow"], indirect=True) def test_no_mlflow_client(no_site_packages): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires mlflow to be installed."): MLflowLogger() ignite-0.5.1/tests/ignite/handlers/test_neptune_logger.py000066400000000000000000000410761465426447700236760ustar00rootroot00000000000000import math import warnings from unittest.mock import MagicMock import pytest import torch from ignite.engine import Engine, Events, State from ignite.handlers.neptune_logger import ( global_step_from_engine, GradsScalarHandler, NeptuneLogger, NeptuneSaver, OptimizerParamsHandler, OutputHandler, WeightsScalarHandler, ) def assert_logger_called_once_with(logger, key, value): result = logger[key].fetch_values() assert len(result.value) == 1 if isinstance(result.value[0], float): assert math.isclose(result.value[0], value, abs_tol=0.01) else: assert result.value[0] == value def test_optimizer_params_handler_wrong_setup(): with pytest.raises(TypeError): OptimizerParamsHandler(optimizer=None) optimizer = MagicMock(spec=torch.optim.Optimizer) handler = OptimizerParamsHandler(optimizer=optimizer) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(TypeError, match="Handler OptimizerParamsHandler works only with NeptuneLogger"): handler(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_optimizer_params(): optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01) wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr") logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 123 wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "lr/group_0", 0.01) logger.stop() wrapper = OptimizerParamsHandler(optimizer, param_name="lr", tag="generator") logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "generator/lr/group_0", 0.01) logger.stop() def test_output_handler_with_wrong_logger_type(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(TypeError, match="Handler OutputHandler works only with NeptuneLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_output_handler_output_transform(): wrapper = OutputHandler("tag", output_transform=lambda x: x) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.output = 12345 mock_engine.state.iteration = 123 wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "tag/output", 12345) logger.stop() wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x}) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "another_tag/loss", 12345) logger.stop() def test_output_handler_metric_names(): wrapper = OutputHandler("tag", metric_names=["a", "b"]) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.iteration = 5 wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "tag/a", 12.23) assert_logger_called_once_with(logger, "tag/b", 23.45) logger.stop() wrapper = OutputHandler("tag", metric_names=["a"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])}) mock_engine.state.iteration = 5 logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) wrapper(mock_engine, logger, Events.ITERATION_STARTED) for key, val in [("tag/a/0", 0.0), ("tag/a/1", 1.0), ("tag/a/2", 2.0), ("tag/a/3", 3.0)]: assert_logger_called_once_with(logger, key, val) logger.stop() wrapper = OutputHandler("tag", metric_names=["a", "c"]) mock_engine = MagicMock() logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine.state = State(metrics={"a": 55.56, "c": "Some text"}) mock_engine.state.iteration = 7 with pytest.warns(UserWarning): wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "tag/a", 55.56) logger.stop() # all metrics wrapper = OutputHandler("tag", metric_names="all") logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.iteration = 5 wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "tag/a", 12.23) assert_logger_called_once_with(logger, "tag/b", 23.45) logger.stop() # log a torch tensor (ndimension = 0) wrapper = OutputHandler("tag", metric_names="all") logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor(12.23), "b": torch.tensor(23.45)}) mock_engine.state.iteration = 5 wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "tag/a", 12.23) assert_logger_called_once_with(logger, "tag/b", 23.45) logger.stop() def test_output_handler_both(): wrapper = OutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x}) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, logger, Events.EPOCH_STARTED) assert_logger_called_once_with(logger, "tag/a", 12.23) assert_logger_called_once_with(logger, "tag/b", 23.45) assert_logger_called_once_with(logger, "tag/loss", 12345) logger.stop() def test_output_handler_with_wrong_global_step_transform_output(): def global_step_transform(*args, **kwargs): return "a" wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 with pytest.raises(TypeError, match="global_step must be int"): wrapper(mock_engine, logger, Events.EPOCH_STARTED) logger.stop() def test_output_handler_with_global_step_from_engine(): mock_another_engine = MagicMock() mock_another_engine.state = State() mock_another_engine.state.epoch = 10 mock_another_engine.state.output = 12.345 wrapper = OutputHandler( "tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_from_engine(mock_another_engine), ) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 1 mock_engine.state.output = 0.123 wrapper(mock_engine, logger, Events.EPOCH_STARTED) assert_logger_called_once_with(logger, "tag/loss", mock_engine.state.output) mock_another_engine.state.epoch = 11 mock_engine.state.output = 1.123 wrapper(mock_engine, logger, Events.EPOCH_STARTED) result = logger["tag/loss"].fetch_values() assert len(result.value) == 2 assert result.value[1] == mock_engine.state.output logger.stop() def test_output_handler_with_global_step_transform(): def global_step_transform(*args, **kwargs): return 10 wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, logger, Events.EPOCH_STARTED) assert_logger_called_once_with(logger, "tag/loss", 12345) logger.stop() def test_output_handler_state_attrs(): wrapper = OutputHandler("tag", state_attributes=["alpha", "beta", "gamma"]) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 5 mock_engine.state.alpha = 3.899 mock_engine.state.beta = torch.tensor(12.23) mock_engine.state.gamma = torch.tensor([21.0, 6.0]) wrapper(mock_engine, logger, Events.ITERATION_STARTED) assert_logger_called_once_with(logger, "tag/alpha", 3.899) assert_logger_called_once_with(logger, "tag/beta", 12.23) assert_logger_called_once_with(logger, "tag/gamma/0", 21.0) assert_logger_called_once_with(logger, "tag/gamma/1", 6.0) logger.stop() def test_weights_scalar_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): WeightsScalarHandler(None) model = MagicMock(spec=torch.nn.Module) with pytest.raises(TypeError, match="Argument reduction should be callable"): WeightsScalarHandler(model, reduction=123) with pytest.raises(TypeError, match="Output of the reduction function should be a scalar"): WeightsScalarHandler(model, reduction=lambda x: x) wrapper = WeightsScalarHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(TypeError, match="Handler WeightsScalarHandler works only with NeptuneLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_weights_scalar_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = WeightsScalarHandler(model, tag=tag) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert_logger_called_once_with(logger, tag_prefix + "weights_norm/fc1/weight", 0.0) assert_logger_called_once_with(logger, tag_prefix + "weights_norm/fc1/bias", 0.0) assert_logger_called_once_with(logger, tag_prefix + "weights_norm/fc2/weight", 12.0) assert_logger_called_once_with(logger, tag_prefix + "weights_norm/fc2/bias", math.sqrt(12.0)) logger.stop() _test() _test(tag="tag") def test_weights_scalar_handler_frozen_layers(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = WeightsScalarHandler(model) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, logger, Events.EPOCH_STARTED) assert_logger_called_once_with(logger, "weights_norm/fc2/weight", 12.0) assert_logger_called_once_with(logger, "weights_norm/fc2/bias", math.sqrt(12.0)) assert not logger.exists("weights_norm/fc1/weight") assert not logger.exists("weights_norm/fc1/bias") logger.stop() def test_grads_scalar_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): GradsScalarHandler(None) model = MagicMock(spec=torch.nn.Module) with pytest.raises(TypeError, match="Argument reduction should be callable"): GradsScalarHandler(model, reduction=123) wrapper = GradsScalarHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(TypeError, match="Handler GradsScalarHandler works only with NeptuneLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_grads_scalar_handler(dummy_model_factory, norm_mock): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = GradsScalarHandler(model, reduction=norm_mock, tag=tag) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 norm_mock.reset_mock() wrapper(mock_engine, logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert logger.exists(tag_prefix + "grads_norm/fc1/weight") assert logger.exists(tag_prefix + "grads_norm/fc1/bias") assert logger.exists(tag_prefix + "grads_norm/fc2/weight") assert logger.exists(tag_prefix + "grads_norm/fc2/bias") logger.stop() _test() _test(tag="tag") def test_grads_scalar_handler_frozen_layers(dummy_model_factory, norm_mock): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = GradsScalarHandler(model, reduction=norm_mock) logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 norm_mock.reset_mock() wrapper(mock_engine, logger, Events.EPOCH_STARTED) assert logger.exists("grads_norm/fc2/weight") assert logger.exists("grads_norm/fc2/bias") assert not logger.exists("grads_norm/fc1/weight") assert not logger.exists("grads_norm/fc1/bias") logger.stop() def test_integration(): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) npt_logger = NeptuneLogger(mode="offline") def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger["test_value"].append(global_step, step=global_step) npt_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) npt_logger.close() def test_integration_as_context_manager(): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) with NeptuneLogger(mode="offline") as npt_logger: trainer = Engine(update_fn) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger["test_value"].append(global_step, step=global_step) npt_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) def test_neptune_saver_serializable(dirname): mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.upload = MagicMock() model = torch.nn.Module() to_save_serializable = {"model": model} saver = NeptuneSaver(mock_logger) fname = dirname / "test.pt" saver(to_save_serializable, fname) assert mock_logger[dirname / "test.pt"].upload.call_count == 1 @pytest.mark.parametrize("model, serializable", [(lambda x: x, False), (torch.nn.Module().to("cpu"), True)]) def test_neptune_saver(model, serializable): mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.upload = MagicMock() to_save_non_serializable = {"model": model} saver = NeptuneSaver(mock_logger) fname = "test.pt" try: with warnings.catch_warnings(): # Ignore torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type # DummyModel. It won't be checked for correctness upon loading. warnings.simplefilter("ignore", category=UserWarning) saver(to_save_non_serializable, fname) except Exception: pass assert mock_logger["model"].upload.call_count == int(serializable) def test_logs_version(): from ignite import __version__ from ignite.handlers.neptune_logger import _INTEGRATION_VERSION_KEY logger = NeptuneLogger( project="tests/dry-run", mode="debug", ) assert logger[_INTEGRATION_VERSION_KEY].fetch() == __version__ ignite-0.5.1/tests/ignite/handlers/test_param_scheduler.py000066400000000000000000001463031465426447700240160ustar00rootroot00000000000000from unittest.mock import MagicMock, patch import numpy as np import pytest import torch from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, ExponentialLR, StepLR from ignite.engine import Engine, Events from ignite.handlers.param_scheduler import ( ConcatScheduler, CosineAnnealingScheduler, create_lr_scheduler_with_warmup, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, ParamScheduler, PiecewiseLinear, ReduceLROnPlateauScheduler, ) from tests.ignite.handlers import MockFP16DeepSpeedZeroOptimizer try: from torch.optim.lr_scheduler import MultiplicativeLR except ImportError: has_multiplicative_lr = False else: from packaging.version import Version # https://github.com/pytorch/pytorch/issues/32756 has_multiplicative_lr = Version(torch.__version__) >= Version("1.5.0") class FakeParamScheduler(ParamScheduler): def get_param(self): return [0] def test_param_scheduler_asserts(): t1 = torch.zeros([1], requires_grad=True) t2 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([{"params": t1, "lr": 0.1}, {"params": t2, "lr": 0.1}]) lr_scheduler = FakeParamScheduler(optimizer, "lr") with pytest.raises(ValueError, match=r"size of value is different than optimizer_param_groups"): lr_scheduler(None) with pytest.raises(TypeError, match=r"Argument state_dict should be a dictionary, but given"): lr_scheduler.load_state_dict(None) with pytest.raises(ValueError, match=r"Required state attribute 'event_index' is absent in provided state_dict"): lr_scheduler.load_state_dict({}) with pytest.raises(TypeError, match=r"Argument optimizer should be torch.optim.Optimizer"): FakeParamScheduler({}, "lr") def test_linear_scheduler_asserts(): with pytest.raises(TypeError, match=r"Argument optimizer should be torch.optim.Optimizer"): LinearCyclicalScheduler({}, "lr", 1, 0, cycle_size=0) tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.0) with pytest.raises(ValueError, match=r"Argument cycle_size should be positive and larger than 1"): LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=0) with pytest.raises(ValueError, match=r"Argument cycle_size should be positive and larger than 1"): LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=1) with pytest.raises( ValueError, match=r"Invalid combination when warmup_duration > 0 and monotonic=False, " r"please use either set warmup_duration=0 or monotonic=True", ): LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=2, warmup_duration=1) def test_linear_scheduler(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.0) scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10) state_dict = scheduler.state_dict() def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) lr_values_in_cycle = [1.0, 0.8, 0.6, 0.4, 0.2, 0.0, 0.2, 0.4, 0.6, 0.8] for _ in range(2): lrs = [] trainer.run([0] * 10, max_epochs=2) assert lrs == pytest.approx([*lr_values_in_cycle, *lr_values_in_cycle]) scheduler.load_state_dict(state_dict) optimizer = torch.optim.SGD([tensor], lr=0) scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10, cycle_mult=2) state_dict = scheduler.state_dict() trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) for _ in range(2): lrs = [] trainer.run([0] * 10, max_epochs=3) assert lrs == list( map( pytest.approx, [ # Cycle 1 1.0, 0.8, 0.6, 0.4, 0.2, 0.0, 0.2, 0.4, 0.6, 0.8, # Cycle 2 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, ], ) ) scheduler.load_state_dict(state_dict) def test_linear_scheduler_warmup_duration(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.0) scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10, warmup_duration=5, monotonic=True) state_dict = scheduler.state_dict() def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) lr_values_in_cycle = [ 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 0.9, 0.8, 0.7, 0.6, ] for _ in range(2): lrs = [] trainer.run([0] * 10, max_epochs=2) assert lrs == pytest.approx(lr_values_in_cycle) scheduler.load_state_dict(state_dict) optimizer = torch.optim.SGD([tensor], lr=0) scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10, cycle_mult=2, warmup_duration=5, monotonic=True) state_dict = scheduler.state_dict() trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) for _ in range(2): lrs = [] trainer.run([0] * 10, max_epochs=3) assert lrs == list( map( pytest.approx, [ # Cycle 1 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, 0.2, 0.4, 0.6, 0.8, # Cycle 2 1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, ], ) ) scheduler.load_state_dict(state_dict) def test_linear_scheduler_cycle_size_two(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=2) data = [0] * 10 max_epochs = 2 simulated_values = LinearCyclicalScheduler.simulate_values( num_events=len(data) * max_epochs, param_name="lr", start_value=1, end_value=0, cycle_size=2 ) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == list( map( pytest.approx, [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], ) ) assert lrs == pytest.approx([v for i, v in simulated_values]) @pytest.mark.parametrize("cyclic_warmup", [False, True]) def test_cosine_annealing_scheduler(cyclic_warmup): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler = CosineAnnealingScheduler(optimizer, "lr", 0, 1, 10, warmup_duration=2 if cyclic_warmup else 0) state_dict = scheduler.state_dict() data = [0] * (10 + int(cyclic_warmup)) max_epochs = 2 simulated_values = CosineAnnealingScheduler.simulate_values( num_events=len(data) * max_epochs, param_name="lr", start_value=0, end_value=1, cycle_size=10, warmup_duration=2 if cyclic_warmup else 0, ) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) lr_values_in_cycle = [ 0.0, 0.02447174185242318, 0.09549150281252627, 0.20610737385376332, 0.3454915028125263, 0.5, 0.6545084971874737, 0.7938926261462365, 0.9045084971874737, 0.9755282581475768, ] lr_values_in_warmup = np.linspace(1.0, 0.0, 2 + 1)[:-1].tolist() if cyclic_warmup else [] for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == pytest.approx([*lr_values_in_cycle, *lr_values_in_warmup, *lr_values_in_cycle]) scheduler.load_state_dict(state_dict) assert lrs == pytest.approx([v for i, v in simulated_values]) def test_concat_scheduler_asserts(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10) scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10) with pytest.raises(TypeError, match=r"Argument schedulers should be a sequence"): ConcatScheduler(schedulers=None, durations=[]) with pytest.raises(ValueError, match=r"Argument schedulers should be of more than one parameter schedulers"): ConcatScheduler(schedulers=[], durations=[]) with pytest.raises(ValueError, match=r"Argument schedulers should be of more than one parameter schedulers"): ConcatScheduler(schedulers=[scheduler_1], durations=[10]) with pytest.raises(TypeError, match=r"Value at index 1 of schedulers should be a parameter scheduler"): ConcatScheduler(schedulers=[scheduler_1, 12], durations=[10]) with pytest.raises(ValueError, match=r"Incorrect number schedulers or duration values"): ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=[10, 5]) with pytest.raises(ValueError, match=r"Argument durations should be list/tuple of integers"): ConcatScheduler(schedulers=[scheduler_1, scheduler_2, scheduler_2], durations=[15, 12.0]) with pytest.raises(TypeError, match=r"Argument durations should be list/tuple"): ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations="abc") with pytest.raises(TypeError, match=r"Argument param_names should be list or tuple"): ConcatScheduler.simulate_values( num_events=123, schedulers=[scheduler_1, scheduler_2], durations=[15], param_names="abc" ) with pytest.raises(ValueError, match=r"Argument param_names should be list or tuple of strings"): ConcatScheduler.simulate_values( num_events=123, schedulers=[scheduler_1, scheduler_2], durations=[15], param_names=[1] ) optimizer_2 = torch.optim.SGD([tensor], lr=0) scheduler_3 = CosineAnnealingScheduler(optimizer_2, "lr", start_value=0.0, end_value=1.0, cycle_size=10) with pytest.raises(ValueError, match=r"schedulers should be related to same optimizer"): ConcatScheduler([scheduler_1, scheduler_3], durations=[30]) scheduler_4 = CosineAnnealingScheduler(optimizer, "lr2", start_value=0.0, end_value=1.0, cycle_size=10) with pytest.raises(ValueError, match=r"schedulers should be related to same param_name"): ConcatScheduler([scheduler_1, scheduler_4], durations=[30]) with pytest.raises(ValueError, match=r"schedulers should be related to same optimizer"): ConcatScheduler.simulate_values(3, [scheduler_1, scheduler_3], durations=[30]) def test_concat_scheduler_state_dict(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10) scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10) durations = [10] concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=durations, save_history=False) steps = 0 for i in range(5): concat_scheduler(engine=None) steps += 1 state_dict = concat_scheduler.state_dict() assert state_dict["durations"] == durations assert state_dict["_current_duration"] == durations[0] - steps assert state_dict["_scheduler_index"] == 0 for _ in range(20): concat_scheduler(None, None) concat_scheduler.load_state_dict(state_dict) assert concat_scheduler.durations == durations assert concat_scheduler._current_duration == durations[0] - steps assert id(concat_scheduler._current_scheduler) == id(scheduler_1) with pytest.raises(ValueError, match=r"Required state attribute 'schedulers' is absent in provided state_dict"): concat_scheduler.load_state_dict({"a": 1}) with pytest.raises(ValueError, match=r"Input state_dict contains 0 state_dicts of concatenated schedulers"): concat_scheduler.load_state_dict({"schedulers": []}) with pytest.raises(TypeError, match=r"Argument state_dict should be a dictionary, but given"): concat_scheduler.load_state_dict(None) @pytest.mark.parametrize("duration_vals_as_np_int", [False, True]) def test_concat_scheduler_two_schedulers(duration_vals_as_np_int): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10) scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10) durations = [10] if duration_vals_as_np_int: durations = [np.int64(t) for t in durations] concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=durations, save_history=True) state_dict = concat_scheduler.state_dict() data = [0] * 10 max_epochs = 2 simulated_values = ConcatScheduler.simulate_values( num_events=len(data) * max_epochs, schedulers=[scheduler_1, scheduler_2], durations=durations ) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, concat_scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == list( map( pytest.approx, [ # Cycle 1 of the LinearCyclicalScheduler 1.0, 0.8, 0.6, 0.4, 0.2, 0.0, 0.2, 0.4, 0.6, 0.8, # Cycle 1 of the CosineAnnealingScheduler 0.0, 0.02447174185242318, 0.09549150281252627, 0.20610737385376332, 0.3454915028125263, 0.5, 0.6545084971874737, 0.7938926261462365, 0.9045084971874737, 0.9755282581475768, ], ) ) state_lrs = trainer.state.param_history["lr"] assert len(state_lrs) == len(lrs) # Unpack singleton lists assert [group[0] for group in state_lrs] == lrs assert lrs == pytest.approx([v for i, v in simulated_values]) concat_scheduler.load_state_dict(state_dict) trainer.state.param_history = None def test_concat_scheduler_two_linear(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.0, end_value=0.1, cycle_size=2) scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.2, end_value=1.0, cycle_size=2) durations = [5] concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=durations, save_history=True) state_dict = concat_scheduler.state_dict() assert concat_scheduler.get_param() == 0.0 data = [0] * 10 max_epochs = 2 simulated_values = ConcatScheduler.simulate_values( num_events=len(data) * max_epochs, schedulers=[scheduler_1, scheduler_2], durations=durations ) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, concat_scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == list( map( pytest.approx, [ # first LinearCyclicalScheduler 0.0, 0.1, 0.0, 0.1, 0.0, # second LinearCyclicalScheduler 0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2, ], ) ) state_lrs = trainer.state.param_history["lr"] assert len(state_lrs) == len(lrs) # Unpack singleton lists assert [group[0] for group in state_lrs] == lrs assert lrs == pytest.approx([v for i, v in simulated_values]) concat_scheduler.load_state_dict(state_dict) trainer.state.param_history = None def test_concat_scheduler_3_schedulers(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.5, cycle_size=20) scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.5, end_value=0.45, cycle_size=10) scheduler_3 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.5, end_value=0.0, cycle_size=20) durations = [10, 5] concat_scheduler = ConcatScheduler( schedulers=[scheduler_1, scheduler_2, scheduler_3], durations=durations, save_history=True ) state_dict = concat_scheduler.state_dict() data = [0] * 10 max_epochs = 2 simulated_values = ConcatScheduler.simulate_values( num_events=len(data) * max_epochs, schedulers=[scheduler_1, scheduler_2, scheduler_3], durations=durations ) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, concat_scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == list( map( pytest.approx, [ # Cycle 1 of the first LinearCyclicalScheduler 1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, # Cycle 1 of the second LinearCyclicalScheduler 0.5, 0.49, 0.48, 0.47, 0.46, # Cycle 1 of the third LinearCyclicalScheduler 0.5, 0.45, 0.4, 0.35, 0.3, ], ) ) state_lrs = trainer.state.param_history["lr"] assert len(state_lrs) == len(lrs) # Unpack singleton lists assert [group[0] for group in state_lrs] == lrs assert lrs == pytest.approx([v for i, v in simulated_values]) concat_scheduler.load_state_dict(state_dict) trainer.state.param_history = None def test_save_param_history(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler = LinearCyclicalScheduler(optimizer, "lr", 1, 0, 10, save_history=True) lrs = [] def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) assert not hasattr(trainer.state, "param_history") trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) trainer.run([0] * 10, max_epochs=2) state_lrs = trainer.state.param_history["lr"] assert len(state_lrs) == len(lrs) # Unpack singleton lists assert [group[0] for group in state_lrs] == lrs def test_lr_scheduler_asserts(): err_msg = r"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler.(_LRScheduler|LRScheduler)" with pytest.raises(TypeError, match=err_msg): LRScheduler(123) with pytest.raises(TypeError, match=err_msg): LRScheduler.simulate_values(1, None) @pytest.mark.parametrize( "torch_lr_scheduler_cls, kwargs", [ (StepLR, ({"step_size": 5, "gamma": 0.5})), (ExponentialLR, ({"gamma": 0.78})), (MultiplicativeLR if has_multiplicative_lr else None, ({"lr_lambda": lambda epoch: 0.95})), ], ) def test_lr_scheduler(torch_lr_scheduler_cls, kwargs): if torch_lr_scheduler_cls is None: return tensor = torch.zeros([1], requires_grad=True) optimizer1 = torch.optim.SGD([tensor], lr=0.01) optimizer2 = torch.optim.SGD([tensor], lr=0.01) optimizer3 = torch.optim.SGD([tensor], lr=0.01) opt_state_dict1 = optimizer1.state_dict() opt_state_dict2 = optimizer2.state_dict() opt_state_dict3 = optimizer3.state_dict() torch_lr_scheduler1 = torch_lr_scheduler_cls(optimizer=optimizer1, **kwargs) scheduler1 = LRScheduler(torch_lr_scheduler1) state_dict1 = scheduler1.state_dict() torch_lr_scheduler2 = torch_lr_scheduler_cls(optimizer=optimizer2, **kwargs) with pytest.warns(UserWarning, match=r"the first lr value from the optimizer, otherwise it will be skipped"): scheduler2 = LRScheduler(torch_lr_scheduler2, use_legacy=True) state_dict2 = scheduler2.state_dict() torch_lr_scheduler3 = torch_lr_scheduler_cls(optimizer=optimizer3, **kwargs) state_dict3 = torch_lr_scheduler3.state_dict() def dummy_update(engine, batch): optimizer1.step() optimizer2.step() optimizer3.step() trainer = Engine(dummy_update) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler1) @trainer.on(Events.ITERATION_STARTED) def save_lr1(engine): lrs1.append(optimizer1.param_groups[0]["lr"]) @trainer.on(Events.ITERATION_STARTED) def save_lr2(engine): lrs2.append(optimizer2.param_groups[0]["lr"]) @trainer.on(Events.ITERATION_STARTED) def save_true_lr(engine): lrs_true.append(optimizer3.param_groups[0]["lr"]) @trainer.on(Events.ITERATION_COMPLETED) def torch_lr_scheduler_step(engine): torch_lr_scheduler3.step() trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler2) for _ in range(2): lrs1 = [] lrs2 = [] lrs_true = [] data = [0] * 10 max_epochs = 2 trainer.run(data, max_epochs=max_epochs) assert lrs_true == pytest.approx(lrs1), f"{_}: {lrs_true} ({len(lrs_true)}) vs {lrs1} ({len(lrs1)})" assert lrs_true == pytest.approx(lrs2), f"{_}: {lrs_true} ({len(lrs_true)}) vs {lrs2} ({len(lrs2)})" optimizer1.load_state_dict(opt_state_dict1) scheduler1.load_state_dict(state_dict1) optimizer2.load_state_dict(opt_state_dict2) scheduler2.load_state_dict(state_dict2) optimizer3.load_state_dict(opt_state_dict3) torch_lr_scheduler3.load_state_dict(state_dict3) optimizer4 = torch.optim.SGD([tensor], lr=0.01) torch_lr_scheduler4 = torch_lr_scheduler_cls(optimizer=optimizer4, **kwargs) simulated_values = LRScheduler.simulate_values(num_events=len(data) * max_epochs, lr_scheduler=torch_lr_scheduler4) assert lrs1 == pytest.approx([v for i, v in simulated_values]) assert lrs2 == pytest.approx([v for i, v in simulated_values]) def test_piecewiselinear_asserts(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) with pytest.raises(TypeError, match=r"Argument milestones_values should be a list or tuple"): PiecewiseLinear(optimizer, "lr", milestones_values=None) with pytest.raises(ValueError, match=r"Argument milestones_values should be with at least one value"): PiecewiseLinear(optimizer, "lr", milestones_values=[]) with pytest.raises(ValueError, match=r"Argument milestones_values should be a list of pairs"): PiecewiseLinear(optimizer, "lr", milestones_values=[(0.5,)]) with pytest.raises(ValueError, match=r"Argument milestones_values should be a list of pairs"): PiecewiseLinear(optimizer, "lr", milestones_values=[(10, 0.5), (0.6,)]) with pytest.raises(ValueError, match=r"Milestones should be increasing integers"): PiecewiseLinear(optimizer, "lr", milestones_values=[(10, 0.5), (5, 0.6)]) with pytest.raises(TypeError, match=r"Value of a milestone should be integer"): PiecewiseLinear(optimizer, "lr", milestones_values=[(0.5, 1)]) @pytest.mark.parametrize("milestones_as_np_int", [True, False]) def test_piecewiselinear(milestones_as_np_int): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) milestones_values = [(5, 0.5), (15, 1.0), (25, 0.0), (35, 1.0), (40, 0.5)] if milestones_as_np_int: milestones_values = [(np.int64(t), v) for t, v in milestones_values] scheduler = PiecewiseLinear(optimizer, "lr", milestones_values=milestones_values) state_dict = scheduler.state_dict() def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) for _ in range(2): lrs = [] trainer.run([0] * 25, max_epochs=2) assert lrs == list( map( pytest.approx, [ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, ], ) ) scheduler.load_state_dict(state_dict) def test_simulate_and_plot_values(): import matplotlib matplotlib.use("Agg") def _test(scheduler_cls, **scheduler_kwargs): if scheduler_cls == LRScheduler: optimizer = scheduler_kwargs["lr_scheduler"].optimizer elif scheduler_cls == ConcatScheduler: optimizer = scheduler_kwargs["optimizer"] del scheduler_kwargs["optimizer"] else: tensor = torch.zeros([1], requires_grad=True) scheduler_kwargs["optimizer"] = torch.optim.SGD([tensor], lr=0.1) optimizer = scheduler_kwargs["optimizer"] max_epochs = 2 data = [0] * 10 simulated_values = scheduler_cls.simulate_values(num_events=len(data) * max_epochs, **scheduler_kwargs) scheduler = scheduler_cls(**scheduler_kwargs) lrs = [] def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) trainer.add_event_handler(Events.ITERATION_STARTED, save_lr) trainer.run(data, max_epochs=max_epochs) assert lrs == pytest.approx([v for i, v in simulated_values]) # reexecute to check if no internal changes # simulated_values = scheduler_cls.simulate_values(num_events=len(data) * max_epochs, # save_history=True, # this will be removed # **scheduler_kwargs) # assert lrs == pytest.approx([v for i, v in simulated_values]) # launch plot values scheduler_cls.plot_values(num_events=len(data) * max_epochs, **scheduler_kwargs) # LinearCyclicalScheduler _test(LinearCyclicalScheduler, param_name="lr", start_value=1.0, end_value=0.0, cycle_size=10) # CosineAnnealingScheduler _test(CosineAnnealingScheduler, param_name="lr", start_value=1.0, end_value=0.0, cycle_size=10) # LRScheduler tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.1) torch_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.5) _test(LRScheduler, lr_scheduler=torch_lr_scheduler) # ConcatScheduler = [LinearCyclicalScheduler, CosineAnnealingScheduler] scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=20) scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10) durations = [10] _test(ConcatScheduler, optimizer=optimizer, schedulers=[scheduler_1, scheduler_2], durations=durations) # ConcatScheduler = [LinearCyclicalScheduler, LRScheduler] tensor = torch.ones([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.001) torch_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=1.5) scheduler_1 = LRScheduler(torch_lr_scheduler) scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.0, cycle_size=10) durations = [10] _test(ConcatScheduler, optimizer=optimizer, schedulers=[scheduler_1, scheduler_2], durations=durations) # PiecewiseLinear tensor = torch.ones([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.001) _test( PiecewiseLinear, optimizer=optimizer, param_name="lr", milestones_values=[(10, 0.5), (20, 0.45), (21, 0.3), (30, 0.1), (40, 0.1)], ) with pytest.raises(ModuleNotFoundError, match=r"This method requires matplotlib to be installed."): with patch.dict("sys.modules", {"matplotlib.pyplot": None}): _test( PiecewiseLinear, optimizer=optimizer, param_name="lr", milestones_values=[(10, 0.5), (20, 0.45), (21, 0.3), (30, 0.1), (40, 0.1)], ) def test_create_lr_scheduler_with_warmup_asserts(): with pytest.raises(TypeError, match=r"Argument lr_scheduler should be a subclass of"): create_lr_scheduler_with_warmup(12, warmup_start_value=0.0, warmup_end_value=0.1, warmup_duration=10) t1 = torch.zeros([1], requires_grad=True) # A) opt lr != warmup_end_value optimizer = torch.optim.SGD([t1], lr=0.2) torch_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.98) with pytest.raises(ValueError, match=r"Argument warmup_duration should be at least 2 events"): create_lr_scheduler_with_warmup( torch_lr_scheduler, warmup_start_value=0.0, warmup_end_value=0.1, warmup_duration=1 ) with pytest.raises(TypeError, match=r"Argument warmup_duration should be integer"): create_lr_scheduler_with_warmup( torch_lr_scheduler, warmup_start_value=0.0, warmup_end_value=0.1, warmup_duration="abc" ) with pytest.raises(TypeError, match=r"Argument output_simulated_values should be a list of None"): simulated_values = () create_lr_scheduler_with_warmup( torch_lr_scheduler, warmup_start_value=0.0, warmup_end_value=0.1, warmup_duration=10, output_simulated_values=simulated_values, ) @pytest.mark.parametrize( "lr_scheduler_name, warmup_start_value, warmup_end_value, warmup_duration, warmup_end_next_value", [ # A) opt lr != warmup_end_value ("ExponentialLR", 0.01, 0.05, 10, 0.2), ("ExponentialLR", 0.01, 0.05, 2, 0.2), # B) opt lr == warmup_end_value ("ExponentialLR", 0.01, 0.2, 10, 0.2 * 0.98), ("ExponentialLR", 0.01, 0.2, 2, 0.2 * 0.98), # C) lr_scheduler start_value != warmup_end_value ("LinearCyclicalScheduler", 0.01, 0.05, 10, 0.8), ("LinearCyclicalScheduler", 0.01, 0.05, 2, 0.8), # D) lr_scheduler start_value == warmup_end_value ("LinearCyclicalScheduler", 0.01, 0.8, 10, 0.8 - (0.8 / 5.0)), ("LinearCyclicalScheduler", 0.01, 0.8, 2, 0.8 - (0.8 / 5.0)), # E) warmup_end_value is None: fall back to case B) ("ExponentialLR", 0.01, None, 10, 0.2 * 0.98), ], ) def test_create_lr_scheduler_with_warmup( lr_scheduler_name, warmup_start_value, warmup_end_value, warmup_duration, warmup_end_next_value ): t1 = torch.zeros([1], requires_grad=True) if lr_scheduler_name == "ExponentialLR": optimizer = torch.optim.SGD([t1], lr=0.2) lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.98) elif lr_scheduler_name == "LinearCyclicalScheduler": optimizer = torch.optim.SGD([t1], lr=0.0) lr_scheduler = LinearCyclicalScheduler( optimizer=optimizer, param_name="lr", start_value=0.8, end_value=0.0, cycle_size=10 ) else: raise ValueError(f"Unknown name: {lr_scheduler_name}") num_iterations = 10 max_epochs = 20 if warmup_end_value is None: expected_warmup_end_value = optimizer.param_groups[0]["lr"] else: expected_warmup_end_value = warmup_end_value simulated_values = [None] * (num_iterations * max_epochs) scheduler = create_lr_scheduler_with_warmup( lr_scheduler, warmup_start_value=warmup_start_value, warmup_end_value=warmup_end_value, warmup_duration=warmup_duration, output_simulated_values=simulated_values, ) state_dict = scheduler.state_dict() trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @trainer.on(Events.ITERATION_STARTED) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) data = [0] * num_iterations for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == pytest.approx([v for _, v in simulated_values]) assert lrs[0] == pytest.approx(warmup_start_value), f"lrs={lrs[: warmup_duration + num_iterations]}" assert lrs[warmup_duration - 1] == pytest.approx( expected_warmup_end_value ), f"lrs={lrs[: warmup_duration + num_iterations]}" assert lrs[warmup_duration] == pytest.approx( warmup_end_next_value ), f"lrs={lrs[: warmup_duration + num_iterations]}" scheduler.load_state_dict(state_dict) @pytest.mark.parametrize("save_history", [False, True]) def test_create_lr_scheduler_with_warmup_on_combined_scheduler(save_history): # Test with a complex scheduler tensor = torch.ones([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0.001) max_epochs = 25 lr_max_value = 0.4 num_iterations_per_epoch = 128 num_iterations = max_epochs * num_iterations_per_epoch warmup_duration = 5 * num_iterations_per_epoch cooldown_duration = 5 * num_iterations_per_epoch scheduler_1 = LinearCyclicalScheduler( optimizer, "lr", start_value=lr_max_value, end_value=lr_max_value * 0.9, cycle_size=(num_iterations - warmup_duration - cooldown_duration) * 2, ) scheduler_2 = LinearCyclicalScheduler( optimizer, "lr", start_value=lr_max_value, end_value=0.0, cycle_size=cooldown_duration * 2 ) lr_scheduler = ConcatScheduler( schedulers=[scheduler_1, scheduler_2], durations=[num_iterations - warmup_duration - cooldown_duration], save_history=False, ) lr_values = [None] * num_iterations scheduler = create_lr_scheduler_with_warmup( lr_scheduler, warmup_start_value=0.0, warmup_end_value=lr_max_value, warmup_duration=warmup_duration, save_history=save_history, output_simulated_values=lr_values, ) state_dict = scheduler.state_dict() trainer = Engine(lambda engine, batch: None) @trainer.on(Events.ITERATION_COMPLETED) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) data = [0] * num_iterations_per_epoch for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == pytest.approx([v for i, v in lr_values]) if save_history: param_history = trainer.state.param_history["lr"] assert lrs == pytest.approx([v[0] for v in param_history]) trainer.state.param_history = None scheduler.load_state_dict(state_dict) def test_create_lr_scheduler_with_warmup_with_real_model(dummy_model_factory): model = dummy_model_factory(with_grads=False, with_frozen_layer=False) init_lr = 0.01 optimizer = torch.optim.SGD(model.parameters(), lr=init_lr) scaled_lr = 0.02 warmup_duration = 5 step_size = 2 gamma = 0.97 output_simulated_values = [None] * 50 create_lr_scheduler_with_warmup( torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma), warmup_start_value=0.0, warmup_end_value=scaled_lr, warmup_duration=warmup_duration, output_simulated_values=output_simulated_values, ) assert output_simulated_values[0] == [0, 0.0] assert output_simulated_values[warmup_duration - 1] == [warmup_duration - 1, scaled_lr] assert output_simulated_values[warmup_duration] == [warmup_duration, init_lr] v = [warmup_duration + step_size, init_lr * gamma] assert output_simulated_values[warmup_duration + step_size] == v def test_param_group_scheduler_asserts(): t1 = torch.zeros([1], requires_grad=True) t2 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([{"params": t1, "lr": 0.1}, {"params": t2, "lr": 0.1}]) lr_scheduler1 = LinearCyclicalScheduler( optimizer, "lr", param_group_index=0, start_value=1.0, end_value=0.0, cycle_size=10 ) lr_scheduler2 = LinearCyclicalScheduler( optimizer, "lr", param_group_index=1, start_value=1.0, end_value=0.0, cycle_size=10 ) with pytest.raises(TypeError, match=r"Argument schedulers should be a list/tuple"): ParamGroupScheduler(schedulers=None, names=["a", "b", "c"]) with pytest.raises(ValueError, match=r"Argument schedulers should be a list/tuple of parameter schedulers"): ParamGroupScheduler(schedulers=[0, 1, 2], names=["a", "b", "c"]) with pytest.raises(ValueError, match=r"Argument schedulers should be a list/tuple of parameter schedulers"): ParamGroupScheduler(schedulers=[lr_scheduler1, "2"], names=["a", "b"]) with pytest.raises(TypeError, match=r"Argument names should be a list/tuple"): ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names="ab") with pytest.raises(ValueError, match=r"Argument names should be a list/tuple of parameter scheduler's names"): ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names=[1, 2]) with pytest.raises(ValueError, match=r"\d should be equal \d"): ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names=["a"]) scheduler = ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names=["a", "b"]) with pytest.raises(TypeError, match=r"Argument state_dict should be a dictionary"): scheduler.load_state_dict(None) with pytest.raises(ValueError, match=r"Required state attribute 'schedulers' is absent in provided state_dict"): scheduler.load_state_dict({"a": 1}) with pytest.raises(ValueError, match=r"Input state_dict contains 0 state_dicts of param group schedulers"): scheduler.load_state_dict({"schedulers": []}) with pytest.raises(ValueError, match=r"Required state attribute 'schedulers' is absent in provided state_dict"): scheduler.load_state_dict({}) with pytest.raises( ValueError, match=r"Name of scheduler from input state dict does not " r"correspond to required one" ): scheduler.load_state_dict({"schedulers": [("a", lr_scheduler1.state_dict()), ("bad_name", {})]}) @pytest.mark.parametrize("param_groups_setting", ["single_optim", "multi_optim"]) def test_param_group_scheduler(param_groups_setting): t1 = torch.zeros([1], requires_grad=True) t2 = torch.zeros([1], requires_grad=True) if param_groups_setting == "single_optim": optimizer = torch.optim.SGD([{"params": t1, "lr": 0.1}, {"params": t2, "lr": 0.1}]) lr_scheduler1 = LinearCyclicalScheduler( optimizer, "lr", param_group_index=0, start_value=1.0, end_value=0.0, cycle_size=10 ) lr_scheduler2 = LinearCyclicalScheduler( optimizer, "lr", param_group_index=1, start_value=1.0, end_value=0.0, cycle_size=10 ) else: optimizer_1 = torch.optim.SGD(params=[t1], lr=0.1) optimizer_2 = torch.optim.SGD(params=[t2], lr=0.1) lr_scheduler1 = LinearCyclicalScheduler(optimizer_1, "lr", start_value=1.0, end_value=0.0, cycle_size=10) lr_scheduler2 = LinearCyclicalScheduler(optimizer_2, "lr", start_value=1.0, end_value=0.0, cycle_size=10) lr_schedulers = [lr_scheduler1, lr_scheduler2] num_iterations = 10 max_epochs = 20 scheduler = ParamGroupScheduler(lr_schedulers, names=[f"s_{i}" for i in range(len(lr_schedulers))]) state_dict = scheduler.state_dict() trainer = Engine(lambda engine, batch: None) lrs = [] @trainer.on(Events.ITERATION_STARTED, lrs) def save_lr(_, lrs): lrs.append(scheduler.get_param()) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) data = [0] * num_iterations for _ in range(2): lrs.clear() trainer.run(data, max_epochs=max_epochs) assert [lr[0] for lr in lrs] == pytest.approx([lr[1] for lr in lrs]) scheduler.load_state_dict(state_dict) values = ParamGroupScheduler.simulate_values(max_epochs * num_iterations, lr_schedulers) assert [lr[1] for lr in values] == pytest.approx([lr[2] for lr in values]) assert [lr[0] for lr in lrs] == pytest.approx([lr[1] for lr in values]) @pytest.mark.parametrize( "scheduler_cls, kwargs", [ (LinearCyclicalScheduler, {"param_name": "lr", "start_value": 1.0, "end_value": 0.0, "cycle_size": 10}), ( PiecewiseLinear, {"param_name": "lr", "milestones_values": [(5, 0.5), (15, 1.0), (25, 0.0), (35, 1.0), (40, 0.5)]}, ), (CosineAnnealingScheduler, {"param_name": "lr", "start_value": 0.0, "end_value": 1.0, "cycle_size": 10}), (ExponentialLR, {"gamma": 0.98}), (StepLR, {"step_size": 50, "gamma": 0.5}), ], ) def test_scheduler_with_param_groups(scheduler_cls, kwargs): t1 = torch.zeros([1], requires_grad=True) t2 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([{"params": t1, "lr": 0.1}, {"params": t2, "lr": 0.1}]) lr_scheduler = scheduler_cls(optimizer, **kwargs) if not isinstance(lr_scheduler, ParamScheduler): lr_scheduler = LRScheduler(lr_scheduler) num_iterations = 10 max_epochs = 20 state_dict = lr_scheduler.state_dict() trainer = Engine(lambda engine, batch: None) @trainer.on(Events.ITERATION_COMPLETED) def save_lr(): lrs.append((optimizer.param_groups[0]["lr"], optimizer.param_groups[1]["lr"])) trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler) data = [0] * num_iterations for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert [lr[0] for lr in lrs] == pytest.approx([lr[1] for lr in lrs]) lr_scheduler.load_state_dict(state_dict) def test_lr_scheduling_on_non_torch_optimizers(): # tests https://github.com/pytorch/ignite/issues/1162 optimizer = MagicMock() optimizer.param_groups = [{"params": 0}] FakeParamScheduler(optimizer, "lr") tensor = torch.zeros([1], requires_grad=True) base_optimizer = torch.optim.SGD([tensor], lr=0) optimizer = MockFP16DeepSpeedZeroOptimizer(base_optimizer) milestones_values = [(5, 0.5), (15, 1.0)] scheduler = PiecewiseLinear(optimizer, "lr", milestones_values=milestones_values) def save_lr(engine): lrs.append(optimizer.param_groups[0]["lr"]) trainer = Engine(lambda engine, batch: None) trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler) trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr) lrs = [] trainer.run([0] * 15, max_epochs=1) assert lrs == list( map(pytest.approx, [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]) ) def test_reduce_lr_on_plateau_scheduler(): tensor1 = torch.zeros([1], requires_grad=True) tensor2 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([{"params": [tensor1]}, {"params": [tensor2]}], lr=1) data = [0] * 8 max_epochs = 10 trainer = Engine(lambda engine, batch: None) @trainer.on(Events.EPOCH_COMPLETED) def evaluate(): evaluator.run(data) scheduler = ReduceLROnPlateauScheduler( optimizer, metric_name="acc", mode="max", factor=0.5, patience=1, threshold_mode="abs", threshold=1.99, min_lr=1e-7, save_history=True, trainer=trainer, param_group_index=0, ) evaluator = Engine(lambda engine, batch: None) evaluator.state.metrics = {"acc": 0.0} generate_acc = iter([3, 7, 7, 9, 10, 11, 8, 8, 4, 7]) @evaluator.on(Events.COMPLETED) def set_acc(): evaluator.state.metrics["acc"] = next(generate_acc) evaluator.add_event_handler(Events.COMPLETED, scheduler) trainer.run(data, max_epochs=max_epochs) lrs = [param[0] for param in trainer.state.param_history["lr"]] assert lrs == list( map( pytest.approx, [1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25], ) ) assert optimizer.param_groups[1]["lr"] == 1 values = ReduceLROnPlateauScheduler.simulate_values( 5, [10, 9, 9, 9, 8.1], 1.0, save_history=True, factor=0.5, patience=2, threshold=0.1 ) values = np.array(values)[:, 1].tolist() assert values == list( map( pytest.approx, [1.0, 1.0, 1.0, 0.5, 0.5], ) ) def test_reduce_lr_on_plateau_scheduler_asserts(): tensor1 = torch.zeros([1], requires_grad=True) tensor2 = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([{"params": [tensor1]}, {"params": [tensor2]}], lr=1) with pytest.raises(TypeError, match=r"When param_group_index is given, min_lr should be a float, but given"): ReduceLROnPlateauScheduler( optimizer, metric_name="acc", min_lr=[1e-7, 1e-8], param_group_index=0, ) with pytest.raises( ValueError, match=r"Argument engine should have in its 'state', attribute 'metrics' which itself has the metric" ): scheduler = ReduceLROnPlateauScheduler(optimizer, metric_name="acc") evaluator = Engine(lambda engine, batch: None) scheduler(evaluator) with pytest.raises(ValueError, match=r"Length of argument metric_values should be equal to num_events."): metric_values = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6] ReduceLROnPlateauScheduler.simulate_values(5, metric_values, 0.01) @pytest.mark.parametrize("warmup_end_value", [0.23, None]) @pytest.mark.parametrize("T_0", [1, 12]) @pytest.mark.parametrize("T_mult", [1, 3]) def test_create_lr_scheduler_with_warmup_cosine(warmup_end_value, T_0, T_mult): lr = 0.2 steps = 200 warm_steps = 50 warm_start = 0.023 def get_optim(): t1 = torch.zeros([1], requires_grad=True) return torch.optim.SGD([t1], lr=lr) def get_cos_shed(): return CosineAnnealingWarmRestarts(optimizer, T_0=T_0, T_mult=T_mult) optimizer = get_optim() scheduler = get_cos_shed() cosine_lrs = [] for i in range(steps): cosine_lrs.append(optimizer.param_groups[0]["lr"]) scheduler.step() optimizer = get_optim() scheduler = create_lr_scheduler_with_warmup( get_cos_shed(), warmup_start_value=warm_start, warmup_end_value=warmup_end_value, warmup_duration=warm_steps ) warm_lrs = [] real_warm_steps = warm_steps if warmup_end_value is not None else (warm_steps - 1) for epoch in range(real_warm_steps + steps): scheduler(None) warm_lrs.append(optimizer.param_groups[0]["lr"]) if warmup_end_value is not None: np.testing.assert_allclose(np.linspace(warm_start, warmup_end_value, warm_steps), warm_lrs[:warm_steps]) assert warm_lrs[real_warm_steps:] == cosine_lrs else: np.testing.assert_allclose(np.linspace(warm_start, lr, warm_steps), warm_lrs[:warm_steps]) assert warm_lrs[real_warm_steps:] == cosine_lrs ignite-0.5.1/tests/ignite/handlers/test_polyaxon_logger.py000066400000000000000000000242101465426447700240600ustar00rootroot00000000000000import os from unittest.mock import call, MagicMock import pytest import torch from ignite.engine import Engine, Events, State from ignite.handlers.polyaxon_logger import ( global_step_from_engine, OptimizerParamsHandler, OutputHandler, PolyaxonLogger, ) os.environ["POLYAXON_NO_OP"] = "1" def test_output_handler_with_wrong_logger_type(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'OutputHandler' works only with PolyaxonLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_output_handler_output_transform(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.output = 12345 mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with(step=123, **{"tag/output": 12345}) wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with(step=123, **{"another_tag/loss": 12345}) def test_output_handler_metric_names(): wrapper = OutputHandler("tag", metric_names=["a", "b", "c"]) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45, "c": torch.tensor(10.0)}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_called_once_with(step=5, **{"tag/a": 12.23, "tag/b": 23.45, "tag/c": 10.0}) wrapper = OutputHandler("tag", metric_names=["a"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])}) mock_engine.state.iteration = 5 mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_has_calls( [call(step=5, **{"tag/a/0": 0.0, "tag/a/1": 1.0, "tag/a/2": 2.0, "tag/a/3": 3.0})], any_order=True ) wrapper = OutputHandler("tag", metric_names=["a", "c"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 55.56, "c": "Some text"}) mock_engine.state.iteration = 7 mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() with pytest.warns(UserWarning): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_has_calls([call(step=7, **{"tag/a": 55.56})], any_order=True) # all metrics wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45, "c": torch.tensor(10.0)}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_called_once_with(step=5, **{"tag/a": 12.23, "tag/b": 23.45, "tag/c": 10.0}) def test_output_handler_both(): wrapper = OutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_called_once_with(step=5, **{"tag/a": 12.23, "tag/b": 23.45, "tag/loss": 12345}) def test_output_handler_with_wrong_global_step_transform_output(): def global_step_transform(*args, **kwargs): return "a" wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 with pytest.raises(TypeError, match="global_step must be int"): wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) def test_output_handler_with_global_step_transform(): def global_step_transform(*args, **kwargs): return 10 wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.log_metrics.assert_called_once_with(step=10, **{"tag/loss": 12345}) def test_output_handler_with_global_step_from_engine(): mock_another_engine = MagicMock() mock_another_engine.state = State() mock_another_engine.state.epoch = 10 mock_another_engine.state.output = 12.345 wrapper = OutputHandler( "tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_from_engine(mock_another_engine), ) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 1 mock_engine.state.output = 0.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.log_metrics.call_count == 1 mock_logger.log_metrics.assert_has_calls( [call(step=mock_another_engine.state.epoch, **{"tag/loss": mock_engine.state.output})] ) mock_another_engine.state.epoch = 11 mock_engine.state.output = 1.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.log_metrics.call_count == 2 mock_logger.log_metrics.assert_has_calls( [call(step=mock_another_engine.state.epoch, **{"tag/loss": mock_engine.state.output})] ) def test_output_handler_state_attrs(): wrapper = OutputHandler("tag", state_attributes=["alpha", "beta", "gamma"]) mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 5 mock_engine.state.alpha = 3.899 mock_engine.state.beta = torch.tensor(12.21) mock_engine.state.gamma = torch.tensor([21.0, 6.0]) wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with( **{"tag/alpha": 3.899, "tag/beta": torch.tensor(12.21).item(), "tag/gamma/0": 21.0, "tag/gamma/1": 6.0}, step=5 ) def test_optimizer_params_handler_wrong_setup(): with pytest.raises(TypeError): OptimizerParamsHandler(optimizer=None) optimizer = MagicMock(spec=torch.optim.Optimizer) handler = OptimizerParamsHandler(optimizer=optimizer) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler OptimizerParamsHandler works only with PolyaxonLogger"): handler(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_optimizer_params(): optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01) wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr") mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with(**{"lr/group_0": 0.01, "step": 123}) wrapper = OptimizerParamsHandler(optimizer, param_name="lr", tag="generator") mock_logger = MagicMock(spec=PolyaxonLogger) mock_logger.log_metrics = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metrics.assert_called_once_with(**{"generator/lr/group_0": 0.01, "step": 123}) def test_integration(): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) plx_logger = PolyaxonLogger() def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger.log_metrics(step=global_step, **{"test_value": global_step}) plx_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) plx_logger.close() def test_integration_as_context_manager(): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) with PolyaxonLogger() as plx_logger: trainer = Engine(update_fn) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger.log_metrics(step=global_step, **{"test_value": global_step}) plx_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) @pytest.mark.parametrize("no_site_packages", ["polyaxon"], indirect=True) def test_no_polyaxon_client(no_site_packages): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires polyaxon"): PolyaxonLogger() ignite-0.5.1/tests/ignite/handlers/test_state_param_scheduler.py000066400000000000000000000441011465426447700252070ustar00rootroot00000000000000import re from pathlib import Path from unittest.mock import patch import pytest import torch import torch.nn as nn from packaging.version import Version from ignite.engine import Engine, Events from ignite.handlers.state_param_scheduler import ( ExpStateScheduler, LambdaStateScheduler, MultiStepStateScheduler, PiecewiseLinearStateScheduler, StepStateScheduler, ) config1 = (3, [(2, 0), (5, 10)], True, [0.0, 0.0, 3.3333333333333335]) expected_hist2 = [0.0] * 10 + [float(i) for i in range(1, 11)] + [10.0] * 10 config2 = (30, [(10, 0), (20, 10)], True, expected_hist2) config3 = ( PiecewiseLinearStateScheduler, {"param_name": "linear_scheduled_param", "milestones_values": [(3, 12), (5, 10)], "create_new": True}, ) config4 = ( ExpStateScheduler, {"param_name": "exp_scheduled_param", "initial_value": 10, "gamma": 0.99, "create_new": True}, ) config5 = ( MultiStepStateScheduler, { "param_name": "multistep_scheduled_param", "initial_value": 10, "gamma": 0.99, "milestones": [3, 6], "create_new": True, }, ) if Version(torch.__version__) < Version("1.9.0"): torch_testing_assert_close = torch.testing.assert_allclose else: torch_testing_assert_close = torch.testing.assert_close class LambdaState: def __init__(self, initial_value, gamma): self.initial_value = initial_value self.gamma = gamma def __call__(self, event_index): return self.initial_value * self.gamma ** (event_index % 9) config6 = ( LambdaStateScheduler, { "param_name": "custom_scheduled_param", "lambda_obj": LambdaState(initial_value=10, gamma=0.99), "create_new": True, }, ) config7 = ( StepStateScheduler, {"param_name": "step_scheduled_param", "initial_value": 10, "gamma": 0.99, "step_size": 5, "create_new": True}, ) @pytest.mark.parametrize("max_epochs, milestones_values, save_history, expected_param_history", [config1, config2]) def test_pwlinear_scheduler_linear_increase_history( max_epochs, milestones_values, save_history, expected_param_history ): # Testing linear increase engine = Engine(lambda e, b: None) pw_linear_step_parameter_scheduler = PiecewiseLinearStateScheduler( param_name="pwlinear_scheduled_param", milestones_values=milestones_values, save_history=save_history, create_new=True, ) pw_linear_step_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) expected_param_history = expected_param_history assert hasattr(engine.state, "param_history") state_param = engine.state.param_history["pwlinear_scheduled_param"] assert len(state_param) == len(expected_param_history) assert state_param == expected_param_history state_dict = pw_linear_step_parameter_scheduler.state_dict() pw_linear_step_parameter_scheduler.load_state_dict(state_dict) @pytest.mark.parametrize("max_epochs, milestones_values", [(3, [(3, 12), (5, 10)]), (5, [(10, 12), (20, 10)])]) def test_pwlinear_scheduler_step_constant(max_epochs, milestones_values): # Testing step_constant engine = Engine(lambda e, b: None) linear_state_parameter_scheduler = PiecewiseLinearStateScheduler( param_name="pwlinear_scheduled_param", milestones_values=milestones_values, create_new=True ) linear_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) torch_testing_assert_close(getattr(engine.state, "pwlinear_scheduled_param"), float(milestones_values[0][1])) state_dict = linear_state_parameter_scheduler.state_dict() linear_state_parameter_scheduler.load_state_dict(state_dict) @pytest.mark.parametrize( "max_epochs, milestones_values, expected_val", [(2, [(0, 0), (3, 10)], 6.666666666666667), (10, [(0, 0), (20, 10)], 5.0)], ) def test_pwlinear_scheduler_linear_increase(max_epochs, milestones_values, expected_val): # Testing linear increase engine = Engine(lambda e, b: None) linear_state_parameter_scheduler = PiecewiseLinearStateScheduler( param_name="pwlinear_scheduled_param", milestones_values=milestones_values, create_new=True ) linear_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) torch_testing_assert_close(getattr(engine.state, "pwlinear_scheduled_param"), expected_val, atol=0.001, rtol=0.0) state_dict = linear_state_parameter_scheduler.state_dict() linear_state_parameter_scheduler.load_state_dict(state_dict) @pytest.mark.parametrize("max_epochs, milestones_values,", [(3, [(0, 0), (3, 10)]), (40, [(0, 0), (20, 10)])]) def test_pwlinear_scheduler_max_value(max_epochs, milestones_values): # Testing max_value engine = Engine(lambda e, b: None) linear_state_parameter_scheduler = PiecewiseLinearStateScheduler( param_name="linear_scheduled_param", milestones_values=milestones_values, create_new=True ) linear_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) torch_testing_assert_close(getattr(engine.state, "linear_scheduled_param"), float(milestones_values[-1][1])) state_dict = linear_state_parameter_scheduler.state_dict() linear_state_parameter_scheduler.load_state_dict(state_dict) def test_piecewiselinear_asserts(): with pytest.raises(TypeError, match=r"Argument milestones_values should be a list or tuple"): PiecewiseLinearStateScheduler(param_name="linear_scheduled_param", milestones_values=None) with pytest.raises(ValueError, match=r"Argument milestones_values should be with at least one value"): PiecewiseLinearStateScheduler(param_name="linear_scheduled_param", milestones_values=[]) with pytest.raises(ValueError, match=r"Argument milestones_values should be a list of pairs"): PiecewiseLinearStateScheduler(param_name="linear_scheduled_param", milestones_values=[(0.5,)]) with pytest.raises(ValueError, match=r"Argument milestones_values should be a list of pairs"): PiecewiseLinearStateScheduler(param_name="linear_scheduled_param", milestones_values=[(10, 0.5), (0.6,)]) with pytest.raises(ValueError, match=r"Milestones should be increasing integers"): PiecewiseLinearStateScheduler(param_name="linear_scheduled_param", milestones_values=[(10, 0.5), (5, 0.6)]) with pytest.raises(TypeError, match=r"Value of a milestone should be integer"): PiecewiseLinearStateScheduler(param_name="linear_scheduled_param", milestones_values=[(0.5, 1)]) @pytest.mark.parametrize("max_epochs, initial_value, gamma", [(3, 10, 0.99), (40, 5, 0.98)]) def test_exponential_scheduler(max_epochs, initial_value, gamma): engine = Engine(lambda e, b: None) exp_state_parameter_scheduler = ExpStateScheduler( param_name="exp_scheduled_param", initial_value=initial_value, gamma=gamma, create_new=True ) exp_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) torch_testing_assert_close(getattr(engine.state, "exp_scheduled_param"), initial_value * gamma**max_epochs) state_dict = exp_state_parameter_scheduler.state_dict() exp_state_parameter_scheduler.load_state_dict(state_dict) @pytest.mark.parametrize("max_epochs, initial_value, gamma, step_size", [(3, 10, 0.99, 5), (40, 5, 0.98, 22)]) def test_step_scheduler(max_epochs, initial_value, gamma, step_size): engine = Engine(lambda e, b: None) step_state_parameter_scheduler = StepStateScheduler( param_name="step_scheduled_param", initial_value=initial_value, gamma=gamma, step_size=step_size, create_new=True, ) step_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) torch_testing_assert_close( getattr(engine.state, "step_scheduled_param"), initial_value * gamma ** (max_epochs // step_size) ) state_dict = step_state_parameter_scheduler.state_dict() step_state_parameter_scheduler.load_state_dict(state_dict) from bisect import bisect_right @pytest.mark.parametrize( "max_epochs, initial_value, gamma, milestones", [(3, 10, 0.99, [3, 6]), (40, 5, 0.98, [3, 6, 9, 10, 11])] ) def test_multistep_scheduler(max_epochs, initial_value, gamma, milestones): engine = Engine(lambda e, b: None) multi_step_state_parameter_scheduler = MultiStepStateScheduler( param_name="multistep_scheduled_param", initial_value=initial_value, gamma=gamma, milestones=milestones, create_new=True, ) multi_step_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=max_epochs) torch_testing_assert_close( getattr(engine.state, "multistep_scheduled_param"), initial_value * gamma ** bisect_right(milestones, max_epochs), ) state_dict = multi_step_state_parameter_scheduler.state_dict() multi_step_state_parameter_scheduler.load_state_dict(state_dict) def test_custom_scheduler(): engine = Engine(lambda e, b: None) class LambdaState: def __init__(self, initial_value, gamma): self.initial_value = initial_value self.gamma = gamma def __call__(self, event_index): return self.initial_value * self.gamma ** (event_index % 9) lambda_state_parameter_scheduler = LambdaStateScheduler( param_name="custom_scheduled_param", lambda_obj=LambdaState(initial_value=10, gamma=0.99), create_new=True ) lambda_state_parameter_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=2) torch_testing_assert_close( getattr(engine.state, "custom_scheduled_param"), LambdaState(initial_value=10, gamma=0.99)(2) ) engine.run([0] * 8, max_epochs=20) torch_testing_assert_close( getattr(engine.state, "custom_scheduled_param"), LambdaState(initial_value=10, gamma=0.99)(20) ) state_dict = lambda_state_parameter_scheduler.state_dict() lambda_state_parameter_scheduler.load_state_dict(state_dict) def test_custom_scheduler_asserts(): class LambdaState: def __init__(self, initial_value, gamma): self.initial_value = initial_value self.gamma = gamma with pytest.raises(ValueError, match=r"Expected lambda_obj to be callable."): lambda_state_parameter_scheduler = LambdaStateScheduler( param_name="custom_scheduled_param", lambda_obj=LambdaState(initial_value=10, gamma=0.99), create_new=True ) @pytest.mark.parametrize("scheduler_cls, scheduler_kwargs", [config3, config4, config5, config6]) def test_simulate_and_plot_values(scheduler_cls, scheduler_kwargs): import matplotlib matplotlib.use("Agg") event = Events.EPOCH_COMPLETED max_epochs = 2 data = [0] * 10 scheduler = scheduler_cls(**scheduler_kwargs) trainer = Engine(lambda engine, batch: None) scheduler.attach(trainer, event) trainer.run(data, max_epochs=max_epochs) # launch plot values scheduler_cls.plot_values(num_events=len(data) * max_epochs, **scheduler_kwargs) @pytest.mark.parametrize("save_history", [False, True]) @pytest.mark.parametrize("scheduler_cls, scheduler_kwargs", [config3, config4, config5, config6]) def test_simulate_values(scheduler_cls, scheduler_kwargs, save_history): max_epochs = 2 data = [0] * 10 scheduler_kwargs["save_history"] = save_history scheduler_cls.simulate_values(num_events=len(data) * max_epochs, **scheduler_kwargs) def test_torch_save_load(dirname): lambda_state_parameter_scheduler = LambdaStateScheduler( param_name="custom_scheduled_param", lambda_obj=LambdaState(initial_value=10, gamma=0.99), create_new=True ) filepath = Path(dirname) / "dummy_lambda_state_parameter_scheduler.pt" torch.save(lambda_state_parameter_scheduler, filepath) loaded_lambda_state_parameter_scheduler = torch.load(filepath) engine1 = Engine(lambda e, b: None) lambda_state_parameter_scheduler.attach(engine1, Events.EPOCH_COMPLETED) engine1.run([0] * 8, max_epochs=2) torch_testing_assert_close( getattr(engine1.state, "custom_scheduled_param"), LambdaState(initial_value=10, gamma=0.99)(2) ) engine2 = Engine(lambda e, b: None) loaded_lambda_state_parameter_scheduler.attach(engine2, Events.EPOCH_COMPLETED) engine2.run([0] * 8, max_epochs=2) torch_testing_assert_close( getattr(engine2.state, "custom_scheduled_param"), LambdaState(initial_value=10, gamma=0.99)(2) ) torch_testing_assert_close( getattr(engine1.state, "custom_scheduled_param"), getattr(engine2.state, "custom_scheduled_param") ) def test_simulate_and_plot_values_no_matplotlib(): with pytest.raises(ModuleNotFoundError, match=r"This method requires matplotlib to be installed."): with patch.dict("sys.modules", {"matplotlib.pyplot": None}): event = Events.EPOCH_COMPLETED max_epochs = 2 data = [0] * 10 kwargs = { "param_name": "multistep_scheduled_param", "initial_value": 10, "gamma": 0.99, "milestones": [3, 6], "create_new": True, } scheduler = MultiStepStateScheduler(**kwargs) trainer = Engine(lambda engine, batch: None) scheduler.attach(trainer, event) trainer.run(data, max_epochs=max_epochs) # launch plot values MultiStepStateScheduler.plot_values(num_events=len(data) * max_epochs, **kwargs) def test_multiple_scheduler_with_save_history(): engine_multiple_schedulers = Engine(lambda e, b: None) configs = [config3, config4, config5, config6, config7] for scheduler, config in configs: if "save_history" in config: del config["save_history"] _scheduler = scheduler(**config, save_history=True) _scheduler.attach(engine_multiple_schedulers) engine_multiple_schedulers.run([0] * 8, max_epochs=2) for scheduler, config in configs: engine = Engine(lambda e, b: None) _scheduler = scheduler(**config, save_history=True) _scheduler.attach(engine) engine.run([0] * 8, max_epochs=2) torch_testing_assert_close( engine_multiple_schedulers.state.param_history[config["param_name"]], engine.state.param_history[config["param_name"]], ) def test_docstring_examples(): # LambdaStateScheduler engine = Engine(lambda e, b: None) class LambdaState: def __init__(self, initial_value, gamma): self.initial_value = initial_value self.gamma = gamma def __call__(self, event_index): return self.initial_value * self.gamma ** (event_index % 9) param_scheduler = LambdaStateScheduler(param_name="param", lambda_obj=LambdaState(10, 0.99), create_new=True) param_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=2) # PiecewiseLinearStateScheduler engine = Engine(lambda e, b: None) param_scheduler = PiecewiseLinearStateScheduler( param_name="param", milestones_values=[(10, 0.5), (20, 0.45), (21, 0.3), (30, 0.1), (40, 0.1)], create_new=True ) param_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=40) # ExpStateScheduler engine = Engine(lambda e, b: None) param_scheduler = ExpStateScheduler(param_name="param", initial_value=10, gamma=0.99, create_new=True) param_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=2) # StepStateScheduler engine = Engine(lambda e, b: None) param_scheduler = StepStateScheduler(param_name="param", initial_value=10, gamma=0.99, step_size=5, create_new=True) param_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=10) # MultiStepStateScheduler engine = Engine(lambda e, b: None) param_scheduler = MultiStepStateScheduler( param_name="param", initial_value=10, gamma=0.99, milestones=[3, 6], create_new=True ) param_scheduler.attach(engine, Events.EPOCH_COMPLETED) engine.run([0] * 8, max_epochs=10) def test_param_scheduler_attach_exception(): trainer = Engine(lambda e, b: None) param_name = "state_param" setattr(trainer.state, param_name, None) save_history = True create_new = True param_scheduler = PiecewiseLinearStateScheduler( param_name=param_name, milestones_values=[(0, 0.0), (10, 0.999)], save_history=save_history, create_new=create_new, ) with pytest.raises( ValueError, match=r"Attribute '" + re.escape(param_name) + "' already exists in the engine.state. " r"This may be a conflict between multiple handlers. " r"Please choose another name.", ): param_scheduler.attach(trainer, Events.ITERATION_COMPLETED) def test_param_scheduler_attach_warning(): trainer = Engine(lambda e, b: None) param_name = "state_param" save_history = True create_new = False param_scheduler = PiecewiseLinearStateScheduler( param_name=param_name, milestones_values=[(0, 0.0), (10, 0.999)], save_history=save_history, create_new=create_new, ) with pytest.warns( UserWarning, match=r"Attribute '" + re.escape(param_name) + "' is not defined in the engine.state. " r"PiecewiseLinearStateScheduler will create it. Remove this warning by setting create_new=True.", ): param_scheduler.attach(trainer, Events.ITERATION_COMPLETED) def test_param_scheduler_with_ema_handler(): from ignite.handlers import EMAHandler model = nn.Linear(2, 1) trainer = Engine(lambda e, b: model(b)) data = torch.rand(100, 2) param_name = "ema_decay" ema_handler = EMAHandler(model) ema_handler.attach(trainer, name=param_name, event=Events.ITERATION_COMPLETED) ema_decay_scheduler = PiecewiseLinearStateScheduler( param_name=param_name, milestones_values=[(0, 0.0), (10, 0.999)], save_history=True ) ema_decay_scheduler.attach(trainer, Events.ITERATION_COMPLETED) trainer.run(data, max_epochs=20) ignite-0.5.1/tests/ignite/handlers/test_stores.py000066400000000000000000000030401465426447700221650ustar00rootroot00000000000000import pytest from ignite.engine.engine import Engine, Events from ignite.handlers import EpochOutputStore @pytest.fixture def dummy_evaluator(): def dummy_process_function(engine, batch): return 1, 0 dummy_evaluator = Engine(dummy_process_function) return dummy_evaluator @pytest.fixture def eos(): return EpochOutputStore() def test_no_transform(dummy_evaluator, eos): eos.attach(dummy_evaluator) dummy_evaluator.run(range(1)) assert eos.data == [(1, 0)] def test_transform(dummy_evaluator): eos = EpochOutputStore(output_transform=lambda x: x[0]) eos.attach(dummy_evaluator) dummy_evaluator.run(range(1)) assert eos.data == [1] def test_reset(dummy_evaluator, eos): eos.attach(dummy_evaluator) dummy_evaluator.run(range(2)) eos.reset() assert eos.data == [] def test_update_one_iteration(dummy_evaluator, eos): eos.attach(dummy_evaluator) dummy_evaluator.run(range(1)) assert len(eos.data) == 1 def test_update_five_iterations(dummy_evaluator, eos): eos.attach(dummy_evaluator) dummy_evaluator.run(range(5)) assert len(eos.data) == 5 def test_attatch(dummy_evaluator, eos): eos.attach(dummy_evaluator) assert dummy_evaluator.has_event_handler(eos.reset, Events.EPOCH_STARTED) assert dummy_evaluator.has_event_handler(eos.update, Events.ITERATION_COMPLETED) def test_store_data(dummy_evaluator, eos): eos.attach(dummy_evaluator, name="eval_data") dummy_evaluator.run(range(1)) assert dummy_evaluator.state.eval_data == eos.data ignite-0.5.1/tests/ignite/handlers/test_tensorboard_logger.py000066400000000000000000000573041465426447700245430ustar00rootroot00000000000000import math import os from unittest.mock import ANY, call, MagicMock, patch import pytest import torch from ignite.engine import Engine, Events, State from ignite.handlers.tensorboard_logger import ( global_step_from_engine, GradsHistHandler, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, TensorboardLogger, WeightsHistHandler, WeightsScalarHandler, ) def test_optimizer_params_handler_wrong_setup(): with pytest.raises(TypeError): OptimizerParamsHandler(optimizer=None) optimizer = MagicMock(spec=torch.optim.Optimizer) handler = OptimizerParamsHandler(optimizer=optimizer) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler OptimizerParamsHandler works only with TensorboardLogger"): handler(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_getattr_method(): # Create a mock SummaryWriter object mock_writer = MagicMock() # Assign the mock object to the writer attribute of a TensorboardLoggerinstance logger = TensorboardLogger() logger.writer = mock_writer # Test that a method passed through the __getattr__ method calls thecorresponding method on the mock object logger.add_scalar("loss", 0.5) mock_writer.add_scalar.assert_called_once_with("loss", 0.5) def test_optimizer_params(): optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01) wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr") mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.writer.add_scalar.assert_called_once_with("lr/group_0", 0.01, 123) wrapper = OptimizerParamsHandler(optimizer, param_name="lr", tag="generator") mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.writer.add_scalar.assert_called_once_with("generator/lr/group_0", 0.01, 123) def test_output_handler_with_wrong_logger_type(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'OutputHandler' works only with TensorboardLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_output_handler_output_transform(): wrapper = OutputHandler("tag", output_transform=lambda x: x) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.output = 12345 mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.writer.add_scalar.assert_called_once_with("tag/output", 12345, 123) wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.writer.add_scalar.assert_called_once_with("another_tag/loss", 12345, 123) def test_output_handler_metric_names(): wrapper = OutputHandler("tag", metric_names=["a", "b"]) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.writer.add_scalar.call_count == 2 mock_logger.writer.add_scalar.assert_has_calls([call("tag/a", 12.23, 5), call("tag/b", 23.45, 5)], any_order=True) wrapper = OutputHandler("tag", metric_names=["a"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])}) mock_engine.state.iteration = 5 mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.writer.add_scalar.call_count == 4 mock_logger.writer.add_scalar.assert_has_calls( [call("tag/a/0", 0.0, 5), call("tag/a/1", 1.0, 5), call("tag/a/2", 2.0, 5), call("tag/a/3", 3.0, 5)], any_order=True, ) wrapper = OutputHandler("tag", metric_names=["a", "c"]) mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 55.56, "c": "Some text"}) mock_engine.state.iteration = 7 mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() with pytest.warns(UserWarning): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.writer.add_scalar.call_count == 1 mock_logger.writer.add_scalar.assert_has_calls([call("tag/a", 55.56, 7)], any_order=True) # all metrics wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.writer.add_scalar.call_count == 2 mock_logger.writer.add_scalar.assert_has_calls([call("tag/a", 12.23, 5), call("tag/b", 23.45, 5)], any_order=True) # log a torch tensor (ndimension = 0) wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": torch.tensor(12.23), "b": torch.tensor(23.45)}) mock_engine.state.iteration = 5 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.writer.add_scalar.call_count == 2 mock_logger.writer.add_scalar.assert_has_calls( [call("tag/a", torch.tensor(12.23).item(), 5), call("tag/b", torch.tensor(23.45).item(), 5)], any_order=True ) def test_output_handler_both(): wrapper = OutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State(metrics={"a": 12.23, "b": 23.45}) mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.writer.add_scalar.call_count == 3 mock_logger.writer.add_scalar.assert_has_calls( [call("tag/a", 12.23, 5), call("tag/b", 23.45, 5), call("tag/loss", 12345, 5)], any_order=True ) def test_output_handler_with_wrong_global_step_transform_output(): def global_step_transform(*args, **kwargs): return "a" wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 with pytest.raises(TypeError, match="global_step must be int"): wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) def test_output_handler_with_global_step_from_engine(): mock_another_engine = MagicMock() mock_another_engine.state = State() mock_another_engine.state.epoch = 10 mock_another_engine.state.output = 12.345 wrapper = OutputHandler( "tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_from_engine(mock_another_engine), ) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 1 mock_engine.state.output = 0.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.writer.add_scalar.call_count == 1 mock_logger.writer.add_scalar.assert_has_calls( [call("tag/loss", mock_engine.state.output, mock_another_engine.state.epoch)] ) mock_another_engine.state.epoch = 11 mock_engine.state.output = 1.123 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.writer.add_scalar.call_count == 2 mock_logger.writer.add_scalar.assert_has_calls( [call("tag/loss", mock_engine.state.output, mock_another_engine.state.epoch)] ) def test_output_handler_with_global_step_transform(): def global_step_transform(*args, **kwargs): return 10 wrapper = OutputHandler("tag", output_transform=lambda x: {"loss": x}, global_step_transform=global_step_transform) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 mock_engine.state.output = 12345 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) assert mock_logger.writer.add_scalar.call_count == 1 mock_logger.writer.add_scalar.assert_has_calls([call("tag/loss", 12345, 10)]) def test_weights_scalar_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = WeightsScalarHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'WeightsScalarHandler' works only with TensorboardLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_weights_scalar_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = WeightsScalarHandler(model, tag=tag) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert mock_logger.writer.add_scalar.call_count == 4 mock_logger.writer.add_scalar.assert_has_calls( [ call(tag_prefix + "weights_norm/fc1/weight", 0.0, 5), call(tag_prefix + "weights_norm/fc1/bias", 0.0, 5), call(tag_prefix + "weights_norm/fc2/weight", 12.0, 5), call(tag_prefix + "weights_norm/fc2/bias", pytest.approx(math.sqrt(12.0)), 5), ], any_order=True, ) _test() _test(tag="tag") def test_weights_scalar_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = WeightsScalarHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_scalar.assert_called_once_with("weights_norm/fc2/weight", 12.0, 5) mock_logger.writer.reset_mock() wrapper = WeightsScalarHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_scalar.assert_has_calls( [ call("model/weights_norm/fc1/weight", 0.0, 5), call("model/weights_norm/fc1/bias", 0.0, 5), ], any_order=True, ) assert mock_logger.writer.add_scalar.call_count == 2 mock_logger.writer.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = WeightsScalarHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_scalar.assert_has_calls( [ call("model/weights_norm/fc1/bias", 0.0, 5), call("model/weights_norm/fc2/bias", pytest.approx(math.sqrt(12.0)), 5), ], any_order=True, ) assert mock_logger.writer.add_scalar.call_count == 2 def test_weights_hist_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = WeightsHistHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'WeightsHistHandler' works only with TensorboardLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_weights_hist_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = WeightsHistHandler(model, tag=tag) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert mock_logger.writer.add_histogram.call_count == 4 mock_logger.writer.add_histogram.assert_has_calls( [ call(tag=tag_prefix + "weights/fc1/weight", values=ANY, global_step=5), call(tag=tag_prefix + "weights/fc1/bias", values=ANY, global_step=5), call(tag=tag_prefix + "weights/fc2/weight", values=ANY, global_step=5), call(tag=tag_prefix + "weights/fc2/bias", values=ANY, global_step=5), ], any_order=True, ) _test() _test(tag="tag") def test_weights_hist_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = WeightsHistHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_histogram.assert_called_once_with(tag="weights/fc2/weight", values=ANY, global_step=5) mock_logger.writer.reset_mock() wrapper = WeightsHistHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="model/weights/fc1/weight", values=ANY, global_step=5), call(tag="model/weights/fc1/bias", values=ANY, global_step=5), ], any_order=True, ) assert mock_logger.writer.add_histogram.call_count == 2 mock_logger.writer.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = WeightsHistHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="model/weights/fc1/bias", values=ANY, global_step=5), call(tag="model/weights/fc2/bias", values=ANY, global_step=5), ], any_order=True, ) assert mock_logger.writer.add_histogram.call_count == 2 def test_grads_scalar_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = GradsScalarHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'GradsScalarHandler' works only with TensorboardLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_grads_scalar_handler(dummy_model_factory, norm_mock): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = GradsScalarHandler(model, reduction=norm_mock, tag=tag) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 norm_mock.reset_mock() wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" mock_logger.writer.add_scalar.assert_has_calls( [ call(tag_prefix + "grads_norm/fc1/weight", ANY, 5), call(tag_prefix + "grads_norm/fc1/bias", ANY, 5), call(tag_prefix + "grads_norm/fc2/weight", ANY, 5), call(tag_prefix + "grads_norm/fc2/bias", ANY, 5), ], any_order=True, ) assert mock_logger.writer.add_scalar.call_count == 4 assert norm_mock.call_count == 4 _test() _test(tag="tag") def test_grads_scalar_handler_whitelist(dummy_model_factory, norm_mock): model = dummy_model_factory() wrapper = GradsScalarHandler(model, reduction=norm_mock, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_scalar.assert_called_once_with("grads_norm/fc2/weight", ANY, 5) mock_logger.writer.reset_mock() wrapper = GradsScalarHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_scalar.assert_has_calls( [ call("model/grads_norm/fc1/weight", ANY, 5), call("model/grads_norm/fc1/bias", ANY, 5), ], any_order=True, ) assert mock_logger.writer.add_scalar.call_count == 2 mock_logger.writer.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = GradsScalarHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_scalar.assert_has_calls( [ call("model/grads_norm/fc1/bias", ANY, 5), call("model/grads_norm/fc2/bias", ANY, 5), ], any_order=True, ) assert mock_logger.writer.add_scalar.call_count == 2 def test_grads_hist_handler_wrong_setup(): model = MagicMock(spec=torch.nn.Module) wrapper = GradsHistHandler(model) mock_logger = MagicMock() mock_engine = MagicMock() with pytest.raises(RuntimeError, match="Handler 'GradsHistHandler' works only with TensorboardLogger"): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) def test_grads_hist_handler(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=False) # define test wrapper to test with and without optional tag def _test(tag=None): wrapper = GradsHistHandler(model, tag=tag) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) tag_prefix = f"{tag}/" if tag else "" assert mock_logger.writer.add_histogram.call_count == 4 mock_logger.writer.add_histogram.assert_has_calls( [ call(tag=tag_prefix + "grads/fc1/weight", values=ANY, global_step=5), call(tag=tag_prefix + "grads/fc1/bias", values=ANY, global_step=5), call(tag=tag_prefix + "grads/fc2/weight", values=ANY, global_step=5), call(tag=tag_prefix + "grads/fc2/bias", values=ANY, global_step=5), ], any_order=True, ) _test() _test(tag="tag") def test_grads_hist_handler_whitelist(dummy_model_factory): model = dummy_model_factory() wrapper = GradsHistHandler(model, whitelist=["fc2.weight"]) mock_logger = MagicMock(spec=TensorboardLogger) mock_logger.writer = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_histogram.assert_called_once_with(tag="grads/fc2/weight", values=ANY, global_step=5) mock_logger.writer.reset_mock() wrapper = GradsHistHandler(model, tag="model", whitelist=["fc1"]) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="model/grads/fc1/weight", values=ANY, global_step=5), call(tag="model/grads/fc1/bias", values=ANY, global_step=5), ], any_order=True, ) assert mock_logger.writer.add_histogram.call_count == 2 mock_logger.writer.reset_mock() def weight_selector(n, _): return "bias" in n wrapper = GradsHistHandler(model, tag="model", whitelist=weight_selector) wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="model/grads/fc1/bias", values=ANY, global_step=5), call(tag="model/grads/fc2/bias", values=ANY, global_step=5), ], any_order=True, ) assert mock_logger.writer.add_histogram.call_count == 2 def test_integration(dirname): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) trainer = Engine(update_fn) tb_logger = TensorboardLogger(log_dir=dirname) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger.writer.add_scalar("test_value", global_step, global_step) tb_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) tb_logger.close() # Check if event files are present written_files = os.listdir(dirname) written_files = [f for f in written_files if "tfevents" in f] assert len(written_files) > 0 def test_integration_as_context_manager(dirname): n_epochs = 5 data = list(range(50)) losses = torch.rand(n_epochs * len(data)) losses_iter = iter(losses) def update_fn(engine, batch): return next(losses_iter) with TensorboardLogger(log_dir=dirname) as tb_logger: trainer = Engine(update_fn) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger.writer.add_scalar("test_value", global_step, global_step) tb_logger.attach(trainer, log_handler=dummy_handler, event_name=Events.EPOCH_COMPLETED) trainer.run(data, max_epochs=n_epochs) # Check if event files are present written_files = os.listdir(dirname) written_files = [f for f in written_files if "tfevents" in f] assert len(written_files) > 0 def test_no_tensorboardX_package(dirname): from torch.utils.tensorboard import SummaryWriter with patch.dict("sys.modules", {"tensorboardX": None}): tb_logger = TensorboardLogger(log_dir=dirname) assert isinstance(tb_logger.writer, SummaryWriter), type(tb_logger.writer) tb_logger.close() def test_no_torch_utils_tensorboard_package(dirname): from tensorboardX import SummaryWriter with patch.dict("sys.modules", {"torch.utils.tensorboard": None}): tb_logger = TensorboardLogger(log_dir=dirname) assert isinstance(tb_logger.writer, SummaryWriter), type(tb_logger.writer) tb_logger.close() def test_no_tensorboardX_nor_torch_utils_tensorboard(): with patch.dict("sys.modules", {"tensorboardX": None, "torch.utils.tensorboard": None}): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires either tensorboardX or torch"): TensorboardLogger(log_dir=None) ignite-0.5.1/tests/ignite/handlers/test_terminate_on_nan.py000066400000000000000000000050311465426447700241700ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.engine import Engine, Events, State from ignite.handlers import TerminateOnNan @pytest.mark.parametrize( "state_output,should_terminate", [ (1.0, False), (torch.tensor(123.45), False), (torch.asin(torch.tensor([1.0, 2.0, 0.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])), True), (torch.asin(torch.randn(4, 4)), True), ((10.0, 1.0 / torch.tensor([1.0, 2.0, 0.0, 3.0]), 1.0), True), ((1.0, torch.tensor(1.0), "abc"), False), (1.0 / torch.randint(0, 2, size=(4, 4)).type(torch.float), True), ((float("nan"), 10.0), True), (float("inf"), True), ([float("nan"), 10.0], True), (np.array([1.0, 2.0]), False), ], ) def test_terminate_on_nan_and_inf(state_output, should_terminate): torch.manual_seed(12) def update_fn(engine, batch): pass trainer = Engine(update_fn) trainer.state = State() h = TerminateOnNan() trainer.state.output = state_output if isinstance(state_output, np.ndarray): h._output_transform = lambda x: x.tolist() h(trainer) assert trainer.should_terminate == should_terminate def test_with_terminate_on_nan(): torch.manual_seed(12) data = [1.0, 0.8, (torch.rand(4, 4), torch.rand(4, 4)), torch.rand(5), torch.asin(torch.randn(4, 4)), 0.0, 1.0] def update_fn(engine, batch): return batch trainer = Engine(update_fn) h = TerminateOnNan() trainer.add_event_handler(Events.ITERATION_COMPLETED, h) trainer.run(data, max_epochs=2) assert trainer.state.iteration == 5 def test_with_terminate_on_inf(): torch.manual_seed(12) data = [ 1.0, 0.8, torch.rand(4, 4), (1.0 / torch.randint(0, 2, size=(4,)).type(torch.float), torch.tensor(1.234)), torch.rand(5), torch.asin(torch.randn(4, 4)), 0.0, 1.0, ] def update_fn(engine, batch): return batch trainer = Engine(update_fn) h = TerminateOnNan() trainer.add_event_handler(Events.ITERATION_COMPLETED, h) trainer.run(data, max_epochs=2) assert trainer.state.iteration == 4 def test_without_terminate_on_nan_inf(): data = [1.0, 0.8, torch.rand(4, 4), (torch.rand(5), torch.rand(5, 4)), 0.0, 1.0] def update_fn(engine, batch): return batch trainer = Engine(update_fn) h = TerminateOnNan() trainer.add_event_handler(Events.ITERATION_COMPLETED, h) trainer.run(data, max_epochs=2) assert trainer.state.iteration == len(data) * 2 ignite-0.5.1/tests/ignite/handlers/test_time_limit.py000066400000000000000000000017351465426447700230130ustar00rootroot00000000000000import time import pytest from ignite.engine import Engine, Events from ignite.handlers import TimeLimit def test_arg_validation(): with pytest.raises(ValueError, match=r"Argument limit_sec should be a positive integer."): TimeLimit(limit_sec=-5) with pytest.raises(TypeError, match=r"Argument limit_sec should be an integer."): TimeLimit(limit_sec="abc") def _train_func(engine, batch): time.sleep(1) @pytest.mark.parametrize("n_iters, limit", [(20, 10), (5, 10)]) def test_terminate_on_time_limit(n_iters, limit): started = time.time() trainer = Engine(_train_func) @trainer.on(Events.TERMINATE) def _(): trainer.state.is_terminated = True trainer.add_event_handler(Events.ITERATION_COMPLETED, TimeLimit(limit)) trainer.state.is_terminated = False trainer.run(range(n_iters)) elapsed = round(time.time() - started) assert elapsed <= limit + 1 assert trainer.state.is_terminated == (n_iters > limit) ignite-0.5.1/tests/ignite/handlers/test_time_profilers.py000066400000000000000000000761501465426447700237050ustar00rootroot00000000000000import sys import time from unittest.mock import patch import pytest from pytest import approx from ignite.engine import Engine, EventEnum, Events from ignite.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler if sys.platform.startswith("darwin"): pytest.skip("Skip if on MacOS", allow_module_level=True) def _do_nothing_update_fn(engine, batch): pass def get_prepared_engine_for_basic_profiler(true_event_handler_time): dummy_trainer = Engine(_do_nothing_update_fn) @dummy_trainer.on(Events.STARTED) def delay_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.COMPLETED) def delay_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_STARTED) def delay_epoch_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_COMPLETED) def delay_epoch_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.ITERATION_STARTED) def delay_iter_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.ITERATION_COMPLETED) def delay_iter_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.GET_BATCH_STARTED) def delay_get_batch_started(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.GET_BATCH_COMPLETED) def delay_get_batch_completed(engine): time.sleep(true_event_handler_time) return dummy_trainer def get_prepared_engine_for_handlers_profiler(true_event_handler_time): HANDLERS_SLEEP_COUNT = 11 PROCESSING_SLEEP_COUNT = 3 class CustomEvents(EventEnum): CUSTOM_STARTED = "custom_started" CUSTOM_COMPLETED = "custom_completed" def dummy_train_step(engine, batch): engine.fire_event(CustomEvents.CUSTOM_STARTED) time.sleep(true_event_handler_time) engine.fire_event(CustomEvents.CUSTOM_COMPLETED) dummy_trainer = Engine(dummy_train_step) dummy_trainer.register_events(*CustomEvents) @dummy_trainer.on(Events.STARTED) def delay_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.COMPLETED) def delay_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_STARTED) def delay_epoch_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_COMPLETED) def delay_epoch_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.ITERATION_STARTED) def delay_iter_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.ITERATION_COMPLETED) def delay_iter_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.GET_BATCH_STARTED) def delay_get_batch_started(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.GET_BATCH_COMPLETED) def delay_get_batch_completed(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(CustomEvents.CUSTOM_STARTED) def delay_custom_started(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(CustomEvents.CUSTOM_COMPLETED) def delay_custom_completed(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_STARTED(once=1)) def do_something_once_on_1_epoch(): time.sleep(true_event_handler_time) return dummy_trainer, HANDLERS_SLEEP_COUNT, PROCESSING_SLEEP_COUNT def test_profilers_wrong_inputs(): profiler = BasicTimeProfiler() with pytest.raises(TypeError, match=r"Argument engine should be ignite.engine.Engine"): profiler.attach(None) with pytest.raises(ModuleNotFoundError, match=r"Need pandas to write results as files"): with patch.dict("sys.modules", {"pandas": None}): profiler.write_results("") profiler = HandlersTimeProfiler() with pytest.raises(TypeError, match=r"Argument engine should be ignite.engine.Engine"): profiler.attach(None) with pytest.raises(ModuleNotFoundError, match=r"Need pandas to write results as files"): with patch.dict("sys.modules", {"pandas": None}): profiler.write_results("") def test_dataflow_timer_basic_profiler(): true_dataflow_time_per_ele = 0.1 true_max_epochs = 1 true_num_iters = 2 def dummy_data_loader(data): while True: for d in data: time.sleep(true_dataflow_time_per_ele) yield d dummy_data = range(true_num_iters) profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) dummy_trainer.run(dummy_data_loader(dummy_data), max_epochs=true_max_epochs, epoch_length=true_num_iters) results = profiler.get_results() dataflow_results = results["dataflow_stats"] assert dataflow_results["min/index"][0] == approx(true_dataflow_time_per_ele, abs=1e-1) assert dataflow_results["max/index"][0] == approx(true_dataflow_time_per_ele, abs=1e-1) assert dataflow_results["mean"] == approx(true_dataflow_time_per_ele, abs=1e-1) assert dataflow_results["std"] == approx(0.0, abs=1e-1) assert dataflow_results["total"] == approx(true_num_iters * true_dataflow_time_per_ele, abs=1e-1) def test_dataflow_timer_handlers_profiler(): true_dataflow_time_per_ele = 0.1 true_max_epochs = 1 true_num_iters = 2 def dummy_data_loader(data): while True: for d in data: time.sleep(true_dataflow_time_per_ele) yield d dummy_data = range(true_num_iters) profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) dummy_trainer.run(dummy_data_loader(dummy_data), max_epochs=true_max_epochs, epoch_length=true_num_iters) results = profiler.get_results() dataflow_results = results[-1] assert dataflow_results[0] == "Dataflow" # event name assert dataflow_results[1] == "None" # total assert dataflow_results[2] == approx(true_num_iters * true_dataflow_time_per_ele, abs=1e-1) # min assert dataflow_results[3][0] == approx(true_dataflow_time_per_ele, abs=1e-1) # max assert dataflow_results[4][0] == approx(true_dataflow_time_per_ele, abs=1e-1) # mean assert dataflow_results[5] == approx(true_dataflow_time_per_ele, abs=1e-1) # stddev assert dataflow_results[6] == approx(0.0, abs=1e-1) def test_processing_timer_basic_profiler(): true_processing_time = 0.1 true_max_epochs = 2 true_num_iters = 2 def train_updater(engine, batch): time.sleep(true_processing_time) profiler = BasicTimeProfiler() dummy_trainer = Engine(train_updater) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() processing_results = results["processing_stats"] assert processing_results["min/index"][0] == approx(true_processing_time, abs=1e-1) assert processing_results["max/index"][0] == approx(true_processing_time, abs=1e-1) assert processing_results["mean"] == approx(true_processing_time, abs=1e-1) assert processing_results["std"] == approx(0.0, abs=1e-1) assert processing_results["total"] == approx(true_max_epochs * true_num_iters * true_processing_time, abs=1e-1) def test_processing_timer_handlers_profiler(): true_processing_time = 0.1 true_max_epochs = 2 true_num_iters = 2 def train_updater(engine, batch): time.sleep(true_processing_time) profiler = HandlersTimeProfiler() dummy_trainer = Engine(train_updater) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() processing_results = results[-2] assert processing_results[0] == "Processing" # event name assert processing_results[1] == "None" # total assert processing_results[2] == approx(true_max_epochs * true_num_iters * true_processing_time, abs=1e-1) # min assert processing_results[3][0] == approx(true_processing_time, abs=1e-1) # max assert processing_results[4][0] == approx(true_processing_time, abs=1e-1) # mean assert processing_results[5] == approx(true_processing_time, abs=1e-1) # stddev assert processing_results[6] == approx(0.0, abs=1e-1) def test_event_handler_started_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.STARTED) def delay_start(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["STARTED"] assert event_results["total"] == approx(true_event_handler_time, abs=1e-1) def test_event_handler_started_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.STARTED) def delay_start(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_start" in event_results[0] assert event_results[1] == "STARTED" assert event_results[2] == approx(true_event_handler_time, abs=1e-1) # total def test_event_handler_completed_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.COMPLETED) def delay_complete(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["COMPLETED"] assert event_results["total"] == approx(true_event_handler_time, abs=1e-1) def test_event_handler_completed_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.COMPLETED) def delay_complete(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_complete" in event_results[0] assert event_results[1] == "COMPLETED" assert event_results[2] == approx(true_event_handler_time, abs=1e-1) # total def test_event_handler_epoch_started_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 1 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.EPOCH_STARTED) def delay_epoch_start(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["EPOCH_STARTED"] assert event_results["min/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["max/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["mean"] == approx(true_event_handler_time, abs=1e-1) assert event_results["std"] == approx(0.0, abs=1e-1) assert event_results["total"] == approx(true_max_epochs * true_event_handler_time, abs=1e-1) def test_event_handler_epoch_started_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 1 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.EPOCH_STARTED) def delay_epoch_start(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_epoch_start" in event_results[0] assert event_results[1] == "EPOCH_STARTED" assert event_results[2] == approx(true_max_epochs * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_event_handler_epoch_completed_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 1 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.EPOCH_COMPLETED) def delay_epoch_complete(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["EPOCH_COMPLETED"] assert event_results["min/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["max/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["mean"] == approx(true_event_handler_time, abs=1e-1) assert event_results["std"] == approx(0.0, abs=1e-1) assert event_results["total"] == approx(true_max_epochs * true_event_handler_time, abs=1e-1) def test_event_handler_epoch_completed_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 2 true_num_iters = 1 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.EPOCH_COMPLETED) def delay_epoch_complete(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_epoch_complete" in event_results[0] assert event_results[1] == "EPOCH_COMPLETED" assert event_results[2] == approx(true_max_epochs * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_event_handler_iteration_started_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.ITERATION_STARTED) def delay_iter_start(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["ITERATION_STARTED"] assert event_results["min/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["max/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["mean"] == approx(true_event_handler_time, abs=1e-1) assert event_results["std"] == approx(0.0, abs=1e-1) assert event_results["total"] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) def test_event_handler_iteration_started_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.ITERATION_STARTED) def delay_iter_start(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_iter_start" in event_results[0] assert event_results[1] == "ITERATION_STARTED" assert event_results[2] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_event_handler_iteration_completed_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.ITERATION_COMPLETED) def delay_iter_complete(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["ITERATION_COMPLETED"] assert event_results["min/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["max/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["mean"] == approx(true_event_handler_time, abs=1e-1) assert event_results["std"] == approx(0.0, abs=1e-1) assert event_results["total"] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) def test_event_handler_iteration_completed_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.ITERATION_COMPLETED) def delay_iter_complete(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_iter_complete" in event_results[0] assert event_results[1] == "ITERATION_COMPLETED" assert event_results[2] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_event_handler_get_batch_started_basic_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.GET_BATCH_STARTED) def delay_get_batch_started(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["GET_BATCH_STARTED"] assert event_results["min/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["max/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["mean"] == approx(true_event_handler_time, abs=1e-1) assert event_results["std"] == approx(0.0, abs=1e-1) assert event_results["total"] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) def test_event_handler_get_batch_started_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.GET_BATCH_STARTED) def delay_get_batch_started(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_get_batch_started" in event_results[0] assert event_results[1] == "GET_BATCH_STARTED" assert event_results[2] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_event_handler_get_batch_completed(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.GET_BATCH_COMPLETED) def delay_get_batch_completed(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"]["GET_BATCH_COMPLETED"] assert event_results["min/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["max/index"][0] == approx(true_event_handler_time, abs=1e-1) assert event_results["mean"] == approx(true_event_handler_time, abs=1e-1) assert event_results["std"] == approx(0.0, abs=1e-1) assert event_results["total"] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) def test_event_handler_get_batch_completed_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.GET_BATCH_COMPLETED) def delay_get_batch_completed(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "delay_get_batch_completed" in event_results[0] assert event_results[1] == "GET_BATCH_COMPLETED" assert event_results[2] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_neg_event_filter_threshold_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 1 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.EPOCH_STARTED(once=2)) def do_something_once_on_2_epoch(): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "do_something_once_on_2_epoch" in event_results[0] assert event_results[1] == "EPOCH_STARTED" assert event_results[2] == "not triggered" def test_pos_event_filter_threshold_handlers_profiler(): true_event_handler_time = HandlersTimeProfiler.EVENT_FILTER_THESHOLD_TIME true_max_epochs = 2 true_num_iters = 1 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.EPOCH_STARTED(once=2)) def do_something_once_on_2_epoch(): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results[0] assert "do_something_once_on_2_epoch" in event_results[0] assert event_results[1] == "EPOCH_STARTED" assert event_results[2] == approx( (true_max_epochs * true_num_iters * true_event_handler_time) / 2, abs=1e-1 ) # total def test_custom_event_with_arg_handlers_profiler(): true_event_handler_time = 0.1 true_max_epochs = 1 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) dummy_trainer.register_events("custom_event") profiler.attach(dummy_trainer) @dummy_trainer.on(Events.ITERATION_COMPLETED(every=1)) def trigger_custom_event(): dummy_trainer.fire_event("custom_event") args = [122, 324] @dummy_trainer.on("custom_event", args) def on_custom_event(args): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = None for row in results: if row[1] == "custom_event": event_results = row break assert event_results is not None assert "on_custom_event" in event_results[0] assert event_results[2] == approx(true_max_epochs * true_num_iters * true_event_handler_time, abs=1e-1) # total assert event_results[3][0] == approx(true_event_handler_time, abs=1e-1) # min assert event_results[4][0] == approx(true_event_handler_time, abs=1e-1) # max assert event_results[5] == approx(true_event_handler_time, abs=1e-1) # mean assert event_results[6] == approx(0.0, abs=1e-1) # stddev def test_event_handler_total_time_basic_profiler(): true_event_handler_time = 0.125 true_max_epochs = 1 true_num_iters = 1 profiler = BasicTimeProfiler() dummy_trainer = Engine(_do_nothing_update_fn) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.STARTED) def delay_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.COMPLETED) def delay_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_STARTED) def delay_epoch_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.EPOCH_COMPLETED) def delay_epoch_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.ITERATION_STARTED) def delay_iter_start(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.ITERATION_COMPLETED) def delay_iter_complete(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.GET_BATCH_STARTED) def delay_get_batch_started(engine): time.sleep(true_event_handler_time) @dummy_trainer.on(Events.GET_BATCH_COMPLETED) def delay_get_batch_completed(engine): time.sleep(true_event_handler_time) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() event_results = results["event_handlers_stats"] assert event_results["total_time"].item() == approx(true_event_handler_time * 8, abs=1e-1) def test_event_handler_total_time_handlers_profiler(): true_event_handler_time = 0.125 true_max_epochs = 1 true_num_iters = 1 profiler = HandlersTimeProfiler() dummy_trainer, handlers_sleep_count, processing_sleep_count = get_prepared_engine_for_handlers_profiler( true_event_handler_time ) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) results = profiler.get_results() total_handler_stats = results[-3] # total result row total_processing_stats = results[-2] # processing result row assert total_handler_stats[2] == approx(true_event_handler_time * handlers_sleep_count, abs=1e-1) # total time assert total_processing_stats[2] == approx(true_event_handler_time * processing_sleep_count, abs=1e-1) # total time def test_write_results_basic_profiler(dirname): true_event_handler_time = 0.125 true_max_epochs = 3 true_num_iters = 2 profiler = BasicTimeProfiler() dummy_trainer = get_prepared_engine_for_basic_profiler(true_event_handler_time) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) fp = dirname / "test_log.csv" profiler.write_results(fp) assert fp.is_file() file_length = 0 with open(fp) as f: for _ in f: file_length += 1 assert file_length == (true_max_epochs * true_num_iters) + 1 def test_write_results_handlers_profiler(dirname): true_event_handler_time = 0.125 true_max_epochs = 3 true_num_iters = 2 profiler = HandlersTimeProfiler() dummy_trainer, _, _ = get_prepared_engine_for_handlers_profiler(true_event_handler_time) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) fp = dirname / "test_log.csv" profiler.write_results(fp) assert fp.is_file() file_length = 0 with open(fp) as f: for _ in f: file_length += 1 assert file_length == (true_max_epochs * true_num_iters) + 1 def test_print_results_basic_profiler(capsys): true_max_epochs = 1 true_num_iters = 5 profiler = BasicTimeProfiler() dummy_trainer = get_prepared_engine_for_basic_profiler(true_event_handler_time=0.0125) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) BasicTimeProfiler.print_results(profiler.get_results()) captured = capsys.readouterr() out = captured.out assert "BasicTimeProfiler._" not in out assert "nan" not in out def test_print_results_handlers_profiler_handlers_profiler(capsys): true_max_epochs = 1 true_num_iters = 5 profiler = HandlersTimeProfiler() dummy_trainer, _, _ = get_prepared_engine_for_handlers_profiler(true_event_handler_time=0.0125) profiler.attach(dummy_trainer) dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) HandlersTimeProfiler.print_results(profiler.get_results()) captured = capsys.readouterr() out = captured.out assert "HandlersTimeProfiler." not in out assert "Timer." not in out def test_get_intermediate_results_during_run_basic_profiler(capsys): true_event_handler_time = 0.0645 true_max_epochs = 2 true_num_iters = 5 profiler = BasicTimeProfiler() dummy_trainer = get_prepared_engine_for_basic_profiler(true_event_handler_time) profiler.attach(dummy_trainer) @dummy_trainer.on(Events.ITERATION_COMPLETED(every=3)) def log_results(_): results = profiler.get_results() profiler.print_results(results) captured = capsys.readouterr() out = captured.out assert "BasicTimeProfiler._" not in out assert "nan" not in out assert " min/index: (0.0, " not in out, out dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs) ignite-0.5.1/tests/ignite/handlers/test_timing.py000066400000000000000000000023451465426447700221440ustar00rootroot00000000000000import sys import time import pytest from ignite.engine import Engine, Events from ignite.handlers import Timer if sys.platform.startswith("darwin"): pytest.skip("Skip if on MacOS", allow_module_level=True) def test_timer(): sleep_t = 0.2 n_iter = 3 def _train_func(engine, batch): time.sleep(sleep_t) def _test_func(engine, batch): time.sleep(sleep_t) trainer = Engine(_train_func) tester = Engine(_test_func) t_total = Timer() t_batch = Timer(average=True) t_train = Timer() t_total.attach(trainer) t_batch.attach( trainer, pause=Events.ITERATION_COMPLETED, resume=Events.ITERATION_STARTED, step=Events.ITERATION_COMPLETED ) t_train.attach(trainer, pause=Events.EPOCH_COMPLETED, resume=Events.EPOCH_STARTED) @trainer.on(Events.EPOCH_COMPLETED) def run_validation(trainer): tester.run(range(n_iter)) # Run "training" trainer.run(range(n_iter)) assert pytest.approx(t_total.value(), abs=1e-1) == 2 * n_iter * sleep_t assert pytest.approx(t_batch.value(), abs=1e-1) == sleep_t assert pytest.approx(t_train.value(), abs=1e-1) == n_iter * sleep_t t_total.reset() assert pytest.approx(t_total.value(), abs=1e-1) == 0.0 ignite-0.5.1/tests/ignite/handlers/test_tqdm_logger.py000066400000000000000000000441761465426447700231710ustar00rootroot00000000000000# -*- coding: utf-8 -*- import sys import time from argparse import Namespace from unittest.mock import patch import numpy as np import pytest import torch from packaging.version import Version from ignite.engine import Engine, Events from ignite.handlers import ProgressBar, TerminateOnNan from ignite.metrics import RunningAverage if sys.platform.startswith("win"): pytest.skip("Skip on Windows", allow_module_level=True) def get_tqdm_version(): import tqdm return Version(tqdm.__version__) def update_fn(engine, batch): a = 1 engine.state.metrics["a"] = a return a def test_pbar_errors(): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires tqdm to be installed"): with patch.dict("sys.modules", {"tqdm.autonotebook": None}): ProgressBar(ncols=80) pbar = ProgressBar(ncols=80) with pytest.raises(ValueError, match=r"Logging event abc is not in allowed"): pbar.attach(Engine(lambda e, b: None), event_name=Namespace(name="abc")) def test_pbar(capsys): n_epochs = 2 loader = [1, 2] engine = Engine(update_fn) pbar = ProgressBar(ncols=80) pbar.attach(engine, ["a"]) engine.run(loader, max_epochs=n_epochs) captured = capsys.readouterr() err = captured.err.split("\r") err = list(map(lambda x: x.strip(), err)) err = list(filter(None, err)) if get_tqdm_version() < Version("4.49.0"): expected = "Epoch 8 -*- , a=1 [00:00<00:00]" else: expected = "Epoch [2/2]: [1/2] 50%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ , a=1 [00:00 None: super(DummyInceptionMetric, self).__init__( num_features=num_features, feature_extractor=feature_extractor, output_transform=output_transform, device=device, ) def reset(self): pass def compute(self): pass def update(self, output): self._extract_features(output) def test_dummy_metric(): with pytest.raises(ValueError, match=r"Argument num_features must be greater to zero, got:"): DummyInceptionMetric(num_features=-1, feature_extractor=torch.nn.Identity()).update(torch.rand(2, 0)) with pytest.raises(ValueError, match=r"feature_extractor output must be a tensor of dim 2, got: 1"): DummyInceptionMetric(num_features=1000, feature_extractor=torch.nn.Identity()).update(torch.rand(3)) with pytest.raises(ValueError, match=r"Batch size should be greater than one, got: 0"): DummyInceptionMetric(num_features=1000, feature_extractor=torch.nn.Identity()).update(torch.rand(0, 0)) with pytest.raises(ValueError, match=r"num_features returned by feature_extractor should be 1000, got: 0"): DummyInceptionMetric(num_features=1000, feature_extractor=torch.nn.Identity()).update(torch.rand(2, 0)) with pytest.raises(ValueError, match=r"Argument num_features must be provided, if feature_extractor is specified."): DummyInceptionMetric(feature_extractor=torch.nn.Identity()) with pytest.raises(TypeError, match=r"Argument feature_extractor must be of type torch.nn.Module, got"): DummyInceptionMetric(num_features=1000, feature_extractor=lambda x: x) assert isinstance(DummyInceptionMetric(num_features=10)._feature_extractor, torch.nn.Identity) def test_inception_extractor_wrong_inputs(): with pytest.raises(ValueError, match=r"Inputs should be a tensor of dim 4"): InceptionModel(return_features=True)(torch.rand(2)) with pytest.raises(ValueError, match=r"Inputs should be a tensor with 3 channels"): InceptionModel(return_features=True)(torch.rand(2, 2, 2, 0)) def test_inception_model_probability(): x = torch.rand(2, 3, 299, 299) y = InceptionModel(return_features=False)(x) assert pytest.approx(torch.sum(y[0]).item()) == 1.0 assert pytest.approx(torch.sum(y[1]).item()) == 1.0 assert torch.all(0 <= y) @pytest.fixture() def mock_no_torchvision(): with patch.dict("sys.modules", {"torchvision": None}): yield torchvision def test_no_torchvision(mock_no_torchvision): with pytest.raises(ModuleNotFoundError, match=r"This module requires torchvision to be installed."): InceptionModel(return_features=True) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_device_mismatch_cuda(): images = torch.rand(10, 3, 299, 299) result = InceptionModel(return_features=False, device="cuda")(images) assert result.is_cuda assert result.shape == torch.Size([10, 1000]) result = InceptionModel(return_features=False)(images.cuda()) assert not result.is_cuda assert result.shape == torch.Size([10, 1000]) images = torch.rand(10, 5) result = DummyInceptionMetric(num_features=5, device="cuda")._extract_features(images) assert result.is_cuda assert result.shape == torch.Size([10, 5]) result = DummyInceptionMetric(num_features=5)._extract_features(images.cuda()) assert not result.is_cuda assert result.shape == torch.Size([10, 5]) ignite-0.5.1/tests/ignite/metrics/nlp/000077500000000000000000000000001465426447700176775ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/metrics/nlp/__init__.py000066400000000000000000000050561465426447700220160ustar00rootroot00000000000000__all__ = ["CorpusForTest"] class CorpusForTest: def __init__(self, lower_split=False): def preproc(text): if lower_split: return text.lower().split() else: return text # BLEU Paper examples self.cand_1 = preproc("the the the the the the the") self.ref_1a = preproc("The cat is on the mat") self.ref_1b = preproc("There is a cat on the mat") self.cand_2a = preproc( "It is a guide to action which ensures that the military always obeys the commands of the party" ) self.cand_2b = preproc("It is to insure the troops forever hearing the activity guidebook that " "party direct") self.ref_2a = preproc( "It is a guide to action that ensures that the military will forever heed " "Party commands" ) self.ref_2b = preproc( "It is the guiding principle which guarantees the military forces always being under the command of " "the Party" ) self.ref_2c = preproc("It is the practical guide for the army always to heed the directions of the party") self.cand_3 = preproc("of the") self.references_1 = [self.ref_1a, self.ref_1b] self.references_2 = [self.ref_2a, self.ref_2b, self.ref_2c] self.sample_1 = ([self.cand_1], [self.references_1]) self.sample_2 = ([self.cand_3], [self.references_2]) self.sample_3 = ([self.cand_2a], [self.references_2]) self.sample_4 = ([self.cand_2b], [self.references_2]) self.sample_5 = ([self.cand_2a, self.cand_2b], [self.references_2, self.references_2]) self.references_3 = [self.ref_2a, self.ref_2b] self.references_4 = [self.ref_2b, self.ref_2c] self.references_5 = [self.ref_2a, self.ref_2c] self.chunks = [ ([self.cand_1], [self.references_1]), ([self.cand_2a], [self.references_2]), ([self.cand_2b], [self.references_2]), ([self.cand_1], [[self.ref_1a]]), ([self.cand_2a], [self.references_3]), ([self.cand_2b], [self.references_3]), ([self.cand_1], [[self.ref_1b]]), ([self.cand_2a], [self.references_4]), ([self.cand_2b], [self.references_4]), ([self.cand_1], [self.references_1]), ([self.cand_2a], [self.references_5]), ([self.cand_2b], [self.references_5]), ([self.cand_1], [[self.ref_1a]]), ([self.cand_2a], [[self.ref_2a]]), ([self.cand_2b], [[self.ref_2c]]), ] ignite-0.5.1/tests/ignite/metrics/nlp/test_bleu.py000066400000000000000000000301041465426447700222350ustar00rootroot00000000000000import os import warnings from collections import Counter import pytest import torch from nltk.translate.bleu_score import corpus_bleu, sentence_bleu, SmoothingFunction import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.nlp import Bleu from . import CorpusForTest corpus = CorpusForTest(lower_split=True) def test_wrong_inputs(): with pytest.raises(ValueError, match=r"ngram order must be greater than zero"): Bleu(ngram=0) with pytest.raises(ValueError, match=r"Smooth is not valid"): Bleu(smooth="fake") with pytest.raises(ValueError, match=r"nb of candidates should be equal to nb of reference lists"): Bleu()._corpus_bleu(references=[[0], [0]], candidates=[[0]]) with pytest.raises(NotComputableError): Bleu().compute() with pytest.raises(ValueError, match='Average must be either "macro" or "micro"'): Bleu(average="macros") parametrize_args = ( "candidates, references", [ ([["a", "a", "a", "b", "c"]], [[["a", "b", "c"], ["a", "a", "d"]]]), corpus.sample_1, corpus.sample_2, corpus.sample_3, corpus.sample_4, ], ) def _test(candidates, references, average, smooth="no_smooth", smooth_nltk_fn=None, ngram_range=8): for i in range(1, ngram_range): weights = tuple([1 / i] * i) bleu = Bleu(ngram=i, average=average, smooth=smooth) if average == "macro": with warnings.catch_warnings(): warnings.simplefilter("ignore") reference = sentence_bleu( references[0], candidates[0], weights=weights, smoothing_function=smooth_nltk_fn ) assert pytest.approx(reference) == bleu._sentence_bleu(references[0], candidates[0]) elif average == "micro": with warnings.catch_warnings(): warnings.simplefilter("ignore") reference = corpus_bleu(references, candidates, weights=weights, smoothing_function=smooth_nltk_fn) assert pytest.approx(reference) == bleu._corpus_bleu(references, candidates) bleu.update((candidates, references)) assert pytest.approx(reference) == bleu.compute() @pytest.mark.parametrize(*parametrize_args) def test_macro_bleu(candidates, references): _test(candidates, references, "macro") @pytest.mark.parametrize(*parametrize_args) def test_micro_bleu(candidates, references): _test(candidates, references, "micro") @pytest.mark.parametrize(*parametrize_args) def test_macro_bleu_smooth1(candidates, references): _test(candidates, references, "macro", "smooth1", SmoothingFunction().method1) @pytest.mark.parametrize(*parametrize_args) def test_micro_bleu_smooth1(candidates, references): _test(candidates, references, "micro", "smooth1", SmoothingFunction().method1) @pytest.mark.parametrize(*parametrize_args) def test_macro_bleu_nltk_smooth2(candidates, references): _test(candidates, references, "macro", "nltk_smooth2", SmoothingFunction().method2) @pytest.mark.parametrize(*parametrize_args) def test_micro_bleu_nltk_smooth2(candidates, references): _test(candidates, references, "micro", "nltk_smooth2", SmoothingFunction().method2) @pytest.mark.parametrize(*parametrize_args) def test_macro_bleu_smooth2(candidates, references): _test(candidates, references, "macro", "smooth2", SmoothingFunction().method2, 3) @pytest.mark.parametrize(*parametrize_args) def test_micro_bleu_smooth2(candidates, references): _test(candidates, references, "micro", "smooth2", SmoothingFunction().method2, 3) def test_accumulation_macro_bleu(): bleu = Bleu(ngram=4, smooth="smooth2") bleu.update(([corpus.cand_1], [corpus.references_1])) bleu.update(([corpus.cand_2a], [corpus.references_2])) bleu.update(([corpus.cand_2b], [corpus.references_2])) bleu.update(([corpus.cand_3], [corpus.references_2])) value = bleu._sentence_bleu(corpus.references_1, corpus.cand_1) value += bleu._sentence_bleu(corpus.references_2, corpus.cand_2a) value += bleu._sentence_bleu(corpus.references_2, corpus.cand_2b) value += bleu._sentence_bleu(corpus.references_2, corpus.cand_3) assert bleu.compute() == value / 4 def test_accumulation_micro_bleu(): bleu = Bleu(ngram=4, smooth="smooth2", average="micro") bleu.update(([corpus.cand_1], [corpus.references_1])) bleu.update(([corpus.cand_2a], [corpus.references_2])) bleu.update(([corpus.cand_2b], [corpus.references_2])) bleu.update(([corpus.cand_3], [corpus.references_2])) value = bleu._corpus_bleu( [corpus.references_1, corpus.references_2, corpus.references_2, corpus.references_2], [corpus.cand_1, corpus.cand_2a, corpus.cand_2b, corpus.cand_3], ) assert bleu.compute() == value def test_bleu_batch_macro(): bleu = Bleu(ngram=4) # Batch size 3 hypotheses = [corpus.cand_1, corpus.cand_2a, corpus.cand_2b] refs = [corpus.references_1, corpus.references_2, corpus.references_2] bleu.update((hypotheses, refs)) with warnings.catch_warnings(): warnings.simplefilter("ignore") reference_bleu_score = ( sentence_bleu(refs[0], hypotheses[0]) + sentence_bleu(refs[1], hypotheses[1]) + sentence_bleu(refs[2], hypotheses[2]) ) / 3 assert pytest.approx(bleu.compute()) == reference_bleu_score value = 0 for _hypotheses, _refs in zip(hypotheses, refs): value += bleu._sentence_bleu(_refs, _hypotheses) bleu.update(([_hypotheses], [_refs])) ref_1 = value / len(refs) ref_2 = bleu.compute() assert pytest.approx(ref_1) == reference_bleu_score assert pytest.approx(ref_2) == reference_bleu_score def test_bleu_batch_micro(): bleu = Bleu(ngram=4, average="micro") # Batch size 3 hypotheses = [corpus.cand_1, corpus.cand_2a, corpus.cand_2b] refs = [corpus.references_1, corpus.references_2, corpus.references_2] bleu.update((hypotheses, refs)) with warnings.catch_warnings(): warnings.simplefilter("ignore") reference_bleu_score = corpus_bleu(refs, hypotheses) assert pytest.approx(bleu.compute()) == reference_bleu_score assert pytest.approx(bleu._corpus_bleu(refs, hypotheses)) == reference_bleu_score @pytest.mark.parametrize( "candidates, references", [ (corpus.cand_1, corpus.references_1), (corpus.cand_2a, corpus.references_2), (corpus.cand_2b, corpus.references_2), (corpus.cand_1, corpus.references_1), ], ) def test_n_gram_counter(candidates, references): bleu = Bleu(ngram=4) hyp_length, ref_length = bleu._n_gram_counter([references], [candidates], Counter(), Counter()) assert hyp_length == len(candidates) ref_lens = (len(reference) for reference in references) closest_ref_len = min(ref_lens, key=lambda ref_len: (abs(ref_len - len(candidates)), ref_len)) assert ref_length == closest_ref_len def _test_macro_distrib_integration(device): from ignite.engine import Engine rank = idist.get_rank() size = len(corpus.chunks) data = [] for c in corpus.chunks: data += idist.get_world_size() * [c] def update(_, i): return data[i + size * rank] def _test(metric_device): engine = Engine(update) m = Bleu(ngram=4, smooth="smooth2") m.attach(engine, "bleu") engine.run(data=list(range(size)), max_epochs=1) assert "bleu" in engine.state.metrics ref_bleu = 0 for candidates, references in data: with warnings.catch_warnings(): warnings.simplefilter("ignore") ref_bleu += sentence_bleu( references[0], candidates[0], weights=[0.25, 0.25, 0.25, 0.25], smoothing_function=SmoothingFunction().method2, ) assert pytest.approx(engine.state.metrics["bleu"]) == ref_bleu / len(data) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_micro_distrib_integration(device): from ignite.engine import Engine rank = idist.get_rank() size = len(corpus.chunks) data = [] for c in corpus.chunks: data += idist.get_world_size() * [c] def update(_, i): return data[i + size * rank] def _test(metric_device): engine = Engine(update) m = Bleu(ngram=4, smooth="smooth2", average="micro") m.attach(engine, "bleu") engine.run(data=list(range(size)), max_epochs=1) assert "bleu" in engine.state.metrics ref_bleu = 0 references = [] candidates = [] for _candidates, _references in data: references.append(_references[0]) candidates.append(_candidates[0]) with warnings.catch_warnings(): warnings.simplefilter("ignore") ref_bleu += corpus_bleu( references, candidates, weights=[0.25, 0.25, 0.25, 0.25], smoothing_function=SmoothingFunction().method2, ) assert pytest.approx(engine.state.metrics["bleu"]) == ref_bleu _test("cpu") if device.type != "xla": _test(idist.device()) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_macro_distrib_integration(device) _test_micro_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_macro_distrib_integration(device) _test_micro_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_macro_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_micro_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_macro_distrib_integration(device) _test_micro_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_macro_distrib_integration(device) _test_micro_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_macro_distrib_integration(device) _test_micro_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_macro_distrib_integration(device) _test_micro_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/nlp/test_rouge.py000066400000000000000000000213341465426447700224340ustar00rootroot00000000000000import os import filelock import nltk import pytest import rouge as pyrouge import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.nlp import Rouge from ignite.metrics.nlp.rouge import compute_ngram_scores, RougeL, RougeN from . import CorpusForTest @pytest.fixture(scope="session", autouse=True) def download_nltk_punkt(worker_id, tmp_path_factory): root_tmp_dir = tmp_path_factory.getbasetemp().parent while True: try: with filelock.FileLock(root_tmp_dir / "nltk_download.lock", timeout=0.2) as fn: fn.acquire() nltk.download("punkt") fn.release() break except filelock._error.Timeout: pass corpus = CorpusForTest() @pytest.mark.parametrize( "candidate, reference, n, expected_precision, expected_recall", [ ([], [], 1, 0, 0), ("abc", "ab", 1, 2 / 3, 2 / 2), ("abc", "ab", 2, 1 / 2, 1 / 1), ("abc", "ab", 3, 0, 0), ("ab", "abc", 1, 2 / 2, 2 / 3), ("ab", "cde", 1, 0 / 2, 0 / 3), ("aab", "aace", 1, 2 / 3, 2 / 4), ("aa", "aaa", 1, 2 / 2, 2 / 3), ("aaa", "aa", 1, 2 / 3, 2 / 2), ], ) def test_compute_ngram_scores(candidate, reference, n, expected_precision, expected_recall): scores = compute_ngram_scores(candidate, reference, n=n) assert pytest.approx(scores.precision()) == expected_precision assert pytest.approx(scores.recall()) == expected_recall def test_wrong_inputs(): with pytest.raises(ValueError, match=r"ngram order must be greater than zero"): RougeN(ngram=0) with pytest.raises(ValueError, match=r"alpha must be in interval \[0, 1\]"): RougeN(alpha=-1) with pytest.raises(ValueError, match=r"alpha must be in interval \[0, 1\]"): RougeN(alpha=2) with pytest.raises(ValueError, match=r"multiref : valid values are \['best', 'average'\] "): RougeN(multiref="") with pytest.raises(ValueError, match=r"variant must be 'L' or integer greater to zero"): Rouge(variants=["error"]) with pytest.raises(NotComputableError): RougeL().compute() with pytest.raises(ValueError): Rouge(multiref="unknown") @pytest.mark.parametrize( "ngram, candidate, reference, expected", [ (1, [1, 2, 3], [1, 2], (2 / 3, 2 / 2)), (2, [1, 2, 3], [1, 2], (1 / 2, 1 / 1)), (1, "abcdef", "zbdfz", (3 / 6, 3 / 5)), (2, "abcdef", "zbdfz", (0, 0)), ], ) def test_rouge_n_alpha(ngram, candidate, reference, expected): for alpha in [0, 1, 0.3, 0.5, 0.8]: rouge = RougeN(ngram=ngram, alpha=alpha) rouge.update(([candidate], [[reference]])) results = rouge.compute() assert results[f"Rouge-{ngram}-P"] == expected[0] assert results[f"Rouge-{ngram}-R"] == expected[1] try: F = expected[0] * expected[1] / ((1 - alpha) * expected[0] + alpha * expected[1]) except ZeroDivisionError: F = 0 assert results[f"Rouge-{ngram}-F"] == F @pytest.mark.parametrize( "candidates, references", [corpus.sample_1, corpus.sample_2, corpus.sample_3, corpus.sample_4, corpus.sample_5] ) def test_rouge_metrics(candidates, references): for multiref in ["average", "best"]: # PERL 1.5.5 reference apply_avg = multiref == "average" apply_best = multiref == "best" evaluator = pyrouge.Rouge( metrics=["rouge-n", "rouge-l"], max_n=4, apply_avg=apply_avg, apply_best=apply_best, alpha=0.5, stemming=False, ensure_compatibility=False, ) scores = evaluator.get_scores(candidates, references) lower_split_references = [ [ref.lower().split() for ref in refs_per_candidate] for refs_per_candidate in references ] lower_split_candidates = [candidate.lower().split() for candidate in candidates] m = Rouge(variants=[1, 2, 4, "L"], multiref=multiref, alpha=0.5) m.update((lower_split_candidates, lower_split_references)) results = m.compute() for key in ["1", "2", "4", "L"]: assert pytest.approx(results[f"Rouge-{key}-R"], abs=1e-4) == scores[f"rouge-{key.lower()}"]["r"] assert pytest.approx(results[f"Rouge-{key}-P"], abs=1e-4) == scores[f"rouge-{key.lower()}"]["p"] assert pytest.approx(results[f"Rouge-{key}-F"], abs=1e-4) == scores[f"rouge-{key.lower()}"]["f"] def _test_distrib_integration(device): from ignite.engine import Engine rank = idist.get_rank() size = len(corpus.chunks) data = [] for c in corpus.chunks: data += idist.get_world_size() * [c] def update(_, i): candidate, references = data[i + size * rank] lower_split_references = [reference.lower().split() for reference in references[0]] lower_split_candidate = candidate[0].lower().split() return [lower_split_candidate], [lower_split_references] def _test(metric_device): engine = Engine(update) m = Rouge(variants=[1, 2, "L"], alpha=0.5, device=metric_device) m.attach(engine, "rouge") engine.run(data=list(range(size)), max_epochs=1) assert "rouge" in engine.state.metrics evaluator = pyrouge.Rouge( metrics=["rouge-n", "rouge-l"], max_n=4, apply_avg=True, apply_best=False, alpha=0.5, stemming=False, ensure_compatibility=False, ) rouge_1_f, rouge_2_f, rouge_l_f = (0, 0, 0) for candidate, references in data: scores = evaluator.get_scores(candidate, references) rouge_1_f += scores["rouge-1"]["f"] rouge_2_f += scores["rouge-2"]["f"] rouge_l_f += scores["rouge-l"]["f"] assert pytest.approx(engine.state.metrics["Rouge-1-F"], abs=1e-4) == rouge_1_f / len(data) assert pytest.approx(engine.state.metrics["Rouge-2-F"], abs=1e-4) == rouge_2_f / len(data) assert pytest.approx(engine.state.metrics["Rouge-L-F"], abs=1e-4) == rouge_l_f / len(data) _test("cpu") if device.type != "xla": _test(idist.device()) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/nlp/test_utils.py000066400000000000000000000037121465426447700224530ustar00rootroot00000000000000import pytest from ignite.metrics.nlp.utils import lcs, modified_precision, ngrams @pytest.mark.parametrize( "sequence, n, expected_keys, expected_values", [ ([], 1, [], []), ([0, 1, 2], 1, [(0,), (1,), (2,)], [1, 1, 1]), ([0, 1, 2], 2, [(0, 1), (1, 2)], [1, 1]), ([0, 1, 2], 3, [(0, 1, 2)], [1]), ([0, 0, 0], 1, [(0,)], [3]), ([0, 0, 0], 2, [(0, 0)], [2]), ("abcde", 4, [("a", "b", "c", "d"), ("b", "c", "d", "e")], [1, 1]), ], ) def test_ngrams(sequence, n, expected_keys, expected_values): ngrams_counter = ngrams(sequence=sequence, n=n) assert list(ngrams_counter.values()) == expected_values assert list(ngrams_counter.keys()) == expected_keys @pytest.mark.parametrize( "seq_a, seq_b, expected", [([], [], 0), ([0, 1, 2], [0, 1, 2], 3), ([0, 1, 2], [0, 3, 2], 2), ("academy", "abracadabra", 4)], ) def test_lcs(seq_a, seq_b, expected): assert lcs(seq_a, seq_b) == expected def test_modified_precision_empty(): for k in range(1, 5): n, d = modified_precision([[]], [], k) assert n == 0 and d == 0 n, d = modified_precision([[]], [0], k) assert n == 0 and d == (k == 1) n, d = modified_precision([[0]], [], k) assert n == 0 and d == 0 n, d = modified_precision([[]], list(range(k)), k) assert n == 0 and d == 1 n, d = modified_precision([list(range(k))], [], k) assert n == 0 and d == 0 @pytest.mark.parametrize( "references, candidate, expected", [ ([[0, 0, 0], [1, 2]], [1, 2, 3, 4], ((2, 4), (1, 3), (0, 2))), ([[0, 1, 2], [0, 0, 3]], [0, 0, 0, 1, 2], ((4, 5), (3, 4), (1, 3))), ([[0, 1, 2], [3, 0, 3]], [3, 0, 0, 1, 2], ((4, 5), (3, 4), (1, 3))), ], ) def test_modified_precision(references, candidate, expected): for n, (e_n, e_d) in enumerate(expected, start=1): n, d = modified_precision(references, candidate, n) assert n == e_n and d == e_d ignite-0.5.1/tests/ignite/metrics/regression/000077500000000000000000000000001465426447700212665ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/metrics/regression/__init__.py000066400000000000000000000000001465426447700233650ustar00rootroot00000000000000ignite-0.5.1/tests/ignite/metrics/regression/test__base.py000066400000000000000000000042451465426447700237550ustar00rootroot00000000000000from typing import Optional import numpy as np import pytest import torch import ignite.distributed as idist from ignite.metrics.regression._base import _BaseRegression, _torch_median def test_base_regression_shapes(): class L1(_BaseRegression): def reset(self): self._sum_of_errors = 0.0 def _update(self, output): y_pred, y = output errors = torch.abs(y.view_as(y_pred) - y_pred) self._sum_of_errors += torch.sum(errors).item() def compute(self): return self._sum_of_errors m = L1() with pytest.raises(ValueError, match=r"Input y_pred should have shape \(N,\) or \(N, 1\)"): y = torch.rand([1, 1, 1]) m.update((y, y)) with pytest.raises(ValueError, match=r"Input y should have shape \(N,\) or \(N, 1\)"): y = torch.rand([1, 1, 1]) m.update((torch.rand(1, 1), y)) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(2), torch.rand(2, 1))) with pytest.raises(TypeError, match=r"Input y_pred dtype should be float"): y = torch.tensor([1, 1]) m.update((y, y)) with pytest.raises(TypeError, match=r"Input y dtype should be float"): y = torch.tensor([1, 1]) m.update((y.float(), y)) @pytest.mark.parametrize("size", [100, 101, (30, 3), (31, 3)]) def test_torch_median_numpy(size, device: Optional[str] = None): data = torch.rand(size).to(device) assert _torch_median(data) == np.median(data.cpu().numpy()) @pytest.mark.tpu @pytest.mark.parametrize("size", [100, 101, (30, 3), (31, 3)]) @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_on_even_size_xla(size): device = "xla" test_torch_median_numpy(size, device=device) @pytest.mark.parametrize("size", [100, 101, (30, 3), (31, 3)]) @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_on_even_size_gpu(size): test_torch_median_numpy(size, device="cuda") @pytest.mark.parametrize("size", [100, 101, (30, 3), (31, 3)]) def test_create_even_size_cpu(size): test_torch_median_numpy(size, device="cpu") ignite-0.5.1/tests/ignite/metrics/regression/test_canberra_metric.py000066400000000000000000000213101465426447700260140ustar00rootroot00000000000000import os import numpy as np import pytest import torch from sklearn.metrics import DistanceMetric import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics.regression import CanberraMetric def test_wrong_input_shapes(): m = CanberraMetric() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_compute(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = CanberraMetric() canberra = DistanceMetric.get_metric("canberra") m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (np.abs(ground_truth - a) / (np.abs(a) + np.abs(ground_truth))).sum() assert m.compute() == pytest.approx(np_sum) assert canberra.pairwise([a, ground_truth])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += ((np.abs(ground_truth - b)) / (np.abs(b) + np.abs(ground_truth))).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([a, b]) v2 = np.hstack([ground_truth, ground_truth]) assert canberra.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += ((np.abs(ground_truth - c)) / (np.abs(c) + np.abs(ground_truth))).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([v1, c]) v2 = np.hstack([v2, ground_truth]) assert canberra.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - d) / (np.abs(d) + np.abs(ground_truth))).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([v1, d]) v2 = np.hstack([v2, ground_truth]) assert canberra.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = CanberraMetric() m.attach(engine, "cm") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() canberra = DistanceMetric.get_metric("canberra") data = list(range(y_pred.shape[0] // batch_size)) cm = engine.run(data, max_epochs=1).metrics["cm"] assert canberra.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(cm) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_error_is_not_nan(): m = CanberraMetric() m.update((torch.zeros(4), torch.zeros(4))) assert not (torch.isnan(m._sum_of_errors).any() or torch.isinf(m._sum_of_errors).any()), m._sum_of_errors def _test_distrib_compute(device): rank = idist.get_rank() canberra = DistanceMetric.get_metric("canberra") def _test(metric_device): metric_device = torch.device(metric_device) m = CanberraMetric(device=metric_device) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() assert canberra.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(res) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() canberra = DistanceMetric.get_metric("canberra") def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = CanberraMetric(device=metric_device) m.attach(engine, "cm") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "cm" in engine.state.metrics res = engine.state.metrics["cm"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() assert pytest.approx(res) == canberra.pairwise([np_y_preds, np_y_true])[0][1] metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_fractional_absolute_error.py000066400000000000000000000210651465426447700301340ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import FractionalAbsoluteError def test_zero_sample(): m = FractionalAbsoluteError() with pytest.raises( NotComputableError, match=r"FractionalAbsoluteError must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = FractionalAbsoluteError() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_compute(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = FractionalAbsoluteError() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (2 * np.abs((a - ground_truth)) / (np.abs(a) + np.abs(ground_truth))).sum() np_len = len(a) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += (2 * np.abs((b - ground_truth)) / (np.abs(b) + np.abs(ground_truth))).sum() np_len += len(b) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += (2 * np.abs((c - ground_truth)) / (np.abs(c) + np.abs(ground_truth))).sum() np_len += len(c) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += (2 * np.abs((d - ground_truth)) / (np.abs(d) + np.abs(ground_truth))).sum() np_len += len(d) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = FractionalAbsoluteError() m.attach(engine, "fab") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) fab = engine.run(data, max_epochs=1).metrics["fab"] np_sum = (2 * np.abs((np_y_pred - np_y)) / (np.abs(np_y_pred) + np.abs(np_y))).sum() np_len = len(y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(fab) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = FractionalAbsoluteError(device=metric_device) y_pred = torch.rand(size=(100,), device=device) y = torch.rand(size=(100,), device=device) m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() np_sum = (2 * np.abs((np_y_pred - np_y)) / (np.abs(np_y_pred) + np.abs(np_y))).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) fae = FractionalAbsoluteError(device=metric_device) fae.attach(engine, "fae") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "fae" in engine.state.metrics res = engine.state.metrics["fae"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() np_y = y_true.cpu().numpy() np_y_pred = y_preds.cpu().numpy() np_sum = (2 * np.abs((np_y_pred - np_y)) / (np.abs(np_y_pred) + np.abs(np_y))).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert pytest.approx(res) == np_ans metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_fractional_bias.py000066400000000000000000000214271465426447700260250ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import FractionalBias def test_zero_sample(): m = FractionalBias() with pytest.raises( NotComputableError, match=r"FractionalBias must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = FractionalBias() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_fractional_bias(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = FractionalBias() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (2 * (ground_truth - a) / (a + ground_truth)).sum() np_len = len(a) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += (2 * (ground_truth - b) / (b + ground_truth)).sum() np_len += len(b) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += (2 * (ground_truth - c) / (c + ground_truth)).sum() np_len += len(c) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += (2 * (ground_truth - d) / (d + ground_truth)).sum() np_len += len(d) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = FractionalBias() m.attach(engine, "fb") np_y = y.double().numpy().ravel() np_y_pred = y_pred.double().numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) fb = engine.run(data, max_epochs=1).metrics["fb"] np_sum = (2 * (np_y - np_y_pred) / (np_y_pred + np_y)).sum() np_len = len(y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(fb) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_error_is_not_nan(): m = FractionalBias() m.update((torch.zeros(4), torch.zeros(4))) assert not (torch.isnan(m._sum_of_errors).any() or torch.isinf(m._sum_of_errors).any()), m._sum_of_errors def _test_distrib_compute(device, tol=1e-5): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = FractionalBias(device=metric_device) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() np_sum = (2 * (np_y - np_y_pred) / (np_y_pred + np_y + 1e-30)).sum() np_len = len(y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(res, rel=tol) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device, tol=1e-5): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,), dtype=torch.double).to(device) y_preds = torch.rand(size=(n_iters * batch_size,), dtype=torch.double).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = FractionalBias(device=metric_device) m.attach(engine, "fb") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "fb" in engine.state.metrics res = engine.state.metrics["fb"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() np_sum = (2 * (np_y_true - np_y_preds) / (np_y_preds + np_y_true + 1e-30)).sum() np_len = len(y_preds) np_ans = np_sum / np_len assert pytest.approx(res, rel=tol) == np_ans metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device, tol=1e-4) _test_distrib_integration(device, tol=1e-4) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device, tol=1e-4) _test_distrib_integration(device, tol=1e-4) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_geometric_mean_absolute_error.py000066400000000000000000000213121465426447700307630ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import GeometricMeanAbsoluteError def test_zero_sample(): m = GeometricMeanAbsoluteError() with pytest.raises( NotComputableError, match=r"GeometricMeanAbsoluteError must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = GeometricMeanAbsoluteError() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_compute(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) np_prod = 1.0 m = GeometricMeanAbsoluteError() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - a) np_prod = np.multiply.reduce(errors) * np_prod np_len = len(a) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - b) np_prod = np.multiply.reduce(errors) * np_prod np_len += len(b) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - c) np_prod = np.multiply.reduce(errors) * np_prod np_len += len(c) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - d) np_prod = np.multiply.reduce(errors) * np_prod np_len += len(d) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = GeometricMeanAbsoluteError() m.attach(engine, "gmae") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) gmae = engine.run(data, max_epochs=1).metrics["gmae"] sum_errors = (np.log(np.abs(np_y - np_y_pred))).sum() np_len = len(y_pred) np_ans = np.exp(sum_errors / np_len) assert np_ans == pytest.approx(gmae) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for i in range(5): # check multiple random inputs as random exact occurencies are rare torch.manual_seed(12 + i) test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = GeometricMeanAbsoluteError(device=metric_device) torch.manual_seed(10 + rank) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() sum_errors = (np.log(np.abs(np_y - np_y_pred))).sum() np_len = len(y_pred) np_ans = np.exp(sum_errors / np_len) assert np_ans == pytest.approx(res) for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = GeometricMeanAbsoluteError(device=metric_device) m.attach(engine, "gmae") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "gmae" in engine.state.metrics res = engine.state.metrics["gmae"] np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() sum_errors = (np.log(np.abs(np_y_true - np_y_preds))).sum() np_len = len(y_preds) np_ans = np.exp(sum_errors / np_len) assert pytest.approx(res) == np_ans metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(11 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_geometric_mean_relative_absolute_error.py000066400000000000000000000163531465426447700326670ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import GeometricMeanRelativeAbsoluteError def test_zero_sample(): m = GeometricMeanRelativeAbsoluteError() with pytest.raises( NotComputableError, match=r"GeometricMeanRelativeAbsoluteError must have at least one example before it can be computed", ): m.compute() def test_wrong_input_shapes(): m = GeometricMeanRelativeAbsoluteError() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_compute(): size = 51 np_y_pred = np.random.rand(size) np_y = np.random.rand(size) np_gmrae = np.exp(np.log(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())).mean()) m = GeometricMeanRelativeAbsoluteError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() m.update((y_pred, y)) assert np_gmrae == pytest.approx(m.compute()) def test_integration(): y_pred = torch.rand(size=(100,)) y = torch.rand(size=(100,)) batch_size = 10 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = GeometricMeanRelativeAbsoluteError() m.attach(engine, "gmrae") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) gmrae = engine.run(data, max_epochs=1).metrics["gmrae"] sum_errors = np.log(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())).sum() np_len = len(y_pred) np_ans = np.exp(sum_errors / np_len) assert np_ans == pytest.approx(gmrae) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = GeometricMeanRelativeAbsoluteError(device=metric_device) y_pred = torch.rand(size=(100,), device=device) y = torch.rand(size=(100,), device=device) m.update((y_pred, y)) y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() np_gmrae = np.exp(np.log(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())).mean()) assert m.compute() == pytest.approx(np_gmrae, rel=1e-4) for i in range(3): torch.manual_seed(12 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() torch.manual_seed(12) def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) gmrae = GeometricMeanRelativeAbsoluteError(device=metric_device) gmrae.attach(engine, "gmrae") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "gmrae" in engine.state.metrics res = engine.state.metrics["gmrae"] np_y = y_true.cpu().numpy() np_y_pred = y_preds.cpu().numpy() np_gmrae = np.exp(np.log(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())).mean()) assert pytest.approx(res, rel=1e-4) == np_gmrae metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_manhattan_distance.py000066400000000000000000000211271465426447700265270ustar00rootroot00000000000000import os import numpy as np import pytest import torch from sklearn.metrics import DistanceMetric import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics.regression import ManhattanDistance def test_wrong_input_shapes(): m = ManhattanDistance() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_mahattan_distance(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = ManhattanDistance() manhattan = DistanceMetric.get_metric("manhattan") m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = np.abs(ground_truth - a).sum() assert m.compute() == pytest.approx(np_sum) assert manhattan.pairwise([a, ground_truth])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += np.abs(ground_truth - b).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([a, b]) v2 = np.hstack([ground_truth, ground_truth]) assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += np.abs(ground_truth - c).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([v1, c]) v2 = np.hstack([v2, ground_truth]) assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += np.abs(ground_truth - d).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([v1, d]) v2 = np.hstack([v2, ground_truth]) assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = ManhattanDistance() m.attach(engine, "md") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() manhattan = DistanceMetric.get_metric("manhattan") data = list(range(y_pred.shape[0] // batch_size)) md = engine.run(data, max_epochs=1).metrics["md"] assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(md) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_error_is_not_nan(): m = ManhattanDistance() m.update((torch.zeros(4), torch.zeros(4))) assert not (torch.isnan(m._sum_of_errors).any() or torch.isinf(m._sum_of_errors).any()), m._sum_of_errors def _test_distrib_compute(device): rank = idist.get_rank() manhattan = DistanceMetric.get_metric("manhattan") def _test(metric_device): metric_device = torch.device(metric_device) m = ManhattanDistance(device=metric_device) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(res) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() manhattan = DistanceMetric.get_metric("manhattan") def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = ManhattanDistance(device=metric_device) m.attach(engine, "md") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "md" in engine.state.metrics res = engine.state.metrics["md"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() assert pytest.approx(res) == manhattan.pairwise([np_y_preds, np_y_true])[0][1] metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_maximum_absolute_error.py000066400000000000000000000201651465426447700274670ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MaximumAbsoluteError def test_zero_sample(): m = MaximumAbsoluteError() with pytest.raises( NotComputableError, match=r"MaximumAbsoluteError must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = MaximumAbsoluteError() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_maximum_absolute_error(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = MaximumAbsoluteError() np_ans = -1 m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_max = np.max(np.abs((a - ground_truth))) np_ans = np_max if np_max > np_ans else np_ans assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_max = np.max(np.abs((b - ground_truth))) np_ans = np_max if np_max > np_ans else np_ans assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_max = np.max(np.abs((c - ground_truth))) np_ans = np_max if np_max > np_ans else np_ans assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_max = np.max(np.abs((d - ground_truth))) np_ans = np_max if np_max > np_ans else np_ans assert m.compute() == pytest.approx(np_ans) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MaximumAbsoluteError() m.attach(engine, "mae") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) mae = engine.run(data, max_epochs=1).metrics["mae"] np_max = np.max(np.abs((np_y_pred - np_y))) assert np_max == pytest.approx(mae) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = MaximumAbsoluteError(device=metric_device) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() np_max = np.max(np.abs((np_y_pred - np_y))) assert np_max == pytest.approx(res) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = MaximumAbsoluteError(device=metric_device) m.attach(engine, "mae") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mae" in engine.state.metrics res = engine.state.metrics["mae"] np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() np_max = np.max(np.abs((np_y_preds - np_y_true))) assert pytest.approx(res) == np_max metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_mean_absolute_relative_error.py000066400000000000000000000216231465426447700306250ustar00rootroot00000000000000import os import numpy as np import pytest import torch from pytest import approx, raises import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MeanAbsoluteRelativeError def test_wrong_input_shapes(): m = MeanAbsoluteRelativeError() with raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_mean_absolute_relative_error(): a = torch.rand(4) b = torch.rand(4) c = torch.rand(4) d = torch.rand(4) ground_truth = torch.rand(4) m = MeanAbsoluteRelativeError() m.update((a, ground_truth)) abs_error_a = torch.sum(torch.abs(ground_truth - a) / torch.abs(ground_truth)) num_samples_a = a.size()[0] sum_error = abs_error_a sum_samples = num_samples_a MARE_a = sum_error / sum_samples assert m.compute() == approx(MARE_a.item()) m.update((b, ground_truth)) abs_error_b = torch.sum(torch.abs(ground_truth - b) / torch.abs(ground_truth)) num_samples_b = b.size()[0] sum_error += abs_error_b sum_samples += num_samples_b MARE_b = sum_error / sum_samples assert m.compute() == approx(MARE_b.item()) m.update((c, ground_truth)) abs_error_c = torch.sum(torch.abs(ground_truth - c) / torch.abs(ground_truth)) num_samples_c = c.size()[0] sum_error += abs_error_c sum_samples += num_samples_c MARE_c = sum_error / sum_samples assert m.compute() == approx(MARE_c.item()) m.update((d, ground_truth)) abs_error_d = torch.sum(torch.abs(ground_truth - d) / torch.abs(ground_truth)) num_samples_d = d.size()[0] sum_error += abs_error_d sum_samples += num_samples_d MARE_d = sum_error / sum_samples assert m.compute() == approx(MARE_d.item()) def test_zero_div(): a = torch.tensor([2.0, -1.0, -1.0, 2.0]) ground_truth = torch.tensor([0.0, 0.5, 0.2, 1.0]) m = MeanAbsoluteRelativeError() with raises(NotComputableError, match=r"The ground truth has 0"): m.update((a, ground_truth)) def test_zero_sample(): m = MeanAbsoluteRelativeError() with raises(NotComputableError, match=r"MeanAbsoluteRelativeError must have at least one sample"): m.compute() def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MeanAbsoluteRelativeError() m.attach(engine, "mare") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) mare = engine.run(data, max_epochs=1).metrics["mare"] abs_error = np.sum(abs(np_y - np_y_pred) / abs(np_y)) num_samples = len(y_pred) res = abs_error / num_samples assert res == approx(mare) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = MeanAbsoluteRelativeError(device=metric_device) y_pred = torch.randint(1, 11, size=(10,), device=device).float() y = torch.randint(1, 11, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() abs_error = np.sum(abs(np_y - np_y_pred) / abs(np_y)) num_samples = len(y_pred) np_res = abs_error / num_samples assert np_res == approx(res) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = MeanAbsoluteRelativeError(device=metric_device) m.attach(engine, "mare") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mare" in engine.state.metrics mare = engine.state.metrics["mare"] np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() abs_error = np.sum(abs(np_y_true - np_y_preds) / abs(np_y_true)) num_samples = len(y_preds) np_res = abs_error / num_samples assert approx(mare) == np_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_mean_error.py000066400000000000000000000176741465426447700250470ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MeanError def test_zero_sample(): m = MeanError() with pytest.raises(NotComputableError, match=r"MeanError must have at least one example before it can be computed"): m.compute() def test_wrong_input_shapes(): m = MeanError() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_mean_error(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = MeanError() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (ground_truth - a).sum() np_len = len(a) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += (ground_truth - b).sum() np_len += len(b) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += (ground_truth - c).sum() np_len += len(c) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += (ground_truth - d).sum() np_len += len(d) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MeanError() m.attach(engine, "me") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) me = engine.run(data, max_epochs=1).metrics["me"] np_sum = (np_y - np_y_pred).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(me, rel=1e-4) def get_test_cases(): test_cases = [ (torch.rand(size=(50,)), torch.rand(size=(50,)), 1), (torch.rand(size=(50, 1)), torch.rand(size=(50, 1)), 10), ] return test_cases for _ in range(5): test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = MeanError(device=metric_device) y_pred = torch.rand(size=(100,), device=device) y = torch.rand(size=(100,), device=device) m.update((y_pred, y)) y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() np_sum = (np_y - np_y_pred).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans, rel=1e-5) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device, tol=1e-5): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) me = MeanError(device=metric_device) me.attach(engine, "me") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "me" in engine.state.metrics res = engine.state.metrics["me"] np_y = y_true.cpu().numpy() np_y_pred = y_preds.cpu().numpy() np_sum = (np_y - np_y_pred).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert pytest.approx(res, rel=tol) == np_ans metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_mean_normalized_bias.py000066400000000000000000000210551465426447700270440ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MeanNormalizedBias def test_zero_sample(): m = MeanNormalizedBias() with pytest.raises( NotComputableError, match=r"MeanNormalizedBias must have at least one example before it can be computed" ): m.compute() def test_zero_gt(): a = np.random.randn(4) ground_truth = np.zeros(4) m = MeanNormalizedBias() with pytest.raises(NotComputableError, match=r"The ground truth has 0."): m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) def test_wrong_input_shapes(): m = MeanNormalizedBias() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_mean_error(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = MeanNormalizedBias() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = ((ground_truth - a) / ground_truth).sum() np_len = len(a) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += ((ground_truth - b) / ground_truth).sum() np_len += len(b) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += ((ground_truth - c) / ground_truth).sum() np_len += len(c) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += ((ground_truth - d) / ground_truth).sum() np_len += len(d) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MeanNormalizedBias() m.attach(engine, "mnb") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) mnb = engine.run(data, max_epochs=1).metrics["mnb"] np_sum = ((np_y - np_y_pred) / np_y).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(mnb) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = MeanNormalizedBias(device=metric_device) y_pred = torch.randint(1, 11, size=(10,), device=device).float() y = torch.randint(1, 11, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() np_sum = ((np_y - np_y_pred) / np_y).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(res) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = MeanNormalizedBias(device=metric_device) m.attach(engine, "mnb") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mnb" in engine.state.metrics res = engine.state.metrics["mnb"] np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() np_sum = ((np_y_true - np_y_preds) / np_y_true).sum() np_len = len(np_y_preds) np_ans = np_sum / np_len assert pytest.approx(res) == np_ans metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_median_absolute_error.py000066400000000000000000000204001465426447700272370ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MedianAbsoluteError def test_zero_sample(): m = MedianAbsoluteError() with pytest.raises( NotComputableError, match=r"EpochMetric must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = MedianAbsoluteError() with pytest.raises(ValueError, match=r"Predictions should be of shape"): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Targets should be of shape"): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError, match=r"Predictions should be of shape"): m.update((torch.rand(4, 1, 2), torch.rand(4))) with pytest.raises(ValueError, match=r"Targets should be of shape"): m.update((torch.rand(4), torch.rand(4, 1, 2))) def test_median_absolute_error(): # See https://github.com/torch/torch7/pull/182 # For even number of elements, PyTorch returns middle element # NumPy returns average of middle elements # Size of dataset will be odd for these tests size = 51 np_y_pred = np.random.rand(size) np_y = np.random.rand(size) np_median_absolute_error = np.median(np.abs(np_y - np_y_pred)) m = MedianAbsoluteError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() m.update((y_pred, y)) assert np_median_absolute_error == pytest.approx(m.compute()) def test_median_absolute_error_2(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) np_median_absolute_error = np.median(np.abs(np_y - np_y_pred)) m = MedianAbsoluteError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() batch_size = 16 n_iters = size // batch_size + 1 for i in range(n_iters): idx = i * batch_size m.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) assert np_median_absolute_error == pytest.approx(m.compute()) def test_integration_median_absolute_error(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) np_median_absolute_error = np.median(np.abs(np_y - np_y_pred)) batch_size = 15 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MedianAbsoluteError() m.attach(engine, "median_absolute_error") data = list(range(size // batch_size)) median_absolute_error = engine.run(data, max_epochs=1).metrics["median_absolute_error"] assert np_median_absolute_error == pytest.approx(median_absolute_error) def _test_distrib_compute(device): def _test(metric_device): metric_device = torch.device(metric_device) m = MedianAbsoluteError(device=metric_device) size = 105 y_pred = torch.randint(1, 10, size=(size, 1), dtype=torch.double, device=device) y = torch.randint(1, 10, size=(size, 1), dtype=torch.double, device=device) m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy().ravel() np_y = y.cpu().numpy().ravel() res = m.compute() e = np.abs(np_y - np_y_pred) np_res = np.median(e) assert pytest.approx(res) == np_res rank = idist.get_rank() for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 105 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = MedianAbsoluteError(device=metric_device) m.attach(engine, "mae") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mae" in engine.state.metrics res = engine.state.metrics["mae"] np_y_true = y_true.cpu().numpy().ravel() np_y_preds = y_preds.cpu().numpy().ravel() e = np.abs(np_y_true - np_y_preds) np_res = np.median(e) assert pytest.approx(res) == np_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: rank = idist.get_rank() for i in range(2): torch.manual_seed(10 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_median_absolute_percentage_error.py000066400000000000000000000210611465426447700314400ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MedianAbsolutePercentageError def test_zero_sample(): m = MedianAbsolutePercentageError() with pytest.raises( NotComputableError, match=r"EpochMetric must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = MedianAbsolutePercentageError() with pytest.raises(ValueError, match=r"Predictions should be of shape"): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Targets should be of shape"): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError, match=r"Predictions should be of shape"): m.update((torch.rand(4, 1, 2), torch.rand(4))) with pytest.raises(ValueError, match=r"Targets should be of shape"): m.update((torch.rand(4), torch.rand(4, 1, 2))) def test_median_absolute_percentage_error(): # See https://github.com/torch/torch7/pull/182 # For even number of elements, PyTorch returns middle element # NumPy returns average of middle elements # Size of dataset will be odd for these tests size = 51 np_y_pred = np.random.rand(size) np_y = np.random.rand(size) np_median_absolute_percentage_error = 100.0 * np.median(np.abs(np_y - np_y_pred) / np.abs(np_y)) m = MedianAbsolutePercentageError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() m.update((y_pred, y)) assert np_median_absolute_percentage_error == pytest.approx(m.compute()) def test_median_absolute_percentage_error_2(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) np_median_absolute_percentage_error = 100.0 * np.median(np.abs(np_y - np_y_pred) / np.abs(np_y)) m = MedianAbsolutePercentageError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() batch_size = 16 n_iters = size // batch_size + 1 for i in range(n_iters): idx = i * batch_size m.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) assert np_median_absolute_percentage_error == pytest.approx(m.compute()) def test_integration_median_absolute_percentage_error(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) np_median_absolute_percentage_error = 100.0 * np.median(np.abs(np_y - np_y_pred) / np.abs(np_y)) batch_size = 15 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MedianAbsolutePercentageError() m.attach(engine, "median_absolute_percentage_error") data = list(range(size // batch_size)) median_absolute_percentage_error = engine.run(data, max_epochs=1).metrics["median_absolute_percentage_error"] assert np_median_absolute_percentage_error == pytest.approx(median_absolute_percentage_error) def _test_distrib_compute(device): def _test(metric_device): metric_device = torch.device(metric_device) m = MedianAbsolutePercentageError(device=metric_device) size = 105 y_pred = torch.randint(1, 10, size=(size, 1), dtype=torch.double, device=device) y = torch.randint(1, 10, size=(size, 1), dtype=torch.double, device=device) m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy().ravel() np_y = y.cpu().numpy().ravel() res = m.compute() e = np.abs(np_y - np_y_pred) / np.abs(np_y) np_res = 100.0 * np.median(e) assert pytest.approx(res) == np_res rank = idist.get_rank() for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 size = 105 y_true = torch.rand(size=(n_iters * size,)).to(device) y_preds = torch.rand(size=(n_iters * size,)).to(device) def update(engine, i): return ( y_preds[i * size : (i + 1) * size], y_true[i * size : (i + 1) * size], ) engine = Engine(update) m = MedianAbsolutePercentageError(device=metric_device) m.attach(engine, "mape") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mape" in engine.state.metrics res = engine.state.metrics["mape"] np_y_true = y_true.cpu().numpy().ravel() np_y_preds = y_preds.cpu().numpy().ravel() e = np.abs(np_y_true - np_y_preds) / np.abs(np_y_true) np_res = 100.0 * np.median(e) assert pytest.approx(res) == np_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: rank = idist.get_rank() for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_median_relative_absolute_error.py000066400000000000000000000210541465426447700311400ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import MedianRelativeAbsoluteError def test_zero_sample(): m = MedianRelativeAbsoluteError() with pytest.raises( NotComputableError, match=r"EpochMetric must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = MedianRelativeAbsoluteError() with pytest.raises(ValueError, match=r"Predictions should be of shape"): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Targets should be of shape"): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError, match=r"Predictions should be of shape"): m.update((torch.rand(4, 1, 2), torch.rand(4))) with pytest.raises(ValueError, match=r"Targets should be of shape"): m.update((torch.rand(4), torch.rand(4, 1, 2))) def test_median_relative_absolute_error(): # See https://github.com/torch/torch7/pull/182 # For even number of elements, PyTorch returns middle element # NumPy returns average of middle elements # Size of dataset will be odd for these tests size = 51 np_y_pred = np.random.rand(size) np_y = np.random.rand(size) np_median_absolute_relative_error = np.median(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())) m = MedianRelativeAbsoluteError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() m.update((y_pred, y)) assert np_median_absolute_relative_error == pytest.approx(m.compute()) def test_median_relative_absolute_error_2(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) np_median_absolute_relative_error = np.median(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())) m = MedianRelativeAbsoluteError() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() batch_size = 16 n_iters = size // batch_size + 1 for i in range(n_iters + 1): idx = i * batch_size m.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) assert np_median_absolute_relative_error == pytest.approx(m.compute()) def test_integration_median_relative_absolute_error_with_output_transform(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) np_median_absolute_relative_error = np.median(np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean())) batch_size = 15 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MedianRelativeAbsoluteError() m.attach(engine, "median_absolute_relative_error") data = list(range(size // batch_size)) median_absolute_relative_error = engine.run(data, max_epochs=1).metrics["median_absolute_relative_error"] assert np_median_absolute_relative_error == pytest.approx(median_absolute_relative_error) def _test_distrib_compute(device): def _test(metric_device): metric_device = torch.device(metric_device) m = MedianRelativeAbsoluteError(device=metric_device) torch.manual_seed(10 + rank) size = 151 y_pred = torch.randint(1, 10, size=(size, 1), dtype=torch.double, device=device) y = torch.randint(1, 10, size=(size, 1), dtype=torch.double, device=device) m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy().ravel() np_y = y.cpu().numpy().ravel() res = m.compute() e = np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean()) np_res = np.median(e) assert pytest.approx(res) == np_res rank = idist.get_rank() for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 size = 151 y_true = torch.rand(size=(size,)).to(device) y_preds = torch.rand(size=(size,)).to(device) def update(engine, i): return ( y_preds[i * size : (i + 1) * size], y_true[i * size : (i + 1) * size], ) engine = Engine(update) m = MedianRelativeAbsoluteError(device=metric_device) m.attach(engine, "mare") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert "mare" in engine.state.metrics res = engine.state.metrics["mare"] np_y_true = y_true.cpu().numpy().ravel() np_y_preds = y_preds.cpu().numpy().ravel() e = np.abs(np_y_true - np_y_preds) / np.abs(np_y_true - np_y_true.mean()) np_res = np.median(e) assert pytest.approx(res) == np_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: rank = idist.get_rank() for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_pearson_correlation.py000066400000000000000000000176571465426447700267670ustar00rootroot00000000000000from typing import Tuple import numpy as np import pytest import torch from scipy.stats import pearsonr from torch import Tensor import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import PearsonCorrelation def np_corr_eps(np_y_pred: np.ndarray, np_y: np.ndarray, eps: float = 1e-8): cov = np.cov(np_y_pred, np_y, ddof=0)[0, 1] std_y_pred = np.std(np_y_pred, ddof=0) std_y = np.std(np_y, ddof=0) corr = cov / np.clip(std_y_pred * std_y, eps, None) return corr def scipy_corr(np_y_pred: np.ndarray, np_y: np.ndarray): corr = pearsonr(np_y_pred, np_y) return corr.statistic def test_zero_sample(): m = PearsonCorrelation() with pytest.raises( NotComputableError, match=r"PearsonCorrelation must have at least one example before it can be computed" ): m.compute() def test_wrong_input_shapes(): m = PearsonCorrelation() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_degenerated_sample(): # one sample m = PearsonCorrelation() y_pred = torch.tensor([1.0]) y = torch.tensor([1.0]) m.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y_pred.numpy() np_res = np_corr_eps(np_y_pred, np_y) assert pytest.approx(np_res) == m.compute() # constant samples m.reset() y_pred = torch.ones(10).float() y = torch.zeros(10).float() m.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y_pred.numpy() np_res = np_corr_eps(np_y_pred, np_y) assert pytest.approx(np_res) == m.compute() def test_pearson_correlation(): a = np.random.randn(4).astype(np.float32) b = np.random.randn(4).astype(np.float32) c = np.random.randn(4).astype(np.float32) d = np.random.randn(4).astype(np.float32) ground_truth = np.random.randn(4).astype(np.float32) m = PearsonCorrelation() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_ans = scipy_corr(a, ground_truth) assert m.compute() == pytest.approx(np_ans, rel=1e-4) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_ans = scipy_corr(np.concatenate([a, b]), np.concatenate([ground_truth] * 2)) assert m.compute() == pytest.approx(np_ans, rel=1e-4) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_ans = scipy_corr(np.concatenate([a, b, c]), np.concatenate([ground_truth] * 3)) assert m.compute() == pytest.approx(np_ans, rel=1e-4) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_ans = scipy_corr(np.concatenate([a, b, c, d]), np.concatenate([ground_truth] * 4)) assert m.compute() == pytest.approx(np_ans, rel=1e-4) @pytest.fixture(params=list(range(2))) def test_case(request): # correlated sample x = torch.randn(size=[50]).float() y = x + torch.randn_like(x) * 0.1 return [ (x, y, 1), (torch.rand(size=(50, 1)).float(), torch.rand(size=(50, 1)).float(), 10), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_integration(n_times, test_case: Tuple[Tensor, Tensor, int]): y_pred, y, batch_size = test_case def update_fn(engine: Engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = PearsonCorrelation() m.attach(engine, "corr") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) corr = engine.run(data, max_epochs=1).metrics["corr"] np_ans = scipy_corr(np_y_pred, np_y) assert pytest.approx(np_ans, rel=2e-4) == corr def test_accumulator_detached(): corr = PearsonCorrelation() y_pred = torch.tensor([2.0, 3.0], requires_grad=True) y = torch.tensor([-2.0, -1.0]) corr.update((y_pred, y)) assert all( (not accumulator.requires_grad) for accumulator in ( corr._sum_of_products, corr._sum_of_y_pred_squares, corr._sum_of_y_preds, corr._sum_of_y_squares, corr._sum_of_ys, ) ) @pytest.mark.usefixtures("distributed") class TestDistributed: def test_compute(self): rank = idist.get_rank() device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) torch.manual_seed(10 + rank) for metric_device in metric_devices: m = PearsonCorrelation(device=metric_device) y_pred = torch.rand(size=[100], device=device) y = torch.rand(size=[100], device=device) m.update((y_pred, y)) y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() np_ans = scipy_corr(np_y_pred, np_y) assert pytest.approx(np_ans, rel=2e-4) == m.compute() @pytest.mark.parametrize("n_epochs", [1, 2]) def test_integration(self, n_epochs: int): tol = 2e-4 rank = idist.get_rank() device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) n_iters = 80 batch_size = 16 for metric_device in metric_devices: torch.manual_seed(12 + rank) y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) engine = Engine( lambda e, i: ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) ) corr = PearsonCorrelation(device=metric_device) corr.attach(engine, "corr") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "corr" in engine.state.metrics res = engine.state.metrics["corr"] np_y = y_true.cpu().numpy() np_y_pred = y_preds.cpu().numpy() np_ans = scipy_corr(np_y_pred, np_y) assert pytest.approx(np_ans, rel=tol) == res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: corr = PearsonCorrelation(device=metric_device) devices = ( corr._device, corr._sum_of_products.device, corr._sum_of_y_pred_squares.device, corr._sum_of_y_preds.device, corr._sum_of_y_squares.device, corr._sum_of_ys.device, ) for dev in devices: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([2.0, 3.0]) y = torch.tensor([-1.0, 1.0]) corr.update((y_pred, y)) devices = ( corr._device, corr._sum_of_products.device, corr._sum_of_y_pred_squares.device, corr._sum_of_y_preds.device, corr._sum_of_y_squares.device, corr._sum_of_ys.device, ) for dev in devices: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/regression/test_r2_score.py000066400000000000000000000166161465426447700244270ustar00rootroot00000000000000import os import numpy as np import pytest import torch from sklearn.metrics import r2_score import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.regression import R2Score def test_zero_sample(): m = R2Score() with pytest.raises(NotComputableError, match=r"R2Score must have at least one example before it can be computed"): m.compute() def test_wrong_input_shapes(): m = R2Score() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_r2_score(): size = 51 np_y_pred = np.random.rand(size) np_y = np.random.rand(size) m = R2Score() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() m.update((y_pred, y)) assert r2_score(np_y, np_y_pred) == pytest.approx(m.compute()) def test_r2_score_2(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) m = R2Score() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) m.reset() batch_size = 16 n_iters = size // batch_size + 1 for i in range(n_iters): idx = i * batch_size m.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) assert r2_score(np_y, np_y_pred) == pytest.approx(m.compute()) def test_integration_r2_score(): np.random.seed(1) size = 105 np_y_pred = np.random.rand(size, 1) np_y = np.random.rand(size, 1) np.random.shuffle(np_y) batch_size = 15 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = R2Score() m.attach(engine, "r2_score") data = list(range(size // batch_size)) r_squared = engine.run(data, max_epochs=1).metrics["r2_score"] assert r2_score(np_y, np_y_pred) == pytest.approx(r_squared) def _test_distrib_compute(device, tol=1e-6): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = R2Score(device=metric_device) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() assert r2_score(np_y, np_y_pred) == pytest.approx(res, abs=tol) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.randint(0, 10, size=(n_iters * batch_size,)).to(device).float() y_preds = torch.randint(0, 10, size=(n_iters * batch_size,)).to(device).float() def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) r2 = R2Score(device=metric_device) r2.attach(engine, "r2") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "r2" in engine.state.metrics res = engine.state.metrics["r2"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = r2_score(y_true.cpu().numpy(), y_preds.cpu().numpy()) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device, tol=1e-3) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device, tol=1e-3) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/regression/test_wave_hedges_distance.py000066400000000000000000000176151465426447700270440ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics.regression import WaveHedgesDistance def test_wrong_input_shapes(): m = WaveHedgesDistance() with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4), torch.rand(4, 1))) with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1), torch.rand(4))) def test_compute(): a = np.random.randn(4) b = np.random.randn(4) c = np.random.randn(4) d = np.random.randn(4) ground_truth = np.random.randn(4) m = WaveHedgesDistance() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (np.abs(ground_truth - a) / np.maximum.reduce([a, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - b) / np.maximum.reduce([b, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - c) / np.maximum.reduce([c, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - d) / np.maximum.reduce([d, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) def test_integration(): def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = WaveHedgesDistance() m.attach(engine, "whd") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) whd = engine.run(data, max_epochs=1).metrics["whd"] np_sum = (np.abs(np_y - np_y_pred) / np.maximum.reduce([np_y_pred, np_y])).sum() assert np_sum == pytest.approx(whd) def get_test_cases(): test_cases = [ (torch.rand(size=(100,)), torch.rand(size=(100,)), 10), (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def _test_distrib_compute(device): rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) m = WaveHedgesDistance(device=metric_device) y_pred = torch.randint(0, 10, size=(10,), device=device).float() y = torch.randint(0, 10, size=(10,), device=device).float() m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() res = m.compute() np_sum = (np.abs(np_y - np_y_pred) / (np.maximum.reduce([np_y_pred, np_y]) + 1e-30)).sum() assert np_sum == pytest.approx(res) for i in range(3): torch.manual_seed(10 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 y_true = torch.rand(size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = WaveHedgesDistance(device=metric_device) m.attach(engine, "whm") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "whm" in engine.state.metrics res = engine.state.metrics["whm"] np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() np_sum = (np.abs(np_y_true - np_y_preds) / (np.maximum.reduce([np_y_preds, np_y_true]) + 1e-30)).sum() assert pytest.approx(res) == np_sum metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_accumulation.py000066400000000000000000000445011465426447700232070ustar00rootroot00000000000000import os import numpy as np import pytest import torch from torch.nn import Linear from torch.optim import SGD import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.accumulation import Average, GeometricAverage, VariableAccumulation torch.manual_seed(15) def test_variable_accumulation_wrong_inputs(): with pytest.raises(TypeError, match=r"Argument op should be a callable"): VariableAccumulation(1) with pytest.raises(TypeError, match=r"Output should be a number or torch.Tensor,"): mean_acc = VariableAccumulation(lambda a, x: a + x) mean_acc.update((1, 2)) with pytest.raises(TypeError, match=r"Output should be a number or torch.Tensor,"): mean_acc = VariableAccumulation(lambda a, x: a + x) mean_acc.update("a") def test_variable_accumulation_mean_variable(): mean_var = VariableAccumulation(lambda a, x: a + x) y_true = torch.rand(100) for y in y_true: mean_var.update(y) a, n = mean_var.compute() assert a.item() == pytest.approx(y_true.sum().item()) assert n == len(y_true) mean_var = VariableAccumulation(lambda a, x: a + x) y_true = torch.rand(100, 10) for y in y_true: mean_var.update(y) a, n = mean_var.compute() assert a.numpy() == pytest.approx(y_true.sum(dim=0).numpy()) assert n == len(y_true) mean_var = VariableAccumulation(lambda a, x: a + x.sum(dim=0)) # iterate by batch of 16 samples y_true = torch.rand(8, 16, 10) for y in y_true: mean_var.update(y) a, n = mean_var.compute() assert a.numpy() == pytest.approx(y_true.reshape(-1, 10).sum(dim=0).numpy()) assert n == y_true.shape[0] * y_true.shape[1] def test_average(): with pytest.raises(NotComputableError): v = Average() v.compute() mean_var = Average() y_true = torch.rand(100) + torch.randint(0, 10, size=(100,)).float() for y in y_true: mean_var.update(y.item()) m = mean_var.compute() assert m.item() == pytest.approx(y_true.mean().item()) mean_var = Average() y_true = torch.rand(100, 10) + torch.randint(0, 10, size=(100, 10)).float() for y in y_true: mean_var.update(y) m = mean_var.compute() assert m.numpy() == pytest.approx(y_true.mean(dim=0).numpy()) mean_var = Average() y_true = torch.rand(8, 16, 10) + torch.randint(0, 10, size=(8, 16, 10)).float() for y in y_true: mean_var.update(y) m = mean_var.compute() assert m.numpy() == pytest.approx(y_true.reshape(-1, 10).mean(dim=0).numpy()) def _geom_mean(t): np_t = t.numpy() return np.exp(np.mean(np.log(np_t), axis=0)) def _mean(y_true): return y_true.mean(dim=0).numpy() def test_geom_average(): with pytest.raises(NotComputableError): v = GeometricAverage() v.compute() mean_var = GeometricAverage() y_true = torch.rand(100) + torch.randint(0, 10, size=(100,)).float() for y in y_true: mean_var.update(y.item()) m = mean_var.compute() assert m == pytest.approx(_geom_mean(y_true)) mean_var = GeometricAverage() y_true = torch.rand(100, 10) + torch.randint(0, 10, size=(100, 10)).float() for y in y_true: mean_var.update(y) m = mean_var.compute() np.testing.assert_almost_equal(m.numpy(), _geom_mean(y_true), decimal=5) mean_var = GeometricAverage() y_true = torch.rand(8, 16, 10) + torch.randint(0, 10, size=(8, 16, 10)).float() for y in y_true: mean_var.update(y) m = mean_var.compute() np.testing.assert_almost_equal(m.numpy(), _geom_mean(y_true.reshape(-1, 10)), decimal=5) @pytest.mark.parametrize("metric_cls, true_result_fn", [(Average, _mean), (GeometricAverage, _geom_mean)]) @pytest.mark.parametrize("shape", [[100, 12], [100]]) def test_integration(metric_cls, true_result_fn, shape): assert len(shape) > 0 and len(shape) < 3 custom_variable = 10.0 + 5.0 * torch.rand(shape) def update_fn(engine, batch): output = custom_variable[engine.state.iteration - 1] output = output.item() if output.ndimension() < 1 else output return 0, output engine = Engine(update_fn) custom_var_mean = metric_cls(output_transform=lambda output: output[1]) custom_var_mean.attach(engine, "agg_custom_var") state = engine.run([0] * shape[0]) np.testing.assert_almost_equal( np.array(state.metrics["agg_custom_var"]), true_result_fn(custom_variable), decimal=5 ) metric_state = custom_var_mean.state_dict() saved_num_examples = custom_var_mean.num_examples saved_accumulator = custom_var_mean.accumulator custom_var_mean.reset() assert custom_var_mean.num_examples == 0 assert custom_var_mean.accumulator == 0 custom_var_mean.load_state_dict(metric_state) assert custom_var_mean.num_examples == saved_num_examples assert (custom_var_mean.accumulator == saved_accumulator).all() def test_compute_mean_std(): n = 8 b = 12 c = 3 w = h = 64 true_data = np.arange(0, n * b * h * w * c, dtype="float64").reshape(n * b, c, h, w) - (n * b * c * w * h * 0.75) mean = true_data.transpose((0, 2, 3, 1)).reshape(-1, c).mean(axis=0) std = true_data.transpose((0, 2, 3, 1)).reshape(-1, c).std(axis=0) train_loader = torch.from_numpy(true_data).reshape(n, b, c, h, w) def compute_mean_std(engine, batch): _b, _c = batch.shape[:2] data = batch.reshape(_b, _c, -1).to(dtype=torch.float64) _mean = torch.mean(data, dim=-1) _mean2 = torch.mean(data**2, dim=-1) return {"mean": _mean, "mean^2": _mean2} compute_engine = Engine(compute_mean_std) img_mean = Average(output_transform=lambda output: output["mean"]) img_mean2 = Average(output_transform=lambda output: output["mean^2"]) img_mean.attach(compute_engine, "mean") img_mean2.attach(compute_engine, "mean2") state = compute_engine.run(train_loader) state.metrics["std"] = torch.sqrt(state.metrics["mean2"] - state.metrics["mean"] ** 2) np.testing.assert_almost_equal(state.metrics["mean"].numpy(), mean, decimal=7) np.testing.assert_almost_equal(state.metrics["std"].numpy(), std, decimal=5) def _test_distrib_variable_accumulation(device): def _test(metric_device): mean_var = VariableAccumulation(lambda a, x: a + x, device=metric_device) y_true = torch.rand(100, device=device, dtype=torch.float64) for y in y_true: mean_var.update(y) y_true = idist.all_reduce(y_true) a, n = mean_var.compute() assert a.item() == pytest.approx(y_true.sum().item()) assert n == len(y_true) * idist.get_world_size() # check if call compute twice a, n = mean_var.compute() assert a.item() == pytest.approx(y_true.sum().item()) assert n == len(y_true) * idist.get_world_size() mean_var = VariableAccumulation(lambda a, x: a + x, device=metric_device) y_true = torch.rand(50, 10, device=device, dtype=torch.float64) for y in y_true: mean_var.update(y) y_true = idist.all_reduce(y_true) a, n = mean_var.compute() assert n == len(y_true) * idist.get_world_size() np.testing.assert_almost_equal(a.cpu().numpy(), y_true.sum(dim=0).cpu().numpy(), decimal=4) a, n = mean_var.compute() assert n == len(y_true) * idist.get_world_size() np.testing.assert_almost_equal(a.cpu().numpy(), y_true.sum(dim=0).cpu().numpy(), decimal=4) # check multiple random inputs as random exact occurencies are rare for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_average(device): def _test(metric_device): with pytest.raises(NotComputableError): v = Average(device=metric_device) v.compute() mean_var = Average(device=metric_device) y_true = torch.rand(100, dtype=torch.float64) + torch.randint(0, 10, size=(100,)).double() y_true = y_true.to(device) for y in y_true: mean_var.update(y) m = mean_var.compute() y_true = idist.all_reduce(y_true) assert m.item() == pytest.approx(y_true.mean().item() / idist.get_world_size()) mean_var = Average(device=metric_device) y_true = torch.rand(100, 10, dtype=torch.float64) + torch.randint(0, 10, size=(100, 10)).double() y_true = y_true.to(device) for y in y_true: mean_var.update(y) m = mean_var.compute() y_true = idist.all_reduce(y_true) np.testing.assert_almost_equal( m.cpu().numpy(), y_true.mean(dim=0).cpu().numpy() / idist.get_world_size(), decimal=5 ) # check multiple random inputs as random exact occurencies are rare for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_geom_average(device): def _test(metric_device): with pytest.raises(NotComputableError): v = GeometricAverage(device=metric_device) v.compute() decimal = 5 if device.type != "xla" else 4 mean_var = GeometricAverage(device=metric_device) y_true = torch.rand(100, dtype=torch.float64) + torch.randint(0, 10, size=(100,)).double() y_true = y_true.to(device) for y in y_true: mean_var.update(y) m = mean_var.compute() log_y_true = torch.log(y_true) log_y_true = idist.all_reduce(log_y_true) np.testing.assert_almost_equal( m, torch.exp(log_y_true.mean(dim=0) / idist.get_world_size()).item(), decimal=decimal ) mean_var = GeometricAverage(device=metric_device) y_true = torch.rand(100, 10, dtype=torch.float64) + torch.randint(0, 10, size=(100, 10)).double() y_true = y_true.to(device) for y in y_true: mean_var.update(y) m = mean_var.compute() log_y_true = torch.log(y_true) log_y_true = idist.all_reduce(log_y_true) np.testing.assert_almost_equal( m.cpu().numpy(), torch.exp(log_y_true.mean(dim=0) / idist.get_world_size()).cpu().numpy(), decimal=decimal ) # check multiple random inputs as random exact occurencies are rare for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _dist_mean(y_true): y_true = idist.all_reduce(y_true) / idist.get_world_size() if len(y_true.shape) > 2: y_true = y_true.reshape(-1, y_true.shape[-1]) return y_true.mean(dim=0).cpu().numpy() def _dist_geom_mean(y_true): log_y_true = torch.log(y_true) log_y_true = idist.all_reduce(log_y_true) if len(log_y_true.shape) > 2: log_y_true = log_y_true.reshape(-1, log_y_true.shape[-1]) np_t = log_y_true.cpu().numpy() return np.exp(np.mean(np_t, axis=0) / idist.get_world_size()) def _test_distrib_integration(device): def _test(metric_cls, shape, true_result_fn, metric_device, tol=1e-5): size = 100 custom_variable = 10.0 + 5.0 * torch.rand(size, *shape, dtype=torch.float64) custom_variable = custom_variable.to(device) def update_fn(engine, batch): return 0, custom_variable[engine.state.iteration - 1] engine = Engine(update_fn) custom_var_mean = metric_cls(output_transform=lambda output: output[1], device=metric_device) custom_var_mean.attach(engine, "agg_custom_var") state = engine.run([0] * size) true_val = true_result_fn(custom_variable) assert len(true_val) == shape[-1] np.testing.assert_almost_equal( state.metrics["agg_custom_var"].cpu().numpy(), true_val, decimal=int(np.log10(1.0 / tol)) ) size = 100 custom_variable = 10.0 + 5.0 * torch.rand(size, dtype=torch.float64) custom_variable = custom_variable.to(device) def update_fn(engine, batch): return 0, custom_variable[engine.state.iteration - 1].item() engine = Engine(update_fn) custom_var_mean = metric_cls(output_transform=lambda output: output[1], device=metric_device) custom_var_mean.attach(engine, "agg_custom_var") state = engine.run([0] * size) assert state.metrics["agg_custom_var"] == pytest.approx(true_result_fn(custom_variable), abs=tol) metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(Average, (12,), _dist_mean, metric_device) _test(Average, (4, 12), _dist_mean, metric_device) _test(GeometricAverage, (12,), _dist_geom_mean, metric_device, tol=1e-4) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: m = VariableAccumulation(lambda a, x: x, device=metric_device) assert m._device == metric_device assert ( m.accumulator.device == metric_device ), f"{type(m.accumulator.device)}:{m.accumulator.device} vs {type(metric_device)}:{metric_device}" m.update(torch.tensor(1, device=device)) assert ( m.accumulator.device == metric_device ), f"{type(m.accumulator.device)}:{m.accumulator.device} vs {type(metric_device)}:{metric_device}" def _test_apex_average(device, amp_mode, opt_level): assert amp_mode == "apex" assert device == "cuda" model = Linear(1, 1) if device: model.to(device) model.weight.data.zero_() model.bias.data.zero_() optimizer = SGD(model.parameters(), 0.1) from apex import amp model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level) mean_var = VariableAccumulation(lambda a, x: a + x) y_true = torch.rand(100).float().to(device) for y in y_true: mean_var.update(y) a, n = mean_var.compute() assert a.item() == pytest.approx(y_true.sum().item()) assert n == len(y_true) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_variable_accumulation(device) _test_distrib_average(device) _test_distrib_geom_average(device) _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_variable_accumulation(device) _test_distrib_average(device) _test_distrib_geom_average(device) _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = idist.device() nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_variable_accumulation, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_average, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_geom_average, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_variable_accumulation(device) _test_distrib_average(device) _test_distrib_geom_average(device) _test_distrib_integration(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_variable_accumulation(device) _test_distrib_average(device) _test_distrib_geom_average(device) _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) # Enable this test when apex issue is fixed # @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") # @pytest.mark.skipif(not find_spec("apex"), reason="Skip if no APEX") @pytest.mark.skip(reason="Temporarily disabled, as it fails because of an issue from apex side") def test_apex_average_on_cuda(): device = "cuda" _test_apex_average(device, amp_mode="apex", opt_level="O0") _test_apex_average(device, amp_mode="apex", opt_level="O1") _test_apex_average(device, amp_mode="apex", opt_level="O2") _test_apex_average(device, amp_mode="apex", opt_level="O3") @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_variable_accumulation(device) _test_distrib_average(device) _test_distrib_geom_average(device) _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_variable_accumulation(device) _test_distrib_average(device) _test_distrib_geom_average(device) _test_distrib_integration(device) _test_distrib_accumulator_device(device) ignite-0.5.1/tests/ignite/metrics/test_accuracy.py000066400000000000000000000640311465426447700223150ustar00rootroot00000000000000import os from typing import Callable, Union from unittest.mock import MagicMock import pytest import torch from packaging.version import Version from sklearn.metrics import accuracy_score import ignite.distributed as idist from ignite.engine import Engine, State from ignite.exceptions import NotComputableError from ignite.metrics import Accuracy torch.manual_seed(12) def test_no_update(): acc = Accuracy() with pytest.raises(NotComputableError, match=r"Accuracy must have at least one example before it can be computed"): acc.compute() def test__check_shape(): acc = Accuracy() with pytest.raises(ValueError, match=r"y and y_pred must have compatible shapes"): acc._check_shape((torch.randint(0, 2, size=(10, 1, 5, 12)).long(), torch.randint(0, 2, size=(10, 5, 6)).long())) with pytest.raises(ValueError, match=r"y and y_pred must have compatible shapes"): acc._check_shape((torch.randint(0, 2, size=(10, 1, 6)).long(), torch.randint(0, 2, size=(10, 5, 6)).long())) with pytest.raises(ValueError, match=r"y and y_pred must have compatible shapes"): acc._check_shape((torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 5)).long())) def test__check_type(): acc = Accuracy() with pytest.raises(RuntimeError, match=r"Invalid shapes of y"): acc._check_type((torch.rand([1, 1, 1]), torch.rand([1]))) def test_binary_wrong_inputs(): acc = Accuracy() with pytest.raises(ValueError, match=r"For binary cases, y must be comprised of 0's and 1's"): # y has not only 0 or 1 values acc.update((torch.randint(0, 2, size=(10,)).long(), torch.arange(0, 10).long())) with pytest.raises(ValueError, match=r"For binary cases, y_pred must be comprised of 0's and 1's"): # y_pred values are not thresholded to 0, 1 values acc.update((torch.rand(10), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError, match=r"y must have shape of "): # incompatible shapes acc.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError, match=r"y must have shape of "): # incompatible shapes acc.update((torch.randint(0, 2, size=(10, 5, 6)).long(), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError, match=r"y must have shape of "): # incompatible shapes acc.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5, 6)).long())) @pytest.fixture(params=range(12)) def test_data_binary(request): return [ # Binary accuracy on input of shape (N, 1) or (N, ) (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1), (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), # Binary accuracy on input of shape (N, L) (torch.randint(0, 2, size=(10, 5)).long(), torch.randint(0, 2, size=(10, 5)).long(), 1), (torch.randint(0, 2, size=(10, 8)).long(), torch.randint(0, 2, size=(10, 8)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 5)).long(), torch.randint(0, 2, size=(50, 5)).long(), 16), (torch.randint(0, 2, size=(50, 8)).long(), torch.randint(0, 2, size=(50, 8)).long(), 16), # Binary accuracy on input of shape (N, H, W, ...) (torch.randint(0, 2, size=(4, 1, 12, 10)).long(), torch.randint(0, 2, size=(4, 1, 12, 10)).long(), 1), (torch.randint(0, 2, size=(15, 1, 20, 10)).long(), torch.randint(0, 2, size=(15, 1, 20, 10)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 1, 12, 10)).long(), torch.randint(0, 2, size=(50, 1, 12, 10)).long(), 16), (torch.randint(0, 2, size=(50, 1, 20, 10)).long(), torch.randint(0, 2, size=(50, 1, 20, 10)).long(), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_binary_input(n_times, test_data_binary): acc = Accuracy() y_pred, y, batch_size = test_data_binary acc.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size acc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: acc.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert acc._type == "binary" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) def test_multiclass_wrong_inputs(): acc = Accuracy() with pytest.raises(ValueError): # incompatible shapes acc.update((torch.rand(10, 5, 4), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes acc.update((torch.rand(10, 5, 6), torch.randint(0, 5, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible shapes acc.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long())) @pytest.fixture(params=range(11)) def test_data_multiclass(request): return [ # Multiclass input data of shape (N, ) and (N, C) (torch.rand(10, 4), torch.randint(0, 4, size=(10,)).long(), 1), (torch.rand(10, 10, 1), torch.randint(0, 18, size=(10, 1)).long(), 1), (torch.rand(10, 18), torch.randint(0, 18, size=(10,)).long(), 1), (torch.rand(4, 10), torch.randint(0, 10, size=(4,)).long(), 1), # 2-classes (torch.rand(4, 2), torch.randint(0, 2, size=(4,)).long(), 1), (torch.rand(100, 5), torch.randint(0, 5, size=(100,)).long(), 16), # Multiclass input data of shape (N, L) and (N, C, L) (torch.rand(10, 4, 5), torch.randint(0, 4, size=(10, 5)).long(), 1), (torch.rand(4, 10, 5), torch.randint(0, 10, size=(4, 5)).long(), 1), (torch.rand(100, 9, 7), torch.randint(0, 9, size=(100, 7)).long(), 16), # Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...) (torch.rand(4, 5, 12, 10), torch.randint(0, 5, size=(4, 12, 10)).long(), 1), (torch.rand(100, 3, 8, 8), torch.randint(0, 3, size=(100, 8, 8)).long(), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_multiclass_input(n_times, test_data_multiclass): acc = Accuracy() y_pred, y, batch_size = test_data_multiclass acc.reset() if batch_size > 1: # Batched Updates n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size acc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: acc.update((y_pred, y)) np_y_pred = y_pred.numpy().argmax(axis=1).ravel() np_y = y.numpy().ravel() assert acc._type == "multiclass" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) def to_numpy_multilabel(y): # reshapes input array to (N x ..., C) y = y.transpose(1, 0).cpu().numpy() num_classes = y.shape[0] y = y.reshape((num_classes, -1)).transpose(1, 0) return y def test_multilabel_wrong_inputs(): acc = Accuracy(is_multilabel=True) with pytest.raises(ValueError): # incompatible shapes acc.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible y_pred acc.update((torch.rand(10, 5), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible y acc.update((torch.randint(0, 5, size=(10, 5, 6)), torch.rand(10))) with pytest.raises(ValueError): # incompatible binary shapes acc.update((torch.randint(0, 2, size=(10, 1)), torch.randint(0, 2, size=(10, 1)).long())) @pytest.fixture(params=range(12)) def test_data_multilabel(request): return [ # Multilabel input data of shape (N, C) and (N, C) (torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long(), 1), (torch.randint(0, 2, size=(10, 7)).long(), torch.randint(0, 2, size=(10, 7)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 16), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 16), # Multilabel input data of shape (N, H, W) (torch.randint(0, 2, size=(10, 5, 10)).long(), torch.randint(0, 2, size=(10, 5, 10)).long(), 1), (torch.randint(0, 2, size=(10, 4, 10)).long(), torch.randint(0, 2, size=(10, 4, 10)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 5, 10)).long(), torch.randint(0, 2, size=(50, 5, 10)).long(), 16), (torch.randint(0, 2, size=(50, 4, 10)).long(), torch.randint(0, 2, size=(50, 4, 10)).long(), 16), # Multilabel input data of shape (N, C, H, W, ...) and (N, C, H, W, ...) (torch.randint(0, 2, size=(4, 5, 12, 10)).long(), torch.randint(0, 2, size=(4, 5, 12, 10)).long(), 1), (torch.randint(0, 2, size=(4, 10, 12, 8)).long(), torch.randint(0, 2, size=(4, 10, 12, 8)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 5, 12, 10)).long(), torch.randint(0, 2, size=(50, 5, 12, 10)).long(), 16), (torch.randint(0, 2, size=(50, 10, 12, 8)).long(), torch.randint(0, 2, size=(50, 10, 12, 8)).long(), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_multilabel_input(n_times, test_data_multilabel): acc = Accuracy(is_multilabel=True) y_pred, y, batch_size = test_data_multilabel if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size acc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: acc.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) def test_incorrect_type(): acc = Accuracy() # Start as binary data y_pred = torch.randint(0, 2, size=(4,)) y = torch.ones(4).long() acc.update((y_pred, y)) # And add a multiclass data y_pred = torch.rand(4, 4) y = torch.ones(4).long() with pytest.raises(RuntimeError): acc.update((y_pred, y)) def _test_distrib_multilabel_input_NHW(device): # Multilabel input data of shape (N, C, H, W, ...) and (N, C, H, W, ...) rank = idist.get_rank() def _test(metric_device): metric_device = torch.device(metric_device) acc = Accuracy(is_multilabel=True, device=metric_device) torch.manual_seed(10 + rank) y_pred = torch.randint(0, 2, size=(4, 5, 8, 10), device=device).long() y = torch.randint(0, 2, size=(4, 5, 8, 10), device=device).long() acc.update((y_pred, y)) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" n = acc._num_examples assert n == y.numel() / y.size(dim=1) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = to_numpy_multilabel(y_pred.cpu()) # (N, C, H, W, ...) -> (N * H * W ..., C) np_y = to_numpy_multilabel(y.cpu()) # (N, C, H, W, ...) -> (N * H * W ..., C) assert acc._type == "multilabel" res = acc.compute() assert n == acc._num_examples assert isinstance(res, float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(res) acc.reset() torch.manual_seed(10 + rank) y_pred = torch.randint(0, 2, size=(4, 7, 10, 8), device=device).long() y = torch.randint(0, 2, size=(4, 7, 10, 8), device=device).long() acc.update((y_pred, y)) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" n = acc._num_examples assert n == y.numel() / y.size(dim=1) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = to_numpy_multilabel(y_pred.cpu()) # (N, C, H, W, ...) -> (N * H * W ..., C) np_y = to_numpy_multilabel(y.cpu()) # (N, C, H, W, ...) -> (N * H * W ..., C) assert acc._type == "multilabel" res = acc.compute() assert n == acc._num_examples assert isinstance(res, float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(res) # check that result is not changed res = acc.compute() assert n == acc._num_examples assert isinstance(res, float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(res) # Batched Updates acc.reset() torch.manual_seed(10 + rank) y_pred = torch.randint(0, 2, size=(80, 5, 8, 10), device=device).long() y = torch.randint(0, 2, size=(80, 5, 8, 10), device=device).long() batch_size = 16 n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size acc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" n = acc._num_examples assert n == y.numel() / y.size(dim=1) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y_pred = to_numpy_multilabel(y_pred.cpu()) # (N, C, L, ...) -> (N * L * ..., C) np_y = to_numpy_multilabel(y.cpu()) # (N, C, L, ...) -> (N * L ..., C) assert acc._type == "multilabel" res = acc.compute() assert n == acc._num_examples assert isinstance(res, float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(res) # check multiple random inputs as random exact occurencies are rare for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_integration_multiclass(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 n_classes = 10 torch.manual_seed(12 + rank) y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) acc = Accuracy(device=metric_device) acc.attach(engine, "acc") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" assert "acc" in engine.state.metrics res = engine.state.metrics["acc"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = accuracy_score(y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy()) assert pytest.approx(res) == true_res metric_state = acc.state_dict() saved__num_correct = acc._num_correct saved__num_examples = acc._num_examples acc.reset() acc.load_state_dict(metric_state) assert acc._num_examples == saved__num_examples assert (acc._num_correct == saved__num_correct).all() metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for _ in range(2): _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) def _test_distrib_integration_multilabel(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 n_classes = 10 torch.manual_seed(12 + rank) y_true = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 8, 10)).to(device) y_preds = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 8, 10)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, ...], y_true[i * batch_size : (i + 1) * batch_size, ...], ) engine = Engine(update) acc = Accuracy(is_multilabel=True, device=metric_device) acc.attach(engine, "acc") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" assert "acc" in engine.state.metrics res = engine.state.metrics["acc"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = accuracy_score(to_numpy_multilabel(y_true), to_numpy_multilabel(y_preds)) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for _ in range(2): _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: acc = Accuracy(device=metric_device) assert acc._device == metric_device assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" y_pred = torch.randint(0, 2, size=(10,), device=device, dtype=torch.long) y = torch.randint(0, 2, size=(10,), device=device, dtype=torch.long) acc.update((y_pred, y)) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" def _test_distrib_integration_list_of_tensors_or_numbers(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 n_classes = 10 torch.manual_seed(12 + rank) y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(_, i): return ( [v for v in y_preds[i * batch_size : (i + 1) * batch_size, ...]], [v.item() for v in y_true[i * batch_size : (i + 1) * batch_size]], ) engine = Engine(update) acc = Accuracy(device=metric_device) acc.attach(engine, "acc") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" assert "acc" in engine.state.metrics res = engine.state.metrics["acc"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = accuracy_score(y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy()) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for _ in range(2): _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="Skip if < 1.7.0") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_integration_list_of_tensors_or_numbers(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="Skip if < 1.7.0") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_integration_list_of_tensors_or_numbers(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_multilabel_input_NHW, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_multiclass, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_multilabel, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_list_of_tensors_or_numbers, (device,), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_integration_list_of_tensors_or_numbers(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_integration_list_of_tensors_or_numbers(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_integration_list_of_tensors_or_numbers(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_integration_list_of_tensors_or_numbers(device) def test_skip_unrolling(): class DummyAcc(Accuracy): def __init__( self, true_output, output_transform: Callable = lambda x: x, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), skip_unrolling: bool = False, ): super(DummyAcc, self).__init__( output_transform=output_transform, is_multilabel=False, device=device, skip_unrolling=skip_unrolling ) self.true_output = true_output def update(self, output): assert output == self.true_output a_pred = torch.randint(0, 2, size=(8, 1)) b_pred = torch.randint(0, 2, size=(8, 1)) y_pred = [a_pred, b_pred] a_true = torch.randint(0, 2, size=(8, 1)) b_true = torch.randint(0, 2, size=(8, 1)) y_true = [a_true, b_true] acc = DummyAcc(true_output=(y_pred, y_true), skip_unrolling=True) state = State(output=(y_pred, y_true)) engine = MagicMock(state=state) acc.iteration_completed(engine) ignite-0.5.1/tests/ignite/metrics/test_average_precision.py000066400000000000000000000317431465426447700242140ustar00rootroot00000000000000import os from unittest.mock import patch import pytest import sklearn import torch from sklearn.metrics import average_precision_score import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import AveragePrecision torch.manual_seed(12) @pytest.fixture() def mock_no_sklearn(): with patch.dict("sys.modules", {"sklearn.metrics": None}): yield sklearn def test_no_sklearn(mock_no_sklearn): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires scikit-learn to be installed."): AveragePrecision() def test_no_update(): ap = AveragePrecision() with pytest.raises( NotComputableError, match=r"EpochMetric must have at least one example before it can be computed" ): ap.compute() def test_input_types(): ap = AveragePrecision() ap.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) ap.update(output1) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): ap.update((torch.randint(0, 5, size=(4, 3)), torch.randint(0, 2, size=(4, 3)))) with pytest.raises(ValueError, match=r"Incoherent types between input y and stored targets"): ap.update((torch.rand(4, 3), torch.randint(0, 2, size=(4, 3)).to(torch.int32))) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): ap.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) def test_check_shape(): ap = AveragePrecision() with pytest.raises(ValueError, match=r"Predictions should be of shape"): ap._check_shape((torch.tensor(0), torch.tensor(0))) with pytest.raises(ValueError, match=r"Predictions should be of shape"): ap._check_shape((torch.rand(4, 3, 1), torch.rand(4, 3))) with pytest.raises(ValueError, match=r"Targets should be of shape"): ap._check_shape((torch.rand(4, 3), torch.rand(4, 3, 1))) @pytest.fixture(params=[item for item in range(8)]) def test_data_binary_and_multilabel(request): return [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 1), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), # Binary input data of shape (N, L) (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 1), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 16), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_binary_and_multilabel_inputs(n_times, test_data_binary_and_multilabel): y_pred, y, batch_size = test_data_binary_and_multilabel ap = AveragePrecision() ap.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size ap.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: ap.update((y_pred, y)) np_y = y.numpy() np_y_pred = y_pred.numpy() res = ap.compute() assert isinstance(res, float) assert average_precision_score(np_y, np_y_pred) == pytest.approx(res) @pytest.fixture(params=[item for item in range(4)]) def test_data_integration_binary_and_multilabel(request): return [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(100,)).long(), torch.randint(0, 2, size=(100,)).long(), 10), (torch.randint(0, 2, size=(100, 1)).long(), torch.randint(0, 2, size=(100, 1)).long(), 10), # Binary input data of shape (N, L) (torch.randint(0, 2, size=(100, 3)).long(), torch.randint(0, 2, size=(100, 3)).long(), 10), (torch.randint(0, 2, size=(100, 4)).long(), torch.randint(0, 2, size=(100, 4)).long(), 10), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_integration_binary_and_mulitlabel_inputs(n_times, test_data_integration_binary_and_multilabel): y_pred, y, batch_size = test_data_integration_binary_and_multilabel def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) ap_metric = AveragePrecision() ap_metric.attach(engine, "ap") np_y = y.numpy() np_y_pred = y_pred.numpy() np_ap = average_precision_score(np_y, np_y_pred) data = list(range(y_pred.shape[0] // batch_size)) ap = engine.run(data, max_epochs=1).metrics["ap"] assert isinstance(ap, float) assert np_ap == pytest.approx(ap) def _test_distrib_binary_and_multilabel_inputs(device): rank = idist.get_rank() torch.manual_seed(12) def _test(y_pred, y, batch_size, metric_device): metric_device = torch.device(metric_device) ap = AveragePrecision(device=metric_device) torch.manual_seed(10 + rank) ap.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size ap.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: ap.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() res = ap.compute() assert isinstance(res, float) assert average_precision_score(np_y, np_y_pred) == pytest.approx(res) def get_test_cases(): test_cases = [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1), (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), # Binary input data of shape (N, L) (torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long(), 1), (torch.randint(0, 2, size=(10, 7)).long(), torch.randint(0, 2, size=(10, 7)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 16), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 16), ] return test_cases for _ in range(3): test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: y_pred = y_pred.to(device) y = y.to(device) _test(y_pred, y, batch_size, "cpu") if device.type != "xla": _test(y_pred, y, batch_size, idist.device()) def _test_distrib_integration_binary_input(device): rank = idist.get_rank() n_iters = 80 batch_size = 16 n_classes = 2 def _test(y_preds, y_true, n_epochs, metric_device, update_fn): metric_device = torch.device(metric_device) engine = Engine(update_fn) ap = AveragePrecision(device=metric_device) ap.attach(engine, "ap") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert "ap" in engine.state.metrics res = engine.state.metrics["ap"] true_res = average_precision_score(y_true.cpu().numpy(), y_preds.cpu().numpy()) assert pytest.approx(res) == true_res def get_tests(is_N): torch.manual_seed(12 + rank) if is_N: y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size).to(device) def update_fn(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) else: y_true = torch.randint(0, n_classes, size=(n_iters * batch_size, 10)).to(device) y_preds = torch.randint(0, n_classes, size=(n_iters * batch_size, 10)).to(device) def update_fn(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size, :], ) return y_preds, y_true, update_fn metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for _ in range(2): # Binary input data of shape (N,) y_preds, y_true, update_fn = get_tests(is_N=True) _test(y_preds, y_true, n_epochs=1, metric_device=metric_device, update_fn=update_fn) _test(y_preds, y_true, n_epochs=2, metric_device=metric_device, update_fn=update_fn) # Binary input data of shape (N, L) y_preds, y_true, update_fn = get_tests(is_N=False) _test(y_preds, y_true, n_epochs=1, metric_device=metric_device, update_fn=update_fn) _test(y_preds, y_true, n_epochs=2, metric_device=metric_device, update_fn=update_fn) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_binary_and_multilabel_inputs, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_binary_input, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_classification_report.py000066400000000000000000000241101465426447700251030ustar00rootroot00000000000000import json import os import pytest import torch from packaging.version import Version import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics.classification_report import ClassificationReport def _test_integration_multiclass(device, output_dict): rank = idist.get_rank() def _test(metric_device, n_classes, labels=None): classification_report = ClassificationReport(device=metric_device, output_dict=output_dict, labels=labels) n_iters = 80 batch_size = 16 y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) classification_report.attach(engine, "cr") data = list(range(n_iters)) engine.run(data=data) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "cr" in engine.state.metrics res = engine.state.metrics["cr"] res2 = classification_report.compute() assert res == res2 assert isinstance(res, dict if output_dict else str) if not output_dict: res = json.loads(res) from sklearn.metrics import classification_report as sklearn_classification_report sklearn_result = sklearn_classification_report( y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), output_dict=True, zero_division=1 ) for i in range(n_classes): label_i = labels[i] if labels else str(i) assert sklearn_result[str(i)]["precision"] == pytest.approx(res[label_i]["precision"]) assert sklearn_result[str(i)]["f1-score"] == pytest.approx(res[label_i]["f1-score"]) assert sklearn_result[str(i)]["recall"] == pytest.approx(res[label_i]["recall"]) assert sklearn_result["macro avg"]["precision"] == pytest.approx(res["macro avg"]["precision"]) assert sklearn_result["macro avg"]["recall"] == pytest.approx(res["macro avg"]["recall"]) assert sklearn_result["macro avg"]["f1-score"] == pytest.approx(res["macro avg"]["f1-score"]) metric_state = classification_report.state_dict() classification_report.reset() classification_report.load_state_dict(metric_state) res2 = classification_report.compute() if not output_dict: res2 = json.loads(res2) for i in range(n_classes): label_i = labels[i] if labels else str(i) assert res2[label_i]["precision"] == res[label_i]["precision"] assert res2[label_i]["f1-score"] == res[label_i]["f1-score"] assert res2[label_i]["recall"] == res[label_i]["recall"] assert res2["macro avg"]["precision"] == res["macro avg"]["precision"] assert res2["macro avg"]["recall"] == res["macro avg"]["recall"] assert res2["macro avg"]["f1-score"] == res["macro avg"]["f1-score"] for i in range(5): torch.manual_seed(12 + rank + i) # check multiple random inputs as random exact occurencies are rare metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(metric_device, 2, ["label0", "label1"]) _test(metric_device, 2) _test(metric_device, 3, ["label0", "label1", "label2"]) _test(metric_device, 3) _test(metric_device, 4, ["label0", "label1", "label2", "label3"]) _test(metric_device, 4) def _test_integration_multilabel(device, output_dict): rank = idist.get_rank() def _test(metric_device, n_epochs, labels=None): classification_report = ClassificationReport(device=metric_device, output_dict=output_dict, is_multilabel=True) n_iters = 10 batch_size = 16 n_classes = 7 y_true = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 6, 8)).to(device) y_preds = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 6, 8)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, ...], y_true[i * batch_size : (i + 1) * batch_size, ...], ) engine = Engine(update) classification_report.attach(engine, "cr") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "cr" in engine.state.metrics res = engine.state.metrics["cr"] res2 = classification_report.compute() assert res == res2 assert isinstance(res, dict if output_dict else str) if not output_dict: res = json.loads(res) np_y_preds = to_numpy_multilabel(y_preds) np_y_true = to_numpy_multilabel(y_true) from sklearn.metrics import classification_report as sklearn_classification_report sklearn_result = sklearn_classification_report(np_y_true, np_y_preds, output_dict=True, zero_division=1) for i in range(n_classes): label_i = labels[i] if labels else str(i) assert sklearn_result[str(i)]["precision"] == pytest.approx(res[label_i]["precision"]) assert sklearn_result[str(i)]["f1-score"] == pytest.approx(res[label_i]["f1-score"]) assert sklearn_result[str(i)]["recall"] == pytest.approx(res[label_i]["recall"]) assert sklearn_result["macro avg"]["precision"] == pytest.approx(res["macro avg"]["precision"]) assert sklearn_result["macro avg"]["recall"] == pytest.approx(res["macro avg"]["recall"]) assert sklearn_result["macro avg"]["f1-score"] == pytest.approx(res["macro avg"]["f1-score"]) for i in range(3): torch.manual_seed(12 + rank + i) # check multiple random inputs as random exact occurencies are rare metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(metric_device, 1) _test(metric_device, 2) _test(metric_device, 1, ["0", "1", "2", "3", "4", "5", "6"]) _test(metric_device, 2, ["0", "1", "2", "3", "4", "5", "6"]) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="Skip if < 1.7.0") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_integration_multiclass(device, True) _test_integration_multiclass(device, False) _test_integration_multilabel(device, True) _test_integration_multilabel(device, False) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="Skip if < 1.7.0") def test_distrib_gloo_cpu_or_gpu(local_rank, distributed_context_single_node_gloo): device = idist.device() _test_integration_multiclass(device, True) _test_integration_multiclass(device, False) _test_integration_multilabel(device, True) _test_integration_multilabel(device, False) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_integration_multiclass, (device, True), np=nproc, do_init=True) gloo_hvd_executor(_test_integration_multiclass, (device, False), np=nproc, do_init=True) gloo_hvd_executor(_test_integration_multilabel, (device, True), np=nproc, do_init=True) gloo_hvd_executor(_test_integration_multilabel, (device, False), np=nproc, do_init=True) def _test_distrib_xla_nprocs(index): device = idist.device() _test_integration_multiclass(device, True) _test_integration_multiclass(device, False) _test_integration_multilabel(device, True) _test_integration_multilabel(device, False) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) def to_numpy_multilabel(y): # reshapes input array to (N x ..., C) y = y.transpose(1, 0).cpu().numpy() num_classes = y.shape[0] y = y.reshape((num_classes, -1)).transpose(1, 0) return y @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_integration_multiclass(device, True) _test_integration_multiclass(device, False) _test_integration_multilabel(device, True) _test_integration_multilabel(device, False) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_integration_multiclass(device, True) _test_integration_multiclass(device, False) _test_integration_multilabel(device, True) _test_integration_multilabel(device, False) ignite-0.5.1/tests/ignite/metrics/test_cohen_kappa.py000066400000000000000000000272141465426447700227750ustar00rootroot00000000000000import os from unittest.mock import patch import pytest import sklearn import torch from sklearn.metrics import cohen_kappa_score import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import CohenKappa torch.manual_seed(12) @pytest.fixture() def mock_no_sklearn(): with patch.dict("sys.modules", {"sklearn.metrics": None}): yield sklearn def test_no_sklearn(mock_no_sklearn): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires scikit-learn to be installed."): CohenKappa() def test_no_update(): ck = CohenKappa() with pytest.raises( NotComputableError, match=r"EpochMetric must have at least one example before it can be computed" ): ck.compute() def test_input_types(): ck = CohenKappa() ck.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) ck.update(output1) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): ck.update((torch.randint(0, 5, size=(4, 3)), torch.randint(0, 2, size=(4, 3)))) with pytest.raises(ValueError, match=r"Incoherent types between input y and stored targets"): ck.update((torch.rand(4, 3), torch.randint(0, 2, size=(4, 3)).to(torch.int32))) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): ck.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) def test_check_shape(): ck = CohenKappa() with pytest.raises(ValueError, match=r"Predictions should be of shape"): ck._check_shape((torch.tensor(0), torch.tensor(0))) with pytest.raises(ValueError, match=r"Predictions should be of shape"): ck._check_shape((torch.rand(4, 3, 1), torch.rand(4, 3))) with pytest.raises(ValueError, match=r"Targets should be of shape"): ck._check_shape((torch.rand(4, 3), torch.rand(4, 3, 1))) def test_cohen_kappa_wrong_weights_type(): with pytest.raises(ValueError, match=r"Kappa Weighting type must be"): ck = CohenKappa(weights=7) with pytest.raises(ValueError, match=r"Kappa Weighting type must be"): ck = CohenKappa(weights="dd") @pytest.fixture(params=range(4)) def test_data_binary(request): return [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1), (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) @pytest.mark.parametrize("weights", [None, "linear", "quadratic"]) def test_binary_input(n_times, weights, test_data_binary): y_pred, y, batch_size = test_data_binary ck = CohenKappa(weights) ck.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size ck.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: ck.update((y_pred, y)) np_y = y.numpy() np_y_pred = y_pred.numpy() res = ck.compute() assert isinstance(res, float) assert cohen_kappa_score(np_y, np_y_pred, weights=weights) == pytest.approx(res) def test_multilabel_inputs(): ck = CohenKappa() with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"): ck.reset() ck.update((torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long())) ck.compute() with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"): ck.reset() ck.update((torch.randint(0, 2, size=(10, 6)).long(), torch.randint(0, 2, size=(10, 6)).long())) ck.compute() with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"): ck.reset() ck.update((torch.randint(0, 2, size=(10, 8)).long(), torch.randint(0, 2, size=(10, 8)).long())) ck.compute() @pytest.fixture(params=range(2)) def test_data_integration_binary(request): return [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 10), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 10), ][request.param] @pytest.mark.parametrize("n_times", range(5)) @pytest.mark.parametrize("weights", [None, "linear", "quadratic"]) def test_integration_binary_input(n_times, weights, test_data_integration_binary): y_pred, y, batch_size = test_data_integration_binary def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) ck_metric = CohenKappa(weights=weights) ck_metric.attach(engine, "ck") np_y = y.numpy() np_y_pred = y_pred.numpy() np_ck = cohen_kappa_score(np_y, np_y_pred, weights=weights) data = list(range(y_pred.shape[0] // batch_size)) ck = engine.run(data, max_epochs=1).metrics["ck"] assert isinstance(ck, float) assert np_ck == pytest.approx(ck) def _test_distrib_binary_input(device): rank = idist.get_rank() def _test(y_pred, y, batch_size, metric_device): metric_device = torch.device(metric_device) ck = CohenKappa(device=metric_device) ck.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size ck.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: ck.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() res = ck.compute() assert isinstance(res, float) assert cohen_kappa_score(np_y, np_y_pred) == pytest.approx(res) def get_test_cases(): test_cases = [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1), (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), ] return test_cases for i in range(3): torch.manual_seed(10 + rank + i) test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size, "cpu") if device.type != "xla": _test(y_pred, y, batch_size, idist.device()) def _test_distrib_integration_binary_input(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 16 torch.manual_seed(12 + rank) # Binary input data of shape (N,) or (N, 1) y_true = torch.randint(0, 2, size=(n_iters * batch_size,)).to(device) y_preds = torch.randint(0, 2, size=(n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) ck = CohenKappa(device=metric_device) ck.attach(engine, "ck") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert "ck" in engine.state.metrics res = engine.state.metrics["ck"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = cohen_kappa_score(y_true.cpu().numpy(), y_preds.cpu().numpy()) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for _ in range(2): _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_binary_input(device) _test_distrib_integration_binary_input(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_binary_input(device) _test_distrib_integration_binary_input(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_binary_input, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_binary_input, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_binary_input(device) _test_distrib_integration_binary_input(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_binary_input(device) _test_distrib_integration_binary_input(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_binary_input(device) _test_distrib_integration_binary_input(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_binary_input(device) _test_distrib_integration_binary_input(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_confusion_matrix.py000066400000000000000000000554031465426447700241150ustar00rootroot00000000000000import os import numpy as np import pytest import torch from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import ConfusionMatrix, IoU, JaccardIndex, mIoU from ignite.metrics.confusion_matrix import cmAccuracy, cmPrecision, cmRecall, DiceCoefficient torch.manual_seed(12) def test_no_update(): cm = ConfusionMatrix(10) with pytest.raises(NotComputableError, match=r"Confusion matrix must have at least one example before it "): cm.compute() def test_num_classes_wrong_input(): with pytest.raises(ValueError, match="Argument num_classes needs to be > 1"): ConfusionMatrix(num_classes=1) def test_multiclass_wrong_inputs(): cm = ConfusionMatrix(10) with pytest.raises( ValueError, match=r"y_pred must have shape \(batch_size, num_classes " r"\(currently set to 10\), ...\)" ): cm.update((torch.rand(10), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError, match=r"y_pred does not have correct number of classes:"): cm.update((torch.rand(10, 5, 4), torch.randint(0, 2, size=(10,)).long())) with pytest.raises( ValueError, match=r"y_pred must have shape \(batch_size, num_classes " r"\(currently set to 10\), ...\) " r"and y must have ", ): cm.update((torch.rand(4, 10, 12, 12), torch.randint(0, 10, size=(10,)).long())) with pytest.raises(ValueError, match=r"y and y_pred must have compatible shapes."): cm.update((torch.rand(4, 10, 12, 14), torch.randint(0, 10, size=(4, 5, 6)).long())) with pytest.raises(ValueError, match=r"Argument average can None or one of"): ConfusionMatrix(num_classes=10, average="abc") with pytest.raises(ValueError, match=r"Argument average should be one of 'samples', 'recall', 'precision'"): ConfusionMatrix.normalize(None, None) @pytest.fixture(params=[item for item in range(10)]) def test_data(request): return [ # Multiclass input data of shape (N, ) (torch.rand(10, 4), torch.randint(0, 4, size=(10,)).long(), 4, 1), (torch.rand(4, 10), torch.randint(0, 10, size=(4,)).long(), 10, 1), (torch.rand(4, 2), torch.randint(0, 2, size=(4,)).long(), 2, 1), (torch.rand(100, 5), torch.randint(0, 5, size=(100,)).long(), 5, 16), # Multiclass input data of shape (N, L) (torch.rand(10, 4, 5), torch.randint(0, 4, size=(10, 5)).long(), 4, 1), (torch.rand(4, 10, 5), torch.randint(0, 10, size=(4, 5)).long(), 10, 1), (torch.rand(100, 9, 7), torch.randint(0, 9, size=(100, 7)).long(), 9, 16), # Multiclass input data of shape (N, H, W, ...) (torch.rand(4, 5, 12, 10), torch.randint(0, 5, size=(4, 12, 10)).long(), 5, 1), (torch.rand(4, 5, 10, 12, 8), torch.randint(0, 5, size=(4, 10, 12, 8)).long(), 5, 1), (torch.rand(100, 3, 8, 8), torch.randint(0, 3, size=(100, 8, 8)).long(), 3, 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_multiclass_input(n_times, test_data): y_pred, y, num_classes, batch_size = test_data cm = ConfusionMatrix(num_classes=num_classes) cm.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size cm.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: cm.update((y_pred, y)) np_y_pred = y_pred.numpy().argmax(axis=1).ravel() np_y = y.numpy().ravel() assert np.all(confusion_matrix(np_y, np_y_pred, labels=list(range(num_classes))) == cm.compute().numpy()) def test_ignored_out_of_num_classes_indices(): num_classes = 21 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(4, num_classes, 12, 10) y = torch.randint(0, 255, size=(4, 12, 10)).long() cm.update((y_pred, y)) np_y_pred = y_pred.numpy().argmax(axis=1).ravel() np_y = y.numpy().ravel() assert np.all(confusion_matrix(np_y, np_y_pred, labels=list(range(num_classes))) == cm.compute().numpy()) def get_y_true_y_pred(): # Generate an image with labels 0 (background), 1, 2 # 3 classes: y_true = np.zeros((30, 30), dtype=np.int32) y_true[1:11, 1:11] = 1 y_true[15:25, 15:25] = 2 y_pred = np.zeros((30, 30), dtype=np.int32) y_pred[5:15, 1:11] = 1 y_pred[20:30, 20:30] = 2 return y_true, y_pred def compute_th_y_true_y_logits(y_true, y_pred): # Create torch.tensor from numpy th_y_true = torch.from_numpy(y_true).unsqueeze(0) # Create logits torch.tensor: num_classes = max(np.max(y_true), np.max(y_pred)) + 1 y_probas = np.ones((num_classes,) + y_true.shape) * -10 for i in range(num_classes): y_probas[i, (y_pred == i)] = 720 th_y_logits = torch.from_numpy(y_probas).unsqueeze(0) return th_y_true, th_y_logits def test_multiclass_images(): num_classes = 3 cm = ConfusionMatrix(num_classes=num_classes) y_true, y_pred = get_y_true_y_pred() # Compute confusion matrix with sklearn true_res = confusion_matrix(y_true.reshape(-1), y_pred.reshape(-1)) th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = cm.compute().numpy() assert np.all(true_res == res) # Another test on batch of 2 images num_classes = 3 cm = ConfusionMatrix(num_classes=num_classes) # Create a batch of two images: th_y_true1 = torch.from_numpy(y_true).reshape(1, 30, 30) th_y_true2 = torch.from_numpy(y_true.transpose()).reshape(1, 30, 30) th_y_true = torch.cat([th_y_true1, th_y_true2], dim=0) # Create a batch of 2 logits tensors y_probas = np.ones((3, 30, 30)) * -10 y_probas[0, (y_pred == 0)] = 720 y_probas[1, (y_pred == 1)] = 720 y_probas[2, (y_pred == 2)] = 768 th_y_logits1 = torch.from_numpy(y_probas).reshape(1, 3, 30, 30) y_probas = np.ones((3, 30, 30)) * -10 y_probas[0, (y_pred.transpose() == 0)] = 720 y_probas[1, (y_pred.transpose() == 2)] = 720 y_probas[2, (y_pred.transpose() == 1)] = 768 th_y_logits2 = torch.from_numpy(y_probas).reshape(1, 3, 30, 30) th_y_logits = torch.cat([th_y_logits1, th_y_logits2], dim=0) # Update metric & compute output = (th_y_logits, th_y_true) cm.update(output) res = cm.compute().numpy() # Compute confusion matrix with sklearn true_res = confusion_matrix(th_y_true.numpy().reshape(-1), np.argmax(th_y_logits.numpy(), axis=1).reshape(-1)) assert np.all(true_res == res) def test_iou_wrong_input(): with pytest.raises(TypeError, match="Argument cm should be instance of ConfusionMatrix"): IoU(None) cm = ConfusionMatrix(num_classes=10) with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given -1"): IoU(cm, ignore_index=-1) with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given a"): IoU(cm, ignore_index="a") with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given 10"): IoU(cm, ignore_index=10) with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given 11"): IoU(cm, ignore_index=11) @pytest.mark.parametrize("average", [None, "samples"]) def test_iou(average): y_true, y_pred = get_y_true_y_pred() th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_res = [0, 0, 0] for index in range(3): bin_y_true = y_true == index bin_y_pred = y_pred == index intersection = bin_y_true & bin_y_pred union = bin_y_true | bin_y_pred true_res[index] = intersection.sum() / union.sum() cm = ConfusionMatrix(num_classes=3, average=average) iou_metric = IoU(cm) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = iou_metric.compute().numpy() assert np.all(res == true_res) for ignore_index in range(3): cm = ConfusionMatrix(num_classes=3) iou_metric = IoU(cm, ignore_index=ignore_index) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = iou_metric.compute().numpy() true_res_ = true_res[:ignore_index] + true_res[ignore_index + 1 :] assert np.all(res == true_res_), f"{ignore_index}: {res} vs {true_res_}" with pytest.raises(ValueError, match=r"ConfusionMatrix should have average attribute either"): cm = ConfusionMatrix(num_classes=3, average="precision") IoU(cm) def test_miou(): y_true, y_pred = get_y_true_y_pred() th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_res = [0, 0, 0] for index in range(3): bin_y_true = y_true == index bin_y_pred = y_pred == index intersection = bin_y_true & bin_y_pred union = bin_y_true | bin_y_pred true_res[index] = intersection.sum() / union.sum() true_res_ = np.mean(true_res) cm = ConfusionMatrix(num_classes=3) iou_metric = mIoU(cm) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = iou_metric.compute().numpy() assert res == true_res_ for ignore_index in range(3): cm = ConfusionMatrix(num_classes=3) iou_metric = mIoU(cm, ignore_index=ignore_index) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = iou_metric.compute().numpy() true_res_ = np.mean(true_res[:ignore_index] + true_res[ignore_index + 1 :]) assert res == true_res_, f"{ignore_index}: {res} vs {true_res_}" def test_cm_accuracy(): y_true, y_pred = get_y_true_y_pred() th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_acc = accuracy_score(y_true.reshape(-1), y_pred.reshape(-1)) cm = ConfusionMatrix(num_classes=3) acc_metric = cmAccuracy(cm) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = acc_metric.compute().numpy() assert pytest.approx(res) == true_acc def test_cm_precision(): y_true, y_pred = np.random.randint(0, 10, size=(1000,)), np.random.randint(0, 10, size=(1000,)) th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_pr = precision_score(y_true.reshape(-1), y_pred.reshape(-1), average="macro") cm = ConfusionMatrix(num_classes=10) pr_metric = cmPrecision(cm, average=True) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = pr_metric.compute().numpy() assert pytest.approx(res) == true_pr true_pr = precision_score(y_true.reshape(-1), y_pred.reshape(-1), average=None) cm = ConfusionMatrix(num_classes=10) pr_metric = cmPrecision(cm, average=False) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = pr_metric.compute().numpy() assert np.all(res == true_pr) def test_cm_recall(): y_true, y_pred = np.random.randint(0, 10, size=(1000,)), np.random.randint(0, 10, size=(1000,)) th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_re = recall_score(y_true.reshape(-1), y_pred.reshape(-1), average="macro") cm = ConfusionMatrix(num_classes=10) re_metric = cmRecall(cm, average=True) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = re_metric.compute().numpy() assert pytest.approx(res) == true_re true_re = recall_score(y_true.reshape(-1), y_pred.reshape(-1), average=None) cm = ConfusionMatrix(num_classes=10) re_metric = cmRecall(cm, average=False) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = re_metric.compute().numpy() assert np.all(res == true_re) def test_cm_with_average(): num_classes = 5 y_pred = torch.rand(40, num_classes) y = torch.randint(0, num_classes, size=(40,)).long() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() np_y = y.numpy().ravel() cm = ConfusionMatrix(num_classes=num_classes, average="samples") cm.update((y_pred, y)) true_res = confusion_matrix(np_y, np_y_pred, labels=list(range(num_classes))) * 1.0 / len(np_y) res = cm.compute().numpy() np.testing.assert_almost_equal(true_res, res) cm = ConfusionMatrix(num_classes=num_classes, average="recall") cm.update((y_pred, y)) true_re = recall_score(np_y, np_y_pred, average=None, labels=list(range(num_classes))) res = cm.compute().numpy().diagonal() np.testing.assert_almost_equal(true_re, res) res = cm.compute().numpy() true_res = confusion_matrix(np_y, np_y_pred, normalize="true") np.testing.assert_almost_equal(true_res, res) cm = ConfusionMatrix(num_classes=num_classes, average="precision") cm.update((y_pred, y)) true_pr = precision_score(np_y, np_y_pred, average=None, labels=list(range(num_classes))) res = cm.compute().numpy().diagonal() np.testing.assert_almost_equal(true_pr, res) res = cm.compute().numpy() true_res = confusion_matrix(np_y, np_y_pred, normalize="pred") np.testing.assert_almost_equal(true_res, res) def test_dice_coefficient_wrong_input(): with pytest.raises(TypeError, match="Argument cm should be instance of ConfusionMatrix"): DiceCoefficient(None) cm = ConfusionMatrix(num_classes=10) with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given -1"): DiceCoefficient(cm, ignore_index=-1) with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given a"): DiceCoefficient(cm, ignore_index="a") with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given 10"): DiceCoefficient(cm, ignore_index=10) with pytest.raises(ValueError, match=r"ignore_index should be integer and in the range of \[0, 10\), but given 11"): DiceCoefficient(cm, ignore_index=11) def test_dice_coefficient(): y_true, y_pred = get_y_true_y_pred() th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_res = [0, 0, 0] for index in range(3): bin_y_true = y_true == index bin_y_pred = y_pred == index # dice coefficient: 2*intersection(x, y) / (|x| + |y|) # union(x, y) = |x| + |y| - intersection(x, y) intersection = bin_y_true & bin_y_pred union = bin_y_true | bin_y_pred true_res[index] = 2.0 * intersection.sum() / (union.sum() + intersection.sum()) cm = ConfusionMatrix(num_classes=3) dice_metric = DiceCoefficient(cm) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = dice_metric.compute().numpy() np.testing.assert_allclose(res, true_res) for ignore_index in range(3): cm = ConfusionMatrix(num_classes=3) dice_metric = DiceCoefficient(cm, ignore_index=ignore_index) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = dice_metric.compute().numpy() true_res_ = true_res[:ignore_index] + true_res[ignore_index + 1 :] assert np.all(res == true_res_), f"{ignore_index}: {res} vs {true_res_}" def _test_distrib_multiclass_images(device): def _test(metric_device): num_classes = 3 cm = ConfusionMatrix(num_classes=num_classes, device=metric_device) y_true, y_pred = get_y_true_y_pred() # Compute confusion matrix with sklearn true_res = confusion_matrix(y_true.reshape(-1), y_pred.reshape(-1)) th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) th_y_true = th_y_true.to(device) th_y_logits = th_y_logits.to(device) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = cm.compute().cpu().numpy() / idist.get_world_size() assert np.all(true_res == res) # Another test on batch of 2 images num_classes = 3 cm = ConfusionMatrix(num_classes=num_classes, device=metric_device) # Create a batch of two images: th_y_true1 = torch.from_numpy(y_true).reshape(1, 30, 30) th_y_true2 = torch.from_numpy(y_true.transpose()).reshape(1, 30, 30) th_y_true = torch.cat([th_y_true1, th_y_true2], dim=0) th_y_true = th_y_true.to(device) # Create a batch of 2 logits tensors y_probas = np.ones((3, 30, 30)) * -10 y_probas[0, (y_pred == 0)] = 720 y_probas[1, (y_pred == 1)] = 720 y_probas[2, (y_pred == 2)] = 768 th_y_logits1 = torch.from_numpy(y_probas).reshape(1, 3, 30, 30) y_probas = np.ones((3, 30, 30)) * -10 y_probas[0, (y_pred.transpose() == 0)] = 720 y_probas[1, (y_pred.transpose() == 2)] = 720 y_probas[2, (y_pred.transpose() == 1)] = 768 th_y_logits2 = torch.from_numpy(y_probas).reshape(1, 3, 30, 30) th_y_logits = torch.cat([th_y_logits1, th_y_logits2], dim=0) # check update if input is on another device th_y_logits = th_y_logits.to(device) # Update metric & compute output = (th_y_logits, th_y_true) cm.update(output) res = cm.compute().cpu().numpy() # Compute confusion matrix with sklearn th_y_true = idist.all_gather(th_y_true) th_y_logits = idist.all_gather(th_y_logits) np_y_true = th_y_true.cpu().numpy().reshape(-1) np_y_pred = np.argmax(th_y_logits.cpu().numpy(), axis=1).reshape(-1) true_res = confusion_matrix(np_y_true, np_y_pred) assert np.all(true_res == res) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: cm = ConfusionMatrix(num_classes=3, device=metric_device) assert cm._device == metric_device assert ( cm.confusion_matrix.device == metric_device ), f"{type(cm.confusion_matrix.device)}:{cm._num_correct.device} vs {type(metric_device)}:{metric_device}" y_true, y_pred = get_y_true_y_pred() th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) cm.update((th_y_logits, th_y_true)) assert ( cm.confusion_matrix.device == metric_device ), f"{type(cm.confusion_matrix.device)}:{cm._num_correct.device} vs {type(metric_device)}:{metric_device}" @pytest.mark.parametrize("average", [None, "samples"]) def test_jaccard_index(average): y_true, y_pred = get_y_true_y_pred() th_y_true, th_y_logits = compute_th_y_true_y_logits(y_true, y_pred) true_res = [0, 0, 0] for index in range(3): bin_y_true = y_true == index bin_y_pred = y_pred == index intersection = bin_y_true & bin_y_pred union = bin_y_true | bin_y_pred true_res[index] = intersection.sum() / union.sum() cm = ConfusionMatrix(num_classes=3, average=average) jaccard_index = JaccardIndex(cm) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = jaccard_index.compute().numpy() assert np.all(res == true_res) for ignore_index in range(3): cm = ConfusionMatrix(num_classes=3) jaccard_index_metric = JaccardIndex(cm, ignore_index=ignore_index) # Update metric output = (th_y_logits, th_y_true) cm.update(output) res = jaccard_index_metric.compute().numpy() true_res_ = true_res[:ignore_index] + true_res[ignore_index + 1 :] assert np.all(res == true_res_), f"{ignore_index}: {res} vs {true_res_}" with pytest.raises(ValueError, match=r"ConfusionMatrix should have average attribute either"): cm = ConfusionMatrix(num_classes=3, average="precision") JaccardIndex(cm) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_multiclass_images(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_multiclass_images(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_multiclass_images, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_multiclass_images(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_multiclass_images(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_multiclass_images(device) _test_distrib_accumulator_device(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_multiclass_images(device) _test_distrib_accumulator_device(device) ignite-0.5.1/tests/ignite/metrics/test_cosine_similarity.py000066400000000000000000000114751465426447700242550ustar00rootroot00000000000000from typing import Tuple import numpy as np import pytest import torch from torch import Tensor import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import CosineSimilarity def test_zero_sample(): cos_sim = CosineSimilarity() with pytest.raises( NotComputableError, match=r"CosineSimilarity must have at least one example before it can be computed" ): cos_sim.compute() @pytest.fixture(params=list(range(4))) def test_case(request): return [ (torch.randn((100, 50)), torch.randn((100, 50)), 10 ** np.random.uniform(-8, 0), 1), ( torch.normal(1.0, 2.0, size=(100, 10)), torch.normal(3.0, 4.0, size=(100, 10)), 10 ** np.random.uniform(-8, 0), 1, ), # updated batches (torch.rand((100, 128)), torch.rand((100, 128)), 10 ** np.random.uniform(-8, 0), 16), ( torch.normal(0.0, 5.0, size=(100, 30)), torch.normal(5.0, 1.0, size=(100, 30)), 10 ** np.random.uniform(-8, 0), 16, ), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case: Tuple[Tensor, Tensor, float, int]): y_pred, y, eps, batch_size = test_case cos = CosineSimilarity(eps=eps) cos.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size cos.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: cos.update((y_pred, y)) np_y = y.numpy() np_y_pred = y_pred.numpy() np_y_norm = np.clip(np.linalg.norm(np_y, axis=1, keepdims=True), eps, None) np_y_pred_norm = np.clip(np.linalg.norm(np_y_pred, axis=1, keepdims=True), eps, None) np_res = np.sum((np_y / np_y_norm) * (np_y_pred / np_y_pred_norm), axis=1) np_res = np.mean(np_res) assert isinstance(cos.compute(), float) assert pytest.approx(np_res, rel=2e-5) == cos.compute() def test_accumulator_detached(): cos = CosineSimilarity() y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.ones(2, 2, dtype=torch.float) cos.update((y_pred, y)) assert not cos._sum_of_cos_similarities.requires_grad @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): tol = 2e-5 n_iters = 100 batch_size = 10 n_dims = 100 rank = idist.get_rank() torch.manual_seed(12 + rank) device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: y_true = torch.randn((n_iters * batch_size, n_dims)).float().to(device) y_preds = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_dims)).float().to(device) engine = Engine( lambda e, i: ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) ) m = CosineSimilarity(device=metric_device) m.attach(engine, "cosine_similarity") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "cosine_similarity" in engine.state.metrics res = engine.state.metrics["cosine_similarity"] y_true_np = y_true.cpu().numpy() y_preds_np = y_preds.cpu().numpy() y_true_norm = np.clip(np.linalg.norm(y_true_np, axis=1, keepdims=True), 1e-8, None) y_preds_norm = np.clip(np.linalg.norm(y_preds_np, axis=1, keepdims=True), 1e-8, None) true_res = np.sum((y_true_np / y_true_norm) * (y_preds_np / y_preds_norm), axis=1) true_res = np.mean(true_res) assert pytest.approx(res, rel=tol) == true_res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: cos = CosineSimilarity(device=metric_device) for dev in (cos._device, cos._sum_of_cos_similarities.device): assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]]).float() y = torch.ones(2, 2).float() cos.update((y_pred, y)) for dev in (cos._device, cos._sum_of_cos_similarities.device): assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_dill.py000066400000000000000000000013131465426447700214410ustar00rootroot00000000000000import dill from ignite.metrics import Metric class Accumulation(Metric): def __init__(self): self.value = 0 super(Accumulation, self).__init__() def reset(self): self.value = 0 def compute(self): return self.value def update(self, output): self.value += output def test_metric(): def _test(m, values, e): for v in values: m.update(v) assert m.compute() == e metric = Accumulation() m1 = dill.loads(dill.dumps(metric)) values = list(range(10)) expected = sum(values) _test(m1, values, expected) metric.update(5) m2 = dill.loads(dill.dumps(metric)) _test(m2, values, expected + 5) ignite-0.5.1/tests/ignite/metrics/test_entropy.py000066400000000000000000000106531465426447700222240ustar00rootroot00000000000000import numpy as np import pytest import torch from scipy.special import softmax from scipy.stats import entropy as scipy_entropy import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import Entropy def np_entropy(np_y_pred: np.ndarray): prob = softmax(np_y_pred, axis=1) ent = np.mean(scipy_entropy(prob, axis=1)) return ent def test_zero_sample(): ent = Entropy() with pytest.raises(NotComputableError, match=r"Entropy must have at least one example before it can be computed"): ent.compute() def test_invalid_shape(): ent = Entropy() y_pred = torch.randn(10).float() with pytest.raises(ValueError, match=r"y_pred must be in the shape of \(B, C\) or \(B, C, ...\), got"): ent.update((y_pred, None)) @pytest.fixture(params=[item for item in range(4)]) def test_case(request): return [ (torch.randn((100, 10)), torch.randint(0, 10, size=[100]), 1), (torch.rand((100, 500)), torch.randint(0, 500, size=[100]), 1), # updated batches (torch.normal(0.0, 5.0, size=(100, 10)), torch.randint(0, 10, size=[100]), 16), (torch.normal(5.0, 3.0, size=(100, 200)), torch.randint(0, 200, size=[100]), 16), # image segmentation (torch.randn((100, 5, 32, 32)), torch.randint(0, 5, size=(100, 32, 32)), 16), (torch.randn((100, 5, 224, 224)), torch.randint(0, 5, size=(100, 224, 224)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case): ent = Entropy() y_pred, y, batch_size = test_case ent.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size ent.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: ent.update((y_pred, y)) np_res = np_entropy(y_pred.numpy()) assert isinstance(ent.compute(), float) assert pytest.approx(ent.compute()) == np_res def test_accumulator_detached(): ent = Entropy() y_pred = torch.tensor([[2.0, 3.0], [-2.0, -1.0]], requires_grad=True) y = torch.zeros(2) ent.update((y_pred, y)) assert not ent._sum_of_entropies.requires_grad @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): tol = 1e-6 device = idist.device() rank = idist.get_rank() torch.manual_seed(12 + rank) n_iters = 100 batch_size = 10 n_cls = 50 metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: y_true = torch.randint(0, n_cls, size=[n_iters * batch_size], dtype=torch.long).to(device) y_preds = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_cls), dtype=torch.float).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = Entropy(device=metric_device) m.attach(engine, "entropy") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "entropy" in engine.state.metrics res = engine.state.metrics["entropy"] true_res = np_entropy(y_preds.cpu().numpy()) assert pytest.approx(res, rel=tol) == true_res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: device = torch.device(device) ent = Entropy(device=metric_device) for dev in [ent._device, ent._sum_of_entropies.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) ent.update((y_pred, y)) for dev in [ent._device, ent._sum_of_entropies.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_epoch_metric.py000066400000000000000000000161741465426447700231710ustar00rootroot00000000000000import pytest import torch import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics import EpochMetric from ignite.metrics.epoch_metric import EpochMetricWarning, NotComputableError def test_epoch_metric_wrong_setup_or_input(): # Wrong compute function with pytest.raises(TypeError, match=r"Argument compute_fn should be callable."): EpochMetric(12345) def compute_fn(y_preds, y_targets): return 0.0 em = EpochMetric(compute_fn) # Wrong input dims with pytest.raises(ValueError, match=r"Predictions should be of shape"): output = (torch.tensor(0), torch.tensor(0)) em.update(output) # Wrong input dims with pytest.raises(ValueError, match=r"Targets should be of shape"): output = (torch.rand(4, 3), torch.rand(4, 3, 1)) em.update(output) # Wrong input dims with pytest.raises(ValueError, match=r"Predictions should be of shape"): output = (torch.rand(4, 3, 1), torch.rand(4, 3)) em.update(output) em.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output1) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): output2 = (torch.randint(0, 5, size=(4, 3)), torch.randint(0, 2, size=(4, 3))) em.update(output2) with pytest.raises(ValueError, match=r"Incoherent types between input y and stored targets"): output2 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3)).to(torch.int32)) em.update(output2) with pytest.raises( NotComputableError, match="EpochMetric must have at least one example before it can be computed" ): em = EpochMetric(compute_fn) em.compute() def test_epoch_metric(): def compute_fn(y_preds, y_targets): return 0.0 em = EpochMetric(compute_fn) em.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output1) output2 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output2) assert all([t.device.type == "cpu" for t in em._predictions + em._targets]) assert torch.equal(em._predictions[0], output1[0]) assert torch.equal(em._predictions[1], output2[0]) assert torch.equal(em._targets[0], output1[1]) assert torch.equal(em._targets[1], output2[1]) assert em.compute() == 0.0 # test when y and y_pred are (batch_size, 1) that are squeezed to (batch_size, ) em.reset() output1 = (torch.rand(4, 1), torch.randint(0, 2, size=(4, 1), dtype=torch.long)) em.update(output1) output2 = (torch.rand(4, 1), torch.randint(0, 2, size=(4, 1), dtype=torch.long)) em.update(output2) assert all([t.device.type == "cpu" for t in em._predictions + em._targets]) assert torch.equal(em._predictions[0], output1[0][:, 0]) assert torch.equal(em._predictions[1], output2[0][:, 0]) assert torch.equal(em._targets[0], output1[1][:, 0]) assert torch.equal(em._targets[1], output2[1][:, 0]) assert em.compute() == 0.0 def test_mse_epoch_metric(): def compute_fn(y_preds, y_targets): return torch.mean(((y_preds - y_targets.type_as(y_preds)) ** 2)).item() em = EpochMetric(compute_fn) em.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output1) output2 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output2) output3 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output3) preds = torch.cat([output1[0], output2[0], output3[0]], dim=0) targets = torch.cat([output1[1], output2[1], output3[1]], dim=0) result = em.compute() assert result == compute_fn(preds, targets) em.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output1) output2 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output2) output3 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) em.update(output3) preds = torch.cat([output1[0], output2[0], output3[0]], dim=0) targets = torch.cat([output1[1], output2[1], output3[1]], dim=0) result = em.compute() assert result == compute_fn(preds, targets) def test_bad_compute_fn(): def compute_fn(y_preds, y_targets): # Following will raise the error: # The size of tensor a (3) must match the size of tensor b (4) # at non-singleton dimension 1 return torch.mean(y_preds - y_targets).item() em = EpochMetric(compute_fn) em.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 4), dtype=torch.long)) with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"): em.update(output1) def test_check_compute_fn(): def compute_fn(y_preds, y_targets): raise Exception em = EpochMetric(compute_fn, check_compute_fn=True) em.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"): em.update(output1) em = EpochMetric(compute_fn, check_compute_fn=False) em.update(output1) def test_distrib_integration(distributed): device = idist.device() if idist.device().type != "xla" else "cpu" rank = idist.get_rank() torch.manual_seed(40 + rank) n_iters = 3 batch_size = 2 n_classes = 7 y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,), device=device) y_preds = torch.rand(n_iters * batch_size, n_classes, device=device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) def assert_data_fn(all_preds, all_targets): return (all_preds.argmax(dim=1) == all_targets).sum().item() ep_metric = EpochMetric(assert_data_fn, check_compute_fn=False, device=device) ep_metric.attach(engine, "epm") data = list(range(n_iters)) engine.run(data=data, max_epochs=3) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) ep_metric_true = (y_preds.argmax(dim=1) == y_true).sum().item() assert engine.state.metrics["epm"] == ep_metric_true assert ep_metric.compute() == ep_metric_true def test_skip_unrolling(): def compute_fn(y_preds, y_targets): return 0.0 em = EpochMetric(compute_fn, skip_unrolling=True) em.reset() output1 = (torch.rand(4, 2), torch.randint(0, 2, size=(4, 2), dtype=torch.long)) em.update(output1) output2 = (torch.rand(4, 2), torch.randint(0, 2, size=(4, 2), dtype=torch.long)) em.update(output2) assert all([t.device.type == "cpu" for t in em._predictions + em._targets]) assert torch.equal(em._predictions[0], output1[0]) assert torch.equal(em._predictions[1], output2[0]) assert torch.equal(em._targets[0], output1[1]) assert torch.equal(em._targets[1], output2[1]) assert em.compute() == 0.0 ignite-0.5.1/tests/ignite/metrics/test_fbeta.py000066400000000000000000000166041465426447700216070ustar00rootroot00000000000000import os import numpy as np import pytest import torch from sklearn.metrics import fbeta_score import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics import Fbeta, Precision, Recall torch.manual_seed(12) def test_wrong_inputs(): with pytest.raises(ValueError, match=r"Beta should be a positive integer"): Fbeta(0.0) with pytest.raises(ValueError, match=r"Input precision metric should have average=False"): p = Precision(average="micro") Fbeta(1.0, precision=p) with pytest.raises(ValueError, match=r"Input recall metric should have average=False"): r = Recall(average="samples") Fbeta(1.0, recall=r) with pytest.raises(ValueError, match=r"If precision argument is provided, output_transform should be None"): p = Precision(average=False) Fbeta(1.0, precision=p, output_transform=lambda x: x) with pytest.raises(ValueError, match=r"If recall argument is provided, output_transform should be None"): r = Recall(average=False) Fbeta(1.0, recall=r, output_transform=lambda x: x) def _output_transform(output): return output["y_pred"], output["y"] @pytest.mark.parametrize( "p, r, average, output_transform", [ (None, None, False, None), (None, None, True, None), (None, None, False, _output_transform), (None, None, True, _output_transform), (Precision(average=False), Recall(average=False), False, None), (Precision(average=False), Recall(average=False), True, None), ], ) def test_integration(p, r, average, output_transform): np.random.seed(1) n_iters = 10 batch_size = 10 n_classes = 10 y_true = np.arange(0, n_iters * batch_size, dtype="int64") % n_classes y_pred = 0.2 * np.random.rand(n_iters * batch_size, n_classes) for i in range(n_iters * batch_size): if np.random.rand() > 0.4: y_pred[i, y_true[i]] = 1.0 else: j = np.random.randint(0, n_classes) y_pred[i, j] = 0.7 y_true_batch_values = iter(y_true.reshape(n_iters, batch_size)) y_pred_batch_values = iter(y_pred.reshape(n_iters, batch_size, n_classes)) def update_fn(engine, batch): y_true_batch = next(y_true_batch_values) y_pred_batch = next(y_pred_batch_values) if output_transform is not None: return {"y_pred": torch.from_numpy(y_pred_batch), "y": torch.from_numpy(y_true_batch)} return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) evaluator = Engine(update_fn) f2 = Fbeta(beta=2.0, average=average, precision=p, recall=r, output_transform=output_transform) f2.attach(evaluator, "f2") data = list(range(n_iters)) state = evaluator.run(data, max_epochs=1) f2_true = fbeta_score(y_true, np.argmax(y_pred, axis=-1), average="macro" if average else None, beta=2.0) np.testing.assert_allclose(np.array(f2_true), np.array(state.metrics["f2"])) def _test_distrib_integration(device): rank = idist.get_rank() def _test(p, r, average, n_epochs, metric_device): n_iters = 60 batch_size = 16 n_classes = 7 torch.manual_seed(12 + rank) y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) fbeta = Fbeta(beta=2.5, average=average, device=metric_device) fbeta.attach(engine, "f2.5") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "f2.5" in engine.state.metrics res = engine.state.metrics["f2.5"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = fbeta_score( y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), beta=2.5, average="macro" if average else None, ) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(None, None, average=True, n_epochs=1, metric_device=metric_device) _test(None, None, average=True, n_epochs=2, metric_device=metric_device) precision = Precision(average=False, device=metric_device) recall = Recall(average=False, device=metric_device) _test(precision, recall, average=False, n_epochs=1, metric_device=metric_device) _test(precision, recall, average=False, n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) ignite-0.5.1/tests/ignite/metrics/test_frequency.py000066400000000000000000000103711465426447700225220ustar00rootroot00000000000000import os import sys import time import pytest import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.metrics import Frequency if sys.platform.startswith("darwin"): pytest.skip("Skip if on MacOS", allow_module_level=True) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_nondistributed_average(): artificial_time = 1 # seconds num_tokens = 100 average_upper_bound = num_tokens / artificial_time average_lower_bound = average_upper_bound * 0.9 freq_metric = Frequency() freq_metric.reset() time.sleep(artificial_time) freq_metric.update(num_tokens) average = freq_metric.compute() assert average_lower_bound < average < average_upper_bound def _test_frequency_with_engine(workers=None, lower_bound_factor=0.8, upper_bound_factor=1.1, every=1): if workers is None: workers = idist.get_world_size() artificial_time = 1.0 / workers # seconds total_tokens = 400 // workers batch_size = 128 // workers estimated_wps = batch_size * workers / artificial_time def update_fn(engine, batch): time.sleep(artificial_time) return {"ntokens": len(batch)} engine = Engine(update_fn) wps_metric = Frequency(output_transform=lambda x: x["ntokens"]) event = Events.ITERATION_COMPLETED(every=every) wps_metric.attach(engine, "wps", event_name=event) @engine.on(event) def assert_wps(e): wps = e.state.metrics["wps"] # Skip iterations 2, 3, 4 if backend is Horovod on CUDA, # wps is abnormally low for these iterations # otherwise, other values of wps are OK if idist.model_name() == "horovod-dist" and e.state.iteration in (2, 3, 4): return low_wps = estimated_wps * lower_bound_factor high_wps = estimated_wps * upper_bound_factor assert low_wps < wps <= high_wps, f"{e.state.iteration}: {low_wps} < {wps} <= {high_wps}" data = [[i] * batch_size for i in range(0, total_tokens, batch_size)] engine.run(data, max_epochs=2) @pytest.mark.skipif(sys.platform.startswith("win"), reason="Skip on Windows") def test_frequency_with_engine(): _test_frequency_with_engine(workers=1) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_frequency_with_engine_distributed(distributed_context_single_node_gloo): _test_frequency_with_engine(workers=idist.get_world_size()) def test_frequency_with_engine_with_every(): _test_frequency_with_engine(workers=1, every=1) _test_frequency_with_engine(workers=1, every=10) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_frequency_with_engine_distributed_with_every(distributed_context_single_node_gloo): _test_frequency_with_engine(workers=idist.get_world_size(), every=1) _test_frequency_with_engine(workers=idist.get_world_size(), every=10) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_frequency_with_engine, (None, 0.8, 1), np=nproc, do_init=True) gloo_hvd_executor(_test_frequency_with_engine, (None, 0.8, 10), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): _test_frequency_with_engine(workers=idist.get_world_size(), every=10) def _test_distrib_xla_nprocs(index): _test_frequency_with_engine(workers=idist.get_world_size(), every=10) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_gpu_info.py000066400000000000000000000107751465426447700223370ustar00rootroot00000000000000from unittest.mock import Mock, patch import pytest import torch from ignite.engine import Engine, State from ignite.metrics import GpuInfo def test_no_pynvml_package(): with patch.dict("sys.modules", {"pynvml.smi": None}): with pytest.raises(ModuleNotFoundError, match="This contrib module requires pynvml to be installed."): GpuInfo() @pytest.mark.skipif(torch.cuda.is_available(), reason="Skip if GPU") def test_no_gpu(): with pytest.raises(RuntimeError, match="This contrib module requires available GPU"): GpuInfo() def _test_gpu_info(device="cpu"): gpu_info = GpuInfo() # increase code cov gpu_info.reset() gpu_info.update(None) t = torch.rand(4, 10, 100, 100).to(device) data = gpu_info.compute() assert len(data) > 0 assert "fb_memory_usage" in data[0] mem_report = data[0]["fb_memory_usage"] assert "used" in mem_report and "total" in mem_report assert mem_report["total"] > 0.0 assert mem_report["used"] > t.shape[0] * t.shape[1] * t.shape[2] * t.shape[3] / 1024.0 / 1024.0 assert "utilization" in data[0] util_report = data[0]["utilization"] assert "gpu_util" in util_report # with Engine engine = Engine(lambda engine, batch: 0.0) engine.state = State(metrics={}) gpu_info.completed(engine, name="gpu") assert "gpu:0 mem(%)" in engine.state.metrics assert isinstance(engine.state.metrics["gpu:0 mem(%)"], int) assert int(mem_report["used"] * 100.0 / mem_report["total"]) == engine.state.metrics["gpu:0 mem(%)"] if util_report["gpu_util"] != "N/A": assert "gpu:0 util(%)" in engine.state.metrics assert isinstance(engine.state.metrics["gpu:0 util(%)"], int) assert int(util_report["gpu_util"]) == engine.state.metrics["gpu:0 util(%)"] else: assert "gpu:0 util(%)" not in engine.state.metrics @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_gpu_info_on_cuda(): _test_gpu_info(device="cuda") query_resp = None @pytest.fixture def mock_pynvml_module(): with patch.dict( "sys.modules", { "pynvml": Mock(name="pynvml"), "pynvml.smi": Mock(name="pynvml.smi"), "pynvml.smi.nvidia_smi": Mock(name="pynvml.smi.nvidia_smi"), }, ): import pynvml from pynvml.smi import nvidia_smi def query(*args, **kwargs): return query_resp def getInstance(): nvsmi = Mock() nvsmi.DeviceQuery = Mock(side_effect=query) return nvsmi nvidia_smi.getInstance = Mock(side_effect=getInstance) yield pynvml @pytest.fixture def mock_gpu_is_available(): with patch("ignite.metrics.gpu_info.torch.cuda") as mock_cuda: mock_cuda.is_available.return_value = True yield mock_cuda @pytest.mark.skipif(torch.cuda.is_available(), reason="No need to mock if has GPU") def test_gpu_info_mock(mock_pynvml_module, mock_gpu_is_available): global query_resp query_resp = {"gpu": [{"fb_memory_usage": {"used": 100.0, "total": 11000.0}, "utilization": {"gpu_util": 50.0}}]} assert torch.cuda.is_available() _test_gpu_info() # Tests https://github.com/pytorch/ignite/issues/1040 query_resp = {"gpu": [{"fb_memory_usage": {"used": 100.0, "total": 11000.0}, "utilization": {"gpu_util": "N/A"}}]} _test_gpu_info() def _test_with_custom_query(resp, warn_msg, check_compute=False): from pynvml.smi import nvidia_smi def query(*args, **kwargs): return resp def getInstance(): nvsmi = Mock() nvsmi.DeviceQuery = Mock(side_effect=query) return nvsmi nvidia_smi.getInstance = Mock(side_effect=getInstance) gpu_info = GpuInfo() if check_compute: with pytest.warns(UserWarning, match=warn_msg): gpu_info.compute() # with Engine engine = Engine(lambda engine, batch: 0.0) engine.state = State(metrics={}) with pytest.warns(UserWarning, match=warn_msg): gpu_info.completed(engine, name="gpu info") # No GPU info _test_with_custom_query(resp={}, warn_msg=r"No GPU information available", check_compute=True) # No GPU memory info _test_with_custom_query(resp={"gpu": [{"utilization": {}}]}, warn_msg=r"No GPU memory usage information available") # No GPU utilization info _test_with_custom_query( resp={"gpu": [{"fb_memory_usage": {}}]}, warn_msg=r"No GPU utilization information available" ) ignite-0.5.1/tests/ignite/metrics/test_js_divergence.py000066400000000000000000000122331465426447700233270ustar00rootroot00000000000000from typing import Tuple import numpy as np import pytest import torch from scipy.spatial.distance import jensenshannon from scipy.special import softmax from torch import Tensor import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import JSDivergence def scipy_js_div(np_y_pred: np.ndarray, np_y: np.ndarray) -> float: y_pred_prob = softmax(np_y_pred, axis=1) y_prob = softmax(np_y, axis=1) # jensenshannon computes the sqrt of the JS divergence js_mean = np.mean(np.square(jensenshannon(y_pred_prob, y_prob, axis=1))) return js_mean def test_zero_sample(): js_div = JSDivergence() with pytest.raises( NotComputableError, match=r"JSDivergence must have at least one example before it can be computed" ): js_div.compute() def test_shape_mismatch(): js_div = JSDivergence() y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.tensor([[-2.0, 1.0]], dtype=torch.float) with pytest.raises(ValueError, match=r"y_pred and y must be in the same shape, got"): js_div.update((y_pred, y)) def test_invalid_shape(): js_div = JSDivergence() y_pred = torch.tensor([2.0, 3.0], dtype=torch.float) y = torch.tensor([4.0, 5.0], dtype=torch.float) with pytest.raises(ValueError, match=r"y_pred must be in the shape of \(B, C\) or \(B, C, ...\), got"): js_div.update((y_pred, y)) @pytest.fixture(params=list(range(4))) def test_case(request): return [ (torch.randn((100, 10)), torch.rand((100, 10)), 1), (torch.rand((100, 500)), torch.randn((100, 500)), 1), # updated batches (torch.normal(0.0, 5.0, size=(100, 10)), torch.rand((100, 10)), 16), (torch.normal(5.0, 3.0, size=(100, 200)), torch.rand((100, 200)), 16), # image segmentation (torch.randn((100, 5, 32, 32)), torch.rand((100, 5, 32, 32)), 16), (torch.rand((100, 5, 224, 224)), torch.randn((100, 5, 224, 224)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case: Tuple[Tensor, Tensor, int]): y_pred, y, batch_size = test_case js_div = JSDivergence() js_div.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size js_div.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: js_div.update((y_pred, y)) res = js_div.compute() np_y_pred = y_pred.numpy() np_y = y.numpy() np_res = scipy_js_div(np_y_pred, np_y) assert isinstance(res, float) assert pytest.approx(np_res, rel=1e-4) == res def test_accumulator_detached(): js_div = JSDivergence() y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.tensor([[-2.0, 1.0], [2.0, 3.0]], dtype=torch.float) js_div.update((y_pred, y)) assert not js_div._sum_of_kl.requires_grad @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): tol = 1e-4 n_iters = 100 batch_size = 10 n_dims = 100 rank = idist.get_rank() torch.manual_seed(12 + rank) device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: y_true = torch.randn((n_iters * batch_size, n_dims)).float().to(device) y_preds = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_dims)).float().to(device) engine = Engine( lambda e, i: ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) ) m = JSDivergence(device=metric_device) m.attach(engine, "js_div") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "js_div" in engine.state.metrics res = engine.state.metrics["js_div"] y_true_np = y_true.cpu().numpy() y_preds_np = y_preds.cpu().numpy() true_res = scipy_js_div(y_preds_np, y_true_np) assert pytest.approx(true_res, rel=tol) == res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: js_div = JSDivergence(device=metric_device) for dev in (js_div._device, js_div._sum_of_kl.device): assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]]).float() y = torch.ones(2, 2).float() js_div.update((y_pred, y)) for dev in (js_div._device, js_div._sum_of_kl.device): assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_kl_divergence.py000066400000000000000000000120741465426447700233240ustar00rootroot00000000000000from typing import Tuple import numpy as np import pytest import torch from scipy.special import softmax from scipy.stats import entropy from torch import Tensor import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import KLDivergence def scipy_kl_div(np_y_pred: np.ndarray, np_y: np.ndarray) -> float: y_pred_prob = softmax(np_y_pred, axis=1) y_prob = softmax(np_y, axis=1) kl_mean = entropy(y_prob, y_pred_prob, axis=1).mean() return kl_mean def test_zero_sample(): kl_div = KLDivergence() with pytest.raises( NotComputableError, match=r"KLDivergence must have at least one example before it can be computed" ): kl_div.compute() def test_shape_mismatch(): kl_div = KLDivergence() y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.tensor([[-2.0, 1.0]], dtype=torch.float) with pytest.raises(ValueError, match=r"y_pred and y must be in the same shape, got"): kl_div.update((y_pred, y)) def test_invalid_shape(): kl_div = KLDivergence() y_pred = torch.tensor([2.0, 3.0], dtype=torch.float) y = torch.tensor([4.0, 5.0], dtype=torch.float) with pytest.raises(ValueError, match=r"y_pred must be in the shape of \(B, C\) or \(B, C, ...\), got"): kl_div.update((y_pred, y)) @pytest.fixture(params=list(range(4))) def test_case(request): return [ (torch.randn((100, 10)), torch.rand((100, 10)), 1), (torch.rand((100, 500)), torch.randn((100, 500)), 1), # updated batches (torch.normal(0.0, 5.0, size=(100, 10)), torch.rand((100, 10)), 16), (torch.normal(5.0, 3.0, size=(100, 200)), torch.rand((100, 200)), 16), # image segmentation (torch.randn((100, 5, 32, 32)), torch.rand((100, 5, 32, 32)), 16), (torch.rand((100, 5, 224, 224)), torch.randn((100, 5, 224, 224)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case: Tuple[Tensor, Tensor, int]): y_pred, y, batch_size = test_case kl_div = KLDivergence() kl_div.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size kl_div.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: kl_div.update((y_pred, y)) res = kl_div.compute() np_y_pred = y_pred.numpy() np_y = y.numpy() np_res = scipy_kl_div(np_y_pred, np_y) assert isinstance(res, float) assert pytest.approx(np_res, rel=1e-4) == res def test_accumulator_detached(): kl_div = KLDivergence() y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.tensor([[-2.0, 1.0], [2.0, 3.0]], dtype=torch.float) kl_div.update((y_pred, y)) assert not kl_div._sum_of_kl.requires_grad @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): tol = 1e-4 n_iters = 100 batch_size = 10 n_dims = 100 rank = idist.get_rank() torch.manual_seed(12 + rank) device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: y_true = torch.randn((n_iters * batch_size, n_dims)).float().to(device) y_preds = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_dims)).float().to(device) engine = Engine( lambda e, i: ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) ) m = KLDivergence(device=metric_device) m.attach(engine, "kl_div") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "kl_div" in engine.state.metrics res = engine.state.metrics["kl_div"] y_true_np = y_true.cpu().numpy() y_preds_np = y_preds.cpu().numpy() true_res = scipy_kl_div(y_preds_np, y_true_np) assert pytest.approx(true_res, rel=tol) == res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: kl_div = KLDivergence(device=metric_device) for dev in (kl_div._device, kl_div._sum_of_kl.device): assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0, 3.0], [-2.0, 1.0]]).float() y = torch.ones(2, 2).float() kl_div.update((y_pred, y)) for dev in (kl_div._device, kl_div._sum_of_kl.device): assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_loss.py000066400000000000000000000274651465426447700215150ustar00rootroot00000000000000import os from typing import Tuple from unittest.mock import MagicMock import pytest import torch from numpy.testing import assert_almost_equal from torch import nn from torch.nn.functional import mse_loss, nll_loss import ignite.distributed as idist from ignite.engine import State from ignite.exceptions import NotComputableError from ignite.metrics import Loss, Precision class DummyLoss1(Loss): def __init__(self, loss_fn, true_output, output_transform=lambda x: x): super(DummyLoss1, self).__init__(loss_fn, output_transform=output_transform) print(true_output) self.true_output = true_output def reset(self): pass def compute(self): pass def update(self, output): assert output == self.true_output def test_output_as_mapping_without_criterion_kwargs(): y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) criterion_kwargs = {} loss_metric = DummyLoss1(nll_loss, true_output=(y_pred, y, criterion_kwargs)) state = State(output=({"y_pred": y_pred, "y": y, "criterion_kwargs": {}})) engine = MagicMock(state=state) loss_metric.iteration_completed(engine) def test_output_as_mapping_with_criterion_kwargs(): y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) criterion_kwargs = {"reduction": "sum"} loss_metric = DummyLoss1(nll_loss, true_output=(y_pred, y, criterion_kwargs)) state = State(output=({"y_pred": y_pred, "y": y, "criterion_kwargs": {"reduction": "sum"}})) engine = MagicMock(state=state) loss_metric.iteration_completed(engine) def y_test_1(requires_grad=False, device=None): return ( torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]], device=device, requires_grad=requires_grad).log(), torch.tensor([2, 2], device=device).long(), 1.1512925625, ) def y_test_2(): return ( torch.tensor([[0.1, 0.3, 0.6], [0.6, 0.2, 0.2], [0.2, 0.7, 0.1]]).log(), torch.tensor([2, 0, 2]).long(), 1.1253643036, ) def y_test_3(): return torch.tensor([[0.1, 0.3, 0.6], [0.6, 0.2, 0.2]]).log(), torch.tensor([2, 0]).long() def test_zero_div(): loss = Loss(nll_loss) with pytest.raises(NotComputableError, match=r"Loss must have at least one example before it can be computed"): loss.compute() @pytest.mark.parametrize("criterion", [nll_loss, nn.NLLLoss()]) def test_compute(criterion): loss = Loss(criterion) y_pred, y, expected_loss = y_test_1() loss.update((y_pred, y)) assert_almost_equal(loss.compute(), expected_loss) y_pred, y, expected_loss = y_test_2() loss.update((y_pred, y)) assert_almost_equal(loss.compute(), expected_loss) # average def test_non_averaging_loss(): loss = Loss(nn.NLLLoss(reduction="none")) y_pred, y, _ = y_test_1() with pytest.raises(ValueError): loss.update((y_pred, y)) def test_gradient_based_loss(): # Tests https://github.com/pytorch/ignite/issues/1674 x = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]], requires_grad=True) y_pred = x.mm(torch.randn(size=(3, 1))) def loss_fn(y_pred, x): gradients = torch.autograd.grad( outputs=y_pred, inputs=x, grad_outputs=torch.ones_like(y_pred), create_graph=True )[0] gradients = gradients.flatten(start_dim=1) return gradients.norm(2, dim=1).mean() loss = Loss(loss_fn) loss.update((y_pred, x)) def test_kwargs_loss(): loss = Loss(nll_loss) y_pred, y, _ = y_test_1() kwargs = {"weight": torch.tensor([0.1, 0.1, 0.1])} loss.update((y_pred, y, kwargs)) expected_value = nll_loss(y_pred, y, **kwargs) assert_almost_equal(loss.compute(), expected_value) def test_reset(): loss = Loss(nll_loss) y_pred, y = y_test_3() loss.update((y_pred, y)) loss.compute() loss.reset() with pytest.raises(NotComputableError): loss.compute() def _test_distrib_compute_on_criterion(device, y_test_1, y_test_2, tol=None): def _test(metric_device, y_test_1, y_test_2): criterion = nn.NLLLoss().to(device) loss = Loss(criterion, device=metric_device) y_pred, y, _ = y_test_1 loss.update((y_pred, y)) n = loss._num_examples assert n == len(y) res = loss.compute() assert n == loss._num_examples y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) true_loss_value = criterion(y_pred, y) assert_almost_equal(res, true_loss_value.item()) loss.reset() y_pred, y, _ = y_test_2 loss.update((y_pred, y)) n = loss._num_examples res = loss.compute() assert n == loss._num_examples y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) true_loss_value = criterion(y_pred, y) if tol is None: assert_almost_equal(res, true_loss_value.item()) else: assert pytest.approx(res, rel=tol) == true_loss_value.item() _test("cpu", y_test_1, y_test_2) if device.type != "xla": _test(idist.device(), y_test_1, y_test_2) def _test_distrib_accumulator_device(device, y_test_1): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: loss = Loss(nll_loss, device=metric_device) assert loss._device == metric_device assert ( loss._sum.device == metric_device ), f"{type(loss._sum.device)}:{loss._sum.device} vs {type(metric_device)}:{metric_device}" y_pred, y, _ = y_test_1 loss.update((y_pred, y)) assert ( loss._sum.device == metric_device ), f"{type(loss._sum.device)}:{loss._sum.device} vs {type(metric_device)}:{metric_device}" def test_sum_detached(): loss = Loss(nll_loss) y_pred, y, _ = y_test_1(requires_grad=True) loss.update((y_pred, y)) assert not loss._sum.requires_grad @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute_on_criterion(device, y_test_1(), y_test_2()) _test_distrib_accumulator_device(device, y_test_1()) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute_on_criterion(device, y_test_1(), y_test_2()) _test_distrib_accumulator_device(device, y_test_1()) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute_on_criterion, (device, y_test_1(), y_test_2()), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device, y_test_1()), np=nproc, do_init=True) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute_on_criterion(device, y_test_1(), y_test_2()) _test_distrib_accumulator_device(device, y_test_1()) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute_on_criterion(device, y_test_1(), y_test_2()) _test_distrib_accumulator_device(device, y_test_1()) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute_on_criterion(device, y_test_1(), y_test_2(), tol=1e-6) _test_distrib_accumulator_device(device, y_test_1()) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute_on_criterion(device, y_test_1(), y_test_2()) _test_distrib_accumulator_device(device, y_test_1()) def test_override_required_output_keys(): # https://github.com/pytorch/ignite/issues/1415 from ignite.engine import create_supervised_evaluator counter = [0] class DummyLoss2(Loss): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def update(self, output): y_pred, y, criterion_kwargs = output assert y_pred.shape == (4, 3) assert y.shape == (4,) assert criterion_kwargs == c_kwargs assert y.equal(data[counter[0]][1]) counter[0] += 1 def reset(self): pass def compute(self): pass model = nn.Linear(10, 3) metrics = {"Precision": Precision(), "DummyLoss2": DummyLoss2(nll_loss)} # global criterion kwargs c_kwargs = {"reduction": "sum"} evaluator = create_supervised_evaluator( model, metrics=metrics, output_transform=lambda x, y, y_pred: {"x": x, "y": y, "y_pred": y_pred, "criterion_kwargs": c_kwargs}, ) data = [ (torch.rand(4, 10), torch.randint(0, 3, size=(4,))), (torch.rand(4, 10), torch.randint(0, 3, size=(4,))), (torch.rand(4, 10), torch.randint(0, 3, size=(4,))), ] evaluator.run(data) class CustomMultiMSELoss(nn.Module): def __init__(self) -> None: super().__init__() def forward( self, y_pred: Tuple[torch.Tensor, torch.Tensor], y_true: Tuple[torch.Tensor, torch.Tensor] ) -> torch.Tensor: a_true, b_true = y_true a_pred, b_pred = y_pred return mse_loss(a_pred, a_true) + mse_loss(b_pred, b_true) class DummyLoss3(Loss): def __init__(self, loss_fn, expected_loss, output_transform=lambda x: x, skip_unrolling=False): super(DummyLoss3, self).__init__(loss_fn, output_transform=output_transform, skip_unrolling=skip_unrolling) self._expected_loss = expected_loss self._loss_fn = loss_fn def reset(self): pass def compute(self): pass def update(self, output): y_pred, y_true = output calculated_loss = self._loss_fn(y_pred=y_pred, y_true=y_true) assert calculated_loss == self._expected_loss def test_skip_unrolling_loss(): a_pred = torch.rand(8, 1) b_pred = torch.rand(8, 1) y_pred = [a_pred, b_pred] a_true = torch.rand(8, 1) b_true = torch.rand(8, 1) y_true = [a_true, b_true] multi_output_mse_loss = CustomMultiMSELoss() expected_loss = multi_output_mse_loss(y_pred=y_pred, y_true=y_true) loss_metric = DummyLoss3(loss_fn=multi_output_mse_loss, expected_loss=expected_loss, skip_unrolling=True) state = State(output=(y_pred, y_true)) engine = MagicMock(state=state) loss_metric.iteration_completed(engine) ignite-0.5.1/tests/ignite/metrics/test_maximum_mean_discrepancy.py000066400000000000000000000137571465426447700255750ustar00rootroot00000000000000from typing import Tuple import numpy as np import pytest import torch from torch import Tensor import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import MaximumMeanDiscrepancy def np_mmd2(x: np.ndarray, y: np.ndarray, var: float = 1.0): n = x.shape[0] x = x.reshape(n, -1) y = y.reshape(n, -1) a = np.arange(n) ii, jj = np.meshgrid(a, a, indexing="ij") XX = np.exp(-np.square(x[ii] - x[jj]).sum(axis=2) / (var * 2)) XX = (np.sum(XX) - n) / (n * (n - 1)) XY = np.exp(-np.square(x[ii] - y[jj]).sum(axis=2) / (var * 2)) XY = np.sum(XY) / (n * n) YY = np.exp(-np.square(y[ii] - y[jj]).sum(axis=2) / (var * 2)) YY = (np.sum(YY) - n) / (n * (n - 1)) mmd2 = np.clip(XX + YY - XY * 2, 0.0, None) return mmd2 def test_zero_sample(): mmd = MaximumMeanDiscrepancy() with pytest.raises( NotComputableError, match=r"MaximumMeanDiscrepacy must have at least one batch before it can be computed" ): mmd.compute() def test_shape_mismatch(): mmd = MaximumMeanDiscrepancy() x = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.tensor([[-2.0, 1.0]], dtype=torch.float) with pytest.raises(ValueError, match=r"x and y must be in the same shape, got"): mmd.update((x, y)) def test_invalid_shape(): mmd = MaximumMeanDiscrepancy() x = torch.tensor([2.0, 3.0], dtype=torch.float) y = torch.tensor([4.0, 5.0], dtype=torch.float) with pytest.raises(ValueError, match=r"x must be in the shape of \(B, ...\), got"): mmd.update((x, y)) @pytest.fixture(params=list(range(4))) def test_case(request): return [ (torch.randn((100, 10)), torch.rand((100, 10)), 10 ** np.random.uniform(-1.0, 0.0), 1), (torch.rand((100, 500)), torch.randn((100, 500)), 10 ** np.random.uniform(-1.0, 0.0), 1), # updated batches (torch.normal(0.0, 5.0, size=(100, 10)), torch.rand((100, 10)), 10 ** np.random.uniform(-1.0, 0.0), 16), (torch.normal(5.0, 3.0, size=(100, 200)), torch.rand((100, 200)), 10 ** np.random.uniform(-1.0, 0.0), 16), # image segmentation (torch.randn((100, 5, 32, 32)), torch.rand((100, 5, 32, 32)), 10 ** np.random.uniform(-1.0, 0.0), 32), (torch.rand((100, 5, 224, 224)), torch.randn((100, 5, 224, 224)), 10 ** np.random.uniform(-1.0, 0.0), 32), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case: Tuple[Tensor, Tensor, float, int]): x, y, var, batch_size = test_case mmd = MaximumMeanDiscrepancy(var=var) mmd.reset() if batch_size > 1: np_mmd2_sum = 0.0 n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size x_batch, y_batch = x[idx : idx + batch_size], y[idx : idx + batch_size] mmd.update((x_batch, y_batch)) np_mmd2_sum += np_mmd2(x_batch.cpu().numpy(), y_batch.cpu().numpy(), var) np_res = np.sqrt(np_mmd2_sum / n_iters) else: mmd.update((x, y)) np_res = np.sqrt(np_mmd2(x.cpu().numpy(), y.cpu().numpy(), var)) res = mmd.compute() assert isinstance(res, float) assert pytest.approx(np_res, abs=1e-4) == res def test_accumulator_detached(): mmd = MaximumMeanDiscrepancy() x = torch.tensor([[2.0, 3.0], [-2.0, 1.0]], dtype=torch.float) y = torch.tensor([[-2.0, 1.0], [2.0, 3.0]], dtype=torch.float) mmd.update((x, y)) assert not any(acc.requires_grad for acc in (mmd._xx_sum, mmd._yy_sum, mmd._xy_sum)) @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): tol = 1e-4 n_iters = 100 batch_size = 10 n_dims = 100 rank = idist.get_rank() torch.manual_seed(12 + rank) device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: y = torch.randn((n_iters * batch_size, n_dims)).float().to(device) x = torch.normal(2.0, 3.0, size=(n_iters * batch_size, n_dims)).float().to(device) def data_loader(i): return x[i * batch_size : (i + 1) * batch_size], y[i * batch_size : (i + 1) * batch_size] engine = Engine(lambda e, i: data_loader(i)) m = MaximumMeanDiscrepancy(device=metric_device) m.attach(engine, "mmd") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) x = idist.all_gather(x) y = idist.all_gather(y) assert "mmd" in engine.state.metrics res = engine.state.metrics["mmd"] # compute numpy mmd true_res = 0.0 for i in range(n_iters): x_batch, y_batch = data_loader(i) x_np = x_batch.cpu().numpy() y_np = y_batch.cpu().numpy() true_res += np_mmd2(x_np, y_np) true_res = np.sqrt(true_res / n_iters) assert pytest.approx(true_res, abs=tol) == res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: mmd = MaximumMeanDiscrepancy(device=metric_device) devices = (mmd._device, mmd._xx_sum.device, mmd._yy_sum.device, mmd._xy_sum.device) for dev in devices: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" x = torch.tensor([[2.0, 3.0], [-2.0, 1.0]]).float() y = torch.ones(2, 2).float() mmd.update((x, y)) devices = (mmd._device, mmd._xx_sum.device, mmd._yy_sum.device, mmd._xy_sum.device) for dev in devices: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_mean_absolute_error.py000066400000000000000000000147741465426447700245630ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import MeanAbsoluteError def test_no_update(): mae = MeanAbsoluteError() with pytest.raises( NotComputableError, match=r"MeanAbsoluteError must have at least one example before it can be computed" ): mae.compute() @pytest.fixture(params=[item for item in range(4)]) def test_case(request): return [ (torch.randint(0, 10, size=(100, 1)), torch.randint(0, 10, size=(100, 1)), 1), (torch.randint(-10, 10, size=(100, 5)), torch.randint(-10, 10, size=(100, 5)), 1), # updated batches (torch.randint(0, 10, size=(100, 1)), torch.randint(0, 10, size=(100, 1)), 16), (torch.randint(-20, 20, size=(100, 5)), torch.randint(-20, 20, size=(100, 5)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case): mae = MeanAbsoluteError() y_pred, y, batch_size = test_case mae.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size mae.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: mae.update((y_pred, y, batch_size)) np_y = y.numpy() np_y_pred = y_pred.numpy() np_res = (np.abs(np_y_pred - np_y)).sum() / np_y.shape[0] assert isinstance(mae.compute(), float) assert mae.compute() == np_res def _test_distrib_integration(device): import numpy as np from ignite.engine import Engine rank = idist.get_rank() def _test(metric_device): n_iters = 80 batch_size = 50 torch.manual_seed(12 + rank) y_true = torch.arange(0, n_iters * batch_size, dtype=torch.float).to(device) y_preds = torch.ones(n_iters * batch_size, dtype=torch.float).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = MeanAbsoluteError(device=metric_device) m.attach(engine, "mae") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mae" in engine.state.metrics res = engine.state.metrics["mae"] true_res = np.mean(np.abs((y_true - y_preds).cpu().numpy())) assert pytest.approx(res) == true_res _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: mae = MeanAbsoluteError(device=metric_device) for dev in [mae._device, mae._sum_of_absolute_errors.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) mae.update((y_pred, y)) for dev in [mae._device, mae._sum_of_absolute_errors.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" def test_accumulator_detached(): mae = MeanAbsoluteError() y_pred = torch.tensor([[2.0], [-2.0]], requires_grad=True) y = torch.zeros(2) mae.update((y_pred, y)) assert not mae._sum_of_absolute_errors.requires_grad @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_mean_pairwise_distance.py000066400000000000000000000156211465426447700252210ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import MeanPairwiseDistance def test_zero_sample(): mpd = MeanPairwiseDistance() with pytest.raises( NotComputableError, match=r"MeanAbsoluteError must have at least one example before it can be computed" ): mpd.compute() @pytest.fixture(params=[item for item in range(4)]) def test_case(request): return [ (torch.randint(0, 10, size=(100, 1)), torch.randint(0, 10, size=(100, 1)), 1), (torch.randint(-20, 20, size=(100, 5)), torch.randint(-20, 20, size=(100, 5)), 1), # updated batches (torch.randint(0, 10, size=(100, 1)), torch.randint(0, 10, size=(100, 1)), 16), (torch.randint(-20, 20, size=(100, 5)), torch.randint(-20, 20, size=(100, 5)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case): mpd = MeanPairwiseDistance() y_pred, y, batch_size = test_case mpd.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size mpd.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: mpd.update((y_pred, y)) np_res = np.mean(torch.pairwise_distance(y_pred, y, p=mpd._p, eps=mpd._eps).numpy()) assert isinstance(mpd.compute(), float) assert pytest.approx(mpd.compute()) == np_res def _test_distrib_integration(device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12 + rank) def _test(metric_device): n_iters = 100 batch_size = 50 y_true = torch.rand(n_iters * batch_size, 10).to(device) y_preds = torch.rand(n_iters * batch_size, 10).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, ...], y_true[i * batch_size : (i + 1) * batch_size, ...], ) engine = Engine(update) m = MeanPairwiseDistance(device=metric_device) m.attach(engine, "mpwd") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mpwd" in engine.state.metrics res = engine.state.metrics["mpwd"] true_res = [] for i in range(n_iters * idist.get_world_size()): true_res.append( torch.pairwise_distance( y_true[i * batch_size : (i + 1) * batch_size, ...], y_preds[i * batch_size : (i + 1) * batch_size, ...], p=m._p, eps=m._eps, ) .cpu() .numpy() ) true_res = np.array(true_res).ravel() true_res = true_res.mean() assert pytest.approx(res) == true_res _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: mpd = MeanPairwiseDistance(device=metric_device) for dev in [mpd._device, mpd._sum_of_distances.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[3.0, 4.0], [-3.0, -4.0]]) y = torch.zeros(2, 2) mpd.update((y_pred, y)) for dev in [mpd._device, mpd._sum_of_distances.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" def test_accumulator_detached(): mpd = MeanPairwiseDistance() y_pred = torch.tensor([[3.0, 4.0], [-3.0, -4.0]], requires_grad=True) y = torch.zeros(2, 2) mpd.update((y_pred, y)) assert not mpd._sum_of_distances.requires_grad @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_mean_squared_error.py000066400000000000000000000150511465426447700243760ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import MeanSquaredError def test_zero_sample(): mse = MeanSquaredError() with pytest.raises( NotComputableError, match=r"MeanSquaredError must have at least one example before it can be computed" ): mse.compute() @pytest.fixture(params=[item for item in range(4)]) def test_case(request): return [ (torch.randint(0, 10, size=(100, 1)), torch.randint(0, 10, size=(100, 1)), 1), (torch.randint(-20, 20, size=(100, 5)), torch.randint(-20, 20, size=(100, 5)), 1), # updated batches (torch.randint(0, 10, size=(100, 1)), torch.randint(0, 10, size=(100, 1)), 16), (torch.randint(-20, 20, size=(100, 5)), torch.randint(-20, 20, size=(100, 5)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case): mse = MeanSquaredError() y_pred, y, batch_size = test_case mse.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size mse.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: mse.update((y_pred, y)) np_y = y.numpy() np_y_pred = y_pred.numpy() np_res = np.power((np_y - np_y_pred), 2.0).sum() / np_y.shape[0] assert isinstance(mse.compute(), float) assert mse.compute() == np_res def _test_distrib_integration(device, tol=1e-6): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12 + rank) def _test(metric_device): n_iters = 100 batch_size = 10 y_true = torch.arange(0, n_iters * batch_size, dtype=torch.float).to(device) y_preds = torch.ones(n_iters * batch_size, dtype=torch.float).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) m = MeanSquaredError(device=metric_device) m.attach(engine, "mse") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mse" in engine.state.metrics res = engine.state.metrics["mse"] true_res = np.mean(np.power((y_true - y_preds).cpu().numpy(), 2.0)) assert pytest.approx(res, rel=tol) == true_res _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: device = torch.device(device) mse = MeanSquaredError(device=metric_device) for dev in [mse._device, mse._sum_of_squared_errors.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) mse.update((y_pred, y)) for dev in [mse._device, mse._sum_of_squared_errors.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" def test_accumulator_detached(): mse = MeanSquaredError() y_pred = torch.tensor([[2.0], [-2.0]], requires_grad=True) y = torch.zeros(2) mse.update((y_pred, y)) assert not mse._sum_of_squared_errors.requires_grad @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device, tol=1e-4) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device, tol=1e-4) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_metric.py000066400000000000000000001307051465426447700220100ustar00rootroot00000000000000import numbers import os from typing import Dict, List from unittest.mock import MagicMock import numpy as np import pytest import torch from packaging.version import Version from pytest import approx, raises from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score import ignite.distributed as idist from ignite.engine import Engine, Events, State from ignite.metrics import Accuracy, ConfusionMatrix, Precision, Recall from ignite.metrics.metric import ( BatchFiltered, BatchWise, EpochWise, Metric, reinit__is_reduced, RunningBatchWise, RunningEpochWise, SingleEpochRunningBatchWise, sync_all_reduce, ) from ignite.utils import _tree_map class DummyMetric1(Metric): def __init__(self, true_output, output_transform=lambda x: x): super(DummyMetric1, self).__init__(output_transform=output_transform) self.true_output = true_output def reset(self): pass def compute(self): pass def update(self, output): assert output == self.true_output def test_no_transform(): y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) metric = DummyMetric1(true_output=(y_pred, y)) state = State(output=(y_pred, y)) engine = MagicMock(state=state) metric.iteration_completed(engine) def test_transform(): y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) def transform(output): pred_dict, target_dict = output return pred_dict["y"], target_dict["y"] metric = DummyMetric1(true_output=(y_pred, y), output_transform=transform) state = State(output=({"y": y_pred}, {"y": y})) engine = MagicMock(state=state) metric.iteration_completed(engine) def test_output_as_mapping_wrong_keys(): metric = DummyMetric1(true_output=(0, 1)) state = State(output=({"y1": 0, "y2": 1})) engine = MagicMock(state=state) with pytest.raises( ValueError, match=r"When transformed engine's output is a mapping, " r"it should contain \('y_pred', 'y'\) keys" ): metric.iteration_completed(engine) def test_output_as_mapping_keys_is_none(): class DummyMetric(Metric): required_output_keys = None def reset(self): pass def compute(self): pass def update(self, output): pass metric = DummyMetric() assert metric.required_output_keys is None state = State(output=({"y1": 0, "y2": 1})) engine = MagicMock(state=state) with pytest.raises(TypeError, match=r"Transformed engine output for DummyMetric metric should be a tuple/list"): metric.iteration_completed(engine) def test_output_as_mapping(): y_pred = torch.tensor([[2.0], [-2.0]]) y = torch.zeros(2) metric = DummyMetric1(true_output=(y_pred, y)) state = State(output=({"y_pred": y_pred, "y": y})) engine = MagicMock(state=state) metric.iteration_completed(engine) def test_no_grad(): y_pred = torch.zeros(4, requires_grad=True) y = torch.zeros(4, requires_grad=False) class DummyMetric(Metric): def reset(self): pass def compute(self): pass def update(self, output): y_pred, y = output mse = torch.pow(y_pred - y.view_as(y_pred), 2) assert y_pred.requires_grad assert not mse.requires_grad metric = DummyMetric() state = State(output=(y_pred, y)) engine = MagicMock(state=state) metric.iteration_completed(engine) def test_arithmetics(): class ListGatherMetric(Metric): def __init__(self, index): self.index = index super(ListGatherMetric, self).__init__() def reset(self): self.list_ = [] def update(self, output): self.list_ = output def compute(self): return self.list_[self.index] m0 = ListGatherMetric(0) m1 = ListGatherMetric(1) m2 = ListGatherMetric(2) # __add__ m0_plus_m1 = m0 + m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_plus_m1.compute() == 11 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_plus_m1.compute() == 22 m2_plus_2 = m2 + 2 m2.update([1, 10, 100]) assert m2_plus_2.compute() == 102 m2_plus_2 = 2 + m2 m2.update([1, 10, 100]) assert m2_plus_2.compute() == 102 # __sub__ m0_minus_m1 = m0 - m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_minus_m1.compute() == -9 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_minus_m1.compute() == -18 m2_minus_2 = m2 - 2 m2.update([1, 10, 100]) assert m2_minus_2.compute() == 98 m2_minus_2 = 2 - m2 m2.update([1, 10, 100]) assert m2_minus_2.compute() == -98 # __mul__ m0_times_m1 = m0 * m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_times_m1.compute() == 10 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_times_m1.compute() == 40 m2_times_2 = m2 * 2 m2.update([1, 10, 100]) assert m2_times_2.compute() == 200 m2_times_2 = 2 * m2 m2.update([1, 10, 100]) assert m2_times_2.compute() == 200 # __pow__ m0_pow_m1 = m0**m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_pow_m1.compute() == 1 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_pow_m1.compute() == 2**20 m2_pow_2 = m2**2 m2.update([1, 10, 100]) assert m2_pow_2.compute() == 10000 m2_pow_2 = 0.99**m2 m2.update([1, 10, 100]) assert m2_pow_2.compute() == 0.3660323412732292 # __mod__ m0_mod_m1 = m0 % m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_mod_m1.compute() == 1 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_mod_m1.compute() == 2 m2_mod_2 = m2 % 2 m2.update([1, 10, 100]) assert m2_mod_2.compute() == 0 # __truediv__ m0_truediv_m1 = m0 / m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_truediv_m1.compute() == approx(0.1) m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_truediv_m1.compute() == approx(0.1) m2_truediv_2 = m2 / 2 m2.update([1, 10, 100]) assert m2_truediv_2.compute() == approx(50.0) m2_truediv_2 = 200 / m2 m2.update([1, 10, 100]) assert m2_truediv_2.compute() == approx(2.0) m0_truediv_m1 = m0.__truediv__(m1) m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_truediv_m1.compute() == approx(0.1) m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_truediv_m1.compute() == approx(0.1) m2_truediv_2 = m2.__truediv__(2) m2.update([1, 10, 100]) assert m2_truediv_2.compute() == approx(50.0) m2_truediv_2 = m2.__rtruediv__(200) m2.update([1, 10, 100]) assert m2_truediv_2.compute() == approx(2.0) # __floordiv__ m0_floordiv_m1 = m0 // m1 m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_floordiv_m1.compute() == 0 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_floordiv_m1.compute() == 0 m2_floordiv_2 = m2 // 2 m2.update([1, 10, 100]) assert m2_floordiv_2.compute() == 50 def test_attach(): class CountMetric(Metric): def __init__(self, value): self.reset_count = 0 super(CountMetric, self).__init__() self.reset_count = 0 self.compute_count = 0 self.update_count = 0 self.value = value def reset(self): self.reset_count += 1 def compute(self): self.compute_count += 1 return self.value def update(self, output): self.update_count += 1 def process_function(*args, **kwargs): return 1 engine = Engine(process_function) m1 = CountMetric(123) m2 = CountMetric(456) m1.attach(engine, "m1") m2.attach(engine, "m2_1") m2.attach(engine, "m2_2") engine.run(range(10), 5) assert engine.state.metrics["m1"] == 123 assert engine.state.metrics["m2_1"] == 456 assert engine.state.metrics["m2_2"] == 456 assert m1.reset_count == 5 assert m1.compute_count == 5 assert m1.update_count == 50 assert m2.reset_count == 5 assert m2.compute_count == 10 assert m2.update_count == 50 assert m1.is_attached(engine) assert m2.is_attached(engine) def test_detach(): class DummyMetric(Metric): required_output_keys = None def reset(self): pass def compute(self): pass def update(self, output): pass def process_function(*args, **kwargs): return 1 engine = Engine(process_function) m1 = DummyMetric() m2 = DummyMetric() m1.attach(engine, "m1") m2.attach(engine, "m2_1") m2.attach(engine, "m2_2") m1.detach(engine) m2.detach(engine) engine.run(range(10), 5) assert "m1" not in engine.state.metrics assert "m2_1" not in engine.state.metrics assert "m2_2" not in engine.state.metrics assert not m1.is_attached(engine) assert not m2.is_attached(engine) def test_integration(): np.random.seed(1) n_iters = 10 batch_size = 10 n_classes = 10 y_true = np.arange(0, n_iters * batch_size, dtype="int64") % n_classes y_pred = 0.2 * np.random.rand(n_iters * batch_size, n_classes) for i in range(n_iters * batch_size): if np.random.rand() > 0.4: y_pred[i, y_true[i]] = 1.0 else: j = np.random.randint(0, n_classes) y_pred[i, j] = 0.7 y_true_batch_values = iter(y_true.reshape(n_iters, batch_size)) y_pred_batch_values = iter(y_pred.reshape(n_iters, batch_size, n_classes)) def update_fn(engine, batch): y_true_batch = next(y_true_batch_values) y_pred_batch = next(y_pred_batch_values) return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) evaluator = Engine(update_fn) precision = Precision(average=False) recall = Recall(average=False) F1 = precision * recall * 2 / (precision + recall) precision.attach(evaluator, "precision") recall.attach(evaluator, "recall") F1.attach(evaluator, "f1") data = list(range(n_iters)) state = evaluator.run(data, max_epochs=1) precision_true = precision_score(y_true, np.argmax(y_pred, axis=-1), average=None) recall_true = recall_score(y_true, np.argmax(y_pred, axis=-1), average=None) f1_true = f1_score(y_true, np.argmax(y_pred, axis=-1), average=None) precision = state.metrics["precision"].numpy() recall = state.metrics["recall"].numpy() f1 = state.metrics["f1"].numpy() assert precision_true == approx(precision), f"{precision_true} vs {precision}" assert recall_true == approx(recall), f"{recall_true} vs {recall}" assert f1_true == approx(f1), f"{f1_true} vs {f1}" def test_abstract_class(): with raises(TypeError): Metric() def test_pytorch_operators(): def _test(composed_metric, metric_name, compute_true_value_fn): metrics = { metric_name: composed_metric, } y_pred = torch.rand(15, 10, 5).float() y = torch.randint(0, 5, size=(15, 10)).long() def update_fn(engine, batch): y_pred, y = batch return y_pred, y validator = Engine(update_fn) for name, metric in metrics.items(): metric.attach(validator, name) def data(y_pred, y): for i in range(y_pred.shape[0]): yield (y_pred[i], y[i]) d = data(y_pred, y) state = validator.run(d, max_epochs=1, epoch_length=y_pred.shape[0]) assert set(state.metrics.keys()) == set([metric_name]) np_y_pred = np.argmax(y_pred.numpy(), axis=-1).ravel() np_y = y.numpy().ravel() assert state.metrics[metric_name] == approx(compute_true_value_fn(np_y_pred, np_y)) precision_1 = Precision(average=False) precision_2 = Precision(average=False) norm_summed_precision = (precision_1 + precision_2).norm(p=10) def compute_true_norm_summed_precision(y_pred, y): p1 = precision_score(y, y_pred, average=None) p2 = precision_score(y, y_pred, average=None) return np.linalg.norm(p1 + p2, ord=10) _test(norm_summed_precision, "mean summed precision", compute_true_value_fn=compute_true_norm_summed_precision) precision = Precision(average=False) recall = Recall(average=False) sum_precision_recall = (precision + recall).sum() def compute_sum_precision_recall(y_pred, y): p = precision_score(y, y_pred, average=None) r = recall_score(y, y_pred, average=None) return np.sum(p + r) _test(sum_precision_recall, "sum precision recall", compute_true_value_fn=compute_sum_precision_recall) precision = Precision(average=False) recall = Recall(average=False) f1 = (precision * recall * 2 / (precision + recall + 1e-20)).mean() def compute_f1(y_pred, y): f1 = f1_score(y, y_pred, average="macro") return f1 _test(f1, "f1", compute_true_value_fn=compute_f1) def test_indexing_metric(): def _test(ignite_metric, sklearn_metic, sklearn_args, index, num_classes=5): y_pred = torch.rand(15, 10, num_classes).float() y = torch.randint(0, num_classes, size=(15, 10)).long() def update_fn(engine, batch): y_pred, y = batch return y_pred, y metrics = {"metric": ignite_metric[index], "metric_wo_index": ignite_metric} validator = Engine(update_fn) for name, metric in metrics.items(): metric.attach(validator, name) def data(y_pred, y): for i in range(y_pred.shape[0]): yield (y_pred[i], y[i]) d = data(y_pred, y) state = validator.run(d, max_epochs=1, epoch_length=y_pred.shape[0]) sklearn_output = sklearn_metic( y.view(-1).numpy(), y_pred.view(-1, num_classes).argmax(dim=1).numpy(), **sklearn_args ) assert (state.metrics["metric_wo_index"][index] == state.metrics["metric"]).all() assert np.allclose(state.metrics["metric"].numpy(), sklearn_output) num_classes = 5 labels = list(range(0, num_classes, 2)) _test(Precision(), precision_score, {"labels": labels, "average": None}, index=labels) labels = list(range(num_classes - 1, 0, -2)) _test(Precision(), precision_score, {"labels": labels, "average": None}, index=labels) labels = [1] _test(Precision(), precision_score, {"labels": labels, "average": None}, index=labels) labels = list(range(0, num_classes, 2)) _test(Recall(), recall_score, {"labels": labels, "average": None}, index=labels) labels = list(range(num_classes - 1, 0, -2)) _test(Recall(), recall_score, {"labels": labels, "average": None}, index=labels) labels = [1] _test(Recall(), recall_score, {"labels": labels, "average": None}, index=labels) # np.ix_ is used to allow for a 2D slice of a matrix. This is required to get accurate result from # ConfusionMatrix. ConfusionMatrix must be sliced the same row-wise and column-wise. labels = list(range(0, num_classes, 2)) _test(ConfusionMatrix(num_classes), confusion_matrix, {"labels": labels}, index=np.ix_(labels, labels)) labels = list(range(num_classes - 1, 0, -2)) _test(ConfusionMatrix(num_classes), confusion_matrix, {"labels": labels}, index=np.ix_(labels, labels)) labels = [1] _test(ConfusionMatrix(num_classes), confusion_matrix, {"labels": labels}, index=np.ix_(labels, labels)) class DummyMetric2(Metric): @reinit__is_reduced def reset(self): pass def compute(self): pass @reinit__is_reduced def update(self, output): pass def _test_compute_with_sync_all_reduce_doesnt_change_attributes(device): class DummyMetric3(Metric): @reinit__is_reduced def reset(self): self.a = torch.tensor(0.0, device=self._device) self.b = 0.0 def update(self, output): self.a += torch.tensor(1.0) self.b += 1.0 @sync_all_reduce("a", "b") def compute(self): return self.a.item(), self.b metric_device = device if torch.device(device).type != "xla" else "cpu" metric = DummyMetric3(device=metric_device) metric.update(None) assert metric.a.item() == metric.b == 1.0 metric.compute() assert metric.a.item() == metric.b == 1.0 def _test_invalid_sync_all_reduce(device): class InvalidMetric(Metric): @reinit__is_reduced def reset(self): self.a = torch.tensor([0.0, 1.0, 2.0, 3.0], requires_grad=False) self.c = 0.0 self.n = 0 self.m = -1 self.d = "a string" def compute(self): pass def update(self): pass @sync_all_reduce("a:sum") def invalid_reduction_op_1(self): pass @sync_all_reduce("c:MaX") def invalid_reduction_op_2(self): pass @sync_all_reduce("n:MINN") def invalid_reduction_op_3(self): pass @sync_all_reduce("m:PROduCT") def invalid_reduction_op_4(self): pass @sync_all_reduce("missingattr") def invalid_reduction_op_5(self): pass @sync_all_reduce("d") def invalid_reduction_op_6(self): pass metric_device = device if torch.device(device).type != "xla" else "cpu" m = InvalidMetric(device=metric_device) m.reset() if idist.get_world_size() > 1: with pytest.raises(ValueError, match=r"Reduction operation is not valid"): m.invalid_reduction_op_1() with pytest.raises(ValueError, match=r"Reduction operation is not valid"): m.invalid_reduction_op_2() with pytest.raises(ValueError, match=r"Reduction operation is not valid"): m.invalid_reduction_op_3() with pytest.raises(ValueError, match=r"Reduction operation is not valid"): m.invalid_reduction_op_4() with pytest.raises(ValueError, match=r"has no attribute named `missingattr`."): m.invalid_reduction_op_5() with pytest.raises( TypeError, match=r"Attribute provided to sync_all_reduce should be a number or tensor but `d`" ): m.invalid_reduction_op_6() def _test_distrib_sync_all_reduce_decorator(device): class DummyMetric(Metric): @reinit__is_reduced def reset(self): # SUM op self.a = torch.tensor([0.0, 1.0, 2.0, 3.0], device=self._device, requires_grad=False) self.a_nocomp = self.a.clone().to("cpu") self.b = torch.tensor(1.0, dtype=torch.float64, device=self._device, requires_grad=False) self.b_nocomp = self.b.clone().to("cpu") self.c = 0.0 self.c_nocomp = self.c self.n = 0 self.n_nocomp = self.n # MAX op self.m = -1 # MIN op self.k = 10000 # initialize number of updates to test (MAX, MIN) ops self.num_updates = 0 # PRODUCT op self.prod = torch.tensor([2.0, 3.0], device=self._device, requires_grad=False) self.prod_nocomp = self.prod.clone().to("cpu") @sync_all_reduce("a", "b", "c", "n:SUM", "m:MAX", "k:MIN", "prod:PRODUCT") def compute(self): assert (self.a.cpu() == (self.a_nocomp + 10) * idist.get_world_size()).all() assert (self.b.cpu() == (self.b_nocomp - 5) * idist.get_world_size()).all() assert self.c == pytest.approx((self.c_nocomp + 1.23456) * idist.get_world_size()) assert self.n == (self.n_nocomp + 1) * idist.get_world_size() assert self.m == self.num_updates * (idist.get_world_size() - 1) - 1 assert self.k == 10000 - self.num_updates * (idist.get_world_size() - 1) temp_prod_nocomp = 5 * self.prod_nocomp # new variable for the recomputing temp_prod_nocomp = temp_prod_nocomp.pow(idist.get_world_size()) assert (self.prod.cpu() == temp_prod_nocomp).all() @reinit__is_reduced def update(self, output): # SUM op self.n += 1 self.c += 1.23456 self.a += 10.0 self.b -= 5.0 # MAX op self.m += idist.get_rank() # MIN op self.k -= idist.get_rank() # numper of updates for (MAX, MIN) ops self.num_updates += 1 # PRODUCT op self.prod *= 5 metric_device = device if torch.device(device).type != "xla" else "cpu" m = DummyMetric(device=metric_device) m.update(None) m.compute() # check if attributes are restored to their original values after previous `compute` m.compute() def _test_creating_on_xla_fails(device): with pytest.raises(ValueError, match=r"Cannot create metric on an XLA device. Use device='cpu' instead."): DummyMetric2(device=device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="Skip if < 1.7.0") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_invalid_sync_all_reduce(device) _test_compute_with_sync_all_reduce_doesnt_change_attributes(device) test_state_dict() test_load_state_dict() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(Version(torch.__version__) < Version("1.7.0"), reason="Skip if < 1.7.0") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_invalid_sync_all_reduce(device) _test_compute_with_sync_all_reduce_doesnt_change_attributes(device) test_state_dict() test_load_state_dict() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = "cpu" if not torch.cuda.is_available() else "cuda" nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_sync_all_reduce_decorator, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_invalid_sync_all_reduce, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_compute_with_sync_all_reduce_doesnt_change_attributes, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_invalid_sync_all_reduce(device) _test_compute_with_sync_all_reduce_doesnt_change_attributes(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_invalid_sync_all_reduce(device) _test_compute_with_sync_all_reduce_doesnt_change_attributes(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_creating_on_xla_fails(device) _test_invalid_sync_all_reduce(device) _test_compute_with_sync_all_reduce_doesnt_change_attributes(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_creating_on_xla_fails(device) _test_invalid_sync_all_reduce(device) _test_compute_with_sync_all_reduce_doesnt_change_attributes(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) def test_completed(): class DummyMetric(Metric): def reset(self): pass def compute(self): pass def update(self, output): pass m = DummyMetric() # tensor engine = MagicMock(state=State(metrics={})) m.compute = MagicMock(return_value=torch.tensor(1.0)) m.completed(engine, "metric") assert engine.state.metrics == {"metric": 1.0} assert isinstance(engine.state.metrics["metric"], numbers.Number) # mapping engine = MagicMock(state=State(metrics={})) metrics = {"foo": 1, "bar": torch.tensor(2.0), "baz": {"qux": "quux"}} m.compute = MagicMock(return_value=metrics) with pytest.raises(ValueError, match=r"Argument name 'foo' is conflicting with mapping keys"): m.completed(engine, "foo") m.completed(engine, "metric") metrics["metric"] = metrics assert engine.state.metrics == metrics # other engine = MagicMock(state=State(metrics={})) m.compute = MagicMock(return_value="foo") m.completed(engine, "metric") assert engine.state.metrics == {"metric": "foo"} @pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU") def test_completed_on_cuda(): # Checks https://github.com/pytorch/ignite/issues/1635#issuecomment-863026919 class DummyMetric(Metric): def reset(self): pass def compute(self): return torch.tensor([1.0, 2.0, 3.0], device="cuda") def update(self, output): pass m = DummyMetric() # tensor engine = MagicMock(state=State(metrics={})) m.completed(engine, "metric") assert "metric" in engine.state.metrics assert isinstance(engine.state.metrics["metric"], torch.Tensor) assert engine.state.metrics["metric"].device.type == "cpu" def test_usage_exception(): engine = Engine(lambda e, b: b) m = DummyMetric2() with pytest.raises(TypeError, match=r"Unhandled usage type"): m.attach(engine, "dummy", usage=1) with pytest.raises( ValueError, match=r"usage should be '\(Running\)EpochWise.usage_name' or '\(\(SingleEpoch\)Running\)BatchWise.usage_name'", ): m.attach(engine, "dummy", usage="fake") class DummyAccumulateInListMetric(Metric): def __init__(self): super(DummyAccumulateInListMetric, self).__init__() self.value = [] def reset(self): self.value = [] def compute(self): return self.value def update(self, output): self.value.append(output) @pytest.mark.parametrize("usage", ["epoch_wise", EpochWise.usage_name, EpochWise()]) def test_epochwise_usage(usage): engine = Engine(lambda e, b: b) m = DummyAccumulateInListMetric() m.attach(engine, "ewm", usage=usage) @engine.on(Events.EPOCH_COMPLETED) def _(): ewm = engine.state.metrics["ewm"] assert len(ewm) == 3 assert ewm == [0, 1, 2] engine.run([0, 1, 2], max_epochs=10) m.detach(engine, usage=usage) class DummyAccumulateMetric(Metric): def __init__(self): super(DummyAccumulateMetric, self).__init__() self.value = 0 def reset(self): self.value = 0 def compute(self): return self.value def update(self, output): self.value += output @pytest.mark.parametrize("usage", ["running_epoch_wise", RunningEpochWise.usage_name, RunningEpochWise()]) def test_running_epochwise_usage(usage): engine = Engine(lambda e, b: e.state.metrics["ewm"]) engine.state.metrics["ewm"] = 0 @engine.on(Events.EPOCH_STARTED) def _(): engine.state.metrics["ewm"] += 1 m = DummyAccumulateMetric() m.attach(engine, "rewm", usage=usage) @engine.on(Events.EPOCH_COMPLETED) def _(): assert engine.state.metrics["rewm"] == sum(range(engine.state.epoch + 1)) engine.run([0, 1, 2], max_epochs=10) m.detach(engine, usage=usage) @pytest.mark.parametrize("usage", ["batch_wise", BatchWise.usage_name, BatchWise()]) def test_batchwise_usage(usage): engine = Engine(lambda e, b: b) m = DummyAccumulateInListMetric() m.attach(engine, "bwm", usage=usage) @engine.on(Events.ITERATION_COMPLETED) def _(): bwm = engine.state.metrics["bwm"] assert len(bwm) == 1 assert bwm[0] == (engine.state.iteration - 1) % 3 engine.run([0, 1, 2], max_epochs=10) m.detach(engine, usage=usage) @pytest.mark.parametrize("usage", ["running_batch_wise", RunningBatchWise.usage_name, RunningBatchWise()]) def test_running_batchwise_usage(usage): engine = Engine(lambda e, b: b) m = DummyAccumulateMetric() m.attach(engine, "rbwm", usage=usage) @engine.on(Events.EPOCH_COMPLETED) def _(): assert engine.state.metrics["rbwm"] == 6 * engine.state.epoch engine.run([0, 1, 2, 3], max_epochs=10) m.detach(engine, usage=usage) @pytest.mark.parametrize( "usage", ["single_epoch_running_batch_wise", SingleEpochRunningBatchWise.usage_name, SingleEpochRunningBatchWise()] ) def test_single_epoch_running_batchwise_usage(usage): engine = Engine(lambda e, b: b) m = DummyAccumulateMetric() m.attach(engine, "rbwm", usage=usage) @engine.on(Events.EPOCH_COMPLETED) def _(): assert engine.state.metrics["rbwm"] == 6 engine.run([0, 1, 2, 3], max_epochs=10) m.detach(engine, usage=usage) def test_batchfiltered_usage(): class MyMetric(Metric): def __init__(self): super(MyMetric, self).__init__() self.value = [] def reset(self): self.value = [] def compute(self): return self.value def update(self, output): self.value.append(output) engine = Engine(lambda e, b: b) m = MyMetric() usage = BatchFiltered(every=2) m.attach(engine, "bfm", usage=usage) @engine.on(Events.EPOCH_COMPLETED) def _(): bfm = engine.state.metrics["bfm"] assert len(bfm) == 2 assert bfm[0] == 1 engine.run([0, 1, 2, 3], max_epochs=10) def test_override_required_output_keys(): # https://discuss.pytorch.org/t/how-access-inputs-in-custom-ignite-metric/91221/5 import torch.nn as nn from ignite.engine import create_supervised_evaluator counter = [0] class CustomMetric(Metric): required_output_keys = ("y_pred", "y", "x") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def update(self, output): y_pred, y, x = output assert y_pred.shape == (4, 3) assert y.shape == (4,) assert x.shape == (4, 10) assert x.equal(data[counter[0]][0]) assert y.equal(data[counter[0]][1]) counter[0] += 1 def reset(self): pass def compute(self): pass model = nn.Linear(10, 3) metrics = {"Precision": Precision(), "CustomMetric": CustomMetric()} evaluator = create_supervised_evaluator( model, metrics=metrics, output_transform=lambda x, y, y_pred: {"x": x, "y": y, "y_pred": y_pred} ) data = [ (torch.rand(4, 10), torch.randint(0, 3, size=(4,))), (torch.rand(4, 10), torch.randint(0, 3, size=(4,))), (torch.rand(4, 10), torch.randint(0, 3, size=(4,))), ] evaluator.run(data) @pytest.mark.parametrize("shapes", [[(10,), ()], [(5, 32, 32), (5, 32, 32)]]) def test_list_of_tensors_and_numbers(shapes): def check_fn(output): assert len(output) == 2 assert isinstance(output[0], torch.Tensor) assert isinstance(output[1], torch.Tensor) assert output[0].shape == (1,) + shapes[0] assert output[1].shape == (1,) + shapes[1] def get_data(gt_as_scalar=False): return [ ( [torch.rand(shapes[0]) for _ in range(3 + i)], # predictions [ torch.rand(shapes[1]).item() if gt_as_scalar else torch.rand(shapes[1]) for _ in range(3 + i) ], # ground truth ) for i in range(5) ] class MyMetric(Metric): def __init__(self, check_fn): super(MyMetric, self).__init__() self.check_fn = check_fn def reset(self): pass def compute(self): pass def update(self, output): self.check_fn(output) engine = Engine(lambda e, b: b) m = MyMetric(check_fn) m.attach(engine, "m") data = get_data() engine.run(data) if len(shapes[1]) == 0: data = get_data(gt_as_scalar=True) engine.run(data) def test_list_of_tensors_and_numbers_unsupported_output(): class MyMetric(Metric): def reset(self): pass def compute(self): pass def update(self, output): pass engine = Engine(lambda e, b: ([0, 1, 2], [0, 1, 2], [0, 1, 2])) m = MyMetric() m.attach(engine, "m") with pytest.raises(ValueError, match=r"Output should have 2 items of the same length"): engine.run([0] * 10) engine = Engine(lambda e, b: ([0, 1, 2], [0, 1, 2, 4])) m = MyMetric() m.attach(engine, "m") with pytest.raises(ValueError, match=r"Output should have 2 items of the same length"): engine.run([0] * 10) class DummyMetric4(Metric): _state_dict_all_req_keys = ( "dnumber", "fnumber", "tensor", "tensor2", "metric", "metric_dict", "metric_list", "initially_none", ) @staticmethod def gen_expected_state(value): expected_state = { "dnumber": value + 1, "fnumber": value + 2.234, "tensor": torch.tensor(value + 2.5), "tensor2": torch.tensor(value + 3.5), "metric": { "_num_correct": torch.tensor(value + 3), "_num_examples": value + 4, }, "metric_dict": { "m1": { "_num_correct": torch.tensor(value + 5), "_num_examples": value + 6, }, "m2": { "_numerator": torch.tensor([value + 7, value + 8]), "_denominator": torch.tensor([value + 9, value + 10]), "_weight": value, "_updated": True, }, "n": value + 12, }, "metric_list": [ { "_numerator": torch.tensor([value + 11, value + 12]), "_denominator": torch.tensor([value + 13, value + 14]), "_weight": value, "_updated": True, }, { "_numerator": torch.tensor([value + 15, value + 16]), "_denominator": torch.tensor([value + 17, value + 18]), "_weight": value, "_updated": True, }, value + 234, ], "initially_none": None, } return expected_state def __init__(self, value): super().reset() self.expected_state = DummyMetric4.gen_expected_state(value) self.dnumber = self.expected_state["dnumber"] self.fnumber = self.expected_state["fnumber"] self.tensor = self.expected_state["tensor"] self.tensor2 = self.expected_state["tensor2"] self.metric = Accuracy() self.metric._num_correct = self.expected_state["metric"]["_num_correct"] self.metric._num_examples = self.expected_state["metric"]["_num_examples"] self.metric_dict: Dict[str, Metric] = { "m1": Accuracy(), "m2": Precision(), "n": self.expected_state["metric_dict"]["n"], } self.metric_dict["m1"]._num_correct = self.expected_state["metric_dict"]["m1"]["_num_correct"] self.metric_dict["m1"]._num_examples = self.expected_state["metric_dict"]["m1"]["_num_examples"] self.metric_dict["m2"]._numerator = self.expected_state["metric_dict"]["m2"]["_numerator"] self.metric_dict["m2"]._denominator = self.expected_state["metric_dict"]["m2"]["_denominator"] self.metric_dict["m2"]._weight = self.expected_state["metric_dict"]["m2"]["_weight"] self.metric_dict["m2"]._updated = self.expected_state["metric_dict"]["m2"]["_updated"] self.metric_list: List[Metric] = [ Recall(), Precision(), self.expected_state["metric_list"][2], ] self.metric_list[0]._numerator = self.expected_state["metric_list"][0]["_numerator"] self.metric_list[0]._denominator = self.expected_state["metric_list"][0]["_denominator"] self.metric_list[0]._weight = self.expected_state["metric_list"][0]["_weight"] self.metric_list[0]._updated = self.expected_state["metric_list"][0]["_updated"] self.metric_list[1]._numerator = self.expected_state["metric_list"][1]["_numerator"] self.metric_list[1]._denominator = self.expected_state["metric_list"][1]["_denominator"] self.metric_list[1]._weight = self.expected_state["metric_list"][1]["_weight"] self.metric_list[1]._updated = self.expected_state["metric_list"][1]["_updated"] self.initially_none = None def reset(self): self.dnumber = -1 self.fnumber = -2.0 self.tensor = torch.tensor([-3]) self.tensor2 = 0 self.metric.reset() for m in self.metric_dict.values(): if isinstance(m, Metric): m.reset() for m in self.metric_list: if isinstance(m, Metric): m.reset() self.initially_none = None def update(self, output): pass def compute(self): pass def test_wrong_state_dict(): class WrongMetric(Metric): _state_dict_all_req_keys = ("object",) def __init__(self, value): super().__init__() self.object = value def reset(self): pass def update(self, output): pass def compute(self): pass metric = WrongMetric(object()) with pytest.raises(TypeError, match="Found attribute of unsupported type. Currently, supported types include"): metric.state_dict() delattr(metric, "object") with pytest.raises(ValueError, match="Found a value in _state_dict_all_req_keys that is not among"): metric.state_dict() def test_wrong_load_state_dict(): metric = DummyMetric4(1) with pytest.raises(TypeError, match="Argument state_dict should be a dictionary"): metric.load_state_dict(123) with pytest.raises(ValueError, match="Incorrect state_dict object. Argument state_dict should be a dictionary"): metric.load_state_dict({"abc": 123}) with pytest.raises(ValueError, match="Expected a list of state_dicts of size equal world_size"): metric.load_state_dict({Metric._Metric__state_dict_key_per_rank: []}) # @pytest.mark.distributed # @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") # @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") # def test_distrib_state_dict_metric_in_metric(distributed_context_single_node_nccl): # class _TestMetric(Metric): # _state_dict_all_req_keys = ("metric", ) # def __init__(self): # self.metric = Accuracy() # def reset(self): # self.metric.reset() # def update(self, output): # self.metric.update(output) # def compute(self): # return self.metric.compute() # m = _TestMetric() # m.update(( # torch.rand(4, 10), # torch.randint(0, 10, size=(4, )), # )) # rank = idist.get_rank() # import time # time.sleep(rank * 0.1) # print("m: ", m.state_dict()) # assert False def test_state_dict(): metric = DummyMetric4(1) state = metric.state_dict() assert isinstance(state, dict) and len(state) == 1 and Metric._Metric__state_dict_key_per_rank in state rank = idist.get_rank() ws = idist.get_world_size() list_state_dicts = state[Metric._Metric__state_dict_key_per_rank] assert len(list_state_dicts) == ws state = list_state_dicts[rank] expected_state = metric.expected_state assert state.keys() == expected_state.keys() # Flatten expected state and output state and compare values output_flatten = [] expected_flatten = [] def get_func(flatten): def wrapper(x, key): if isinstance(x, Metric): flatten.extend([(key, getattr(x, k)) for k in x._state_dict_all_req_keys]) else: flatten.append((key, x)) return wrapper _tree_map(get_func(expected_flatten), expected_state) _tree_map(get_func(output_flatten), state) assert len(output_flatten) == len(expected_flatten) and len(expected_flatten) > 0, ( expected_flatten, output_flatten, ) for key_output, key_expected in zip(output_flatten, expected_flatten): key1, output = key_output key2, expected = key_expected assert key1 == key2, (key1, key2) if isinstance(output, torch.Tensor): assert isinstance(expected, torch.Tensor) assert (output == expected).all(), (output, expected) else: assert output == expected, (output, expected) def test_load_state_dict(): metric = DummyMetric4(1) state = metric.state_dict() metric.reset() metric.initially_none = 1 metric.load_state_dict(state) rank = idist.get_rank() world_size = idist.get_world_size() assert len(state[Metric._Metric__state_dict_key_per_rank]) == world_size expected_state = state[Metric._Metric__state_dict_key_per_rank][rank] # Flatten expected state and output state and compare values output_flatten = [] expected_flatten = [] def get_func(flatten): def wrapper(x, **kwargs): if isinstance(x, Metric): flatten.extend([getattr(x, k) for k in x._state_dict_all_req_keys]) else: flatten.append(x) return wrapper _tree_map(get_func(expected_flatten), expected_state) _tree_map(get_func(output_flatten), {key: getattr(metric, key) for key in metric._state_dict_all_req_keys}) assert len(output_flatten) == len(expected_flatten) and len(expected_flatten) > 0, ( expected_flatten, output_flatten, ) for output, expected in zip(output_flatten, expected_flatten): if isinstance(output, torch.Tensor): assert isinstance(expected, torch.Tensor) assert (output == expected).all(), (output, expected) else: assert output == expected, (output, expected) class DummyMetric5(Metric): def __init__(self, true_output, output_transform=lambda x: x, skip_unrolling=False): super(DummyMetric5, self).__init__(output_transform=output_transform, skip_unrolling=skip_unrolling) self.true_output = true_output def reset(self): pass def compute(self): pass def update(self, output): assert output == self.true_output def test_skip_unrolling(): # y_pred and y are ouputs recieved from a multi_output model a_pred = torch.rand(8, 1) b_pred = torch.rand(8, 1) y_pred = [a_pred, b_pred] a_true = torch.rand(8, 1) b_true = torch.rand(8, 1) y_true = [a_true, b_true] metric = DummyMetric5(true_output=(y_pred, y_true), skip_unrolling=True) state = State(output=(y_pred, y_true)) engine = MagicMock(state=state) metric.iteration_completed(engine) class DummyMetric6(Metric): def reset(self): pass def compute(self): pass def update(self, output): pass def __call__(self, value): pass def test_access_to_metric_dunder_attributes(): metric = DummyMetric6() import inspect # `inspect.signature` accesses `__signature__` attribute of the metric. assert "value" in inspect.signature(metric).parameters.keys() ignite-0.5.1/tests/ignite/metrics/test_metric_group.py000066400000000000000000000072271465426447700232260ustar00rootroot00000000000000import pytest import torch from ignite import distributed as idist from ignite.engine import Engine from ignite.metrics import Accuracy, MetricGroup, Precision torch.manual_seed(41) def test_update(): precision = Precision() accuracy = Accuracy() group = MetricGroup({"precision": Precision(), "accuracy": Accuracy()}) y_pred = torch.randint(0, 2, (100,)) y = torch.randint(0, 2, (100,)) precision.update((y_pred, y)) accuracy.update((y_pred, y)) group.update((y_pred, y)) assert precision.state_dict() == group.metrics["precision"].state_dict() assert accuracy.state_dict() == group.metrics["accuracy"].state_dict() def test_output_transform(): def drop_first(output): y_pred, y = output return (y_pred[1:], y[1:]) precision = Precision(output_transform=drop_first) accuracy = Accuracy(output_transform=drop_first) group = MetricGroup( {"precision": Precision(output_transform=drop_first), "accuracy": Accuracy(output_transform=drop_first)} ) y_pred = torch.randint(0, 2, (100,)) y = torch.randint(0, 2, (100,)) precision.update(drop_first(drop_first((y_pred, y)))) accuracy.update(drop_first(drop_first((y_pred, y)))) group.update(drop_first((y_pred, y))) assert precision.state_dict() == group.metrics["precision"].state_dict() assert accuracy.state_dict() == group.metrics["accuracy"].state_dict() def test_compute(): precision = Precision() accuracy = Accuracy() group = MetricGroup({"precision": Precision(), "accuracy": Accuracy()}) for _ in range(3): y_pred = torch.randint(0, 2, (100,)) y = torch.randint(0, 2, (100,)) precision.update((y_pred, y)) accuracy.update((y_pred, y)) group.update((y_pred, y)) assert group.compute() == {"precision": precision.compute(), "accuracy": accuracy.compute()} precision.reset() accuracy.reset() group.reset() assert precision.state_dict() == group.metrics["precision"].state_dict() assert accuracy.state_dict() == group.metrics["accuracy"].state_dict() @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): rank = idist.get_rank() torch.manual_seed(12 + rank) n_epochs = 3 n_iters = 5 batch_size = 10 device = idist.device() y_true = torch.randint(0, 2, size=(n_iters * batch_size,)).to(device) y_pred = torch.randint(0, 2, (n_iters * batch_size,)).to(device) def update(_, i): return ( y_pred[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) precision = Precision() precision.attach(engine, "precision") accuracy = Accuracy() accuracy.attach(engine, "accuracy") group = MetricGroup({"eval_metrics.accuracy": Accuracy(), "eval_metrics.precision": Precision()}) group.attach(engine, "eval_metrics") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "eval_metrics" in engine.state.metrics assert "eval_metrics.accuracy" in engine.state.metrics assert "eval_metrics.precision" in engine.state.metrics assert engine.state.metrics["eval_metrics"] == { "eval_metrics.accuracy": engine.state.metrics["accuracy"], "eval_metrics.precision": engine.state.metrics["precision"], } assert engine.state.metrics["eval_metrics.accuracy"] == engine.state.metrics["accuracy"] assert engine.state.metrics["eval_metrics.precision"] == engine.state.metrics["precision"] ignite-0.5.1/tests/ignite/metrics/test_metrics_lambda.py000066400000000000000000000436101465426447700234710ustar00rootroot00000000000000import os import numpy as np import pytest import torch from pytest import approx from sklearn.metrics import f1_score, precision_score, recall_score import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics import Accuracy, Metric, MetricsLambda, Precision, Recall class ListGatherMetric(Metric): def __init__(self, index): super(ListGatherMetric, self).__init__() self.index = index def reset(self): self.list_ = None def update(self, output): self.list_ = output def compute(self): return self.list_[self.index] def test_metrics_lambda(): m0 = ListGatherMetric(0) m1 = ListGatherMetric(1) m2 = ListGatherMetric(2) def process_function(engine, data): return data engine = Engine(process_function) def plus(this, other): return this + other m0_plus_m1 = MetricsLambda(plus, m0, other=m1) m2_plus_2 = MetricsLambda(plus, m2, 2) m0_plus_m1.attach(engine, "m0_plus_m1") m2_plus_2.attach(engine, "m2_plus_2") engine.run([[1, 10, 100]]) assert engine.state.metrics["m0_plus_m1"] == 11 assert engine.state.metrics["m2_plus_2"] == 102 engine.run([[2, 20, 200]]) assert engine.state.metrics["m0_plus_m1"] == 22 assert engine.state.metrics["m2_plus_2"] == 202 # metrics are partially attached assert not m0.is_attached(engine) assert not m1.is_attached(engine) assert not m2.is_attached(engine) # a dependency is detached m0.detach(engine) # so the lambda metric is too assert not m0_plus_m1.is_attached(engine) # the lambda is attached again m0_plus_m1.attach(engine, "m0_plus_m1") assert m0_plus_m1.is_attached(engine) # metrics are always partially attached assert not m0.is_attached(engine) m0_plus_m1.detach(engine) assert not m0_plus_m1.is_attached(engine) # detached (and no longer partially attached) assert not m0.is_attached(engine) def test_metrics_lambda_reset(): m0 = ListGatherMetric(0) m1 = ListGatherMetric(1) m2 = ListGatherMetric(2) m0.update([1, 10, 100]) m1.update([1, 10, 100]) m2.update([1, 10, 100]) def fn(x, y, z, t): return 1 m = MetricsLambda(fn, m0, m1, z=m2, t=0) # initiating a new instance of MetricsLambda must reset # its argument metrics assert m0.list_ is None assert m1.list_ is None assert m2.list_ is None m0.update([1, 10, 100]) m1.update([1, 10, 100]) m2.update([1, 10, 100]) m.reset() assert m0.list_ is None assert m1.list_ is None assert m2.list_ is None def test_metrics_lambda_update_and_attach_together(): y_pred = torch.randint(0, 2, size=(15, 10, 4)).float() y = torch.randint(0, 2, size=(15, 10, 4)).long() def update_fn(engine, batch): y_pred, y = batch return y_pred, y engine = Engine(update_fn) precision = Precision(average=False) recall = Recall(average=False) def Fbeta(r, p, beta): return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) F1.attach(engine, "f1") with pytest.raises(ValueError, match=r"MetricsLambda is already attached to an engine"): F1.update((y_pred, y)) y_pred = torch.randint(0, 2, size=(15, 10, 4)).float() y = torch.randint(0, 2, size=(15, 10, 4)).long() F1 = MetricsLambda(Fbeta, recall, precision, 1) F1.update((y_pred, y)) engine = Engine(update_fn) with pytest.raises(ValueError, match=r"The underlying metrics are already updated"): F1.attach(engine, "f1") F1.reset() F1.attach(engine, "f1") def test_metrics_lambda_update(): """ Test if the underlying metrics are updated """ y_pred = torch.randint(0, 2, size=(15, 10, 4)).float() y = torch.randint(0, 2, size=(15, 10, 4)).long() precision = Precision(average=False) recall = Recall(average=False) def Fbeta(r, p, beta): return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) F1.update((y_pred, y)) assert precision._updated assert recall._updated F1.reset() assert not precision._updated assert not recall._updated """ Test multiple updates and if the inputs of the underlying metrics are updated multiple times """ y_pred1 = torch.randint(0, 2, size=(15,)) y1 = torch.randint(0, 2, size=(15,)) y_pred2 = torch.randint(0, 2, size=(15,)) y2 = torch.randint(0, 2, size=(15,)) F1.update((y_pred1, y1)) F1.update((y_pred2, y2)) # Compute true_positives and positives for precision correct1 = y1 * y_pred1 all_positives1 = y_pred1.sum(dim=0) if correct1.sum() == 0: true_positives1 = torch.zeros_like(all_positives1) else: true_positives1 = correct1.sum(dim=0) correct2 = y2 * y_pred2 all_positives2 = y_pred2.sum(dim=0) if correct2.sum() == 0: true_positives2 = torch.zeros_like(all_positives2) else: true_positives2 = correct2.sum(dim=0) true_positives = true_positives1 + true_positives2 positives = all_positives1 + all_positives2 assert precision._type == "binary" assert precision._numerator == true_positives assert precision._denominator == positives # Computing positivies for recall is different positives1 = y1.sum(dim=0) positives2 = y2.sum(dim=0) positives = positives1 + positives2 assert recall._type == "binary" assert recall._numerator == true_positives assert recall._denominator == positives """ Test compute """ F1.reset() F1.update((y_pred1, y1)) F1_metrics_lambda = F1.compute() F1_sklearn = f1_score(y1.numpy(), y_pred1.numpy()) assert pytest.approx(F1_metrics_lambda) == F1_sklearn @pytest.mark.parametrize("attach_pr_re", [True, False]) def test_integration(attach_pr_re): torch.manual_seed(1) n_iters = 10 batch_size = 10 n_classes = 10 y_true = torch.arange(0, n_iters * batch_size) % n_classes y_pred = 0.2 * torch.rand(n_iters * batch_size, n_classes) for i in range(n_iters * batch_size): if torch.rand(1) > 0.4: y_pred[i, y_true[i]] = 1.0 else: j = torch.randint(0, n_classes, size=(1,)) y_pred[i, j] = 0.7 y_true_batch_values = iter(y_true.reshape(n_iters, batch_size)) y_pred_batch_values = iter(y_pred.reshape(n_iters, batch_size, n_classes)) def update_fn(engine, batch): y_true_batch = next(y_true_batch_values) y_pred_batch = next(y_pred_batch_values) return y_pred_batch, y_true_batch evaluator = Engine(update_fn) precision = Precision(average=False) recall = Recall(average=False) def Fbeta(r, p, beta): return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) if attach_pr_re: precision.attach(evaluator, "precision") recall.attach(evaluator, "recall") F1.attach(evaluator, "f1") data = list(range(n_iters)) state = evaluator.run(data, max_epochs=1) precision_true = precision_score(y_true, y_pred.argmax(dim=-1), average=None) recall_true = recall_score(y_true, y_pred.argmax(dim=-1), average=None) f1_true = f1_score(y_true, y_pred.argmax(dim=-1), average="macro") assert f1_true == approx(state.metrics["f1"]), f"{f1_true} vs {state.metrics['f1']}" if attach_pr_re: precision = state.metrics["precision"].numpy() recall = state.metrics["recall"].numpy() assert precision_true == approx(precision), f"{precision_true} vs {precision}" assert recall_true == approx(recall), f"{recall_true} vs {recall}" metric_state = F1.state_dict() F1.reset() F1.load_state_dict(metric_state) f1_value = F1.compute() assert f1_value == state.metrics["f1"] def test_load_state_dict(): acc = Accuracy() error = 1.0 - acc acc.update( ( torch.randint(0, 2, size=(8,)), torch.randint(0, 2, size=(8,)), ) ) e = error.compute() a = acc.compute() assert 1.0 - a == e metric_state = error.state_dict() error.reset() error.load_state_dict(metric_state) e2 = error.compute() assert e2 == e def test_state_metrics(): y_pred = torch.randint(0, 2, size=(15, 10, 4)).float() y = torch.randint(0, 2, size=(15, 10, 4)).long() def update_fn(engine, batch): y_pred, y = batch return y_pred, y evaluator = Engine(update_fn) precision = Precision(average=False) recall = Recall(average=False) F1 = precision * recall * 2 / (precision + recall + 1e-20) F1 = MetricsLambda(lambda t: torch.mean(t).item(), F1) precision.attach(evaluator, "precision") recall.attach(evaluator, "recall") F1.attach(evaluator, "f1") def data(y_pred, y): for i in range(y_pred.shape[0]): yield (y_pred[i], y[i]) d = data(y_pred, y) state = evaluator.run(d, max_epochs=1, epoch_length=y_pred.shape[0]) assert set(state.metrics.keys()) == set(["precision", "recall", "f1"]) def test_state_metrics_ingredients_not_attached(): y_pred = torch.randint(0, 2, size=(15, 10, 4)).float() y = torch.randint(0, 2, size=(15, 10, 4)).long() def update_fn(engine, batch): y_pred, y = batch return y_pred, y evaluator = Engine(update_fn) precision = Precision(average=False) recall = Recall(average=False) F1 = precision * recall * 2 / (precision + recall + 1e-20) F1 = MetricsLambda(lambda t: torch.mean(t).item(), F1) F1.attach(evaluator, "F1") def data(y_pred, y): for i in range(y_pred.shape[0]): yield (y_pred[i], y[i]) d = data(y_pred, y) state = evaluator.run(d, max_epochs=1, epoch_length=y_pred.shape[0]) assert set(state.metrics.keys()) == set(["F1"]) def test_recursive_attachment(): def _test(composed_metric, metric_name, compute_true_value_fn): metrics = { metric_name: composed_metric, } y_pred = torch.randint(0, 2, size=(15, 10, 4)).float() y = torch.randint(0, 2, size=(15, 10, 4)).long() def update_fn(engine, batch): y_pred, y = batch return y_pred, y validator = Engine(update_fn) for name, metric in metrics.items(): metric.attach(validator, name) def data(y_pred, y): for i in range(y_pred.shape[0]): yield (y_pred[i], y[i]) d = data(y_pred, y) state = validator.run(d, max_epochs=1, epoch_length=y_pred.shape[0]) assert set(state.metrics.keys()) == set([metric_name]) np_y_pred = y_pred.numpy().ravel() np_y = y.numpy().ravel() assert state.metrics[metric_name] == approx(compute_true_value_fn(np_y_pred, np_y)) precision_1 = Precision() precision_2 = Precision() summed_precision = precision_1 + precision_2 def compute_true_summed_precision(y_pred, y): p1 = precision_score(y, y_pred) p2 = precision_score(y, y_pred) return p1 + p2 _test(summed_precision, "summed precision", compute_true_value_fn=compute_true_summed_precision) precision_1 = Precision() precision_2 = Precision() mean_precision = (precision_1 + precision_2) / 2 def compute_true_mean_precision(y_pred, y): p1 = precision_score(y, y_pred) p2 = precision_score(y, y_pred) return (p1 + p2) * 0.5 _test(mean_precision, "mean precision", compute_true_value_fn=compute_true_mean_precision) precision_1 = Precision() precision_2 = Precision() some_metric = 2.0 + 0.2 * (precision_1 * precision_2 + precision_1 - precision_2) ** 0.5 def compute_true_somemetric(y_pred, y): p1 = precision_score(y, y_pred) p2 = precision_score(y, y_pred) return 2.0 + 0.2 * (p1 * p2 + p1 - p2) ** 0.5 _test(some_metric, "some metric", compute_true_somemetric) def _test_distrib_integration(device): rank = idist.get_rank() n_iters = 10 batch_size = 10 n_classes = 10 def _test(metric_device): y_true = torch.arange(0, n_iters * batch_size, dtype=torch.int64).to(device) % n_classes y_pred = 0.2 * torch.rand(n_iters * batch_size, n_classes).to(device) for i in range(n_iters * batch_size): if np.random.rand() > 0.4: y_pred[i, y_true[i]] = 1.0 else: j = np.random.randint(0, n_classes) y_pred[i, j] = 0.7 def update_fn(engine, i): y_true_batch = y_true[i * batch_size : (i + 1) * batch_size, ...] y_pred_batch = y_pred[i * batch_size : (i + 1) * batch_size, ...] return y_pred_batch, y_true_batch evaluator = Engine(update_fn) precision = Precision(average=False, device=metric_device) recall = Recall(average=False, device=metric_device) def Fbeta(r, p, beta): return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) F1.attach(evaluator, "f1") another_f1 = (1.0 + precision * recall * 2 / (precision + recall + 1e-20)).mean().item() another_f1.attach(evaluator, "ff1") data = list(range(n_iters)) state = evaluator.run(data, max_epochs=1) y_pred = idist.all_gather(y_pred) y_true = idist.all_gather(y_true) assert "f1" in state.metrics assert "ff1" in state.metrics f1_true = f1_score(y_true.view(-1).cpu(), y_pred.view(-1, n_classes).argmax(dim=-1).cpu(), average="macro") assert f1_true == approx(state.metrics["f1"]) assert 1.0 + f1_true == approx(state.metrics["ff1"]) for i in range(3): torch.manual_seed(12 + rank + i) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_metrics_on_diff_devices(device): n_classes = 10 n_iters = 12 batch_size = 16 rank = idist.get_rank() torch.manual_seed(12 + rank) y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) evaluator = Engine(update) precision = Precision(average=False, device="cpu") recall = Recall(average=False, device=device) def Fbeta(r, p, beta): return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) F1.attach(evaluator, "f1") another_f1 = (1.0 + precision * recall * 2 / (precision + recall + 1e-20)).mean().item() another_f1.attach(evaluator, "ff1") data = list(range(n_iters)) state = evaluator.run(data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "f1" in state.metrics assert "ff1" in state.metrics f1_true = f1_score(y_true.view(-1).cpu(), y_preds.view(-1, n_classes).argmax(dim=-1).cpu(), average="macro") assert f1_true == approx(state.metrics["f1"]) assert 1.0 + f1_true == approx(state.metrics["ff1"]) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_metrics_on_diff_devices(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_metrics_on_diff_devices, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_metrics_on_diff_devices(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_multilabel_confusion_matrix.py000066400000000000000000000435651465426447700263350ustar00rootroot00000000000000import numpy as np import pytest import torch from sklearn.metrics import multilabel_confusion_matrix import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics.multilabel_confusion_matrix import MultiLabelConfusionMatrix torch.manual_seed(12) def test_no_update(): cm = MultiLabelConfusionMatrix(10) with pytest.raises( NotComputableError, match=r"Confusion matrix must have at least one example before it can be computed" ): cm.compute() def test_num_classes_wrong_input(): with pytest.raises(ValueError, match="Argument num_classes needs to be > 1"): MultiLabelConfusionMatrix(num_classes=1) def test_multiclass_wrong_inputs(): cm = MultiLabelConfusionMatrix(10) with pytest.raises( ValueError, match=r"y_pred must at least have shape \(batch_size, num_classes \(currently set to 10\), ...\)" ): cm.update((torch.rand(10), torch.randint(0, 2, size=(10, 10)).long())) with pytest.raises( ValueError, match=r"y must at least have shape \(batch_size, num_classes \(currently set to 10\), ...\)" ): cm.update((torch.rand(10, 10), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError, match=r"y_pred and y have different batch size: 10 vs 8"): cm.update((torch.rand(10, 10), torch.randint(0, 2, size=(8, 10)).long())) with pytest.raises(ValueError, match=r"y does not have correct number of classes: 9 vs 10"): cm.update((torch.rand(10, 10), torch.randint(0, 2, size=(10, 9)).long())) with pytest.raises(ValueError, match=r"y_pred does not have correct number of classes: 3 vs 10"): cm.update((torch.rand(10, 3), torch.randint(0, 2, size=(10, 10)).long())) with pytest.raises(ValueError, match=r"y and y_pred shapes must match."): cm.update((torch.rand(10, 10, 2), torch.randint(0, 2, size=(10, 10)).long())) with pytest.raises( ValueError, match=r"y_pred must be of any type: \(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64\)", ): cm.update((torch.rand(10, 10), torch.rand(10, 10))) with pytest.raises( ValueError, match=r"y must be of any type: \(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64\)" ): cm.update((torch.rand(10, 10).type(torch.int32), torch.rand(10, 10))) with pytest.raises(ValueError, match=r"y_pred must be a binary tensor"): y = torch.randint(0, 2, size=(10, 10)).long() y_pred = torch.randint(0, 2, size=(10, 10)).long() y_pred[0, 0] = 2 cm.update((y_pred, y)) with pytest.raises(ValueError, match=r"y must be a binary tensor"): y = torch.randint(0, 2, size=(10, 10)).long() y_pred = torch.randint(0, 2, size=(10, 10)).long() y[0, 0] = 2 cm.update((y_pred, y)) def get_y_true_y_pred(): # Generate an image with labels 0 (background), 1, 2 # 3 classes: y_true = np.zeros((1, 3, 30, 30), dtype=np.int64) y_true[0, 0, 5:17, 7:11] = 1 y_true[0, 1, 1:11, 1:11] = 1 y_true[0, 2, 15:25, 15:25] = 1 y_pred = np.zeros((1, 3, 30, 30), dtype=np.int64) y_pred[0, 0, 0:7, 8:15] = 1 y_pred[0, 1, 5:15, 1:11] = 1 y_pred[0, 2, 20:30, 20:30] = 1 return y_true, y_pred def test_multiclass_images(): num_classes = 3 cm = MultiLabelConfusionMatrix(num_classes=num_classes) y_true, y_pred = get_y_true_y_pred() # Compute confusion matrix with sklearn sklearn_CM = multilabel_confusion_matrix( y_true.transpose((0, 2, 3, 1)).reshape(-1, 3), y_pred.transpose((0, 2, 3, 1)).reshape(-1, 3) ) # Update metric output = (torch.tensor(y_pred), torch.tensor(y_true)) cm.update(output) ignite_CM = cm.compute().cpu().numpy() assert np.all(ignite_CM == sklearn_CM) # Another test on batch of 2 images cm = MultiLabelConfusionMatrix(num_classes=num_classes) # Create a batch of two images: th_y_true1 = torch.tensor(y_true) th_y_true2 = torch.tensor(y_true.transpose(0, 1, 3, 2)) th_y_true = torch.cat([th_y_true1, th_y_true2], dim=0) th_y_pred1 = torch.tensor(y_pred) th_y_pred2 = torch.tensor(y_pred.transpose(0, 1, 3, 2)) th_y_pred = torch.cat([th_y_pred1, th_y_pred2], dim=0) # Update metric & compute output = (th_y_pred, th_y_true) cm.update(output) ignite_CM = cm.compute().cpu().numpy() # Compute confusion matrix with sklearn th_y_true = idist.all_gather(th_y_true) th_y_pred = idist.all_gather(th_y_pred) np_y_true = th_y_true.cpu().numpy().transpose((0, 2, 3, 1)).reshape(-1, 3) np_y_pred = th_y_pred.cpu().numpy().transpose((0, 2, 3, 1)).reshape(-1, 3) sklearn_CM = multilabel_confusion_matrix(np_y_true, np_y_pred) assert np.all(ignite_CM == sklearn_CM) def _test_distrib_multiclass_images(device): def _test(metric_device): num_classes = 3 cm = MultiLabelConfusionMatrix(num_classes=num_classes, device=metric_device) y_true, y_pred = get_y_true_y_pred() # Compute confusion matrix with sklearn sklearn_CM = multilabel_confusion_matrix( y_true.transpose((0, 2, 3, 1)).reshape(-1, 3), y_pred.transpose((0, 2, 3, 1)).reshape(-1, 3) ) # Update metric output = (torch.tensor(y_pred).to(device), torch.tensor(y_true).to(device)) cm.update(output) ignite_CM = cm.compute().cpu().numpy() assert np.all(ignite_CM == sklearn_CM) # Another test on batch of 2 images num_classes = 3 cm = MultiLabelConfusionMatrix(num_classes=num_classes, device=metric_device) # Create a batch of two images: th_y_true1 = torch.tensor(y_true) th_y_true2 = torch.tensor(y_true.transpose(0, 1, 3, 2)) th_y_true = torch.cat([th_y_true1, th_y_true2], dim=0) th_y_true = th_y_true.to(device) th_y_pred1 = torch.tensor(y_pred) th_y_pred2 = torch.tensor(y_pred.transpose(0, 1, 3, 2)) th_y_pred = torch.cat([th_y_pred1, th_y_pred2], dim=0) th_y_pred = th_y_pred.to(device) # Update metric & compute output = (th_y_pred, th_y_true) cm.update(output) ignite_CM = cm.compute().cpu().numpy() # Compute confusion matrix with sklearn th_y_true = idist.all_gather(th_y_true) th_y_pred = idist.all_gather(th_y_pred) np_y_true = th_y_true.cpu().numpy().transpose((0, 2, 3, 1)).reshape(-1, 3) np_y_pred = th_y_pred.cpu().numpy().transpose((0, 2, 3, 1)).reshape(-1, 3) sklearn_CM = multilabel_confusion_matrix(np_y_true, np_y_pred) assert np.all(ignite_CM == sklearn_CM) _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: cm = MultiLabelConfusionMatrix(num_classes=3, device=metric_device) assert cm._device == metric_device assert ( cm.confusion_matrix.device == metric_device ), f"{type(cm.confusion_matrix.device)}:{cm._num_correct.device} vs {type(metric_device)}:{metric_device}" y_true, y_pred = get_y_true_y_pred() cm.update((torch.tensor(y_pred), torch.tensor(y_true))) assert ( cm.confusion_matrix.device == metric_device ), f"{type(cm.confusion_matrix.device)}:{cm._num_correct.device} vs {type(metric_device)}:{metric_device}" def test_simple_2D_input(): # Tests for 2D inputs with normalized = True and False num_iters = 5 num_samples = 100 num_classes = 10 torch.manual_seed(0) for _ in range(num_iters): target = torch.randint(0, 2, size=(num_samples, num_classes)) prediction = torch.randint(0, 2, size=(num_samples, num_classes)) sklearn_CM = multilabel_confusion_matrix(target.numpy(), prediction.numpy()) mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) mlcm.update([prediction, target]) ignite_CM = mlcm.compute().numpy() assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) mlcm = MultiLabelConfusionMatrix(num_classes, normalized=True) mlcm.update([prediction, target]) ignite_CM_normalized = mlcm.compute().numpy() sklearn_CM_normalized = sklearn_CM / sklearn_CM.sum(axis=(1, 2))[:, None, None] assert np.allclose(sklearn_CM_normalized, ignite_CM_normalized) def test_simple_ND_input(): num_iters = 5 num_samples = 100 num_classes = 10 torch.manual_seed(0) size_3d = 4 for _ in range(num_iters): # 3D tests target = torch.randint(0, 2, size=(num_samples, num_classes, size_3d)) prediction = torch.randint(0, 2, size=(num_samples, num_classes, size_3d)) mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) mlcm.update([prediction, target]) ignite_CM = mlcm.compute().numpy() target_reshaped = target.permute(0, 2, 1).reshape(size_3d * num_samples, num_classes) prediction_reshaped = prediction.permute(0, 2, 1).reshape(size_3d * num_samples, num_classes) sklearn_CM = multilabel_confusion_matrix(target_reshaped.numpy(), prediction_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) size_4d = 4 for _ in range(num_iters): # 4D tests target = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d)) prediction = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d)) mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) mlcm.update([prediction, target]) ignite_CM = mlcm.compute().numpy() target_reshaped = target.permute(0, 2, 3, 1).reshape(size_3d * size_4d * num_samples, num_classes) prediction_reshaped = prediction.permute(0, 2, 3, 1).reshape(size_3d * size_4d * num_samples, num_classes) sklearn_CM = multilabel_confusion_matrix(target_reshaped.numpy(), prediction_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) size_5d = 4 for _ in range(num_iters): # 5D tests target = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d, size_5d)) prediction = torch.randint(0, 2, size=(num_samples, num_classes, size_3d, size_4d, size_5d)) mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) mlcm.update([prediction, target]) ignite_CM = mlcm.compute().numpy() target_reshaped = target.permute(0, 2, 3, 4, 1).reshape(size_3d * size_4d * size_5d * num_samples, num_classes) prediction_reshaped = prediction.permute(0, 2, 3, 4, 1).reshape( size_3d * size_4d * size_5d * num_samples, num_classes ) sklearn_CM = multilabel_confusion_matrix(target_reshaped.numpy(), prediction_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) def test_simple_batched(): num_iters = 5 num_samples = 100 num_classes = 10 batch_size = 1 torch.manual_seed(0) for _ in range(num_iters): # 2D tests mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) targets = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes)) predictions = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes)) for i in range(int(num_samples / batch_size)): target_sample = targets[i] prediction_sample = predictions[i] mlcm.update([prediction_sample, target_sample]) ignite_CM = mlcm.compute().numpy() targets_reshaped = targets.reshape(-1, num_classes) predictions_reshaped = predictions.reshape(-1, num_classes) sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) size_3d = 4 for _ in range(num_iters): # 3D tests mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) targets = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d)) predictions = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d)) for i in range(int(num_samples / batch_size)): target_sample = targets[i] prediction_sample = predictions[i] mlcm.update([prediction_sample, target_sample]) ignite_CM = mlcm.compute().numpy() targets_reshaped = targets.permute(0, 1, 3, 2).reshape(-1, num_classes) predictions_reshaped = predictions.permute(0, 1, 3, 2).reshape(-1, num_classes) sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) size_4d = 4 for _ in range(num_iters): # 4D tests mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) targets = torch.randint(0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d)) predictions = torch.randint( 0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d) ) for i in range(int(num_samples / batch_size)): target_sample = targets[i] prediction_sample = predictions[i] mlcm.update([prediction_sample, target_sample]) ignite_CM = mlcm.compute().numpy() targets_reshaped = targets.permute(0, 1, 3, 4, 2).reshape(-1, num_classes) predictions_reshaped = predictions.permute(0, 1, 3, 4, 2).reshape(-1, num_classes) sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) size_5d = 4 for _ in range(num_iters): # 5D tests mlcm = MultiLabelConfusionMatrix(num_classes, normalized=False) targets = torch.randint( 0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d, size_5d) ) predictions = torch.randint( 0, 2, size=(int(num_samples / batch_size), batch_size, num_classes, size_3d, size_4d, size_5d) ) for i in range(int(num_samples / batch_size)): target_sample = targets[i] prediction_sample = predictions[i] mlcm.update([prediction_sample, target_sample]) ignite_CM = mlcm.compute().numpy() targets_reshaped = targets.permute(0, 1, 3, 4, 5, 2).reshape(-1, num_classes) predictions_reshaped = predictions.permute(0, 1, 3, 4, 5, 2).reshape(-1, num_classes) sklearn_CM = multilabel_confusion_matrix(targets_reshaped.numpy(), predictions_reshaped.numpy()) assert np.all(sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)) # @pytest.mark.distributed # @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") # @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") # def test_distrib_nccl_gpu(distributed_context_single_node_nccl): # device = idist.device() # _test_distrib_multiclass_images(device) # _test_distrib_accumulator_device(device) # @pytest.mark.distributed # @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") # def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): # device = idist.device() # _test_distrib_multiclass_images(device) # _test_distrib_accumulator_device(device) # @pytest.mark.distributed # @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") # @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") # def test_distrib_hvd(gloo_hvd_executor): # device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") # nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() # gloo_hvd_executor(_test_distrib_multiclass_images, (device,), np=nproc, do_init=True) # gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) # @pytest.mark.multinode_distributed # @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") # @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") # def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): # # device = idist.device() # _test_distrib_multiclass_images(device) # _test_distrib_accumulator_device(device) # @pytest.mark.multinode_distributed # @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") # @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") # def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): # # device = idist.device() # _test_distrib_multiclass_images(device) # _test_distrib_accumulator_device(device) # @pytest.mark.tpu # @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") # @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") # def test_distrib_single_device_xla(): # device = idist.device() # _test_distrib_multiclass_images(device) # _test_distrib_accumulator_device(device) # def _test_distrib_xla_nprocs(index): # device = idist.device() # _test_distrib_multiclass_images(device) # _test_distrib_accumulator_device(device) # @pytest.mark.tpu # @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") # @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") # def test_distrib_xla_nprocs(xmp_executor): # n = int(os.environ["NUM_TPU_WORKERS"]) # xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_mutual_information.py000066400000000000000000000114421465426447700244350ustar00rootroot00000000000000from typing import Tuple import numpy as np import pytest import torch from scipy.special import softmax from scipy.stats import entropy from torch import Tensor import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import MutualInformation def np_mutual_information(np_y_pred: np.ndarray) -> float: prob = softmax(np_y_pred, axis=1) marginal_ent = entropy(np.mean(prob, axis=0)) conditional_ent = np.mean(entropy(prob, axis=1)) return max(0.0, marginal_ent - conditional_ent) def test_zero_sample(): mi = MutualInformation() with pytest.raises( NotComputableError, match=r"MutualInformation must have at least one example before it can be computed" ): mi.compute() def test_invalid_shape(): mi = MutualInformation() y_pred = torch.randn(10).float() with pytest.raises(ValueError, match=r"y_pred must be in the shape of \(B, C\) or \(B, C, ...\), got"): mi.update((y_pred, None)) @pytest.fixture(params=list(range(4))) def test_case(request): return [ (torch.randn((100, 10)).float(), torch.randint(0, 10, size=[100]), 1), (torch.rand((100, 500)).float(), torch.randint(0, 500, size=[100]), 1), # updated batches (torch.normal(0.0, 5.0, size=(100, 10)).float(), torch.randint(0, 10, size=[100]), 16), (torch.normal(5.0, 3.0, size=(100, 200)).float(), torch.randint(0, 200, size=[100]), 16), # image segmentation (torch.randn((100, 5, 32, 32)).float(), torch.randint(0, 5, size=(100, 32, 32)), 16), (torch.randn((100, 5, 224, 224)).float(), torch.randint(0, 5, size=(100, 224, 224)), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_compute(n_times, test_case: Tuple[Tensor, Tensor, int]): mi = MutualInformation() y_pred, y, batch_size = test_case mi.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size mi.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: mi.update((y_pred, y)) np_res = np_mutual_information(y_pred.numpy()) res = mi.compute() assert isinstance(res, float) assert pytest.approx(np_res, rel=1e-4) == res def test_accumulator_detached(): mi = MutualInformation() y_pred = torch.tensor([[2.0, 3.0], [-2.0, -1.0]], requires_grad=True) y = torch.zeros(2) mi.update((y_pred, y)) assert not mi._sum_of_probabilities.requires_grad @pytest.mark.usefixtures("distributed") class TestDistributed: def test_integration(self): tol = 1e-4 n_iters = 100 batch_size = 10 n_cls = 50 device = idist.device() rank = idist.get_rank() torch.manual_seed(12 + rank) metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: y_true = torch.randint(0, n_cls, size=[n_iters * batch_size], dtype=torch.long).to(device) y_preds = torch.normal(0.0, 3.0, size=(n_iters * batch_size, n_cls), dtype=torch.float).to(device) engine = Engine( lambda e, i: ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) ) m = MutualInformation(device=metric_device) m.attach(engine, "mutual_information") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "mutual_information" in engine.state.metrics res = engine.state.metrics["mutual_information"] true_res = np_mutual_information(y_preds.cpu().numpy()) assert pytest.approx(true_res, rel=tol) == res def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(device) for metric_device in metric_devices: mi = MutualInformation(device=metric_device) devices = (mi._device, mi._sum_of_probabilities.device) for dev in devices: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[2.0, 3.0], [-2.0, -1.0]], requires_grad=True) y = torch.zeros(2) mi.update((y_pred, y)) devices = (mi._device, mi._sum_of_probabilities.device) for dev in devices: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_precision.py000066400000000000000000000600551465426447700225200ustar00rootroot00000000000000import warnings import pytest import torch from sklearn.exceptions import UndefinedMetricWarning from sklearn.metrics import precision_score import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import Precision torch.manual_seed(12) def test_no_update(): precision = Precision() assert precision._updated is False with pytest.raises(NotComputableError, match=r"Precision must have at least one example before it can be computed"): precision.compute() assert precision._updated is False def test_average_parameter(): with pytest.raises(ValueError, match="Argument average should be None or a boolean or one of values"): Precision(average=1) pr = Precision(average="samples") with pytest.raises( ValueError, match=r"Argument average='samples' is incompatible with binary and multiclass input data." ): pr.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long())) assert pr._updated is False pr = Precision(average="samples") with pytest.raises( ValueError, match=r"Argument average='samples' is incompatible with binary and multiclass input data." ): pr.update((torch.rand(10, 3), torch.randint(0, 3, size=(10,)).long())) assert pr._updated is False pr = Precision(average=True) assert pr._average == "macro" def test_binary_wrong_inputs(): pr = Precision() assert pr._updated is False with pytest.raises(ValueError, match=r"For binary cases, y must be comprised of 0's and 1's"): # y has not only 0 or 1 values pr.update((torch.randint(0, 2, size=(10,)).long(), torch.arange(0, 10).long())) assert pr._updated is False with pytest.raises(ValueError, match=r"For binary cases, y_pred must be comprised of 0's and 1's"): # y_pred values are not thresholded to 0, 1 values pr.update((torch.rand(10), torch.randint(0, 2, size=(10,)).long())) assert pr._updated is False with pytest.raises(ValueError, match=r"y must have shape of"): # incompatible shapes pr.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) assert pr._updated is False with pytest.raises(ValueError, match=r"y must have shape of"): # incompatible shapes pr.update((torch.randint(0, 2, size=(10, 5, 6)).long(), torch.randint(0, 2, size=(10,)).long())) assert pr._updated is False with pytest.raises(ValueError, match=r"y must have shape of"): # incompatible shapes pr.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5, 6)).long())) assert pr._updated is False with pytest.warns( RuntimeWarning, match="`y` and `y_pred` should be of dtype long when entry type is binary and average!=False", ): pr = Precision(average=None) pr.update((torch.randint(0, 2, size=(10,)).float(), torch.randint(0, 2, size=(10,)))) with pytest.warns( RuntimeWarning, match="`y` and `y_pred` should be of dtype long when entry type is binary and average!=False", ): pr = Precision(average=None) pr.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).float())) def ignite_average_to_scikit_average(average, data_type: str): if average in [None, "micro", "samples", "weighted", "macro"]: return average if average is False: if data_type == "binary": return "binary" else: return None elif average is True: return "macro" else: raise ValueError(f"Wrong average parameter `{average}`") @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_binary_input(average): pr = Precision(average=average) assert pr._updated is False def _test(y_pred, y, batch_size): pr.reset() assert pr._updated is False if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size pr.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert pr._updated is True assert isinstance(pr.compute(), torch.Tensor if not average else float) pr_compute = pr.compute().numpy() if not average else pr.compute() sk_average_parameter = ignite_average_to_scikit_average(average, "binary") assert precision_score( np_y, np_y_pred, average=sk_average_parameter, labels=[0, 1], zero_division=0 ) == pytest.approx(pr_compute) def get_test_cases(): test_cases = [ # Binary accuracy on input of shape (N, 1) or (N, ) (torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)), 1), (torch.randint(0, 2, size=(10, 1)), torch.randint(0, 2, size=(10, 1)), 1), # updated batches (torch.randint(0, 2, size=(50,)), torch.randint(0, 2, size=(50,)), 16), (torch.randint(0, 2, size=(50, 1)), torch.randint(0, 2, size=(50, 1)), 16), # Binary accuracy on input of shape (N, L) (torch.randint(0, 2, size=(10, 5)), torch.randint(0, 2, size=(10, 5)), 1), (torch.randint(0, 2, size=(10, 1, 5)), torch.randint(0, 2, size=(10, 1, 5)), 1), # updated batches (torch.randint(0, 2, size=(50, 5)), torch.randint(0, 2, size=(50, 5)), 16), (torch.randint(0, 2, size=(50, 1, 5)), torch.randint(0, 2, size=(50, 1, 5)), 16), # Binary accuracy on input of shape (N, H, W) (torch.randint(0, 2, size=(10, 12, 10)), torch.randint(0, 2, size=(10, 12, 10)), 1), (torch.randint(0, 2, size=(10, 1, 12, 10)), torch.randint(0, 2, size=(10, 1, 12, 10)), 1), # updated batches (torch.randint(0, 2, size=(50, 12, 10)), torch.randint(0, 2, size=(50, 12, 10)), 16), (torch.randint(0, 2, size=(50, 1, 12, 10)), torch.randint(0, 2, size=(50, 1, 12, 10)), 16), # Corner case with all zeros predictions (torch.zeros(size=(10,), dtype=torch.long), torch.randint(0, 2, size=(10,)), 1), (torch.zeros(size=(10, 1), dtype=torch.long), torch.randint(0, 2, size=(10, 1)), 1), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_multiclass_wrong_inputs(): pr = Precision() assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes pr.update((torch.rand(10, 5, 4), torch.randint(0, 2, size=(10,)).long())) assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes pr.update((torch.rand(10, 5, 6), torch.randint(0, 5, size=(10, 5)).long())) assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes pr.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long())) assert pr._updated is False pr = Precision(average=True) assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes between two updates pr.update((torch.rand(10, 5), torch.randint(0, 5, size=(10,)).long())) pr.update((torch.rand(10, 6), torch.randint(0, 5, size=(10,)).long())) assert pr._updated is True with pytest.raises(ValueError): # incompatible shapes between two updates pr.update((torch.rand(10, 5, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) pr.update((torch.rand(10, 6, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) assert pr._updated is True pr = Precision(average=False) assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes between two updates pr.update((torch.rand(10, 5), torch.randint(0, 5, size=(10,)).long())) pr.update((torch.rand(10, 6), torch.randint(0, 5, size=(10,)).long())) assert pr._updated is True with pytest.raises(ValueError): # incompatible shapes between two updates pr.update((torch.rand(10, 5, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) pr.update((torch.rand(10, 6, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) assert pr._updated is True with pytest.warns( RuntimeWarning, match="`y` should be of dtype long when entry type is multiclass", ): pr = Precision() pr.update((torch.rand(10, 5), torch.randint(0, 5, size=(10,)).float())) @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_multiclass_input(average): pr = Precision(average=average) assert pr._updated is False def _test(y_pred, y, batch_size): pr.reset() assert pr._updated is False if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size pr.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: pr.update((y_pred, y)) num_classes = y_pred.shape[1] np_y_pred = y_pred.argmax(dim=1).numpy().ravel() np_y = y.numpy().ravel() assert pr._type == "multiclass" assert pr._updated is True assert isinstance(pr.compute(), torch.Tensor if not average else float) pr_compute = pr.compute().numpy() if not average else pr.compute() sk_average_parameter = ignite_average_to_scikit_average(average, "multiclass") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) sk_compute = precision_score(np_y, np_y_pred, labels=range(0, num_classes), average=sk_average_parameter) assert sk_compute == pytest.approx(pr_compute) def get_test_cases(): test_cases = [ # Multiclass input data of shape (N, ) and (N, C) (torch.rand(10, 6), torch.randint(0, 6, size=(10,)), 1), (torch.rand(10, 4), torch.randint(0, 4, size=(10,)), 1), # updated batches (torch.rand(50, 6), torch.randint(0, 6, size=(50,)), 16), (torch.rand(50, 4), torch.randint(0, 4, size=(50,)), 16), # Multiclass input data of shape (N, L) and (N, C, L) (torch.rand(10, 5, 8), torch.randint(0, 5, size=(10, 8)), 1), (torch.rand(10, 8, 12), torch.randint(0, 8, size=(10, 12)), 1), # updated batches (torch.rand(50, 5, 8), torch.randint(0, 5, size=(50, 8)), 16), (torch.rand(50, 8, 12), torch.randint(0, 8, size=(50, 12)), 16), # Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...) (torch.rand(10, 5, 18, 16), torch.randint(0, 5, size=(10, 18, 16)), 1), (torch.rand(10, 7, 20, 12), torch.randint(0, 7, size=(10, 20, 12)), 1), # updated batches (torch.rand(50, 5, 18, 16), torch.randint(0, 5, size=(50, 18, 16)), 16), (torch.rand(50, 7, 20, 12), torch.randint(0, 7, size=(50, 20, 12)), 16), # Corner case with all zeros predictions (torch.zeros(size=(10, 6)), torch.randint(0, 6, size=(10,)), 1), (torch.zeros(size=(10, 4)), torch.randint(0, 4, size=(10,)), 1), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_multilabel_wrong_inputs(): pr = Precision(is_multilabel=True) assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes pr.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).long())) assert pr._updated is False with pytest.raises(ValueError): # incompatible y_pred pr.update((torch.rand(10, 5), torch.randint(0, 2, size=(10, 5)).long())) assert pr._updated is False with pytest.raises(ValueError): # incompatible y pr.update((torch.randint(0, 5, size=(10, 5, 6)), torch.rand(10))) assert pr._updated is False with pytest.raises(ValueError): # incompatible shapes between two updates pr.update((torch.randint(0, 2, size=(20, 5)), torch.randint(0, 2, size=(20, 5)).long())) pr.update((torch.randint(0, 2, size=(20, 6)), torch.randint(0, 2, size=(20, 6)).long())) assert pr._updated is True def to_numpy_multilabel(y): # reshapes input array to (N x ..., C) y = y.transpose(1, 0).cpu().numpy() num_classes = y.shape[0] y = y.reshape((num_classes, -1)).transpose(1, 0) return y @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted", "samples"]) def test_multilabel_input(average): pr = Precision(average=average, is_multilabel=True) assert pr._updated is False def _test(y_pred, y, batch_size): pr.reset() assert pr._updated is False if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size pr.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: pr.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert pr._type == "multilabel" assert pr._updated is True pr_compute = pr.compute().numpy() if not average else pr.compute() sk_average_parameter = ignite_average_to_scikit_average(average, "multilabel") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average=sk_average_parameter) == pytest.approx(pr_compute) def get_test_cases(): test_cases = [ # Multilabel input data of shape (N, C) (torch.randint(0, 2, size=(10, 5)), torch.randint(0, 2, size=(10, 5)), 1), (torch.randint(0, 2, size=(10, 4)), torch.randint(0, 2, size=(10, 4)), 1), # updated batches (torch.randint(0, 2, size=(50, 5)), torch.randint(0, 2, size=(50, 5)), 16), (torch.randint(0, 2, size=(50, 4)), torch.randint(0, 2, size=(50, 4)), 16), # Multilabel input data of shape (N, C, L) (torch.randint(0, 2, size=(10, 5, 10)), torch.randint(0, 2, size=(10, 5, 10)), 1), (torch.randint(0, 2, size=(10, 4, 10)), torch.randint(0, 2, size=(10, 4, 10)), 1), # updated batches (torch.randint(0, 2, size=(50, 5, 10)), torch.randint(0, 2, size=(50, 5, 10)), 16), (torch.randint(0, 2, size=(50, 4, 10)), torch.randint(0, 2, size=(50, 4, 10)), 16), # Multilabel input data of shape (N, C, H, W) (torch.randint(0, 2, size=(10, 5, 18, 16)), torch.randint(0, 2, size=(10, 5, 18, 16)), 1), (torch.randint(0, 2, size=(10, 4, 20, 23)), torch.randint(0, 2, size=(10, 4, 20, 23)), 1), # updated batches (torch.randint(0, 2, size=(50, 5, 18, 16)), torch.randint(0, 2, size=(50, 5, 18, 16)), 16), (torch.randint(0, 2, size=(50, 4, 20, 23)), torch.randint(0, 2, size=(50, 4, 20, 23)), 16), # Corner case with all zeros predictions (torch.zeros(size=(10, 5)), torch.randint(0, 2, size=(10, 5)), 1), (torch.zeros(size=(10, 4)), torch.randint(0, 2, size=(10, 4)), 1), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_incorrect_type(average): # Tests changing of type during training pr = Precision(average=average) assert pr._updated is False y_pred = torch.softmax(torch.rand(4, 4), dim=1) y = torch.ones(4).long() pr.update((y_pred, y)) assert pr._updated is True y_pred = torch.randint(0, 2, size=(4,)) y = torch.ones(4).long() with pytest.raises(RuntimeError): pr.update((y_pred, y)) assert pr._updated is True @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_incorrect_y_classes(average): pr = Precision(average=average) assert pr._updated is False y_pred = torch.randint(0, 2, size=(10, 4)).float() y = torch.randint(4, 5, size=(10,)).long() with pytest.raises(ValueError): pr.update((y_pred, y)) assert pr._updated is False @pytest.mark.usefixtures("distributed") class TestDistributed: @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro"]) @pytest.mark.parametrize("n_epochs", [1, 2]) def test_integration_multiclass(self, average, n_epochs): rank = idist.get_rank() torch.manual_seed(12 + rank) n_iters = 60 batch_size = 16 n_classes = 7 metric_devices = [torch.device("cpu")] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) pr = Precision(average=average, device=metric_device) pr.attach(engine, "pr") assert pr._updated is False data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "pr" in engine.state.metrics assert pr._updated is True res = engine.state.metrics["pr"] if isinstance(res, torch.Tensor): # Fixes https://github.com/pytorch/ignite/issues/1635#issuecomment-863026919 assert res.device.type == "cpu" res = res.cpu().numpy() sk_average_parameter = ignite_average_to_scikit_average(average, "multiclass") true_res = precision_score( y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), average=sk_average_parameter ) assert pytest.approx(res) == true_res @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro", "samples"]) @pytest.mark.parametrize("n_epochs", [1, 2]) def test_integration_multilabel(self, average, n_epochs): rank = idist.get_rank() torch.manual_seed(12 + rank) n_iters = 60 batch_size = 16 n_classes = 7 metric_devices = ["cpu"] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: y_true = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 6, 8)).to(device) y_preds = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 6, 8)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, ...], y_true[i * batch_size : (i + 1) * batch_size, ...], ) engine = Engine(update) pr = Precision(average=average, is_multilabel=True, device=metric_device) pr.attach(engine, "pr") assert pr._updated is False data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "pr" in engine.state.metrics assert pr._updated is True res = engine.state.metrics["pr"] res2 = pr.compute() if isinstance(res, torch.Tensor): res = res.cpu().numpy() res2 = res2.cpu().numpy() assert (res == res2).all() else: assert res == res2 np_y_preds = to_numpy_multilabel(y_preds) np_y_true = to_numpy_multilabel(y_true) assert pr._type == "multilabel" sk_average_parameter = ignite_average_to_scikit_average(average, "multilabel") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y_true, np_y_preds, average=sk_average_parameter) == pytest.approx(res) @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro"]) def test_accumulator_device(self, average): # Binary accuracy on input of shape (N, 1) or (N, ) metric_devices = [torch.device("cpu")] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: pr = Precision(average=average, device=metric_device) assert pr._device == metric_device assert pr._updated is False # Since the shape of the accumulated amount isn't known before the first update # call, the internal variables aren't tensors on the right device yet. y_pred = torch.randint(0, 2, size=(10,)) y = torch.randint(0, 2, size=(10,)).long() pr.update((y_pred, y)) assert pr._updated is True assert ( pr._numerator.device == metric_device ), f"{type(pr._numerator.device)}:{pr._numerator.device} vs {type(metric_device)}:{metric_device}" if average != "samples": # For average='samples', `_denominator` is of type `int` so it has not `device` member. assert ( pr._denominator.device == metric_device ), f"{type(pr._denominator.device)}:{pr._denominator.device} vs {type(metric_device)}:{metric_device}" if average == "weighted": assert pr._weight.device == metric_device, f"{type(pr._weight.device)}:{pr._weight.device} vs " f"{type(metric_device)}:{metric_device}" @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro", "samples"]) def test_multilabel_accumulator_device(self, average): # Multiclass input data of shape (N, ) and (N, C) metric_devices = [torch.device("cpu")] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: pr = Precision(is_multilabel=True, average=average, device=metric_device) assert pr._device == metric_device assert pr._updated is False y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() pr.update((y_pred, y)) assert pr._updated is True assert ( pr._numerator.device == metric_device ), f"{type(pr._numerator.device)}:{pr._numerator.device} vs {type(metric_device)}:{metric_device}" if average != "samples": # For average='samples', `_denominator` is of type `int` so it has not `device` member. assert ( pr._denominator.device == metric_device ), f"{type(pr._denominator.device)}:{pr._denominator.device} vs {type(metric_device)}:{metric_device}" if average == "weighted": assert pr._weight.device == metric_device, f"{type(pr._weight.device)}:{pr._weight.device} vs " f"{type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_precision_recall_curve.py000066400000000000000000000264031465426447700252450ustar00rootroot00000000000000import os from typing import Tuple from unittest.mock import patch import numpy as np import pytest import sklearn import torch from sklearn.metrics import precision_recall_curve import ignite.distributed as idist from ignite.engine import Engine from ignite.metrics.epoch_metric import EpochMetricWarning from ignite.metrics.precision_recall_curve import PrecisionRecallCurve @pytest.fixture() def mock_no_sklearn(): with patch.dict("sys.modules", {"sklearn.metrics": None}): yield sklearn def test_no_sklearn(mock_no_sklearn): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires scikit-learn to be installed."): y = torch.tensor([1, 1]) pr_curve = PrecisionRecallCurve() pr_curve.update((y, y)) pr_curve.compute() def test_precision_recall_curve(): size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,)) np_y[size // 2 :] = 1 sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred) precision_recall_curve_metric = PrecisionRecallCurve() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) precision_recall_curve_metric.update((y_pred, y)) precision, recall, thresholds = precision_recall_curve_metric.compute() precision = precision.numpy() recall = recall.numpy() thresholds = thresholds.numpy() assert pytest.approx(precision) == sk_precision assert pytest.approx(recall) == sk_recall # assert thresholds almost equal, due to numpy->torch->numpy conversion np.testing.assert_array_almost_equal(thresholds, sk_thresholds) def test_integration_precision_recall_curve_with_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,)) np_y[size // 2 :] = 1 np.random.shuffle(np_y) sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred) batch_size = 10 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) precision_recall_curve_metric = PrecisionRecallCurve(output_transform=lambda x: (x[1], x[2])) precision_recall_curve_metric.attach(engine, "precision_recall_curve") data = list(range(size // batch_size)) precision, recall, thresholds = engine.run(data, max_epochs=1).metrics["precision_recall_curve"] precision = precision.numpy() recall = recall.numpy() thresholds = thresholds.numpy() assert pytest.approx(precision) == sk_precision assert pytest.approx(recall) == sk_recall # assert thresholds almost equal, due to numpy->torch->numpy conversion np.testing.assert_array_almost_equal(thresholds, sk_thresholds) def test_integration_precision_recall_curve_with_activated_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y_pred_sigmoid = torch.sigmoid(torch.from_numpy(np_y_pred)).numpy() np_y = np.zeros((size,)) np_y[size // 2 :] = 1 np.random.shuffle(np_y) sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y, np_y_pred_sigmoid) batch_size = 10 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) precision_recall_curve_metric = PrecisionRecallCurve(output_transform=lambda x: (torch.sigmoid(x[1]), x[2])) precision_recall_curve_metric.attach(engine, "precision_recall_curve") data = list(range(size // batch_size)) precision, recall, thresholds = engine.run(data, max_epochs=1).metrics["precision_recall_curve"] precision = precision.cpu().numpy() recall = recall.cpu().numpy() thresholds = thresholds.cpu().numpy() assert pytest.approx(precision) == sk_precision assert pytest.approx(recall) == sk_recall # assert thresholds almost equal, due to numpy->torch->numpy conversion np.testing.assert_array_almost_equal(thresholds, sk_thresholds) def test_check_compute_fn(): y_pred = torch.zeros((8, 13)) y_pred[:, 1] = 1 y_true = torch.zeros_like(y_pred) output = (y_pred, y_true) em = PrecisionRecallCurve(check_compute_fn=True) em.reset() with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"): em.update(output) em = PrecisionRecallCurve(check_compute_fn=False) em.update(output) def _test_distrib_compute(device): rank = idist.get_rank() def _test(y_pred, y, batch_size, metric_device): metric_device = torch.device(metric_device) prc = PrecisionRecallCurve(device=metric_device) prc.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size prc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: prc.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() res = prc.compute() assert isinstance(res, Tuple) assert precision_recall_curve(np_y, np_y_pred)[0] == pytest.approx(res[0].cpu().numpy()) assert precision_recall_curve(np_y, np_y_pred)[1] == pytest.approx(res[1].cpu().numpy()) assert precision_recall_curve(np_y, np_y_pred)[2] == pytest.approx(res[2].cpu().numpy()) def get_test_cases(): test_cases = [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)), 1), (torch.randint(0, 2, size=(10, 1)), torch.randint(0, 2, size=(10, 1)), 1), # updated batches (torch.randint(0, 2, size=(50,)), torch.randint(0, 2, size=(50,)), 16), (torch.randint(0, 2, size=(50, 1)), torch.randint(0, 2, size=(50, 1)), 16), ] return test_cases for i in range(3): torch.manual_seed(12 + rank + i) test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: y_pred = y_pred.to(device) y = y.to(device) _test(y_pred, y, batch_size, "cpu") if device.type != "xla": _test(y_pred, y, batch_size, idist.device()) def _test_distrib_integration(device): rank = idist.get_rank() def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 batch_size = 151 torch.manual_seed(12 + rank) y_true = torch.randint(0, 2, (n_iters * batch_size,)).to(device) y_preds = torch.randint(0, 2, (n_iters * batch_size,)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) prc = PrecisionRecallCurve(device=metric_device) prc.attach(engine, "prc") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_true = idist.all_gather(y_true) y_preds = idist.all_gather(y_preds) assert "prc" in engine.state.metrics precision, recall, thresholds = engine.state.metrics["prc"] np_y_true = y_true.cpu().numpy().ravel() np_y_preds = y_preds.cpu().numpy().ravel() sk_precision, sk_recall, sk_thresholds = precision_recall_curve(np_y_true, np_y_preds) assert precision.shape == sk_precision.shape assert recall.shape == sk_recall.shape assert thresholds.shape == sk_thresholds.shape assert pytest.approx(precision.cpu().numpy()) == sk_precision assert pytest.approx(recall.cpu().numpy()) == sk_recall assert pytest.approx(thresholds.cpu().numpy()) == sk_thresholds metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for _ in range(2): _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_compute, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device) _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_psnr.py000066400000000000000000000172531465426447700215110ustar00rootroot00000000000000import numpy as np import pytest import torch from skimage.metrics import peak_signal_noise_ratio as ski_psnr import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import PSNR from ignite.utils import manual_seed def test_zero_div(): psnr = PSNR(1.0) with pytest.raises(NotComputableError, match="PSNR must have at least one example before it can be computed"): psnr.compute() def test_invalid_psnr(): y_pred = torch.rand(1, 3, 8, 8) y = torch.rand(1, 3, 8, 8) psnr = PSNR(1.0) with pytest.raises(TypeError, match="Expected y_pred and y to have the same data type."): psnr.update((y_pred, y.double())) with pytest.raises(ValueError, match="Expected y_pred and y to have the same shape."): psnr.update((y_pred, y.squeeze(dim=0))) @pytest.fixture(params=["float", "YCbCr", "uint8", "NHW shape"]) def test_data(request, available_device): manual_seed(42) if request.param == "float": y_pred = torch.rand(8, 3, 28, 28, device=available_device) y = y_pred * 0.8 elif request.param == "YCbCr": y_pred = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=available_device) y = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=available_device) elif request.param == "uint8": y_pred = torch.randint(0, 256, (4, 3, 16, 16), dtype=torch.uint8, device=available_device) y = (y_pred * 0.8).to(torch.uint8) elif request.param == "NHW shape": y_pred = torch.rand(8, 28, 28, device=available_device) y = y_pred * 0.8 else: raise ValueError(f"Wrong fixture parameter, given {request.param}") return (y_pred, y) def test_psnr(test_data, available_device): y_pred, y = test_data data_range = (y.max() - y.min()).cpu().item() psnr = PSNR(data_range=data_range, device=available_device) psnr.update(test_data) psnr_compute = psnr.compute() np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() np_psnr = 0 for np_y_pred_, np_y_ in zip(np_y_pred, np_y): np_psnr += ski_psnr(np_y_, np_y_pred_, data_range=data_range) assert psnr_compute > 0.0 assert isinstance(psnr_compute, float) assert np.allclose(psnr_compute, np_psnr / np_y.shape[0]) def _test( y_pred, y, data_range, metric_device, n_iters, batch_size, atol, output_transform=lambda x: x, compute_y_channel=False, ): def update(engine, i): return ( y_pred[i * batch_size : (i + 1) * batch_size], y[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) psnr = PSNR(data_range=data_range, output_transform=output_transform, device=metric_device) psnr.attach(engine, "psnr") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y = idist.all_gather(y) y_pred = idist.all_gather(y_pred) assert "psnr" in engine.state.metrics result = engine.state.metrics["psnr"] assert result > 0.0 if compute_y_channel: np_y_pred = y_pred[:, 0, ...].cpu().numpy() np_y = y[:, 0, ...].cpu().numpy() else: np_y_pred = y_pred.cpu().numpy() np_y = y.cpu().numpy() np_psnr = 0 for np_y_pred_, np_y_ in zip(np_y_pred, np_y): np_psnr += ski_psnr(np_y_, np_y_pred_, data_range=data_range) assert np.allclose(result, np_psnr / np_y.shape[0], atol=atol) @pytest.mark.usefixtures("distributed") class TestDistributed: def test_input_float(self): device = idist.device() def get_test_cases(): y_pred = torch.rand(n_iters * batch_size, 2, 2, device=device) y = y_pred * 0.65 return y_pred, y n_iters = 100 batch_size = 10 rank = idist.get_rank() for i in range(3): # check multiple random inputs as random exact occurencies are rare torch.manual_seed(42 + rank + i) y_pred, y = get_test_cases() _test(y_pred, y, 1, "cpu", n_iters, batch_size, atol=1e-8) if device.type != "xla": _test(y_pred, y, 1, idist.device(), n_iters, batch_size, atol=1e-8) def test_multilabel_input_YCbCr(self): device = idist.device() def get_test_cases(): y_pred = torch.randint(16, 236, (n_iters * batch_size, 1, 12, 12), dtype=torch.uint8, device=device) cbcr_pred = torch.randint(16, 241, (n_iters * batch_size, 2, 12, 12), dtype=torch.uint8, device=device) y = torch.randint(16, 236, (n_iters * batch_size, 1, 12, 12), dtype=torch.uint8, device=device) cbcr = torch.randint(16, 241, (n_iters * batch_size, 2, 12, 12), dtype=torch.uint8, device=device) y_pred, y = torch.cat((y_pred, cbcr_pred), dim=1), torch.cat((y, cbcr), dim=1) return y_pred, y n_iters = 100 batch_size = 10 def out_fn(x): return x[0][:, 0, ...], x[1][:, 0, ...] rank = idist.get_rank() for i in range(3): # check multiple random inputs as random exact occurencies are rare torch.manual_seed(42 + rank + i) y_pred, y = get_test_cases() _test( y_pred, y, 220, "cpu", n_iters, batch_size, atol=1e-8, output_transform=out_fn, compute_y_channel=True ) if device.type != "xla": dev = idist.device() _test( y_pred, y, 220, dev, n_iters, batch_size, atol=1e-8, output_transform=out_fn, compute_y_channel=True ) def test_multilabel_input_uint8(self): device = idist.device() def get_test_cases(): y_pred = torch.randint(0, 256, (n_iters * batch_size, 3, 16, 16), device=device, dtype=torch.uint8) y = (y_pred * 0.65).to(torch.uint8) return y_pred, y n_iters = 100 batch_size = 10 rank = idist.get_rank() for i in range(3): # check multiple random inputs as random exact occurencies are rare torch.manual_seed(42 + rank + i) y_pred, y = get_test_cases() _test(y_pred, y, 100, "cpu", n_iters, batch_size, atol=1e-8) if device.type != "xla": _test(y_pred, y, 100, idist.device(), n_iters, batch_size, atol=1e-8) def test_multilabel_input_NHW(self): device = idist.device() def get_test_cases(): y_pred = torch.rand(n_iters * batch_size, 28, 28, device=device) y = y_pred * 0.8 return y_pred, y n_iters = 100 batch_size = 10 rank = idist.get_rank() for i in range(3): # check multiple random inputs as random exact occurencies are rare torch.manual_seed(42 + rank + i) y_pred, y = get_test_cases() _test(y_pred, y, 10, "cpu", n_iters, batch_size, atol=1e-8) if device.type != "xla": _test(y_pred, y, 10, idist.device(), n_iters, batch_size, atol=1e-8) def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if torch.device(device).type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: psnr = PSNR(data_range=1.0, device=metric_device) dev = psnr._device assert dev == metric_device, f"{dev} vs {metric_device}" y_pred = torch.rand(2, 3, 28, 28, dtype=torch.float, device=device) y = y_pred * 0.65 psnr.update((y_pred, y)) dev = psnr._sum_of_batchwise_psnr.device assert dev == metric_device, f"{dev} vs {metric_device}" ignite-0.5.1/tests/ignite/metrics/test_recall.py000066400000000000000000000577521465426447700220010ustar00rootroot00000000000000import warnings import pytest import torch from sklearn.exceptions import UndefinedMetricWarning from sklearn.metrics import recall_score import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import Recall torch.manual_seed(12) def test_no_update(): recall = Recall() assert recall._updated is False with pytest.raises(NotComputableError, match=r"Recall must have at least one example before it can be computed"): recall.compute() assert recall._updated is False recall = Recall(is_multilabel=True) assert recall._updated is False with pytest.raises(NotComputableError, match=r"Recall must have at least one example before it can be computed"): recall.compute() assert recall._updated is False def test_average_parameter(): re = Recall(average="samples") with pytest.raises( ValueError, match=r"Argument average='samples' is incompatible with binary and multiclass input data." ): re.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long())) assert re._updated is False re = Recall(average="samples") with pytest.raises( ValueError, match=r"Argument average='samples' is incompatible with binary and multiclass input data." ): re.update((torch.rand(10, 3), torch.randint(0, 3, size=(10,)).long())) assert re._updated is False re = Recall(average=True) assert re._average == "macro" def test_binary_wrong_inputs(): re = Recall() assert re._updated is False with pytest.raises(ValueError, match=r"For binary cases, y must be comprised of 0's and 1's"): # y has not only 0 or 1 values re.update((torch.randint(0, 2, size=(10,)), torch.arange(0, 10).long())) assert re._updated is False with pytest.raises(ValueError, match=r"For binary cases, y_pred must be comprised of 0's and 1's"): # y_pred values are not thresholded to 0, 1 values re.update((torch.rand(10, 1), torch.randint(0, 2, size=(10,)).long())) assert re._updated is False with pytest.raises(ValueError, match=r"y must have shape of"): # incompatible shapes re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10, 5)).long())) assert re._updated is False with pytest.raises(ValueError, match=r"y must have shape of"): # incompatible shapes re.update((torch.randint(0, 2, size=(10, 5, 6)), torch.randint(0, 2, size=(10,)).long())) assert re._updated is False with pytest.raises(ValueError, match=r"y must have shape of"): # incompatible shapes re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10, 5, 6)).long())) assert re._updated is False with pytest.warns( RuntimeWarning, match="`y` and `y_pred` should be of dtype long when entry type is binary and average!=False", ): re = Recall(average=None) re.update((torch.randint(0, 2, size=(10,)).float(), torch.randint(0, 2, size=(10,)))) with pytest.warns( RuntimeWarning, match="`y` and `y_pred` should be of dtype long when entry type is binary and average!=False", ): re = Recall(average=None) re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).float())) def ignite_average_to_scikit_average(average, data_type: str): if average in [None, "micro", "samples", "weighted", "macro"]: return average if average is False: if data_type == "binary": return "binary" else: return None elif average is True: return "macro" else: raise ValueError(f"Wrong average parameter `{average}`") @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_binary_input(average): re = Recall(average=average) assert re._updated is False def _test(y_pred, y, batch_size): re.reset() assert re._updated is False if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size re.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert re._updated is True assert isinstance(re.compute(), torch.Tensor if not average else float) re_compute = re.compute().numpy() if not average else re.compute() sk_average_parameter = ignite_average_to_scikit_average(average, "binary") assert recall_score(np_y, np_y_pred, average=sk_average_parameter, labels=[0, 1]) == pytest.approx(re_compute) def get_test_cases(): test_cases = [ # Binary accuracy on input of shape (N, 1) or (N, ) (torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)), 1), (torch.randint(0, 2, size=(10, 1)), torch.randint(0, 2, size=(10, 1)), 1), # updated batches (torch.randint(0, 2, size=(50,)), torch.randint(0, 2, size=(50,)), 16), (torch.randint(0, 2, size=(50, 1)), torch.randint(0, 2, size=(50, 1)), 16), # Binary accuracy on input of shape (N, L) (torch.randint(0, 2, size=(10, 5)), torch.randint(0, 2, size=(10, 5)), 1), (torch.randint(0, 2, size=(10, 1, 5)), torch.randint(0, 2, size=(10, 1, 5)), 1), # updated batches (torch.randint(0, 2, size=(50, 5)), torch.randint(0, 2, size=(50, 5)), 16), (torch.randint(0, 2, size=(50, 1, 5)), torch.randint(0, 2, size=(50, 1, 5)), 16), # Binary accuracy on input of shape (N, H, W) (torch.randint(0, 2, size=(10, 12, 10)), torch.randint(0, 2, size=(10, 12, 10)), 1), (torch.randint(0, 2, size=(10, 1, 12, 10)), torch.randint(0, 2, size=(10, 1, 12, 10)), 1), # updated batches (torch.randint(0, 2, size=(50, 12, 10)), torch.randint(0, 2, size=(50, 12, 10)), 16), (torch.randint(0, 2, size=(50, 1, 12, 10)), torch.randint(0, 2, size=(50, 1, 12, 10)), 16), # Corner case with all zeros predictions (torch.zeros(size=(10,), dtype=torch.long), torch.randint(0, 2, size=(10,)), 1), (torch.zeros(size=(10, 1), dtype=torch.long), torch.randint(0, 2, size=(10, 1)), 1), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_multiclass_wrong_inputs(): re = Recall() assert re._updated is False with pytest.raises(ValueError): # incompatible shapes re.update((torch.rand(10, 5, 4), torch.randint(0, 2, size=(10,)).long())) assert re._updated is False with pytest.raises(ValueError): # incompatible shapes re.update((torch.rand(10, 5, 6), torch.randint(0, 5, size=(10, 5)).long())) assert re._updated is False with pytest.raises(ValueError): # incompatible shapes re.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long())) assert re._updated is False re = Recall(average=True) assert re._updated is False with pytest.raises(ValueError): # incompatible shapes between two updates re.update((torch.rand(10, 5), torch.randint(0, 5, size=(10,)).long())) re.update((torch.rand(10, 6), torch.randint(0, 5, size=(10,)).long())) assert re._updated is True with pytest.raises(ValueError): # incompatible shapes between two updates re.update((torch.rand(10, 5, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) re.update((torch.rand(10, 6, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) assert re._updated is True re = Recall(average=False) assert re._updated is False with pytest.raises(ValueError): # incompatible shapes between two updates re.update((torch.rand(10, 5), torch.randint(0, 5, size=(10,)).long())) re.update((torch.rand(10, 6), torch.randint(0, 5, size=(10,)).long())) assert re._updated is True with pytest.raises(ValueError): # incompatible shapes between two updates re.update((torch.rand(10, 5, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) re.update((torch.rand(10, 6, 12, 14), torch.randint(0, 5, size=(10, 12, 14)).long())) assert re._updated is True with pytest.warns( RuntimeWarning, match="`y` should be of dtype long when entry type is multiclass", ): re = Recall() re.update((torch.rand(10, 5), torch.randint(0, 5, size=(10,)).float())) @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_multiclass_input(average): re = Recall(average=average) assert re._updated is False def _test(y_pred, y, batch_size): re.reset() assert re._updated is False if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size re.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: re.update((y_pred, y)) num_classes = y_pred.shape[1] np_y_pred = y_pred.argmax(dim=1).numpy().ravel() np_y = y.numpy().ravel() assert re._type == "multiclass" assert re._updated is True assert isinstance(re.compute(), torch.Tensor if not average else float) re_compute = re.compute().numpy() if not average else re.compute() sk_average_parameter = ignite_average_to_scikit_average(average, "multiclass") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) sk_compute = recall_score(np_y, np_y_pred, labels=range(0, num_classes), average=sk_average_parameter) assert sk_compute == pytest.approx(re_compute) def get_test_cases(): test_cases = [ # Multiclass input data of shape (N, ) and (N, C) (torch.rand(10, 6), torch.randint(0, 6, size=(10,)), 1), (torch.rand(10, 4), torch.randint(0, 4, size=(10,)), 1), # updated batches (torch.rand(50, 6), torch.randint(0, 6, size=(50,)), 16), (torch.rand(50, 4), torch.randint(0, 4, size=(50,)), 16), # Multiclass input data of shape (N, L) and (N, C, L) (torch.rand(10, 5, 8), torch.randint(0, 5, size=(10, 8)), 1), (torch.rand(10, 8, 12), torch.randint(0, 8, size=(10, 12)), 1), # updated batches (torch.rand(50, 5, 8), torch.randint(0, 5, size=(50, 8)), 16), (torch.rand(50, 8, 12), torch.randint(0, 8, size=(50, 12)), 16), # Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...) (torch.rand(10, 5, 18, 16), torch.randint(0, 5, size=(10, 18, 16)), 1), (torch.rand(10, 7, 20, 12), torch.randint(0, 7, size=(10, 20, 12)), 1), # updated batches (torch.rand(50, 5, 18, 16), torch.randint(0, 5, size=(50, 18, 16)), 16), (torch.rand(50, 7, 20, 12), torch.randint(0, 7, size=(50, 20, 12)), 16), # Corner case with all zeros predictions (torch.zeros(size=(10, 6)), torch.randint(0, 6, size=(10,)), 1), (torch.zeros(size=(10, 4)), torch.randint(0, 4, size=(10,)), 1), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) def test_multilabel_wrong_inputs(): re = Recall(is_multilabel=True) assert re._updated is False with pytest.raises(ValueError): # incompatible shapes re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).long())) assert re._updated is False with pytest.raises(ValueError): # incompatible y_pred re.update((torch.rand(10, 5), torch.randint(0, 2, size=(10, 5)).long())) assert re._updated is False with pytest.raises(ValueError): # incompatible y re.update((torch.randint(0, 5, size=(10, 5, 6)), torch.rand(10))) assert re._updated is False with pytest.raises(ValueError): # incompatible shapes between two updates re.update((torch.randint(0, 2, size=(20, 5)), torch.randint(0, 2, size=(20, 5)).long())) re.update((torch.randint(0, 2, size=(20, 6)), torch.randint(0, 2, size=(20, 6)).long())) assert re._updated is True def to_numpy_multilabel(y): # reshapes input array to (N x ..., C) y = y.transpose(1, 0).cpu().numpy() num_classes = y.shape[0] y = y.reshape((num_classes, -1)).transpose(1, 0) return y @pytest.mark.parametrize("average", [None, False, "macro", "micro", "samples"]) def test_multilabel_input(average): re = Recall(average=average, is_multilabel=True) assert re._updated is False def _test(y_pred, y, batch_size): re.reset() assert re._updated is False if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size re.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: re.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert re._type == "multilabel" assert re._updated is True re_compute = re.compute().numpy() if not average else re.compute() sk_average_parameter = ignite_average_to_scikit_average(average, "multilabel") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average=sk_average_parameter) == pytest.approx(re_compute) def get_test_cases(): test_cases = [ # Multilabel input data of shape (N, C) (torch.randint(0, 2, size=(10, 5)), torch.randint(0, 2, size=(10, 5)), 1), (torch.randint(0, 2, size=(10, 4)), torch.randint(0, 2, size=(10, 4)), 1), # updated batches (torch.randint(0, 2, size=(50, 5)), torch.randint(0, 2, size=(50, 5)), 16), (torch.randint(0, 2, size=(50, 4)), torch.randint(0, 2, size=(50, 4)), 16), # Multilabel input data of shape (N, H, W) (torch.randint(0, 2, size=(10, 5, 10)), torch.randint(0, 2, size=(10, 5, 10)), 1), (torch.randint(0, 2, size=(10, 4, 10)), torch.randint(0, 2, size=(10, 4, 10)), 1), # updated batches (torch.randint(0, 2, size=(50, 5, 10)), torch.randint(0, 2, size=(50, 5, 10)), 16), (torch.randint(0, 2, size=(50, 4, 10)), torch.randint(0, 2, size=(50, 4, 10)), 16), # Multilabel input data of shape (N, C, H, W, ...) (torch.randint(0, 2, size=(10, 5, 18, 16)), torch.randint(0, 2, size=(10, 5, 18, 16)), 1), (torch.randint(0, 2, size=(10, 4, 20, 23)), torch.randint(0, 2, size=(10, 4, 20, 23)), 1), # updated batches (torch.randint(0, 2, size=(50, 5, 18, 16)), torch.randint(0, 2, size=(50, 5, 18, 16)), 16), (torch.randint(0, 2, size=(50, 4, 20, 23)), torch.randint(0, 2, size=(50, 4, 20, 23)), 16), # Corner case with all zeros predictions (torch.zeros(size=(10, 5)), torch.randint(0, 2, size=(10, 5)), 1), (torch.zeros(size=(10, 4)), torch.randint(0, 2, size=(10, 4)), 1), ] return test_cases for _ in range(5): # check multiple random inputs as random exact occurencies are rare test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size) @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_incorrect_type(average): # Tests changing of type during training re = Recall(average=average) assert re._updated is False y_pred = torch.softmax(torch.rand(4, 4), dim=1) y = torch.ones(4).long() re.update((y_pred, y)) assert re._updated is True y_pred = torch.zeros(4) y = torch.ones(4).long() with pytest.raises(RuntimeError): re.update((y_pred, y)) assert re._updated is True @pytest.mark.parametrize("average", [None, False, "macro", "micro", "weighted"]) def test_incorrect_y_classes(average): re = Recall(average=average) assert re._updated is False y_pred = torch.randint(0, 2, size=(10, 4)).float() y = torch.randint(4, 5, size=(10,)).long() with pytest.raises(ValueError): re.update((y_pred, y)) assert re._updated is False @pytest.mark.usefixtures("distributed") class TestDistributed: @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro"]) @pytest.mark.parametrize("n_epochs", [1, 2]) def test_integration_multiclass(self, average, n_epochs): rank = idist.get_rank() torch.manual_seed(12 + rank) n_iters = 60 batch_size = 16 n_classes = 7 metric_devices = [torch.device("cpu")] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) re = Recall(average=average, device=metric_device) re.attach(engine, "re") assert re._updated is False data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "re" in engine.state.metrics assert re._updated is True res = engine.state.metrics["re"] if isinstance(res, torch.Tensor): # Fixes https://github.com/pytorch/ignite/issues/1635#issuecomment-863026919 assert res.device.type == "cpu" res = res.cpu().numpy() sk_average_parameter = ignite_average_to_scikit_average(average, "multiclass") true_res = recall_score( y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), average=sk_average_parameter ) assert pytest.approx(res) == true_res @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro", "samples"]) @pytest.mark.parametrize("n_epochs", [1, 2]) def test_integration_multilabel(self, average, n_epochs): rank = idist.get_rank() torch.manual_seed(12 + rank) n_iters = 60 batch_size = 16 n_classes = 7 metric_devices = ["cpu"] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: y_true = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 6, 8)).to(device) y_preds = torch.randint(0, 2, size=(n_iters * batch_size, n_classes, 6, 8)).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, ...], y_true[i * batch_size : (i + 1) * batch_size, ...], ) engine = Engine(update) re = Recall(average=average, is_multilabel=True, device=metric_device) re.attach(engine, "re") assert re._updated is False data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "re" in engine.state.metrics assert re._updated is True res = engine.state.metrics["re"] res2 = re.compute() if isinstance(res, torch.Tensor): res = res.cpu().numpy() res2 = res2.cpu().numpy() assert (res == res2).all() else: assert res == res2 np_y_preds = to_numpy_multilabel(y_preds) np_y_true = to_numpy_multilabel(y_true) assert re._type == "multilabel" sk_average_parameter = ignite_average_to_scikit_average(average, "multilabel") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y_true, np_y_preds, average=sk_average_parameter) == pytest.approx(res) @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro"]) def test_accumulator_device(self, average): # Binary accuracy on input of shape (N, 1) or (N, ) metric_devices = [torch.device("cpu")] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: re = Recall(average=average, device=metric_device) assert re._device == metric_device assert re._updated is False # Since the shape of the accumulated amount isn't known before the first update # call, the internal variables aren't tensors on the right device yet. y_reed = torch.randint(0, 2, size=(10,)) y = torch.randint(0, 2, size=(10,)).long() re.update((y_reed, y)) assert re._updated is True assert ( re._numerator.device == metric_device ), f"{type(re._numerator.device)}:{re._numerator.device} vs {type(metric_device)}:{metric_device}" if average != "samples": # For average='samples', `_denominator` is of type `int` so it has not `device` member. assert ( re._denominator.device == metric_device ), f"{type(re._denominator.device)}:{re._denominator.device} vs {type(metric_device)}:{metric_device}" if average == "weighted": assert re._weight.device == metric_device, f"{type(re._weight.device)}:{re._weight.device} vs " f"{type(metric_device)}:{metric_device}" @pytest.mark.parametrize("average", [False, "macro", "weighted", "micro", "samples"]) def test_multilabel_accumulator_device(self, average): # Multiclass input data of shape (N, ) and (N, C) metric_devices = [torch.device("cpu")] device = idist.device() if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: re = Recall(is_multilabel=True, average=average, device=metric_device) assert re._device == metric_device assert re._updated is False y_reed = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() re.update((y_reed, y)) assert re._updated is True assert ( re._numerator.device == metric_device ), f"{type(re._numerator.device)}:{re._numerator.device} vs {type(metric_device)}:{metric_device}" if average != "samples": # For average='samples', `_denominator` is of type `int` so it has not `device` member. assert ( re._denominator.device == metric_device ), f"{type(re._denominator.device)}:{re._denominator.device} vs {type(metric_device)}:{metric_device}" if average == "weighted": assert re._weight.device == metric_device, f"{type(re._weight.device)}:{re._weight.device} vs " f"{type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_roc_auc.py000066400000000000000000000325151465426447700221400ustar00rootroot00000000000000import os from unittest.mock import patch import pytest import sklearn import torch from sklearn.metrics import roc_auc_score import ignite.distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics import ROC_AUC from ignite.metrics.epoch_metric import EpochMetricWarning torch.manual_seed(12) @pytest.fixture() def mock_no_sklearn(): with patch.dict("sys.modules", {"sklearn.metrics": None}): yield sklearn def test_no_sklearn(mock_no_sklearn): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires scikit-learn to be installed."): ROC_AUC() def test_no_update(): roc_auc = ROC_AUC() with pytest.raises( NotComputableError, match=r"EpochMetric must have at least one example before it can be computed" ): roc_auc.compute() def test_input_types(): roc_auc = ROC_AUC() roc_auc.reset() output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long)) roc_auc.update(output1) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): roc_auc.update((torch.randint(0, 5, size=(4, 3)), torch.randint(0, 2, size=(4, 3)))) with pytest.raises(ValueError, match=r"Incoherent types between input y and stored targets"): roc_auc.update((torch.rand(4, 3), torch.randint(0, 2, size=(4, 3)).to(torch.int32))) with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"): roc_auc.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) def test_check_shape(): roc_auc = ROC_AUC() with pytest.raises(ValueError, match=r"Predictions should be of shape"): roc_auc._check_shape((torch.tensor(0), torch.tensor(0))) with pytest.raises(ValueError, match=r"Predictions should be of shape"): roc_auc._check_shape((torch.rand(4, 3, 1), torch.rand(4, 3))) with pytest.raises(ValueError, match=r"Targets should be of shape"): roc_auc._check_shape((torch.rand(4, 3), torch.rand(4, 3, 1))) @pytest.fixture(params=range(8)) def test_data_binary_and_multilabel(request): return [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 1), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), # Binary input data of shape (N, L) (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 1), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 16), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 16), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_binary_and_multilabel_inputs(n_times, test_data_binary_and_multilabel): y_pred, y, batch_size = test_data_binary_and_multilabel roc_auc = ROC_AUC() roc_auc.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size roc_auc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: roc_auc.update((y_pred, y)) np_y = y.numpy() np_y_pred = y_pred.numpy() res = roc_auc.compute() assert isinstance(res, float) assert roc_auc_score(np_y, np_y_pred) == pytest.approx(res) def test_check_compute_fn(): y_pred = torch.zeros((8, 13)) y_pred[:, 1] = 1 y_true = torch.zeros_like(y_pred) output = (y_pred, y_true) em = ROC_AUC(check_compute_fn=True) em.reset() with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"): em.update(output) em = ROC_AUC(check_compute_fn=False) em.update(output) @pytest.fixture(params=range(4)) def test_data_integration_binary_and_multilabel(request): return [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(100,)).long(), torch.randint(0, 2, size=(100,)).long(), 10), (torch.randint(0, 2, size=(100, 1)).long(), torch.randint(0, 2, size=(100, 1)).long(), 10), # Binary input data of shape (N, L) (torch.randint(0, 2, size=(100, 3)).long(), torch.randint(0, 2, size=(100, 3)).long(), 10), (torch.randint(0, 2, size=(100, 4)).long(), torch.randint(0, 2, size=(100, 4)).long(), 10), ][request.param] @pytest.mark.parametrize("n_times", range(5)) def test_integration_binary_and_multilabel_inputs(n_times, test_data_integration_binary_and_multilabel): y_pred, y, batch_size = test_data_integration_binary_and_multilabel def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) roc_auc_metric = ROC_AUC() roc_auc_metric.attach(engine, "roc_auc") np_y = y.numpy() np_y_pred = y_pred.numpy() np_roc_auc = roc_auc_score(np_y, np_y_pred) data = list(range(y_pred.shape[0] // batch_size)) roc_auc = engine.run(data, max_epochs=1).metrics["roc_auc"] assert isinstance(roc_auc, float) assert np_roc_auc == pytest.approx(roc_auc) def _test_distrib_binary_and_multilabel_inputs(device): rank = idist.get_rank() def _test(y_pred, y, batch_size, metric_device): metric_device = torch.device(metric_device) roc_auc = ROC_AUC(device=metric_device) roc_auc.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size roc_auc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: roc_auc.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() res = roc_auc.compute() assert isinstance(res, float) assert roc_auc_score(np_y, np_y_pred) == pytest.approx(res) def get_test_cases(): test_cases = [ # Binary input data of shape (N,) or (N, 1) (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1), (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1), # updated batches (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16), (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16), # Binary input data of shape (N, L) (torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long(), 1), (torch.randint(0, 2, size=(10, 7)).long(), torch.randint(0, 2, size=(10, 7)).long(), 1), # updated batches (torch.randint(0, 2, size=(50, 4)).long(), torch.randint(0, 2, size=(50, 4)).long(), 16), (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 16), ] return test_cases for i in range(5): torch.manual_seed(12 + rank + i) test_cases = get_test_cases() for y_pred, y, batch_size in test_cases: _test(y_pred, y, batch_size, "cpu") if device.type != "xla": _test(y_pred, y, batch_size, idist.device()) def _test_distrib_integration_binary_input(device): rank = idist.get_rank() n_iters = 80 batch_size = 16 n_classes = 2 def _test(y_preds, y_true, n_epochs, metric_device, update_fn): metric_device = torch.device(metric_device) engine = Engine(update_fn) roc_auc = ROC_AUC(device=metric_device) roc_auc.attach(engine, "roc_auc") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "roc_auc" in engine.state.metrics res = engine.state.metrics["roc_auc"] true_res = roc_auc_score(y_true.cpu().numpy(), y_preds.cpu().numpy()) assert pytest.approx(res) == true_res def get_tests(is_N): if is_N: y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size).to(device) def update_fn(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) else: y_true = torch.randint(0, n_classes, size=(n_iters * batch_size, 10)).to(device) y_preds = torch.rand(n_iters * batch_size, 10).to(device) def update_fn(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size], ) return y_preds, y_true, update_fn metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: for i in range(2): torch.manual_seed(12 + rank + i) # Binary input data of shape (N,) y_preds, y_true, update_fn = get_tests(is_N=True) _test(y_preds, y_true, n_epochs=1, metric_device=metric_device, update_fn=update_fn) _test(y_preds, y_true, n_epochs=2, metric_device=metric_device, update_fn=update_fn) # Binary input data of shape (N, L) y_preds, y_true, update_fn = get_tests(is_N=False) _test(y_preds, y_true, n_epochs=1, metric_device=metric_device, update_fn=update_fn) _test(y_preds, y_true, n_epochs=2, metric_device=metric_device, update_fn=update_fn) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_binary_and_multilabel_inputs, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_integration_binary_input, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_binary_and_multilabel_inputs(device) _test_distrib_integration_binary_input(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_roc_curve.py000066400000000000000000000127021465426447700225100ustar00rootroot00000000000000from unittest.mock import patch import numpy as np import pytest import sklearn import torch from sklearn.metrics import roc_curve from ignite import distributed as idist from ignite.engine import Engine from ignite.exceptions import NotComputableError from ignite.metrics.epoch_metric import EpochMetricWarning from ignite.metrics.roc_auc import RocCurve def test_wrong_setup(): def compute_fn(y_preds, y_targets): return 0.0 with pytest.raises(NotComputableError, match="RocCurve must have at least one example before it can be computed"): metric = RocCurve(compute_fn) metric.compute() @pytest.fixture() def mock_no_sklearn(): with patch.dict("sys.modules", {"sklearn.metrics": None}): yield sklearn def test_no_sklearn(mock_no_sklearn): with pytest.raises(ModuleNotFoundError, match=r"This contrib module requires scikit-learn to be installed"): RocCurve() def test_roc_curve(): size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,)) np_y[size // 2 :] = 1 sk_fpr, sk_tpr, sk_thresholds = roc_curve(np_y, np_y_pred) roc_curve_metric = RocCurve() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) roc_curve_metric.update((y_pred, y)) fpr, tpr, thresholds = roc_curve_metric.compute() assert np.array_equal(fpr, sk_fpr) assert np.array_equal(tpr, sk_tpr) # assert thresholds almost equal, due to numpy->torch->numpy conversion np.testing.assert_array_almost_equal(thresholds, sk_thresholds) def test_integration_roc_curve_with_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,)) np_y[size // 2 :] = 1 np.random.shuffle(np_y) sk_fpr, sk_tpr, sk_thresholds = roc_curve(np_y, np_y_pred) batch_size = 10 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) roc_curve_metric = RocCurve(output_transform=lambda x: (x[1], x[2])) roc_curve_metric.attach(engine, "roc_curve") data = list(range(size // batch_size)) fpr, tpr, thresholds = engine.run(data, max_epochs=1).metrics["roc_curve"] assert np.array_equal(fpr, sk_fpr) assert np.array_equal(tpr, sk_tpr) # assert thresholds almost equal, due to numpy->torch->numpy conversion np.testing.assert_array_almost_equal(thresholds, sk_thresholds) def test_integration_roc_curve_with_activated_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y_pred_sigmoid = torch.sigmoid(torch.from_numpy(np_y_pred)).numpy() np_y = np.zeros((size,)) np_y[size // 2 :] = 1 np.random.shuffle(np_y) sk_fpr, sk_tpr, sk_thresholds = roc_curve(np_y, np_y_pred_sigmoid) batch_size = 10 def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) roc_curve_metric = RocCurve(output_transform=lambda x: (torch.sigmoid(x[1]), x[2])) roc_curve_metric.attach(engine, "roc_curve") data = list(range(size // batch_size)) fpr, tpr, thresholds = engine.run(data, max_epochs=1).metrics["roc_curve"] assert np.array_equal(fpr, sk_fpr) assert np.array_equal(tpr, sk_tpr) # assert thresholds almost equal, due to numpy->torch->numpy conversion np.testing.assert_array_almost_equal(thresholds, sk_thresholds) def test_check_compute_fn(): y_pred = torch.zeros((8, 13)) y_pred[:, 1] = 1 y_true = torch.zeros_like(y_pred) output = (y_pred, y_true) em = RocCurve(check_compute_fn=True) em.reset() with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"): em.update(output) em = RocCurve(check_compute_fn=False) em.update(output) def test_distrib_integration(distributed): rank = idist.get_rank() torch.manual_seed(41 + rank) n_batches, batch_size = 5, 10 y = torch.randint(0, 2, size=(n_batches * batch_size,)) y_pred = torch.rand((n_batches * batch_size,)) def update(engine, i): return ( y_pred[i * batch_size : (i + 1) * batch_size], y[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) device = torch.device("cpu") if idist.device().type == "xla" else idist.device() metric = RocCurve(device=device) metric.attach(engine, "roc_curve") data = list(range(n_batches)) engine.run(data=data, max_epochs=1) fpr, tpr, thresholds = engine.state.metrics["roc_curve"] assert isinstance(fpr, torch.Tensor) and fpr.device == device assert isinstance(tpr, torch.Tensor) and tpr.device == device assert isinstance(thresholds, torch.Tensor) and thresholds.device == device y = idist.all_gather(y) y_pred = idist.all_gather(y_pred) sk_fpr, sk_tpr, sk_thresholds = roc_curve(y.cpu().numpy(), y_pred.cpu().numpy()) np.testing.assert_array_almost_equal(fpr.cpu().numpy(), sk_fpr) np.testing.assert_array_almost_equal(tpr.cpu().numpy(), sk_tpr) np.testing.assert_array_almost_equal(thresholds.cpu().numpy(), sk_thresholds) ignite-0.5.1/tests/ignite/metrics/test_root_mean_squared_error.py000066400000000000000000000123521465426447700254420ustar00rootroot00000000000000import os import numpy as np import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import RootMeanSquaredError def test_zero_sample(): rmse = RootMeanSquaredError() with pytest.raises( NotComputableError, match=r"MeanSquaredError must have at least one example before it can be computed" ): rmse.compute() @pytest.fixture(params=[0, 1, 2, 3]) def test_data(request): return [ (torch.empty(10).uniform_(0, 10), torch.empty(10).uniform_(0, 10), 1), (torch.empty(10, 1).uniform_(-10, 10), torch.empty(10, 1).uniform_(-10, 10), 1), # updated batches (torch.empty(50).uniform_(0, 10), torch.empty(50).uniform_(0, 10), 16), (torch.empty(50, 1).uniform_(-10, 10), torch.empty(50, 1).uniform_(-10, 10), 16), ][request.param] @pytest.mark.parametrize("n_times", range(3)) def test_compute(n_times, test_data): rmse = RootMeanSquaredError() y_pred, y, batch_size = test_data rmse.reset() if batch_size > 1: n_iters = y.shape[0] // batch_size + 1 for i in range(n_iters): idx = i * batch_size rmse.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) else: rmse.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() np_res = np.sqrt(np.power((np_y - np_y_pred), 2.0).sum() / np_y.shape[0]) res = rmse.compute() assert isinstance(res, float) assert pytest.approx(res) == np_res def _test_distrib_integration(device, tol=1e-6): from ignite.engine import Engine rank = idist.get_rank() def _test(metric_device): n_iters = 2 batch_size = 3 torch.manual_seed(12 + rank) y_true = torch.arange(0, n_iters * batch_size, dtype=torch.float).to(device) y_preds = (rank + 1) * torch.ones(n_iters * batch_size, dtype=torch.float).to(device) def update(engine, i): return y_preds[i * batch_size : (i + 1) * batch_size], y_true[i * batch_size : (i + 1) * batch_size] engine = Engine(update) m = RootMeanSquaredError(device=metric_device) m.attach(engine, "rmse") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "rmse" in engine.state.metrics res = engine.state.metrics["rmse"] true_res = np.sqrt(np.mean(np.square((y_true - y_preds).cpu().numpy()))) assert pytest.approx(res, rel=tol) == true_res _test("cpu") if device.type != "xla": _test(idist.device()) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device, tol=1e-4) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device, tol=1e-4) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/metrics/test_running_average.py000066400000000000000000000361411465426447700236760ustar00rootroot00000000000000import warnings from functools import partial from itertools import accumulate import numpy as np import pytest import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.metrics import Accuracy, RunningAverage from ignite.metrics.metric import RunningBatchWise, RunningEpochWise, SingleEpochRunningBatchWise def test_wrong_input_args(): with pytest.raises(TypeError, match=r"Argument src should be a Metric or None."): RunningAverage(src=[12, 34]) with pytest.raises(ValueError, match=r"Argument alpha should be a float between"): RunningAverage(alpha=-1.0) with pytest.raises(ValueError, match=r"Argument output_transform should be None if src is a Metric"): RunningAverage(Accuracy(), output_transform=lambda x: x[0]) with pytest.raises(ValueError, match=r"Argument output_transform should not be None if src corresponds"): RunningAverage() with pytest.raises(ValueError, match=r"Argument device should be None if src is a Metric"): RunningAverage(Accuracy(), device="cpu") with pytest.warns(UserWarning, match=r"`epoch_bound` is deprecated and will be removed in the future."): m = RunningAverage(Accuracy(), epoch_bound=True) @pytest.mark.filterwarnings("ignore") @pytest.mark.parametrize("epoch_bound, usage", [(False, RunningBatchWise()), (True, SingleEpochRunningBatchWise())]) def test_epoch_bound(epoch_bound, usage): with warnings.catch_warnings(): metric = RunningAverage(output_transform=lambda _: _, epoch_bound=epoch_bound) e1 = Engine(lambda _, __: None) e2 = Engine(lambda _, __: None) metric.attach(e1, "") metric.epoch_bound = None metric.attach(e2, "", usage) e1._event_handlers == e2._event_handlers @pytest.mark.parametrize("usage", [RunningBatchWise(), SingleEpochRunningBatchWise()]) def test_integration_batchwise(usage): torch.manual_seed(10) alpha = 0.98 n_iters = 10 batch_size = 10 n_classes = 10 max_epochs = 3 data = list(range(n_iters)) loss = torch.arange(n_iters, dtype=torch.float) y_true = torch.randint(0, n_classes, size=(n_iters, batch_size)) y_pred = torch.rand(n_iters, batch_size, n_classes) accuracy_running_averages = torch.tensor( list( accumulate( map( lambda y_yp: torch.sum(y_yp[1].argmax(dim=-1) == y_yp[0]).item() / y_yp[0].size(0), zip( y_true if isinstance(usage, SingleEpochRunningBatchWise) else y_true.repeat(max_epochs, 1), y_pred if isinstance(usage, SingleEpochRunningBatchWise) else y_pred.repeat(max_epochs, 1, 1), ), ), lambda ra, acc: ra * alpha + (1 - alpha) * acc, ) ) ) if isinstance(usage, SingleEpochRunningBatchWise): accuracy_running_averages = accuracy_running_averages.repeat(max_epochs) loss_running_averages = torch.tensor( list( accumulate( loss if isinstance(usage, SingleEpochRunningBatchWise) else loss.repeat(max_epochs), lambda ra, loss_item: ra * alpha + (1 - alpha) * loss_item, ) ) ) if isinstance(usage, SingleEpochRunningBatchWise): loss_running_averages = loss_running_averages.repeat(max_epochs) def update_fn(_, i): loss_value = loss[i] y_true_batch = y_true[i] y_pred_batch = y_pred[i] return loss_value, y_pred_batch, y_true_batch trainer = Engine(update_fn) acc_metric = RunningAverage(Accuracy(output_transform=lambda x: [x[1], x[2]]), alpha=alpha) acc_metric.attach(trainer, "running_avg_accuracy", usage) avg_output = RunningAverage(output_transform=lambda x: x[0], alpha=alpha) avg_output.attach(trainer, "running_avg_loss", usage) metric_acc_running_averages = [] metric_loss_running_averages = [] @trainer.on(Events.ITERATION_COMPLETED) def _(engine): metric_acc_running_averages.append(engine.state.metrics["running_avg_accuracy"]) metric_loss_running_averages.append(engine.state.metrics["running_avg_loss"]) trainer.run(data, max_epochs=3) assert (torch.tensor(metric_acc_running_averages) == accuracy_running_averages).all() assert (torch.tensor(metric_loss_running_averages) == loss_running_averages).all() metric_state = acc_metric.state_dict() saved__value = acc_metric._value saved_src__num_correct = acc_metric.src._num_correct saved_src__num_examples = acc_metric.src._num_examples acc_metric.reset() acc_metric.load_state_dict(metric_state) assert acc_metric._value == saved__value assert acc_metric.src._num_examples == saved_src__num_examples assert (acc_metric.src._num_correct == saved_src__num_correct).all() metric_state = avg_output.state_dict() saved__value = avg_output._value assert avg_output.src is None avg_output.reset() avg_output.load_state_dict(metric_state) assert avg_output._value == saved__value assert avg_output.src is None def test_integration_epochwise(): torch.manual_seed(10) alpha = 0.98 n_iters = 10 batch_size = 10 n_classes = 10 max_epochs = 3 data = list(range(n_iters)) y_true = torch.randint(0, n_classes, size=(n_iters, batch_size)) y_pred = torch.rand(max_epochs, n_iters, batch_size, n_classes) accuracy_running_averages = torch.tensor( list( accumulate( map( lambda y_pred_epoch: torch.sum(y_pred_epoch.argmax(dim=-1) == y_true).item() / y_true.numel(), y_pred, ), lambda ra, acc: ra * alpha + (1 - alpha) * acc, ) ) ) def update_fn(engine, i): y_true_batch = y_true[i] y_pred_batch = y_pred[engine.state.epoch - 1, i] return y_pred_batch, y_true_batch trainer = Engine(update_fn) acc_metric = RunningAverage(Accuracy(), alpha=alpha) acc_metric.attach(trainer, "running_avg_accuracy", RunningEpochWise()) metric_acc_running_averages = [] @trainer.on(Events.EPOCH_COMPLETED) def _(engine): metric_acc_running_averages.append(engine.state.metrics["running_avg_accuracy"]) trainer.run(data, max_epochs=3) assert (torch.tensor(metric_acc_running_averages) == accuracy_running_averages).all() @pytest.mark.parametrize("usage", [RunningBatchWise(), SingleEpochRunningBatchWise(), RunningEpochWise()]) def test_multiple_attach(usage): n_iters = 100 errD_values = iter(np.random.rand(n_iters)) errG_values = iter(np.random.rand(n_iters)) D_x_values = iter(np.random.rand(n_iters)) D_G_z1 = iter(np.random.rand(n_iters)) D_G_z2 = iter(np.random.rand(n_iters)) def update_fn(engine, batch): return { "errD": next(errD_values), "errG": next(errG_values), "D_x": next(D_x_values), "D_G_z1": next(D_G_z1), "D_G_z2": next(D_G_z2), } trainer = Engine(update_fn) alpha = 0.98 # attach running average monitoring_metrics = ["errD", "errG", "D_x", "D_G_z1", "D_G_z2"] for metric in monitoring_metrics: foo = partial(lambda x, metric: x[metric], metric=metric) RunningAverage(alpha=alpha, output_transform=foo).attach(trainer, metric, usage) @trainer.on(usage.COMPLETED) def check_values(engine): values = [] for metric in monitoring_metrics: values.append(engine.state.metrics[metric]) values = set(values) assert len(values) == len(monitoring_metrics) data = list(range(n_iters)) trainer.run(data) @pytest.mark.filterwarnings("ignore") @pytest.mark.parametrize("epoch_bound", [True, False, None]) @pytest.mark.parametrize("src", [Accuracy(), None]) @pytest.mark.parametrize("usage", [RunningBatchWise(), SingleEpochRunningBatchWise(), RunningEpochWise()]) def test_detach(epoch_bound, src, usage): with warnings.catch_warnings(): m = RunningAverage(src, output_transform=(lambda _: _) if src is None else None, epoch_bound=epoch_bound) e = Engine(lambda _, __: None) m.attach(e, "m", usage) for event_handlers in e._event_handlers.values(): assert len(event_handlers) != 0 m.detach(e, usage) for event_handlers in e._event_handlers.values(): assert len(event_handlers) == 0 def test_output_is_tensor(): m = RunningAverage(output_transform=lambda x: x) m.update(torch.rand(10, requires_grad=True).mean()) v = m.compute() assert isinstance(v, torch.Tensor) assert not v.requires_grad m.update(torch.rand(10, requires_grad=True).mean()) v = m.compute() assert isinstance(v, torch.Tensor) assert not v.requires_grad m.update(torch.rand(10, requires_grad=True).mean()) v = m.compute() assert isinstance(v, torch.Tensor) assert not v.requires_grad @pytest.mark.usefixtures("distributed") class TestDistributed: @pytest.mark.parametrize("usage", [RunningBatchWise(), SingleEpochRunningBatchWise()]) def test_src_is_output(self, usage): device = idist.device() rank = idist.get_rank() n_iters = 10 n_epochs = 3 # Data per rank data = list(range(n_iters)) rank_loss_count = n_epochs * n_iters all_loss_values = torch.arange(0, rank_loss_count * idist.get_world_size(), dtype=torch.float64).to(device) loss_values = iter(all_loss_values[rank_loss_count * rank : rank_loss_count * (rank + 1)]) def update_fn(engine, batch): loss_value = next(loss_values) return loss_value.item() trainer = Engine(update_fn) alpha = 0.98 metric_device = device if device.type != "xla" else "cpu" avg_output = RunningAverage(output_transform=lambda x: x, alpha=alpha, device=metric_device) avg_output.attach(trainer, "running_avg_output", usage) @trainer.on(usage.STARTED) def reset_running_avg_output(engine): engine.state.running_avg_output = None @trainer.on(usage.ITERATION_COMPLETED) def running_avg_output_update(engine): i = engine.state.iteration - 1 o = sum([all_loss_values[i + r * rank_loss_count] for r in range(idist.get_world_size())]).item() o /= idist.get_world_size() if engine.state.running_avg_output is None: engine.state.running_avg_output = o else: engine.state.running_avg_output = engine.state.running_avg_output * alpha + (1.0 - alpha) * o @trainer.on(usage.COMPLETED) def assert_equal_running_avg_output_values(engine): it = engine.state.iteration assert ( engine.state.running_avg_output == engine.state.metrics["running_avg_output"] ), f"{it}: {engine.state.running_avg_output} vs {engine.state.metrics['running_avg_output']}" trainer.run(data, max_epochs=3) @pytest.mark.parametrize("usage", [RunningBatchWise(), SingleEpochRunningBatchWise(), RunningEpochWise()]) def test_src_is_metric(self, usage): device = idist.device() rank = idist.get_rank() n_iters = 10 n_epochs = 3 batch_size = 10 n_classes = 10 def _test(metric_device): data = list(range(n_iters)) np.random.seed(12) all_y_true_batch_values = np.random.randint( 0, n_classes, size=(idist.get_world_size(), n_epochs * n_iters, batch_size) ) all_y_pred_batch_values = np.random.rand(idist.get_world_size(), n_epochs * n_iters, batch_size, n_classes) y_true_batch_values = iter(all_y_true_batch_values[rank, ...]) y_pred_batch_values = iter(all_y_pred_batch_values[rank, ...]) def update_fn(engine, batch): y_true_batch = next(y_true_batch_values) y_pred_batch = next(y_pred_batch_values) return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) trainer = Engine(update_fn) alpha = 0.98 acc_metric = RunningAverage(Accuracy(device=metric_device), alpha=alpha) acc_metric.attach(trainer, "running_avg_accuracy", usage) running_avg_acc = [ None, ] true_acc_metric = Accuracy(device=metric_device) @trainer.on(Events.ITERATION_COMPLETED) def manual_running_avg_acc(engine): iteration = engine.state.iteration if not isinstance(usage, RunningEpochWise) or ((iteration - 1) % n_iters) == 0: true_acc_metric.reset() if ((iteration - 1) % n_iters) == 0 and isinstance(usage, SingleEpochRunningBatchWise): running_avg_acc[0] = None for j in range(idist.get_world_size()): output = ( torch.from_numpy(all_y_pred_batch_values[j, iteration - 1, :, :]), torch.from_numpy(all_y_true_batch_values[j, iteration - 1, :]), ) true_acc_metric.update(output) if not isinstance(usage, RunningEpochWise) or (iteration % n_iters) == 0: batch_acc = true_acc_metric._num_correct.item() * 1.0 / true_acc_metric._num_examples if running_avg_acc[0] is None: running_avg_acc[0] = batch_acc else: running_avg_acc[0] = running_avg_acc[0] * alpha + (1.0 - alpha) * batch_acc engine.state.running_avg_acc = running_avg_acc[0] @trainer.on(Events.ITERATION_COMPLETED) def assert_equal_running_avg_acc_values(engine): print(engine.state.iteration) if not isinstance(usage, RunningEpochWise) or ( (engine.state.iteration > 1) and ((engine.state.iteration % n_iters) == 1) ): assert ( engine.state.running_avg_acc == engine.state.metrics["running_avg_accuracy"] ), f"{engine.state.running_avg_acc} vs {engine.state.metrics['running_avg_accuracy']}" trainer.run(data, max_epochs=3) _test("cpu") if device.type != "xla": _test(idist.device()) def test_accumulator_device(self): device = idist.device() metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: # Don't test the src=Metric case because compute() returns a scalar, # so the metric doesn't accumulate on the device specified avg = RunningAverage(output_transform=lambda x: x, device=metric_device) assert avg._device == metric_device # Value is None until the first update then compute call for _ in range(3): avg.update(torch.tensor(1.0, device=device)) avg.compute() assert ( avg._value.device == metric_device ), f"{type(avg._value.device)}:{avg._value.device} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_ssim.py000066400000000000000000000262411465426447700214770ustar00rootroot00000000000000from typing import Sequence, Union import numpy as np import pytest import torch from skimage.metrics import structural_similarity as ski_ssim import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import SSIM def test_zero_div(): ssim = SSIM(data_range=1.0) with pytest.raises(NotComputableError): ssim.compute() def test_invalid_ssim(): y_pred = torch.rand(1, 1, 4, 4) y = y_pred + 0.125 with pytest.raises(ValueError, match=r"Expected kernel_size to have odd positive number."): ssim = SSIM(data_range=1.0, kernel_size=2) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Expected kernel_size to have odd positive number."): ssim = SSIM(data_range=1.0, kernel_size=-1) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Argument kernel_size should be either int or a sequence of int."): ssim = SSIM(data_range=1.0, kernel_size=1.0) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Argument sigma should be either float or a sequence of float."): ssim = SSIM(data_range=1.0, sigma=-1) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Expected sigma to have positive number."): ssim = SSIM(data_range=1.0, sigma=(-1, -1)) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Argument sigma should be either float or a sequence of float."): ssim = SSIM(data_range=1.0, sigma=1) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Expected y_pred and y to have the same shape."): y = y.squeeze(dim=0) ssim = SSIM(data_range=1.0) ssim.update((y_pred, y)) ssim.compute() with pytest.raises(ValueError, match=r"Expected y_pred and y to have BxCxHxW shape."): y = y.squeeze(dim=0) ssim = SSIM(data_range=1.0) ssim.update((y, y)) ssim.compute() with pytest.raises(TypeError, match=r"Expected y_pred and y to have the same data type."): y = y.double() ssim = SSIM(data_range=1.0) ssim.update((y_pred, y)) ssim.compute() @pytest.mark.parametrize( "shape, kernel_size, gaussian, use_sample_covariance", [[(8, 3, 224, 224), 7, False, True], [(12, 3, 28, 28), 11, True, False]], ) def test_ssim(available_device, shape, kernel_size, gaussian, use_sample_covariance): y_pred = torch.rand(shape, device=available_device) y = y_pred * 0.8 compare_ssim_ignite_skiimg( y_pred, y, available_device, kernel_size=kernel_size, gaussian=gaussian, use_sample_covariance=use_sample_covariance, ) def compare_ssim_ignite_skiimg( y_pred: torch.Tensor, y: torch.Tensor, device: torch.device, precision: float = 2e-5, # default to float32 expected precision *, skimg_y_pred: Union[np.ndarray, None] = None, skimg_y: Union[np.ndarray, None] = None, data_range: float = 1.0, kernel_size: Union[int, Sequence[int]] = 11, gaussian: bool = True, use_sample_covariance: bool = False, ): sigma = 1.5 ssim = SSIM(data_range=data_range, sigma=sigma, device=device) ssim.update((y_pred, y)) ignite_ssim = ssim.compute() if y_pred.dtype == torch.bfloat16: y_pred = y_pred.to(dtype=torch.float16) if skimg_y_pred is None: skimg_y_pred = y_pred.cpu().numpy() if skimg_y is None: skimg_y = skimg_y_pred * 0.8 skimg_ssim = ski_ssim( skimg_y_pred, skimg_y, win_size=kernel_size, sigma=sigma, channel_axis=1, gaussian_weights=gaussian, data_range=data_range, use_sample_covariance=use_sample_covariance, ) assert isinstance(ignite_ssim, float) assert np.allclose(ignite_ssim, skimg_ssim, atol=precision) @pytest.mark.parametrize( "metric_device, y_pred_device", [ [torch.device("cpu"), torch.device("cpu")], [torch.device("cpu"), torch.device("cuda")], [torch.device("cuda"), torch.device("cpu")], [torch.device("cuda"), torch.device("cuda")], ], ) def test_ssim_device(available_device, metric_device, y_pred_device): if available_device == "cpu": pytest.skip("This test requires a cuda device.") data_range = 1.0 sigma = 1.5 shape = (12, 5, 256, 256) ssim = SSIM(data_range=data_range, sigma=sigma, device=metric_device) y_pred = torch.rand(shape, device=y_pred_device) y = y_pred * 0.8 if metric_device == torch.device("cuda") and y_pred_device == torch.device("cpu"): with pytest.warns(UserWarning): ssim.update((y_pred, y)) else: ssim.update((y_pred, y)) if metric_device == torch.device("cuda") or y_pred_device == torch.device("cuda"): # A tensor will always have the device index set excepted_device = torch.device("cuda:0") else: excepted_device = torch.device("cpu") assert ssim._kernel.device == excepted_device def test_ssim_variable_batchsize(available_device): # Checks https://github.com/pytorch/ignite/issues/2532 sigma = 1.5 data_range = 1.0 ssim = SSIM(data_range=data_range, sigma=sigma) y_preds = [ torch.rand(12, 3, 28, 28, device=available_device), torch.rand(12, 3, 28, 28, device=available_device), torch.rand(8, 3, 28, 28, device=available_device), torch.rand(16, 3, 28, 28, device=available_device), torch.rand(1, 3, 28, 28, device=available_device), torch.rand(30, 3, 28, 28, device=available_device), ] y_true = [v * 0.8 for v in y_preds] for y_pred, y in zip(y_preds, y_true): ssim.update((y_pred, y)) out = ssim.compute() ssim.reset() ssim.update((torch.cat(y_preds), torch.cat(y_true))) expected = ssim.compute() assert np.allclose(out, expected) def test_ssim_variable_channel(available_device): y_preds = [ torch.rand(12, 5, 28, 28, device=available_device), torch.rand(12, 4, 28, 28, device=available_device), torch.rand(12, 7, 28, 28, device=available_device), torch.rand(12, 3, 28, 28, device=available_device), torch.rand(12, 11, 28, 28, device=available_device), torch.rand(12, 6, 28, 28, device=available_device), ] y_true = [v * 0.8 for v in y_preds] for y_pred, y in zip(y_preds, y_true): compare_ssim_ignite_skiimg(y_pred, y, available_device) @pytest.mark.parametrize( "dtype, precision", [(torch.bfloat16, 2e-3), (torch.float16, 4e-4), (torch.float32, 2e-5), (torch.float64, 2e-5)] ) def test_cuda_ssim_dtypes(available_device, dtype, precision): # Checks https://github.com/pytorch/ignite/pull/3034 if available_device == "cpu" and dtype in [torch.float16, torch.bfloat16]: pytest.skip(reason=f"Unsupported dtype {dtype} on CPU device") shape = (12, 3, 28, 28) y_pred = torch.rand(shape, device=available_device, dtype=dtype) y = y_pred * 0.8 compare_ssim_ignite_skiimg(y_pred, y, available_device, precision) @pytest.mark.parametrize( "shape, kernel_size, gaussian, use_sample_covariance", [[(8, 3, 224, 224), 7, False, True], [(12, 3, 28, 28), 11, True, False]], ) def test_ssim_uint8(available_device, shape, kernel_size, gaussian, use_sample_covariance): y_pred = torch.randint(0, 255, shape, device=available_device, dtype=torch.uint8) y = (y_pred * 0.8).to(dtype=torch.uint8) sigma = 1.5 data_range = 255 ssim = SSIM(data_range=data_range, sigma=sigma, device=available_device) ssim.update((y_pred, y)) ignite_ssim = ssim.compute() skimg_pred = y_pred.cpu().numpy() skimg_y = (skimg_pred * 0.8).astype(np.uint8) skimg_ssim = ski_ssim( skimg_pred, skimg_y, win_size=kernel_size, sigma=sigma, channel_axis=1, gaussian_weights=gaussian, data_range=data_range, use_sample_covariance=use_sample_covariance, ) assert isinstance(ignite_ssim, float) assert np.allclose(ignite_ssim, skimg_ssim, atol=1e-5) @pytest.mark.usefixtures("distributed") class TestDistributed: @pytest.mark.parametrize("metric_device", ["cpu", "process_device"]) def test_integration(self, metric_device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12 + rank) n_iters = 100 batch_size = 10 device = idist.device() if metric_device == "process_device": metric_device = device if device.type != "xla" else "cpu" y_pred = torch.rand(n_iters * batch_size, 3, 28, 28, dtype=torch.float, device=device) y = y_pred * 0.65 def update(engine, i): return ( y_pred[i * batch_size : (i + 1) * batch_size, ...], y[i * batch_size : (i + 1) * batch_size, ...], ) engine = Engine(update) SSIM(data_range=1.0, device=metric_device).attach(engine, "ssim") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) assert "ssim" in engine.state.metrics res = engine.state.metrics["ssim"] np_pred = y_pred.cpu().numpy() np_true = np_pred * 0.65 true_res = ski_ssim( np_pred, np_true, win_size=11, sigma=1.5, channel_axis=1, gaussian_weights=True, data_range=1.0, use_sample_covariance=False, ) tol = 1e-3 if device.type == "xla" else 1e-4 # Isn't better to ask `distributed` about backend info? assert pytest.approx(res, abs=tol) == true_res engine = Engine(update) SSIM(data_range=1.0, gaussian=False, kernel_size=7, device=metric_device).attach(engine, "ssim") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) assert "ssim" in engine.state.metrics res = engine.state.metrics["ssim"] np_pred = y_pred.cpu().numpy() np_true = np_pred * 0.65 true_res = ski_ssim(np_pred, np_true, win_size=7, channel_axis=1, gaussian_weights=False, data_range=1.0) assert pytest.approx(res, abs=tol) == true_res @pytest.mark.parametrize("metric_device", [torch.device("cpu"), "process_device"]) def test_accumulator_device(self, metric_device): device = idist.device() if metric_device == "process_device": metric_device = torch.device(device if device.type != "xla" else "cpu") ssim = SSIM(data_range=1.0, device=metric_device) assert ssim._kernel is None assert isinstance(ssim._kernel_2d, torch.Tensor) for dev in [ssim._device, ssim._kernel_2d.device]: assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" y_pred = torch.rand(2, 3, 28, 28, dtype=torch.float, device=device) y = y_pred * 0.65 ssim.update((y_pred, y)) dev = ssim._sum_of_ssim.device assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}" ignite-0.5.1/tests/ignite/metrics/test_top_k_categorical_accuracy.py000066400000000000000000000153321465426447700260460ustar00rootroot00000000000000import os import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import TopKCategoricalAccuracy def test_zero_div(): acc = TopKCategoricalAccuracy(2) with pytest.raises( NotComputableError, match=r"TopKCategoricalAccuracy must have at least one example before it can be computed" ): acc.compute() def test_compute(): acc = TopKCategoricalAccuracy(2) y_pred = torch.FloatTensor([[0.2, 0.4, 0.6, 0.8], [0.8, 0.6, 0.4, 0.2]]) y = torch.ones(2).long() acc.update((y_pred, y)) assert isinstance(acc.compute(), float) assert acc.compute() == 0.5 acc.reset() y_pred = torch.FloatTensor([[0.4, 0.8, 0.2, 0.6], [0.8, 0.6, 0.4, 0.2]]) y = torch.ones(2).long() acc.update((y_pred, y)) assert isinstance(acc.compute(), float) assert acc.compute() == 1.0 def top_k_accuracy(y_true, y_pred, k=5, normalize=True): import numpy as np # Taken from # https://github.com/scikit-learn/scikit-learn/blob/4685cb5c50629aba4429f6701585f82fc3eee5f7/ # sklearn/metrics/classification.py#L187 if len(y_true.shape) == 2: y_true = np.argmax(y_true, axis=1) num_obs, num_labels = y_pred.shape idx = num_labels - k - 1 counter = 0.0 argsorted = np.argsort(y_pred, axis=1) for i in range(num_obs): if y_true[i] in argsorted[i, idx + 1 :]: counter += 1.0 if normalize: return counter * 1.0 / num_obs else: return counter def _test_distrib_integration(device): from ignite.engine import Engine def _test(n_epochs, metric_device): n_iters = 100 batch_size = 16 n_classes = 10 y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device) y_preds = torch.rand(n_iters * batch_size, n_classes).to(device) def update(engine, i): return ( y_preds[i * batch_size : (i + 1) * batch_size, :], y_true[i * batch_size : (i + 1) * batch_size], ) engine = Engine(update) k = 5 acc = TopKCategoricalAccuracy(k=k, device=metric_device) acc.attach(engine, "acc") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) y_preds = idist.all_gather(y_preds) y_true = idist.all_gather(y_true) assert "acc" in engine.state.metrics res = engine.state.metrics["acc"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() true_res = top_k_accuracy(y_true.cpu().numpy(), y_preds.cpu().numpy(), k=k) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) rank = idist.get_rank() for i in range(3): torch.manual_seed(12 + rank + i) for metric_device in metric_devices: _test(n_epochs=1, metric_device=metric_device) _test(n_epochs=2, metric_device=metric_device) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: acc = TopKCategoricalAccuracy(2, device=metric_device) assert acc._device == metric_device assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" y_pred = torch.tensor([[0.2, 0.4, 0.6, 0.8], [0.8, 0.6, 0.4, 0.2]]) y = torch.ones(2).long() acc.update((y_pred, y)) assert ( acc._num_correct.device == metric_device ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}" @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_nccl_gpu(distributed_context_single_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_distrib_hvd(gloo_hvd_executor): device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count() gloo_hvd_executor(_test_distrib_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.multinode_distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_single_device_xla(): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device) _test_distrib_accumulator_device(device) @pytest.mark.tpu @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars") @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package") def test_distrib_xla_nprocs(xmp_executor): n = int(os.environ["NUM_TPU_WORKERS"]) xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n) ignite-0.5.1/tests/ignite/test_utils.py000066400000000000000000000233311465426447700202130ustar00rootroot00000000000000import logging import platform import sys from collections import namedtuple import pytest import torch from packaging.version import Version from ignite.engine import Engine, Events from ignite.utils import _to_str_list, convert_tensor, deprecated, hash_checkpoint, setup_logger, to_onehot def test_convert_tensor(): x = torch.tensor([0.0]) tensor = convert_tensor(x) assert torch.is_tensor(tensor) x = torch.tensor([0.0]) tensor = convert_tensor(x, device="cpu", non_blocking=True) assert torch.is_tensor(tensor) x = torch.tensor([0.0]) tensor = convert_tensor(x, device="cpu", non_blocking=False) assert torch.is_tensor(tensor) x = [torch.tensor([0.0]), torch.tensor([0.0])] list_ = convert_tensor(x) assert isinstance(list_, list) assert torch.is_tensor(list_[0]) assert torch.is_tensor(list_[1]) x = (torch.tensor([0.0]), torch.tensor([0.0])) tuple_ = convert_tensor(x) assert isinstance(tuple_, tuple) assert torch.is_tensor(tuple_[0]) assert torch.is_tensor(tuple_[1]) Point = namedtuple("Point", ["x", "y"]) x = Point(torch.tensor([0.0]), torch.tensor([0.0])) tuple_ = convert_tensor(x) assert isinstance(tuple_, Point) assert torch.is_tensor(tuple_[0]) assert torch.is_tensor(tuple_[1]) x = {"a": torch.tensor([0.0]), "b": torch.tensor([0.0])} dict_ = convert_tensor(x) assert isinstance(dict_, dict) assert torch.is_tensor(dict_["a"]) assert torch.is_tensor(dict_["b"]) assert convert_tensor("a") == "a" with pytest.raises(TypeError): convert_tensor(12345) @pytest.mark.parametrize( "input_data,expected", [ (42, ["42.0000"]), ([{"a": 15, "b": torch.tensor([2.0])}], ["a: 15.0000", "b: [2.0000]"]), ({"a": 10, "b": 2.33333}, ["a: 10.0000", "b: 2.3333"]), ({"x": torch.tensor(0.1234), "y": [1, 2.3567]}, ["x: 0.1234", "y: 1.0000, 2.3567"]), (({"nested": [3.1415, torch.tensor(0.0001)]},), ["nested: 3.1415, 0.0001"]), ( {"large_vector": torch.tensor(range(20))}, ["large_vector: [0.0000, 1.0000, 2.0000, 3.0000, 4.0000, 5.0000, 6.0000, 7.0000, 8.0000, 9.0000, ...]"], ), ({"large_matrix": torch.randn(5, 5)}, ["large_matrix: Shape[5, 5]"]), ({"empty": []}, ["empty: "]), ([], []), ({"none": None}, ["none: "]), ({1: 100, 2: 200}, ["1: 100.0000", "2: 200.0000"]), ], ) def test__to_str_list(input_data, expected): assert _to_str_list(input_data) == expected def test_to_onehot(): indices = torch.tensor([0, 1, 2, 3], dtype=torch.long) actual = to_onehot(indices, 4) expected = torch.eye(4, dtype=torch.uint8) assert actual.equal(expected) y = torch.randint(0, 21, size=(1000,)) y_ohe = to_onehot(y, num_classes=21) y2 = torch.argmax(y_ohe, dim=1) assert y.equal(y2) y = torch.randint(0, 21, size=(4, 250, 255)) y_ohe = to_onehot(y, num_classes=21) y2 = torch.argmax(y_ohe, dim=1) assert y.equal(y2) y = torch.randint(0, 21, size=(4, 150, 155, 4, 6)) y_ohe = to_onehot(y, num_classes=21) y2 = torch.argmax(y_ohe, dim=1) assert y.equal(y2) # Test with `TorchScript` x = torch.tensor([0, 1, 2, 3]) # Test the raw `to_onehot` function scripted_to_onehot = torch.jit.script(to_onehot) assert scripted_to_onehot(x, 4).allclose(to_onehot(x, 4)) # Test inside `torch.nn.Module` class SLP(torch.nn.Module): def __init__(self): super(SLP, self).__init__() self.linear = torch.nn.Linear(4, 1) def forward(self, x): x = to_onehot(x, 4) return self.linear(x.to(torch.float)) eager_model = SLP() scripted_model = torch.jit.script(eager_model) assert eager_model(x).allclose(scripted_model(x)) def test_dist_setup_logger(): logger = setup_logger("trainer", level=logging.CRITICAL, distributed_rank=1) assert logger.level != logging.CRITICAL def test_setup_logger(capsys, dirname): trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) assert len(trainer.logger.handlers) == 0 trainer.logger.addHandler(logging.NullHandler()) trainer.logger.addHandler(logging.NullHandler()) trainer.logger.addHandler(logging.NullHandler()) fp = dirname / "log" def _test(stream): trainer.logger = setup_logger("trainer", stream=stream, filepath=fp, reset=True) evaluator.logger = setup_logger("evaluator", stream=stream, filepath=fp, reset=True) assert len(trainer.logger.handlers) == 2 assert len(evaluator.logger.handlers) == 2 @trainer.on(Events.EPOCH_COMPLETED) def _(_): evaluator.run([0, 1, 2]) trainer.run([0, 1, 2, 3, 4, 5], max_epochs=5) captured = capsys.readouterr() if stream is sys.stdout: err = captured.out.split("\n") else: err = captured.err.split("\n") with open(fp, "r") as h: data = h.readlines() for source in [err, data]: assert "trainer INFO: Engine run starting with max_epochs=5." in source[0] assert "evaluator INFO: Engine run starting with max_epochs=1." in source[1] _test(stream=None) _test(stream=sys.stderr) _test(stream=sys.stdout) # Needed by windows to release FileHandler in the loggers logging.shutdown() def _setup_a_logger_and_dump(name, message): logger = setup_logger(name) logger.info(message) def test_override_setup_logger(capsys): _setup_a_logger_and_dump(__name__, "test_override_setup_logger") source = capsys.readouterr().err.split("\n") assert "tests.ignite.test_utils INFO: test_override_setup_logger" in source[0] # change the logger level of _setup_a_logger_and_dump setup_logger(name=__name__, level=logging.WARNING, reset=True) _setup_a_logger_and_dump(__name__, "test_override_setup_logger") source = capsys.readouterr().err.split("\n") assert source[0] == "" # Needed by windows to release FileHandler in the loggers logging.shutdown() @pytest.mark.parametrize("encoding", [None, "utf-8"]) def test_setup_logger_encoding(encoding, dirname): fp = dirname / "log.txt" logger = setup_logger(name="logger", filepath=fp, encoding=encoding, reset=True) test_words = ["say hello", "say δ½ ε₯½", "say こんにけわ", "say μ•ˆλ…•ν•˜μ„Έμš”", "say ΠΏΡ€ΠΈΠ²Π΅Ρ‚"] for w in test_words: logger.info(w) logging.shutdown() with open(fp, "r", encoding=encoding) as h: data = h.readlines() if platform.system() == "Windows" and encoding is None: flatten_data = "\n".join(data) assert test_words[0] in flatten_data for word in test_words[1:]: assert word not in flatten_data else: assert len(data) == len(test_words) for expected, output in zip(test_words, data): assert expected in output def test_deprecated(): # Test on function without docs, @deprecated without reasons @deprecated("0.4.2", "0.6.0") def func_no_docs(): return 24 assert func_no_docs.__doc__ == "**Deprecated function**.\n\n .. deprecated:: 0.4.2" # Test on function with docs, @deprecated without reasons @deprecated("0.4.2", "0.6.0") def func_no_reasons(): """Docs are cool""" return 24 assert func_no_reasons.__doc__ == "**Deprecated function**.\n\n Docs are cool.. deprecated:: 0.4.2" # Test on function with docs, @deprecated with reasons @deprecated("0.4.2", "0.6.0", reasons=("r1", "r2")) def func_no_warnings(): """Docs are very cool""" return 24 assert ( func_no_warnings.__doc__ == "**Deprecated function**.\n\n Docs are very cool.. deprecated:: 0.4.2\n\n\t\n\t- r1\n\t- r2" ) # Tests that the function emits DeprecationWarning @deprecated("0.4.2", "0.6.0", reasons=("r1", "r2")) def func_check_warning(): """Docs are very ...""" return 24 with pytest.deprecated_call(): assert func_check_warning() == 24 with pytest.warns( DeprecationWarning, match="This function has been deprecated since version 0.4.2 and will be removed in version 0.6.0." + "\n Please refer to the documentation for more details.", ): # Trigger a warning. func_check_warning() # Test that the function raises Exception @deprecated("0.4.2", "0.6.0", reasons=("reason1", "reason2"), raise_exception=True) def func_with_everything(): return 1 with pytest.raises(Exception) as exec_info: func_with_everything() assert ( str(exec_info.value) == "This function has been deprecated since version 0.4.2 and will be removed in version 0.6.0." + "\n Please refer to the documentation for more details." ) def test_smoke__utils(): from ignite._utils import apply_to_tensor, apply_to_type, convert_tensor, to_onehot # noqa: F401 @pytest.mark.skipif(Version(torch.__version__) < Version("1.5.0"), reason="Skip if < 1.5.0") def test_hash_checkpoint(tmp_path): # download lightweight model from torchvision.models import squeezenet1_0 model = squeezenet1_0() torch.hub.download_url_to_file( "https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth", f"{tmp_path}/squeezenet1_0.pt" ) hash_checkpoint_path, sha_hash = hash_checkpoint(f"{tmp_path}/squeezenet1_0.pt", str(tmp_path)) model.load_state_dict(torch.load(str(hash_checkpoint_path), "cpu"), True) assert sha_hash[:8] == "b66bff10" assert hash_checkpoint_path.name == f"squeezenet1_0-{sha_hash[:8]}.pt" # test non-existent checkpoint_path with pytest.raises(FileNotFoundError, match=r"not_found.pt does not exist in *"): hash_checkpoint(f"{tmp_path}/not_found.pt", tmp_path) ignite-0.5.1/tests/run_code_style.bat000066400000000000000000000006221465426447700176670ustar00rootroot00000000000000@ECHO OFF if "%1" == "lint" goto lint if "%1" == "fmt" goto fmt if "%1" == "mypy" goto mypy if "%1" == "install" goto install goto end :lint flake8 ignite tests examples --config setup.cfg ufmt diff . goto end :fmt ufmt format . goto end :mypy mypy --config-file mypy.ini goto end :install pip install --upgrade flake8 "black==24.3.0" "usort==1.0.8.post1" "ufmt==2.5.1" "mypy" goto end :end popd ignite-0.5.1/tests/run_code_style.sh000077500000000000000000000005271465426447700175420ustar00rootroot00000000000000#!/bin/bash set -xeu if [ $1 = "lint" ]; then flake8 ignite tests examples --config setup.cfg ufmt diff . elif [ $1 = "fmt" ]; then ufmt format . elif [ $1 = "mypy" ]; then mypy --config-file mypy.ini elif [ $1 = "install" ]; then pip install --upgrade flake8 "black==24.3.0" "usort==1.0.8.post1" "ufmt==2.5.1" "mypy" fi ignite-0.5.1/tests/run_cpu_tests.sh000066400000000000000000000016511465426447700174150ustar00rootroot00000000000000#!/bin/bash source "$(dirname "$0")/common_test_functionality.sh" set -xeu skip_distrib_tests=${SKIP_DISTRIB_TESTS:-0} use_last_failed=${USE_LAST_FAILED:-0} match_tests_expression=${1:-""} run_tests \ --core_args "--tx 4*popen//python=python -vvv tests/ignite" \ --cache_dir ".cpu-not-distrib" \ --skip_distrib_tests "${skip_distrib_tests}" \ --use_coverage 1 \ --match_tests_expression "${match_tests_expression}" \ --use_last_failed ${use_last_failed} # https://pubs.opengroup.org/onlinepubs/009695399/utilities/xcu_chap02.html#tag_02_06_02 if [ "${skip_distrib_tests}" -eq "1" ]; then exit 0 fi # Run 2 processes with --dist=each run_tests \ --core_args "-m distributed -vvv tests/ignite" \ --world_size 2 \ --cache_dir ".cpu-distrib" \ --skip_distrib_tests 0 \ --use_coverage 1 \ --match_tests_expression "${match_tests_expression}" \ --use_last_failed ${use_last_failed} ignite-0.5.1/tests/run_gpu_tests.sh000066400000000000000000000024571465426447700174260ustar00rootroot00000000000000#!/bin/bash source "$(dirname "$0")/common_test_functionality.sh" set -xeu skip_distrib_tests=${SKIP_DISTRIB_TESTS:-1} use_last_failed=${USE_LAST_FAILED:-0} ngpus=${1:-1} match_tests_expression=${2:-""} if [ -z "$match_tests_expression" ]; then cuda_pattern="cuda" else cuda_pattern="cuda and $match_tests_expression" fi run_tests \ --core_args "-vvv tests/ignite" \ --cache_dir ".gpu-cuda" \ --skip_distrib_tests "${skip_distrib_tests}" \ --use_coverage 1 \ --match_tests_expression "${cuda_pattern}" \ --use_last_failed ${use_last_failed} # https://pubs.opengroup.org/onlinepubs/009695399/utilities/xcu_chap02.html#tag_02_06_02 if [ "${skip_distrib_tests}" -eq "1" ]; then exit 0 fi run_tests \ --core_args "-vvv -m distributed tests/ignite" \ --cache_dir ".gpu-distrib" \ --skip_distrib_tests 0 \ --use_coverage 1 \ --match_tests_expression "${match_tests_expression}" \ --use_last_failed ${use_last_failed} if [ ${ngpus} -gt 1 ]; then run_tests \ --core_args "-vvv -m distributed tests/ignite" \ --world_size "${ngpus}" \ --cache_dir ".gpu-distrib-multi" \ --skip_distrib_tests 0 \ --use_coverage 1 \ --match_tests_expression "${match_tests_expression}" \ --use_last_failed ${use_last_failed} fi ignite-0.5.1/tests/run_multinode_tests_in_docker.sh000066400000000000000000000042741465426447700226470ustar00rootroot00000000000000#!/bin/bash # Tests configuration: if [[ -z "$1" || "$1" -lt 2 ]]; then echo "nnodes setting default to 2" export nnodes=2 else export nnodes=$1 fi if [[ -z "$2" || "$2" -lt 1 ]]; then echo "nproc_per_node setting default to 4" export nproc_per_node=4 else export nproc_per_node=$2 fi if [ -z "$3" ]; then echo "gpu setting default to 0 ( False )" export gpu=0 else export gpu=$3 fi # Start script from ignite root folder if [ ! -d tests ]; then echo "Ignite tests folder is not found. Please run script from ignite's root folder" exit 1 fi docker_image="pytorchignite/tests:latest" docker build -t $docker_image -<

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