pax_global_header00006660000000000000000000000064140100656500014507gustar00rootroot0000000000000052 comment=5adf3d919999a03b4037d88e54292c6a7a1db90d ignite-0.4.3/000077500000000000000000000000001401006565000127725ustar00rootroot00000000000000ignite-0.4.3/.circleci/000077500000000000000000000000001401006565000146255ustar00rootroot00000000000000ignite-0.4.3/.circleci/config.yml000066400000000000000000000402661401006565000166250ustar00rootroot00000000000000version: 2.1 parameters: pytorch_stable_image: type: string # https://hub.docker.com/r/pytorch/pytorch/tags default: "pytorch/pytorch:1.7.0-cuda11.0-cudnn8-runtime" pytorch_stable_image_devel: type: string # https://hub.docker.com/r/pytorch/pytorch/tags default: "pytorch/pytorch:1.7.0-cuda11.0-cudnn8-devel" workingdir: type: string default: "/tmp/ignite" should_build_docker_images: type: boolean default: false should_publish_docker_images: type: boolean default: false build_docker_image_pytorch_version: type: string default: "1.7.0-cuda11.0-cudnn8" build_docker_image_hvd_version: type: string default: "v0.21.0" build_docker_image_msdp_version: type: string default: "v0.3.8" # ------------------------------------------------------------------------------------- # Environments to run the jobs in # ------------------------------------------------------------------------------------- one_gpu: &one_gpu machine: # https://circleci.com/docs/2.0/configuration-reference/#available-linux-gpu-images image: ubuntu-1604-cuda-10.1:201909-23 # CUDA 10.1, docker 19.03.0-ce, nvidia-docker 2.2.2 docker_layer_caching: true # https://circleci.com/product/features/resource-classes/#linux-vm resource_class: gpu.small one_gpu_windows: &one_gpu_windows machine: resource_class: windows.gpu.nvidia.medium image: windows-server-2019-nvidia:stable shell: bash.exe two_gpus: &two_gpus machine: # https://circleci.com/docs/2.0/configuration-reference/#available-linux-gpu-images image: ubuntu-1604-cuda-10.1:201909-23 # CUDA 10.1, docker 19.03.0-ce, nvidia-docker 2.2.2 docker_layer_caching: true # https://circleci.com/product/features/resource-classes/#linux-vm resource_class: gpu.medium # ------------------------------------------------------------------------------------- # Re-usable commands # ------------------------------------------------------------------------------------- install_latest_nvidia: &install_latest_nvidia - run: name: Install latest NVidia-driver and CUDA command: | sudo apt-get purge nvidia* && sudo apt-get autoremove sudo apt-get update && sudo apt-get install -y --no-install-recommends nvidia-455 cuda-drivers-455 # Install nvidia-container-runtime sudo apt-get install -y nvidia-container-runtime # Reload driver : https://stackoverflow.com/a/45319156/6309199 # lsof | grep nvidia -> kill Xvfb sudo lsof | grep "/usr/bin/Xvfb" | head -1 | awk '{print $2}' | xargs -I {} sudo kill -9 {} # lsmod | grep nvidia sudo rmmod nvidia_uvm && sudo rmmod nvidia_drm && sudo rmmod nvidia_modeset && sudo rmmod nvidia # reload driver nvidia-smi pull_pytorch_stable_image: &pull_pytorch_stable_image - run: name: Pull PyTorch Stable Image command: | docker pull << pipeline.parameters.pytorch_stable_image >> pull_pytorch_stable_devel_image: &pull_pytorch_stable_devel_image - run: name: Pull PyTorch Stable Develop Image command: | docker pull << pipeline.parameters.pytorch_stable_image_devel >> run_pytorch_container: &run_pytorch_container - run: name: Start Pytorch container environment: wd: << pipeline.parameters.workingdir >> command: | docker run --gpus=all --rm -itd --shm-size 16G -v ${wd}:/ignite -w /ignite --name pthd << pipeline.parameters.pytorch_stable_image >> docker exec -it pthd nvidia-smi docker exec -it pthd ls run_pytorch_devel_container: &run_pytorch_devel_container - run: name: Start Pytorch dev container environment: wd: << pipeline.parameters.workingdir >> command: | docker run --gpus=all --rm -itd --shm-size 16G -v ${wd}:/ignite -w /ignite --name pthd << pipeline.parameters.pytorch_stable_image_devel >> docker exec -it pthd nvidia-smi docker exec -it pthd ls install_dependencies: &install_dependencies - run: name: Install dependencies command: | docker exec -it pthd pip install -r requirements-dev.txt export install_ignite_cmd='python setup.py install' docker exec -it pthd /bin/bash -c "$install_ignite_cmd" # ------------------------------------------------------------------------------------- # Jobs to run # ------------------------------------------------------------------------------------- jobs: one_gpu_tests: <<: *one_gpu working_directory: << pipeline.parameters.workingdir >> steps: - checkout - run: name: Trigger job if modified command: | bash .circleci/trigger_if_modified.sh "^(ignite|tests|examples|\.circleci).*" - <<: *install_latest_nvidia - <<: *pull_pytorch_stable_image - <<: *run_pytorch_container - <<: *install_dependencies - run: name: Run GPU Unit Tests and Examples command: | # pytest on cuda export test_cmd='bash tests/run_gpu_tests.sh' docker exec -it pthd /bin/bash -c "${test_cmd}" # MNIST tests # 1) mnist.py export minst1_cmd='CUDA_VISIBLE_DEVICES=0 python examples/mnist/mnist.py --epochs=1' docker exec -it pthd /bin/bash -c "$minst1_cmd" # 2) mnist_with_visdom.py export visdom_script_cmd='python -c "from visdom.server import download_scripts; download_scripts()"' export visdom_cmd='python -m visdom.server' docker exec -d pthd /bin/bash -c "$visdom_script_cmd && $visdom_cmd" export sleep_cmd='sleep 10' export mnist2_cmd='python examples/mnist/mnist_with_visdom.py --epochs=1' docker exec -it pthd /bin/bash -c "$sleep_cmd && $mnist2_cmd" # 3.1) mnist_with_tensorboard.py with tbX export mnist3_cmd='CUDA_VISIBLE_DEVICES=0 python examples/mnist/mnist_with_tensorboard.py --epochs=1' docker exec -it pthd /bin/bash -c "$mnist3_cmd" # uninstall tensorboardX export pip_cmd='pip uninstall -y tensorboardX' docker exec -it pthd /bin/bash -c "$pip_cmd" # 3.2) mnist_with_tensorboard.py with native torch tb docker exec -it pthd /bin/bash -c "$mnist3_cmd" # 4) mnist_save_resume_engine.py # save export mnist4_cmd='CUDA_VISIBLE_DEVICES=0 python examples/mnist/mnist_save_resume_engine.py --epochs=2 --crash_iteration 1100' docker exec -it pthd /bin/bash -c "$mnist4_cmd" # resume export mnist4_cmd='CUDA_VISIBLE_DEVICES=0 python examples/mnist/mnist_save_resume_engine.py --epochs=2 --resume_from=/tmp/mnist_save_resume/checkpoint_1.pt' docker exec -it pthd /bin/bash -c "$mnist4_cmd" - run: name: Codecov upload command: | bash <(curl -s https://codecov.io/bash) -Z -F gpu one_gpu_windows_tests: <<: *one_gpu_windows working_directory: << pipeline.parameters.workingdir >> steps: - checkout - run: name: Trigger job if modified command: | bash .circleci/trigger_if_modified.sh "^(ignite|tests|examples|\.circleci).*" - run: name: Install dependencies command: | conda --version conda install -y pytorch torchvision cudatoolkit=10.1 -c pytorch pip install -r requirements-dev.txt pip install . - run: name: Run GPU Unit Tests command: | # pytest on cuda SKIP_DISTRIB_TESTS=1 bash tests/run_gpu_tests.sh two_gpus_tests: <<: *two_gpus working_directory: << pipeline.parameters.workingdir >> steps: - checkout - run: name: Trigger job if modified command: | bash .circleci/trigger_if_modified.sh "^(ignite|tests|examples|\.circleci).*" - <<: *install_latest_nvidia - <<: *pull_pytorch_stable_image - <<: *run_pytorch_container - <<: *install_dependencies - run: name: Run 1 Node 2 GPUs Unit Tests command: | export test_cmd='bash tests/run_gpu_tests.sh 2' docker exec -it pthd /bin/bash -c "${test_cmd}" - run: name: Codecov upload command: | bash <(curl -s https://codecov.io/bash) -Z -F gpu-2 two_gpus_check_dist_cifar10_example: <<: *two_gpus working_directory: << pipeline.parameters.workingdir >> steps: - checkout - run: name: Trigger job if modified command: | bash .circleci/trigger_if_modified.sh "^(ignite|tests|examples|\.circleci).*" - <<: *install_latest_nvidia - <<: *pull_pytorch_stable_image - <<: *run_pytorch_container - <<: *install_dependencies - run: name: "Install additional example dependencies" command: | docker exec -it pthd pip install fire - run: name: "Run without backend" command: | export example_path="examples/contrib/cifar10" # initial run export stop_cmd="--stop_iteration=500" export test_cmd="CI=1 python ${example_path}/main.py run" docker exec -it pthd /bin/bash -c "${test_cmd} ${stop_cmd}" # resume export resume_opt="--resume-from=/tmp/output-cifar10/resnet18_backend-None-1_stop-on-500/training_checkpoint_400.pt" docker exec -it pthd /bin/bash -c "${test_cmd} --num_epochs=7 ${resume_opt}" - run: name: "Run with NCCL backend using torch dist launch" command: | export example_path="examples/contrib/cifar10" # initial run export stop_cmd="--stop_iteration=500" export test_cmd="CI=1 python -u -m torch.distributed.launch --nproc_per_node=2 --use_env ${example_path}/main.py run --backend=nccl" docker exec -it pthd /bin/bash -c "${test_cmd} ${stop_cmd}" # resume export resume_opt="--resume-from=/tmp/output-cifar10/resnet18_backend-nccl-2_stop-on-500/training_checkpoint_400.pt" docker exec -it pthd /bin/bash -c "${test_cmd} --num_epochs=7 ${resume_opt}" - run: name: "Run with NCCL backend using spawn" command: | export example_path="examples/contrib/cifar10" # initial run export stop_cmd="--stop_iteration=500" export test_cmd="CI=1 python -u ${example_path}/main.py run --backend=nccl --nproc_per_node=2" docker exec -it pthd /bin/bash -c "${test_cmd} ${stop_cmd}" # resume export resume_opt="--resume-from=/tmp/output-cifar10/resnet18_backend-nccl-2_stop-on-500/training_checkpoint_400.pt" docker exec -it pthd /bin/bash -c "${test_cmd} --num_epochs=7 ${resume_opt}" two_gpus_hvd_tests: <<: *two_gpus working_directory: << pipeline.parameters.workingdir >> steps: - checkout - run: name: Trigger job if modified command: | bash .circleci/trigger_if_modified.sh "^(ignite|tests|examples|\.circleci).*" - <<: *install_latest_nvidia - <<: *pull_pytorch_stable_devel_image - <<: *run_pytorch_devel_container - <<: *install_dependencies - run: name: "Install Horovod with NCCL GPU ops" command: | # Following https://github.com/horovod/horovod/blob/master/Dockerfile.test.gpu # and https://github.com/horovod/horovod/issues/1944#issuecomment-628192778 docker exec -it pthd /bin/bash -c "apt-get update && apt-get install -y git" docker exec -it pthd /bin/bash -c "git clone --recursive https://github.com/horovod/horovod.git /horovod && cd /horovod && python setup.py sdist" docker exec -it pthd /bin/bash -c "conda install -y cmake=3.16 nccl=2.7 -c conda-forge" docker exec -it pthd /bin/bash -c 'cd /horovod && HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_LINK=SHARED HOROVOD_WITHOUT_MPI=1 HOROVOD_WITH_PYTORCH=1 pip install -v $(ls /horovod/dist/horovod-*.tar.gz) && ldconfig' docker exec -it pthd horovodrun --check-build - run: name: Run 1 Node 2 GPUs Unit Tests command: | export test_cmd='bash tests/run_gpu_tests.sh' docker exec -it pthd /bin/bash -c "${test_cmd}" # no CUDA devices Horovod tests export test_cmd='CUDA_VISIBLE_DEVICES="" pytest --cov ignite --cov-append --cov-report term-missing --cov-report xml -vvv tests/ -m distributed' docker exec -it pthd /bin/bash -c "${test_cmd}" - run: name: Codecov upload command: | bash <(curl -s https://codecov.io/bash) -Z -F gpu-2-hvd - run: name: "Check CIFAR10 using horovodrun" command: | docker exec -it pthd pip install fire export example_path="examples/contrib/cifar10" # initial run export stop_cmd="--stop_iteration=500" export test_cmd="cd ${example_path} && CI=1 horovodrun -np 2 python -u main.py run --backend=horovod" docker exec -it pthd /bin/bash -c "${test_cmd} ${stop_cmd}" # resume export resume_opt="--resume-from=/tmp/output-cifar10/resnet18_backend-horovod-2_stop-on-500/training_checkpoint_400.pt" docker exec -it pthd /bin/bash -c "${test_cmd} --num_epochs=7 ${resume_opt}" - run: name: "Check CIFAR10 using spawn" command: | export example_path="examples/contrib/cifar10" # initial run export stop_cmd="--stop_iteration=500" export test_cmd="cd ${example_path} && CI=1 python -u main.py run --backend=horovod --nproc_per_node=2" docker exec -it pthd /bin/bash -c "${test_cmd} ${stop_cmd}" # resume export resume_opt="--resume-from=/tmp/output-cifar10/resnet18_backend-horovod-2_stop-on-500/training_checkpoint_400.pt" docker exec -it pthd /bin/bash -c "${test_cmd} --num_epochs=7 ${resume_opt}" build_publish_docker_images: machine: # https://circleci.com/docs/2.0/configuration-reference/#available-machine-images image: ubuntu-2004:202010-01 docker_layer_caching: true # https://circleci.com/docs/2.0/configuration-reference/#machine-executor-linux resource_class: 2xlarge working_directory: << pipeline.parameters.workingdir >> steps: - checkout - run: name: Build all Horovod flavoured PyTorch-Ignite images command: | cd docker export PTH_VERSION=<< pipeline.parameters.build_docker_image_pytorch_version >> export HVD_VERSION=<< pipeline.parameters.build_docker_image_hvd_version >> sh ./hvd/build_all.sh - run: name: Build all PyTorch-Ignite images command: | cd docker export PTH_VERSION=<< pipeline.parameters.build_docker_image_pytorch_version >> sh ./main/build_all.sh - run: name: Build all MS DeepSpeed flavoured PyTorch-Ignite images command: | cd docker export PTH_VERSION=<< pipeline.parameters.build_docker_image_pytorch_version >> export MSDP_VERSION=<< pipeline.parameters.build_docker_image_msdp_version >> sh ./msdp/build_all.sh - run: name: List built images command: docker images | grep pytorchignite - when: condition: << pipeline.parameters.should_publish_docker_images >> steps: - run: name: Push all PyTorch-Ignite Docker images command: | cd docker sh ./push_all.sh # ------------------------------------------------------------------------------------- # Workflows # ------------------------------------------------------------------------------------- workflows: version: 2 gpu_tests: unless: << pipeline.parameters.should_build_docker_images >> jobs: - one_gpu_tests - one_gpu_windows_tests - two_gpus_tests - two_gpus_check_dist_cifar10_example - two_gpus_hvd_tests docker_images: when: << pipeline.parameters.should_build_docker_images >> jobs: - build_publish_docker_images ignite-0.4.3/.circleci/trigger_if_modified.sh000066400000000000000000000023031401006565000211400ustar00rootroot00000000000000#!/bin/bash # Script is taken from https://circleci.com/developer/orbs/orb/roopakv/swissknife#commands-run_if_modified # Usage: sh trigger_if_modified.sh [base-branch] # - for example: sh trigger_if_modified.sh "^(ignite|tests|examples|\.circleci).*" if [ -z "$1" ]; then echo "Pattern should be provided. Usage: sh trigger_if_modified.sh " exit 1 fi pattern=$1 if [ -z "$2" ]; then base_branch=master else base_branch=$2 fi echo "- Pattern: ${pattern}" echo "- Base branch: ${base_branch}" if [ -z "$BASH" ]; then echo Bash not installed. exit 1 fi git status >/dev/null 2>&1 || { echo >&2 "Not in a git directory or no git"; exit 1; } circleci-agent >/dev/null 2>&1 || { echo >&2 "No Circle CI agent. These are in all Circle CI containers"; exit 1; } if [ "$CIRCLE_BRANCH" == "master" ]; then echo "Skip checking modified files if on master" exit 0 fi FILES_MODIFIED="" setcommit () { FILES_MODIFIED=$(git diff --name-only origin/${base_branch}..HEAD | grep -i -E ${pattern}) } setcommit || true if [ -z "$FILES_MODIFIED" ]; then echo "Files not modified. Halting job" circleci-agent step halt else echo "Files modified: ${FILES_MODIFIED}, continuing steps" fiignite-0.4.3/.github/000077500000000000000000000000001401006565000143325ustar00rootroot00000000000000ignite-0.4.3/.github/FUNDING.yml000066400000000000000000000013231401006565000161460ustar00rootroot00000000000000# 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: # 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-0.4.3/.github/ISSUE_TEMPLATE/000077500000000000000000000000001401006565000165155ustar00rootroot00000000000000ignite-0.4.3/.github/ISSUE_TEMPLATE/bug-report.md000066400000000000000000000011431401006565000211240ustar00rootroot00000000000000--- name: "\U0001F41B Bug Report" about: Submit a bug report to help us improve Ignite --- ## ๐Ÿ› 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-0.4.3/.github/ISSUE_TEMPLATE/documentation.md000066400000000000000000000004711401006565000217120ustar00rootroot00000000000000--- name: "\U0001F4DA Documentation" about: Report an issue, comment or suggestion related to project's docs labels: 'docs' --- ## ๐Ÿ“š Documentation ignite-0.4.3/.github/ISSUE_TEMPLATE/feature-request.md000066400000000000000000000012111401006565000221530ustar00rootroot00000000000000--- name: "\U0001F680 Feature Request" about: Submit a proposal/request for a new Ingite feature --- ## ๐Ÿš€ Feature ignite-0.4.3/.github/ISSUE_TEMPLATE/questions-help-support.md000066400000000000000000000010101401006565000235210ustar00rootroot00000000000000--- name: "โ“Questions/Help/Support" about: Do you have a question? labels: 'question' --- ## โ“ Questions/Help/Support ignite-0.4.3/.github/ISSUE_TEMPLATE/user-feedback.md000066400000000000000000000005121401006565000215350ustar00rootroot00000000000000--- 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-0.4.3/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000003371401006565000201360ustar00rootroot00000000000000Fixes #{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-0.4.3/.github/workflows/000077500000000000000000000000001401006565000163675ustar00rootroot00000000000000ignite-0.4.3/.github/workflows/binaries-nightly-release.yml000066400000000000000000000030011401006565000237720ustar00rootroot00000000000000name: 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@v2 - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: 3.8 - 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 pip install --upgrade --no-cache-dir twine python setup.py install - name: Build and Publish Conda binaries shell: bash -l {0} env: ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} UPLOAD_USER: "pytorch-nightly" run: | chmod +x ./conda.recipe/build_and_upload.sh ./conda.recipe/build_and_upload.sh - name: Build and Publish PyPi binaries shell: bash -l {0} run: | python setup.py sdist bdist_wheel twine check dist/* TWINE_USERNAME="${{ secrets.PYPI_USER }}" TWINE_PASSWORD="${{ secrets.PYPI_TOKEN }}" twine upload --verbose dist/* ignite-0.4.3/.github/workflows/binaries-release.yml000066400000000000000000000022261401006565000223260ustar00rootroot00000000000000name: Stable Releases on: release: types: [published] jobs: build-publish: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: miniconda-version: "latest" python-version: 3.8 - name: Install dependencies shell: bash -l {0} run: | conda install -y pytorch torchvision cpuonly -c pytorch pip install -r requirements-dev.txt pip install --upgrade --no-cache-dir twine python setup.py install - name: Build and Publish Conda binaries shell: bash -l {0} env: ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }} UPLOAD_USER: "pytorch" run: | chmod +x ./conda.recipe/build_and_upload.sh ./conda.recipe/build_and_upload.sh - name: Build and Publish PyPI binaries shell: bash -l {0} run: | python setup.py sdist bdist_wheel twine check dist/* TWINE_USERNAME="${{ secrets.PYPI_USER }}" TWINE_PASSWORD="${{ secrets.PYPI_TOKEN }}" twine upload --verbose dist/* ignite-0.4.3/.github/workflows/build_docs.sh000066400000000000000000000002101401006565000210230ustar00rootroot00000000000000sphinx-versioning --use-master-conf --use-master-templates build --greatest-tag --whitelist-branches master docs/source docs/build/html ignite-0.4.3/.github/workflows/code-style.yml000066400000000000000000000016221401006565000211630ustar00rootroot00000000000000name: Format python code on: push: paths-ignore: - "assets/**" - ".circleci/**" - "docker/**" - "docs/**" - "conda.recipe" - "**.md" pull_request: paths-ignore: - "assets/**" - ".circleci/**" - "docker/**" - "docs/**" - "conda.recipe" - "**.md" jobs: code-style: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: python-version: "3.8" - run: | python -m pip install autopep8 "black==19.10b0" "isort==5.7.0" autopep8 --recursive --in-place --aggressive --aggressive . black . isort . - name: Commit and push changes uses: stefanzweifel/git-auto-commit-action@v4 with: commit_message: "autopep8 fix" env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} ignite-0.4.3/.github/workflows/docker-publish.yml000066400000000000000000000016651401006565000220350ustar00rootroot00000000000000name: Trigger Build And Publish Docker Images on: push: branches: - master paths: - docker/** - ".github/workflows/docker-publish.yml" release: types: [published] pull_request: paths: - docker/** - ".github/workflows/docker-publish.yml" jobs: build-publish: name: Trigger Build and Push Docker images to Docker Hub runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: python-version: 3.7 - name: Trigger Circle-CI pipeline env: CIRCLE_TOKEN: ${{ secrets.CIRCLE_TOKEN }} run: | pip install requests if [ $GITHUB_EVENT_NAME == 'pull_request' ]; then should_publish_docker_images=false; else should_publish_docker_images=true; fi branch=$GITHUB_REF python -u .github/workflows/trigger_circle_ci.py $should_publish_docker_images $branch ignite-0.4.3/.github/workflows/docs.yml000066400000000000000000000016251401006565000200460ustar00rootroot00000000000000name: Build docs on: push: branches: [master] release: types: [published] jobs: build-deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: python-version: 3.7 - run: sudo npm install katex@latest -g - uses: actions/cache@v2 with: path: ~/.cache/pip key: pip-${{ 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: ${{ secrets.GITHUB_TOKEN }} publish_dir: docs/build/html publish_branch: gh-pages commit_message: Deploy pytorch/ignite docs ignite-0.4.3/.github/workflows/hvd-tests.yml000066400000000000000000000037531401006565000210430ustar00rootroot00000000000000name: Run Horovod tests on: push: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - ".github/workflows/hvd-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - ".github/workflows/hvd-tests.yml" jobs: horovod-tests: runs-on: ubuntu-latest strategy: matrix: python-version: [3.7] pytorch-channel: [pytorch] steps: - uses: actions/checkout@v2 - name: Get year & week number id: get-date run: echo "::set-output name=date::$(/bin/date "+%Y-%U")" shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | pip3 install -U --user pip echo "::set-output name=dir::$(pip3 cache dir)" shell: bash -l {0} - uses: actions/cache@v2 with: path: | ~/conda_pkgs_dir ${{ steps.pip-cache.outputs.dir }} key: ${{ steps.get-date.outputs.date }}-horovod-${{ hashFiles('requirements-dev.txt') }} - 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: | #install other dependencies conda install pytorch torchvision cpuonly -c ${{ matrix.pytorch-channel }} pip install -r requirements-dev.txt pip install horovod python setup.py install - name: Run Tests shell: bash -l {0} run: | SKIP_DISTRIB_TESTS=${{ matrix.skip-distrib-tests }} bash tests/run_cpu_tests.sh - name: Upload coverage to Codecov uses: codecov/codecov-action@v1 with: file: ./coverage.xml flags: hvd-cpu fail_ci_if_error: true ignite-0.4.3/.github/workflows/install_docs_deps.sh000066400000000000000000000006641401006565000224220ustar00rootroot00000000000000# remove pkg-resources as it causes failure when installing https://github.com/vfdev-5/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/vfdev-5/sphinxcontrib-versioning.git ignite-0.4.3/.github/workflows/pytorch-version-tests.yml000066400000000000000000000040011401006565000234200ustar00rootroot00000000000000name: 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 * * *" jobs: build: runs-on: ubuntu-latest strategy: max-parallel: 10 fail-fast: false matrix: python-version: [3.6, 3.7, 3.8] pytorch-version: [1.7.0, 1.6.0, 1.5.1, 1.4.0, 1.3.1] exclude: - pytorch-version: 1.3.1 python-version: 3.8 steps: - uses: actions/checkout@v2 - name: Get year & week number id: get-date run: echo "::set-output name=date::$(/bin/date "+%Y-%U")" shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | pip3 install -U --user pip echo "::set-output name=dir::$(pip3 cache dir)" shell: bash -l {0} - uses: actions/cache@v2 with: path: | ~/conda_pkgs_dir ${{ steps.pip-cache.outputs.dir }} 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=${{ matrix.python-version }} -c pytorch pip install -r requirements-dev.txt python setup.py install - name: Run Tests shell: bash -l {0} run: | pytest tests -vvv ignite-0.4.3/.github/workflows/tpu-tests.yml000066400000000000000000000062211401006565000210630ustar00rootroot00000000000000name: Run TPU tests on: push: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_tpu_tests.sh" - ".github/workflows/tpu-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_tpu_tests.sh" - ".github/workflows/tpu-tests.yml" jobs: tpu-tests: runs-on: ubuntu-latest strategy: fail-fast: false matrix: xla-version: [nightly] steps: - uses: actions/checkout@v2 - name: Set up Python 3.6 uses: actions/setup-python@v2 with: python-version: "3.6.10" architecture: "x64" - name: Get year & week number id: get-date run: echo "::set-output name=date::$(/bin/date "+%Y-%U")" shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | pip3 install -U pip echo "::set-output name=dir::$(pip cache dir)" shell: bash -l {0} - uses: actions/cache@v2 with: path: | ${{ steps.pip-cache.outputs.dir }} 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 }}-${{ matrix.xla-version }}- - name: Install Torch XLA and others run: | ## Install gsutil echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list sudo apt-get install -y apt-transport-https ca-certificates gnupg curl curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - sudo apt-get update && sudo apt-get install -y google-cloud-sdk ## Install openblas and mkl sudo apt-get install -y libopenblas-dev libomp5 pip install mkl ## Download torch & xla gsutil cp gs://tpu-pytorch/wheels/torch-${{ matrix.xla-version }}-cp36-cp36m-linux_x86_64.whl . gsutil cp gs://tpu-pytorch/wheels/torch_xla-${{ matrix.xla-version }}-cp36-cp36m-linux_x86_64.whl . ## Install torch & xla pip install torch-${{ matrix.xla-version }}-cp36-cp36m-linux_x86_64.whl pip install torch_xla-${{ matrix.xla-version }}-cp36-cp36m-linux_x86_64.whl ## Install test deps and Ignite pip install -r requirements-dev.txt python setup.py install - name: Run Tests run: | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/hostedtoolcache/Python/3.6.10/x64/lib export XRT_DEVICE_MAP="CPU:0;/job:localservice/replica:0/task:0/device:XLA_CPU:0" export XRT_WORKERS="localservice:0;grpc://localhost:40934" python -c "import torch_xla; print('torch xla version:', torch_xla.__version__)" bash tests/run_tpu_tests.sh - name: Upload coverage to Codecov uses: codecov/codecov-action@v1 with: file: ./coverage.xml flags: tpu fail_ci_if_error: true ignite-0.4.3/.github/workflows/trigger_circle_ci.py000066400000000000000000000075251401006565000224110ustar00rootroot00000000000000import json import os import sys import time import requests def assert_result(result, expected_code): if result.status_code != expected_code: raise RuntimeError(f"{result.status_code}: {result.text}") def get_output(result_text, required_keys): output = json.loads(result_text) if not all([v in output for v in required_keys]): raise RuntimeError(f"Output does not contain required fields: {required_keys}\n" f"Output is: {output}") return output def trigger_new_pipeline(data, headers): result = requests.post( "https://circleci.com/api/v2/project/gh/pytorch/ignite/pipeline", data=json.dumps(data), headers=headers ) assert_result(result, 201) output = get_output(result.text, ["id",]) return output["id"] def assert_pipeline_created(pipeline_id, headers): while True: result = requests.get(f"https://circleci.com/api/v2/pipeline/{pipeline_id}", headers=headers) assert_result(result, 200) output = get_output(result.text, ["state", "errors"]) if output["state"] == "errored": raise RuntimeError(f"Pipeline is errored: {output['errors']}") if output["state"] == "created": break time.sleep(2) def get_workflow_id(pipeline_id, headers): while True: result = requests.get(f"https://circleci.com/api/v2/pipeline/{pipeline_id}/workflow", headers=headers) assert_result(result, 200) output = get_output(result.text, ["items",]) items = output["items"] if len(items) > 1: raise RuntimeError(f"Incorrect number of workflow ids: {len(items)} != 1\n" f"items: {items}") if len(items) < 1: continue item_0 = items[0] if "id" not in item_0: raise RuntimeError("Workflow info does not contain 'id'\n" f"Info: {item_0}") return item_0["id"] def assert_workflows_successful(pipeline_id, headers): workflow_id = get_workflow_id(pipeline_id, headers) base_url = "https://app.circleci.com/pipelines/github/pytorch/ignite" url = None while True: result = requests.get(f"https://circleci.com/api/v2/workflow/{workflow_id}", headers=headers) assert_result(result, 200) output = get_output(result.text, ["name", "status", "pipeline_number"]) if url is None: url = f"{base_url}/{output['pipeline_number']}/workflows/{workflow_id}" print(f"Circle CI workflow: {url}") if output["status"] in ["error", "failing", "canceled", "not_run", "failed"]: raise RuntimeError(f"Workflow failed: {output['status']}\n" f"See {url}") if output["status"] == "success": print("\nWorkflow successful") break time.sleep(30) print(".", end=" ") if __name__ == "__main__": print("Trigger new pipeline on Circle-CI") if "CIRCLE_TOKEN" not in os.environ: raise RuntimeError( "Can not find CIRCLE_TOKEN env variable.\n" "Please, export CIRCLE_TOKEN= before calling this script" ) argv = sys.argv if len(argv) != 3: raise RuntimeError("Usage: python trigger_circle_ci.py ") should_publish_docker_images = json.loads(argv[1]) branch = argv[2] print(f"- should_publish_docker_images: {should_publish_docker_images}") print(f"- Branch: {branch}") headers = {"authorization": "Basic", "content-type": "application/json", "Circle-Token": os.environ["CIRCLE_TOKEN"]} data = { "branch": branch, "parameters": { "should_build_docker_images": True, "should_publish_docker_images": should_publish_docker_images, }, } unique_pipeline_id = trigger_new_pipeline(data, headers) assert_pipeline_created(unique_pipeline_id, headers) assert_workflows_successful(unique_pipeline_id, headers) ignite-0.4.3/.github/workflows/unit-tests.yml000066400000000000000000000133211401006565000212310ustar00rootroot00000000000000name: Run unit tests on: push: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - "examples/**.py" - ".github/workflows/unit-tests.yml" pull_request: paths: - "ignite/**" - "tests/ignite/**" - "tests/run_cpu_tests.sh" - "examples/**.py" - ".github/workflows/unit-tests.yml" jobs: build: runs-on: ${{ matrix.os }} strategy: max-parallel: 10 fail-fast: false matrix: os: [ubuntu-latest] python-version: [3.6, 3.7, 3.8] pytorch-channel: [pytorch, pytorch-nightly] include: # includes a single build on windows - os: windows-latest pytorch-channel: pytorch python-version: 3.7 skip-distrib-tests: 1 # includes a single build on macosx - os: macos-latest pytorch-channel: pytorch python-version: 3.7 skip-distrib-tests: 1 steps: - uses: actions/checkout@v2 - name: Get year & week number id: get-date run: | echo "::set-output name=date::$(/bin/date "+%Y-%U")" shell: bash -l {0} - name: Get pip cache dir id: pip-cache run: | pip3 install -U --user pip echo "::set-output name=dir::$(pip3 cache dir)" shell: bash -l {0} - uses: actions/cache@v2 with: path: | ~/conda_pkgs_dir ${{ steps.pip-cache.outputs.dir }} 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 torchvision cpuonly -c ${{ matrix.pytorch-channel }} pip install -r requirements-dev.txt python setup.py install - name: Check code formatting shell: bash -l {0} run: | pip install flake8 "black==19.10b0" "isort==5.7.0" flake8 ignite/ tests/ examples/ black --check . isort -c . - name: Run Mypy shell: bash -l {0} if: ${{ matrix.os == 'ubuntu-latest' }} run: | pip install mypy mypy --config-file mypy.ini - name: Run Tests shell: bash -l {0} run: | SKIP_DISTRIB_TESTS=${{ matrix.skip-distrib-tests }} bash tests/run_cpu_tests.sh - name: Upload coverage to Codecov uses: codecov/codecov-action@v1 with: file: ./coverage.xml flags: cpu fail_ci_if_error: true - name: Run MNIST Examples shell: bash -l {0} run: | # MNIST # 1) mnist.py python examples/mnist/mnist.py --epochs=1 - name: Run MNIST with loggers Examples shell: bash -l {0} if: ${{ matrix.os == 'ubuntu-latest' }} run: | # 2) mnist_with_visdom.py python -c "from visdom.server 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 shell: bash -l {0} 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 shell: bash -l {0} 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 shell: bash -l {0} 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 shell: bash -l {0} 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 shell: bash -l {0} if: ${{ matrix.os == 'ubuntu-latest' }} run: | #fast-neural-style #train mkdir -p ~/.cache/torch/checkpoints/ && wget "https://download.pytorch.org/models/vgg16-397923af.pth" -O ~/.cache/torch/checkpoints/vgg16-397923af.pth python examples/fast_neural_style/neural_style.py train --epochs 1 --cuda 0 --dataset test --dataroot . --image_size 32 --style_image examples/fast_neural_style/images/style_images/mosaic.jpg --style_size 32 ignite-0.4.3/.gitignore000066400000000000000000000006541401006565000147670ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files *.pyc __pycache__/ *.py[cod] *$py.class # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Documentation /docs/src/ /docs/build/ ignite-0.4.3/.pre-commit-config.yaml000066400000000000000000000015021401006565000172510ustar00rootroot00000000000000exclude: "^conda.recipe" repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v2.3.0 hooks: - id: check-toml - id: check-yaml - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/prettier/pre-commit rev: main hooks: - id: prettier - repo: https://github.com/asottile/seed-isort-config rev: v1.9.4 hooks: - id: seed-isort-config args: [--exclude=^((examples|docs)/.*)$] - repo: https://github.com/timothycrosley/isort rev: 4.3.21-2 hooks: - id: isort - repo: https://github.com/python/black rev: 19.10b0 hooks: - id: black language_version: python3.8 - repo: https://gitlab.com/pycqa/flake8 rev: 3.7.7 hooks: - id: flake8 args: [--append-config=tox.ini] ignite-0.4.3/CITATION000066400000000000000000000004771401006565000141370ustar00rootroot00000000000000@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-0.4.3/CODE_OF_CONDUCT.md000066400000000000000000000064251401006565000156000ustar00rootroot00000000000000# 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-0.4.3/CONTRIBUTING.md000066400000000000000000000220121401006565000152200ustar00rootroot00000000000000# 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 youโ€™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 developersโ€™ 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. ## Developing Ignite ### Quickstart guide for first-time contributors
- Install [miniconda](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/index.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 To get the development installation with the necessary dependencies, run the following: ```bash git clone https://github.com/pytorch/ignite.git cd ignite python setup.py develop pip install -r requirements-dev.txt pip install flake8 "black==19.10b0" "isort==5.7.0" mypy ``` ### 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()` 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/), [black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/) tools 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. ```bash # This should autoformat the files isort -rc . black . # Run lint checking flake8 ignite/ tests/ examples/ # 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 black 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 black, isort 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, black 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, isort or black # Fix any flake8 errors by following their suggestions # isort and black 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`): ```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 Mypy checks: To run mypy to check the optional static type: ```bash mypy --config-file mypy.ini ``` 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 If you are not familiar with creating a Pull Request, here are some guides: - http://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request - https://help.github.com/articles/creating-a-pull-request/ ##### 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](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) for formatting docstrings 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. 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. 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 `0.0.0.0:` (e.g. `0.0.0.0:1234`) and click to `html` folder. ignite-0.4.3/LICENSE000066400000000000000000000027501401006565000140030ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2018, PyTorch 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-0.4.3/README.md000066400000000000000000000567751401006565000142750ustar00rootroot00000000000000
| ![image](https://img.shields.io/badge/-Tests:-black?style=flat-square) [![image](https://github.com/pytorch/ignite/workflows/Run%20unit%20tests/badge.svg)](https://github.com/pytorch/ignite/actions) [![image](https://img.shields.io/badge/-GPU%20tests-black?style=flat-square)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master)[![image](https://circleci.com/gh/pytorch/ignite.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master) [![image](https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/ignite/index.html) | | :--- | | ![image](https://img.shields.io/badge/-Stable%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch/ignite/badges/version.svg)](https://anaconda.org/pytorch/ignite) [![image](https://anaconda.org/pytorch/ignite/badges/downloads.svg)](https://anaconda.org/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pypi.org/project/pytorch-ignite/) [![image](https://pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite) | | ![image](https://img.shields.io/badge/-Nightly%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch-nightly/ignite/badges/version.svg)](https://anaconda.org/pytorch-nightly/ignite) [![image](https://img.shields.io/badge/PyPI-pre%20releases-brightgreen)](https://pypi.org/project/pytorch-ignite/#history) | | ![image](https://img.shields.io/badge/-Features:-black?style=flat-square) [![image](https://img.shields.io/badge/docker-hub-blue)](https://hub.docker.com/u/pytorchignite) [![image](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org) [![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Twitter](https://img.shields.io/badge/news-twitter-blue)](https://twitter.com/pytorch_ignite) | | ![image](https://img.shields.io/badge/-Supported_PyTorch/Python_versions:-black?style=flat-square) [![link](https://img.shields.io/badge/-check_here-blue)](https://github.com/pytorch/ignite/actions?query=workflow%3A%22PyTorch+version+tests%22) |
## 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 - [Why Ignite?](#why-ignite) - [Installation](#installation) - [Getting Started](#getting-started) - [Documentation](#documentation) - [Examples](#examples) - [Tutorials](#tutorials) - [Reproducible Training Examples](#reproducible-training-examples) - [Communication](#communication) - [Contributing](#contributing) - [Projects using Ignite](#projects-using-ignite) - [About the team](#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 # E.g. 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 say, 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 can be 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/contrib/metrics.html#regression-metrics). - Users can also [compose their own 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 ``` Available pre-built images are : - `pytorchignite/base:latest | pytorchignite/hvd-base:latest` - `pytorchignite/apex:latest | pytorchignite/hvd-apex:latest | pytorchignite/msdp-apex:latest` - `pytorchignite/vision:latest | pytorchignite/hvd-vision:latest` - `pytorchignite/apex-vision:latest | pytorchignite/hvd-apex-vision:latest | pytorchignite/msdp-apex-vision:latest` - `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.org/ignite/quickstart.html) - [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch.org/ignite/concepts.html) - 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 - [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: - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg) - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5) # Examples Complete list of examples can be found [here](https://pytorch.org/ignite/examples.html). ## 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/contrib/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 with mixed precision by [nvidia/apex](https://github.com/NVIDIA/apex/) - Experiments tracking with [MLflow](https://mlflow.org/), [Polyaxon](https://polyaxon.com/) or [ClearML](https://github.com/allegroai/clearml/) # 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 Slack](https://pytorch.slack.com) at #pytorch-ignite channel. [Request access](https://bit.ly/ptslack). - [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) ## 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/optuna/using-optuna-to-optimize-pytorch-ignite-hyperparameters-b680f55ac746) ## 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) ## 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/pfnet/optuna/blob/master/examples/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) 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 in this list, so please send a PR with brief description of the project. # About the team & Disclaimer This repository is 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.org/ignite/master/about.html) page for a list of core contributors. For usage questions and issues, please see the various channels [here](#communication). 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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. Donโ€™t let it overlap with text, images, or other elements. - Do not try and recreate or modify the logo. 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/…‹‰^๎ึXNhqฅ„I(W2~jฆf๊zฎ็bf๋bvhนnh‰>f‹๖iคฎkงฆ๋’Ž๋zพ„ฆk‡Ž๋tukร&์‡ถ„๘ฐkภ>lน&์Ž^ืลๆkปn๋cNiลฆ์พึhอ๖kz์ว\ฮฆgัžkถถlh^๋ฤ๋ฆ†๋ทึ่าF์มv์ำึlหmž๎l1ึkฯiปฎkc๎ั๎kฤฮ๋ฮFษlแVmค~lวFmุŽnๅfํ้vํk†๋แํฮr5cัๅYกZNล๊ล{cฏฆด3่ฑ&k›ๆsU๋ืžํๅ^ๅะmbc้~๋ึถoศ–mึ๏ฦฮํๅp๚พoา–mm๏๏ว๏฿Ž๎ย~ํำฆp{vnmmุ๊พpเ†pงึo่žp๚.๎ฯ๐ฟ๐งl๎Ÿpฏl๗owp ๏ง๖๏ยnkF๎ยlทp๏o่fq ฏ๑ื๑฿๐$gp o๒ื๎งๆ๎ะn๐oซถH-o๓f]ๅ{<_foXื๗ๆ๎V–o4ฯqt๐4วqไV๒%ฯpว๑–๓6oW w๓9฿sทๆ๏;๗s?‡sลt<ŸsAทg๙๖p)oqC฿๓D_t5ทs8WrD฿ํ>W๋g๒JW๓M็tI็๓Fท๓8๗๔W๔เ†tLท]D7u&ฮ๕AGuu u&ืtW๏t ๓)ฟg3๗n}ฦ๒Šิr่€;ignite-0.4.3/assets/tldr/teaser.ipynb000066400000000000000000000351511401006565000175740ustar00rootroot00000000000000{ "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 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_torch_launch = \"WORLD_SIZE\" in os.environ\n", "\n", "if in_colab:\n", " VERSION = \"nightly\"\n", " !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\n", " !python pytorch-xla-env-setup.py --version $VERSION" ] }, { "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.contrib.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", " 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", " 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", " # - 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_dataset, test_dataset = get_train_test_datasets(config.get(\"data_path\", \".\"))\n", "\n", " if idist.get_rank() == 0:\n", " # Ensure that only rank 0 download the dataset\n", " idist.barrier()\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_torch_launch):\n", "\n", " backend = None # or \"nccl\", \"gloo\", \"xla-tpu\" ...\n", " nproc_per_node = None # or N to spawn N processes\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", "# $ python -m torch.distributed.launch --nproc_per_node=2 --use_env main.py\n", "#\n", "if __name__ == \"__main__\" and with_torch_launch:\n", "\n", " backend = \"nccl\" # or \"nccl\", \"gloo\", \"xla-tpu\" ...\n", " nproc_per_node = None # or N to spawn N processes\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\" # or \"nccl\", \"gloo\", \"xla-tpu\" ...\n", " nproc_per_node = 8 # or N to spawn N processes\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/contrib/cifar10\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }ignite-0.4.3/assets/tldr/teaser.py000066400000000000000000000147301401006565000171030ustar00rootroot00000000000000import 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.contrib.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_torch_launch = "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): 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) 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): # - Get train/test datasets if idist.get_rank() > 0: # Ensure that only rank 0 download the dataset idist.barrier() train_dataset, test_dataset = get_train_test_datasets(config.get("data_path", ".")) if idist.get_rank() == 0: # Ensure that only 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.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_torch_launch): backend = None # or "nccl", "gloo", "xla-tpu" ... nproc_per_node = None # or N to spawn N processes config = { "model": "resnet18", "dataset": "cifar10", } with idist.Parallel(backend=backend, nproc_per_node=nproc_per_node) as parallel: parallel.run(training, config) # --- Multiple GPUs --- # $ python -m torch.distributed.launch --nproc_per_node=2 --use_env main.py # if __name__ == "__main__" and with_torch_launch: backend = "nccl" # or "nccl", "gloo", "xla-tpu" ... nproc_per_node = None # or N to spawn N processes 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" # or "nccl", "gloo", "xla-tpu" ... nproc_per_node = 8 # or N to spawn N processes 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/contrib/cifar10 ignite-0.4.3/codecov.yml000066400000000000000000000003331401006565000151360ustar00rootroot00000000000000coverage: precision: 2 round: down range: "95...100" status: patch: default: target: 90 project: default: threshold: 1% changes: false comment: false ignore: - "tests/" ignite-0.4.3/conda.recipe/000077500000000000000000000000001401006565000153245ustar00rootroot00000000000000ignite-0.4.3/conda.recipe/build_and_upload.sh000066400000000000000000000016711401006565000211520ustar00rootroot00000000000000#!/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 ls conda_build/*/*.tar.bz2 | xargs -I {} anaconda -v -t $ANACONDA_TOKEN upload -u $UPLOAD_USER {} ignite-0.4.3/conda.recipe/meta.yaml000066400000000000000000000012621401006565000171370ustar00rootroot00000000000000{% 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-0.4.3/docker/000077500000000000000000000000001401006565000142415ustar00rootroot00000000000000ignite-0.4.3/docker/README.md000066400000000000000000000064061401006565000155260ustar00rootroot00000000000000# 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` - [msdp/Dockerfile.msdp-apex-base](msdp/Dockerfile.msdp-apex-base): multi-stage MSDeepSpeed build with latest Pytorch, Ignite image with minimal dependencies - `docker pull pytorchignite/msdp-base:latest` - [msdp/Dockerfile.msdp-apex-vision](msdp/Dockerfile.msdp-apex-vision): base MSDeepSpeed build with useful computer vision libraries - `docker pull pytorchignite/msdp-vision:latest` - [msdp/Dockerfile.msdp-apex-nlp](msdp/Dockerfile.msdp-apex-nlp): base MSDeepSpeed build with useful NLP libraries - `docker pull pytorchignite/msdp-nlp:latest` ## How to use ```bash docker run -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash ``` ## Building the image yourself Dockerfiles are supplied to build images with dependencies that ease the use of Ignite for computer vision / NLP tasks: ```bash cd main docker build -t pytorchignite/base:latest -f Dockerfile.base . ``` ignite-0.4.3/docker/hvd/000077500000000000000000000000001401006565000150225ustar00rootroot00000000000000ignite-0.4.3/docker/hvd/Dockerfile.hvd-apex000066400000000000000000000051311401006565000205270ustar00rootroot00000000000000# Multi-stage build # Dockerfile.hvd-apex ARG PTH_VERSION # 1/Building apex with pytorch:*-devel FROM pytorch/pytorch:${PTH_VERSION}-devel AS apex-hvd-builder # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch ARG ARG_TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" ENV TORCH_CUDA_ARCH_LIST=$ARG_TORCH_CUDA_ARCH_LIST # 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 wheel --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . ARG HVD_VERSION # Build Horovod RUN apt-get update && apt-get install -y git && \ git clone --recursive --depth 1 --branch ${HVD_VERSION} https://github.com/horovod/horovod.git /horovod && \ conda install -y cmake=3.16 nccl=2.7 -c conda-forge && \ cd /horovod && \ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_LINK=SHARED HOROVOD_WITHOUT_MPI=1 HOROVOD_WITH_PYTORCH=1 pip wheel --no-cache-dir . && \ rm -rf /var/lib/apt/lists/* # Build runtime image FROM pytorch/pytorch:${PTH_VERSION}-runtime # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch # 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/America/New_York /etc/localtime && \ apt-get install -y tzdata && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get -y install --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm # 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 # Horovod RUN conda install -y nccl=2.7 -c conda-forge ENV LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH COPY --from=apex-hvd-builder /horovod/horovod-*.whl /horovod/ RUN cd /horovod && \ pip install --no-cache-dir horovod-*.whl && \ rm -fr /horovod ignite-0.4.3/docker/hvd/Dockerfile.hvd-apex-nlp000066400000000000000000000004441401006565000213200ustar00rootroot00000000000000# Dockerfile.hvd-apex-nlp FROM pytorchignite/hvd-apex:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir torchtext \ transformers \ spacy \ nltk ignite-0.4.3/docker/hvd/Dockerfile.hvd-apex-vision000066400000000000000000000014461401006565000220410ustar00rootroot00000000000000# Dockerfile.hvd-apex-vision FROM pytorchignite/hvd-apex:latest # Install opencv dependencies RUN apt-get update && \ apt-get -y install --no-install-recommends libglib2.0 \ libsm6 \ libxext6 \ libxrender-dev && \ rm -rf /var/lib/apt/lists/* # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python \ py_config_runner \ pillow \ clearml ignite-0.4.3/docker/hvd/Dockerfile.hvd-base000066400000000000000000000036061401006565000205110ustar00rootroot00000000000000# Multi-stage build # Dockerfile.hvd-base ARG PTH_VERSION FROM pytorch/pytorch:${PTH_VERSION}-devel as builder # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch ARG HVD_VERSION # Build Horovod RUN apt-get update && apt-get install -y git && \ git clone --recursive --depth 1 --branch ${HVD_VERSION} https://github.com/horovod/horovod.git /horovod && \ conda install -y cmake=3.16 nccl=2.7 -c conda-forge && \ cd /horovod && \ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_LINK=SHARED HOROVOD_WITHOUT_MPI=1 HOROVOD_WITH_PYTORCH=1 pip wheel --no-cache-dir . && \ rm -rf /var/lib/apt/lists/* # Build runtime image FROM pytorch/pytorch:${PTH_VERSION}-runtime # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/America/New_York /etc/localtime && \ apt-get install -y tzdata && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get -y install --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm # 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 # Horovod RUN conda install -y nccl=2.7 -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 /horovod ignite-0.4.3/docker/hvd/Dockerfile.hvd-nlp000066400000000000000000000004371401006565000203670ustar00rootroot00000000000000# Dockerfile.hvd-nlp FROM pytorchignite/hvd-base:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir torchtext \ transformers \ spacy \ nltk ignite-0.4.3/docker/hvd/Dockerfile.hvd-vision000066400000000000000000000014411401006565000211010ustar00rootroot00000000000000# Dockerfile.hvd-vision FROM pytorchignite/hvd-base:latest # Install opencv dependencies RUN apt-get update && \ apt-get -y install --no-install-recommends libglib2.0 \ libsm6 \ libxext6 \ libxrender-dev && \ rm -rf /var/lib/apt/lists/* # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python \ py_config_runner \ pillow \ clearml ignite-0.4.3/docker/hvd/build_all.sh000066400000000000000000000026061401006565000173110ustar00rootroot00000000000000#!/bin/bash echo "Build all Horovod flavoured PyTorch-Ignite images" 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 $limit ]; then exit 1 fi } # Start script from ignite docker folder if [ ! -d hvd ]; then echo "Can not find 'hvd' folder" echo "Usage: sh hvd/build_all.sh" exit 1 fi curr_dir=$PWD cd $curr_dir/hvd set -eu image_tag="" if [[ -z "${PTH_VERSION}" ]]; then echo "PTH_VERSION is not set" exit 1 fi if [[ -z "${HVD_VERSION}" ]]; then echo "HVD_VERSION is not set" exit 1 fi pth_version=${PTH_VERSION} hvd_version=${HVD_VERSION} for image_name in "hvd-base" "hvd-vision" "hvd-nlp" "hvd-apex" "hvd-apex-vision" "hvd-apex-nlp" do retry "docker build --build-arg PTH_VERSION=${pth_version} --build-arg HVD_VERSION=${hvd_version} -t pytorchignite/${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} done cd $curr_dir ignite-0.4.3/docker/main/000077500000000000000000000000001401006565000151655ustar00rootroot00000000000000ignite-0.4.3/docker/main/Dockerfile.apex000066400000000000000000000035641401006565000201230ustar00rootroot00000000000000# Multi-stage build # Dockerfile.apex ARG PTH_VERSION # 1/Building apex with pytorch:*-devel FROM pytorch/pytorch:${PTH_VERSION}-devel AS apex-builder # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch ARG ARG_TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" ENV TORCH_CUDA_ARCH_LIST=$ARG_TORCH_CUDA_ARCH_LIST # 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 $tmp_apex_path && \ pip wheel --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . # 2/ Build the runtime image FROM pytorch/pytorch:${PTH_VERSION}-runtime # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch 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/America/New_York /etc/localtime && \ apt-get install -y tzdata && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get -y install --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm # 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 ignite-0.4.3/docker/main/Dockerfile.apex-nlp000066400000000000000000000004341401006565000207030ustar00rootroot00000000000000# Dockerfile.apex-nlp FROM pytorchignite/apex:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir torchtext \ transformers \ spacy \ nltk ignite-0.4.3/docker/main/Dockerfile.apex-vision000066400000000000000000000014361401006565000214240ustar00rootroot00000000000000# Dockerfile.apex-vision FROM pytorchignite/apex:latest # Install opencv dependencies RUN apt-get update && \ apt-get -y install --no-install-recommends libglib2.0 \ libsm6 \ libxext6 \ libxrender-dev && \ rm -rf /var/lib/apt/lists/* # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python \ py_config_runner \ pillow \ clearml ignite-0.4.3/docker/main/Dockerfile.base000066400000000000000000000017451401006565000200770ustar00rootroot00000000000000# Dockerfile.base ARG PTH_VERSION FROM pytorch/pytorch:${PTH_VERSION}-runtime # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/America/New_York /etc/localtime && \ apt-get install -y tzdata && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get -y install --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm # 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 ignite-0.4.3/docker/main/Dockerfile.nlp000066400000000000000000000004271401006565000177520ustar00rootroot00000000000000# Dockerfile.nlp FROM pytorchignite/base:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir torchtext \ transformers \ spacy \ nltk ignite-0.4.3/docker/main/Dockerfile.vision000066400000000000000000000014311401006565000204640ustar00rootroot00000000000000# Dockerfile.vision FROM pytorchignite/base:latest # Install opencv dependencies RUN apt-get update && \ apt-get -y install --no-install-recommends libglib2.0 \ libsm6 \ libxext6 \ libxrender-dev && \ rm -rf /var/lib/apt/lists/* # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python \ py_config_runner \ pillow \ clearml ignite-0.4.3/docker/main/build_all.sh000066400000000000000000000023121401006565000174460ustar00rootroot00000000000000#!/bin/bash echo "Build all PyTorch-Ignite images" 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 $limit ]; then exit 1 fi } # Start script from ignite docker folder if [ ! -d main ]; then echo "Can not find 'main' folder" echo "Usage: sh main/build_all.sh" exit 1 fi curr_dir=$PWD cd $curr_dir/main set -eu image_tag="" if [[ -z "${PTH_VERSION}" ]]; then echo "PTH_VERSION is not set" exit 1 fi pth_version=${PTH_VERSION} for image_name in "base" "vision" "nlp" "apex" "apex-vision" "apex-nlp" do retry "docker build --build-arg PTH_VERSION=${pth_version} -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} done cd $curr_dir ignite-0.4.3/docker/msdp/000077500000000000000000000000001401006565000152045ustar00rootroot00000000000000ignite-0.4.3/docker/msdp/Dockerfile.msdp-apex000066400000000000000000000045261401006565000211020ustar00rootroot00000000000000# Multi-stage build # Dockerfile.msdp-apex ARG PTH_VERSION # 1/Building apex with pytorch:*-devel FROM pytorch/pytorch:${PTH_VERSION}-devel AS apex-msdp-builder # Temporary hack to install pth 1.7.1 RUN conda install -y pytorch torchvision cudatoolkit=11.0 -c pytorch ARG ARG_TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" ENV TORCH_CUDA_ARCH_LIST=$ARG_TORCH_CUDA_ARCH_LIST # 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 wheel --no-cache-dir --global-option="--cpp_ext" --global-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 # Install tzdata / git RUN apt-get update && \ ln -fs /usr/share/zoneinfo/America/New_York /etc/localtime && \ apt-get install -y tzdata && \ dpkg-reconfigure --frontend noninteractive tzdata && \ apt-get -y install --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Ignite main dependencies RUN pip install --upgrade --no-cache-dir pytorch-ignite \ tensorboard \ tqdm # 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 ignite-0.4.3/docker/msdp/Dockerfile.msdp-apex-nlp000066400000000000000000000004461401006565000216660ustar00rootroot00000000000000# Dockerfile.msdp-apex-nlp FROM pytorchignite/msdp-apex:latest # Ignite NLP dependencies RUN pip install --upgrade --no-cache-dir torchtext \ transformers \ spacy \ nltk ignite-0.4.3/docker/msdp/Dockerfile.msdp-apex-vision000066400000000000000000000014501401006565000224000ustar00rootroot00000000000000# Dockerfile.msdp-apex-vision FROM pytorchignite/msdp-apex:latest # Install opencv dependencies RUN apt-get update && \ apt-get -y install --no-install-recommends libglib2.0 \ libsm6 \ libxext6 \ libxrender-dev && \ rm -rf /var/lib/apt/lists/* # Ignite vision dependencies RUN pip install --upgrade --no-cache-dir albumentations \ image-dataset-viz \ numpy \ opencv-python \ py_config_runner \ pillow \ clearml ignite-0.4.3/docker/msdp/build_all.sh000066400000000000000000000025661401006565000175000ustar00rootroot00000000000000#!/bin/bash echo "Build all MS DeepSpeed flavoured PyTorch-Ignite images" 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 $limit ]; then exit 1 fi } # Start script from ignite docker folder if [ ! -d msdp ]; then echo "Can not find 'msdp' folder" echo "Usage: sh msdp/build_all.sh" exit 1 fi curr_dir=$PWD cd $curr_dir/msdp set -eu image_tag="" if [[ -z "${PTH_VERSION}" ]]; then echo "PTH_VERSION is not set" exit 1 fi if [[ -z "${MSDP_VERSION}" ]]; then echo "MSDP_VERSION is not set" exit 1 fi pth_version=${PTH_VERSION} msdp_version=${MSDP_VERSION} for image_name in "msdp-apex" "msdp-apex-vision" "msdp-apex-nlp" do retry "docker build --build-arg PTH_VERSION=${pth_version} --build-arg MSDP_VERSION=${msdp_version} -t pytorchignite/${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} done cd $curr_dir ignite-0.4.3/docker/push_all.sh000066400000000000000000000034401401006565000164050ustar00rootroot00000000000000#!/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 set -eu echo $DOCKER_TOKEN | docker login --username=$DOCKER_USER --password-stdin set -xeu 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 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 # If use locally, mind to clean dangling images # docker images | grep 'pytorchignite\|' | awk '{print $3}' | xargs docker rmi -f # or # docker image prune ignite-0.4.3/docs/000077500000000000000000000000001401006565000137225ustar00rootroot00000000000000ignite-0.4.3/docs/Makefile000066400000000000000000000020221401006565000153560ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = ignite SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(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 http://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 .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-0.4.3/docs/make.bat000066400000000000000000000014561401006565000153350ustar00rootroot00000000000000@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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{% if not embedded %} {% if sphinx_version >= "1.8.0" %} {%- for scriptfile in script_files %} {{ js_tag(scriptfile) }} {%- endfor %} {% else %} {%- for scriptfile in script_files %} {%- endfor %} {% endif %} {% endif %} {%- block footer %} {% endblock %} {% include "cookie_banner.html" %} ignite-0.4.3/docs/source/_templates/theme_variables.jinja000066400000000000000000000006641401006565000235340ustar00rootroot00000000000000{%- set external_urls = { 'github': 'https://github.com/pytorch/ignite', 'github_issues': 'https://github.com/pytorch/ignite/issues', 'contributing': 'https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md', 'docs': 'https://pytorch.org/ignite/index.html', 'home': 'https://pytorch.org/ignite', 'concepts': 'concepts.html', 'quickstart': 'quickstart.html', 'examples': 'examples.html', 'faq': 'faq.html' } -%}ignite-0.4.3/docs/source/about.rst000066400000000000000000000077011401006565000170730ustar00rootroot00000000000000About us ======== History ------- This project was started by Alykhan Tejani `@alykhantejani `_, Francisco Massa `@fmassa `_ and Joost van Amersfoort `@y0ast `_ in the end of 2017 and was highly inspired by `torchnet `_. In 2018 project core teams has grown and the first public release was published in June 2018. Since then, several releases have appeared following a ~5 month cycle, and a thriving international community has been leading the development. Governance ---------- This project is purely open-source community effort. Project's affiliation to `PyTorch group `_ is due to historical reasons and it has the same implication to the project's governance as for other community projects from `PyTorch Ecosystem `_. The decision making process and governance structure of the project is described in the `governance document `_. Authors ------- The following people are currently core contributors to PyTorch-Igniteโ€™s development and maintenance: - Victor Fomin `@vfdev-5 `_ - Anmol Joshi `@anmolsjoshi `_ - Sylvain Desroziers `@sdesrozis `_ Emeritus Core Developers ------------------------ The following people have been active contributors in the past, but are no longer active in the project: - Alykhan Tejani `@alykhantejani `_ - Jason Kriss `@jasonkriss `_ Join Core Team -------------- We are looking for motivated contributors to become collaborators and help out with the project. We can start considering a candidate after several successfully merged Github pull requests. If you are interested, for more details, please, contact Victor (@vfdev-5) on `PyTorch Slack `_ or via email `vfdev.5` at `gmail.com`. Citing PyTorch-Ignite --------------------- If you use pytorch-ignite in a scientific publication, we would appreciate citations to the project. .. code-block:: text @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}}, } Acknowledgements ---------------- We gratefully acknowledge the contributions of `all open-source developers `_ who helped get this project off of the ground and supported the project. We especially appreciate amazing work of Isabela Presedo-Floyd `@isabela-pf `_ on project's logo, its `elaboration `_ and `guidelines `_. Our new logos can be found `here `_. We are also grateful to Soumith Chintala `@soumith `_, Joe Spisak `@jspisak `_ from Facebook for their help and to `PyTorch group `_ for infrastructure support (GPU CI and hosting of conda releases and our documentation). In particular, Victor Fomin `@vfdev-5 `_ acknowledges `Magellium `_ and `Quansight `_ for supporting project's development. Sylvain Desroziers `@sdesrozis `_ also gratefully acknowledges the support of `IFPEN `_. We also acknowledge `scikit-learn project `_'s documentation on governance, author's presentation etc from which this project is highly inspired. ignite-0.4.3/docs/source/concepts.rst000066400000000000000000000371071401006565000176020ustar00rootroot00000000000000Concepts ======== Engine ------ The **essence** of the framework is the class :class:`~ignite.engine.engine.Engine`, an abstraction that loops a given number of times over provided data, executes a processing function and returns a result: .. code-block:: python while epoch < max_epochs: # run an epoch on data data_iter = iter(data) while True: try: batch = next(data_iter) output = process_function(batch) iter_counter += 1 except StopIteration: data_iter = iter(data) if iter_counter == epoch_length: break Thus, a model trainer is simply an engine that loops multiple times over the training dataset and updates model parameters. Similarly, model evaluation can be done with an engine that runs a single time over the validation dataset and computes metrics. For example, model trainer for a supervised task: .. code-block:: python def train_step(trainer, batch): model.train() optimizer.zero_grad() x, y = prepare_batch(batch) y_pred = model(x) loss = loss_fn(y_pred, y) loss.backward() optimizer.step() return loss.item() trainer = Engine(train_step) trainer.run(data, max_epochs=100) The type of output of the training step (i.e. ``loss.item()`` in the above example) is not restricted. Training step function can return everything user wants. Output is set to ``trainer.state.output`` and can be used further for any type of processing. .. Note :: By default, epoch length is defined by ``len(data)``. However, a user can also manually define the epoch length as a number of iterations to loop over. In this way, the input data can be an iterator. .. code-block:: python trainer.run(data, max_epochs=100, epoch_length=200) If data is a finite data iterator with unknown length (for user), argument ``epoch_length`` can be omitted and it will be automatically determined when data iterator is exhausted. **Training logic of any complexity** can be coded with ``train_step`` method and a trainer can be constructed using this method. Argument ``batch`` in ``train_step`` function is user-defined and can contain any data required for a single iteration. .. code-block:: python model_1 = ... model_2 = ... # ... optimizer_1 = ... optimizer_2 = ... # ... criterion_1 = ... criterion_2 = ... # ... def train_step(trainer, batch): data_1 = batch["data_1"] data_2 = batch["data_2"] # ... model_1.train() optimizer_1.zero_grad() loss_1 = forward_pass(data_1, model_1, criterion_1) loss_1.backward() optimizer_1.step() # ... model_2.train() optimizer_2.zero_grad() loss_2 = forward_pass(data_2, model_2, criterion_2) loss_2.backward() optimizer_2.step() # ... # User can return any type of structure. return { "loss_1": loss_1, "loss_2": loss_2, # ... } trainer = Engine(train_step) trainer.run(data, max_epochs=100) For multi-models training examples like GAN's, please, see our :doc:`examples`. Events and Handlers ------------------- To improve the :class:`~ignite.engine.engine.Engine`'s flexibility, an event system is introduced which facilitates interaction on each step of the run: - *engine is started/completed* - *epoch is started/completed* - *batch iteration is started/completed* Complete list of events can be found at :class:`~ignite.engine.events.Events`. Thus, a user can execute a custom code as an event handler. Handlers can be any function: e.g. lambda, simple function, class method etc. The first argument can be optionally `engine`, but not necessarily. Let us consider in more detail what happens when :meth:`~ignite.engine.engine.Engine.run` is called: .. code-block:: python fire_event(Events.STARTED) while epoch < max_epochs: fire_event(Events.EPOCH_STARTED) # run once on data for batch in data: fire_event(Events.ITERATION_STARTED) output = process_function(batch) fire_event(Events.ITERATION_COMPLETED) fire_event(Events.EPOCH_COMPLETED) fire_event(Events.COMPLETED) At first, *"engine is started"* event is fired and all its event handlers are executed (we will see in the next paragraph how to add event handlers). Next, `while` loop is started and *"epoch is started"* event occurs, etc. Every time an event is fired, attached handlers are executed. Attaching an event handler is simple using method :meth:`~ignite.engine.engine.Engine.add_event_handler` or :meth:`~ignite.engine.engine.Engine.on` decorator: .. code-block:: python trainer = Engine(update_model) trainer.add_event_handler(Events.STARTED, lambda _: print("Start training")) # or @trainer.on(Events.STARTED) def on_training_started(engine): print("Another message of start training") # or even simpler, use only what you need ! @trainer.on(Events.STARTED) def on_training_started(): print("Another message of start training") # attach handler with args, kwargs mydata = [1, 2, 3, 4] def on_training_ended(data): print(f"Training is ended. mydata={data}") trainer.add_event_handler(Events.COMPLETED, on_training_ended, mydata) Event handlers can be detached via :meth:`~ignite.engine.engine.Engine.remove_event_handler` or via the :class:`~ignite.engine.events.RemovableEventHandle` reference returned by :meth:`~ignite.engine.engine.Engine.add_event_handler`. This can be used to reuse a configured engine for multiple loops: .. code-block:: python model = ... train_loader, validation_loader, test_loader = ... trainer = create_supervised_trainer(model, optimizer, loss) evaluator = create_supervised_evaluator(model, metrics={"acc": Accuracy()}) def log_metrics(engine, title): print(f"Epoch: {trainer.state.epoch} - {title} accuracy: {engine.state.metrics['acc']:.2f}") @trainer.on(Events.EPOCH_COMPLETED) def evaluate(trainer): with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "train"): evaluator.run(train_loader) with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "validation"): evaluator.run(validation_loader) with evaluator.add_event_handler(Events.COMPLETED, log_metrics, "test"): evaluator.run(test_loader) trainer.run(train_loader, max_epochs=100) Event handlers can be also configured to be called with a user pattern: every n-th events, once or using a custom event filtering function: .. code-block:: python model = ... train_loader, validation_loader, test_loader = ... trainer = create_supervised_trainer(model, optimizer, loss) @trainer.on(Events.ITERATION_COMPLETED(every=50)) def log_training_loss_every_50_iterations(): print(f"{trainer.state.epoch} / {trainer.state.max_epochs} : {trainer.state.iteration} - loss: {trainer.state.output:.2f}") @trainer.on(Events.EPOCH_STARTED(once=25)) def do_something_once_on_25_epoch(): # do something 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 trainer.run(train_loader, max_epochs=100) Custom events `````````````` The user can also define custom events. Events defined by user should inherit from :class:`~ignite.engine.events.EventEnum` and be registered with :meth:`~ignite.engine.engine.Engine.register_events` in an `engine`. .. code-block:: python from ignite.engine import EventEnum class CustomEvents(EventEnum): """ Custom events defined by user """ CUSTOM_STARTED = 'custom_started' CUSTOM_COMPLETED = 'custom_completed' engine.register_events(*CustomEvents) These events could be used to attach any handler and are fired using :meth:`~ignite.engine.engine.Engine.fire_event`. .. code-block:: python @engine.on(CustomEvents.CUSTOM_STARTED) def call_on_custom_event(engine): # do something @engine.on(Events.STARTED) def fire_custom_events(engine): engine.fire_event(CustomEvents.CUSTOM_STARTED) .. Note :: See the source code of :class:`~ignite.contrib.engines.create_supervised_tbptt_trainer` for an example of usage of custom events. If you want to use filtering with custom events (e.g. ``CustomEvents.CUSTOM_STARTED(every=5)``), you need to do 3 more things: - ``engine.state`` should have corresponding attributes for the events, e.g. ``engine.state.custom_started`` - you need to pass a dict `event_to_attr` to :meth:`~ignite.engine.engine.Engine.register_events`, which maps between events and state attributes, e.g. .. code-block:: python event_to_attr = { CustomEvents.CUSTOM_STARTED: "custom_started", CustomEvents.CUSTOM_COMPLETED: "custom_completed", } - you should increase the counter for the event whenever you fire the event, e.g. ``engine.state.custom_started += 1`` .. warning:: This solution for filtering is a temporary workaround and may change in the future. Handlers ```````` Library provides a set of built-in handlers to checkpoint the training pipeline, to save best models, to stop training if no improvement, to use experiment tracking system, etc. They can be found in the following two modules: - :doc:`handlers` - :doc:`contrib/handlers` Some classes can be simply added to :class:`~ignite.engine.engine.Engine` as a callable function. For example, .. code-block:: python from ignite.handlers import TerminateOnNan trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan()) Others provide an ``attach`` method to internally add several handlers to :class:`~ignite.engine.engine.Engine`: .. code-block:: python from ignite.contrib.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 as a histogram after each epoch tb_logger.attach( trainer, event_name=Events.EPOCH_COMPLETED, log_handler=WeightsHistHandler(model) ) Timeline and events ``````````````````` Below the events and some typical handlers are displayed on a timeline for a training loop with evaluation after every epoch: .. image:: _static/img/concepts/timeline_and_events.png :target: _static/img/concepts/timeline_and_events.png State ----- A state is introduced in :class:`~ignite.engine.engine.Engine` to store the output of the `process_function`, current epoch, iteration and other helpful information. Each :class:`~ignite.engine.engine.Engine` contains a :class:`~ignite.engine.events.State`, which includes the following: - **engine.state.seed**: Seed to set at each data "epoch". - **engine.state.epoch**: Number of epochs the engine has completed. Initializated as 0 and the first epoch is 1. - **engine.state.iteration**: Number of iterations the engine has completed. Initialized as 0 and the first iteration is 1. - **engine.state.max_epochs**: Number of epochs to run for. Initializated as 1. - **engine.state.output**: The output of the `process_function` defined for the :class:`~ignite.engine.engine.Engine`. See below. - etc Other attributes can be found in the docs of :class:`~ignite.engine.events.State`. In the code below, `engine.state.output` will store the batch loss. This output is used to print the loss at every iteration. .. code-block:: python def update(engine, batch): x, y = batch y_pred = model(inputs) loss = loss_fn(y_pred, y) optimizer.zero_grad() loss.backward() optimizer.step() return loss.item() def on_iteration_completed(engine): iteration = engine.state.iteration epoch = engine.state.epoch loss = engine.state.output print(f"Epoch: {epoch}, Iteration: {iteration}, Loss: {loss}") trainer.add_event_handler(Events.ITERATION_COMPLETED, on_iteration_completed) Since there is no restrictions on the output of `process_function`, Ignite provides `output_transform` argument for its :class:`~ignite.metrics` and :class:`~ignite.handlers`. Argument `output_transform` is a function used to transform `engine.state.output` for intended use. Below we'll see different types of `engine.state.output` and how to transform them. In the code below, `engine.state.output` will be a list of loss, y_pred, y for the processed batch. If we want to attach :class:`~ignite.metrics.Accuracy` to the engine, `output_transform` will be needed to get y_pred and y from `engine.state.output`. Let's see how that is done: .. code-block:: python def update(engine, batch): x, y = batch y_pred = model(inputs) loss = loss_fn(y_pred, y) optimizer.zero_grad() loss.backward() optimizer.step() return loss.item(), y_pred, y trainer = Engine(update) @trainer.on(Events.EPOCH_COMPLETED) def print_loss(engine): epoch = engine.state.epoch loss = engine.state.output[0] print (f'Epoch {epoch}: train_loss = {loss}') accuracy = Accuracy(output_transform=lambda x: [x[1], x[2]]) accuracy.attach(trainer, 'acc') trainer.run(data, max_epochs=10) Similar to above, but this time the output of the `process_function` is a dictionary of loss, y_pred, y for the processed batch, this is how the user can use `output_transform` to get y_pred and y from `engine.state.output`. See below: .. code-block:: python def update(engine, batch): x, y = batch y_pred = model(inputs) loss = loss_fn(y_pred, y) optimizer.zero_grad() loss.backward() optimizer.step() return {'loss': loss.item(), 'y_pred': y_pred, 'y': y} trainer = Engine(update) @trainer.on(Events.EPOCH_COMPLETED) def print_loss(engine): epoch = engine.state.epoch loss = engine.state.output['loss'] print (f'Epoch {epoch}: train_loss = {loss}') accuracy = Accuracy(output_transform=lambda x: [x['y_pred'], x['y']]) accuracy.attach(trainer, 'acc') trainer.run(data, max_epochs=10) .. Note :: A good practice is to use :class:`~ignite.engine.events.State` also as a storage of user data created in update or handler functions. For example, we would like to save `new_attribute` in the `state`: .. code-block:: python def user_handler_function(engine): engine.state.new_attribute = 12345 Metrics ------- Library provides a list of out-of-the-box metrics for various Machine Learning tasks. Two way of computing metrics are supported : 1) online and 2) storing the entire output history. Metrics can be attached to :class:`~ignite.engine.engine.Engine`: .. code-block:: python from ignite.metrics import Accuracy accuracy = Accuracy() accuracy.attach(evaluator, "accuracy") state = evaluator.run(validation_data) print("Result:", state.metrics) # > {"accuracy": 0.12345} or can be used as stand-alone objects: .. code-block:: python from ignite.metrics import Accuracy accuracy = Accuracy() accuracy.reset() for y_pred, y in get_prediction_target(): accuracy.update((y_pred, y)) print("Result:", accuracy.compute()) Complete list of metrics and the API can be found in :doc:`metrics` module. **Where to go next?** Checkout our :doc:`examples` and tutorial notebooks. ignite-0.4.3/docs/source/conf.py000066400000000000000000000237641401006565000165350ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/stable/config # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath("../..")) import ignite import pytorch_sphinx_theme from datetime import datetime # -- Project information ----------------------------------------------------- project = "ignite" author = "PyTorch-Ignite Contributors" copyright = f"{datetime.now().year}, {author}" # The short X.Y version try: version = os.environ["code_version"] if "master" in version: version = "master (" + ignite.__version__ + ")" else: version = version.replace("v", "") except KeyError: version = ignite.__version__ # The full version, including alpha/beta/rc tags release = "master" # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autosummary", "sphinx.ext.doctest", "sphinx.ext.intersphinx", "sphinx.ext.todo", "sphinx.ext.coverage", "sphinxcontrib.katex", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.autosectionlabel", ] # katex options katex_prerender = True # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # The language for content autogenerated by Sphinx. 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 = None # 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_theme = "pytorch_sphinx_theme" html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] html_theme_options = { "canonical_url": "https://pytorch.org/ignite/index.html", "collapse_navigation": False, "display_version": True, "logo_only": 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 = { "css_files": [ # 'https://fonts.googleapis.com/css?family=Lato', # '_static/css/pytorch_theme.css' "_static/css/ignite_theme.css" ], } # -- 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_typehints = "signature" # -- A patch that turns-off cross refs for type annotations ------------------ import sphinx.domains.python from docutils import nodes from sphinx import addnodes # replaces pending_xref node with desc_type for type annotations sphinx.domains.python.type_to_xref = lambda t, e=None: addnodes.desc_type("", nodes.Text(t)) # -- Autosummary patch to get list of a classes, funcs automatically ---------- from importlib import import_module from inspect import getmembers, isclass, isfunction import sphinx.ext.autosummary from sphinx.ext.autosummary import Autosummary from docutils.parsers.rst import directives from docutils.statemachine import StringList class BetterAutosummary(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 names = [name for name in names if getattr(module, name).__doc__ is not None] 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() # Patch original Autosummary sphinx.ext.autosummary.Autosummary = BetterAutosummary ignite-0.4.3/docs/source/contrib/000077500000000000000000000000001401006565000166625ustar00rootroot00000000000000ignite-0.4.3/docs/source/contrib/engines.rst000066400000000000000000000012051401006565000210420ustar00rootroot00000000000000ignite.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-0.4.3/docs/source/contrib/handlers.rst000066400000000000000000000362041401006565000212210ustar00rootroot00000000000000ignite.contrib.handlers ======================= Contribution module of handlers custom_events ------------- .. automodule:: ignite.contrib.handlers.custom_events :members: param_scheduler --------------- .. currentmodule:: ignite.contrib.handlers.param_scheduler .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.param_scheduler :members: lr_finder --------- .. currentmodule:: ignite.contrib.handlers.lr_finder .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.lr_finder :members: time_profilers --------------- .. currentmodule:: ignite.contrib.handlers.time_profilers .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.time_profilers :members: stores ------- .. currentmodule:: ignite.contrib.handlers.stores .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.stores :members: tensorboard_logger ------------------ See `tensorboardX mnist example `_ and `CycleGAN and EfficientNet notebooks `_ for detailed usage. .. currentmodule:: ignite.contrib.handlers.tensorboard_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.tensorboard_logger :members: :inherited-members: visdom_logger ------------- See `visdom mnist example `_ for detailed usage. .. currentmodule:: ignite.contrib.handlers.visdom_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.visdom_logger :members: :inherited-members: neptune_logger ------------------ See `neptune mnist example `_ for detailed usage. .. currentmodule:: ignite.contrib.handlers.neptune_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.neptune_logger :members: :inherited-members: mlflow_logger ------------- .. currentmodule:: ignite.contrib.handlers.mlflow_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.mlflow_logger :members: :inherited-members: tqdm_logger ----------- See `tqdm mnist example `_ for detailed usage. .. currentmodule:: ignite.contrib.handlers.tqdm_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.tqdm_logger :members: polyaxon_logger --------------- .. currentmodule:: ignite.contrib.handlers.polyaxon_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.polyaxon_logger :members: :inherited-members: wandb_logger --------------- See `wandb mnist example `_ for detailed usage. .. currentmodule:: ignite.contrib.handlers.wandb_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.wandb_logger :members: :inherited-members: clearml_logger --------------- See `clearml mnist example `_ for detailed usage. .. currentmodule:: ignite.contrib.handlers.clearml_logger .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.handlers.clearml_logger :members: :inherited-members: trains_logger -------------- .. note:: ``trains_logger`` was renamed to ``clearml_logger``. Please refer to :ref:`clearml_logger`. More on parameter scheduling ---------------------------- In this section there are visual examples of various parameter schedulings that can be achieved. Example with :class:`ignite.contrib.handlers.CosineAnnealingScheduler` `````````````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.contrib.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.contrib.handlers.LinearCyclicalScheduler` ````````````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.contrib.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.contrib.handlers.ConcatScheduler` ````````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.contrib.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.contrib.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.contrib.handlers.LRScheduler` ````````````````````````````````````````````````````````` .. code-block:: python import numpy as np import matplotlib.pylab as plt from ignite.contrib.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.contrib.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 ignite-0.4.3/docs/source/contrib/metrics.rst000066400000000000000000000025641401006565000210710ustar00rootroot00000000000000ignite.contrib.metrics ====================== Contribution module of metrics .. currentmodule:: ignite.contrib.metrics .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.contrib.metrics :members: :imported-members: Regression metrics ------------------ .. currentmodule:: ignite.contrib.metrics.regression .. automodule:: ignite.contrib.metrics.regression Module :mod:`ignite.contrib.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". .. _`Botchkarev 2018`: https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf Complete list of metrics: .. currentmodule:: ignite.contrib.metrics.regression .. autosummary:: :nosignatures: :autolist: .. autoclass:: CanberraMetric .. autoclass:: FractionalAbsoluteError .. autoclass:: FractionalBias .. autoclass:: GeometricMeanAbsoluteError .. autoclass:: GeometricMeanRelativeAbsoluteError .. autoclass:: ManhattanDistance .. autoclass:: MaximumAbsoluteError .. autoclass:: MeanAbsoluteRelativeError .. autoclass:: MeanError .. autoclass:: MeanNormalizedBias .. autoclass:: MedianAbsoluteError .. autoclass:: MedianAbsolutePercentageError .. autoclass:: MedianRelativeAbsoluteError .. autoclass:: R2Score .. autoclass:: WaveHedgesDistance ignite-0.4.3/docs/source/distributed.rst000066400000000000000000000067571401006565000203150ustar00rootroot00000000000000ignite.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 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: :autolist: .. automodule:: ignite.distributed.auto :members: ignite.distributed.launcher --------------------------- .. currentmodule:: ignite.distributed.launcher .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.distributed.launcher :members: 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-0.4.3/docs/source/engine.rst000066400000000000000000000226111401006565000172230ustar00rootroot00000000000000ignite.engine ============== Main module of the library containing: ignite.engine.engine .. currentmodule:: ignite.engine.engine .. autosummary:: :nosignatures: :autolist: ignite.engine.events .. currentmodule:: ignite.engine.events .. autosummary:: :nosignatures: :autolist: ignite.engine.deterministic (helper methods for deterministic training) .. currentmodule:: ignite.engine.deterministic .. autosummary:: :nosignatures: :autolist: and helper methods to define supervised trainer and evaluator: .. currentmodule:: ignite.engine .. autosummary:: :nosignatures: :autolist-functions: More details about those structures can be found in :doc:`concepts`. .. currentmodule:: ignite.engine.engine .. autoclass:: Engine :members: .. autofunction:: ignite.engine.create_supervised_trainer .. autofunction:: ignite.engine.create_supervised_evaluator 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` 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 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 = ... to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler} 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 = ... to_load = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler} 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 `_ ignite.engine.events -------------------- .. currentmodule:: ignite.engine.events .. autoclass:: CallableEventWithFilter :members: .. autoclass:: Events :members: .. autoclass:: EventEnum .. autoclass:: State .. autoclass:: RemovableEventHandle :members: :undoc-members: ignite.engine.deterministic --------------------------- 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` .. currentmodule:: ignite.engine.deterministic .. automodule:: ignite.engine.deterministic :members: 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-0.4.3/docs/source/examples.rst000066400000000000000000000075541401006565000176050ustar00rootroot00000000000000Examples ======== We provide several examples using `ignite` to display how it helps to write compact and full-featured training loops in several lines of code: MNIST example ------------- Basic neural network training on MNIST dataset with/without :mod:`ignite.contrib` module: - `MNIST with ignite.contrib TQDM/Tensorboard/Visdom loggers `_ - `MNIST with native TQDM/Tensorboard/Visdom logging `_ These examples are ported from `pytorch/examples `_. Distributed examples -------------------- Training a ResNet on CIFAR10 in various configurations: 1) single gpu 2) single node multiple gpus 3) multiple nodes and multiple gpus 4) single or multiple TPUs - `CIFAR10 `_ - This example displays usage of :doc:`distributed` helper module. Other examples -------------- - `DCGAN `_ - plain Deep Convolution Generative Adversarial Networks training - `Reinforcement Learning `_ - Actor/Critic and Reinforce methods on Cart-Pole task - `Fast Neural Style `_ - Artistic style transfer implementation. These examples are ported from `pytorch/examples `_. Notebooks --------- - `Text Classification using Convolutional Neural Networks `_ - `Variational Auto Encoders `_ - `Training Cycle-GAN on Horses to Zebras with Nvidia/Apex `_ - `Another training Cycle-GAN on Horses to Zebras with Native Torch CUDA AMP `_ - `Finetuning EfficientNet-B0 on CIFAR100 `_ - `Convolutional Neural Networks for Classifying Fashion-MNIST Dataset `_ - `Hyperparameters tuning with Ax `_ - `Basic example of LR finder on MNIST `_ - `Benchmark mixed precision training on Cifar100: torch.cuda.amp vs nvidia/apex `_ - `MNIST training on a single TPU `_ All notebooks can be opened on Google Colab with a link: .. code-block:: text https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/ Reproducible trainings ---------------------- Inspired by `torchvision/references `_, we provide several reproducible baselines for vision tasks: - `ImageNet `_ - `Pascal VOC2012 `_ Features: - Distributed training with mixed precision by `nvidia/apex `_ - Experiments tracking with `MLflow `_ or `Polyaxon `_ or `ClearML `_ ignite-0.4.3/docs/source/exceptions.rst000066400000000000000000000002411401006565000201320ustar00rootroot00000000000000ignite.exceptions ================= .. currentmodule:: ignite.exceptions .. autosummary:: :nosignatures: :autolist: .. autoclass:: NotComputableError ignite-0.4.3/docs/source/faq.rst000066400000000000000000000350051401006565000165260ustar00rootroot00000000000000FAQ === In this section we grouped answers on frequently asked questions and some best practices of using `ignite`. Each engine has its own Events ------------------------------ It is important to understand that engines have their own events. For example, we defined a trainer and an evaluator: .. code-block:: python @trainer.on(Events.EPOCH_COMPLETED) def in_training_loop_on_epoch_completed(engine): evaluator.run(val_loader) # this starts another loop on validation dataset to compute metrics @evaluator.on(Events.COMPLETED) def when_validation_loop_is_done(engine): # do something with computed metrics etc # -> early stopping or reduce LR on plateau # or just log them Trainer engine has its own loop and runs multiple times over the training dataset. When a training epoch is over we launch evaluator engine and run a single time of over the validation dataset. **Evaluator has its own loop**. Therefore, it runs only one epoch and `Events.EPOCH_COMPLETED` is equivalent to `Events.COMPLETED`. As a consequence, the following code is correct too: .. code-block:: python handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer) evaluator.add_event_handler(Events.COMPLETED, handler) best_model_saver = ModelCheckpoint('/tmp/models', 'best', score_function=score_function) evaluator.add_event_handler(Events.COMPLETED, best_model_saver, {'mymodel': model}) More details :ref:`Events and Handlers`. Creating Custom Events based on Forward/Backward Pass ----------------------------------------------------- There are cases where the user might want to add events based on the loss calculation and backward pass. Ignite provides flexibility to the user to allow for this: .. code-block:: python from ignite.engine import EventEnum class BackpropEvents(EventEnum): """ Events based on back propagation """ BACKWARD_STARTED = 'backward_started' BACKWARD_COMPLETED = 'backward_completed' OPTIM_STEP_COMPLETED = 'optim_step_completed' def update(engine, batch): model.train() opitmizer.zero_grad() x, y = process_batch(batch) y_pred = model(x) loss = loss_fn(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) return loss.item() trainer = Engine(update) trainer.register_events(*BackpropEvents) @trainer.on(BackpropEvents.BACKWARD_STARTED) def function_before_backprop(engine): # insert custom function here .. Note :: Events defined by user should inherit from :class:`~ignite.engine.events.EventEnum` More detailed implementation can be found in `TBPTT Trainer <_modules/ignite/contrib/engines/tbptt.html#create_supervised_tbptt_trainer>`_. Gradients accumulation ---------------------- A best practice to use if we need to increase effectively the batch size on limited GPU resources. There several ways to do this, the most simple is the following: .. code-block:: python accumulation_steps = 4 def update_fn(engine, batch): model.train() x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) y_pred = model(x) loss = criterion(y_pred, y) / accumulation_steps loss.backward() if engine.state.iteration % accumulation_steps == 0: optimizer.step() optimizer.zero_grad() return loss.item() trainer = Engine(update_fn) Based on `this blog article `_ and `this code `_. Working with iterators ---------------------- If data provider for training or validation is an iterator (infinite or finite with known or unknown size), here are basic examples of how to setup trainer or evaluator. Infinite iterator for training `````````````````````````````` Let's use an infinite data iterator as training dataflow .. code-block:: python import torch from ignite.engine import Engine, Events 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) In this case we will obtain the following output: .. code-block:: text 1/3 : 1 - 63.862 1/3 : 2 - 64.042 1/3 : 3 - 63.936 1/3 : 4 - 64.141 1/3 : 5 - 64.767 2/3 : 6 - 63.791 2/3 : 7 - 64.565 2/3 : 8 - 63.602 2/3 : 9 - 63.995 2/3 : 10 - 63.943 3/3 : 11 - 63.831 3/3 : 12 - 64.276 3/3 : 13 - 64.148 3/3 : 14 - 63.920 3/3 : 15 - 64.226 If we do not specify `epoch_length`, we can stop the training explicitly by calling :meth:`~ignite.engine.engine.Engine.terminate` In this case, there will be only a single epoch defined. .. code-block:: python import torch from ignite.engine import Engine, Events 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)) We obtain the following output: .. code-block:: text 1/1 : 1 - 63.862 1/1 : 2 - 64.042 1/1 : 3 - 63.936 1/1 : 4 - 64.141 1/1 : 5 - 64.767 1/1 : 6 - 63.791 1/1 : 7 - 64.565 1/1 : 8 - 63.602 1/1 : 9 - 63.995 1/1 : 10 - 63.943 1/1 : 11 - 63.831 1/1 : 12 - 64.276 1/1 : 13 - 64.148 1/1 : 14 - 63.920 1/1 : 15 - 64.226 Same code can be used for validating models. Finite iterator with unknown length ``````````````````````````````````` Let's use a finite data iterator but with unknown length (for user). In case of training, we would like to perform several passes over the dataflow and thus we need to restart the data iterator when it is exhausted. In the code, we do not specify `epoch_length` which will be automatically determined. .. code-block:: python import torch from ignite.engine import Engine, Events 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) In case of validation, the code is simply .. code-block:: python import torch from ignite.engine import Engine, Events 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) Finite iterator with known length ````````````````````````````````` Let's use a finite data iterator with known size for training or validation. If we need to restart the data iterator, we can do this either as in case of unknown size by attaching the restart handler on `@trainer.on(Events.DATALOADER_STOP_ITERATION)`, but here we will do this explicitly on iteration: .. code-block:: python import torch from ignite.engine import Engine, Events 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) In case of validation, the code is simply .. code-block:: python import torch from ignite.engine import Engine, Events 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) Switching data provider during the training ------------------------------------------- User can easily switch data provider during the training using :meth:`~ignite.engine.engine.Engine.set_data`. See an example in the documentation of the method. Time profiling during training ------------------------------ User can fetch times in several manners depending on complexity of required time profiling: Single epoch and total time ``````````````````````````` Simpliest way to fetch time of single epoch and complete training is to use ``engine.state.times["EPOCH_COMPLETED"]`` and ``engine.state.times["COMPLETED"]``: .. code-block:: python trainer = ... @trainer.on(Events.EPOCH_COMPLETED) def log_epoch_time(): print(f"{trainer.state.epoch}: {trainer.state.times['EPOCH_COMPLETED']}") @trainer.on(Events.COMPLETED) def log_total_time(): print(f"Total: {trainer.state.times['COMPLETED']}") For details, see :class:`~ignite.engine.events.State`. Basic time profiling ```````````````````` User can setup :class:`~ignite.contrib.handlers.time_profilers.BasicTimeProfiler` to fetch times spent in data processing, training step, event handlers: .. code-block:: python from ignite.contrib.handlers import BasicTimeProfiler trainer = ... # Create an object of the profiler and attach an engine to it profiler = BasicTimeProfiler() profiler.attach(trainer) @trainer.on(Events.EPOCH_COMPLETED(every=10)) def log_intermediate_results(): profiler.print_results(profiler.get_results()) trainer.run(dataloader, max_epochs=3) Typical output: .. 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 For details, see :class:`~ignite.contrib.handlers.time_profilers.BasicTimeProfiler`. Event handlers time profiling ````````````````````````````` If you want to get time breakdown per handler basis then you can setup :class:`~ignite.contrib.handlers.time_profilers.HandlersTimeProfiler`: .. code-block:: python from ignite.contrib.handlers import HandlersTimeProfiler trainer = ... # Create an object of the profiler and attach an engine to it profiler = HandlersTimeProfiler() profiler.attach(trainer) @trainer.on(Events.EPOCH_COMPLETED(every=10)) def log_intermediate_results(): profiler.print_results(profiler.get_results()) trainer.run(dataloader, max_epochs=3) Typical output: .. 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] For details, see :class:`~ignite.contrib.handlers.time_profilers.HandlersTimeProfiler`. Custom time measures ```````````````````` Custom time measures can be performed using :class:`~ignite.handlers.Timer`. See its docstring for details. Other questions --------------- Other questions and answers can be also found on the github among the issues labeled by `question `_ and on the forum `Discuss.PyTorch `_, category "Ignite". ignite-0.4.3/docs/source/governance.rst000066400000000000000000000041321401006565000201030ustar00rootroot00000000000000PyTorch-Ignite governance ========================= The purpose of this document is to formalize the governance process used by the PyTorch-Ignite project, to clarify how decisions are made and how the various elements of our community interact. This document establishes a decision-making structure that takes into account feedback from all members of the community and strives to find consensus, while avoiding any deadlocks. This is a meritocratic, consensus-based community project. Anyone with an interest in the project can join the community, contribute to the project design and participate in the decision making process. This document describes how that participation takes place and how to set about earning merit within the project community. Roles And Responsibilities -------------------------- Contributors ```````````` Contributors are community members who contribute in concrete ways to the project. Anyone can become a contributor, and contributions can take many forms as detailed in the `contributors guide `_. Core developers ``````````````` Core developers are community members who have shown their continued implication in the project through ongoing engagement with the community and contributions. They have shown they can be trusted to maintain PyTorch-Ignite with care. Being a core developer allows contributors to more easily carry on with their project related activities by giving them direct access to the projectโ€™s repository and is represented as being an organization member on the PyTorch-Ignite GitHub organization. Core developers are expected to review code contributions, can merge approved pull requests, can cast votes for and against merging a pull-request, and can be involved in deciding major changes to the API. New core developers can be nominated by any existing core developers. Core developers that have not contributed to the project (commits or GitHub comments) in the past 2 months will be asked if they want to become emeritus core developers and recant their commit rights until they become active again. ignite-0.4.3/docs/source/handlers.rst000066400000000000000000000011731401006565000175560ustar00rootroot00000000000000ignite.handlers =============== Complete list of handlers ------------------------- .. currentmodule:: ignite.handlers .. autosummary:: :nosignatures: :autolist: .. autoclass:: Checkpoint :members: reset, setup_filename_pattern, load_objects, state_dict, load_state_dict, get_default_score_fn .. autoclass:: ignite.handlers.checkpoint.BaseSaveHandler :members: __call__, remove .. autoclass:: DiskSaver .. autoclass:: ModelCheckpoint .. autoclass:: EarlyStopping .. autoclass:: Timer :members: .. autoclass:: TerminateOnNan .. autofunction:: global_step_from_engine .. autoclass:: TimeLimit :members:ignite-0.4.3/docs/source/index.rst000066400000000000000000000071311401006565000170650ustar00rootroot00000000000000Ignite Your Networks! ===================== :mod:`ignite` is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. .. raw:: html *Click on the image to see complete code* Features -------- - `Less code than pure PyTorch `_ 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 Installation ============ From `pip `_: .. code:: bash pip install pytorch-ignite From `conda `_: .. code:: bash conda install ignite -c pytorch From source: .. code:: bash pip install git+https://github.com/pytorch/ignite Nightly releases ---------------- From pip: .. code:: bash pip install --pre pytorch-ignite From conda (this suggests to install `pytorch nightly release `_ instead of stable version as dependency): .. code:: bash conda install ignite -c pytorch-nightly Docker Images ------------- Using pre-built images ^^^^^^^^^^^^^^^^^^^^^^ Pull a pre-built docker image from `our Docker Hub `_ and run it with docker v19.03+. .. code:: bash docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest Available pre-built images are : - ``pytorchignite/base:latest | pytorchignite/hvd-base:latest | pytorchignite/msdp-apex-base:latest`` - ``pytorchignite/apex:latest | pytorchignite/hvd-apex:latest`` - ``pytorchignite/vision:latest | pytorchignite/hvd-vision:latest | pytorchignite/msdp-apex-vision:latest`` - ``pytorchignite/apex-vision:latest | pytorchignite/hvd-apex-vision:latest`` - ``pytorchignite/nlp:latest | pytorchignite/hvd-nlp:latest | pytorchignite/msdp-apex-nlp:latest`` - ``pytorchignite/apex-nlp:latest | pytorchignite/hvd-apex-nlp:latest`` For more details, see `here `_. Documentation ============= To get started, please, read :doc:`quickstart` and :doc:`concepts`. .. toctree:: :maxdepth: 2 :caption: Notes quickstart concepts examples faq Library structure ================= - :mod:`ignite`: Core of the library, contains an engine for training and evaluating, most of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks. - :mod:`ignite.contrib`: The contrib directory contains additional modules that can require extra dependencies. Modules vary from TBPTT engine, various optimisation parameter schedulers, experiment tracking system handlers and a metrics module containing many regression metrics. .. 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 .. toctree:: :maxdepth: 1 :caption: Team about governance ignite-0.4.3/docs/source/metrics.rst000066400000000000000000000276121401006565000174320ustar00rootroot00000000000000ignite.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. In practice a user needs to attach the metric instance to an engine. The metric value is then 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.reset()` : resets internal variables and accumulators - :meth:`~ignite.metrics.Metric.update()` : updates internal variables and accumulators with provided batch output ``(y_pred, y)`` - :meth:`~ignite.metrics.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` and override three methods : - :meth:`~ignite.metrics.Metric.reset()` : resets internal variables and accumulators - :meth:`~ignite.metrics.Metric.update()` : updates internal variables and accumulators with provided batch output ``(y_pred, y)`` - :meth:`~ignite.metrics.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") 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`, :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.reset()` is triggered every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.Metric.update()` is triggered every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.Metric.compute()` is triggered every ``EPOCH_COMPLETED``. Usages can be user defined by creating a class inheriting for :class:`~ignite.metrics.MetricUsage`. See the list below of usages. Complete list of usages ``````````````````````` - :class:`~ignite.metrics.MetricUsage` - :class:`~ignite.metrics.EpochWise` - :class:`~ignite.metrics.BatchWise` - :class:`~ignite.metrics.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")`` over ``compute`` method. This means that when ``compute`` method is called, metric's interal variables ``self._num_examples`` and ``self._num_correct`` are summed up over all participating devices. Therefore, once collected, these internal variables can be used to compute the final metric value. Complete list of metrics ------------------------ .. currentmodule:: ignite.metrics .. autosummary:: :nosignatures: :autolist: .. autoclass:: Accuracy .. autoclass:: Average .. autoclass:: ConfusionMatrix .. autofunction:: DiceCoefficient .. autoclass:: EpochMetric .. autofunction:: Fbeta .. autoclass:: GeometricAverage .. autofunction:: IoU .. autofunction:: mIoU .. autoclass:: Loss .. autoclass:: MeanAbsoluteError .. autoclass:: MeanPairwiseDistance .. autoclass:: MeanSquaredError .. autoclass:: Metric :members: .. autoclass:: MetricsLambda .. autoclass:: PSNR .. autoclass:: Precision .. autoclass:: Recall .. autoclass:: RootMeanSquaredError .. autoclass:: RunningAverage .. autoclass:: SSIM .. autoclass:: TopKCategoricalAccuracy .. autoclass:: VariableAccumulation .. autoclass:: MetricUsage .. autoclass:: EpochWise .. autoclass:: BatchWise .. autoclass:: BatchFiltered .. currentmodule:: ignite.metrics.metric .. autofunction:: reinit__is_reduced .. autofunction:: sync_all_reduce ignite-0.4.3/docs/source/quickstart.rst000066400000000000000000000167051401006565000201570ustar00rootroot00000000000000Quick start =========== Welcome to **PyTorch-Ignite** quick start guide that just covers the essentials of getting a project up and walking through the code. In several lines of this given code, you can get your model trained and validated as shown below: Code ---- .. code-block:: python from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator from ignite.metrics import Accuracy, Loss model = Net() train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.8) criterion = nn.NLLLoss() trainer = create_supervised_trainer(model, optimizer, criterion) val_metrics = { "accuracy": Accuracy(), "nll": Loss(criterion) } evaluator = create_supervised_evaluator(model, metrics=val_metrics) @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(trainer): print(f"Epoch[{trainer.state.epoch}] Loss: {trainer.state.output:.2f}") @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(trainer): evaluator.run(train_loader) metrics = evaluator.state.metrics print(f"Training Results - Epoch: {trainer.state.epoch} Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['nll']:.2f}") @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(trainer): evaluator.run(val_loader) metrics = evaluator.state.metrics print(f"Validation Results - Epoch: {trainer.state.epoch} Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['nll']:.2f}") trainer.run(train_loader, max_epochs=100) **Note**: Complete code can be found in the file `examples/mnist/mnist.py `_. Explanation ----------- Now let's break up the code and review it in details. In the first 4 lines, we define our model, training and validation datasets (as `torch.utils.data.DataLoader `_), optimizer and loss function: .. code-block:: python model = Net() train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.8) criterion = nn.NLLLoss() Next we define trainer and evaluator engines. In this example, we are using helper methods as :meth:`~ignite.engine.create_supervised_trainer` and :meth:`~ignite.engine.create_supervised_evaluator`: .. code-block:: python trainer = create_supervised_trainer(model, optimizer, criterion) val_metrics = { "accuracy": Accuracy(), "nll": Loss(criterion) } evaluator = create_supervised_evaluator(model, metrics=val_metrics) Objects ``trainer`` and ``evaluator`` are instances of :class:`~ignite.engine.engine.Engine` - main component of Ignite. :class:`~ignite.engine.engine.Engine` is an abstraction over your training/validation loop. In general, we can define ``trainer`` and ``evaluator`` using directly :class:`~ignite.engine.engine.Engine` class and custom training/validation step logic: .. code-block:: python def train_step(engine, batch): model.train() optimizer.zero_grad() x, y = batch[0].to(device), batch[1].to(device) y_pred = model(x) loss = criterion(y_pred, y) loss.backward() optimizer.step() return loss.item() trainer = Engine(train_step) 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 evaluator = Engine(validation_step) Note that the helper function :meth:`~ignite.engine.create_supervised_evaluator` is to create an evaluator which accepts an argument ``metrics``: .. code-block:: python metrics={ 'accuracy': Accuracy(), 'nll': Loss(loss) } Here we define two metrics: *accuracy* and *loss* to compute on validation dataset. More information on metrics can be found at :doc:`metrics`. The most interesting part of the code snippet is adding event handlers. :class:`~ignite.engine.engine.Engine` allows to add handlers on various events that triggers during the run. When an event is triggered, attached handlers (functions) are executed. Thus, for logging purposes we add a function to be executed at the end of every ``log_interval``-th iteration: .. code-block:: python @trainer.on(Events.ITERATION_COMPLETED(every=log_interval)) def log_training_loss(engine): print(f"Epoch[{engine.state.epoch}] Loss: {engine.state.output:.2f}") or equivalently without the decorator .. code-block:: python def log_training_loss(engine): print(f"Epoch[{engine.state.epoch}] Loss: {engine.state.output:.2f}") trainer.add_event_handler(Events.ITERATION_COMPLETED, log_training_loss) When an epoch ends, we want to compute training and validation metrics [#f1]_. For that purpose, we can run previously defined ``evaluator`` on ``train_loader`` and ``val_loader``. Therefore we attach two additional handlers to the trainer on epoch complete event: .. code-block:: python @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(trainer): evaluator.run(train_loader) metrics = evaluator.state.metrics print(f"Training Results - Epoch[{trainer.state.epoch}] Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['nll']:.2f}") @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(trainer): evaluator.run(val_loader) metrics = evaluator.state.metrics print(f"Validation Results - Epoch[{trainer.state.epoch}] Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['nll']:.2f}") .. Note :: Function :meth:`~ignite.engine.engine.Engine.add_event_handler` (as well as :meth:`~ignite.engine.engine.Engine.on` decorator) also accepts optional `args`, `kwargs` to be passed to the handler. For example: .. code-block:: python trainer.add_event_handler(Events.ITERATION_COMPLETED, log_training_loss, train_loader) Finally, we start the engine on the training dataset and run it during 100 epochs: .. code-block:: python trainer.run(train_loader, max_epochs=100) **Where to go next?** To understand better the concepts of the library, please read :doc:`concepts`. .. rubric:: Footnotes .. [#f1] In this example, we follow a pattern that requires a second pass through the training set. This could be expensive on large datasets (even taking a subset). Another more common pattern is to accumulate measures online over an epoch in the training loop. In this case, metrics are aggregated on a moving model, and thus, we do not want to encourage this pattern. However, if a user still likes to implement the last pattern, it can be easily done by attaching metrics to the trainer as following: .. code-block:: python def custom_output_transform(x, y, y_pred, loss): return { "y": y, "y_pred": y_pred, "loss": loss.item() } trainer = create_supervised_trainer( model, optimizer, criterion, device, output_transform=custom_output_transform ) # Attach metrics: val_metrics = { "accuracy": Accuracy(), "nll": Loss(criterion) } for name, metric in val_metrics.items(): metric.attach(trainer, name) ignite-0.4.3/docs/source/utils.rst000066400000000000000000000002661401006565000171200ustar00rootroot00000000000000ignite.utils ============ Module with helper methods .. currentmodule:: ignite.utils .. autosummary:: :nosignatures: :autolist: .. automodule:: ignite.utils :members: ignite-0.4.3/examples/000077500000000000000000000000001401006565000146105ustar00rootroot00000000000000ignite-0.4.3/examples/contrib/000077500000000000000000000000001401006565000162505ustar00rootroot00000000000000ignite-0.4.3/examples/contrib/cifar10/000077500000000000000000000000001401006565000174755ustar00rootroot00000000000000ignite-0.4.3/examples/contrib/cifar10/.gitignore000066400000000000000000000000321401006565000214600ustar00rootroot00000000000000output cifar10 raw_pytorchignite-0.4.3/examples/contrib/cifar10/README.md000066400000000000000000000102501401006565000207520ustar00rootroot00000000000000# 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` ## Usage: Run the example on a single GPU: ```bash python main.py run ``` 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/" ``` ### 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, 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 python -u -m torch.distributed.launch \ --nnodes=2 \ --nproc_per_node=2 \ --node_rank=0 \ --master_addr=master --master_port=2222 --use_env \ main.py run --backend="nccl" ``` 2. Execute on worker node ```bash python -u -m torch.distributed.launch \ --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 torch.distributed.launch python -u -m torch.distributed.launch --nproc_per_node=2 --use_env main.py run --backend="nccl" --stop_iteration=1000 ``` Resume from the latest checkpoint ```bash python -u -m torch.distributed.launch --nproc_per_node=2 --use_env 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-0.4.3/examples/contrib/cifar10/main.py000066400000000000000000000310521401006565000207740ustar00rootroot00000000000000from datetime import datetime from pathlib import Path import fire import torch import torch.nn as nn import torch.optim as optim import utils import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.contrib.handlers import PiecewiseLinear from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.handlers import Checkpoint, DiskSaver 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", distributed_rank=local_rank) 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"]: try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains 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 3 best models by validation accuracy: common.gen_save_best_models_by_val_score( save_handler=get_save_handler(config), evaluator=evaluator, models={"model": model}, metric_name="accuracy", n_saved=3, trainer=trainer, tag="test", ) # 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: import traceback print(traceback.format_exc()) if rank == 0: tb_logger.close() def run( seed=543, data_path="/tmp/cifar10", output_path="/tmp/output-cifar10/", model="resnet18", 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=200, backend=None, resume_from=None, log_every_iters=15, nproc_per_node=None, stop_iteration=None, with_clearml=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. 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. **spawn_kwargs: Other kwargs to spawn run in child processes: master_addr, master_port, node_rank, nnodes """ # 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_rank() > 0: # Ensure that only rank 0 download the dataset idist.barrier() train_dataset, test_dataset = utils.get_train_test_datasets(config["data_path"]) if idist.get_rank() == 0: # Ensure that only 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"]) # 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"\nEpoch {epoch} - Evaluation time (seconds): {int(elapsed)} - {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__}") 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 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() # Supervised part y_pred = model(x) loss = criterion(y_pred, y) optimizer.zero_grad() loss.backward() optimizer.step() # This can be helpful for XLA to avoid performance slow down if fetch loss.item() every iteration if config["log_every_iters"] > 0 and (engine.state.iteration - 1) % config["log_every_iters"] == 0: batch_loss = loss.item() engine.state.saved_batch_loss = batch_loss else: batch_loss = engine.state.saved_batch_loss return { "batch loss": batch_loss, } trainer = Engine(train_step) trainer.state.saved_batch_loss = -1.0 trainer.state_dict_user_keys.append("saved_batch_loss") 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.contrib.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-0.4.3/examples/contrib/cifar10/utils.py000066400000000000000000000020611401006565000212060ustar00rootroot00000000000000import os 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): if not os.path.exists(path): os.makedirs(path) 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-0.4.3/examples/contrib/cifar100_amp_benchmark/000077500000000000000000000000001401006565000224245ustar00rootroot00000000000000ignite-0.4.3/examples/contrib/cifar100_amp_benchmark/benchmark_fp32.py000066400000000000000000000043441401006565000255670ustar00rootroot00000000000000import 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.contrib.handlers import ProgressBar from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator from ignite.handlers import 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-0.4.3/examples/contrib/cifar100_amp_benchmark/benchmark_nvidia_apex.py000066400000000000000000000046241401006565000273050ustar00rootroot00000000000000import 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.contrib.handlers import ProgressBar from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator from ignite.handlers import 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-0.4.3/examples/contrib/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py000066400000000000000000000056731401006565000277730ustar00rootroot00000000000000import fire import torch from torch.cuda.amp import GradScaler, autocast 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.contrib.handlers import ProgressBar from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator from ignite.handlers import 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-0.4.3/examples/contrib/cifar100_amp_benchmark/utils.py000066400000000000000000000035411401006565000241410ustar00rootroot00000000000000import 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-0.4.3/examples/contrib/cifar10_qat/000077500000000000000000000000001401006565000203425ustar00rootroot00000000000000ignite-0.4.3/examples/contrib/cifar10_qat/.gitignore000066400000000000000000000000321401006565000223250ustar00rootroot00000000000000output cifar10 raw_pytorchignite-0.4.3/examples/contrib/cifar10_qat/README.md000066400000000000000000000047771401006565000216400ustar00rootroot00000000000000# 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-0.4.3/examples/contrib/cifar10_qat/main.py000066400000000000000000000246441401006565000216520ustar00rootroot00000000000000from datetime import datetime from pathlib import Path import fire import torch import torch.nn as nn import torch.optim as optim import utils import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.contrib.handlers import PiecewiseLinear from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.handlers import Checkpoint, DiskSaver 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 = "{}_backend-{}-{}_{}".format(config["model"], 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("Output path: {}".format(config["output_path"])) if "cuda" in device.type: config["cuda device name"] = torch.cuda.get_device_name(local_rank) # 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 3 best models by validation accuracy: common.save_best_model_by_val_score( output_path=config["output_path"], evaluator=evaluator, model=model, metric_name="Accuracy", n_saved=1, trainer=trainer, tag="test", ) trainer.run(train_loader, max_epochs=config["num_epochs"]) 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=200, backend=None, resume_from=None, log_every_iters=15, nproc_per_node=None, **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. **spawn_kwargs: Other kwargs to spawn run in child processes: master_addr, master_port, node_rank, nnodes """ # 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: try: parallel.run(training, config) except Exception as e: raise e def get_dataflow(config): # - Get train/test datasets if idist.get_rank() > 0: # Ensure that only rank 0 download the dataset idist.barrier() train_dataset, test_dataset = utils.get_train_test_datasets(config["data_path"]) if idist.get_rank() == 0: # Ensure that only rank 0 download the dataset idist.barrier() # Setup data loader also adapted to distributed config 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"]) 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} - Time taken (seconds) : {elapsed:.02f} - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): logger.info("Quantization Aware Training {} on CIFAR10".format(config["model"])) logger.info("- PyTorch version: {}".format(torch.__version__)) logger.info("- Ignite version: {}".format(ignite.__version__)) logger.info("\n") logger.info("Configuration:") for key, value in config.items(): logger.info("\t{}: {}".format(key, value)) logger.info("\n") if idist.get_world_size() > 1: logger.info("\nDistributed setting:") logger.info("\tbackend: {}".format(idist.backend())) logger.info("\tworld size: {}".format(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 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() y_pred = model(x) loss = criterion(y_pred, y) optimizer.zero_grad() loss.backward() optimizer.step() 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"], output_path=config["output_path"], 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(), "Checkpoint '{}' is not found".format(checkpoint_fp.as_posix()) logger.info("Resume from a checkpoint: {}".format(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 if __name__ == "__main__": fire.Fire({"run": run}) ignite-0.4.3/examples/contrib/cifar10_qat/pact.py000066400000000000000000000015171401006565000216470ustar00rootroot00000000000000# 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 import torch.nn.functional as F 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-0.4.3/examples/contrib/cifar10_qat/utils.py000066400000000000000000000250601401006565000220570ustar00rootroot00000000000000import os import brevitas import brevitas.nn as qnn import torch import torch.nn as nn import torchvision 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): if not os.path.exists(path): os.makedirs(path) 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-0.4.3/examples/contrib/mnist/000077500000000000000000000000001401006565000174025ustar00rootroot00000000000000ignite-0.4.3/examples/contrib/mnist/README.md000066400000000000000000000030411401006565000206570ustar00rootroot00000000000000# Basic MNIST Example with Ignite and `ignite.contrib` module ported from [pytorch-examples](https://github.com/pytorch/examples/tree/master/mnist) Basic neural network training with Ignite and various built-in loggers from `ignite.contrib`: - TQDM progress bar - Tensorboard - Visdom ### Usage: #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` #### Logging with TQDM progress bar Run the example: ``` python mnist_with_tqdm_logger.py ``` ### Logging with TensorboardX or `torch.utils.tensorboard` Example with training and validation monitoring using Tensorboard. #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - Optionally [TensorboardX](https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX` - Tensorboard: `pip install tensorboard` Optionally, user can install `pynvml` package on Python 3 and log GPU information: used memory, utilization. #### Usage: Run the example: ```bash python mnist_with_tensorboard_logger.py --log_dir=/tmp/tensorboard_logs ``` Start tensorboard: ```bash tensorboard --logdir=/tmp/tensorboard_logs/ ``` ### Logging with Visdom Example with training and validation monitoring using Visdom #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [Visdom](https://github.com/facebookresearch/visdom): `pip install visdom` #### Usage: Run the example: ```bash python mnist_with_visdom_logger.py ``` ignite-0.4.3/examples/contrib/mnist/mnist_with_clearml_logger.py000066400000000000000000000127741401006565000252120ustar00rootroot00000000000000""" 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.contrib.handlers.clearml_logger import ( ClearMLLogger, ClearMLSaver, GradsHistHandler, GradsScalarHandler, WeightsHistHandler, WeightsScalarHandler, global_step_from_engine, ) from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer from ignite.handlers import Checkpoint 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), event_name=Events.ITERATION_COMPLETED(every=100) ) clearml_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_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), event_name=Events.EPOCH_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-0.4.3/examples/contrib/mnist/mnist_with_neptune_logger.py000066400000000000000000000133231401006565000252400ustar00rootroot00000000000000""" MNIST example with training and validation monitoring using Neptune. Requirements: Neptune: `pip install neptune-client` Usage: Run the example: ```bash python mnist_with_neptune_logger.py ``` Go to https://neptune.ai and explore your experiment. Note: You can see an example experiment here: https://ui.neptune.ai/o/shared/org/pytorch-ignite-integration/e/PYTOR-26/charts """ 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.contrib.handlers.neptune_logger import ( GradsScalarHandler, NeptuneLogger, NeptuneSaver, WeightsScalarHandler, global_step_from_engine, ) from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer from ignite.handlers import Checkpoint 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_name="shared/pytorch-ignite-integration", name="ignite-mnist-example", 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-0.4.3/examples/contrib/mnist/mnist_with_tensorboard_logger.py000066400000000000000000000146701401006565000261120ustar00rootroot00000000000000""" 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.contrib.handlers.tensorboard_logger import ( GradsHistHandler, GradsScalarHandler, TensorboardLogger, WeightsHistHandler, WeightsScalarHandler, global_step_from_engine, ) from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer from ignite.handlers import ModelCheckpoint 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.contrib.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), event_name=Events.ITERATION_COMPLETED(every=100)) tb_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_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), event_name=Events.EPOCH_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-0.4.3/examples/contrib/mnist/mnist_with_tqdm_logger.py000066400000000000000000000102531401006565000245260ustar00rootroot00000000000000from 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.contrib.handlers import ProgressBar from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer 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.contrib.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-0.4.3/examples/contrib/mnist/mnist_with_visdom_logger.py000066400000000000000000000127271401006565000250720ustar00rootroot00000000000000""" 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.contrib.handlers.visdom_logger import ( GradsScalarHandler, VisdomLogger, WeightsScalarHandler, global_step_from_engine, ) from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer from ignite.handlers import ModelCheckpoint 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-0.4.3/examples/contrib/mnist/mnist_with_wandb_logger.py000066400000000000000000000125571401006565000246650ustar00rootroot00000000000000""" 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.contrib.handlers.wandb_logger import WandBLogger, global_step_from_engine from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer from ignite.handlers import ModelCheckpoint 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-0.4.3/examples/fast_neural_style/000077500000000000000000000000001401006565000203335ustar00rootroot00000000000000ignite-0.4.3/examples/fast_neural_style/README.md000066400000000000000000000062561401006565000216230ustar00rootroot00000000000000# 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 โ”œโ”€โ”€ MSCOCO โ”‚ โ”œโ”€โ”€ 0 โ”‚ โ”‚ โ”œโ”€โ”€ RY48TY43YT.jpg โ”‚ โ”‚ โ”œโ”€โ”€ 4324J0FNFL.jpg โ”‚ โ”‚ โ”œโ”€โ”€ 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-0.4.3/examples/fast_neural_style/handlers.py000066400000000000000000000022401401006565000225030ustar00rootroot00000000000000import 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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argparse import os import random import sys from collections import OrderedDict import numpy as np import torch import utils from handlers import Progbar from torch.optim import Adam from torch.utils.data import DataLoader from 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 (os.path.exists(args.checkpoint_model_dir)): os.makedirs(args.checkpoint_model_dir) 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-0.4.3/examples/fast_neural_style/transformer_net.py000066400000000000000000000073061401006565000241230ustar00rootroot00000000000000import 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-0.4.3/examples/fast_neural_style/utils.py000066400000000000000000000017261401006565000220530ustar00rootroot00000000000000import torch from 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.ANTIALIAS) elif scale is not None: img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS) 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-0.4.3/examples/fast_neural_style/vgg.py000066400000000000000000000025331401006565000214730ustar00rootroot00000000000000from collections import namedtuple import torch from torchvision import models class Vgg16(torch.nn.Module): def __init__(self, requires_grad=False): super(Vgg16, self).__init__() vgg_pretrained_features = models.vgg16(pretrained=True).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-0.4.3/examples/gan/000077500000000000000000000000001401006565000153555ustar00rootroot00000000000000ignite-0.4.3/examples/gan/README.md000066400000000000000000000004461401006565000166400ustar00rootroot00000000000000# 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-0.4.3/examples/gan/dcgan.py000066400000000000000000000366361401006565000170210ustar00rootroot00000000000000import argparse import os import random import warnings import torch import torch.nn as nn import torch.optim as optim import torch.utils.data as data from ignite.contrib.handlers import ProgressBar from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint, 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 ImportError( "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 = os.path.join(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 = os.path.join(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 = os.path.join(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 numpy as np import pandas as pd except ImportError: warnings.warn("Loss plots will not be generated -- pandas or matplotlib not found") else: df = pd.read_csv(os.path.join(output_dir, LOGS_FNAME), delimiter="\t", index_col="iteration") _ = df.plot(subplots=True, figsize=(20, 20)) _ = plt.xlabel("Iteration number") fig = plt.gcf() path = os.path.join(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" try: os.makedirs(args.output_dir) except FileExistsError: if (not os.path.isdir(args.output_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-0.4.3/examples/mnist/000077500000000000000000000000001401006565000157425ustar00rootroot00000000000000ignite-0.4.3/examples/mnist/README.md000066400000000000000000000101571401006565000172250ustar00rootroot00000000000000# Basic MNIST Example with Ignite ported from [pytorch-examples](https://github.com/pytorch/examples/tree/master/mnist) #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [tqdm](https://github.com/tqdm/tqdm/): `pip install tqdm` #### Usage: Run the example: ``` python mnist.py ``` ### Logging with Tensorboard MNIST example with training and validation monitoring using Tensorboard. Notice that if PyTorch version is less than 1.2, the module TensorboardX is required. #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [TensorboardX](https://github.com/lanpa/tensorboard-pytorch) (if and only if `PyTorch <= 1.2`): `pip install tensorboardX` - Tensorboard: `pip install tensorboard` #### Usage: 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 #### Requirements: - [torchvision](https://github.com/pytorch/vision/): `pip install torchvision` - [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 ``` ### 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-0.4.3/examples/mnist/assets/000077500000000000000000000000001401006565000172445ustar00rootroot00000000000000ignite-0.4.3/examples/mnist/assets/logs-det_run_1_2.png000066400000000000000000011032761401006565000230270ustar00rootroot00000000000000‰PNG  IHDR 0|๘๖ว (iCCPICC ProfileH‰•—XSษ€็–$$$ด@ค„D้Uz l„$PBH"vdQมต bมŠฎŠจธ@velุP”uฑ`CๅM@Wฟ๗๗ฮ๗อฝฮœ9sฮนs'wPb‹DYจูย› ๙3ไ1ูู9U-![ฆ~็'>SG|ฒู้#ฌศE.J‰(‹=๓,ว–์,้๐&ฐQ๙โะhYฮฒบeๆ„ษ˜ ๙‚05"ฒไ๋ฎ^ฦ|ihŽ$ึ 0@ฉ\v`d=ศฦยฌˆ๐!ฝwš ˜ึไฑbcQฎ8'zศ?Zภ“ล 3[,ŸKfS*อŒ๓๒น™ฯc ๛l,ไว&(โD๒๑U ฿“dฦ„ ู;zuฦq/่๚ฦธ.ฐลเL~ธœjฟU:’๑ทZ๙"‘Q๒(ฒ/ู๒วTฌUœGผศ*๕}-qฅŽT+`ค็ว<พซร~ดฤc‡ฐ๓ุ)์"ึ„ี&vkภZฐc2YOไkcxถhy<™ะเง๙ุCsสช&ฑซฑ๋ฑ๛<ิ๒xyฒ—% G4S,H็็1เnอcฒ„œฑc˜vvศ๖~ลึ๒†!฿ำฦฅoบ“ธ—Be๚7๎AGป ฟ๛ฆ3y — Žตqคโ|…—]€S40€{—%ฬศธOเ ‚ภ bA˜๋ฬ‡๋T f€ู`(e`X6€-`;ุ ๖ƒ 4Sเธ ฺภ pฎ•.๐๔w`ABC่ˆbˆ˜!6ˆโ†x#AH8$!)H:"Dคศld!R†”#mH5๒;r9…\Dฺ‘ศCคy|B1”Šjข๚จ9:uCะ04Šฆฃนh!ZŒ.CืกU่^ด=…^Fo ่ ด˜2ฦภŒ0[ฬ ภ"ฑd, csฑRฌซย๖c๐I_ร:ฑ^์#Nฤ้8ท…๋5ร9x.>_Šoภwใu๘๏รฟh=‚ 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•ฑฟฟๆๆf๕SZZŠฅฅ%ต^—.]BmmะภwอปŽ๕ f๊Iฮyฟญ~mm“%8๕aวj&L—5้QB>JจฎฉFey”,}ึจ๗๘‡H€ฒE€zษาŠงf%(žš5ใอsณvžฯ๗ีส๑OBG ั๐F`ฟถง๑8?ุร‘น $&@}˜™;๚7ฏœ‡๕{์c๔F3|า[๐ม˜r5255ฅๆญผŠ‘H€H€H€H€H€H€H€H€H€ฮ ชช*ธn(๛Vซๅt:Uoอห+lm__6ี:๚Q}๎๋ส ’   ุK`{]๙=พumวqฦอŠค|ฟo/-ๆF$@‡@พ๋Tๅฟ8-ษš’ œwA}ฉe*—o๗c๋ฉฉฝG๔เl/OๆF$@$@$@$@$@$@$@$@$็JJJะซึbzv‡‡‡y^#๋ล/tึฃค๙ช๕ .xJzpพเ€ี'        ฐL ุrJ&$         sHเเเฺ์.ฒqณฺด'ว็ฐ…Y%          \'P˜๋d๙H€H€H€H€H€H€H€H€H€H€ฮˆภษษ ฆX          ‹L€ฮน๕Yw         HF€œ“ัแ=          S"๐้วcลีืจIENDฎB`‚ignite-0.4.3/examples/mnist/mnist.py000066400000000000000000000110131401006565000174420ustar00rootroot00000000000000from 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 Events, create_supervised_evaluator, create_supervised_trainer 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-0.4.3/examples/mnist/mnist_save_resume_engine.py000066400000000000000000000254171401006565000234020ustar00rootroot00000000000000from 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 Events, create_supervised_evaluator, create_supervised_trainer from ignite.handlers import Checkpoint, DiskSaver from ignite.metrics import Accuracy, Loss from ignite.utils import manual_seed try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter except ImportError: raise RuntimeError( "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(["{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(["{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) evaluator = create_supervised_evaluator( model, metrics={"accuracy": Accuracy(), "nll": Loss(criterion)}, device=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"] pbar.desc = f"Epoch {engine.state.epoch} - loss: {engine.state.output:.4f} - lr: {lr:.4f}" pbar.update(log_interval) writer.add_scalar("training/loss", engine.state.output, 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} 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-0.4.3/examples/mnist/mnist_with_tensorboard.py000066400000000000000000000132501401006565000231040ustar00rootroot00000000000000""" 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 Events, create_supervised_evaluator, create_supervised_trainer from ignite.metrics import Accuracy, Loss try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter except ImportError: raise RuntimeError( "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-0.4.3/examples/mnist/mnist_with_tensorboard_on_tpu.py000066400000000000000000000141771401006565000245010ustar00rootroot00000000000000""" 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 Events, create_supervised_evaluator, create_supervised_trainer from ignite.metrics import Accuracy, Loss, RunningAverage try: import torch_xla.core.xla_model as xm except ImportError: raise RuntimeError( "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-0.4.3/examples/mnist/mnist_with_visdom.py000066400000000000000000000134621401006565000220700ustar00rootroot00000000000000from 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 Events, create_supervised_evaluator, create_supervised_trainer from ignite.metrics import Accuracy, Loss try: import visdom except ImportError: raise RuntimeError("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-0.4.3/examples/notebooks/000077500000000000000000000000001401006565000166135ustar00rootroot00000000000000ignite-0.4.3/examples/notebooks/Cifar100_bench_amp.ipynb000066400000000000000000000130371401006565000231230ustar00rootroot00000000000000{ "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": "3.7.6" }, "colab": { "name": "Cifar100_bench_amp.ipynb", "provenance": [] } }, "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", "metadata": { "id": "SkRXPuNRfDHX" }, "source": [ "!pip install pytorch-ignite pynvml fire" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9SPEV91DfDHZ" }, "source": [ "2) Let's install Nvidia/Apex package:" ] }, { "cell_type": "code", "metadata": { "id": "fGtxgbj8fDHb" }, "source": [ "# Install Apex:\n", "!pip install --upgrade --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" git+https://github.com/NVIDIA/apex/" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "QnihHXQpfDHb" }, "source": [ "import torch\n", "import torchvision\n", "import ignite\n", "torch.__version__, torchvision.__version__, ignite.__version__" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "6xqqj0q1fDHh" }, "source": [ "!git clone https://github.com/pytorch/ignite.git /tmp/ignite\n", "scriptspath=\"/tmp/ignite/examples/contrib/cifar100_amp_benchmark/\"\n", "setup=f\"cd {scriptspath} && export PYTHONPATH=$PWD:$PYTHONPATH\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gX30i_abfDHi" }, "source": [ "4) Download dataset" ] }, { "cell_type": "code", "metadata": { "id": "ulufk4tsfDHj" }, "source": [ "from torchvision.datasets.cifar import CIFAR100\n", "CIFAR100(root=\"/tmp/cifar100/\", train=True, download=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ai6nBHZKfDHl" }, "source": [ "## Training in fp32" ] }, { "cell_type": "code", "metadata": { "id": "mHwsVTB6fDHq" }, "source": [ "!{setup} && python benchmark_fp32.py /tmp/cifar100/ --batch_size=256 --max_epochs=20" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "n2p-EMwGfDHs" }, "source": [ "## Training with `torch.cuda.amp`" ] }, { "cell_type": "code", "metadata": { "id": "xkuW1EY-fDHs" }, "source": [ "!{setup} && python benchmark_torch_cuda_amp.py /tmp/cifar100/ --batch_size=256 --max_epochs=20" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "A6Pe4cW6fDHu" }, "source": [ "!{setup} && python benchmark_nvidia_apex.py /tmp/cifar100/ --batch_size=256 --max_epochs=20 --opt=\"O1\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1aqdlPSgfDHu" }, "source": [ "!{setup} && python benchmark_nvidia_apex.py /tmp/cifar100/ --batch_size=256 --max_epochs=20 --opt=\"O2\"" ], "execution_count": null, "outputs": [] } ] }ignite-0.4.3/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb000066400000000000000000001011351401006565000253100ustar00rootroot00000000000000{ "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 " ], "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.contrib.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.contrib.handlers import TensorboardLogger, ProgressBar\n", "from ignite.contrib.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} ยฑ {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-0.4.3/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb000066400000000000000000001373361401006565000246620ustar00rootroot00000000000000{ "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", "!pip install --upgrade --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" 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)) โˆ’ 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)) โˆ’ 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) โˆ’ 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) โˆ’ 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) โˆ’ 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)) โˆ’ 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)) โˆ’ 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) โˆ’ 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) โˆ’ 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.contrib.handlers import TensorboardLogger, WandBLogger\n", "from ignite.contrib.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.contrib.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.contrib.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-0.4.3/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb000066400000000000000000001366251401006565000253430ustar00rootroot00000000000000{ "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 $\\text{mean}_{x \\in A}[(D_B(G(x)) โˆ’ 1)^2]$ and cycle loss $\\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right]$\n", "- generator B is trained minimize $\\text{mean}_{y \\in B}[(D_A(F(y)) โˆ’ 1)^2]$ and cycle loss $\\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]$\n", "- discriminators A is trained to minimize $\\text{mean}_{x \\in A}[(D_A(x) โˆ’ 1)^2] + \\text{mean}_{y \\in B}[D_A(F(y))^2]$.\n", "- discriminator B is trained to minimize $\\text{mean}_{y \\in B}[(D_B(y) โˆ’ 1)^2] + \\text{mean}_{x \\in A}[D_B(G(x))^2]$." ] }, { "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) โˆ’ 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)) โˆ’ 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)) โˆ’ 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) โˆ’ 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) โˆ’ 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.contrib.handlers import TensorboardLogger, WandBLogger\n", "from ignite.contrib.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.contrib.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.contrib.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-0.4.3/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb000066400000000000000000001302421401006565000257760ustar00rootroot00000000000000{ "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", "!pip install --upgrade --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" 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 = torch.tensor(drop_connect_rate, requires_grad=False)\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", " 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.contrib.handlers import TensorboardLogger\n", "from ignite.contrib.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.contrib.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-0.4.3/examples/notebooks/FashionMNIST.ipynb000066400000000000000000000601351401006565000220650ustar00rootroot00000000000000{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "FashionMNIST.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": "3.6.9-final" } }, "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", "metadata": { "id": "A7LJdJfU6pra" }, "source": [ "!pip install pytorch-ignite" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7PiU02-fabxz" }, "source": [ "### Importing libraries\n", "\n", "General Data-Science Libraries like numpy, matplotlib and seaborn" ] }, { "cell_type": "code", "metadata": { "id": "-FlbGAEe6prv" }, "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "E-OllAGT6pr-" }, "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" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "YodbjXPi6psK" }, "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" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "Y2dqyxnr6psU" }, "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)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "AST_DtTC6psh" }, "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)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "JXyVBBYw6pst" }, "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()" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "a7EaSDMW6ps5" }, "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 = []" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "4LXaAfga6ptD" }, "source": [ "RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "0mcIq0lc6ptN" }, "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)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "UFQNaZDx6ptV" }, "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) " ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "kVrVyE8X6pth" }, "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)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "qGVhrmoZ6ptp" }, "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})" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "lW6m6Stp6pty" }, "source": [ "trainer.run(train_loader, max_epochs=epochs)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "SptDgBR66pt4" }, "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()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "g4iomcl36pt-" }, "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()" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "SU4y96CI6puH" }, "source": [ "# loading the saved model\n", "def fetch_last_checkpoint_model_filename(model_save_path):\n", " import os\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 os.path.join(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\")" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "uWojxHNy6puN" }, "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\"))" ], "execution_count": null, "outputs": [] }, { "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)" ] } ] }ignite-0.4.3/examples/notebooks/FastaiLRFinder_MNIST.ipynb000066400000000000000000000202361401006565000234300ustar00rootroot00000000000000{ "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": "3.7.1-final" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } }, "colab": { "name": "FastaiLRFinder_MNIST.ipynb", "provenance": [] }, "accelerator": "GPU" }, "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", "metadata": { "id": "wdmoIYIvmvdp" }, "source": [ "!pip install pytorch-ignite" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lP73mDV_nHV9" }, "source": [ "## Import libraries" ] }, { "cell_type": "code", "metadata": { "pycharm": { "is_executing": false }, "id": "lMphyBmmmvdw" }, "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" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "z0epEdj4mvds" }, "source": [ "import ignite\n", "ignite.__version__" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "NEl3QFzZmvd1" }, "source": [ "from ignite.engine import create_supervised_trainer, create_supervised_evaluator\n", "from ignite.metrics import Loss, Accuracy\n", "from ignite.contrib.handlers import FastaiLRFinder, ProgressBar" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "L_wmAdFgmvdx" }, "source": [ "## Loading MNIST" ] }, { "cell_type": "code", "metadata": { "id": "eZeKOgKymvdx" }, "source": [ "mnist_pwd = \"data\"\n", "batch_size= 256" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Hgq0uZHAmvdy" }, "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": "JTLEOUqtmvdy" }, "source": [ "## Model" ] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "6KqS5A4Umvd0" }, "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": "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", "metadata": { "id": "WxaXjwDhmvd2" }, "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": "CTGJPVI6mvd2" }, "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)\n", " \n", "trainer.run(trainloader, max_epochs=10)\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)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "oN0VkPapmvd5" }, "source": [ "lr_finder.plot()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "EHHNoOi8mvd6" }, "source": [ "lr_finder.lr_suggestion()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "NcT19wqkmvd6" }, "source": [ "Let's now setup suggested learning rate to the optimizer and we can train the model with optimal learning rate.\n", "\n", "*Note, that model and optimizer were restored to their initial states when `FastaiLRFinder` finished.*" ] }, { "cell_type": "code", "metadata": { "id": "DJqgyaFnmvd7" }, "source": [ "optimizer.param_groups[0]['lr'] = lr_finder.lr_suggestion()\n", "\n", "trainer.run(trainloader, max_epochs=10)\n", "evaluator.run(testloader)\n", "print(evaluator.state.metrics)" ], "execution_count": null, "outputs": [] } ] }ignite-0.4.3/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb000066400000000000000000000216011401006565000247120ustar00rootroot00000000000000{ "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.contrib.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-0.4.3/examples/notebooks/MNIST_on_TPU.ipynb000066400000000000000000000322211401006565000217740ustar00rootroot00000000000000{ "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 = \"1.5\"\n", "!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\n", "!python pytorch-xla-env-setup.py --version $VERSION" ], "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.contrib.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", " \"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", " \"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-0.4.3/examples/notebooks/TextCNN.ipynb000066400000000000000000001022741401006565000211470ustar00rootroot00000000000000{ "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": "TextCNN.ipynb", "provenance": [] }, "accelerator": "GPU" }, "cells": [ { "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, setup 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 spacy`\n", "\n", "`python -m spacy download en`" ] }, { "cell_type": "code", "metadata": { "id": "7XHAD9x7znj_" }, "source": [ "!pip install pytorch-ignite torchtext spacy\n", "!python -m spacy download en" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lZty7-RWznkA" }, "source": [ "## Import Libraries" ] }, { "cell_type": "code", "metadata": { "id": "VKTazeAkznkB" }, "source": [ "import random" ], "execution_count": null, "outputs": [] }, { "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", "* **data**: A module to setup the data in the form Fields and Labels.\n", "* **datasets**: A module to download NLP datasets.\n", "* **GloVe**: A module to download and use pretrained GloVe embedings." ] }, { "cell_type": "code", "metadata": { "id": "XrXE-f7jznkD" }, "source": [ "from torchtext import data\n", "from torchtext import datasets\n", "from torchtext.vocab import GloVe" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ivAnTyEfznkE" }, "source": [ "We import torch, nn and functional modules to create our models! " ] }, { "cell_type": "code", "metadata": { "id": "gbEFAWr0znkE" }, "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "SEED = 1234\n", "torch.manual_seed(SEED)\n", "torch.cuda.manual_seed(SEED)" ], "execution_count": null, "outputs": [] }, { "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 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. \n", "* **ProgressBar**: Handler to create a tqdm progress bar." ] }, { "cell_type": "code", "metadata": { "id": "enczLgLTznkH" }, "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.contrib.handlers import ProgressBar" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WZYyXYB5znkH" }, "source": [ "## Processing Data" ] }, { "cell_type": "markdown", "metadata": { "id": "tCadZAV_znkH" }, "source": [ "The code below first sets up `TEXT` and `LABEL` as general data objects. \n", "\n", "* `TEXT` converts any text to lowercase and produces tensors with the batch dimension first. \n", "* `LABEL` is a data object that will convert any labels to floats.\n", "\n", "Next IMDB training and test datasets are downloaded, the training data is split into training and validation datasets. It takes TEXT and LABEL as inputs so that the data is processed as specified. " ] }, { "cell_type": "code", "metadata": { "id": "-BttNTcGznkH" }, "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "TEXT = data.Field(lower=True, batch_first=True)\n", "LABEL = data.LabelField(dtype=torch.float)\n", "\n", "train_data, test_data = datasets.IMDB.splits(TEXT, LABEL, root='/tmp/imdb/')\n", "train_data, valid_data = train_data.split(split_ratio=0.8, random_state=random.seed(SEED))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "aAt_J_eDznkI" }, "source": [ "Now we have three sets of the data - train, validation, test. Let's explore what these data objects are and how to extract data from them.\n", "* `train_data` is **torchtext.data.dataset.Dataset**, this is similar to **torch.utils.data.Dataset**.\n", "* `train_data[0]` is **torchtext.data.example.Example**, a Dataset is comprised of many Examples.\n", "* Let's explore the attributes of an example. We see a few methods, but most importantly we see `label` and `text`.\n", "* `example.text` is the text of that example and `example.label` is the label of the example. " ] }, { "cell_type": "code", "metadata": { "id": "GZZxlIWVznkI" }, "source": [ "print('type(train_data) : ', type(train_data))\n", "print('type(train_data[0]) : ',type(train_data[0]))\n", "example = train_data[0]\n", "print('Attributes of Example : ', [attr for attr in dir(example) if '_' not in attr])\n", "print('example.label : ', example.label)\n", "print('example.text[:10] : ', example.text[:10])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "yCrZzrYaznkK" }, "source": [ "Now that we have an idea of what are split datasets look like, lets dig further into `TEXT` and `LABEL`. It is important that we build our vocabulary based on the train dataset as validation and test are **unseen** in our experimenting. \n", "\n", "For `TEXT`, let's download the 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", "We use `TEXT.build_vocab` to do this, let's explore the `TEXT` object more. \n", "\n", "Let's explore what `TEXT` object is and how to extract data from them. We see `TEXT` has a few attributes, let's explore vocab, since we just used the build_vocab function. \n", "\n", "`TEXT.vocab` 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" ] }, { "cell_type": "code", "metadata": { "id": "IfGqPAfEznkK" }, "source": [ "TEXT.build_vocab(train_data, vectors=GloVe(name='6B', dim=100, cache='/tmp/glove/'))\n", "print ('Attributes of TEXT : ', [attr for attr in dir(TEXT) if '_' not in attr])\n", "print ('Attributes of TEXT.vocab : ', [attr for attr in dir(TEXT.vocab) if '_' not in attr])\n", "print ('First 5 values TEXT.vocab.itos : ', TEXT.vocab.itos[0:5]) \n", "print ('First 5 key, value pairs of TEXT.vocab.stoi : ', {key:value for key,value in list(TEXT.vocab.stoi.items())[0:5]}) \n", "print ('Shape of TEXT.vocab.vectors.shape : ', TEXT.vocab.vectors.shape)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PFfeEinqznkL" }, "source": [ "Let's do the same with `LABEL`. We see that there are vectors related to `LABEL`, this is expected because `LABEL` provides the definition of a label of data." ] }, { "cell_type": "code", "metadata": { "id": "5sU3UOXDznkL" }, "source": [ "LABEL.build_vocab(train_data)\n", "print ('Attributes of LABEL : ', [attr for attr in dir(LABEL) if '_' not in attr])\n", "print ('Attributes of LABEL.vocab : ', [attr for attr in dir(LABEL.vocab) if '_' not in attr])\n", "print ('First 5 values LABEL.vocab.itos : ', LABEL.vocab.itos) \n", "print ('First 5 key, value pairs of LABEL.vocab.stoi : ', {key:value for key,value in list(LABEL.vocab.stoi.items())})\n", "print ('Shape of LABEL.vocab.vectors : ', LABEL.vocab.vectors)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ojXZisxLznkM" }, "source": [ "Now we must convert our split datasets into iterators, we'll take advantage of **torchtext.data.BucketIterator**! BucketIterator pads every element of a batch to the length of the longest element of the batch." ] }, { "cell_type": "code", "metadata": { "id": "tM4yWV9YznkM" }, "source": [ "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits((train_data, valid_data, test_data), \n", " batch_size=32,\n", " device=device)" ], "execution_count": null, "outputs": [] }, { "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 setup are process_functions for Ignite's `Engine`.\n", "* `batch.label[0]` is the label of a single example. We can see that `LABEL.vocab.stoi` was used to map the label that originally text into a float.\n", "* `batch.text[0]` is the text of a single example. Similar to label, `TEXT.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 bucket iterator is doing exactly as we hoped!" ] }, { "cell_type": "code", "metadata": { "id": "Ga2xDXfyznkN" }, "source": [ "batch = next(iter(train_iterator))\n", "print('batch.label[0] : ', batch.label[0])\n", "print('batch.text[0] : ', batch.text[0][batch.text[0] != 1])\n", "\n", "lengths = []\n", "for i, batch in enumerate(train_iterator):\n", " x = batch.text\n", " lengths.append(x.shape[1])\n", " if i == 10:\n", " break\n", "\n", "print ('Lengths of first 10 batches : ', lengths)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "63z1tffDznkO" }, "source": [ "class TextCNN(nn.Module):\n", " def __init__(self, vocab_size, embedding_dim, kernel_sizes, num_filters, 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(vocab_size, embedding_dim, padding_idx=1)\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).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_(TEXT.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.')" ], "execution_count": null, "outputs": [] }, { "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 setup. " ] }, { "cell_type": "code", "metadata": { "id": "HM_7LQE3znkP" }, "source": [ "vocab_size, embedding_dim = TEXT.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()" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "Q4ncIcYcznkQ" }, "source": [ "def process_function(engine, batch):\n", " model.train()\n", " optimizer.zero_grad()\n", " x, y = batch.text, batch.label\n", " y_pred = model(x)\n", " loss = criterion(y_pred, y)\n", " loss.backward()\n", " optimizer.step()\n", " return loss.item()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "HiiQr_GYznkQ" }, "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 and y from batch.\n", "* With torch.no_grad(), no gradients are calculated for any succeding steps.\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", "metadata": { "id": "b9-G-9iVznkR" }, "source": [ "def eval_function(engine, batch):\n", " model.eval()\n", " with torch.no_grad():\n", " x, y = batch.text, batch.label\n", " y_pred = model(x)\n", " return y_pred, y" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "k1CxFQs_znkS" }, "source": [ "trainer = Engine(process_function)\n", "train_evaluator = Engine(eval_function)\n", "validation_evaluator = Engine(eval_function)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "P-98lPU9znkS" }, "source": [ "RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "KAK6nXEbznkS" }, "source": [ "def thresholded_output_transform(output):\n", " y_pred, y = output\n", " y_pred = torch.round(y_pred)\n", " return y_pred, y" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "QkcC2R4qznkT" }, "source": [ "Accuracy(output_transform=thresholded_output_transform).attach(train_evaluator, 'accuracy')\n", "Loss(criterion).attach(train_evaluator, 'bce')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "tLtT5f11znkT" }, "source": [ "Accuracy(output_transform=thresholded_output_transform).attach(validation_evaluator, 'accuracy')\n", "Loss(criterion).attach(validation_evaluator, 'bce')" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "qteztuB3znkU" }, "source": [ "pbar = ProgressBar(persist=True, bar_format=\"\")\n", "pbar.attach(trainer, ['loss'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "x4DxUwXfznkU" }, "source": [ "### EarlyStopping - Tracking Validation Loss\n", "\n", "Now we'll setup a Early Stopping 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 5 epochs, the training process will stop early. " ] }, { "cell_type": "code", "metadata": { "id": "wPM6-USgznkU" }, "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)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "3XsmcAA2znkV" }, "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)" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "id": "9Gl6WT0YznkW" }, "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})" ], "execution_count": null, "outputs": [] }, { "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", "metadata": { "scrolled": false, "id": "sPe46cQOznkX" }, "source": [ "trainer.run(train_iterator, max_epochs=20)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OpqXiZUsznkY" }, "source": [ "That's it! We have successfully trained and evaluated a Convolutational Neural Network for Text Classification. " ] } ] }ignite-0.4.3/examples/notebooks/VAE.ipynb000066400000000000000000000652271401006565000203050ustar00rootroot00000000000000{ "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-0.4.3/examples/notebooks/assets/000077500000000000000000000000001401006565000201155ustar00rootroot00000000000000ignite-0.4.3/examples/notebooks/assets/ax_hparams.png000066400000000000000000041234001401006565000227520ustar00rootroot00000000000000‰PNG  IHDR <๖]แG๗ 'iCCPICC ProfileH‰•—XSษ€็–$$$ด@ค„D)าฅืามFH %†„ bGX *"XัUืศbรฎ,‚ฝ?QQึล‚ •7I]{฿;๙ๆ?gฮœ9็น๓@5Š-eกjd sลัม~ฬฤคd&ฉ ๐‡"อๆHDพQQแส๐Ÿ๒๎ด…rีVๆ๋็*๊\ž„9•+แdC>๎ฬ‰s ๔Bฝษฬ\d"ŒhŠa€Meœฎ`Wง*8\nํ9%*›-N@E3“จ”BถrBศอฝ8|6๒gศcฒณg@Vต„l™๚Ÿ๔๘L๑ษfงฐ"น($ข,๖์ณ[ฒณครs˜ภFๅ‹Cขe9ห๊–9#LฦTศ็…ฉ‘5 _pๅ๖2~ย—†ฤ ูเHaอ”สe„Aึƒl,ฬŠา{ฅ 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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 layer3_residual_out->layer3_residual_add pool pool layer3_residual_add->pool classifier_flatten flatten pool->classifier_flatten classifier_conv conv classifier_flatten->classifier_conv classifier_scale scale classifier_conv->classifier_scale logits logits classifier_scale->logits input input input->prep_whiten 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g}ถ๏ึญ[๑อo~3มฅตต52=๔ะ~พ<ฟ„ฦŸ?ฟ‰_NฤšeุฦŽŽŽDLŸืoฤ๘๑็ทz+„gRฃ,ต!'ึ$ฤ๒/2ฉฯ=๛์ณ๑ย /ธ็%ย๑E”ฺwหฮ๋ฯวฅ๘- P@ €@~q!ใ.:‡๒jjjฺ๗x๊ทƒ—๏ฤ~ฆƒ•ฯƒภม ™?หIWlงคใ๑A!ฒฎtC‚@ไ]โAขเc๛X*บœย๋ฟ๋๘ิง>•u‰๔jbษHฬˆผแa|โŸHD† ๅท๛ทSี‘Dุ%”ดXy?Ž„‰๘๗พŸ็ceu%~๐ƒ„HธศผDษ๋ฏฟวœˆ•o๛๑่ฃฦยฉฤBว–azŸU+y๗^D_&๏ป)๏‹0๘฿T้E๎ญ[๕2ีมlซ๊Uๅbฮ<ฟฟฆต/Zœ;wn? ฅD4ฤวก#แ&q&๗"oG^–ฯŠTเ มฦz%š์ซ’จ฿฿?๚ฃ?J๐J™‹ ( P@ €@ฤgด๑–y๙NW9ฮS ๏ฅโ๛rไ๖`%ช้)LมฟงŸ~:!๎ฺcˆภ‹D็jHNf”|(ัxไ‘G’Jyต!ืBไZฎฟ๊Rr๏ตใy๗๗D๐XงฤวG?๚ัุภณ๏Eผ๔๛ำ๘อ/fjฆm๛ญ฿๚ญD`Heฮ2. ๋“@๐ฒ^ …๘‡H”พํ9v์X’2ค฿3ง“T!W+ห๛‘/Ngyy;%ŽCฬH˜ูV%Jถื?/ีผ–แ7น ๆฉฉอุ‡งž"ฆ 0SญL๛%4Ÿไ'แU^–eๆฯ๖หถ;ฅM๖฿๖ # !วฤ+ozx/ผ]“์ฝ”r7ฯสป฿ทป%dw?Iฺƒy฿ู้2฿r฿ฉพ๗๋ป๕แพฟ_ํ๚y–๛n}~ท๏?nฺr~ฺy;๓r๒฿aฟ?Nšร๒ฝ๏lหƒิžŸฆ๛Q\~%v๋@ุIDATำิ๑nyณ๊yทvผŸ฿–}ผ_Y๗๛vฟ–ฯw‡ฝ//็พ›๖วHฃๆศโฝ•ใ… V๙๗๏สUฐCR}ง”Cค~dd$ฺY(‘—๛/๒+Aขx๏๒ฅห กม6๏kฏฝ–ค "ีฺWˆP+=‘่x๘แ‡า.๑"RฎED|xx8•ฉ4Cแ์ูณIฺ’็ฑV*#‘ าฏฅฅฅ%ีฏ„๙U=๚ซฟ๚ซt/๑""~ไศ‘Tท*cึญTฦ{ีง|Nา–หWโ๑วOR๛m=ถ-็๘Gฐหcรวa%ฅtE๕/ํ:“4H"JษŠp0็"๗ถิ๛fll,มั6Kฬ<๑ฤฉยS›๛/œททถ–—ใ„ฐl๋=$มกDXูฏ}ํkษฦ1ณ-๖฿๑Pย๓žฎŠ๗”๋3ฏผ๛};XโO’๖`wzถฬ๗ฃwช๏zn}xท๏๏Wป~žๅพ[Ÿ฿ํ๛๖ŸถœŸ6ฮผœ7ุ๏“ๆฐ|๏ว;๒ ต็ง้ใม~”?—฿4uผ[ฌzญ๏็๗Ÿe๏Wึพฏ‡ๅ๓a๏หหyท๏ฆฝOฯ}๑ตGฤผ๗ม+'^๑ำยฎ7?ญtIENDฎB`‚ignite-0.4.3/examples/references/000077500000000000000000000000001401006565000167315ustar00rootroot00000000000000ignite-0.4.3/examples/references/README.md000066400000000000000000000005231401006565000202100ustar00rootroot00000000000000# Reproducible trainings with Ignite Inspired by [torchvision/references](https://github.com/pytorch/vision/tree/master/references), we provide several reproducible baselines for vision tasks: * [x] Classification * [x] [ImageNet](classification/imagenet) * [x] Segmentation * [x] [Pascal VOC2012](segmentation/pascal_voc2012) ignite-0.4.3/examples/references/classification/000077500000000000000000000000001401006565000217245ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/000077500000000000000000000000001401006565000235155ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/.gitignore000066400000000000000000000001471401006565000255070ustar00rootroot00000000000000.ipynb_checkpoints input output experiments/plx/*.yml experiments/plx/*.yaml .polyaxon/ .polyaxonignoreignite-0.4.3/examples/references/classification/imagenet/NOTES_ClearML.md000066400000000000000000000207771401006565000263030ustar00rootroot00000000000000# Experiments tracking with ClearML [Allegro ClearML](https://allegro.ai/clearml/docs/) is a full system open source ML / DL experiment manager and ML-Ops solution. It is composed of a server, Python SDK and web UI. **Allegro ClearML** enables data scientists and data engineers to effortlessly track, manage, compare and collaborate on their experiments as well as easily manage their training workloads on remote machines. ## Install ClearML Install [clearml](https://github.com/allegroai/clearml) by executing the following command: ```bash pip install --upgrade clearml ``` ## Install requirements ```bash pip install -r requirements.txt ``` We need to also install Nvidia/APEX and libraries for opencv. **Important**, please, check the content of `experiments/setup_opencv.sh` before running the script. ```bash sh experiments/setup_apex.sh sh experiments/setup_opencv.sh ``` #### Download ImageNet dataset Since 10/2019, we need to register an account in order to download the dataset. To download the dataset, use the following form : http://www.image-net.org/download.php ## Setup the environment variables ### Setup the dataset path To configure the path to already existing ImageNet dataset, please specify `DATASET_PATH` environment variable ```bash export DATASET_PATH=/path/to/imagenet # export DATASET_PATH=$PWD/input/imagenet ``` ## Run the experiment code In **ClearML**, when you run the experiment code, `clearml` stores the experiment in [clearml-server](https://github.com/allegroai/clearml-server). By default, `clearml` works with the demo **ClearML Server** ([https://demoapp.trains.allegro.ai/dashboard](https://demoapp.trains.allegro.ai/dashboard)), which is open to anyone (although once a week it is refreshing and deleting all data). You can also set up your own [self-hosted](https://github.com/allegroai/clearml-server) **ClearML Server**. After the experiment code runs once, you can [reproduce the experiment](#reproducing-the-experiment) using the **ClearML Web-App (UI)**, which is part of `clearml-server`. You only need to run the code once to store it in `clearml-server`. ### Setup This setup is a specific for this code and is not required in general usage of ClearML. We setup an output path as a local storage: ```bash export CLEARML_OUTPUT_PATH=/path/to/output/clearml # e.g export CLEARML_OUTPUT_PATH=$PWD/output/clearml ``` This environment variable helps to choose ClearML as experiment tracking system among all others. ### ClearML fileserver setup 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/docs/examples/reporting/artifacts/), 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/docs/examples/reporting/artifacts/ ### Run the code #### Training on single node and single GPU Please, make sure to adapt training data loader batch size to your GPU type. By default, batch size is 64 per process. Execute the following command: ```bash export CLEARML_OUTPUT_PATH=/path/to/output/clearml # e.g export CLEARML_OUTPUT_PATH=$PWD/output/clearml export PYTHONPATH=$PWD/code:$PYTHONPATH py_config_runner ./code/scripts/training.py ./configs/train/baseline_resnet50.py ``` #### Training on single node and multiple GPUs Please, make sure to adapt training data loader batch size to your GPU type. By default, batch size is 64 per process. ```bash export CLEARML_OUTPUT_PATH=/path/to/output/clearml # e.g export CLEARML_OUTPUT_PATH=$PWD/output/clearml export PYTHONPATH=$PWD/code:$PYTHONPATH python -m torch.distributed.launch --nproc 2 --use_env -m py_config_runner ./code/scripts/training.py ./configs/train/baseline_resnet50.py ``` In **ClearML Web-App** a new project named _"ImageNet Training"_ will be created, with an experiment named _"baseline_resnet50"_ inside. In your local environment, the console output includes the URL of the experiment's **RESULTS** page. You can now view your experiment in **ClearML** by clicking the link or copying the URL into your browser. It opens the results in the experiment's details pane, in the **ClearML Web-App (UI)**. #### ClearML automatic Logging When the experiment code runs, **ClearML** automatically logs your environment, code, and the outputs. Which means that you don't need to change your code. All you need is 2 lines of integration at the top of your main script ```python from clearml import Task Task.init("ImageNet Training", "baseline_resnet50") ``` Once it's there, the following will be automatically logged by **ClearML**: - **Resource Monitoring** CPU/GPU utilization, temperature, IO, network, etc - **Development Environment** Python environment, Git (repo, branch, commit) including uncommitted changes - **Configuration** Including configuration files, command line arguments (ArgParser), and general dictionaries - Full **stdout** and **stderr** automatic logging - Model snapshots, with optional automatic upload to central storage. - Artifacts log & store, including shared folders, S3, GS, Azure, and Http/s - Matplotlib / Seaborn / TensorBoard / TensorBoardX scalars, metrics, histograms, images, audio, video, etc Additionally, **ClearML** supports explicit logging by adding calls to the **ClearML** Python client `Logger` class methods in the code. For more information, see [Explicit Reporting](https://allegro.ai/docs/examples/examples_explicit_reporting/) in the **ClearML** documentation. ## Track the experiment and visualize the results In the **ClearML Web-App (UI)**, track the experiment and visualize results in the experiment's details pane, which is organized in tabs and provides the following information; - Source code, uncommitted changes, Python packages and versions, and other information, in the **EXECUTION** tab - Hyperparameters in the **HYPERPARAMETERS** tab - Input model, Configuration, Output model, and other artifacts in the **ARTIFACTS** tab - Experiment Comments and General experiment information in the **INFO** tab - Results in the **RESULTS** tab, including the log, scalar metric plots, plots of any data, and debug samples ## Reproducing the experiments In **ClearML**, reproduce experiments using `clearml-agent` for remote execution. Rerun the same experiment, by making an exact copy of it (a clone), and remotely execute the cloned experiment. First, install `clearml-agent` and then configure it to work with your self-hosted **ClearML Server**. Once `clearml-agent` is installed and configured, run `clearml-agent daemon`. In **ClearML**, we call these _workers_, they pop experiments from a job execution queue and execute them. Every machine with a _clearml-agent daemon_, becomes a registered _worker_ in your **clearml-server** cluster. Using the **ClearML Web-App** you can easily send experiments to be remotely executed on one of these machines. More details can be found on the _clearml-agent_ [github](https://github.com/allegroai/clearml-agent/) ### Install and configure clearml-agent 1. Install `clearml-agent` pip install clearml-agent 1. Configure `clearml-agent` by running the setup wizard clearml-agent init ### Remotely execute the experiment 1. Start a **ClearML** worker. Run a `clearml-agent daemon` listening to a queue For example, run a `clearml-agent daemon` listening to the `default` queue and using multiple GPUs. clearml-agent daemon --gpus 0,1 --queue default 1. Locate the experiment. In the **ClearML Web-App (UI)**, Projects page, click on the project card 1. Make a copy of the experiment 1. In the experiment table, right-click the experiment 1. On the sub-menu, select **Clone** 1. Select the project, type a name for the copy, and type a description, or accept the defaults 1. Click the **CLONE** button The copy of the experiment is created. Its details pane opens. 1. Send the experiment for remote execution, by enqueuing it in one of the job execution queues 1. In the experiment table, right-click the experiment 1. On the sub-menu, select **Enqueue** 1. Select the _default_ queue 1. Click the **ENQUEUE** button The experiment's status changes to Pending. When the experiment reaches the top of the job execution queue, the `clearml-agent deamon` fetches it, its status changes to Running, and `clearml-agent` executes it while logging and monitoring. You can track the experiment while it is in progress, and anytime afterwards. ignite-0.4.3/examples/references/classification/imagenet/NOTES_MLflow.md000066400000000000000000000071611401006565000262140ustar00rootroot00000000000000# Experiments tracking with MLflow User can run ImageNet training using MLflow experiments tracking system on the local machine. ## Requirements We use `conda` and [MLflow](https://github.com/mlflow/mlflow) to handle experiments/runs and all python dependencies. Please, install these tools: - [MLflow](https://github.com/mlflow/mlflow): `pip install mlflow` - [conda](https://conda.io/en/latest/miniconda.html) We need to also install Nvidia/APEX and libraries for opencv. APEX is automatically installed on the first run. Manually, all can be installed with the following commands. **Important**, please, check the content of `experiments/setup_opencv.sh` before running. ```bash sh experiments/setup_apex.sh sh experiments/setup_opencv.sh ``` ## Usage ### Download ImageNet-1k dataset Since 10/2019, we need to register an account in order to download the dataset. To download the dataset, use the following form : http://www.image-net.org/download.php ### Setup dataset path To configure the path to already existing ImageNet dataset, please specify `DATASET_PATH` environment variable ```bash export DATASET_PATH=/path/to/imagenet # export DATASET_PATH=$PWD/input/imagenet ``` ### MLflow setup Setup mlflow output path as a local storage (option with remote storage is not supported): ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns ``` Create once "Trainings" experiment ```bash mlflow experiments create -n Trainings ``` or check existing experiments: ```bash mlflow experiments list ``` ### Training on single node with single GPU Please, make sure to adapt training data loader batch size to your GPU type. By default, batch size is 64. ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns mlflow run experiments/mlflow --experiment-name=Trainings -P config_path=configs/train/baseline_r50.py -P num_gpus=1 ``` ### Training on single node with multiple GPUs For optimal devices usage, please, make sure to adapt training data loader batch size to your infrastructure. By default, batch size is 64 per process. ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns mlflow run experiments/mlflow --experiment-name=Trainings -P config_path=configs/train/baseline_r50.py -P num_gpus=2 ``` ## Training tracking ### MLflow dashboard To visualize experiments and runs, user can start mlflow dashboard: ```bash mlflow server --backend-store-uri /path/to/output/mlruns --default-ainfrastructure/path/to/output/mlruns -p 6026 -h 0.0.0.0 # e.g mlflow server --backend-store-uri $PWD/output/mlruns --default-artifact-root $PWD/output/mlruns -p 6026 -h 0.0.0.0 ``` ### Tensorboard dashboard To visualize experiments and runs, user can start tensorboard: ```bash tensorboard --logdir /path/to/output/mlruns/1 # e.g tensorboard --logdir $PWD/output/mlruns/1 ``` where `/1` points to "Training" experiment. ## Implementation details Files tree description: ``` code configs experiments/mlflow : MLflow related files notebooks ``` ### Experiments - [conda.yaml](experiments/mlflow/conda.yaml): defines all python dependencies necessary for our experimentations - [MLproject](experiments/mlflow/MLproject): defines types of experiments we would like to perform by "entry points": - main : starts single-node multi-GPU training script When we execute ```bash mlflow run experiments/mlflow --experiment-name=Trainings -P config_path=configs/train/baseline_r50.py -P num_gpus=2 ``` it executes `main` entry point from [MLproject](experiments/mlflow/MLproject) and runs provided command. ignite-0.4.3/examples/references/classification/imagenet/NOTES_Polyaxon.md000066400000000000000000000033161401006565000266230ustar00rootroot00000000000000# Experiments tracking with Polyaxon User can run ImageNet training using [Polyaxon experiments tracking system](https://polyaxon.com/). ## Requirements In this case we assume, user has [Polyaxon](https://polyaxon.com/) installed on a machine/cluster/cloud and can schedule experiments with `polyaxon-cli`. ## Usage ### Setup Polyaxon project Create project on the cluster ```bash polyaxon project create --name=imagenet --description="Classification on ImageNet" ``` Initialize local project ```bash polyaxon init imagenet ``` Please rename and modify `experiments/plx/xp_training.yml.tmpl` to `experiments/plx/xp_training.yml` to adapt to your cluster configuration. #### Download ImageNet dataset Since 10/2019, we need to register an account in order to download the dataset. To download the dataset, use the following form : http://www.image-net.org/download.php ### Training on single node with single or multiple GPU For optimal devices usage, please, make sure to adapt training data loader batch size to your infrastructure. By default, batch size is 64 per process. Please, adapt `xp_training.yml` to your cluster configuration and run it, for example, as ```bash polyaxon run -u -f experiments/plx/xp_training.yml --name="baseline_resnet50" --tags=train,resnet50 ``` ## Training tracking Please, see Polyaxon dashboard usage at https://docs.polyaxon.com/ ## Implementation details Files tree description: ``` code configs experiments/plx : Polyaxon related files notebooks ``` ### Experiments File [xp_training.yml.tmpl](experiments/mlflow/xp_training.yml.tmpl) defines all configurations and dependencies necessary for our experimentations. Part `run.cmd` starts single-node multi-GPU training script. ignite-0.4.3/examples/references/classification/imagenet/README.md000066400000000000000000000070231401006565000247760ustar00rootroot00000000000000# Reproducible ImageNet training with Ignite In this example, we provide script and tools to perform reproducible experiments on training neural networks on ImageNet dataset. Features: - Distributed training with mixed precision by [nvidia/apex](https://github.com/NVIDIA/apex/) - Experiments tracking with [MLflow](https://mlflow.org/) or [Polyaxon](https://polyaxon.com/) or [ClearML](https://github.com/allegroai/clearml) ![tb_dashboard](assets/tb_dashboard.png) There are three possible options: 1) Experiments tracking with MLflow, 2) Experiments tracking with Polyaxon or 3) Experiments tracking with ClearML. Experiments tracking with ClearML / MLflow is more suitable for a local machine with GPU(s). For experiments tracking with Polyaxon user needs to have Polyaxon installed on a machine/cluster/cloud and can schedule experiments with `polyaxon-cli`. User can choose one option and skip the descriptions of another option. - Notes for [experiments tracking with MLflow](NOTES_MLflow.md) - Notes for [experiments tracking with Polyaxon](NOTES_Polyaxon.md) - Notes for [experiments tracking with ClearML](NOTES_ClearML.md) ## Implementation details Files tree description: ``` code |___ dataflow : module privides data loaders and various transformers |___ scripts : executable training script |___ utils : other helper modules configs |___ train : training python configuration files experiments |___ mlflow : MLflow related files |___ plx : Polyaxon related files |___ clearml : requirements.txt to install ClearML python package notebooks : jupyter notebooks to check specific parts from code modules ``` ## Code and configs ### [py_config_runner](https://github.com/vfdev-5/py_config_runner) We use [py_config_runner](https://github.com/vfdev-5/py_config_runner) package to execute python scripts with python configuration files. ### Training script Training script is located [code/scripts](code/scripts/) and contains - `training.py`, single training script with possiblity to use one of MLflow / Polayaxon / ClearML experiments tracking systems. Training script contains `run` method required by [py_config_runner](https://github.com/vfdev-5/py_config_runner) to run a script with a configuration. The split between training script and configuration python file is the following. Configuration file being a python script defines necessary components for neural network training: - Dataflow: training/validation/train evaluation data loaders with custom data augmentations - Model - Optimizer - Criterion - LR scheduler - other parameters: device, number of epochs, etc Training script uses these components to setup and run training and validation loops. By default, processing group with "nccl" backend is initialized for distributed configuration (even for a single GPU). Training script is generic, uses [`ignite.distributed` API](https://pytorch.org/ignite/master/distributed.html), and adapts training components to provided distributed configuration (e.g. uses DistribtedDataParallel model wrapper, uses distributed sampling, scales batch size etc). ### Configurations - [baseline_resnet50.py](configs/train/baseline_resnet50.py) : trains ResNet50 ### Results Model | Training Top-1 Accuracy | Training Top-5 Accuracy | Test Top-1 Accuracy | Test Top-5 Accuracy ---|---|---|---|--- ResNet-50 | 78% | 92% | 77% | 94% ## Acknowledgements Part of trainings was done within [Tesla GPU Test Drive](https://www.nvidia.com/en-us/data-center/tesla/gpu-test-drive/) on 2 Nvidia V100 GPUs. ![tb_dashboard_images](assets/tb_dashboard_images.png) 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@€ @€ @€ @€บ œปฬO€ฮษ]H€ @€ @€ @€ @€@"sขey*ิ8ืุฃ) @€ @€ @€ @€ˆ8วธ:• ฮC_ @€ @€ @€ @€ @เ#p๖' @€›ฮ›ม]G€ @€ @€ @€ @€@*sชuy,T8Wุข @€ @€ @€ @€ˆ8Gษ:— ฮ @€ @€ @€ @€ @เ8๛ @€อ็อเฎ#@€ @€ @€ @€ @ •€ภ9ีบ<–*œ+lั  @€ @€ @€ @€D œฃdK€็Œ  @€ @€ @€ @€ ๐œ  @€ภf๓fpื @€ @€ @€ @€ J@เœj]K€ฮถh @€ @€ @€ @€ขฮQฒฮ%@€๓ฦว @€ @€ @€ @€x~็/<ํใ/ฌIENDฎB`‚ignite-0.4.3/examples/references/classification/imagenet/code/000077500000000000000000000000001401006565000244275ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/code/dataflow/000077500000000000000000000000001401006565000262305ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/code/dataflow/__init__.py000066400000000000000000000000001401006565000303270ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/code/dataflow/dataloaders.py000066400000000000000000000045461401006565000310760ustar00rootroot00000000000000from typing import Callable, Optional, Tuple import cv2 import numpy as np from torch.utils.data import DataLoader from torch.utils.data.dataset import Subset from torchvision.datasets import ImageNet import ignite.distributed as idist 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_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 = ImageNet( root_path, split="train", transform=lambda sample: train_transforms(image=sample)["image"], loader=opencv_loader ) val_ds = ImageNet( root_path, split="val", transform=lambda sample: val_transforms(image=sample)["image"], loader=opencv_loader ) if limit_train_num_samples is not None: np.random.seed(limit_train_num_samples) train_indices = np.random.permutation(len(train_ds))[:limit_train_num_samples] train_ds = Subset(train_ds, train_indices) if limit_val_num_samples is not None: np.random.seed(limit_val_num_samples) val_indices = np.random.permutation(len(val_ds))[:limit_val_num_samples] val_ds = Subset(val_ds, val_indices) # random samples for evaluation on training dataset if len(val_ds) < len(train_ds): np.random.seed(len(val_ds)) train_eval_indices = np.random.permutation(len(train_ds))[: len(val_ds)] train_eval_ds = Subset(train_ds, train_eval_indices) else: train_eval_ds = train_ds train_loader = idist.auto_dataloader( train_ds, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True, ) val_batch_size = batch_size * 4 if val_batch_size is None else val_batch_size val_loader = idist.auto_dataloader( val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, ) train_eval_loader = idist.auto_dataloader( train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, ) return train_loader, val_loader, train_eval_loader ignite-0.4.3/examples/references/classification/imagenet/code/dataflow/transforms.py000066400000000000000000000006251401006565000310030ustar00rootroot00000000000000from typing import Callable, Type import torch 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 ignite-0.4.3/examples/references/classification/imagenet/code/dataflow/vis.py000066400000000000000000000040211401006565000274000ustar00rootroot00000000000000from typing import Callable, Optional import numpy as np import torch try: from image_dataset_viz import render_datapoint except ImportError: raise RuntimeError("Install it 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 ignite-0.4.3/examples/references/classification/imagenet/code/scripts/000077500000000000000000000000001401006565000261165ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/code/scripts/training.py000066400000000000000000000265011401006565000303070ustar00rootroot00000000000000# This a training script launched with py_config_runner # It should obligatory contain `run(config, **kwargs)` method from pathlib import Path import torch from apex import amp from py_config_runner.config_utils import TRAINVAL_CONFIG, assert_config, get_params from py_config_runner.utils import set_seed from utils import exp_tracking from utils.handlers import predictions_gt_images_handler import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events, _prepare_batch, create_supervised_evaluator from ignite.metrics import Accuracy, TopKCategoricalAccuracy from ignite.utils import setup_logger def initialize(config): model = config.model.to(config.device) optimizer = config.optimizer # Setup Nvidia/Apex AMP model, optimizer = amp.initialize(model, optimizer, opt_level=getattr(config, "fp16_opt_level", "O2"), num_losses=1) # Adapt model to dist conf model = idist.auto_model(model) criterion = config.criterion.to(config.device) return model, optimizer, criterion def create_trainer(model, optimizer, criterion, train_sampler, config, logger): prepare_batch = config.prepare_batch device = config.device # Setup trainer accumulation_steps = getattr(config, "accumulation_steps", 1) model_output_transform = getattr(config, "model_output_transform", lambda x: x) def train_update_function(engine, batch): model.train() x, y = prepare_batch(batch, device=device, non_blocking=True) y_pred = model(x) y_pred = model_output_transform(y_pred) loss = criterion(y_pred, y) / accumulation_steps with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss: scaled_loss.backward() if engine.state.iteration % accumulation_steps == 0: optimizer.step() optimizer.zero_grad() return { "supervised batch loss": loss.item(), } output_names = getattr(config, "output_names", ["supervised batch loss",]) lr_scheduler = config.lr_scheduler trainer = Engine(train_update_function) trainer.logger = logger to_save = {"model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler, "trainer": trainer, "amp": amp} save_every_iters = getattr(config, "save_every_iters", 1000) common.setup_common_training_handlers( trainer, train_sampler, to_save=to_save, save_every_iters=save_every_iters, output_path=config.output_path.as_posix(), lr_scheduler=lr_scheduler, with_gpu_stats=True, output_names=output_names, with_pbars=False, ) common.ProgressBar(persist=False).attach(trainer, metric_names="all") return trainer def create_evaluators(model, metrics, config): model_output_transform = getattr(config, "model_output_transform", lambda x: x) evaluator_args = dict( model=model, metrics=metrics, device=config.device, non_blocking=True, prepare_batch=config.prepare_batch, output_transform=lambda x, y, y_pred: (model_output_transform(y_pred), y,), ) train_evaluator = create_supervised_evaluator(**evaluator_args) evaluator = create_supervised_evaluator(**evaluator_args) common.ProgressBar(persist=False).attach(train_evaluator) common.ProgressBar(persist=False).attach(evaluator) return evaluator, train_evaluator 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} - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): msg = f"\n- PyTorch version: {torch.__version__}" msg += f"\n- Ignite version: {ignite.__version__}" logger.info(msg) if idist.get_world_size() > 1: msg = "\nDistributed setting:" msg += f"\tbackend: {idist.backend()}" msg += f"\trank: {idist.get_rank()}" msg += f"\tworld size: {idist.get_world_size()}" logger.info(msg) def training(local_rank, config, logger=None): if not getattr(config, "use_fp16", True): raise RuntimeError("This training script uses by default fp16 AMP") torch.backends.cudnn.benchmark = True set_seed(config.seed + local_rank) train_loader, val_loader, train_eval_loader = config.train_loader, config.val_loader, config.train_eval_loader # Setup model, optimizer, criterion model, optimizer, criterion = initialize(config) if not hasattr(config, "prepare_batch"): config.prepare_batch = _prepare_batch # Setup trainer for this specific task trainer = create_trainer(model, optimizer, criterion, train_loader.sampler, config, logger) if getattr(config, "benchmark_dataflow", False): benchmark_dataflow_num_iters = getattr(config, "benchmark_dataflow_num_iters", 1000) DataflowBenchmark(benchmark_dataflow_num_iters, prepare_batch=config.prepare_batch).attach( trainer, train_loader ) # Setup evaluators val_metrics = { "Accuracy": Accuracy(), "Top-5 Accuracy": TopKCategoricalAccuracy(k=5), } if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator, train_evaluator = create_evaluators(model, val_metrics, config) @trainer.on(Events.EPOCH_COMPLETED(every=getattr(config, "val_interval", 1)) | Events.COMPLETED) def run_validation(): epoch = trainer.state.epoch state = train_evaluator.run(train_eval_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(val_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) if getattr(config, "start_by_validation", False): trainer.add_event_handler(Events.STARTED, run_validation) score_metric_name = "Accuracy" if hasattr(config, "es_patience"): common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name) # Store 3 best models by validation accuracy: common.save_best_model_by_val_score( config.output_path.as_posix(), evaluator, model=model, metric_name=score_metric_name, n_saved=3, trainer=trainer, tag="val", ) if idist.get_rank() == 0: tb_logger = common.setup_tb_logging( config.output_path.as_posix(), trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator}, ) exp_tracking_logger = exp_tracking.setup_logging( trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator} ) # Log train/val predictions: tb_logger.attach( evaluator, log_handler=predictions_gt_images_handler( img_denormalize_fn=config.img_denormalize, n_images=15, another_engine=trainer, prefix_tag="validation" ), event_name=Events.ITERATION_COMPLETED(once=len(val_loader) // 2), ) tb_logger.attach( train_evaluator, log_handler=predictions_gt_images_handler( img_denormalize_fn=config.img_denormalize, n_images=15, another_engine=trainer, prefix_tag="training" ), event_name=Events.ITERATION_COMPLETED(once=len(train_eval_loader) // 2), ) trainer.run(train_loader, max_epochs=config.num_epochs) if idist.get_rank() == 0: tb_logger.close() exp_tracking_logger.close() def run(config, **kwargs): """This is the main method to run the training. As this training script is launched with `py_config_runner` it should obligatory contain `run(config, **kwargs)` method. """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled, "Nvidia/Amp requires cudnn backend to be enabled." with idist.Parallel(backend="nccl") as parallel: logger = setup_logger(name="ImageNet Training", distributed_rank=idist.get_rank()) assert_config(config, TRAINVAL_CONFIG) # The following attributes are automatically added by py_config_runner assert hasattr(config, "config_filepath") and isinstance(config.config_filepath, Path) assert hasattr(config, "script_filepath") and isinstance(config.script_filepath, Path) if idist.get_rank() == 0 and exp_tracking.has_clearml: try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.init("ImageNet Training", config.config_filepath.stem) task.connect_configuration(config.config_filepath.as_posix()) log_basic_info(logger, config) config.output_path = Path(exp_tracking.get_output_path()) # dump python files to reproduce the run exp_tracking.log_artifact(config.config_filepath.as_posix()) exp_tracking.log_artifact(config.script_filepath.as_posix()) exp_tracking.log_params(get_params(config, TRAINVAL_CONFIG)) try: parallel.run(training, config, logger=logger) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e class DataflowBenchmark: def __init__(self, num_iters=100, prepare_batch=None): from ignite.handlers import Timer device = idist.device() def upload_to_gpu(engine, batch): if prepare_batch is not None: x, y = prepare_batch(batch, device=device, non_blocking=False) self.num_iters = num_iters self.benchmark_dataflow = Engine(upload_to_gpu) @self.benchmark_dataflow.on(Events.ITERATION_COMPLETED(once=num_iters)) def stop_benchmark_dataflow(engine): engine.terminate() if idist.get_rank() == 0: @self.benchmark_dataflow.on(Events.ITERATION_COMPLETED(every=num_iters // 100)) def show_progress_benchmark_dataflow(engine): print(".", end=" ") self.timer = Timer(average=False) self.timer.attach( self.benchmark_dataflow, start=Events.EPOCH_STARTED, resume=Events.ITERATION_STARTED, pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED, ) def attach(self, trainer, train_loader): from torch.utils.data import DataLoader @trainer.on(Events.STARTED) def run_benchmark(_): if idist.get_rank() == 0: print("-" * 50) print(" - Dataflow benchmark") self.benchmark_dataflow.run(train_loader) t = self.timer.value() if idist.get_rank() == 0: print(" ") print(f" Total time ({self.num_iters} iterations) : {t:.5f} seconds") print(f" time per iteration : {t / self.num_iters} seconds") if isinstance(train_loader, DataLoader): num_images = train_loader.batch_size * self.num_iters print(f" number of images / s : {num_images / t}") print("-" * 50) ignite-0.4.3/examples/references/classification/imagenet/code/utils/000077500000000000000000000000001401006565000255675ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/code/utils/__init__.py000066400000000000000000000000001401006565000276660ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/code/utils/exp_tracking.py000066400000000000000000000067031401006565000306250ustar00rootroot00000000000000# Module for common exp tracking methods import os from pathlib import Path import torch import ignite import ignite.distributed as idist from ignite.contrib.engines import common try: import polyaxon_client.tracking if "POLYAXON_RUN_OUTPUTS_PATH" not in os.environ: raise ImportError("Not in Polyaxon cluster") has_plx = True except ImportError: has_plx = False try: import mlflow if "MLFLOW_TRACKING_URI" not in os.environ: raise ImportError("MLFLOW_TRACKING_URI should be defined") has_mlflow = True except ImportError: has_mlflow = False try: try: import clearml except ImportError: import trains if "CLEARML_OUTPUT_PATH" not in os.environ: raise ImportError("CLEARML_OUTPUT_PATH should be defined") has_clearml = True clearml_output_path = None except ImportError: has_clearml = False def _plx_get_output_path(): from polyaxon_client.tracking import get_outputs_path return get_outputs_path() @idist.one_rank_only() def _plx_log_artifact(fp): from polyaxon_client.tracking import Experiment plx_exp = Experiment() plx_exp.log_artifact(fp) @idist.one_rank_only() def _plx_log_params(params_dict): from polyaxon_client.tracking import Experiment plx_exp = Experiment() plx_exp.log_params( **{"pytorch version": torch.__version__, "ignite version": ignite.__version__,} ) plx_exp.log_params(**params_dict) def _mlflow_get_output_path(): return mlflow.get_artifact_uri() @idist.one_rank_only() def _mlflow_log_artifact(fp): mlflow.log_artifact(fp) @idist.one_rank_only() def _mlflow_log_params(params_dict): mlflow.log_params( {"pytorch version": torch.__version__, "ignite version": ignite.__version__,} ) mlflow.log_params(params_dict) def _clearml_get_output_path(): global clearml_output_path if clearml_output_path is None: from datetime import datetime output_path = Path(os.environ["CLEARML_OUTPUT_PATH"]) output_path = output_path / "clearml" / datetime.now().strftime("%Y%m%d-%H%M%S") clearml_output_path = output_path return clearml_output_path.as_posix() @idist.one_rank_only() def _clearml_log_artifact(fp): try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.current_task() task.upload_artifact(Path(fp).name, fp) @idist.one_rank_only() def _clearml_log_params(params_dict): try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.current_task() task.connect(params_dict) if has_plx: get_output_path = _plx_get_output_path log_params = _plx_log_params setup_logging = common.setup_plx_logging log_artifact = _plx_log_artifact elif has_mlflow: get_output_path = _mlflow_get_output_path log_params = _mlflow_log_params setup_logging = common.setup_mlflow_logging log_artifact = _mlflow_log_artifact elif has_clearml: get_output_path = _clearml_get_output_path log_params = _clearml_log_params setup_logging = common.setup_clearml_logging log_artifact = _clearml_log_artifact else: raise RuntimeError( "No experiment tracking system is setup. " "Please, setup either MLflow, Polyaxon or ClearML. " "For more details see NOTES_*.md" ) ignite-0.4.3/examples/references/classification/imagenet/code/utils/handlers.py000066400000000000000000000021451401006565000277430ustar00rootroot00000000000000import torch from dataflow.vis import make_grid 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-0.4.3/examples/references/classification/imagenet/configs/000077500000000000000000000000001401006565000251455ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/configs/train/000077500000000000000000000000001401006565000262625ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/configs/train/baseline_resnet50.py000066400000000000000000000051361401006565000321500ustar00rootroot00000000000000# 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.dataloaders import get_train_val_loaders from dataflow.transforms import denormalize 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 fp16_opt_level = "O2" val_interval = 2 train_crop_size = 224 val_crop_size = 320 batch_size = 64 * idist.get_world_size() # total batch size num_workers = 10 # ############################## # 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, ) # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=mean, std=std) # ############################## # Setup Model # ############################## model = resnet50(pretrained=False) # ############################## # 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-0.4.3/examples/references/classification/imagenet/configs/train/check_baseline_resnet50.py000066400000000000000000000051671401006565000333110ustar00rootroot00000000000000# 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.dataloaders import get_train_val_loaders from dataflow.transforms import denormalize 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 fp16_opt_level = "O2" val_interval = 2 start_by_validation = True train_crop_size = 224 val_crop_size = 320 batch_size = 64 * idist.get_world_size() # total batch size num_workers = 10 # ############################## # 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, ) # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=mean, std=std) # ############################## # Setup Model # ############################## model = resnet50(pretrained=False) # ############################## # Setup Solver # ############################## num_epochs = 1 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-0.4.3/examples/references/classification/imagenet/experiments/000077500000000000000000000000001401006565000260605ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/experiments/clearml/000077500000000000000000000000001401006565000274775ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/experiments/clearml/requirements.txt000066400000000000000000000000101401006565000327520ustar00rootroot00000000000000clearml ignite-0.4.3/examples/references/classification/imagenet/experiments/mlflow/000077500000000000000000000000001401006565000273605ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/experiments/mlflow/MLproject000066400000000000000000000005351401006565000312050ustar00rootroot00000000000000name: ImageNet conda_env: conda.yaml entry_points: main: parameters: config_path: path num_gpus: float command: "sh ../setup_apex.sh && export PYTHONPATH=$PWD/../../code:$PYTHONPATH && python -m torch.distributed.launch --nproc_per_node={num_gpus} --use_env -m py_config_runner ../../code/scripts/training.py {config_path}" ignite-0.4.3/examples/references/classification/imagenet/experiments/mlflow/conda.yaml000066400000000000000000000004771401006565000313400ustar00rootroot00000000000000name: imagenet_env channels: - pytorch dependencies: - python=3.7 - numpy - cudatoolkit - pytorch - torchvision - pip - pip: - mlflow - albumentations - tqdm - tensorboardX - py_config_runner - pynvml - pytorch-ignite - git+https://github.com/vfdev-5/ImageDatasetViz.git ignite-0.4.3/examples/references/classification/imagenet/experiments/plx/000077500000000000000000000000001401006565000266635ustar00rootroot00000000000000ignite-0.4.3/examples/references/classification/imagenet/experiments/plx/xp_training.yml.tmpl000066400000000000000000000032531401006565000327060ustar00rootroot00000000000000--- version: 1 kind: experiment tags: ["training", "resnet50"] # Setup running node: environment: node_selector: polyaxon: multigpu resources: gpu: requests: 2 limits: 2 # Setup running environment: build: image: pytorch/pytorch:1.5-cuda10.1-cudnn7-devel build_steps: # For opencv - apt-get update && TZ=America/New_York DEBIAN_FRONTEND="noninteractive" apt-get -y install tzdata && apt-get -y install --no-install-recommends libglib2.0 libsm6 libxext6 libxrender-dev git # Install Nvidia/APEX - git clone https://github.com/NVIDIA/apex /tmp/apex && cd /tmp/apex && export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" && pip install --upgrade --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . # Install ignite and other useful packages - pip install --upgrade --pre pytorch-ignite && pip install git+https://github.com/vfdev-5/ImageDatasetViz.git albumentations && pip install polyaxon-client tqdm tensorboardX py_config_runner pynvml declarations: config_file: "baseline_resnet50.py" script_file: "training.py" num_gpus: 2 run: cmd: - export LC_ALL=C.UTF-8 && export LANG=C.UTF-8 - export PYTHONPATH=$PYTHONPATH:$PWD/code/ # Required env variables to define dataset placement - export DATASET_PATH=/path/to/ImageNet-1k/ - export config_file=$PWD/configs/train/{{config_file}} - export script_file=$PWD/code/scripts/{{script_file}} # Copy configuration file to the output - cp $config_file $POLYAXON_RUN_OUTPUTS_PATH - cp $script_file $POLYAXON_RUN_OUTPUTS_PATH - python -m torch.distributed.launch --nproc_per_node={{num_gpus}} -m py_config_runner $script_file $config_fileignite-0.4.3/examples/references/classification/imagenet/experiments/setup_apex.sh000066400000000000000000000006151401006565000305730ustar00rootroot00000000000000#!/bin/bash tmp_apex_path="/tmp/apex" python -c "import apex" res=$? if [ "$res" -eq "1" ]; then echo "Setup NVIDIA Apex" rm -rf $tmp_apex_path git clone https://github.com/NVIDIA/apex $tmp_apex_path cd $tmp_apex_path export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" pip install --upgrade --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . fi ignite-0.4.3/examples/references/classification/imagenet/experiments/setup_opencv.sh000066400000000000000000000006231401006565000311270ustar00rootroot00000000000000#!/bin/bash python -c "import cv2" res=$? if [ "$res" -eq "1" ]; then echo "Install libglib2.0 libsm6 libxext6 libxrender-dev for opencv" apt-get update ln -fs /usr/share/zoneinfo/America/New_York /etc/localtime apt-get install -y tzdata dpkg-reconfigure --frontend noninteractive tzdata apt-get -y install --no-install-recommends libglib2.0 libsm6 libxext6 libxrender-dev fi ignite-0.4.3/examples/references/classification/imagenet/requirements.txt000066400000000000000000000001771401006565000270060ustar00rootroot00000000000000albumentations image-dataset-viz numpy opencv-python py_config_runner pytorch-ignite pillow tensorboard torch torchvision tqdm ignite-0.4.3/examples/references/segmentation/000077500000000000000000000000001401006565000214265ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/000077500000000000000000000000001401006565000240455ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/.gitignore000066400000000000000000000001471401006565000260370ustar00rootroot00000000000000.ipynb_checkpoints input output experiments/plx/*.yml experiments/plx/*.yaml .polyaxon/ .polyaxonignoreignite-0.4.3/examples/references/segmentation/pascal_voc2012/NOTES_ClearML.md000066400000000000000000000221701401006565000266200ustar00rootroot00000000000000# Experiments tracking with ClearML [Allegro ClearML](https://allegro.ai/clearml/docs/) is a full system open source ML / DL experiment manager and ML-Ops solution. It is composed of a server, Python SDK and web UI. **Allegro ClearML** enables data scientists and data engineers to effortlessly track, manage, compare and collaborate on their experiments as well as easily manage their training workloads on remote machines. ## Install ClearML Install [clearml](https://github.com/allegroai/clearml) by executing the following command: ```bash pip install --upgrade clearml ``` ## Install requirements ```bash pip install -r requirements.txt ``` We need to also install Nvidia/APEX and libraries for opencv. **Important**, please, check the content of `experiments/setup_opencv.sh` before running the script. ```bash sh experiments/setup_apex.sh sh experiments/setup_opencv.sh ``` ## Download Pascal VOC2012 and SDB datasets Download and extract the datasets: ```bash python code/scripts/download_dataset.py /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 ## Setup the environment variables ### Setup the dataset path Export the `DATASET_PATH` environment variable for the Pascal VOC2012 dataset. ```bash export DATASET_PATH=/path/to/pascal_voc2012 # e.g. export DATASET_PATH=$PWD/input/ where VOCdevkit is located ``` ### Setup the SBD dataset path Export the `SBD_DATASET_PATH` environment variable for the SBD evaluation dataset. ```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 ``` ## Run the experiment code In **ClearML**, when you run the experiment code, `clearml` stores the experiment in [clearml-server](https://github.com/allegroai/clearml-server). By default, `clearml` works with the demo **ClearML Server** ([https://demoapp.trains.allegro.ai/dashboard](https://demoapp.trains.allegro.ai/dashboard)), which is open to anyone (although once a week it is refreshing and deleting all data). You can also set up your own [self-hosted](https://github.com/allegroai/clearml-server) **ClearML Server**. After the experiment code runs once, you can [reproduce the experiment](#reproducing-the-experiment) using the **ClearML Web-App (UI)**, which is part of `clearml-server`. You only need to run the code once to store it in `clearml-server`. ### Setup This setup is a specific for this code and is not required in general usage of ClearML. We setup an output path as a local storage: ```bash export CLEARML_OUTPUT_PATH=/path/to/output/clearml # e.g export CLEARML_OUTPUT_PATH=$PWD/output/clearml ``` This environment variable helps to choose ClearML as experiment tracking system among all others. ### ClearML fileserver setup The configuration to upload artifact must be done by modifying the `clearml` configuration file `~/clearml.conf` generated by `clearml-init`. According to the [documentation](https://allegro.ai/docs/examples/reporting/artifacts/), the `output_uri` argument can be configured in `sdk.development.default_output_uri` to fileserver uri. If server is self-hosted, `ClearML` fileserver uri is `http://localhost:8081`. For more details, see https://allegro.ai/docs/examples/reporting/artifacts/ ### Run the code #### Training on single node and single GPU Please, make sure to adapt training data loader batch size to your GPU type. For example, a single GPU with 11GB can have a batch size of 8-9. Execute the following command: ```bash export CLEARML_OUTPUT_PATH=/path/to/output/clearml # e.g export CLEARML_OUTPUT_PATH=$PWD/output/clearml python -m py_config_runner ./code/scripts/training.py ./configs/train/baseline_resnet101.py ``` #### Training on single node and multiple GPUs For optimal devices usage, please, make sure to adapt training data loader batch size to your infrasture. For example, a single GPU with 11GB can have a batch size of 8-9, thus, on N devices, we can set it as `N * 9`. ```bash export CLEARML_OUTPUT_PATH=/path/to/output/clearml # e.g export CLEARML_OUTPUT_PATH=$PWD/output/clearml python -m torch.distributed.launch --nproc 2 --use_env -m py_config_runner ./code/scripts/training.py ./configs/train/baseline_resnet101.py ``` In **ClearML Web-App** a new project named _"Pascal-VOC12 Training"_ will be created, with an experiment named _"baseline_resnet101"_ inside. In your local environment, the console output includes the URL of the experiment's **RESULTS** page. You can now view your experiment in **ClearML** by clicking the link or copying the URL into your browser. It opens the results in the experiment's details pane, in the **ClearML Web-App (UI)**. #### ClearML automatic Logging When the experiment code runs, **ClearML** automatically logs your environment, code, and the outputs. Which means that you don't need to change your code. All you need is 2 lines of integration at the top of your main script ```python from clearml import Task Task.init("Pascal-VOC12 Training", "baseline_resnet101") ``` Once it's there, the following will be automatically logged by **ClearML**: - **Resource Monitoring** CPU/GPU utilization, temperature, IO, network, etc - **Development Environment** Python environment, Git (repo, branch, commit) including uncommitted changes - **Configuration** Including configuration files, command line arguments (ArgParser), and general dictionaries - Full **stdout** and **stderr** automatic logging - Model snapshots, with optional automatic upload to central storage. - Artifacts log & store, including shared folders, S3, GS, Azure, and Http/s - Matplotlib / Seaborn / TensorBoard / TensorBoardX scalars, metrics, histograms, images, audio, video, etc Additionally, **ClearML** supports explicit logging by adding calls to the **ClearML** Python client `Logger` class methods in the code. For more information, see [Explicit Reporting](https://allegro.ai/docs/examples/examples_explicit_reporting/) in the **ClearML** documentation. ## Track the experiment and visualize the results In the **ClearML Web-App (UI)**, track the experiment and visualize results in the experiment's details pane, which is organized in tabs and provides the following information; - Source code, uncommitted changes, Python packages and versions, and other information, in the **EXECUTION** tab - Hyperparameters in the **HYPERPARAMETERS** tab - Input model, Configuration, Output model, and other artifacts in the **ARTIFACTS** tab - Experiment Comments and General experiment information in the **INFO** tab - Results in the **RESULTS** tab, including the log, scalar metric plots, plots of any data, and debug samples ## Reproducing the experiments In **ClearML**, reproduce experiments using `clearml-agent` for remote execution. Rerun the same experiment, by making an exact copy of it (a clone), and remotely execute the cloned experiment. First, install `clearml-agent` and then configure it to work with your self-hosted **ClearML Server**. Once `clearml-agent` is installed and configured, run `clearml-agent daemon`. In **ClearML**, we call these _workers_, they pop experiments from a job execution queue and execute them. Every machine with a _clearml-agent daemon_, becomes a registered _worker_ in your **clearml-server** cluster. Using the **ClearML Web-App** you can easily send experiments to be remotely executed on one of these machines. More details can be found on the _clearml-agent_ [github](https://github.com/allegroai/clearml-agent/) ### Install and configure clearml-agent 1. Install `clearml-agent` pip install clearml-agent 1. Configure `clearml-agent` by running the setup wizard clearml-agent init ### Remotely execute the experiment 1. Start a **ClearML** worker. Run a `clearml-agent daemon` listening to a queue For example, run a `clearml-agent daemon` listening to the `default` queue and using multiple GPUs. clearml-agent daemon --gpus 0,1 --queue default 1. Locate the experiment. In the **ClearML Web-App (UI)**, Projects page, click on the project card 1. Make a copy of the experiment 1. In the experiment table, right-click the experiment 1. On the sub-menu, select **Clone** 1. Select the project, type a name for the copy, and type a description, or accept the defaults 1. Click the **CLONE** button The copy of the experiment is created. Its details pane opens. 1. Send the experiment for remote execution, by enqueuing it in one of the job execution queues 1. In the experiment table, right-click the experiment 1. On the sub-menu, select **Enqueue** 1. Select the _default_ queue 1. Click the **ENQUEUE** button The experiment's status changes to Pending. When the experiment reaches the top of the job execution queue, the `clearml-agent deamon` fetches it, its status changes to Running, and `clearml-agent` executes it while logging and monitoring. You can track the experiment while it is in progress, and anytime afterwards. ignite-0.4.3/examples/references/segmentation/pascal_voc2012/NOTES_MLflow.md000066400000000000000000000103511401006565000265370ustar00rootroot00000000000000# Experiments tracking with MLflow User can run Pascal VOC training using MLflow experiments tracking system on the local machine. ## Requirements We use `conda` and [MLflow](https://github.com/mlflow/mlflow) to handle experiments/runs and all python dependencies. Please, install these tools: - [MLflow](https://github.com/mlflow/mlflow): `pip install mlflow` - [conda](https://conda.io/en/latest/miniconda.html) We need to also install Nvidia/APEX and libraries for opencv. APEX is automatically installed on the first run. Manually, all can be installed with the following commands. **Important**, please, check the content of `experiments/setup_opencv.sh` before running. ```bash sh experiments/setup_apex.sh sh experiments/setup_opencv.sh ``` ## Usage ### Download Pascal VOC 2012 and SBD ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns mlflow run experiments/mlflow -e download -P output_path=/path/where/download/ # e.g mlflow run experiments/mlflow -e download -P output_path=$PWD/input ``` ### Setup dataset path To configure the path to already existing PASCAL VOC2012 dataset, please specify `DATASET_PATH` environment variable ```bash export DATASET_PATH=/path/to/pascal_voc2012 # e.g. export DATASET_PATH=$PWD/input/ where VOCdevkit is located ``` #### With SBD dataset Optionally, user can configure the path to already existing SBD dataset, please specify `SBD_DATASET_PATH` environment variable ```bash export SBD_DATASET_PATH=/path/to/SBD/benchmark_RELEASE/dataset/ # e.g. export SBD_DATASET_PATH=/data/SBD/benchmark_RELEASE/dataset/ where "cls img inst train.txt train_noval.txt val.txt" are located ``` ### MLflow setup Setup mlflow output path as a local storage (option with remote storage is not supported): ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns ``` Create once "Trainings" experiment ```bash mlflow experiments create -n Trainings ``` or check existing experiments: ```bash mlflow experiments list ``` ### Training on single node with single GPU Please, make sure to adapt training data loader batch size to your GPU type. For example, a single GPU with 11GB can have a batch size of 8-9. ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns mlflow run experiments/mlflow --experiment-name=Trainings -P config_path=configs/train/baseline_resnet101.py -P num_gpus=1 ``` ### Training on single node with multiple GPUs For optimal devices usage, please, make sure to adapt training data loader batch size to your infrastructure. For example, a single GPU with 11GB can have a batch size of 8-9, thus, on N devices, we can set it as `N * 9`. ```bash export MLFLOW_TRACKING_URI=/path/to/output/mlruns # e.g export MLFLOW_TRACKING_URI=$PWD/output/mlruns mlflow run experiments/mlflow --experiment-name=Trainings -P config_path=configs/train/baseline_resnet101.py -P num_gpus=2 ``` ## Training tracking ### MLflow dashboard To visualize experiments and runs, user can start mlflow dashboard: ```bash mlflow server --backend-store-uri /path/to/output/mlruns --default-artifact-root /path/to/output/mlruns -p 6026 -h 0.0.0.0 # e.g mlflow server --backend-store-uri $PWD/output/mlruns --default-artifact-root $PWD/output/mlruns -p 6026 -h 0.0.0.0 ``` ### Tensorboard dashboard To visualize experiments and runs, user can start tensorboard: ```bash tensorboard --logdir /path/to/output/mlruns/1 # e.g tensorboard --logdir $PWD/output/mlruns/1 ``` where `/1` points to "Training" experiment. ## Implementation details Files tree description: ``` code configs experiments/mlflow : MLflow related files notebooks ``` ### Experiments - [conda.yaml](experiments/mlflow/conda.yaml): defines all python dependencies necessary for our experimentations - [MLproject](experiments/mlflow/MLproject): defines types of experiments we would like to perform by "entry points": - main : starts single-node multi-GPU training script When we execute ```bash mlflow run experiments/mlflow --experiment-name=Trainings -P config_path=configs/train/baseline_resnet101.py -P num_gpus=2 ``` it executes `main` entry point from [MLproject](experiments/mlflow/MLproject) and runs provided command. ignite-0.4.3/examples/references/segmentation/pascal_voc2012/NOTES_Polyaxon.md000066400000000000000000000036231401006565000271540ustar00rootroot00000000000000# Experiments tracking with Polyaxon User can run Pascal VOC training using [Polyaxon experiments tracking system](https://polyaxon.com/). ## Requirements In this case we assume, user has [Polyaxon](https://polyaxon.com/) installed on a machine/cluster/cloud and can schedule experiments with `polyaxon-cli`. ## Usage ### Setup Polyaxon project Create project on the cluster ```bash polyaxon project create --name=pascal-voc2012 --description="Semantic segmentation on Pascal VOC2012" ``` Initialize local project ```bash polyaxon init pascal-voc2012 ``` Please rename and modify `experiments/plx/xp_training.yml.tmpl` to `experiments/plx/xp_training.yml` to adapt to your cluster configuration. #### Download Pascal VOC 2012 and SBD Optionally, it is possible to download the datasets as a job. Please rename and modify `experiments/plx/job_download_datasets.yml.tmpl` to `experiments/plx/job_download_datasets.yml` ```bash polyaxon run -u -f experiments/plx/job_download_datasets.yml ``` ### Training on single node with single or multiple GPU For optimal devices usage, please, make sure to adapt training data loader batch size to your infrastructure. For example, a single GPU with 11GB can have a batch size of 8-9, thus, on N devices, we can set it as `N * 9`. Please, adapt `xp_training.yml` to your cluster configuration and run it, for example, as ```bash polyaxon run -u -f experiments/plx/xp_training.yml --name="baseline_resnet101_sbd" --tags=train,deeplab,sbd ``` ## Training tracking Please, see Polyaxon dashboard usage at https://docs.polyaxon.com/ ## Implementation details Files tree description: ``` code configs experiments/plx : Polyaxon related files notebooks ``` ### Experiments File [xp_training.yml.tmpl](experiments/plx/xp_training.yml.tmpl) defines all configurations and dependencies necessary for our experimentations. Part `run.cmd` starts single-node multi-GPU training script. ignite-0.4.3/examples/references/segmentation/pascal_voc2012/README.md000066400000000000000000000074371401006565000253370ustar00rootroot00000000000000# Reproducible PASCAL VOC2012 training with 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 mixed precision by [nvidia/apex](https://github.com/NVIDIA/apex/) - Experiments tracking with [MLflow](https://mlflow.org/) or [Polyaxon](https://polyaxon.com/) or [ClearML](https://github.com/allegroai/clearml) Tensorboard | MLflow ---|--- ![tb_dashboard](assets/tb_dashboard.png) | ![mlflow_dashboard](assets/mlflow_dashboard.png) There are three possible options: 1) Experiments tracking with MLflow, 2) Experiments tracking with Polyaxon or 3) Experiments tracking with ClearML. Experiments tracking with ClearML / MLflow is more suitable for a local machine with GPU(s). For experiments tracking with Polyaxon user needs to have Polyaxon installed on a machine/cluster/cloud and can schedule experiments with `polyaxon-cli`. User can choose one option and skip the descriptions of another option. - Notes for [experiments tracking with MLflow](NOTES_MLflow.md) - Notes for [experiments tracking with Polyaxon](NOTES_Polyaxon.md) - Notes for [experiments tracking with ClearML](NOTES_ClearML.md) ## Implementation details Files tree description: ``` code |___ dataflow : module privides data loaders and various transformers |___ scripts : executable training script |___ utils : other helper modules configs |___ train : training python configuration files experiments |___ mlflow : MLflow related files |___ plx : Polyaxon related files |___ clearml : requirements.txt to install ClearML python package notebooks : jupyter notebooks to check specific parts from code modules ``` ## Code and configs ### [py_config_runner](https://github.com/vfdev-5/py_config_runner) We use [py_config_runner](https://github.com/vfdev-5/py_config_runner) package to execute python scripts with python configuration files. ### Training script Training script is located [code/scripts](code/scripts/) and contains - `training.py`, single training script with possiblity to use one of MLflow / Polayaxon / ClearML experiments tracking systems. Training script contains `run` method required by [py_config_runner](https://github.com/vfdev-5/py_config_runner) to run a script with a configuration. The split between training script and configuration python file is the following. Configuration file being a python script defines necessary components for neural network training: - Dataflow: training/validation/train evaluation data loaders with custom data augmentations - Model - Optimizer - Criterion - LR scheduler - other parameters: device, number of epochs, etc Training script uses these components to setup and run training and validation loops. By default, processing group with "nccl" backend is initialized for distributed configuration (even for a single GPU). Training script is generic, uses [`ignite.distributed` API](https://pytorch.org/ignite/master/distributed.html), and adapts training components to provided distributed configuration (e.g. uses DistribtedDataParallel model wrapper, uses distributed sampling, scales batch size etc). ### Configurations - [baseline_resnet101.py](configs/train/baseline_resnet101.py) : trains DeeplabV3-ResNet101 on Pascal VOC2012 dataset only - [baseline_resnet101_sbd.py](configs/train/baseline_resnet101_sbd.py) : trains DeeplabV3-ResNet101 on Pascal VOC2012 dataset with SBD ### Results Model | with SBD | Training mIoU+BG | Test mIoU+BG ---|---|---|--- DeepLabV3 ResNet-101 | X | 86% | 68% ## Acknowledgements Part of trainings was done within [Tesla GPU Test Drive](https://www.nvidia.com/en-us/data-center/tesla/gpu-test-drive/) on 2 Nvidia V100 GPUs. ignite-0.4.3/examples/references/segmentation/pascal_voc2012/assets/000077500000000000000000000000001401006565000253475ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/assets/mlflow_dashboard.png000066400000000000000000003042161401006565000313720ustar00rootroot00000000000000‰PNG  IHDRrศEฃQ2ฮ๕๏Wษฒ9BP(สIl!+ๅำจuณ๘‚|ิฏณ,‡?อี(ห ะQn™ๆ”Y๎žๆคYฯฤD†{ข…ษค@™ž‹žหJA๓P่(›๓ุ\สำ๓บฐR™l”ทก IžFไ ฒย%๙9B๏ˆ9f พฯ%Jr—ฬหaHrๆฅFฦฬq.7:xŽ…้฿c<%~(\RsฒภGฒฦLแ_ึลeHโsR#$kd~ฏ#Œ•ิภๆxyKผ(I ?วC’ŸŸ*‰็d๘Jยษุ๔ฐ}*ูŸ4ฆ่/เ ั‡ข€%๚X ๆ‚r8หsฆเ™ล_!เฆคๆะัฤก3x,ณ๙tKs ฆ๏ใ์็~woๆžAŠ„๏พฌห8 ็้๎[bภ‰dผฟ๛ Ÿกืข€ :,‘ wึ‡™ม๒@h ็ษ˜ข•ู'เ†V์B@$ˆK ค‚L หภ*ฐ‚bฐ ์ๅเ8ƒcเ$hmเ"ธn€^p<b0 ^‚1๐LB„‡จ R†4!=ศฒ„์!ศ „ยก8(JxZm€Šกจช„๊ ะY่"t ๊ƒ๎Cƒะ(๔๚ #0–‡ีa}xlปรp$ผNณแ<ธ—มU๐Qธ พ฿€๏ภb๘%<Ž„Œ("Zˆ)bx"!H<’Œ5HRŠT! 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ะŠN ีyอ๚ๅตตIENDฎB`‚ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/000077500000000000000000000000001401006565000247575ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/__init__.py000066400000000000000000000000001401006565000270560ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/dataflow/000077500000000000000000000000001401006565000265605ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/dataflow/__init__.py000066400000000000000000000000001401006565000306570ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/dataflow/dataloaders.py000066400000000000000000000064411401006565000314220ustar00rootroot00000000000000from typing import Callable, Optional, Tuple, Union import numpy as np from dataflow.datasets import TransformedDataset, get_train_dataset, get_train_noval_sbdataset, get_val_dataset from torch.utils.data import DataLoader from torch.utils.data.dataset import ConcatDataset, Subset import ignite.distributed as idist 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, with_sbd: Optional[str] = None, limit_train_num_samples: Optional[int] = None, limit_val_num_samples: Optional[int] = None, ) -> Tuple[DataLoader, DataLoader, DataLoader]: train_ds = get_train_dataset(root_path) val_ds = get_val_dataset(root_path) if with_sbd is not None: sbd_train_ds = get_train_noval_sbdataset(with_sbd) train_ds = ConcatDataset([train_ds, sbd_train_ds]) if limit_train_num_samples is not None: np.random.seed(limit_train_num_samples) train_indices = np.random.permutation(len(train_ds))[:limit_train_num_samples] train_ds = Subset(train_ds, train_indices) if limit_val_num_samples is not None: np.random.seed(limit_val_num_samples) val_indices = np.random.permutation(len(val_ds))[:limit_val_num_samples] val_ds = Subset(val_ds, val_indices) # random samples for evaluation on training dataset if len(val_ds) < len(train_ds): np.random.seed(len(val_ds)) train_eval_indices = np.random.permutation(len(train_ds))[: 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) train_loader = idist.auto_dataloader( train_ds, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True, ) val_batch_size = batch_size * 4 if val_batch_size is None else val_batch_size val_loader = idist.auto_dataloader( val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, ) train_eval_loader = idist.auto_dataloader( train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False, ) return train_loader, val_loader, train_eval_loader def get_inference_dataloader( root_path: str, mode: str, transforms: Callable, batch_size: int = 16, num_workers: int = 8, pin_memory: bool = True, limit_num_samples: Optional[int] = None, ) -> DataLoader: 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) if limit_num_samples is not None: indices = np.random.permutation(len(dataset))[:limit_num_samples] dataset = Subset(dataset, indices) dataset = TransformedDataset(dataset, transform_fn=transforms) loader = DataLoader( dataset, shuffle=False, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory, drop_last=False ) return loader ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/dataflow/datasets.py000066400000000000000000000064231401006565000307470ustar00rootroot00000000000000from typing import Callable, Type import cv2 import numpy as np from PIL import Image from torch.utils.data import Dataset from torchvision.datasets.sbd import SBDataset from torchvision.datasets.voc import VOCSegmentation class TransformedDataset(Dataset): def __init__(self, ds: Dataset, transform_fn: Callable): 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: bool = 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: bool = 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: str, return_meta: bool = False): return VOCSegmentationOpencv( root=root_path, year="2012", image_set="train", download=False, return_meta=return_meta ) def get_val_dataset(root_path: str, return_meta: bool = False): return VOCSegmentationOpencv(root=root_path, year="2012", image_set="val", download=False, return_meta=return_meta) def get_train_noval_sbdataset(root_path: str, return_meta: bool = False): return SBDatasetOpencv(root_path, image_set="train_noval", mode="segmentation", return_meta=return_meta) ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/dataflow/transforms.py000066400000000000000000000015341401006565000313330ustar00rootroot00000000000000import torch from ignite.utils import convert_tensor def ignore_mask_boundaries(force_apply, **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_batch_fp32(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-0.4.3/examples/references/segmentation/pascal_voc2012/code/dataflow/vis.py000066400000000000000000000063261401006565000277420ustar00rootroot00000000000000from typing import Callable, Optional, Union import numpy as np import torch from PIL import Image try: from image_dataset_viz import render_datapoint except ImportError: raise RuntimeError("Install it 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: Union[np.ndarray, Image.Image]) -> Image.Image: if isinstance(mask, np.ndarray): mask = Image.fromarray(mask) mask.putpalette(vocpallete) mask = mask.convert(mode="RGB") return mask def tensor_to_rgb(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_mask: torch.Tensor, img_denormalize_fn: Callable, batch_gt_mask: Optional[torch.Tensor] = 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 ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/scripts/000077500000000000000000000000001401006565000264465ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/scripts/__init__.py000066400000000000000000000000001401006565000305450ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/scripts/download_dataset.py000066400000000000000000000017711401006565000323420ustar00rootroot00000000000000import argparse import os from torchvision.datasets.sbd import SBDataset from torchvision.datasets.voc import VOCSegmentation if __name__ == "__main__": parser = argparse.ArgumentParser("Download Pascal VOC 2012 and SBD segmentation datasets") parser.add_argument("output_path", type=str, help="Path where to download and unzip the dataset") args = parser.parse_args() print("Download Pascal VOC 2012 - Training") VOCSegmentation(args.output_path, image_set="train", download=True) print("Download Pascal VOC 2012 - Validation") VOCSegmentation(args.output_path, image_set="val", download=True) print("Download SBD - Training without Pascal VOC validation part") sbd_path = os.path.join(args.output_path, "SBD") os.makedirs(sbd_path, exist_ok=True) SBDataset(sbd_path, image_set="train_noval", mode="segmentation", download=True) print("Done") print(f"Pascal VOC 2012 is at : {os.path.join(args.output_path, 'VOCdevkit')}") print(f"SBD is at : {sbd_path}") ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/scripts/training.py000066400000000000000000000270421401006565000306400ustar00rootroot00000000000000# This a training script launched with py_config_runner # It should obligatory contain `run(config, **kwargs)` method import sys from collections.abc import Mapping from pathlib import Path import torch from apex import amp from dataflow.datasets import VOCSegmentationOpencv from py_config_runner.config_utils import TRAINVAL_CONFIG, assert_config, get_params from py_config_runner.utils import set_seed from utils import exp_tracking from utils.handlers import predictions_gt_images_handler import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.handlers import DiskSaver from ignite.metrics import ConfusionMatrix, IoU, mIoU from ignite.utils import setup_logger # Adds "code" folder to python path sys.path.insert(0, Path(__file__).parent.parent.as_posix()) def initialize(config): model = config.model.to(config.device) optimizer = config.optimizer # Setup Nvidia/Apex AMP model, optimizer = amp.initialize(model, optimizer, opt_level=getattr(config, "fp16_opt_level", "O2"), num_losses=1) # Adapt model to dist conf model = idist.auto_model(model) criterion = config.criterion.to(config.device) return model, optimizer, criterion def get_save_handler(config): if exp_tracking.has_clearml: from ignite.contrib.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=config.output_path.as_posix()) return DiskSaver(config.output_path.as_posix()) def create_trainer(model, optimizer, criterion, train_sampler, config, logger): prepare_batch = config.prepare_batch device = config.device # Setup trainer accumulation_steps = getattr(config, "accumulation_steps", 1) model_output_transform = getattr(config, "model_output_transform", lambda x: x) def train_update_function(engine, batch): model.train() x, y = prepare_batch(batch, device=device, non_blocking=True) y_pred = model(x) y_pred = model_output_transform(y_pred) loss = criterion(y_pred, y) if isinstance(loss, Mapping): assert "supervised batch loss" in loss loss_dict = loss output = {k: v.item() for k, v in loss_dict.items()} loss = loss_dict["supervised batch loss"] / accumulation_steps else: output = {"supervised batch loss": loss.item()} with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss: scaled_loss.backward() if engine.state.iteration % accumulation_steps == 0: optimizer.step() optimizer.zero_grad() return output output_names = getattr(config, "output_names", ["supervised batch loss",]) lr_scheduler = config.lr_scheduler trainer = Engine(train_update_function) trainer.logger = logger to_save = {"model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler, "trainer": trainer, "amp": amp} save_every_iters = getattr(config, "save_every_iters", 1000) common.setup_common_training_handlers( trainer, train_sampler, to_save=to_save, save_every_iters=save_every_iters, save_handler=get_save_handler(config), lr_scheduler=lr_scheduler, with_gpu_stats=exp_tracking.has_mlflow, output_names=output_names, with_pbars=False, ) if idist.get_rank() == 0: common.ProgressBar(persist=False).attach(trainer, metric_names="all") return trainer def create_evaluators(model, metrics, config): model_output_transform = getattr(config, "model_output_transform", lambda x: x) evaluator_args = dict( model=model, metrics=metrics, device=config.device, non_blocking=True, prepare_batch=config.prepare_batch, output_transform=lambda x, y, y_pred: (model_output_transform(y_pred), y,), ) train_evaluator = create_supervised_evaluator(**evaluator_args) evaluator = create_supervised_evaluator(**evaluator_args) if idist.get_rank() == 0: common.ProgressBar(desc="Evaluation (train)", persist=False).attach(train_evaluator) common.ProgressBar(desc="Evaluation (val)", persist=False).attach(evaluator) return evaluator, train_evaluator 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): {int(elapsed)} - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): msg = f"\n- PyTorch version: {torch.__version__}" msg += f"\n- Ignite version: {ignite.__version__}" msg += f"\n- Cuda device name: {torch.cuda.get_device_name(idist.get_local_rank())}" logger.info(msg) if idist.get_world_size() > 1: msg = "\nDistributed setting:" msg += f"\tbackend: {idist.backend()}" msg += f"\trank: {idist.get_rank()}" msg += f"\tworld size: {idist.get_world_size()}" logger.info(msg) def training(local_rank, config, logger=None): if not getattr(config, "use_fp16", True): raise RuntimeError("This training script uses by default fp16 AMP") torch.backends.cudnn.benchmark = True set_seed(config.seed + local_rank) train_loader, val_loader, train_eval_loader = config.train_loader, config.val_loader, config.train_eval_loader # Setup model, optimizer, criterion model, optimizer, criterion = initialize(config) # Setup trainer for this specific task trainer = create_trainer(model, optimizer, criterion, train_loader.sampler, config, logger) # 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 hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator, train_evaluator = create_evaluators(model, val_metrics, config) val_interval = getattr(config, "val_interval", 1) @trainer.on(Events.EPOCH_COMPLETED(every=val_interval)) def run_validation(): epoch = trainer.state.epoch state = train_evaluator.run(train_eval_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(val_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) if config.num_epochs % val_interval != 0: trainer.add_event_handler(Events.COMPLETED, run_validation) if getattr(config, "start_by_validation", False): trainer.add_event_handler(Events.STARTED, run_validation) score_metric_name = "mIoU_bg" if hasattr(config, "es_patience"): common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name) # Store 3 best models by validation accuracy: common.gen_save_best_models_by_val_score( save_handler=get_save_handler(config), evaluator=evaluator, models=model, metric_name=score_metric_name, n_saved=3, trainer=trainer, tag="val", ) if idist.get_rank() == 0: tb_logger = common.setup_tb_logging( config.output_path.as_posix(), trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator}, ) if not exp_tracking.has_clearml: exp_tracking_logger = exp_tracking.setup_logging( 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 % (getattr(config, "val_interval", 1) * 3) == 0 c2 |= trainer.state.epoch == config.num_epochs return c1 and c2 tb_logger.attach( evaluator, log_handler=predictions_gt_images_handler( img_denormalize_fn=config.img_denormalize, n_images=15, another_engine=trainer, prefix_tag="validation" ), event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter), ) # Log confusion matrix to ClearML: if exp_tracking.has_clearml: @trainer.on(Events.COMPLETED) def compute_and_log_cm(): 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: try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task clearml_logger = Task.current_task().get_logger() clearml_logger.report_confusion_matrix( title="Final Confusion Matrix", series="cm-preds-gt", matrix=cm, iteration=trainer.state.iteration, xlabels=VOCSegmentationOpencv.target_names, ylabels=VOCSegmentationOpencv.target_names, ) trainer.run(train_loader, max_epochs=config.num_epochs) if idist.get_rank() == 0: tb_logger.close() if not exp_tracking.has_clearml: exp_tracking_logger.close() def run(config, **kwargs): """This is the main method to run the training. As this training script is launched with `py_config_runner` it should obligatory contain `run(config, **kwargs)` method. """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled, "Nvidia/Amp requires cudnn backend to be enabled." with idist.Parallel(backend="nccl") as parallel: logger = setup_logger(name="Pascal-VOC12 Training", distributed_rank=idist.get_rank()) assert_config(config, TRAINVAL_CONFIG) # The following attributes are automatically added by py_config_runner assert hasattr(config, "config_filepath") and isinstance(config.config_filepath, Path) assert hasattr(config, "script_filepath") and isinstance(config.script_filepath, Path) if idist.get_rank() == 0 and exp_tracking.has_clearml: try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.init("Pascal-VOC12 Training", config.config_filepath.stem) task.connect_configuration(config.config_filepath.as_posix()) log_basic_info(logger, config) config.output_path = Path(exp_tracking.get_output_path()) # dump python files to reproduce the run exp_tracking.log_artifact(config.config_filepath.as_posix()) exp_tracking.log_artifact(config.script_filepath.as_posix()) exp_tracking.log_params(get_params(config, TRAINVAL_CONFIG)) try: parallel.run(training, config, logger=logger) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/utils/000077500000000000000000000000001401006565000261175ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/utils/__init__.py000066400000000000000000000000001401006565000302160ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/utils/exp_tracking.py000066400000000000000000000067031401006565000311550ustar00rootroot00000000000000# Module for common exp tracking methods import os from pathlib import Path import torch import ignite import ignite.distributed as idist from ignite.contrib.engines import common try: import polyaxon_client.tracking if "POLYAXON_RUN_OUTPUTS_PATH" not in os.environ: raise ImportError("Not in Polyaxon cluster") has_plx = True except ImportError: has_plx = False try: import mlflow if "MLFLOW_TRACKING_URI" not in os.environ: raise ImportError("MLFLOW_TRACKING_URI should be defined") has_mlflow = True except ImportError: has_mlflow = False try: try: import clearml except ImportError: import trains if "CLEARML_OUTPUT_PATH" not in os.environ: raise ImportError("CLEARML_OUTPUT_PATH should be defined") has_clearml = True clearml_output_path = None except ImportError: has_clearml = False def _plx_get_output_path(): from polyaxon_client.tracking import get_outputs_path return get_outputs_path() @idist.one_rank_only() def _plx_log_artifact(fp): from polyaxon_client.tracking import Experiment plx_exp = Experiment() plx_exp.log_artifact(fp) @idist.one_rank_only() def _plx_log_params(params_dict): from polyaxon_client.tracking import Experiment plx_exp = Experiment() plx_exp.log_params( **{"pytorch version": torch.__version__, "ignite version": ignite.__version__,} ) plx_exp.log_params(**params_dict) def _mlflow_get_output_path(): return mlflow.get_artifact_uri() @idist.one_rank_only() def _mlflow_log_artifact(fp): mlflow.log_artifact(fp) @idist.one_rank_only() def _mlflow_log_params(params_dict): mlflow.log_params( {"pytorch version": torch.__version__, "ignite version": ignite.__version__,} ) mlflow.log_params(params_dict) def _clearml_get_output_path(): global clearml_output_path if clearml_output_path is None: from datetime import datetime output_path = Path(os.environ["CLEARML_OUTPUT_PATH"]) output_path = output_path / "clearml" / datetime.now().strftime("%Y%m%d-%H%M%S") clearml_output_path = output_path return clearml_output_path.as_posix() @idist.one_rank_only() def _clearml_log_artifact(fp): try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.current_task() task.upload_artifact(Path(fp).name, fp) @idist.one_rank_only() def _clearml_log_params(params_dict): try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.current_task() task.connect(params_dict) if has_plx: get_output_path = _plx_get_output_path log_params = _plx_log_params setup_logging = common.setup_plx_logging log_artifact = _plx_log_artifact elif has_mlflow: get_output_path = _mlflow_get_output_path log_params = _mlflow_log_params setup_logging = common.setup_mlflow_logging log_artifact = _mlflow_log_artifact elif has_clearml: get_output_path = _clearml_get_output_path log_params = _clearml_log_params setup_logging = common.setup_clearml_logging log_artifact = _clearml_log_artifact else: raise RuntimeError( "No experiment tracking system is setup. " "Please, setup either MLflow, Polyaxon or ClearML. " "For more details see NOTES_*.md" ) ignite-0.4.3/examples/references/segmentation/pascal_voc2012/code/utils/handlers.py000066400000000000000000000022311401006565000302670ustar00rootroot00000000000000import torch from dataflow.vis import make_grid, tensor_to_rgb 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.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-0.4.3/examples/references/segmentation/pascal_voc2012/configs/000077500000000000000000000000001401006565000254755ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/configs/train/000077500000000000000000000000001401006565000266125ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/configs/train/baseline_resnet101.py000066400000000000000000000063251401006565000325560ustar00rootroot00000000000000# 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.dataloaders import get_train_val_loaders from dataflow.transforms import denormalize, ignore_mask_boundaries, prepare_batch_fp32 from torchvision.models.segmentation import deeplabv3_resnet101 import ignite.distributed as idist # ############################## # Global configs # ############################## seed = 19 device = "cuda" debug = False fp16_opt_level = "O2" num_classes = 21 batch_size = 9 * idist.get_world_size() # total batch size val_batch_size = batch_size * 2 num_workers = 12 val_interval = 3 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, ) prepare_batch = prepare_batch_fp32 # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) # ############################## # Setup models # ############################## 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-0.4.3/examples/references/segmentation/pascal_voc2012/configs/train/baseline_resnet101_sbd.py000066400000000000000000000065111401006565000334030ustar00rootroot00000000000000# 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.dataloaders import get_train_val_loaders from dataflow.transforms import denormalize, ignore_mask_boundaries, prepare_batch_fp32 from torchvision.models.segmentation import deeplabv3_resnet101 import ignite.distributed as idist # ############################## # Global configs # ############################## seed = 19 device = "cuda" debug = False fp16_opt_level = "O2" num_classes = 21 batch_size = 9 * idist.get_world_size() # total batch size val_batch_size = batch_size * 2 num_workers = 12 val_interval = 3 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, with_sbd=sbd_data_path, limit_train_num_samples=100 if debug else None, limit_val_num_samples=100 if debug else None, ) prepare_batch = prepare_batch_fp32 # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) # ############################## # Setup models # ############################## 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-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/000077500000000000000000000000001401006565000264105ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/clearml/000077500000000000000000000000001401006565000300275ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/clearml/requirements.txt000066400000000000000000000000101401006565000333020ustar00rootroot00000000000000clearml ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/mlflow/000077500000000000000000000000001401006565000277105ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/mlflow/MLproject000066400000000000000000000010231401006565000315260ustar00rootroot00000000000000name: PascalVOC2012 conda_env: conda.yaml entry_points: download: parameters: output_path: path command: "export PYTHONPATH=$PWD/../../code:$PYTHONPATH && python ../../code/scripts/download_dataset.py {output_path}" main: parameters: config_path: path num_gpus: float command: "sh ../setup_apex.sh && export PYTHONPATH=$PWD/../../code:$PYTHONPATH && python -m torch.distributed.launch --nproc_per_node={num_gpus} --use_env -m py_config_runner ../../code/scripts/training.py {config_path}" ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/mlflow/conda.yaml000066400000000000000000000005051401006565000316600ustar00rootroot00000000000000name: pascal_voc2012_env channels: - pytorch dependencies: - python=3.7 - numpy - cudatoolkit - pytorch - torchvision - pip - pip: - mlflow - albumentations - tqdm - tensorboardX - py_config_runner - pynvml - pytorch-ignite - git+https://github.com/vfdev-5/ImageDatasetViz.git ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/plx/000077500000000000000000000000001401006565000272135ustar00rootroot00000000000000job_download_datasets.yml.tmpl000066400000000000000000000003351401006565000351640ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/plx--- version: 1 kind: job # Setup running environment: build: image: pytorch/pytorch:latest run: cmd: - export PYTHONPATH=$PYTHONPATH:$PWD/code/ - python $PWD/code/scripts/download_dataset.py /path/where/downloadignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/plx/xp_training.yml.tmpl000066400000000000000000000033541401006565000332400ustar00rootroot00000000000000--- version: 1 kind: experiment tags: ["training", "deeplabv3", "sbd"] # Setup running node: environment: node_selector: polyaxon: multigpu resources: gpu: requests: 2 limits: 2 # Setup running environment: build: image: pytorch/pytorch:1.5-cuda10.1-cudnn7-devel build_steps: # For opencv - apt-get update && TZ=America/New_York DEBIAN_FRONTEND="noninteractive" apt-get -y install tzdata && apt-get -y install --no-install-recommends libglib2.0 libsm6 libxext6 libxrender-dev git # Install Nvidia/APEX - git clone https://github.com/NVIDIA/apex /tmp/apex && cd /tmp/apex && export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" && pip install --upgrade --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . # Install ignite and other useful packages - pip install --upgrade --pre pytorch-ignite && pip install git+https://github.com/vfdev-5/ImageDatasetViz.git albumentations && pip install polyaxon-client tqdm tensorboardX py_config_runner pynvml declarations: config_file: "baseline_resnet101_sbd.py" script_file: "training.py" num_gpus: 2 run: cmd: - export LC_ALL=C.UTF-8 && export LANG=C.UTF-8 - export PYTHONPATH=$PYTHONPATH:$PWD/code/ # Required env variables to define dataset placement - export DATASET_PATH=/path/to_VOCdevkit_root - export SBD_DATASET_PATH=/path/to/sbd-dataset/ - export config_file=$PWD/configs/train/{{config_file}} - export script_file=$PWD/code/scripts/{{script_file}} # Copy configuration file to the output - cp $config_file $POLYAXON_RUN_OUTPUTS_PATH - cp $script_file $POLYAXON_RUN_OUTPUTS_PATH - python -m torch.distributed.launch --nproc_per_node={{num_gpus}} -m py_config_runner $script_file $config_fileignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/setup_apex.sh000066400000000000000000000006151401006565000311230ustar00rootroot00000000000000#!/bin/bash tmp_apex_path="/tmp/apex" python -c "import apex" res=$? if [ "$res" -eq "1" ]; then echo "Setup NVIDIA Apex" rm -rf $tmp_apex_path git clone https://github.com/NVIDIA/apex $tmp_apex_path cd $tmp_apex_path export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" pip install --upgrade --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . fi ignite-0.4.3/examples/references/segmentation/pascal_voc2012/experiments/setup_opencv.sh000066400000000000000000000006231401006565000314570ustar00rootroot00000000000000#!/bin/bash python -c "import cv2" res=$? if [ "$res" -eq "1" ]; then echo "Install libglib2.0 libsm6 libxext6 libxrender-dev for opencv" apt-get update ln -fs /usr/share/zoneinfo/America/New_York /etc/localtime apt-get install -y tzdata dpkg-reconfigure --frontend noninteractive tzdata apt-get -y install --no-install-recommends libglib2.0 libsm6 libxext6 libxrender-dev fi ignite-0.4.3/examples/references/segmentation/pascal_voc2012/notebooks/000077500000000000000000000000001401006565000260505ustar00rootroot00000000000000ignite-0.4.3/examples/references/segmentation/pascal_voc2012/notebooks/check_dataflow.ipynb000066400000000000000000206503631401006565000320700ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Check dataflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup local code into PYTHONPATH" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.insert(0, \"../code\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install visualization package dependency" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# !pip install --upgrade git+https://github.com/vfdev-5/ImageDatasetViz.git " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import datasets and dataloaders" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from dataflow.datasets import get_train_dataset, get_val_dataset, get_train_noval_sbdataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from dataflow.dataloaders import get_train_val_loaders" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check datasets" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "assert 'DATASET_PATH' in os.environ\n", "data_path = os.environ['DATASET_PATH']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pylab as plt\n", "%matplotlib inline\n", "\n", "\n", "from dataflow import render_mask\n", "from image_dataset_viz import render_datapoint" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1464, dict_keys(['image', 'mask']), (281, 500, 3), (281, 500))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_ds = get_train_dataset(data_path)\n", "dp = train_ds[0]\n", "len(train_ds), dp.keys(), dp['image'].shape, dp['mask'].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "

" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "plt.imshow(render_datapoint(dp['image'], render_mask(dp['mask'])))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1449, dict_keys(['image', 'mask']), (366, 500, 3), (366, 500))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val_ds = get_val_dataset(data_path)\n", "dp = val_ds[0]\n", "len(val_ds), dp.keys(), dp['image'].shape, dp['mask'].shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "plt.imshow(render_datapoint(dp['image'], render_mask(dp['mask'])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "assert 'SBDATASET_PATH' in os.environ\n", "sbdata_path = os.environ['SBDATASET_PATH']" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5623, dict_keys(['image', 'mask']), (442, 500, 3), (442, 500))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sbd_ds = get_train_noval_sbdataset(sbdata_path)\n", "dp = sbd_ds[0]\n", "len(sbd_ds), dp.keys(), dp['image'].shape, dp['mask'].shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "plt.imshow(render_datapoint(dp['image'], render_mask(dp['mask'])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check dataloaders" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# !pip install albumentations" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import albumentations as A\n", "\n", "from dataflow.transforms import ToTensor, ignore_mask_boundaries\n", "from dataflow import make_grid\n", "from dataflow.transforms import denormalize" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "train_transforms = A.Compose([\n", " A.RandomScale(scale_limit=(0.0, 1.5), interpolation=cv2.INTER_CUBIC, p=1.0),\n", " A.PadIfNeeded(513, 513, border_mode=cv2.BORDER_CONSTANT),\n", " A.RandomCrop(513, 513),\n", " A.HorizontalFlip(),\n", " A.Blur(blur_limit=3),\n", "\n", " A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n", " ignore_mask_boundaries,\n", " ToTensor(),\n", "])\n", "\n", "val_transforms = A.Compose([\n", " A.PadIfNeeded(513, 513, border_mode=cv2.BORDER_CONSTANT),\n", " A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n", " ignore_mask_boundaries,\n", " ToTensor(),\n", "])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "train_loader, val_loader, train_eval_loader = get_train_val_loaders(root_path=data_path, \n", " train_transforms=train_transforms,\n", " val_transforms=val_transforms,\n", " batch_size=4,\n", " val_batch_size=4,\n", " with_sbd=sbdata_path,\n", " random_seed=12)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "train_loader_iter = iter(train_loader)\n", "val_loader_iter = iter(val_loader)\n", "train_eval_loader_iter = iter(train_eval_loader)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from functools import partial\n", "\n", "\n", "for _ in range(3):\n", " train_batch = next(train_loader_iter)\n", "\n", " batch_x = train_batch['image']\n", " batch_y = train_batch['mask']\n", " \n", " plt.figure(figsize=(15, 7))\n", " grid_pred_gt = make_grid(batch_x, batch_y, \n", " img_denormalize_fn=partial(denormalize, \n", " mean=(0.485, 0.456, 0.406), \n", " std=(0.229, 0.224, 0.225)))\n", " plt.imshow(grid_pred_gt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Validation data" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from functools import partial\n", "\n", "\n", "for _ in range(3):\n", " val_batch = next(val_loader_iter)\n", "\n", " batch_x = val_batch['image']\n", " batch_y = val_batch['mask']\n", " \n", " plt.figure(figsize=(15, 7))\n", " grid_pred_gt = make_grid(batch_x, batch_y, \n", " img_denormalize_fn=partial(denormalize, \n", " mean=(0.485, 0.456, 0.406), \n", " std=(0.229, 0.224, 0.225)))\n", " plt.imshow(grid_pred_gt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training data for validation" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from functools import partial\n", "\n", "\n", "for _ in range(3):\n", " train_eval_batch = next(train_eval_loader_iter)\n", "\n", " batch_x = train_eval_batch['image']\n", " batch_y = train_eval_batch['mask']\n", " \n", " plt.figure(figsize=(15, 7))\n", " grid_pred_gt = make_grid(batch_x, batch_y, \n", " img_denormalize_fn=partial(denormalize, \n", " mean=(0.485, 0.456, 0.406), \n", " std=(0.229, 0.224, 0.225)))\n", " plt.imshow(grid_pred_gt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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" } }, "nbformat": 4, "nbformat_minor": 2 } ignite-0.4.3/examples/references/segmentation/pascal_voc2012/notebooks/check_models.ipynb000066400000000000000000000033061401006565000315350ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Check models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DeeplabV3" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from torchvision.models.segmentation import deeplabv3_resnet101\n", "\n", "num_classes = 21\n", "model = deeplabv3_resnet101(num_classes=num_classes)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import torch\n", "\n", "val_size = 513\n", "train_size = int(val_size * 0.7)\n", "\n", "device = 'cuda'\n", "\n", "model.to(device)\n", "\n", "x = torch.rand(4, 3, train_size, train_size).to(device)\n", "y = model(x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([4, 21, 359, 359])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y['out'].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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" } }, "nbformat": 4, "nbformat_minor": 2 } ignite-0.4.3/examples/references/segmentation/pascal_voc2012/requirements.txt000066400000000000000000000001771401006565000273360ustar00rootroot00000000000000albumentations image-dataset-viz numpy opencv-python py_config_runner pytorch-ignite pillow tensorboard torch torchvision tqdm ignite-0.4.3/examples/reinforcement_learning/000077500000000000000000000000001401006565000213275ustar00rootroot00000000000000ignite-0.4.3/examples/reinforcement_learning/README.md000066400000000000000000000004151401006565000226060ustar00rootroot00000000000000# Reinforcement learning training examples with Ignite ported from [pytorch-examples](https://github.com/pytorch/examples/tree/master/reinforcement_learning) ```bash pip install gym # For REINFORCE: python reinforce.py # For actor critic: python actor_critic.py ``` ignite-0.4.3/examples/reinforcement_learning/actor_critic.py000066400000000000000000000113371401006565000243530ustar00rootroot00000000000000import argparse from collections import 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 gym except ImportError: raise RuntimeError("Please install opengym: pip install gym") SavedAction = namedtuple("SavedAction", ["log_prob", "value"]) class Policy(nn.Module): def __init__(self): super(Policy, self).__init__() self.affine1 = nn.Linear(4, 128) self.action_head = nn.Linear(128, 2) self.value_head = nn.Linear(128, 1) self.saved_actions = [] self.rewards = [] def forward(self, x): x = F.relu(self.affine1(x)) action_scores = self.action_head(x) state_values = self.value_head(x) return F.softmax(action_scores, dim=-1), state_values def select_action(model, observation): observation = torch.from_numpy(observation).float() probs, observation_value = model(observation) m = Categorical(probs) action = m.sample() model.saved_actions.append(SavedAction(m.log_prob(action), observation_value)) return action.item() def finish_episode(model, optimizer, gamma, eps): R = 0 saved_actions = model.saved_actions policy_losses = [] value_losses = [] rewards = [] for r in model.rewards[::-1]: R = r + gamma * R rewards.insert(0, R) rewards = torch.tensor(rewards) rewards = (rewards - rewards.mean()) / (rewards.std() + eps) for (log_prob, value), r in zip(saved_actions, rewards): reward = r - value.item() policy_losses.append(-log_prob * reward) value_losses.append(F.smooth_l1_loss(value, torch.tensor([r]))) optimizer.zero_grad() loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum() loss.backward() optimizer.step() del model.rewards[:] del model.saved_actions[:] EPISODE_STARTED = Events.EPOCH_STARTED EPISODE_COMPLETED = Events.EPOCH_COMPLETED def main(env, args): model = Policy() optimizer = optim.Adam(model.parameters(), lr=3e-2) eps = np.finfo(np.float32).eps.item() timesteps = list(range(10000)) def run_single_timestep(engine, timestep): observation = engine.state.observation action = select_action(model, observation) engine.state.observation, reward, done, _ = env.step(action) if args.render: env.render() model.rewards.append(reward) if done: engine.terminate_epoch() engine.state.timestep = timestep trainer = Engine(run_single_timestep) @trainer.on(Events.STARTED) def initialize(engine): engine.state.running_reward = 10 @trainer.on(EPISODE_STARTED) def reset_environment_state(engine): engine.state.observation = env.reset() @trainer.on(EPISODE_COMPLETED) def update_model(engine): t = engine.state.timestep engine.state.running_reward = engine.state.running_reward * 0.99 + t * 0.01 finish_episode(model, optimizer, args.gamma, eps) @trainer.on(EPISODE_COMPLETED(every=args.log_interval)) def log_episode(engine): i_episode = engine.state.epoch print( f"Episode {i_episode}\tLast length: {engine.state.timestep:5d}" f"\tAverage length: {engine.state.running_reward:.2f}" ) @trainer.on(EPISODE_COMPLETED) def should_finish_training(engine): running_reward = engine.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 {engine.state.timestep} time steps!" ) engine.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-v0") env.seed(args.seed) torch.manual_seed(args.seed) main(env, args) ignite-0.4.3/examples/reinforcement_learning/reinforce.py000066400000000000000000000104421401006565000236560ustar00rootroot00000000000000import argparse 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 gym except ImportError: raise RuntimeError("Please install opengym: pip install gym") class Policy(nn.Module): def __init__(self): super(Policy, self).__init__() self.affine1 = nn.Linear(4, 128) self.affine2 = nn.Linear(128, 2) self.saved_log_probs = [] self.rewards = [] def forward(self, x): x = F.relu(self.affine1(x)) action_scores = self.affine2(x) return F.softmax(action_scores, dim=1) def select_action(model, observation): state = torch.from_numpy(observation).float().unsqueeze(0) probs = model(state) m = Categorical(probs) action = m.sample() model.saved_log_probs.append(m.log_prob(action)) return action.item() def finish_episode(model, optimizer, gamma, eps): R = 0 policy_loss = [] rewards = [] for r in model.rewards[::-1]: R = r + gamma * R rewards.insert(0, R) rewards = torch.tensor(rewards) rewards = (rewards - rewards.mean()) / (rewards.std() + eps) for log_prob, reward in zip(model.saved_log_probs, rewards): policy_loss.append(-log_prob * reward) optimizer.zero_grad() policy_loss = torch.cat(policy_loss).sum() policy_loss.backward() optimizer.step() del model.rewards[:] del model.saved_log_probs[:] EPISODE_STARTED = Events.EPOCH_STARTED EPISODE_COMPLETED = Events.EPOCH_COMPLETED def main(env, args): model = Policy() optimizer = optim.Adam(model.parameters(), lr=1e-2) eps = np.finfo(np.float32).eps.item() timesteps = list(range(10000)) def run_single_timestep(engine, timestep): observation = engine.state.observation action = select_action(model, observation) engine.state.observation, reward, done, _ = env.step(action) if args.render: env.render() model.rewards.append(reward) if done: engine.terminate_epoch() engine.state.timestep = timestep trainer = Engine(run_single_timestep) @trainer.on(Events.STARTED) def initialize(engine): engine.state.running_reward = 10 @trainer.on(EPISODE_STARTED) def reset_environment_state(engine): engine.state.observation = env.reset() @trainer.on(EPISODE_COMPLETED) def update_model(engine): t = engine.state.timestep engine.state.running_reward = engine.state.running_reward * 0.99 + t * 0.01 finish_episode(model, optimizer, args.gamma, eps) @trainer.on(EPISODE_COMPLETED(every=args.log_interval)) def log_episode(engine): i_episode = engine.state.epoch print( f"Episode {i_episode}\tLast length: {engine.state.timestep:5d}" f"\tAverage length: {engine.state.running_reward:.2f}" ) @trainer.on(EPISODE_COMPLETED) def should_finish_training(engine): running_reward = engine.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 {engine.state.timestep} time steps!" ) engine.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-v0") env.seed(args.seed) torch.manual_seed(args.seed) main(env, args) ignite-0.4.3/ignite/000077500000000000000000000000001401006565000142515ustar00rootroot00000000000000ignite-0.4.3/ignite/__init__.py000066400000000000000000000002661401006565000163660ustar00rootroot00000000000000import ignite.contrib import ignite.distributed import ignite.engine import ignite.exceptions import ignite.handlers import ignite.metrics import ignite.utils __version__ = "0.4.3" ignite-0.4.3/ignite/_utils.py000066400000000000000000000001461401006565000161230ustar00rootroot00000000000000# For compatibilty from ignite.utils import apply_to_tensor, apply_to_type, convert_tensor, to_onehot ignite-0.4.3/ignite/base/000077500000000000000000000000001401006565000151635ustar00rootroot00000000000000ignite-0.4.3/ignite/base/__init__.py000066400000000000000000000000541401006565000172730ustar00rootroot00000000000000from ignite.base.mixins import Serializable ignite-0.4.3/ignite/base/mixins.py000066400000000000000000000017021401006565000170440ustar00rootroot00000000000000from collections import OrderedDict from collections.abc import Mapping class Serializable: _state_dict_all_req_keys = () # type: tuple _state_dict_one_of_opt_keys = () # type: tuple def state_dict(self) -> OrderedDict: pass 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-0.4.3/ignite/contrib/000077500000000000000000000000001401006565000157115ustar00rootroot00000000000000ignite-0.4.3/ignite/contrib/__init__.py000066400000000000000000000000001401006565000200100ustar00rootroot00000000000000ignite-0.4.3/ignite/contrib/engines/000077500000000000000000000000001401006565000173415ustar00rootroot00000000000000ignite-0.4.3/ignite/contrib/engines/__init__.py000066400000000000000000000001271401006565000214520ustar00rootroot00000000000000from ignite.contrib.engines.tbptt import Tbptt_Events, create_supervised_tbptt_trainer ignite-0.4.3/ignite/contrib/engines/common.py000066400000000000000000000716241401006565000212150ustar00rootroot00000000000000import numbers import warnings from functools import partial from typing import Any, Callable, Dict, Iterable, Mapping, Optional, Sequence, Tuple, Union, cast import torch import torch.nn as nn from torch.optim.lr_scheduler import _LRScheduler from torch.optim.optimizer import Optimizer from torch.utils.data.distributed import DistributedSampler import ignite.distributed as idist from ignite.contrib.handlers import ( ClearMLLogger, LRScheduler, MLflowLogger, NeptuneLogger, PolyaxonLogger, ProgressBar, TensorboardLogger, TrainsLogger, VisdomLogger, WandBLogger, global_step_from_engine, ) from ignite.contrib.handlers.base_logger import BaseLogger from ignite.contrib.handlers.param_scheduler import ParamScheduler from ignite.contrib.metrics import GpuInfo from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, DiskSaver, EarlyStopping, TerminateOnNan from ignite.handlers.checkpoint import BaseSaveHandler from ignite.metrics import RunningAverage 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, _LRScheduler]] = 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, device: Optional[Union[str, torch.device]] = None, 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.TerminateOnNan` - handler to setup learning rate scheduling - :class:`~ignite.handlers.ModelCheckpoint` - :class:`~ignite.metrics.RunningAverage` on `update_function` output - Two progress bars on epochs and optionally on iterations Args: trainer (Engine): trainer engine. Output of trainer's `update_function` should be a dictionary or sequence or a single tensor. train_sampler (torch.utils.data.DistributedSampler, optional): Optional distributed sampler used to call `set_epoch` method on epoch started event. to_save (dict, optional): dictionary with objects to save in the checkpoint. This argument is passed to :class:`~ignite.handlers.Checkpoint` instance. save_every_iters (int, optional): saving interval. By default, `to_save` objects are stored each 1000 iterations. output_path (str, optional): output path to indicate where `to_save` objects are stored. Argument is mutually exclusive with ``save_handler``. lr_scheduler (ParamScheduler or subclass of `torch.optim.lr_scheduler._LRScheduler`): learning rate scheduler as native torch LRScheduler or ignite's parameter scheduler. with_gpu_stats (bool, optional): if True, :class:`~ignite.contrib.metrics.handlers.GpuInfo` is attached to the trainer. This requires `pynvml` package to be installed. output_names (list/tuple, optional): list of names associated with `update_function` output dictionary. with_pbars (bool, optional): if True, two progress bars on epochs and optionally on iterations are attached. Default, True. with_pbar_on_iters (bool, optional): if True, a progress bar on iterations is attached to the trainer. Default, True. log_every_iters (int, optional): logging interval for :class:`~ignite.contrib.metrics.handlers.GpuInfo` and for epoch-wise progress bar. Default, 100. stop_on_nan (bool, optional): if True, :class:`~ignite.handlers.TerminateOnNan` handler is added to the trainer. Default, True. clear_cuda_cache (bool, optional): if True, `torch.cuda.empty_cache()` is called every end of epoch. Default, True. save_handler (callable or :class:`~ignite.handlers.checkpoint.BaseSaveHandler`, optional): 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`. device (str of torch.device, optional): deprecated argument, it will be removed in v0.5.0. """ if device is not None: warnings.warn("Argument device is unused and deprecated. It will be removed in v0.5.0") 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, _LRScheduler]] = 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, torch.optim.lr_scheduler._LRScheduler): trainer.add_event_handler( Events.ITERATION_COMPLETED, lambda engine: cast(_LRScheduler, lr_scheduler).step() ) elif isinstance(lr_scheduler, LRScheduler): trainer.add_event_handler(Events.ITERATION_COMPLETED, lr_scheduler) 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), epoch_bound=False).attach( trainer, n ) 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, _LRScheduler]] = 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: cast(DistributedSampler, 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 (str): logging directory path trainer (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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 (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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 (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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 (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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 (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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 (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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 (Engine): trainer engine optimizers (torch.optim.Optimizer or dict of torch.optim.Optimizer, optional): single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging. evaluators (Engine or dict of Engine, optional): single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging. log_every_iters (int, optional): 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.contrib.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", **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 (callable or :class:`~ignite.handlers.checkpoint.BaseSaveHandler`): 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 (Engine): evaluation engine used to provide the score models (nn.Module or Mapping): model or dictionary with the object to save. Objects should have implemented ``state_dict`` and ``load_state_dict`` methods. metric_name (str): metric name to use for score evaluation. This metric should be present in `evaluator.state.metrics`. n_saved (int, optional): number of best models to store trainer (Engine, optional): trainer engine to fetch the epoch when saving the best model. tag (str, optional): score name prefix: `{tag}_{metric_name}`. By default, tag is "val". **kwargs: optional keyword args to be passed to construct :class:`~ignite.handlers.checkpoint.Checkpoint`. Returns: A :class:`~ignite.handlers.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 = {"model": models} # type: Dict[str, nn.Module] 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=Checkpoint.get_default_score_fn(metric_name), **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", **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 (str): output path to indicate where to save best models evaluator (Engine): evaluation engine used to provide the score model (nn.Module): model to store metric_name (str): metric name to use for score evaluation. This metric should be present in `evaluator.state.metrics`. n_saved (int, optional): number of best models to store trainer (Engine, optional): trainer engine to fetch the epoch when saving the best model. tag (str, optional): score name prefix: `{tag}_{metric_name}`. By default, tag is "val". **kwargs: optional keyword args to be passed to construct :class:`~ignite.handlers.checkpoint.Checkpoint`. Returns: A :class:`~ignite.handlers.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, **kwargs, ) def add_early_stopping_by_val_score( patience: int, evaluator: Engine, trainer: Engine, metric_name: str ) -> 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 (int): number of events to wait if no improvement and then stop the training. evaluator (Engine): evaluation engine used to provide the score trainer (Engine): trainer engine to stop the run if no improvement. metric_name (str): metric name to use for score evaluation. This metric should be present in `evaluator.state.metrics`. Returns: A :class:`~ignite.handlers.EarlyStopping` handler. """ es_handler = EarlyStopping(patience=patience, score_function=get_default_score_fn(metric_name), trainer=trainer) evaluator.add_event_handler(Events.COMPLETED, es_handler) return es_handler ignite-0.4.3/ignite/contrib/engines/tbptt.py000066400000000000000000000110561401006565000210530ustar00rootroot00000000000000# 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 Engine, EventEnum, _prepare_batch 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 (`torch.nn.Module`): the model to train. optimizer (`torch.optim.Optimizer`): the optimizer to use. loss_fn (torch.nn loss function): the loss function to use. tbtt_step (int): the length of time chunks (last one may be smaller). dim (int): axis representing the time dimension. device (str, optional): device type specification (default: None). Applies to batches. non_blocking (bool, optional): 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 (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. .. 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 `_ Returns: Engine: a trainer engine with supervised update function. """ 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-0.4.3/ignite/contrib/handlers/000077500000000000000000000000001401006565000175115ustar00rootroot00000000000000ignite-0.4.3/ignite/contrib/handlers/__init__.py000066400000000000000000000022051401006565000216210ustar00rootroot00000000000000from ignite.contrib.handlers.clearml_logger import ClearMLLogger from ignite.contrib.handlers.custom_events import CustomPeriodicEvent from ignite.contrib.handlers.lr_finder import FastaiLRFinder from ignite.contrib.handlers.mlflow_logger import MLflowLogger from ignite.contrib.handlers.neptune_logger import NeptuneLogger from ignite.contrib.handlers.param_scheduler import ( ConcatScheduler, CosineAnnealingScheduler, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, PiecewiseLinear, create_lr_scheduler_with_warmup, ) from ignite.contrib.handlers.polyaxon_logger import PolyaxonLogger from ignite.contrib.handlers.stores import EpochOutputStore from ignite.contrib.handlers.tensorboard_logger import TensorboardLogger from ignite.contrib.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler from ignite.contrib.handlers.tqdm_logger import ProgressBar from ignite.contrib.handlers.trains_logger import TrainsLogger from ignite.contrib.handlers.visdom_logger import VisdomLogger from ignite.contrib.handlers.wandb_logger import WandBLogger from ignite.handlers import global_step_from_engine # ref ignite-0.4.3/ignite/contrib/handlers/base_logger.py000066400000000000000000000215631401006565000223430ustar00rootroot00000000000000import numbers import warnings from abc import ABCMeta, abstractmethod from typing import Any, Callable, Dict, List, Optional, Sequence, 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): @abstractmethod def __call__(self, engine: Engine, logger: Any, event_name: Union[str, Events]) -> None: pass 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] = 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: raise ValueError("Either metric_names or output_transform 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 def _setup_output_metrics(self, engine: Engine) -> Dict[str, Any]: """Helper method to setup metrics to log """ metrics = {} if self.metric_names is not None: if isinstance(self.metric_names, str) and self.metric_names == "all": metrics = 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[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.update({name: value for name, value in output_dict.items()}) return metrics class BaseWeightsScalarHandler(BaseHandler): """ Helper handler to log model's weights as scalars. """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: if not isinstance(model, torch.nn.Module): raise TypeError(f"Argument model should be of type torch.nn.Module, but given {type(model)}") if not callable(reduction): raise TypeError(f"Argument reduction should be callable, but given {type(reduction)}") def _is_0D_tensor(t: torch.Tensor) -> 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.model = model self.reduction = reduction self.tag = tag class BaseWeightsHistHandler(BaseHandler): """ Helper handler to log model's weights as histograms. """ def __init__(self, model: nn.Module, tag: Optional[str] = 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 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] ) -> RemovableEventHandle: """Attach the logger to the engine and execute `log_handler` function at `event_name` events. Args: engine (Engine): engine object. log_handler (callable): 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`. Returns: :class:`~ignite.engine.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) 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): 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.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): 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.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-0.4.3/ignite/contrib/handlers/clearml_logger.py000066400000000000000000000746321401006565000230550ustar00rootroot00000000000000import numbers 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 import torch from torch.nn import Module from torch.optim import Optimizer import ignite.distributed as idist from ignite.contrib.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHistHandler, BaseWeightsScalarHandler, ) from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine from ignite.handlers.checkpoint import DiskSaver __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: project_name (str): The name of the project in which the experiment will be created. If the project does not exist, it is created. If ``project_name`` is ``None``, the repository name is used. (Optional) task_name (str): The name of Task (experiment). If ``task_name`` is ``None``, the Python experiment script's file name is used. (Optional) task_type (str): Optional. The task type. Valid values are: - ``TaskTypes.training`` (Default) - ``TaskTypes.train`` - ``TaskTypes.testing`` - ``TaskTypes.inference`` Examples: .. code-block:: python from ignite.contrib.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, *_: Any, **kwargs: Any) -> None: try: from clearml import Task from clearml.binding.frameworks.tensorflow_bind import WeightsGradientHistHelper except ImportError: try: # Backwards-compatibility for legacy Trains SDK from trains import Task from trains.binding.frameworks.tensorflow_bind import WeightsGradientHistHelper except ImportError: raise RuntimeError( "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") class _Stub(object): def __call__(self, *_: Any, **__: Any) -> "_Stub": return self def __getattr__(self, attr: str) -> "_Stub": if attr in ("name", "id"): return "" # type: ignore[return-value] return self def __setattr__(self, attr: str, val: Any) -> None: pass self._task = _Stub() else: 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) @classmethod def set_bypass_mode(cls, bypass: bool) -> None: """ Will bypass all outside communication, and will drop all logs. 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. """ setattr(cls, "_bypass", 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)``. Return: If True, all outside communication is skipped. """ return getattr(cls, "_bypass", bool(os.environ.get("CI"))) 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 Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.clearml_logger.global_step_from_engine`. Note: 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[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, ) -> None: super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) 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(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] 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 isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0: logger.clearml_logger.report_scalar(title=self.tag, series=key, iteration=global_step, value=value) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): logger.clearml_logger.report_scalar( title=f"{self.tag}/{key}", series=str(i), iteration=global_step, value=v.item() ) else: warnings.warn(f"ClearMLLogger output_handler can not log metrics value type {type(value)}") class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> 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 iterates over named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) 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.model.named_parameters(): if p.grad is None: continue 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(BaseWeightsHistHandler): """Helper handler to log model's weights as histograms. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights tag (str, optional): common title for all produced plots. For example, 'generator' """ def __init__(self, model: Module, tag: Optional[str] = None) -> None: super(WeightsHistHandler, self).__init__(model, tag=tag) 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.model.named_parameters(): if p.grad is None: continue 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.grad.detach().cpu().numpy(), ) class GradsScalarHandler(BaseWeightsScalarHandler): """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. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: super(GradsScalarHandler, self).__init__(model, reduction, tag=tag) 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.model.named_parameters(): 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.data), iteration=global_step, ) class GradsHistHandler(BaseWeightsHistHandler): """Helper handler to log model's gradients as histograms. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights tag (str, optional): common title for all produced plots. For example, 'generator' """ def __init__(self, model: Module, tag: Optional[str] = None) -> None: super(GradsHistHandler, self).__init__(model, tag=tag) 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.model.named_parameters(): 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.detach().cpu().numpy(), ) class ClearMLSaver(DiskSaver): """ Handler that saves input checkpoint as ClearML artifacts Args: logger (ClearMLLogger, optional): An instance of :class:`~ignite.contrib.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 (str, optional): The default location for output models and other artifacts uploaded by ClearML. For more information, see ``clearml.Task.init``. dirname (str, optional): Directory path where the checkpoint will be saved. If not provided, a temporary directory will be created. Examples: .. code-block:: python from ignite.contrib.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, ) -> None: 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(list) # type: DefaultDict[Union[str, Tuple[str, str]], List[Any]] 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 RuntimeError( "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 import Model from clearml.binding.frameworks import WeightsFileHandler except ImportError: try: # Backwards-compatibility for legacy Trains SDK from trains import Model from trains.binding.frameworks import WeightsFileHandler except ImportError: raise RuntimeError( "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 = (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 (str): 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-0.4.3/ignite/contrib/handlers/custom_events.py000066400000000000000000000107551401006565000227710ustar00rootroot00000000000000import warnings from ignite.engine import EventEnum, Events, State class CustomPeriodicEvent: """DEPRECATED. Use filtered events instead. Handler to define a custom periodic events as a number of elapsed iterations/epochs for an engine. When custom periodic event is created and attached to an engine, the following events are fired: 1) K iterations is specified: - `Events.ITERATIONS__STARTED` - `Events.ITERATIONS__COMPLETED` 1) K epochs is specified: - `Events.EPOCHS__STARTED` - `Events.EPOCHS__COMPLETED` Examples: .. code-block:: python from ignite.engine import Engine, Events from ignite.contrib.handlers import CustomPeriodicEvent # Let's define an event every 1000 iterations cpe1 = CustomPeriodicEvent(n_iterations=1000) cpe1.attach(trainer) # Let's define an event every 10 epochs cpe2 = CustomPeriodicEvent(n_epochs=10) cpe2.attach(trainer) @trainer.on(cpe1.Events.ITERATIONS_1000_COMPLETED) def on_every_1000_iterations(engine): # run a computation after 1000 iterations # ... print(engine.state.iterations_1000) @trainer.on(cpe2.Events.EPOCHS_10_STARTED) def on_every_10_epochs(engine): # run a computation every 10 epochs # ... print(engine.state.epochs_10) Args: n_iterations (int, optional): number iterations of the custom periodic event n_epochs (int, optional): number iterations of the custom periodic event. Argument is optional, but only one, either n_iterations or n_epochs should defined. """ def __init__(self, n_iterations=None, n_epochs=None): warnings.warn( "CustomPeriodicEvent is deprecated since 0.4.0 and will be removed in 0.5.0. Use filtered events instead.", DeprecationWarning, ) if n_iterations is not None: if not isinstance(n_iterations, int): raise TypeError("Argument n_iterations should be an integer") if n_iterations < 1: raise ValueError("Argument n_iterations should be positive") if n_epochs is not None: if not isinstance(n_epochs, int): raise TypeError("Argument n_epochs should be an integer") if n_epochs < 1: raise ValueError("Argument n_epochs should be positive") if (n_iterations is None and n_epochs is None) or (n_iterations and n_epochs): raise ValueError("Either n_iterations or n_epochs should be defined") if n_iterations: prefix = "iterations" self.state_attr = "iteration" self.period = n_iterations if n_epochs: prefix = "epochs" self.state_attr = "epoch" self.period = n_epochs self.custom_state_attr = "{}_{}".format(prefix, self.period) event_name = "{}_{}".format(prefix.upper(), self.period) setattr( self, "Events", EventEnum("Events", " ".join(["{}_STARTED".format(event_name), "{}_COMPLETED".format(event_name)])), ) # Update State.event_to_attr for e in self.Events: State.event_to_attr[e] = self.custom_state_attr # Create aliases self._periodic_event_started = getattr(self.Events, "{}_STARTED".format(event_name)) self._periodic_event_completed = getattr(self.Events, "{}_COMPLETED".format(event_name)) def _on_started(self, engine): setattr(engine.state, self.custom_state_attr, 0) def _on_periodic_event_started(self, engine): if getattr(engine.state, self.state_attr) % self.period == 1: setattr(engine.state, self.custom_state_attr, getattr(engine.state, self.custom_state_attr) + 1) engine.fire_event(self._periodic_event_started) def _on_periodic_event_completed(self, engine): if getattr(engine.state, self.state_attr) % self.period == 0: engine.fire_event(self._periodic_event_completed) def attach(self, engine): engine.register_events(*self.Events) engine.add_event_handler(Events.STARTED, self._on_started) engine.add_event_handler( getattr(Events, "{}_STARTED".format(self.state_attr.upper())), self._on_periodic_event_started ) engine.add_event_handler( getattr(Events, "{}_COMPLETED".format(self.state_attr.upper())), self._on_periodic_event_completed ) ignite-0.4.3/ignite/contrib/handlers/lr_finder.py000066400000000000000000000356361401006565000220440ustar00rootroot00000000000000# coding: utf-8 import contextlib import logging import tempfile import warnings 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 from ignite.contrib.handlers.param_scheduler import LRScheduler, PiecewiseLinear from ignite.engine import Engine, Events from ignite.handlers import Checkpoint 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.contrib.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 """ def __init__(self) -> None: self._diverge_flag = False self._history = {} # type: Dict[str, List[Any]] self._best_loss = None self._lr_schedule = None # type: Optional[Union[LRScheduler, PiecewiseLinear]] self.logger = logging.getLogger(__name__) def _run( self, trainer: Engine, optimizer: Optimizer, output_transform: Callable, num_iter: int, end_lr: 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 num_iter > max_iter: warnings.warn( f"Desired num_iter {num_iter} is unreachable with the current run setup of {max_iter} iteration " f"({trainer.state.max_epochs} epochs)", UserWarning, ) 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, end_lr, num_iter)) else: start_lr = optimizer.param_groups[0]["lr"] self._lr_schedule = PiecewiseLinear( optimizer, param_name="lr", milestones_values=[(0, start_lr), (num_iter, end_lr)] ) 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) # type: ignore[arg-type] 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) lr = self._lr_schedule.get_param() # type: ignore[union-attr] 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 fromm the previous run """ return self._history def plot(self, skip_start: int = 10, skip_end: int = 5, log_lr: bool = True) -> None: """Plots the learning rate range test. This method requires `matplotlib` package to be installed: .. code-block:: bash pip install matplotlib Args: skip_start (int, optional): number of batches to trim from the start. Default: 10. skip_end (int, optional): number of batches to trim from the start. Default: 5. log_lr (bool, optional): True to plot the learning rate in a logarithmic scale; otherwise, plotted in a linear scale. Default: True. """ try: from matplotlib import pyplot as plt except ImportError: raise RuntimeError( "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. Also, handle skip_end=0 # properly so the behaviour is the expected lrs = self._history["lr"] losses = self._history["loss"] 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] # Plot loss as a function of the learning rate plt.plot(lrs, losses) if log_lr: plt.xscale("log") plt.xlabel("Learning rate") plt.ylabel("Loss") plt.show() def lr_suggestion(self) -> Any: """ Returns: learning rate at the minimum numerical gradient """ if not self._history: raise RuntimeError("learning rate finder didn't run yet so lr_suggestion can't be returned") loss = self._history["loss"] grads = torch.tensor([loss[i] - loss[i - 1] for i in range(1, len(loss))]) min_grad_idx = grads.argmin() + 1 return self._history["lr"][int(min_grad_idx)] @contextlib.contextmanager def attach( self, trainer: Engine, to_save: Mapping, output_transform: Callable = lambda output: output, num_iter: Optional[int] = None, end_lr: 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. Usage: .. 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)` Args: trainer (Engine): lr_finder is attached to this trainer. Please, keep in mind that all attached handlers will be executed. to_save (Mapping): dictionary with optimizer and other objects that needs to be restored after running the LR finder. For example, `to_save={'optimizer': optimizer, 'model': model}`. All objects should implement `state_dict` and `load_state_dict` methods. output_transform (callable, optional): function that transforms the trainer's `state.output` after each iteration. It must return the loss of that iteration. num_iter (int, optional): 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`. end_lr (float, optional): upper bound for lr search. Default, 10.0. step_mode (str, optional): "exp" or "linear", which way should the lr be increased from optimizer's initial lr to `end_lr`. Default, "exp". smooth_f (float, optional): loss smoothing factor in range `[0, 1)`. Default, 0.05 diverge_th (float, optional): Used for stopping the search when `current loss > diverge_th * best_loss`. Default, 5.0. Note: lr_finder cannot be attached to more than one trainer at a time. Returns: trainer_with_lr_finder: trainer used for finding the lr """ 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}") # 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()) optimizer = to_save["optimizer"] # Attach handlers if not trainer.has_event_handler(self._run): trainer.add_event_handler( Events.STARTED, self._run, optimizer, output_transform, num_iter, end_lr, 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 (torch.optim.Optimizer): wrapped optimizer. end_lr (float, optional): the initial learning rate which is the lower boundary of the test. Default: 10. num_iter (int, optional): the number of iterations over which the test occurs. Default: 100. last_epoch (int): the index of last epoch. Default: -1. """ def __init__(self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1) -> None: self.end_lr = end_lr self.num_iter = num_iter super(_ExponentialLR, self).__init__(optimizer, last_epoch) def get_lr(self) -> List[float]: # type: ignore curr_iter = self.last_epoch + 1 # type: ignore[attr-defined] r = curr_iter / self.num_iter return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs] # type: ignore[attr-defined] ignite-0.4.3/ignite/contrib/handlers/mlflow_logger.py000066400000000000000000000300301401006565000227160ustar00rootroot00000000000000import numbers import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from torch.optim import Optimizer from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine __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 (str): MLflow tracking uri. See MLflow docs for more details Examples: .. code-block:: python from ignite.contrib.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) -> None: try: import mlflow except ImportError: raise RuntimeError( "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. Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, 'training' metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.mlflow_logger.global_step_from_engine`. Note: 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[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, ) -> None: super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) 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") metrics = self._setup_output_metrics(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] 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." ) rendered_metrics = {} # type: Dict[str, float] for key, value in metrics.items(): if isinstance(value, numbers.Number): rendered_metrics[f"{self.tag} {key}"] = value # type: ignore[assignment] elif isinstance(value, torch.Tensor) and value.ndimension() == 0: rendered_metrics[f"{self.tag} {key}"] = value.item() elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): rendered_metrics[f"{self.tag} {key} {i}"] = v.item() else: warnings.warn(f"MLflowLogger output_handler can not log metrics value type {type(value)}") # Additionally recheck metric names as MLflow rejects non-valid names with MLflowException from mlflow.utils.validation import _VALID_PARAM_AND_METRIC_NAMES for key in list(rendered_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 rendered_metrics[key] logger.log_metrics(rendered_metrics, step=global_step) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, 'generator' """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> 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-0.4.3/ignite/contrib/handlers/neptune_logger.py000066400000000000000000000602051401006565000231030ustar00rootroot00000000000000import numbers import tempfile import warnings from typing import Any, Callable, List, Mapping, Optional, Union import torch import torch.nn as nn from torch.optim import Optimizer import ignite import ignite.distributed as idist from ignite.contrib.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsScalarHandler, ) from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine from ignite.handlers.checkpoint import BaseSaveHandler __all__ = [ "NeptuneLogger", "NeptuneSaver", "OptimizerParamsHandler", "OutputHandler", "WeightsScalarHandler", "GradsScalarHandler", "global_step_from_engine", ] class NeptuneLogger(BaseLogger): """ `Neptune `_ handler to log metrics, model/optimizer parameters, gradients during the training and validation. It can also log model checkpoints to Neptune server. .. code-block:: bash pip install neptune-client Args: api_token (str | None): Required in online mode. Neputne API token, found on https://neptune.ai. Read how to get your API key https://docs.neptune.ai/python-api/tutorials/get-started.html#copy-api-token. project_name (str): Required in online mode. Qualified name of a project in a form of "namespace/project_name" for example "tom/minst-classification". If None, the value of NEPTUNE_PROJECT environment variable will be taken. You need to create the project in https://neptune.ai first. offline_mode (bool): Optional default False. If offline_mode=True no logs will be send to neptune. Usually used for debug purposes. experiment_name (str, optional): Optional. Editable name of the experiment. Name is displayed in the experimentโ€™s Details (Metadata section) and in experiments view as a column. upload_source_files (list, optional): Optional. List of source files to be uploaded. Must be list of str or single str. Uploaded sources are displayed in the experimentโ€™s Source code tab. If None is passed, Python file from which experiment was created will be uploaded. Pass empty list (`[]`) to upload no files. Unix style pathname pattern expansion is supported. For example, you can pass `*.py` to upload all python source files from the current directory. For recursion lookup use `**/*.py` (for Python 3.5 and later). For more information see glob library. params (dict, optional): Optional. Parameters of the experiment. After experiment creation params are read-only. Parameters are displayed in the experimentโ€™s Parameters section and each key-value pair can be viewed in experiments view as a column. properties (dict, optional): Optional default is `{}`. Properties of the experiment. They are editable after experiment is created. Properties are displayed in the experimentโ€™s Details and each key-value pair can be viewed in experiments view as a column. tags (list, optional): Optional default `[]`. Must be list of str. Tags of the experiment. Tags are displayed in the experimentโ€™s Details and can be viewed in experiments view as a column. Examples: .. code-block:: python from ignite.contrib.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 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, 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`. 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, 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`. 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's parameters, e.g. 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 an experiment with neptune tracking here: https://ui.neptune.ai/o/shared/org/pytorch-ignite-integration/e/PYTOR1-18/charts You can save model checkpoints to a Neptune server: .. 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 context manager: .. code-block:: python from ignite.contrib.handlers.neptune_logger import * # We are using the api_token for the anonymous user neptuner but you can use your own. with 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 ) 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: import neptune return getattr(neptune, attr) def __init__(self, *args: Any, **kwargs: Any) -> None: try: import neptune except ImportError: raise RuntimeError( "This contrib module requires neptune-client to be installed. " "You may install neptune with command: \n pip install neptune-client \n" ) if kwargs.get("offline_mode", False): self.mode = "offline" neptune.init(project_qualified_name="dry-run/project", backend=neptune.OfflineBackend()) else: self.mode = "online" neptune.init(api_token=kwargs.get("api_token"), project_qualified_name=kwargs.get("project_name")) kwargs["name"] = kwargs.pop("experiment_name", None) self._experiment_kwargs = { k: v for k, v in kwargs.items() if k not in ["api_token", "project_name", "offline_mode"] } self.experiment = neptune.create_experiment(**self._experiment_kwargs) def close(self) -> None: self.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 Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.neptune_logger.global_step_from_engine`. Note: 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[Union[str, List[str]]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, ) -> None: super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) 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(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] 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 isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0: logger.log_metric(f"{self.tag}/{key}", x=global_step, y=value) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): logger.log_metric(f"{self.tag}/{key}/{i}", x=global_step, y=v.item()) else: warnings.warn(f"NeptuneLogger output_handler can not log metrics value type {type(value)}") class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> 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.log_metric(k, x=global_step, y=v) class WeightsScalarHandler(BaseWeightsScalarHandler): """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. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) 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.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.log_metric( f"{tag_prefix}weights_{self.reduction.__name__}/{name}", x=global_step, y=self.reduction(p.data), ) class GradsScalarHandler(BaseWeightsScalarHandler): """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. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: super(GradsScalarHandler, self).__init__(model, reduction, tag=tag) 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.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.log_metric( f"{tag_prefix}grads_{self.reduction.__name__}/{name}", x=global_step, y=self.reduction(p.grad), ) class NeptuneSaver(BaseSaveHandler): """Handler that saves input checkpoint to the Neptune server. Args: neptune_logger (ignite.contrib.handlers.neptune_logger.NeptuneLogger): an instance of NeptuneLogger class. Examples: .. code-block:: python from ignite.contrib.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 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) # type: ignore[attr-defined] self._logger.log_artifact(tmp.name, filename) @idist.one_rank_only(with_barrier=True) def remove(self, filename: str) -> None: self._logger.delete_artifacts(filename) ignite-0.4.3/ignite/contrib/handlers/param_scheduler.py000066400000000000000000001343511401006565000232300ustar00rootroot00000000000000import itertools import math import numbers import tempfile 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, cast import torch from torch.optim.lr_scheduler import _LRScheduler from torch.optim.optimizer import Optimizer from ignite.engine import Engine class ParamScheduler(metaclass=ABCMeta): """An abstract class for updating an optimizer's parameter value during training. Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): name of optimizer's parameter to update. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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, ) -> 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_group_index = param_group_index self.param_name = param_name self.event_index = 0 self._save_history = save_history self._state_attrs = ["event_index", "param_name", "save_history", "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: # type: ignore 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]] @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 ParamScheduler. Returns: dict: a dictionary containing a whole state of ParamScheduler """ 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 ParamScheduler. Args: state_dict (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 optimizer's parameter values Returns: list of params, or scalar param """ pass @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 (int): number of events during the simulation. **scheduler_kwargs : parameter scheduler configuration kwargs. Returns: list of pairs: [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 @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 (int): 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.pylab as plt except ImportError: raise RuntimeError( "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 CyclicalScheduler(ParamScheduler): """An abstract class for updating an optimizer's parameter value over a cycle of some size. Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): name of optimizer's parameter to update. start_value (float): value at start of cycle. end_value (float): value at the middle of the cycle. cycle_size (int): length of cycle, value should be larger than 1. cycle_mult (float, optional): ratio by which to change the cycle_size. at the end of each cycle (default=1.0). start_value_mult (float, optional): ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult (float, optional): ratio by which to change the end value at the end of each cycle (default=1.0). save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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. """ 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, 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 = int(cycle_size) # Ensure cycle_size is integer self.cycle_mult = cycle_mult self.cycle = 0 self.start_value_mult = start_value_mult self.end_value_mult = end_value_mult 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", ] def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: if self.event_index != 0 and self.event_index % self.cycle_size == 0: self.event_index = 0 self.cycle_size = int(self.cycle_size * self.cycle_mult) self.cycle += 1 self.start_value *= self.start_value_mult self.end_value *= self.end_value_mult return super(CyclicalScheduler, self).__call__(engine, name) 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.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): name of optimizer's parameter to update. start_value (float): value at start of cycle. end_value (float): value at the middle of the cycle. cycle_size (int): length of cycle. cycle_mult (float, optional): ratio by which to change the cycle_size at the end of each cycle (default=1). start_value_mult (float, optional): ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult (float, optional): ratio by which to change the end value at the end of each cycle (default=1.0). save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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: .. code-block:: python from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler scheduler = LinearCyclicalScheduler(optimizer, 'lr', 1e-3, 1e-1, len(train_loader)) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) # # Linearly increases the learning rate from 1e-3 to 1e-1 and back to 1e-3 # over the course of 1 epoch # """ def get_param(self) -> float: cycle_progress = self.event_index / self.cycle_size 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.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): name of optimizer's parameter to update. start_value (float): value at start of cycle. end_value (float): value at the end of the cycle. cycle_size (int): length of cycle. cycle_mult (float, optional): ratio by which to change the cycle_size at the end of each cycle (default=1). start_value_mult (float, optional): ratio by which to change the start value at the end of each cycle (default=1.0). end_value_mult (float, optional): ratio by which to change the end value at the end of each cycle (default=1.0). save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): 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: .. code-block:: python from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler scheduler = CosineAnnealingScheduler(optimizer, 'lr', 1e-1, 1e-3, len(train_loader)) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) # # Anneals the learning rate from 1e-1 to 1e-3 over the course of 1 epoch. # .. code-block:: python from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler optimizer = SGD( [ {"params": model.base.parameters(), 'lr': 0.001}, {"params": model.fc.parameters(), 'lr': 0.01}, ] ) scheduler1 = LinearCyclicalScheduler(optimizer, 'lr', 1e-7, 1e-5, len(train_loader), param_group_index=0) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler1, "lr (base)") scheduler2 = CosineAnnealingScheduler(optimizer, 'lr', 1e-5, 1e-3, len(train_loader), param_group_index=1) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler2, "lr (fc)") .. [Smith17] Smith, Leslie N. "Cyclical learning rates for training neural networks." Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017 """ 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 ParamScheduler): list of parameter schedulers. durations (list of int): list of number of events that lasts a parameter scheduler from schedulers. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). Examples: .. code-block:: python from ignite.contrib.handlers.param_scheduler import ConcatScheduler from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.5, cycle_size=60) scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.5, end_value=0.01, cycle_size=60) combined_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=[30, ]) trainer.add_event_handler(Events.ITERATION_STARTED, combined_scheduler) # # Sets the Learning rate linearly from 0.1 to 0.5 over 30 iterations. Then # starts an annealing schedule from 0.5 to 0.01 over 60 iterations. # The annealing cycles are repeated indefinitely. # """ def __init__(self, schedulers: List[ParamScheduler], durations: List[int], save_history: bool = False) -> None: 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 (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._setup_scheduler() 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, **kwargs: Any, ) -> List[List[int]]: """Method to simulate scheduled values during num_events events. Args: num_events (int): number of events during the simulation. schedulers (list of ParamScheduler): list of parameter schedulers. durations (list of int): list of number of events that lasts a parameter scheduler from schedulers. param_names (list or tuple of str, optional): parameter name or list of parameter names to simulate values. By default, the first scheduler's parameter name is taken. Returns: 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( # type: ignore[call-arg] schedulers=schedulers, save_history=False, durations=durations, **kwargs ) 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 LRScheduler(ParamScheduler): """A wrapper class to call `torch.optim.lr_scheduler` objects as `ignite` handlers. Args: lr_scheduler (subclass of `torch.optim.lr_scheduler._LRScheduler`): lr_scheduler object to wrap. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). .. code-block:: python from ignite.contrib.handlers.param_scheduler import LRScheduler from torch.optim.lr_scheduler import StepLR step_scheduler = StepLR(optimizer, step_size=3, gamma=0.1) scheduler = LRScheduler(step_scheduler) # In this example, we assume to have installed PyTorch>=1.1.0 # (with new `torch.optim.lr_scheduler` behaviour) and # we 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 is will be skipped: trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler) """ def __init__(self, lr_scheduler: _LRScheduler, save_history: bool = False) -> None: if not isinstance(lr_scheduler, _LRScheduler): raise TypeError( "Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, " f"but given {type(lr_scheduler)}" ) self.lr_scheduler = lr_scheduler super(LRScheduler, self).__init__( optimizer=self.lr_scheduler.optimizer, # type: ignore[attr-defined] param_name="lr", save_history=save_history, ) self._state_attrs += ["lr_scheduler"] def __call__(self, engine: Optional[Engine], name: Optional[str] = None) -> None: self.lr_scheduler.last_epoch += 1 # type: ignore[attr-defined] super(LRScheduler, self).__call__(engine, name) 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[attr-defined] lr_list = cast(List[float], self.lr_scheduler.get_lr()) self.lr_scheduler._get_lr_called_within_step = False # type: ignore[attr-defined] 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: _LRScheduler, **kwargs: Any ) -> List[List[int]]: """Method to simulate scheduled values during num_events events. Args: num_events (int): number of events during the simulation. lr_scheduler (subclass of `torch.optim.lr_scheduler._LRScheduler`): lr_scheduler object to wrap. Returns: list of pairs: [event_index, value] """ if not isinstance(lr_scheduler, _LRScheduler): raise TypeError( "Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler, " 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(), # type: ignore[attr-defined] } torch.save(obj, cache_filepath.as_posix()) values = [] scheduler = cls(save_history=False, lr_scheduler=lr_scheduler, **kwargs) # type: ignore[call-arg] for i in range(num_events): params = [p[scheduler.param_name] for p in scheduler.optimizer_param_groups] values.append([i] + params) scheduler(engine=None) obj = torch.load(cache_filepath.as_posix()) lr_scheduler.load_state_dict(obj["lr_scheduler"]) lr_scheduler.optimizer.load_state_dict(obj["optimizer"]) # type: ignore[attr-defined] return values def create_lr_scheduler_with_warmup( lr_scheduler: Union[ParamScheduler, _LRScheduler], 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 (ParamScheduler or subclass of `torch.optim.lr_scheduler._LRScheduler`): learning rate scheduler after the warm-up. warmup_start_value (float): learning rate start value of the warm-up phase. warmup_duration (int): warm-up phase duration, number of events. warmup_end_value (float, optional): learning rate end value of the warm-up phase, (default=None). If None, warmup_end_value is set to optimizer initial lr. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). output_simulated_values (list, optional): 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: learning rate scheduler with linear warm-up. 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: .. code-block:: python torch_lr_scheduler = ExponentialLR(optimizer=optimizer, gamma=0.98) lr_values = [None] * 100 scheduler = create_lr_scheduler_with_warmup(torch_lr_scheduler, warmup_start_value=0.0, warmup_end_value=0.1, warmup_duration=10, output_simulated_values=lr_values) lr_values = np.array(lr_values) # Plot simulated values plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate") # Attach to the trainer trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) """ if not isinstance(lr_scheduler, (ParamScheduler, _LRScheduler)): raise TypeError( "Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler or " f"ParamScheduler, 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 = [] # type: 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, _LRScheduler): init_lr = param_group["lr"] if init_lr != param_group_warmup_end_value: milestones_values.append((warmup_duration, init_lr)) 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 = [ warmup_scheduler, lr_scheduler, ] # type: List[Union[ParamScheduler, ParamGroupScheduler, _LRScheduler]] 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.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): name of optimizer's parameter to update. milestones_values (list of tuples (int, float)): list of tuples (event index, parameter value) represents milestones and parameter. Milestones should be increasing integers. save_history (bool, optional): whether to log the parameter values to `engine.state.param_history`, (default=False). param_group_index (int, optional): optimizer's parameters group to use. Returns: PiecewiseLinear: piecewise linear scheduler .. 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. # """ 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 = [] # type: List[float] milestones = [] # type: 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 ParamScheduler): list/tuple of parameter schedulers. names (list of str): list of names of schedulers. .. code-block:: python optimizer = SGD( [ {"params": model.base.parameters(), 'lr': 0.001), {"params": model.fc.parameters(), 'lr': 0.01), ] ) scheduler1 = LinearCyclicalScheduler(optimizer, 'lr', 1e-7, 1e-5, len(train_loader), param_group_index=0) scheduler2 = CosineAnnealingScheduler(optimizer, 'lr', 1e-5, 1e-3, len(train_loader), param_group_index=1) lr_schedulers = [scheduler1, scheduler2] names = ["lr (base)", "lr (fc)"] scheduler = ParamGroupScheduler(schedulers=lr_schedulers, names=names) # Attach single scheduler to the trainer trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) """ 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 = OrderedDict() # type: Dict[str, List[Any]] 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 (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[_LRScheduler], **kwargs: Any) -> List[List[int]]: """Method to simulate scheduled values during num_events events. Args: num_events (int): number of events during the simulation. schedulers (subclass of `torch.optim.lr_scheduler._LRScheduler`): lr_scheduler object to wrap. Returns: list of pairs: [event_index, value] """ # 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() # type: ignore[attr-defined] torch.save(objs, cache_filepath.as_posix()) values = [] scheduler = cls(schedulers=schedulers, **kwargs) # type: ignore[arg-type] for i in range(num_events): params = [scheduler.get_param() for scheduler in schedulers] # type: ignore[attr-defined] 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"]) # type: ignore[attr-defined] 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-0.4.3/ignite/contrib/handlers/polyaxon_logger.py000066400000000000000000000266201401006565000233010ustar00rootroot00000000000000import numbers import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from torch.optim import Optimizer from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine __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-client `_ package to be installed: .. code-block:: bash pip install polyaxon-client Examples: .. code-block:: python from ignite.contrib.handlers.polyaxon_logger import * # Create a logger plx_logger = PolyaxonLogger() # Log experiment parameters: plx_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 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 ) Args: *args: Positional arguments accepted from `Experiment `_. **kwargs: Keyword arguments accepted from `Experiment `_. """ def __init__(self, *args: Any, **kwargs: Any) -> None: try: from polyaxon_client.tracking import Experiment except ImportError: raise RuntimeError( "This contrib module requires polyaxon-client to be installed. " "Please install it with command: \n pip install polyaxon-client" ) self.experiment = Experiment(*args, **kwargs) 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. Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.polyaxon_logger.global_step_from_engine`. Note: 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[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, ) -> None: super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) 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(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] 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." ) rendered_metrics = {"step": global_step} # type: Dict[str, Union[float, numbers.Number]] for key, value in metrics.items(): if isinstance(value, numbers.Number): rendered_metrics[f"{self.tag}/{key}"] = value elif isinstance(value, torch.Tensor) and value.ndimension() == 0: rendered_metrics[f"{self.tag}/{key}"] = value.item() elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): rendered_metrics[f"{self.tag}/{key}/{i}"] = v.item() else: warnings.warn(f"PolyaxonLogger output_handler can not log metrics value type {type(value)}") logger.log_metrics(**rendered_metrics) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> 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-0.4.3/ignite/contrib/handlers/stores.py000066400000000000000000000031641401006565000214060ustar00rootroot00000000000000from typing import Callable, List, Tuple, Union 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 (callable, optional): a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output , e.g., lambda x: x[0] Examples:: eos = EpochOutputStore() trainer = create_supervised_trainer(model, optimizer, loss) train_evaluator = create_supervised_evaluator(model, metrics) eos.attach(train_evaluator) @trainer.on(Events.EPOCH_COMPLETED) def log_training_results(engine): train_evaluator.run(train_loader) output = eos.data # do something with output, e.g., plotting .. versionadded:: 0.4.2 """ def __init__(self, output_transform: Callable = lambda x: x) -> None: self.data = [] # type: List[Union[int, Tuple[int, int]]] self.output_transform = output_transform def reset(self) -> None: self.data = [] def update(self, engine: Engine) -> None: output = self.output_transform(engine.state.output) self.data.append(output) def attach(self, engine: Engine) -> None: engine.add_event_handler(Events.EPOCH_STARTED, self.reset) engine.add_event_handler(Events.ITERATION_COMPLETED, self.update) ignite-0.4.3/ignite/contrib/handlers/tensorboard_logger.py000066400000000000000000000504071401006565000237520ustar00rootroot00000000000000import numbers import warnings from typing import Any, Callable, List, Optional, Union import torch import torch.nn as nn from torch.optim import Optimizer from ignite.contrib.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHistHandler, BaseWeightsScalarHandler, ) from ignite.engine import Engine, EventEnum, Events from ignite.handlers import global_step_from_engine __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.contrib.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 with we are done tb_logger.close() It is also possible to use the logger as context manager: .. code-block:: python from ignite.contrib.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) -> None: try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter # type: ignore[no-redef] except ImportError: raise RuntimeError( "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 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 and/or metrics Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.tensorboard_logger.global_step_from_engine`. Note: 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[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, ) -> None: super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, EventEnum]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'OutputHandler' works only with TensorboardLogger") metrics = self._setup_output_metrics(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] 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 isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0: logger.writer.add_scalar(f"{self.tag}/{key}", value, global_step) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): logger.writer.add_scalar(f"{self.tag}/{key}/{i}", v.item(), global_step) else: warnings.warn(f"TensorboardLogger output_handler can not log metrics value type {type(value)}") class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None) -> 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 iterates over named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) 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.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_scalar( f"{tag_prefix}weights_{self.reduction.__name__}/{name}", self.reduction(p.data), global_step ) class WeightsHistHandler(BaseWeightsHistHandler): """Helper handler to log model's weights as histograms. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, tag: Optional[str] = None): super(WeightsHistHandler, self).__init__(model, tag=tag) 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.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_histogram( tag=f"{tag_prefix}weights/{name}", values=p.data.detach().cpu().numpy(), global_step=global_step, ) class GradsScalarHandler(BaseWeightsScalarHandler): """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. Examples: .. code-block:: python from ignite.contrib.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=GradsScalarHandler(model, reduction=torch.norm) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None) -> None: super(GradsScalarHandler, self).__init__(model, reduction, tag=tag) 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.model.named_parameters(): 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(BaseWeightsHistHandler): """Helper handler to log model's gradients as histograms. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, tag: Optional[str] = None) -> None: super(GradsHistHandler, self).__init__(model, tag=tag) 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.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_histogram( tag=f"{tag_prefix}grads/{name}", values=p.grad.detach().cpu().numpy(), global_step=global_step ) ignite-0.4.3/ignite/contrib/handlers/time_profilers.py000066400000000000000000000712511401006565000231140ustar00rootroot00000000000000import functools from collections import OrderedDict from typing import Any, Callable, Dict, List, Mapping, Sequence, Tuple, Union, cast import torch from ignite.engine import Engine, EventEnum, Events from ignite.handlers 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.contrib.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') """ events_to_ignore = [ Events.EXCEPTION_RAISED, Events.TERMINATE, Events.TERMINATE_SINGLE_EPOCH, Events.DATALOADER_STOP_ITERATION, ] 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 = {} # type: 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: 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[Union[float], Union[float]]]]: # compute on non-zero data: data = data[data > 0] out = [ ("total", torch.sum(data).item() if len(data) > 0 else "not yet triggered") ] # type: List[Tuple[str, Union[str, float, Tuple[Union[float], Union[float]]]]] if len(data) > 1: out += [ ("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 = sum( [(self.event_handlers_times[e]).sum() for e in Events if e not in self.events_to_ignore] ) # type: Union[int, torch.Tensor] 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)] # type: ignore[list-item] ) 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 .. code-block:: python profiler.write_results('path_to_dir/awesome_filename.csv') Example output: .. 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: print("Need pandas to write results as files") return 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 .. code-block:: python profiler.print_results(results) Example output: .. 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.contrib.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') """ 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 = [] # type: List[float] self.processing_times = [] # type: List[float] self.event_handlers_times = {} # type: 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: 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]]]: """ 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 = round(torch.sum(data).item(), 5) if len(data) > 0 else "not triggered" # type: Union[str, float] min_index = ("None", "None") # type: Tuple[Union[str, float], Union[str, float]] max_index = ("None", "None") # type: Tuple[Union[str, float], Union[str, float]] mean = "None" # type: Union[str, float] std = "None" # type: Union[str, float] 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 .. code-block:: python profiler.write_results('path_to_dir/awesome_filename.csv') Example output: .. 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: print("Need pandas to write results as files") return 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 .. code-block:: python profiler.print_results(results) Example output: .. 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-0.4.3/ignite/contrib/handlers/tqdm_logger.py000066400000000000000000000304571401006565000224000ustar00rootroot00000000000000# -*- coding: utf-8 -*- import warnings from typing import Any, Callable, List, Optional, Union import torch from ignite.contrib.handlers.base_logger import BaseLogger, BaseOutputHandler from ignite.engine import Engine, Events from ignite.engine.events import CallableEventWithFilter, RemovableEventHandle class ProgressBar(BaseLogger): """ TQDM progress bar handler to log training progress and computed metrics. Args: persist (bool, optional): set to ``True`` to persist the progress bar after completion (default = ``False``) bar_format (str, optional): 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ , loss=0.123 [06:17<12:34] Note: When adding attaching the progress bar to an engine, it is recommend 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. """ _events_order = [ Events.STARTED, Events.EPOCH_STARTED, Events.ITERATION_STARTED, Events.ITERATION_COMPLETED, Events.EPOCH_COMPLETED, Events.COMPLETED, ] # type: List[Union[Events, CallableEventWithFilter]] def __init__( self, persist: bool = False, bar_format: str = "{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]", **tqdm_kwargs: Any, ) -> None: try: from tqdm.autonotebook import tqdm except ImportError: raise RuntimeError( "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 (str): 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[str] = None, output_transform: Optional[Callable] = None, event_name: Union[Events, CallableEventWithFilter] = Events.ITERATION_COMPLETED, closing_event_name: Union[Events, CallableEventWithFilter] = Events.EPOCH_COMPLETED, ) -> None: """ Attaches the progress bar to an engine object. Args: engine (Engine): engine object. metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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`. 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 != CallableEventWithFilter.default_event_filter: 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) super(ProgressBar, self).attach(engine, log_handler, event_name) engine.add_event_handler(closing_event_name, self._close) def attach_opt_params_handler( 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: """Intentionally empty""" pass class _OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics Args: description (str): progress bar description. metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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`. """ 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, ) -> 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) 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] metrics = self._setup_output_metrics(engine) rendered_metrics = {} for key, value in metrics.items(): if isinstance(value, torch.Tensor): if value.ndimension() == 0: rendered_metrics[key] = value.item() elif value.ndimension() == 1: for i, v in enumerate(value): k = f"{key}_{i}" rendered_metrics[k] = v.item() else: warnings.warn(f"ProgressBar can not log tensor with {value.ndimension()} dimensions") else: rendered_metrics[key] = value if rendered_metrics: logger.pbar.set_postfix(**rendered_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-0.4.3/ignite/contrib/handlers/trains_logger.py000066400000000000000000000002761401006565000227270ustar00rootroot00000000000000from ignite.contrib.handlers.clearml_logger import ClearMLLogger, ClearMLSaver __all__ = [ "TrainsLogger", "TrainsSaver", ] TrainsLogger = ClearMLLogger TrainsSaver = ClearMLSaver ignite-0.4.3/ignite/contrib/handlers/visdom_logger.py000066400000000000000000000526441401006565000227360ustar00rootroot00000000000000import numbers import os import warnings from typing import Any, Callable, Dict, List, Optional, Union, cast import torch import torch.nn as nn from torch.optim import Optimizer from ignite.contrib.handlers.base_logger import ( BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsScalarHandler, ) from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine __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/facebookresearch/visdom.git Args: server (str, optional): visdom server URL. It can be also specified by environment variable `VISDOM_SERVER_URL` port (int, optional): visdom server's port. It can be also specified by environment variable `VISDOM_PORT` num_workers (int, optional): 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.contrib.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.contrib.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} ) """ 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 RuntimeError( "This contrib module requires visdom package. " "Please install it with command:\n" "pip install git+https://github.com/facebookresearch/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: import concurrent.futures except ImportError: raise RuntimeError( "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 = _DummyExecutor() # type: Union[_DummyExecutor, "ThreadPoolExecutor"] if num_workers > 0: from concurrent.futures import ThreadPoolExecutor 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) -> None: self.windows = {} # type: 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 (VisdomLogger): visdom logger k (str): scalar name which is used to set window title and y-axis label v (int or float): 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 (int): 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 Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.visdom_logger.global_step_from_engine`. show_legend (bool, optional): flag to show legend in the window Note: 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] = None, show_legend: bool = False, ) -> None: super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) _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(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] 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(): values = [] # type: List[Union[float, torch.Tensor]] keys = [] if isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0: values.append(value) # type: ignore[arg-type] keys.append(key) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: values = value # type: ignore[assignment] keys = [f"{key}/{i}" for i in range(len(value))] else: warnings.warn(f"VisdomLogger output_handler can not log metrics value type {type(value)}") for k, v in zip(keys, values): k = f"{self.tag}/{k}" self.add_scalar(logger, k, v, event_name, global_step) logger._save() class OptimizerParamsHandler(BaseOptimizerParamsHandler, _BaseVisDrawer): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" show_legend (bool, optional): flag to show legend in the window """ def __init__( self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None, show_legend: bool = False, ) -> None: 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. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" show_legend (bool, optional): flag to show legend in the window """ def __init__( self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None, show_legend: bool = False, ) -> None: 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 = float(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. Examples: .. code-block:: python from ignite.contrib.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) ) Args: model (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" show_legend (bool, optional): flag to show legend in the window """ def __init__( self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None, show_legend: bool = False, ) -> None: 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(): name = name.replace(".", "/") k = f"{tag_prefix}grads_{self.reduction.__name__}/{name}" v = float(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-0.4.3/ignite/contrib/handlers/wandb_logger.py000066400000000000000000000327761401006565000225340ustar00rootroot00000000000000from typing import Any, Callable, List, Optional, Union from torch.optim import Optimizer from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.engine import Engine, Events from ignite.handlers import global_step_from_engine __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.contrib.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 ) 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) -> None: try: import wandb self._wandb = wandb except ImportError: raise RuntimeError( "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): wandb.init(*args, **kwargs) def __getattr__(self, attr: Any) -> Any: return getattr(self._wandb, attr) # type: ignore[misc] 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 Examples: .. code-block:: python from ignite.contrib.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.contrib.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 ) Args: tag (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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.contrib.handlers.wandb_logger.global_step_from_engine`. sync (bool, optional): If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log. Note: 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[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, sync: Optional[bool] = None, ) -> None: super().__init__(tag, metric_names, output_transform, global_step_transform) 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) # type: ignore[misc] 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(engine) if self.tag is not None: metrics = {f"{self.tag}/{name}": value for name, value in metrics.items()} logger.log(metrics, step=global_step, sync=self.sync) class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.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 ) Args: optimizer (torch.optim.Optimizer or object): torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" sync (bool, optional): If set to False, process calls to log in a seperate thread. Default (None) uses whatever the default value of wandb.log. """ def __init__( self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None, sync: Optional[bool] = None, ) -> 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-0.4.3/ignite/contrib/metrics/000077500000000000000000000000001401006565000173575ustar00rootroot00000000000000ignite-0.4.3/ignite/contrib/metrics/__init__.py000066400000000000000000000004571401006565000214760ustar00rootroot00000000000000import ignite.contrib.metrics.regression from ignite.contrib.metrics.average_precision import AveragePrecision from ignite.contrib.metrics.gpu_info import GpuInfo from ignite.contrib.metrics.precision_recall_curve import PrecisionRecallCurve from ignite.contrib.metrics.roc_auc import ROC_AUC, RocCurve ignite-0.4.3/ignite/contrib/metrics/average_precision.py000066400000000000000000000044241401006565000234220ustar00rootroot00000000000000from typing import Callable import torch from ignite.metrics import EpochMetric def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: try: from sklearn.metrics import average_precision_score except ImportError: raise RuntimeError("This contrib module requires sklearn to be installed.") y_true = y_targets.numpy() y_pred = y_preds.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 (callable, optional): 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 (bool): 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. 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) """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(AveragePrecision, self).__init__( average_precision_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn ) ignite-0.4.3/ignite/contrib/metrics/gpu_info.py000066400000000000000000000102241401006565000215360ustar00rootroot00000000000000# -*- 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 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–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: import pynvml except ImportError: raise RuntimeError( "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") from pynvml.smi import nvidia_smi # 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 = self.nvsmi.DeviceQuery( "memory.used, memory.total, utilization.gpu" ) # type: Dict[str, List[Dict[str, Any]]] 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-0.4.3/ignite/contrib/metrics/precision_recall_curve.py000066400000000000000000000046031401006565000244550ustar00rootroot00000000000000from typing import Any, Callable, Tuple import torch from ignite.metrics 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 RuntimeError("This contrib module requires sklearn to be installed.") y_true = y_targets.numpy() y_pred = y_preds.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 (callable, optional): 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 (bool): 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. 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 activated_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y roc_auc = PrecisionRecallCurve(activated_output_transform) """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(PrecisionRecallCurve, self).__init__( precision_recall_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn ) ignite-0.4.3/ignite/contrib/metrics/regression/000077500000000000000000000000001401006565000215375ustar00rootroot00000000000000ignite-0.4.3/ignite/contrib/metrics/regression/__init__.py000066400000000000000000000025071401006565000236540ustar00rootroot00000000000000from ignite.contrib.metrics.regression.canberra_metric import CanberraMetric from ignite.contrib.metrics.regression.fractional_absolute_error import FractionalAbsoluteError from ignite.contrib.metrics.regression.fractional_bias import FractionalBias from ignite.contrib.metrics.regression.geometric_mean_absolute_error import GeometricMeanAbsoluteError from ignite.contrib.metrics.regression.geometric_mean_relative_absolute_error import GeometricMeanRelativeAbsoluteError from ignite.contrib.metrics.regression.manhattan_distance import ManhattanDistance from ignite.contrib.metrics.regression.maximum_absolute_error import MaximumAbsoluteError from ignite.contrib.metrics.regression.mean_absolute_relative_error import MeanAbsoluteRelativeError from ignite.contrib.metrics.regression.mean_error import MeanError from ignite.contrib.metrics.regression.mean_normalized_bias import MeanNormalizedBias from ignite.contrib.metrics.regression.median_absolute_error import MedianAbsoluteError from ignite.contrib.metrics.regression.median_absolute_percentage_error import MedianAbsolutePercentageError from ignite.contrib.metrics.regression.median_relative_absolute_error import MedianRelativeAbsoluteError from ignite.contrib.metrics.regression.r2_score import R2Score from ignite.contrib.metrics.regression.wave_hedges_distance import WaveHedgesDistance ignite-0.4.3/ignite/contrib/metrics/regression/_base.py000066400000000000000000000054571401006565000231750ustar00rootroot00000000000000from abc import abstractmethod from typing import Callable, Tuple import torch from ignite.metrics import EpochMetric, Metric from ignite.metrics.metric import reinit__is_reduced def _check_output_shapes(output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output if y_pred.shape != y.shape: raise ValueError(f"Input data shapes should be the same, but given {y_pred.shape} and {y.shape}") 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}") 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}") 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 class _BaseRegressionEpoch(EpochMetric): # Base class for all median-based regression metrics # `update` method check the shapes and call internal overloaded method `_update`. # Class internally stores complete history of predictions and targets of type float32. def __init__( self, compute_fn: Callable, output_transform: Callable = lambda x: x, check_compute_fn: bool = True, ) -> None: super(_BaseRegressionEpoch, self).__init__( compute_fn=compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn ) def _check_type(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: _check_output_types(output) super(_BaseRegressionEpoch, self)._check_type(output) def _check_shape(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: _check_output_shapes(output) ignite-0.4.3/ignite/contrib/metrics/regression/canberra_metric.py000066400000000000000000000031641401006565000252350ustar00rootroot00000000000000from typing import Callable, Tuple, Union import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce 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.neighbors.DistanceMetric.html .. versionchanged:: 0.4.3 - Fixed implementation: ``abs`` in denominator. - Works with DDP. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ) -> None: super(CanberraMetric, self).__init__(output_transform, device) @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) / (torch.abs(y_pred) + torch.abs(y)) 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-0.4.3/ignite/contrib/metrics/regression/fractional_absolute_error.py000066400000000000000000000025561401006565000273520ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 """ def reset(self) -> None: self._sum_of_errors = 0.0 self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output 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).item() self._num_examples += y.shape[0] 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 / self._num_examples ignite-0.4.3/ignite/contrib/metrics/regression/fractional_bias.py000066400000000000000000000024201401006565000252270ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 """ def reset(self) -> None: self._sum_of_errors = 0.0 self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = 2 * (y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred)) self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0] 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 / self._num_examples ignite-0.4.3/ignite/contrib/metrics/regression/geometric_mean_absolute_error.py000066400000000000000000000026161401006565000302030ustar00rootroot00000000000000from typing import Tuple, Union, cast import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 """ def reset(self) -> None: self._sum_of_errors = 0.0 # type: Union[float, torch.Tensor] self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = torch.log(torch.abs(y.view_as(y_pred) - y_pred)) self._sum_of_errors += torch.sum(errors) self._num_examples += y.shape[0] 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(cast(torch.Tensor, self._sum_of_errors) / self._num_examples).item() ignite-0.4.3/ignite/contrib/metrics/regression/geometric_mean_relative_absolute_error.py000066400000000000000000000032461401006565000320760ustar00rootroot00000000000000from typing import Tuple, Union, cast import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 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 """ def reset(self) -> None: self._sum_y = 0.0 # type: Union[float, torch.Tensor] self._num_examples = 0 self._sum_of_errors = 0.0 # type: Union[float, torch.Tensor] def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output self._sum_y += y.sum() self._num_examples += y.shape[0] y_mean = self._sum_y / self._num_examples numerator = torch.abs(y.view_as(y_pred) - y_pred) denominator = torch.abs(y.view_as(y_pred) - y_mean) self._sum_of_errors += torch.log(numerator / denominator).sum() def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "GeometricMeanRelativeAbsoluteError must have at least one example before it can be computed." ) return torch.exp(torch.mean(cast(torch.Tensor, self._sum_of_errors) / self._num_examples)).item() ignite-0.4.3/ignite/contrib/metrics/regression/manhattan_distance.py000066400000000000000000000027561401006565000257500ustar00rootroot00000000000000from typing import Callable, Tuple, Union import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce 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.neighbors.DistanceMetric.html .. versionchanged:: 0.4.3 - Fixed sklearn compatibility. - Workes with DDP. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): super(ManhattanDistance, self).__init__(output_transform, device) @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-0.4.3/ignite/contrib/metrics/regression/maximum_absolute_error.py000066400000000000000000000024031401006565000266740ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 """ def reset(self) -> None: self._max_of_absolute_errors = -1 # type: float def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output 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 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-0.4.3/ignite/contrib/metrics/regression/mean_absolute_relative_error.py000066400000000000000000000030421401006565000300320ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError class MeanAbsoluteRelativeError(_BaseRegression): r"""Calculate Mean Absolute Relative Error. .. 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 """ def reset(self) -> None: self._sum_of_absolute_relative_errors = 0.0 self._num_samples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output 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).item() self._num_samples += y.size()[0] 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 / self._num_samples ignite-0.4.3/ignite/contrib/metrics/regression/mean_error.py000066400000000000000000000023271401006565000242460ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 """ def reset(self) -> None: self._sum_of_errors = 0.0 self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = y.view_as(y_pred) - y_pred self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0] 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 / self._num_examples ignite-0.4.3/ignite/contrib/metrics/regression/mean_normalized_bias.py000066400000000000000000000025401401006565000262540ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError 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 """ def reset(self) -> None: self._sum_of_errors = 0.0 self._num_examples = 0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output 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).item() self._num_examples += y.shape[0] 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 / self._num_examples ignite-0.4.3/ignite/contrib/metrics/regression/median_absolute_error.py000066400000000000000000000023331401006565000264560ustar00rootroot00000000000000from typing import Callable import torch from ignite.contrib.metrics.regression._base import _BaseRegressionEpoch 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).item() class MedianAbsoluteError(_BaseRegressionEpoch): 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 """ def __init__(self, output_transform: Callable = lambda x: x): super(MedianAbsoluteError, self).__init__(median_absolute_error_compute_fn, output_transform) ignite-0.4.3/ignite/contrib/metrics/regression/median_absolute_percentage_error.py000066400000000000000000000025461401006565000306610ustar00rootroot00000000000000from typing import Callable import torch from ignite.contrib.metrics.regression._base import _BaseRegressionEpoch 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).item() class MedianAbsolutePercentageError(_BaseRegressionEpoch): 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 """ def __init__(self, output_transform: Callable = lambda x: x): super(MedianAbsolutePercentageError, self).__init__( median_absolute_percentage_error_compute_fn, output_transform ) ignite-0.4.3/ignite/contrib/metrics/regression/median_relative_absolute_error.py000066400000000000000000000025161401006565000303540ustar00rootroot00000000000000from typing import Callable import torch from ignite.contrib.metrics.regression._base import _BaseRegressionEpoch 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).item() class MedianRelativeAbsoluteError(_BaseRegressionEpoch): 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 """ def __init__(self, output_transform: Callable = lambda x: x): super(MedianRelativeAbsoluteError, self).__init__(median_relative_absolute_error_compute_fn, output_transform) ignite-0.4.3/ignite/contrib/metrics/regression/r2_score.py000066400000000000000000000040721401006565000236320ustar00rootroot00000000000000from typing import Callable, Tuple, Union import torch from ignite.contrib.metrics.regression._base import _BaseRegression from ignite.exceptions import NotComputableError from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce 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`. .. versionchanged:: 0.4.3 Works with DDP. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super(R2Score, self).__init__(output_transform, device) @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-0.4.3/ignite/contrib/metrics/regression/wave_hedges_distance.py000066400000000000000000000017731401006565000262540ustar00rootroot00000000000000from typing import Tuple import torch from ignite.contrib.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 """ def reset(self) -> None: self._sum_of_errors = 0.0 def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = torch.abs(y.view_as(y_pred) - y_pred) / torch.max(y_pred, y.view_as(y_pred)) self._sum_of_errors += torch.sum(errors).item() def compute(self) -> float: return self._sum_of_errors ignite-0.4.3/ignite/contrib/metrics/roc_auc.py000066400000000000000000000104611401006565000213460ustar00rootroot00000000000000from typing import Any, Callable, Tuple import torch from ignite.metrics import EpochMetric def roc_auc_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: try: from sklearn.metrics import roc_auc_score except ImportError: raise RuntimeError("This contrib module requires sklearn to be installed.") y_true = y_targets.numpy() y_pred = y_preds.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]: try: from sklearn.metrics import roc_curve except ImportError: raise RuntimeError("This contrib module requires sklearn to be installed.") y_true = y_targets.numpy() y_pred = y_preds.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 (callable, optional): 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 (bool): 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. 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 activated_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y roc_auc = ROC_AUC(activated_output_transform) """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(ROC_AUC, self).__init__( roc_auc_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn ) 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 (callable, optional): 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 (bool): 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. 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 activated_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y roc_auc = RocCurve(activated_output_transform) """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(RocCurve, self).__init__( roc_auc_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn ) ignite-0.4.3/ignite/distributed/000077500000000000000000000000001401006565000165735ustar00rootroot00000000000000ignite-0.4.3/ignite/distributed/__init__.py000066400000000000000000000002651401006565000207070ustar00rootroot00000000000000from ignite.distributed.auto import * from ignite.distributed.comp_models import native, xla from ignite.distributed.launcher import Parallel from ignite.distributed.utils import * ignite-0.4.3/ignite/distributed/auto.py000066400000000000000000000334401401006565000201210ustar00rootroot00000000000000import warnings from typing import Any, Callable, Iterator, List, Optional, Union import torch import torch.nn as nn from torch.optim.optimizer import Optimizer from torch.utils.data import DataLoader, Dataset 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 from ignite.distributed.comp_models import native as idist_native from ignite.distributed.comp_models import 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, `torch DistributedSampler`_ is setup. - if a sampler is provided by user, 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. 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, ) Args: dataset (Dataset): input torch dataset **kwargs: keyword arguments for `torch DataLoader`_. Returns: `torch DataLoader`_ or `XLA MpDeviceLoader`_ for XLA devices .. _torch DataLoader: https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader .. _XLA MpDeviceLoader: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/parallel_loader.py#L178 .. _torch DistributedSampler: https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler """ 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 kwargs.get("sampler", None) is not None: sampler = DistributedProxySampler( kwargs["sampler"], num_replicas=world_size, rank=rank ) # type: Union[DistributedProxySampler, DistributedSampler, Sampler] else: sampler = DistributedSampler( dataset, num_replicas=world_size, rank=rank, shuffle=kwargs.get("shuffle", True) ) # we need to remove "shuffle" from kwargs if sampler is used if "shuffle" in kwargs: del kwargs["shuffle"] 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. 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) Args: model (torch.nn.Module): model to adapt. sync_bn (bool): 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 .. _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 == idist_native.NCCL: if sync_bn: logger.info("Convert batch norm to sync batch norm") model = nn.SyncBatchNorm.convert_sync_batchnorm(model) 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}") model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[lrank,], **kwargs) elif idist.has_native_dist_support and bnd == idist_native.GLOO: if sync_bn: logger.info("Convert batch norm to sync batch norm") model = nn.SyncBatchNorm.convert_sync_batchnorm(model) 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) -> 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. Examples: .. code-block:: python import ignite.distributed as idist optimizer = idist.auto_optim(optimizer) Args: optimizer (Optimizer): input torch optimizer Returns: Optimizer .. _xm.optimizer_step: http://pytorch.org/xla/release/1.5/index.html#torch_xla.core.xla_model.optimizer_step .. versionchanged:: 0.4.2 Added Horovod distributed optimizer. """ 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) 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 .. note:: Input sampler is assumed to have a constant size. Args: sampler (Sampler): Input torch data sampler. num_replicas (int, optional): Number of processes participating in distributed training. rank (int, optional): Rank of the current process within ``num_replicas``. """ 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 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 = [] # type: 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: Optional[Callable] = None) -> None: xm.optimizer_step(self.wrapped_optimizer, barrier=True) ignite-0.4.3/ignite/distributed/comp_models/000077500000000000000000000000001401006565000210745ustar00rootroot00000000000000ignite-0.4.3/ignite/distributed/comp_models/__init__.py000066400000000000000000000025231401006565000232070ustar00rootroot00000000000000from typing import TYPE_CHECKING, List, Tuple, Type, 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 = [ _SerialModel, ] # type: List[Type[Union[_SerialModel, "_NativeDistModel", "_XlaDistModel", "_HorovodDistModel"]]] 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-0.4.3/ignite/distributed/comp_models/base.py000066400000000000000000000225521401006565000223660ustar00rootroot00000000000000import warnings from abc import ABCMeta, abstractmethod from numbers import Number from typing import Any, Callable, List, Optional, Union, cast import torch 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 = None # type: Optional[int] def __init__(self) -> None: self._backend = None # type: Optional[str] self._nproc_per_node = None # type: Optional[int] self._nnodes = None # type: Optional[int] self._node = None # type: Optional[int] 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 = None # type: Any @staticmethod def _encode_str(x: str, device: torch.device) -> torch.Tensor: # use fix padded size size = 1024 if len(x) > size: warnings.warn(f"Input string size {len(x)} is larger than {size} and thus will be truncated") x = x[:size] 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, 1025) return padded_x.unsqueeze(0) @staticmethod def _decode_str(xs: torch.Tensor) -> List[str]: # xs.shape = (n, 1025), e.g. (world_size, 1025) 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, float, 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 tensor = torch.tensor(tensor, device=device, dtype=self._collective_op_dtype) elif isinstance(tensor, str): tensor_to_str = True tensor = self._encode_str(tensor, device) tensor = self._apply_op(tensor, device, fn, *args, **kwargs) if tensor_to_number: if tensor.numel() == 1: return tensor.item() else: 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") -> 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)) def all_gather(self, tensor: Union[torch.Tensor, float, str]) -> Union[torch.Tensor, float, List[float], List[str]]: if not isinstance(tensor, (torch.Tensor, Number, str)): raise TypeError(f"Unhandled input type {type(tensor)}") return self._collective_op(tensor, self._do_all_gather) def broadcast(self, tensor: Union[torch.Tensor, float, str], src: int = 0) -> Union[torch.Tensor, float, str]: if not isinstance(tensor, (torch.Tensor, Number, str)): raise TypeError(f"Unhandled input type {type(tensor)}") rank = self.get_rank() device = self.device() tensor_to_number = tensor_to_str = False if rank != src: if isinstance(tensor, Number): tensor_to_number = True tensor = torch.empty(1, device=self.device(), dtype=torch.float) elif isinstance(tensor, str): tensor_to_str = True tensor = torch.empty(1, 1025, device=self.device(), dtype=torch.long) else: if isinstance(tensor, Number): tensor_to_number = True tensor = torch.tensor([tensor,], device=device, dtype=torch.float) elif isinstance(tensor, str): tensor_to_str = True tensor = self._encode_str(tensor, device) 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") -> torch.Tensor: pass @abstractmethod def _do_all_gather(self, tensor: torch.Tensor) -> torch.Tensor: pass @abstractmethod def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: pass @abstractmethod def barrier(self) -> None: 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") 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") -> Union[torch.Tensor, float]: return tensor def all_gather(self, tensor: Union[torch.Tensor, float, str]) -> Union[torch.Tensor, float, List[float], List[str]]: if isinstance(tensor, torch.Tensor): return tensor return cast(Union[List[float], List[str]], [tensor]) def broadcast(self, tensor: Union[torch.Tensor, float, str], src: int = 0) -> Union[torch.Tensor, float, str]: return tensor def _do_all_reduce(self, tensor: torch.Tensor, op: str = "sum") -> torch.Tensor: pass def _do_all_gather(self, tensor: torch.Tensor) -> torch.Tensor: pass def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: pass def barrier(self) -> None: pass ignite-0.4.3/ignite/distributed/comp_models/horovod.py000066400000000000000000000147001401006565000231300ustar00rootroot00000000000000import os import warnings from typing import Any, Callable, Mapping, Optional, Tuple, cast 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 = backend # type: str 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( # type: ignore[override] 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), np=nproc_per_node, hosts=hosts, **kwargs, ) _reduce_op_map = { "SUM": hvd.mpi_ops.Sum, "AVERAGE": hvd.mpi_ops.Average, "ADASUM": hvd.mpi_ops.Adasum, } def _do_all_reduce(self, tensor: torch.Tensor, op: str = "SUM") -> torch.Tensor: 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_all_gather(self, tensor: torch.Tensor) -> torch.Tensor: if tensor.ndimension() == 0: tensor = tensor.unsqueeze(0) return hvd.allgather(tensor) 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-0.4.3/ignite/distributed/comp_models/native.py000066400000000000000000000332611401006565000227410ustar00rootroot00000000000000import os import subprocess import warnings from distutils.version import LooseVersion from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, cast import torch import torch.distributed as dist import torch.multiprocessing as mp 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 - 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, **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") return _NativeDistModel(backend=backend, **kwargs) def __init__(self, backend: Optional[str] = None, timeout: 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 = None # type: Optional[Dict[str, str]] if backend is not None: self._create_from_backend(backend, timeout=timeout, **kwargs) else: self._init_from_context() def _create_from_backend(self, backend: str, timeout: 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.setup_env_vars() self._local_rank = int(os.environ["LOCAL_RANK"]) # for debug purposes self._master_port = int(os.environ["MASTER_PORT"]) # type: Optional[int] self._master_addr = os.environ["MASTER_ADDR"] # type: Optional[str] init_pg_kwargs = {} if timeout is not None: init_pg_kwargs["timeout"] = timeout dist.init_process_group(backend, init_method="env://", **init_pg_kwargs) # https://github.com/facebookresearch/maskrcnn-benchmark/issues/172 dist.barrier() if backend == dist.Backend.NCCL: torch.cuda.set_device(self._local_rank) self._setup_attrs() def _init_from_context(self) -> None: self._identify_local_rank() # for debug purposes self._master_port = None self._master_addr = None self._setup_attrs() def _compute_nproc_per_node(self) -> int: local_rank = self.get_local_rank() device = torch.device("cpu") if self.backend() == dist.Backend.NCCL: # we manually set cuda device to local rank in order to avoid a hang on all_reduce device = torch.device(f"cuda:{local_rank}") tensor = torch.tensor([self.get_local_rank() + 1]).to(device) dist.all_reduce(tensor, op=dist.ReduceOp.MAX) return int(tensor.item()) def _get_all_hostnames(self) -> List[Tuple[str, ...]]: import socket device = "cpu" if self.backend() == dist.Backend.NCCL: 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(hostnames) # type: Counter 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_JOBID" 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) -> None: self._env_backup = os.environ.copy() if "SLURM_JOBID" in os.environ: self._setup_env_in_slurm() return # check if all necessary env vars are set # if partially defined raise an error necessary_env_vars = ["RANK", "LOCAL_RANK", "WORLD_SIZE"] all_env_vars_defined = [k in os.environ for k in necessary_env_vars] if any(all_env_vars_defined) and not all(all_env_vars_defined): raise RuntimeError( f"PyTorch distributed configuration should define env variables '{necessary_env_vars}'" ) os.environ["RANK"] = os.environ.get("RANK", "0") os.environ["LOCAL_RANK"] = os.environ.get("LOCAL_RANK", "0") os.environ["WORLD_SIZE"] = os.environ.get("WORLD_SIZE", "1") os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "127.0.0.1") os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "15000") def _setup_env_in_slurm(self) -> None: for k in ["SLURM_PROCID", "SLURM_LOCALID", "SLURM_NTASKS", "SLURM_JOB_NODELIST"]: if k not in os.environ: raise RuntimeError(f"SLURM distributed configuration is missing '{k}' in env variables") os.environ["RANK"] = os.environ["SLURM_PROCID"] os.environ["LOCAL_RANK"] = os.environ["SLURM_LOCALID"] os.environ["WORLD_SIZE"] = os.environ["SLURM_NTASKS"] # port should be the same over all process slurm_port = os.environ["SLURM_JOB_ID"] slurm_port = slurm_port[-4:] os.environ["MASTER_PORT"] = str(int(slurm_port) + 15000) # master address is the first hostname of nodes list hostnames = subprocess.check_output(["scontrol", "show", "hostnames", os.environ["SLURM_JOB_NODELIST"]]) os.environ["MASTER_ADDR"] = hostnames.split()[0].decode("utf-8") def get_local_rank(self) -> int: return 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 self.backend() == dist.Backend.NCCL: 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 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: str, master_port: str, kw: Any, ) -> None: from ignite.distributed.utils import _set_model, finalize copy_env_vars = os.environ.copy() os.environ["LOCAL_RANK"] = str(local_rank) os.environ["RANK"] = str(node_rank * nprocs_per_node + local_rank) os.environ["WORLD_SIZE"] = str(world_size) os.environ["MASTER_ADDR"] = str(master_addr) os.environ["MASTER_PORT"] = str(master_port) model = _NativeDistModel.create_from_backend(backend, **kw) _set_model(model) fn(local_rank, *args, **kw_dict) finalize() os.environ.clear() os.environ.update(copy_env_vars) @staticmethod def spawn( # type: ignore[override] fn: Callable, args: Tuple, kwargs_dict: Optional[Mapping] = None, nproc_per_node: int = 1, nnodes: int = 1, node_rank: int = 0, master_addr: str = "127.0.0.1", master_port: int = 2222, backend: str = "nccl", **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 LooseVersion(torch.__version__) >= LooseVersion("1.5.0"): spawn_kwargs["start_method"] = kwargs.get("start_method", "spawn") start_processes = mp.start_processes 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, 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") -> torch.Tensor: if op not in self._reduce_op_map: raise ValueError(f"Unsupported reduction operation: '{op}'") reduce_op = self._reduce_op_map[op] dist.all_reduce(tensor, reduce_op) return tensor def _do_all_gather(self, tensor: torch.Tensor) -> torch.Tensor: if tensor.ndimension() == 0: tensor = tensor.unsqueeze(0) output = [torch.zeros_like(tensor) for _ in range(self.get_world_size())] dist.all_gather(output, tensor) return torch.cat(output, dim=0) def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor: dist.broadcast(tensor, src=src) return tensor def barrier(self) -> None: dist.barrier() ignite-0.4.3/ignite/distributed/comp_models/xla.py000066400000000000000000000124541401006565000222400ustar00rootroot00000000000000from typing import Any, Callable, Mapping, Optional, Tuple, cast 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 = backend # type: str 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( # type: ignore[override] 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") -> torch.Tensor: if op not in self._reduce_op_map: raise ValueError(f"Unsupported reduction operation: '{op}'") op = self._reduce_op_map[op] xm.all_reduce(op, [tensor,]) return tensor def _do_all_gather(self, tensor: torch.Tensor) -> torch.Tensor: # from https://github.com/jysohn23/xla/blob/model-parallel-colab/Gather_Scatter_Broadcast_PyTorch_XLA.ipynb 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,]) return output.reshape(-1, *output.shape[2:]) 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") ignite-0.4.3/ignite/distributed/launcher.py000066400000000000000000000271001401006565000207460ustar00rootroot00000000000000from 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", "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) or 2) only initialize a processing group given the ``backend`` (useful with tools like `torch.distributed.launch`_, `horovodrun`_, etc). Examples: 1) Single node or Multi-node, Multi-GPU training launched with `torch.distributed.launch`_ or `horovodrun`_ tools Single node option with 4 GPUs .. code-block:: bash python -m torch.distributed.launch --nproc_per_node=4 --use_env 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 python -m torch.distributed.launch --nnodes=2 --node_rank=0 --master_addr=master \ --master_port=3344 --nproc_per_node=8 --use_env main.py # or if installed horovod horovodrun -np 16 -H hostname1:8,hostname2:8 python main.py ## node 1 python -m torch.distributed.launch --nnodes=2 --node_rank=1 --master_addr=master \ --master_port=3344 --nproc_per_node=8 --use_env 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 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 with idist.Parallel(backend=backend, 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()) # ... 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 } with idist.Parallel(backend="nccl", **dist_config) as parallel: parallel.run(training, config, a=1, b=2) .. _torch.distributed.launch: https://pytorch.org/docs/stable/distributed.html#launch-utility .. _horovodrun: https://horovod.readthedocs.io/en/latest/api.html#module-horovod.run Args: backend (str, optional): backend to use: `nccl`, `gloo`, `xla-tpu`, `horovod`. If None, no distributed configuration. nproc_per_node (int, optional): optional argument, number of processes per node to specify. If not None, :meth:`~ignite.distributed.Parallel.run` will spawn ``nproc_per_node`` processes that run input function with its arguments. nnodes (int, optional): optional argument, number of nodes participating in distributed configuration. If not None, :meth:`~ignite.distributed.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 (int, optional): 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 (str, optional): 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 (int, optional): optional argument, master node port for torch native backends (`nccl`, `gloo`). Mandatory argument if ``master_addr`` is specified. **spawn_kwargs: kwargs to ``idist.spawn`` function. .. versionchanged:: 0.4.2 ``backend`` now accepts `horovod` distributed framework. """ 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, **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.logger = setup_logger(__name__ + "." + self.__class__.__name__, distributed_rank=0) # distributed_rank=0 <=> explicit rank 0, avoid call idist. Critical for TPU on Colab, avoid context is setup 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, **spawn_kwargs ) if self._spawn_params is not None: self.logger.info(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}") @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, **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): raise ValueError( "If number of nodes larger than one, arguments master_addr and master_port " f"should be specified, but given master_addr={master_addr} and master_port={master_port}" ) params = { "nproc_per_node": nproc_per_node, "nnodes": nnodes, "node_rank": node_rank, "master_addr": master_addr, "master_port": master_port, } 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. Example .. code-block:: python def training(local_rank, config, **kwargs): # ... print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend()) # ... with idist.Parallel(backend=backend) as parallel: parallel.run(training, config, a=1, b=2) Args: func (Callable): 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``. """ if self._spawn_params is not None and self.backend is not None: self.logger.info(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") local_rank = idist.get_local_rank() func(local_rank, *args, **kwargs) self.logger.info("End of run") def __enter__(self) -> "Parallel": if (self.backend is not None) and self._spawn_params is None: idist.initialize(self.backend) self.logger = setup_logger(__name__ + "." + self.__class__.__name__) self.logger.info(f"Initialized processing group with backend: '{self.backend}'") 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(f"Finalized processing group with backend: '{self.backend}'") idist.finalize() ignite-0.4.3/ignite/distributed/utils.py000066400000000000000000000435421401006565000203150ustar00rootroot00000000000000import socket 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", ] _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 (bool): 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 = () # type: 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``. 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) Args: backend (str): backend to use: `nccl`, `gloo`, `xla-tpu`, `horovod` fn (function): 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 (tuple): arguments passed to `fn`. kwargs_dict (Mapping): kwargs passed to `fn`. nproc_per_node (int): 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), `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. .. _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 .. _xmp.spawn: http://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") -> Union[torch.Tensor, float]: """Helper method to perform all reduce operation. Args: tensor (torch.Tensor or number): tensor or number to collect across participating processes. op (str): reduction operation, "SUM" by default. Possible values: "SUM", "PRODUCT", "MIN", "MAX", "AND", "OR". Please, several values are not supported for the backend like "horovod". Returns: torch.Tensor or number """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.all_reduce(tensor, op) def all_gather(tensor: Union[torch.Tensor, float, str]) -> Union[torch.Tensor, float, List[float], List[str]]: """Helper method to perform all gather operation. Args: tensor (torch.Tensor or number or str): tensor or number or str to collect across participating processes. Returns: torch.Tensor of shape ``(world_size * tensor.shape[0], tensor.shape[1], ...)`` if input is a tensor or torch.Tensor of shape ``(world_size, )`` if input is a number or List of strings if input is a string """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.all_gather(tensor) def broadcast(tensor: Union[torch.Tensor, float, str], src: int = 0) -> Union[torch.Tensor, float, str]: """Helper method to perform broadcast operation. Args: tensor (torch.Tensor or number or str): tensor or number or str to broadcast to participating processes. Make sure to respect dtype of torch tensor input for all processes, otherwise execution will crash. src (int): source rank. Default, 0. Returns: torch.Tensor or string or number Examples: .. code-block:: python if idist.get_rank() == 0: t1 = torch.rand(4, 5, 6, device=idist.device()) s1 = "abc" x = 12.3456 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 .. versionadded:: 0.4.2 """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) return _model.broadcast(tensor, src=src) def barrier() -> None: """Helper method to synchronize all processes. """ if _need_to_sync and isinstance(_model, _SerialModel): sync(temporary=True) _model.barrier() 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.initialize` or :meth:`~ignite.distributed.spawn`. Usage: 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) # ... Args: index (int): local rank or current process index """ 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`` Examples: Launch single node multi-GPU training with ``torch.distributed.launch`` utility. .. code-block:: python # >>> python -m torch.distributed.launch --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}") idist.initialize("nccl") local_rank = idist.get_local_rank() train_fn(local_rank, a, b, c) idist.finalize() Args: backend (str, optional): backend: `nccl`, `gloo`, `xla-tpu`, `horovod`. **kwargs: acceptable kwargs according to provided backend: - "nccl" or "gloo" : timeout(=timedelta(minutes=30)). - "horovod" : comm(=None), more info: `hvd_init`_. .. _hvd_init: https://horovod.readthedocs.io/en/latest/api.html#horovod.torch.init .. versionchanged:: 0.4.2 ``backend`` now accepts `horovod` distributed framework. """ 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 (int): rank number of the handler (default: 0). with_barrier (bool): synchronisation with a barrier (default: False). .. 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 ignite-0.4.3/ignite/engine/000077500000000000000000000000001401006565000155165ustar00rootroot00000000000000ignite-0.4.3/ignite/engine/__init__.py000066400000000000000000000157501401006565000176370ustar00rootroot00000000000000from 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 if idist.has_xla_support: import torch_xla.core.xla_model as xm __all__ = [ "State", "create_supervised_trainer", "create_supervised_evaluator", "Engine", "DeterministicEngine", "Events", "EventsList", "EventEnum", "CallableEventWithFilter", "RemovableEventHandle", ] 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: 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 create_supervised_trainer( model: torch.nn.Module, optimizer: torch.optim.Optimizer, loss_fn: Union[Callable, torch.nn.Module], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, prepare_batch: Callable = _prepare_batch, output_transform: Callable = lambda x, y, y_pred, loss: loss.item(), deterministic: bool = False, ) -> Engine: """Factory function for creating a trainer for supervised models. Args: model (`torch.nn.Module`): the model to train. optimizer (`torch.optim.Optimizer`): the optimizer to use. loss_fn (torch.nn loss function): the loss function to use. device (str, optional): 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 (bool, optional): 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 (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. output_transform (callable, optional): 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 (bool, optional): if True, returns deterministic engine of type :class:`~ignite.engine.deterministic.DeterministicEngine`, otherwise :class:`~ignite.engine.engine.Engine` (default: False). 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 `_ Returns: Engine: a trainer engine with supervised update function. """ 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 if on_tpu and not idist.has_xla_support: raise RuntimeError("In order to run on TPU, please install PyTorch XLA") def _update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]: model.train() optimizer.zero_grad() x, y = prepare_batch(batch, device=device, non_blocking=non_blocking) y_pred = model(x) loss = loss_fn(y_pred, y) loss.backward() if on_tpu: xm.optimizer_step(optimizer, barrier=True) else: optimizer.step() return output_transform(x, y, y_pred, loss) trainer = Engine(_update) if not deterministic else DeterministicEngine(_update) return trainer 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, output_transform: Callable = lambda x, y, y_pred: (y_pred, y), ) -> Engine: """ Factory function for creating an evaluator for supervised models. Args: model (`torch.nn.Module`): the model to train. metrics (dict of str - :class:`~ignite.metrics.Metric`): a map of metric names to Metrics. device (str, optional): device type specification (default: None). Applies to batches after starting the engine. Model *will not* be moved. non_blocking (bool, optional): 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 (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`. output_transform (callable, optional): 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. Note: `engine.state.output` for this engine is defind 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 `_ Returns: Engine: an evaluator engine with supervised inference function. """ metrics = metrics or {} def _inference(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) y_pred = model(x) return output_transform(x, y, y_pred) evaluator = Engine(_inference) for name, metric in metrics.items(): metric.attach(evaluator, name) return evaluator ignite-0.4.3/ignite/engine/deterministic.py000066400000000000000000000257411401006565000207440ustar00rootroot00000000000000import random import warnings from collections import OrderedDict from functools import wraps from typing import Any, Callable, Generator, Iterator, List, Optional, cast 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 (torch.utils.data.DataLoader): input dataloader new_batch_sampler (torch.utils.data.sampler.BatchSampler): 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. Usage: 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 Args: batch_sampler (torch.utils.data.sampler.BatchSampler): batch sampler same as used with `torch.utils.data.DataLoader` start_iteration (int, optional): optional 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 = [] # type: List self.batch_sampler = batch_sampler self.start_iteration = start_iteration self.sampler = self.batch_sampler.sampler def setup_batch_indices(self) -> None: 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]) 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 (callable): 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. """ def __init__(self, process_function: Callable): super(DeterministicEngine, self).__init__(process_function) self.state_dict_user_keys.append("rng_states") 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 not hasattr(self.state, "rng_states"): setattr(self.state, "rng_states", None) if torch.cuda.is_available(): torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def _setup_engine(self) -> None: if self.state.dataloader is None: raise RuntimeError( "Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error." ) 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.append(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[attr-defined, 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[attr-defined, 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 else: le = iter_counter if iteration is None: iteration = self.state.iteration manual_seed(self.state.seed + iteration // le) # type: ignore[operator] ignite-0.4.3/ignite/engine/engine.py000066400000000000000000001076131401006565000173450ustar00rootroot00000000000000import functools import logging import time import warnings import weakref from collections import OrderedDict, defaultdict from collections.abc import Mapping from typing import Any, Callable, Dict, 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 (callable): 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 (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 (Events): last event name triggered by the engine. Examples: Create a basic trainer .. code-block:: python def update_model(engine, batch): inputs, targets = batch 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") def __init__(self, process_function: Callable): self._event_handlers = defaultdict(list) # type: Dict[Any, List] self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__) self._process_function = process_function self.last_event_name = None # type: Optional[Events] self.should_terminate = False self.should_terminate_single_epoch = False self.state = State() self._state_dict_user_keys = [] # type: List[str] self._allowed_events = [] # type: List[EventEnum] self._dataloader_iter = None # type: Optional[Iterator[Any]] self._init_iter = [] # type: List[int] 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) 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 (iterable): 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 (dict, optional): A dictionary to map an event to a state attribute. Example usage: .. 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 (callable): 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``. 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`. Returns: :class:`~ignite.engine.RemovableEventHandle`, which can be used to remove the handler. Example usage: .. 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 != CallableEventWithFilter.default_event_filter ): event_filter = event_name.filter handler = self._handler_wrapper(handler, event_name, event_filter) self._assert_allowed_event(event_name) event_args = (Exception(),) if event_name == Events.EXCEPTION_RAISED else () 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) @staticmethod def _assert_non_filtered_event(event_name: Any) -> None: if ( isinstance(event_name, CallableEventWithFilter) and event_name.filter != CallableEventWithFilter.default_event_filter ): raise TypeError( "Argument event_name should not be a filtered event, " "please use event without any event filtering" ) def has_event_handler(self, handler: Callable, event_name: Optional[Any] = None) -> bool: """Check if the specified event has the specified handler. Args: handler (callable): 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 = [event_name] # type: Union[List[Any], Dict[Any, List]] 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() # type: ignore[attr-defined] 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 (callable): 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 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`. Example usage: .. 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"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 terminate(self) -> None: """Sends terminate signal to the engine, so that it terminates completely the run after the current iteration. """ 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 after the current iteration. """ 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 = self._state_dict_all_req_keys + (self._state_dict_one_of_opt_keys[0],) # type: Tuple[str, ...] 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` and `max_epochs`, `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 attributs added by user. Args: state_dict (Mapping): 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: return state.iteration == state.epoch_length * state.max_epochs # type: ignore[operator] 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 (Iterable): Collection of batches allowing repeated iteration (e.g., list or `DataLoader`). Example usage: 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: Iterable, max_epochs: Optional[int] = None, epoch_length: Optional[int] = None, seed: 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 (Iterable): Collection of batches allowing repeated iteration (e.g., list or `DataLoader`). max_epochs (int, optional): 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 (int, optional): 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. seed (int, optional): Deprecated argument since v0.4.0 and will be removed in v0.5.0. Please, use `torch.manual_seed` or :meth:`~ignite.utils.manual_seed`. 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 seed is not None: warnings.warn( "Argument seed is deprecated. It will be removed in 0.5.0. " "Please, use torch.manual_seed or ignite.utils.manual_seed" ) if 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 larger than the start epoch " f"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): # Create new state if max_epochs is None: max_epochs = 1 if epoch_length 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 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" ) self.state.dataloader = data return self._internal_run() @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_engine(self) -> None: if self.state.dataloader is None: raise RuntimeError( "Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error." ) iteration = self.state.iteration self._dataloader_iter = iter(self.state.dataloader) # 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.append(iteration) def _internal_run(self) -> State: self.should_terminate = self.should_terminate_single_epoch = False self._init_timers(self.state) try: start_time = time.time() self._fire_event(Events.STARTED) while self.state.epoch < self.state.max_epochs and not self.should_terminate: # type: ignore[operator] self.state.epoch += 1 self._fire_event(Events.EPOCH_STARTED) if self._dataloader_iter is None: self._setup_engine() time_taken = self._run_once_on_dataset() # time is available for handlers but must be update after fire self.state.times[Events.EPOCH_COMPLETED.name] = time_taken handlers_start_time = time.time() if self.should_terminate: self._fire_event(Events.TERMINATE) else: self._fire_event(Events.EPOCH_COMPLETED) time_taken += time.time() - handlers_start_time # update time wrt handlers self.state.times[Events.EPOCH_COMPLETED.name] = time_taken hours, mins, secs = _to_hours_mins_secs(time_taken) self.logger.info(f"Epoch[{self.state.epoch}] Complete. Time taken: {hours:02d}:{mins:02d}:{secs:02d}") if self.should_terminate: break time_taken = time.time() - start_time # time is available for handlers but must be update 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:02d}") 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(self) -> float: start_time = time.time() # We need to setup iter_counter > 0 if we resume from an iteration iter_counter = self._init_iter.pop() if len(self._init_iter) > 0 else 0 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." ) if self.state.dataloader is None: raise RuntimeError( "Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error." ) while True: try: # Avoid Events.GET_BATCH_STARTED triggered twice when data iter is restarted if self.last_event_name != Events.DATALOADER_STOP_ITERATION: self._fire_event(Events.GET_BATCH_STARTED) self.state.batch = next(self._dataloader_iter) self._fire_event(Events.GET_BATCH_COMPLETED) 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): total_iters = self.state.epoch_length * self.state.max_epochs # type: ignore[operator] 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 self._fire_event(Events.DATALOADER_STOP_ITERATION) self.set_data(self.state.dataloader) should_exit = True continue self.state.iteration += 1 self._fire_event(Events.ITERATION_STARTED) self.state.output = self._process_function(self, self.state.batch) self._fire_event(Events.ITERATION_COMPLETED) # TODO: remove refs on batch to avoid high mem consumption ? -> need verification # self.state.batch = None if self.should_terminate or self.should_terminate_single_epoch: self._fire_event(Events.TERMINATE_SINGLE_EPOCH, iter_counter=iter_counter) self.should_terminate_single_epoch = False self.set_data(self.state.dataloader) break if self.state.epoch_length is not None and iter_counter == self.state.epoch_length: break 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 ignite-0.4.3/ignite/engine/events.py000066400000000000000000000407311401006565000174010ustar00rootroot00000000000000import numbers import warnings import weakref from enum import Enum from types import DynamicClassAttribute from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, List, Optional, 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 (str): The actual enum value. Only needed for internal use. Do not touch! event_filter (callable): 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 (str, optional): 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: if event_filter is None: event_filter = CallableEventWithFilter.default_event_filter 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[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 (callable, optional): a filter function to check if the event should be executed when the event type was fired every (int, optional): a value specifying how often the event should be fired once (int, optional): a value specifying when the event should be fired (if only once) Returns: CallableEventWithFilter: A new event having the same value but a different filter function """ if not ((event_filter is not None) ^ (every is not None) ^ (once is not None)): raise ValueError("Only one of the input arguments should be specified") 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) and not (isinstance(once, numbers.Integral) and once > 0): raise ValueError("Argument every should be integer and positive") 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) # 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: def wrapper(engine: "Engine", event: int) -> bool: if event % every == 0: return True return False return wrapper @staticmethod def once_event_filter(once: int) -> Callable: def wrapper(engine: "Engine", event: int) -> bool: if event == once: return True return False return wrapper @staticmethod def default_event_filter(engine: "Engine", event: int) -> bool: return True def __str__(self) -> str: return "" % (self.name, self.filter) 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 CallableEvents(CallableEventWithFilter): # For backward compatibility def __init__(self, *args: Any, **kwargs: Any) -> None: super(CallableEvents, self).__init__(*args, **kwargs) warnings.warn( "Class ignite.engine.events.CallableEvents is deprecated. It will be removed in 0.5.0. " "Please, use ignite.engine.EventEnum instead", DeprecationWarning, ) class EventEnum(CallableEventWithFilter, Enum): # type: ignore[misc] """Base class for all :class:`~ignite.engine.events.Events`. User defined custom events should also inherit this class. For example, 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): # ... """ pass 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 - โœ” - โœ— - โœ— * - :meth:`~ignite.engine.engine.Engine.terminate_epoch()` - โœ” - โœ” - โœ— * - :meth:`~ignite.engine.engine.Engine.terminate()` - โœ— - โœ” - โœ” 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 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" EPOCH_COMPLETED = "epoch_completed" STARTED = "started" COMPLETED = "completed" ITERATION_STARTED = "iteration_started" ITERATION_COMPLETED = "iteration_completed" EXCEPTION_RAISED = "exception_raised" GET_BATCH_STARTED = "get_batch_started" GET_BATCH_COMPLETED = "get_batch_completed" DATALOADER_STOP_ITERATION = "dataloader_stop_iteration" TERMINATE = "terminate" TERMINATE_SINGLE_EPOCH = "terminate_single_epoch" 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 = [] # type: 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 """ event_to_attr = { 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", } # type: Dict[Union[str, "Events", "CallableEventWithFilter"], str] def __init__(self, **kwargs: Any) -> None: self.iteration = 0 self.epoch = 0 self.epoch_length = None # type: Optional[int] self.max_epochs = None # type: Optional[int] self.output = None # type: Optional[int] self.batch = None # type: Optional[int] self.metrics = {} # type: Dict[str, Any] self.dataloader = None # type: Optional[Union[DataLoader, Iterable[Any]]] self.seed = None # type: Optional[int] self.times = { Events.EPOCH_COMPLETED.name: None, Events.COMPLETED.name: None, } # type: Dict[str, Optional[float]] 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: 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. Example usage: .. 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 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-0.4.3/ignite/engine/utils.py000066400000000000000000000021071401006565000172300ustar00rootroot00000000000000import 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()) # type: ignore[attr-defined] 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, int]: """Convert seconds to hours, mins, and seconds.""" mins, secs = divmod(time_taken, 60) hours, mins = divmod(mins, 60) return round(hours), round(mins), round(secs) ignite-0.4.3/ignite/exceptions.py000066400000000000000000000002261401006565000170040ustar00rootroot00000000000000__all__ = ["NotComputableError"] class NotComputableError(RuntimeError): """ Exception class to raise if Metric cannot be computed. """ ignite-0.4.3/ignite/handlers/000077500000000000000000000000001401006565000160515ustar00rootroot00000000000000ignite-0.4.3/ignite/handlers/__init__.py000066400000000000000000000024701401006565000201650ustar00rootroot00000000000000from typing import Any, Callable, Optional, Union from ignite.engine import Engine from ignite.engine.events import Events from ignite.handlers.checkpoint import Checkpoint, DiskSaver, ModelCheckpoint from ignite.handlers.early_stopping import EarlyStopping from ignite.handlers.terminate_on_nan import TerminateOnNan from ignite.handlers.time_limit import TimeLimit from ignite.handlers.timing import Timer __all__ = [ "ModelCheckpoint", "Checkpoint", "DiskSaver", "Timer", "EarlyStopping", "TerminateOnNan", "global_step_from_engine", "TimeLimit", ] 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): engine which state is used to provide the global step custom_event_name (optional): registered event name. Optional argument, event name to use. Returns: global step """ 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-0.4.3/ignite/handlers/checkpoint.py000066400000000000000000001144141401006565000205570ustar00rootroot00000000000000import collections.abc as collections import numbers import os import tempfile import warnings from abc import ABCMeta, abstractmethod from collections import OrderedDict from tempfile import _TemporaryFileWrapper # type: ignore[attr-defined] from typing import Any, Callable, Dict, List, Mapping, NamedTuple, Optional, Tuple, Union import torch import torch.nn as nn import ignite.distributed as idist from ignite.base import Serializable from ignite.engine import Engine, Events __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 (Mapping): checkpoint dictionary to save. filename (str): filename associated with checkpoint. metadata (Mapping, optional): metadata on checkpoint to save. """ pass @abstractmethod def remove(self, filename: str) -> None: """Method to remove saved checkpoint. Args: filename (str): filename associated with checkpoint. """ pass 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 (Mapping): 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 (callable or :class:`~ignite.handlers.checkpoint.BaseSaveHandler`): 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`. filename_prefix (str, optional): Prefix for the file name to which objects will be saved. See Note for details. score_function (callable, optional): 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 (str, optional): If ``score_function`` not None, it is possible to store its value using ``score_name``. See Notes for more details. n_saved (int, optional): 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 (callable, optional): 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`. archived (bool, optional): Deprecated argument as models saved by ``torch.save`` are already compressed. filename_pattern (str, optional): 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 (bool): 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 (bool): if `True`, the latest equally scored model is stored. Otherwise, the first model. Default, `False`. .. _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, it should be run in all processes, otherwise application can get stuck on 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, DiskSaver 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, DiskSaver('/tmp/models', create_dir=True), 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, DiskSaver('/tmp/models', create_dir=True), 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, DiskSaver, global_step_from_engine trainer = ... evaluator = ... # Setup Accuracy metric computation on evaluator # Run evaluation on epoch completed event # ... score_function = Checkpoint.get_default_score_fn("accuracy") to_save = {'model': model} handler = Checkpoint( to_save, DiskSaver('/tmp/models', create_dir=True), n_saved=2, filename_prefix='best', score_function=score_function, score_name="val_acc", 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_val_acc=0.77.pt", "best_model_10_val_acc=0.78.pt", ] .. versionchanged:: 0.4.3 - Checkpoint can save model with same filename. - Added ``greater_or_equal`` argument. """ Item = NamedTuple("Item", [("priority", int), ("filename", str)]) _state_dict_all_req_keys = ("saved",) def __init__( self, to_save: Mapping, save_handler: Union[Callable, BaseSaveHandler], filename_prefix: str = "", score_function: Optional[Callable] = None, score_name: Optional[str] = None, n_saved: Optional[int] = 1, global_step_transform: Optional[Callable] = None, archived: bool = False, filename_pattern: Optional[str] = None, include_self: bool = False, greater_or_equal: bool = False, ) -> None: 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 (callable(save_handler) or isinstance(save_handler, BaseSaveHandler)): raise TypeError("Argument `save_handler` should be callable or inherit from BaseSaveHandler") if score_function is None and score_name is not None: raise ValueError("If `score_name` is provided, then `score_function` " "should be also provided.") 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 archived: warnings.warn("Argument archived is deprecated and will be removed in 0.5.0") self.to_save = to_save self.filename_prefix = filename_prefix self.save_handler = save_handler self.score_function = score_function self.score_name = score_name self.n_saved = n_saved self.ext = "pt" self.global_step_transform = global_step_transform self.filename_pattern = filename_pattern self._saved = [] # type: List["Checkpoint.Item"] self.include_self = include_self self.greater_or_equal = greater_or_equal 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[str]: if len(self._saved) < 1: return None return 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] 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 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, Dict[Any, Any]]: checkpoint = {} if self.to_save is not None: for k, obj in self.to_save.items(): if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)): obj = obj.module checkpoint[k] = obj.state_dict() return checkpoint @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 (bool): If True, the ``filename_prefix`` is added to the filename pattern: ``{filename_prefix}_{name}...``. Default, True. with_score (bool): 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 (bool): 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 (bool): 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. Example: .. 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: for k, obj in objs.items(): if not hasattr(obj, attr): raise TypeError(f"Object {type(obj)} should have `{attr}` method") @staticmethod def load_objects(to_load: Mapping, checkpoint: Mapping, **kwargs: Any) -> None: """Helper method to apply ``load_state_dict`` on the objects from ``to_load`` using states from ``checkpoint``. Exemples: .. code-block:: python import torch from ignite.engine import Engine, Events from ignite.handlers import ModelCheckpoint, Checkpoint trainer = Engine(lambda engine, batch: None) handler = ModelCheckpoint('/tmp/models', '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 = "/tmp/models/myprefix_checkpoint_40.pth" checkpoint = torch.load(checkpoint_fp) Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint) 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``). Args: to_load (Mapping): a dictionary with objects, e.g. `{"model": model, "optimizer": optimizer, ...}` checkpoint (Mapping): 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) .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/ torch.nn.parallel.DistributedDataParallel.html .. _DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html """ Checkpoint._check_objects(to_load, "load_state_dict") if not isinstance(checkpoint, collections.Mapping): raise TypeError(f"Argument checkpoint should be a dictionary, but given {type(checkpoint)}") if len(kwargs) > 1 or any(k for k in kwargs.keys() if k not in ["strict"]): warnings.warn("kwargs contains keys other than strict and these will be ignored") is_state_dict_strict = kwargs.get("strict", True) 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: if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)): obj = obj.module obj.load_state_dict(checkpoint, strict=is_state_dict_strict) return # multiple objects to load for k, obj in to_load.items(): if k not in checkpoint: raise ValueError(f"Object labeled by '{k}' from `to_load` is not found in the checkpoint") if isinstance(obj, (nn.DataParallel, nn.parallel.DistributedDataParallel)): obj = obj.module if isinstance(obj, torch.nn.Module): obj.load_state_dict(checkpoint[k], strict=is_state_dict_strict) else: obj.load_state_dict(checkpoint[k]) def state_dict(self) -> "OrderedDict[str, List[Tuple[int, str]]]": """Method returns state dict with saved items: list of ``(priority, filename)`` pairs. Can be used to save internal state of the class. """ return OrderedDict([("saved", [(p, f) for p, f in self._saved])]) def load_state_dict(self, state_dict: Mapping) -> None: """Method replace internal state of the class with provided state dict data. Args: state_dict (Mapping): 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 (str): 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 (float): 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. Exemples: .. 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 (str): Directory path where the checkpoint will be saved atomic (bool, optional): 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 (bool, optional): if True, will create directory ``dirname`` if it doesnt exist. require_empty (bool, optional): If True, will raise exception if there are any files in the directory ``dirname``. **kwargs: Accepted keyword arguments for `torch.save` or `xm.save`. .. versionchanged:: 0.4.2 Accept ``kwargs`` for `torch.save` or `xm.save`. """ def __init__( self, dirname: str, atomic: bool = True, create_dir: bool = True, require_empty: bool = True, **kwargs: Any ) -> None: self.dirname = os.path.expanduser(dirname) self._atomic = atomic self._check_and_setup(dirname, create_dir, require_empty) self.kwargs = kwargs @staticmethod @idist.one_rank_only() def _check_and_setup(dirname: str, create_dir: bool, require_empty: bool) -> None: if create_dir: if not os.path.exists(dirname): os.makedirs(dirname) # Ensure that dirname exists if not os.path.exists(dirname): 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 = os.path.join(self.dirname, filename) if idist.has_xla_support: self._save_xla(checkpoint, path) else: self._save_native(checkpoint, path) @idist.one_rank_only() def _save_native(self, checkpoint: Mapping, path: str) -> None: self._save_func(checkpoint, path, torch.save) def _save_xla(self, checkpoint: Mapping, path: str) -> None: 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, rank=idist.get_rank()) def _save_func(self, checkpoint: Mapping, path: str, func: Callable, rank: int = 0) -> None: if not self._atomic: func(checkpoint, path, **self.kwargs) else: tmp_file = None tmp_name = "" tmp = None # type: _TemporaryFileWrapper if rank == 0: 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: if tmp is not None: tmp.close() os.remove(tmp_name) raise else: if tmp is not None: tmp.close() os.rename(tmp.name, path) @idist.one_rank_only() def remove(self, filename: str) -> None: path = os.path.join(self.dirname, filename) os.remove(path) class ModelCheckpoint(Checkpoint): """ModelCheckpoint handler 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`. 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. Argument ``save_as_state_dict`` is deprecated and should not be used. It is considered as True. Argument ``save_interval`` is deprecated and should not be used. Please, use events filtering instead, e.g. :attr:`~ignite.engine.events.Events.ITERATION_STARTED(every=1000)` 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 (str): Directory path where objects will be saved. filename_prefix (str): Prefix for the file names to which objects will be saved. See Notes of :class:`~ignite.handlers.Checkpoint` for more details. score_function (callable, optional): 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 (str, optional): if ``score_function`` not None, it is possible to store its value using `score_name`. See Notes for more details. n_saved (int, optional): Number of objects that should be kept on disk. Older files will be removed. If set to `None`, all objects are kept. atomic (bool, optional): 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 (bool, optional): If True, will raise exception if there are any files starting with ``filename_prefix`` in the directory ``dirname``. create_dir (bool, optional): If True, will create directory ``dirname`` if it does not exist. global_step_transform (callable, optional): 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`. archived (bool, optional): Deprecated argument as models saved by `torch.save` are already compressed. include_self (bool): 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``. **kwargs: Accepted keyword arguments for `torch.save` or `xm.save` in `DiskSaver`. .. versionchanged:: 0.4.2 Accept ``kwargs`` for `torch.save` or `xm.save` Examples: >>> 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) >>> 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) >>> os.listdir('/tmp/models') ['myprefix_mymodel_20.pt', 'myprefix_mymodel_30.pt'] >>> handler.last_checkpoint ['/tmp/models/myprefix_mymodel_30.pt'] """ def __init__( self, dirname: str, filename_prefix: str, save_interval: Optional[int] = None, 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, save_as_state_dict: bool = True, global_step_transform: Optional[Callable] = None, archived: bool = False, include_self: bool = False, **kwargs: Any, ) -> None: if not save_as_state_dict: raise ValueError( "Argument save_as_state_dict is deprecated and should be True." "This argument will be removed in 0.5.0." ) if save_interval is not None: msg = ( "Argument save_interval is deprecated and should be None. This argument will be removed in 0.5.0." "Please, use events filtering instead, e.g. Events.ITERATION_STARTED(every=1000)" ) if save_interval == 1: # Do not break for old version who used `save_interval=1` warnings.warn(msg) else: # No choice raise ValueError(msg) disk_saver = DiskSaver(dirname, atomic=atomic, create_dir=create_dir, require_empty=require_empty, **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, archived=archived, include_self=include_self, ) @property def last_checkpoint(self) -> Union[str, None]: if len(self._saved) < 1: return None if not isinstance(self.save_handler, DiskSaver): raise RuntimeError( f"Unable to save checkpoint, save_handler should be DiskSaver, got {type(self.save_handler)}." ) return os.path.join(self.save_handler.dirname, self._saved[-1].filename) def __call__(self, engine: Engine, to_save: Mapping) -> None: # 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-0.4.3/ignite/handlers/early_stopping.py000066400000000000000000000074771401006565000215010ustar00rootroot00000000000000from collections import OrderedDict from typing import Callable, Mapping, Optional, cast 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 (int): Number of events to wait if no improvement and then stop the training. score_function (callable): 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 (Engine): trainer engine to stop the run if no improvement. min_delta (float, optional): 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 (bool, optional): 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 = None # type: Optional[float] 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]": return OrderedDict([("counter", self.counter), ("best_score", cast(float, self.best_score))]) def load_state_dict(self, state_dict: Mapping) -> None: super().load_state_dict(state_dict) self.counter = state_dict["counter"] self.best_score = state_dict["best_score"] ignite-0.4.3/ignite/handlers/terminate_on_nan.py000066400000000000000000000041061401006565000217440ustar00rootroot00000000000000import 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 (callable, optional): 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-0.4.3/ignite/handlers/time_limit.py000066400000000000000000000030611401006565000205570ustar00rootroot00000000000000import 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 (int, optional): 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-0.4.3/ignite/handlers/timing.py000066400000000000000000000107121401006565000177130ustar00rootroot00000000000000from 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 (bool, optional): 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: >>> 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: >>> 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: >>> 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: >>> 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.EPOCH_STARTED, ... resume=Events.ITERATION_STARTED, ... pause=Events.ITERATION_COMPLETED, ... step=Events.ITERATION_COMPLETED) """ def __init__(self, average: bool = False) -> None: 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): Engine that this timer will be attached to. start (Events): Event which should start (reset) the timer. pause (Events): Event which should pause the timer. resume (Events, optional): Event which should resume the timer. step (Events, optional): Event which should call the `step` method of the counter. Returns: self (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": self._t0 = perf_counter() self.total = 0.0 self.step_count = 0.0 self.running = True return self def pause(self, *args: Any) -> None: if self.running: self.total += self._elapsed() self.running = False def resume(self, *args: Any) -> None: if not self.running: self.running = True self._t0 = perf_counter() def value(self) -> float: 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: self.step_count += 1.0 def _elapsed(self) -> float: return perf_counter() - self._t0 ignite-0.4.3/ignite/metrics/000077500000000000000000000000001401006565000157175ustar00rootroot00000000000000ignite-0.4.3/ignite/metrics/__init__.py000066400000000000000000000030641401006565000200330ustar00rootroot00000000000000from ignite.metrics.accumulation import Average, GeometricAverage, VariableAccumulation from ignite.metrics.accuracy import Accuracy from ignite.metrics.confusion_matrix import ConfusionMatrix, DiceCoefficient, IoU, mIoU from ignite.metrics.epoch_metric import EpochMetric from ignite.metrics.fbeta import Fbeta from ignite.metrics.frequency import Frequency from ignite.metrics.loss import Loss 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.metrics_lambda import MetricsLambda from ignite.metrics.precision import Precision from ignite.metrics.psnr import PSNR from ignite.metrics.recall import Recall 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", "MetricsLambda", "MeanAbsoluteError", "MeanPairwiseDistance", "MeanSquaredError", "ConfusionMatrix", "TopKCategoricalAccuracy", "Average", "DiceCoefficient", "EpochMetric", "Fbeta", "GeometricAverage", "IoU", "mIoU", "Precision", "PSNR", "Recall", "RootMeanSquaredError", "RunningAverage", "VariableAccumulation", "Frequency", "SSIM", ] ignite-0.4.3/ignite/metrics/accumulation.py000066400000000000000000000176661401006565000207750ustar00rootroot00000000000000import numbers from typing import Any, 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 (callable): 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 (callable, optional): 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 (str or torch.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. """ required_output_keys = None def __init__( self, op: Callable, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): 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) @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) or isinstance(output, 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 output.device != self._device: output = output.to(self._device) 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)` Examples: .. code-block:: python evaluator = ... custom_var_mean = Average(output_transform=lambda output: output['custom_var']) custom_var_mean.attach(evaluator, 'mean_custom_var') state = evaluator.run(dataset) # state.metrics['mean_custom_var'] -> average of output['custom_var'] Args: output_transform (callable, optional): 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 (str or torch.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, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): 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) @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`. 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)` Args: output_transform (callable, optional): 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 (str or torch.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, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ): 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) @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-0.4.3/ignite/metrics/accuracy.py000066400000000000000000000162001401006565000200620ustar00rootroot00000000000000from 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"), ): self._is_multilabel = is_multilabel self._type = None # type: Optional[str] self._num_classes = None # type: Optional[int] super(_BaseClassification, self).__init__(output_transform=output_transform, device=device) 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 = y_pred.shape # type: Tuple[int, ...] 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)`` or ``{'y_pred': y_pred, 'y': 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. 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: .. code-block:: python def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y binary_accuracy = Accuracy(thresholded_output_transform) Args: output_transform (callable, optional): 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 (bool, optional): flag to use in multilabel case. By default, False. device (str or torch.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, output_transform: Callable = lambda x: x, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): super(Accuracy, self).__init__(output_transform=output_transform, is_multilabel=is_multilabel, device=device) @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-0.4.3/ignite/metrics/confusion_matrix.py000066400000000000000000000317461401006565000216730ustar00rootroot00000000000000import 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"] class ConfusionMatrix(Metric): """Calculates confusion matrix for multi-class data. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': 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 (int): Number of classes, should be > 1. See notes for more details. average (str, optional): 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 (callable, optional): 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 (str or torch.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: 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. 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: .. code-block:: python def binary_one_hot_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred).round().long() y_pred = ignite.utils.to_onehot(y_pred, 2) y = y.long() return y_pred, y metrics = { "confusion_matrix": ConfusionMatrix(2, output_transform=binary_one_hot_output_transform), } evaluator = create_supervised_evaluator( model, metrics=metrics, output_transform=lambda x, y, y_pred: (y_pred, y) ) """ def __init__( self, num_classes: int, average: Optional[str] = None, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): 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) @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 = y_pred.shape # type: Tuple[int, ...] 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: 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.ConfusionMatrix` metric. .. math:: \text{J}(A, B) = \frac{ \lvert A \cap B \rvert }{ \lvert A \cup B \rvert } Args: cm (ConfusionMatrix): instance of confusion matrix metric ignore_index (int, optional): index to ignore, e.g. background index Returns: MetricsLambda Examples: .. code-block:: python train_evaluator = ... cm = ConfusionMatrix(num_classes=num_classes) IoU(cm, ignore_index=0).attach(train_evaluator, 'IoU') state = train_evaluator.run(train_dataset) # state.metrics['IoU'] -> tensor of shape (num_classes - 1, ) """ 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 non-negative integer, but given {ignore_index}") # Increase floating point precision and pass to CPU cm = cm.type(torch.DoubleTensor) iou = cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag() + 1e-15) # type: MetricsLambda if ignore_index is not None: ignore_idx = ignore_index # type: int # 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.ConfusionMatrix` metric. Args: cm (ConfusionMatrix): instance of confusion matrix metric ignore_index (int, optional): index to ignore, e.g. background index Returns: MetricsLambda Examples: .. code-block:: python train_evaluator = ... cm = ConfusionMatrix(num_classes=num_classes) mIoU(cm, ignore_index=0).attach(train_evaluator, 'mean IoU') state = train_evaluator.run(train_dataset) # state.metrics['mean IoU'] -> scalar """ iou = IoU(cm=cm, ignore_index=ignore_index).mean() # type: MetricsLambda return iou def cmAccuracy(cm: ConfusionMatrix) -> MetricsLambda: """Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric. Args: cm (ConfusionMatrix): instance of confusion matrix metric Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.type(torch.DoubleTensor) accuracy = cm.diag().sum() / (cm.sum() + 1e-15) # type: MetricsLambda return accuracy def cmPrecision(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda: """Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric. Args: cm (ConfusionMatrix): instance of confusion matrix metric average (bool, optional): if True metric value is averaged over all classes Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.type(torch.DoubleTensor) precision = cm.diag() / (cm.sum(dim=0) + 1e-15) # type: MetricsLambda if average: mean = precision.mean() # type: MetricsLambda return mean return precision def cmRecall(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda: """ Calculates recall using :class:`~ignite.metrics.ConfusionMatrix` metric. Args: cm (ConfusionMatrix): instance of confusion matrix metric average (bool, optional): if True metric value is averaged over all classes Returns: MetricsLambda """ # Increase floating point precision and pass to CPU cm = cm.type(torch.DoubleTensor) recall = cm.diag() / (cm.sum(dim=1) + 1e-15) # type: MetricsLambda if average: mean = recall.mean() # type: MetricsLambda return mean return recall def DiceCoefficient(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda: """Calculates Dice Coefficient for a given :class:`~ignite.metrics.ConfusionMatrix` metric. Args: cm (ConfusionMatrix): instance of confusion matrix metric ignore_index (int, optional): index to ignore, e.g. background index """ 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 non-negative integer, but given {ignore_index}") # Increase floating point precision and pass to CPU cm = cm.type(torch.DoubleTensor) dice = 2.0 * cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) + 1e-15) # type: MetricsLambda if ignore_index is not None: ignore_idx = ignore_index # type: int # 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 ignite-0.4.3/ignite/metrics/epoch_metric.py000066400000000000000000000143621401006565000207400ustar00rootroot00000000000000import warnings from typing import Callable, List, Sequence, Tuple, Union, cast 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_classes)``. Output datatype should be `float32`. Target datatype should be `long`. .. 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)`` or ``{'y_pred': y_pred, 'y': y}``. If target shape is ``(batch_size, n_classes)`` and ``n_classes > 1`` than it should be binary: e.g. ``[[0, 1, 0, 1], ]``. Args: compute_fn (callable): a callable with the signature (`torch.tensor`, `torch.tensor`) takes as the input `predictions` and `targets` and returns a scalar. Input tensors will be on specified ``device`` (see arg below). output_transform (callable, optional): 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 (bool): 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 (str or torch.device, optional): optional device specification for internal storage. 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``. """ def __init__( self, compute_fn: Callable, output_transform: Callable = lambda x: x, check_compute_fn: bool = True, device: Union[str, torch.device] = torch.device("cpu"), ) -> 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) @reinit__is_reduced def reset(self) -> None: self._predictions = [] # type: List[torch.Tensor] self._targets = [] # type: List[torch.Tensor] 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_classes) or (batch_size, ).") if y.ndimension() not in (1, 2): raise ValueError("Targets should be of shape (batch_size, n_classes) or (batch_size, ).") if y.ndimension() == 2: if not torch.equal(y ** 2, y): raise ValueError("Targets should be binary (0 or 1).") 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.") _prediction_tensor = torch.cat(self._predictions, dim=0) _target_tensor = torch.cat(self._targets, dim=0) ws = idist.get_world_size() if ws > 1 and not self._is_reduced: # 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._is_reduced = True result = 0.0 if idist.get_rank() == 0: # Run compute_fn on zero rank only result = self.compute_fn(_prediction_tensor, _target_tensor) if ws > 1: # broadcast result to all processes result = cast(float, idist.broadcast(result, src=0)) return result class EpochMetricWarning(UserWarning): pass ignite-0.4.3/ignite/metrics/fbeta.py000066400000000000000000000060451401006565000173570ustar00rootroot00000000000000from 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. Args: beta (float): weight of precision in harmonic mean average (bool, optional): 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, optional): precision object metric with `average=False` to compute F-beta score recall (Precision, optional): recall object metric with `average=False` to compute F-beta score output_transform (callable, optional): 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 (str or torch.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 """ 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-0.4.3/ignite/metrics/frequency.py000066400000000000000000000054721401006565000203020ustar00rootroot00000000000000from typing import Any, 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 EpochWise, Metric, MetricUsage, reinit__is_reduced, sync_all_reduce class Frequency(Metric): """Provides metrics for the number of examples processed per second. Examples: .. 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ , wps=400 [00:17<00:35] """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu") ) -> None: super(Frequency, self).__init__(output_transform=output_transform, device=device) @reinit__is_reduced def reset(self) -> None: self._timer = Timer() self._acc = 0 self._n = 0 self._elapsed = 0.0 super(Frequency, self).reset() @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-0.4.3/ignite/metrics/loss.py000066400000000000000000000057221401006565000172570ustar00rootroot00000000000000from typing import Callable, Dict, Sequence, Tuple, Union, cast 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 (callable): 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 (callable): 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 (callable): a callable taking a target tensor that returns the first dimension size (usually the batch size). device (str or torch.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. """ required_output_keys = None def __init__( self, loss_fn: Callable, output_transform: Callable = lambda x: x, batch_size: Callable = lambda x: len(x), device: Union[str, torch.device] = torch.device("cpu"), ): super(Loss, self).__init__(output_transform, device=device) 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 = {} # type: Dict else: y_pred, y, kwargs = cast(Tuple[torch.Tensor, torch.Tensor, Dict], output) average_loss = self._loss_fn(y_pred.detach(), y.detach(), **kwargs) 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-0.4.3/ignite/metrics/mean_absolute_error.py000066400000000000000000000026721401006565000223270ustar00rootroot00000000000000from 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)`` or ``{'y_pred': y_pred, 'y': y}``. """ @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-0.4.3/ignite/metrics/mean_pairwise_distance.py000066400000000000000000000032531401006565000227710ustar00rootroot00000000000000from 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)`` or ``{'y_pred': y_pred, 'y': y}``. """ def __init__( self, p: int = 2, eps: float = 1e-6, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: super(MeanPairwiseDistance, self).__init__(output_transform, device=device) 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-0.4.3/ignite/metrics/mean_squared_error.py000066400000000000000000000026641401006565000221560ustar00rootroot00000000000000from 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 \left(y_{i} - x_{i} \right)^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)`` or ``{'y_pred': y_pred, 'y': y}``. """ @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-0.4.3/ignite/metrics/metric.py000066400000000000000000000523121401006565000175570ustar00rootroot00000000000000import warnings from abc import ABCMeta, abstractmethod from collections.abc import Mapping from functools import wraps from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union import torch import ignite.distributed as idist from ignite.engine import CallableEventWithFilter, Engine, Events if TYPE_CHECKING: from ignite.metrics.metrics_lambda import MetricsLambda __all__ = ["Metric", "MetricUsage", "EpochWise", "BatchWise", "BatchFiltered"] 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.started`. completed: event when the metric completes. This event will be associated to :meth:`~ignite.metrics.Metric.completed`. iteration_completed: event when the metric updates. This event will be associated to :meth:`~ignite.metrics.Metric.iteration_completed`. """ 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.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.Metric.completed` on every ``EPOCH_COMPLETED``. Attributes: usage_name (str): usage name string """ usage_name = "epoch_wise" def __init__(self) -> None: super(EpochWise, self).__init__( started=Events.EPOCH_STARTED, completed=Events.EPOCH_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, ) class BatchWise(MetricUsage): """ Batch-wise usage of Metrics. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.Metric.started` on every ``ITERATION_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.Metric.completed` on every ``ITERATION_COMPLETED``. Attributes: usage_name (str): usage name string """ usage_name = "batch_wise" def __init__(self) -> None: super(BatchWise, self).__init__( started=Events.ITERATION_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.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.Metric.iteration_completed` on filtered ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.Metric.completed` on every ``EPOCH_COMPLETED``. Args: *args: Positional arguments to setup :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED(*args, **kwargs)` **kwargs: Keyword arguments to setup :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED(*args, **kwargs)` handled by :meth:`~ignite.metrics.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(metaclass=ABCMeta): """ Base class for all Metrics. Args: output_transform (callable, optional): 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 (str or torch.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. Attributes: required_output_keys (tuple): dictionary defines required keys to be found in ``engine.state.output`` if the latter is a dictionary. Default, ``("y_pred", "y")``. This is useful with custom metrics that can require other arguments than predictions ``y_pred`` and targets ``y``. See notes below for an example. Note: 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. .. 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. """ # public class attribute required_output_keys = ("y_pred", "y") # type: Optional[Tuple] # 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"), ): self._output_transform = output_transform # Check device if distributed is initialized: if idist.get_world_size() > 1: # check if reset and update methods are decorated. Compute may not be decorated if not (hasattr(self.reset, "_decorated") and hasattr(self.update, "_decorated")): warnings.warn( f"{self.__class__.__name__} class does not support distributed setting. " "Computed result is not collected across all computing devices", RuntimeWarning, ) # 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._is_reduced = False self.reset() @abstractmethod def reset(self) -> None: """ Resets the metric to it's 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 it's 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.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.attach`. Args: engine (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.attach`. Args: engine (Engine): the engine to which the metric must be attached """ 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) 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.attach`. Args: engine (Engine): the engine to which the metric must be attached name (str): the name of the metric used as key in dict `engine.state.metrics` .. versionchanged:: 0.4.3 Added dict in metrics results. """ 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) and len(result.size()) == 0: result = result.item() engine.state.metrics[name] = result def _check_usage(self, usage: Union[str, MetricUsage]) -> MetricUsage: if isinstance(usage, str): if usage == EpochWise.usage_name: usage = EpochWise() elif usage == BatchWise.usage_name: usage = BatchWise() else: raise ValueError(f"usage should be 'EpochWise.usage_name' or 'BatchWise.usage_name', get {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 (Engine): the engine to which the metric must be attached name (str): the name of the metric to attach usage (str or MetricUsage, optional): the usage of the metric. Valid string values should be :attr:`ignite.metrics.EpochWise.usage_name` (default) or :attr:`ignite.metrics.BatchWise.usage_name`. Example: .. 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.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 (Engine): the engine from which the metric must be detached usage (str or MetricUsage, optional): the usage of the metric. Valid string values should be 'epoch_wise' (default) or 'batch_wise'. Example: .. 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 (Engine): the engine checked from which the metric should be attached usage (str or MetricUsage, optional): 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 __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 __div__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x.__div__(y), self, other) def __rdiv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x.__div__(y), other, self) 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 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. See :doc:`metrics` on how to use it. Args: *attrs: attribute names of decorated class """ 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() if len(attrs) > 0 and not self._is_reduced: if ws > 1: for attr in attrs: t = getattr(self, attr, None) if t is not None: t = idist.all_reduce(t) self._is_reduced = True setattr(self, attr, t) else: self._is_reduced = True return func(self, *args, **kwargs) 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. """ @wraps(func) def wrapper(self: Metric, *args: Any, **kwargs: Any) -> None: func(self, *args, **kwargs) self._is_reduced = False setattr(wrapper, "_decorated", True) return wrapper ignite-0.4.3/ignite/metrics/metrics_lambda.py000066400000000000000000000131711401006565000212420ustar00rootroot00000000000000import itertools from typing import Any, Callable, Optional, Union import torch from ignite.engine import Engine, Events 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 does not recursively update the metrics it depends on. When reset, all its dependency metrics would be resetted. When attach, all its dependency metrics would be attached automatically (but partially, e.g :meth:`~ignite.metrics.Metric.is_attached()` will return False). Args: f (callable): the function that defines the computation args (sequence): Sequence of other metrics or something else that will be fed to ``f`` as arguments. Example: .. code-block:: python 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) 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) """ def __init__(self, f: Callable, *args: Any, **kwargs: Any) -> None: self.function = f self.args = args self.kwargs = kwargs self.engine = None # type: Optional[Engine] 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() @reinit__is_reduced def update(self, output: Any) -> None: # NB: this method does not recursively update dependency metrics, # which might cause duplicate update issue. To update this metric, # users should manually update its dependencies. pass 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: 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-0.4.3/ignite/metrics/precision.py000066400000000000000000000201021401006565000202570ustar00rootroot00000000000000import warnings from typing import Callable, Sequence, Union, cast 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 from ignite.utils import to_onehot __all__ = ["Precision"] class _BasePrecisionRecall(_BaseClassification): def __init__( self, output_transform: Callable = lambda x: x, average: bool = False, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): if idist.get_world_size() > 1: if (not average) and is_multilabel: warnings.warn( "Precision/Recall metrics do not work in distributed setting when average=False " "and is_multilabel=True. Results are not reduced across computing devices. Computed result " "corresponds to the local rank's (single process) result.", RuntimeWarning, ) self._average = average self.eps = 1e-20 super(_BasePrecisionRecall, self).__init__( output_transform=output_transform, is_multilabel=is_multilabel, device=device ) @reinit__is_reduced def reset(self) -> None: self._true_positives = 0 # type: Union[int, torch.Tensor] self._positives = 0 # type: Union[int, torch.Tensor] if self._is_multilabel: init_value = 0.0 if self._average else [] self._true_positives = torch.tensor(init_value, dtype=torch.float64, device=self._device) self._positives = torch.tensor(init_value, dtype=torch.float64, device=self._device) super(_BasePrecisionRecall, self).reset() def compute(self) -> Union[torch.Tensor, float]: is_scalar = not isinstance(self._positives, torch.Tensor) or self._positives.ndim == 0 if is_scalar and self._positives == 0: raise NotComputableError( f"{self.__class__.__name__} must have at least one example before it can be computed." ) if not (self._type == "multilabel" and not self._average): if not self._is_reduced: self._true_positives = idist.all_reduce(self._true_positives) # type: ignore[assignment] self._positives = idist.all_reduce(self._positives) # type: ignore[assignment] self._is_reduced = True # type: bool result = self._true_positives / (self._positives + self.eps) if self._average: return cast(torch.Tensor, result).mean().item() else: return result class Precision(_BasePrecisionRecall): r"""Calculates precision for binary and multiclass 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)`` or ``{'y_pred': y_pred, 'y': 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, ...). 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: .. code-block:: python def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y precision = Precision(output_transform=thresholded_output_transform) In multilabel cases, average parameter should be True. However, if user would like to compute F1 metric, for example, average parameter should be False. This can be done as shown below: .. code-block:: python precision = Precision(average=False, is_multilabel=True) recall = Recall(average=False, is_multilabel=True) F1 = precision * recall * 2 / (precision + recall + 1e-20) F1 = MetricsLambda(lambda t: torch.mean(t).item(), F1) .. warning:: In multilabel cases, if average is False, 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. .. warning:: In multilabel cases, if average is False, current implementation does not work with distributed computations. Results are not reduced across the GPUs. Computed result corresponds to the local rank's (single GPU) result. Args: output_transform (callable, optional): 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 (bool, optional): if True, precision is computed as the unweighted average (across all classes in multiclass case), otherwise, returns a tensor with the precision (for each class in multiclass case). is_multilabel (bool, optional) flag to use in multilabel case. By default, value is False. If True, average parameter should be True and the average is computed across samples, instead of classes. device (str or torch.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, output_transform: Callable = lambda x: x, average: bool = False, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): super(Precision, self).__init__( output_transform=output_transform, average=average, is_multilabel=is_multilabel, device=device ) @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": y_pred = y_pred.view(-1) y = y.view(-1) elif self._type == "multiclass": num_classes = y_pred.size(1) if y.max() + 1 > num_classes: raise ValueError( f"y_pred contains less classes than y. Number of predicted classes is {num_classes}" f" and element in y has invalid class = {y.max().item() + 1}." ) y = to_onehot(y.view(-1), num_classes=num_classes) indices = torch.argmax(y_pred, dim=1).view(-1) y_pred = to_onehot(indices, num_classes=num_classes) elif self._type == "multilabel": # if y, y_pred shape is (N, C, ...) -> (C, N x ...) num_classes = y_pred.size(1) y_pred = torch.transpose(y_pred, 1, 0).reshape(num_classes, -1) y = torch.transpose(y, 1, 0).reshape(num_classes, -1) # 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 all_positives = y_pred.sum(dim=0) if correct.sum() == 0: true_positives = torch.zeros_like(all_positives) else: true_positives = correct.sum(dim=0) if self._type == "multilabel": if not self._average: self._true_positives = torch.cat([self._true_positives, true_positives], dim=0) # type: torch.Tensor self._positives = torch.cat([self._positives, all_positives], dim=0) # type: torch.Tensor else: self._true_positives += torch.sum(true_positives / (all_positives + self.eps)) self._positives += len(all_positives) else: self._true_positives += true_positives self._positives += all_positives ignite-0.4.3/ignite/metrics/psnr.py000066400000000000000000000105421401006565000172550ustar00rootroot00000000000000from typing import Callable, Optional, 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 `_. - `y_pred` and `y` **must** have (batch_size, ...) shape. - `y_pred` and `y` **must** have same dtype and same shape. Args: data_range (int or float): 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 (callable, optional): A callable that is used to transform the Engineโ€™s process_functionโ€™s output into the form expected by the metric. device (str or torch.device): specifies which device updates are accumulated on. Setting the metricโ€™s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU. Example: 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, ...}``. .. code-block:: python def process_function(engine, batch): # ... return y_pred, y engine = Engine(process_function) psnr = PSNR(data_range=1.0) psnr.attach(engine, "psnr") # ... state = engine.run(data) print(f"PSNR: {state.metrics['psnr']}") 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, .. code-block:: python 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(engine, "psnr") # ... state = engine.run(data) print(f"PSNR: {state.metrics['psrn']}") .. versionadded:: 0.4.3 """ def __init__( self, data_range: Union[int, float], output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super().__init__(output_transform=output_transform, device=device) 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) -> torch.Tensor: 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 ignite-0.4.3/ignite/metrics/recall.py000066400000000000000000000132101401006565000175300ustar00rootroot00000000000000from typing import Callable, Sequence, Union import torch from ignite.metrics.metric import reinit__is_reduced from ignite.metrics.precision import _BasePrecisionRecall from ignite.utils import to_onehot __all__ = ["Recall"] class Recall(_BasePrecisionRecall): r"""Calculates recall for binary and multiclass 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)`` or ``{'y_pred': y_pred, 'y': 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, ...). 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: .. code-block:: python def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y recall = Recall(output_transform=thresholded_output_transform) In multilabel cases, average parameter should be True. However, if user would like to compute F1 metric, for example, average parameter should be False. This can be done as shown below: .. code-block:: python precision = Precision(average=False, is_multilabel=True) recall = Recall(average=False, is_multilabel=True) F1 = precision * recall * 2 / (precision + recall + 1e-20) F1 = MetricsLambda(lambda t: torch.mean(t).item(), F1) .. warning:: In multilabel cases, if average is False, 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. .. warning:: In multilabel cases, if average is False, current implementation does not work with distributed computations. Results are not reduced across the GPUs. Computed result corresponds to the local rank's (single GPU) result. Args: output_transform (callable, optional): 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 (bool, optional): if True, precision is computed as the unweighted average (across all classes in multiclass case), otherwise, returns a tensor with the precision (for each class in multiclass case). is_multilabel (bool, optional) flag to use in multilabel case. By default, value is False. If True, average parameter should be True and the average is computed across samples, instead of classes. device (str or torch.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, output_transform: Callable = lambda x: x, average: bool = False, is_multilabel: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): super(Recall, self).__init__( output_transform=output_transform, average=average, is_multilabel=is_multilabel, device=device ) @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": y_pred = y_pred.view(-1) y = y.view(-1) elif self._type == "multiclass": num_classes = y_pred.size(1) if y.max() + 1 > num_classes: raise ValueError( f"y_pred contains less classes than y. Number of predicted classes is {num_classes}" f" and element in y has invalid class = {y.max().item() + 1}." ) y = to_onehot(y.view(-1), num_classes=num_classes) indices = torch.argmax(y_pred, dim=1).view(-1) y_pred = to_onehot(indices, num_classes=num_classes) elif self._type == "multilabel": # if y, y_pred shape is (N, C, ...) -> (C, N x ...) num_classes = y_pred.size(1) y_pred = torch.transpose(y_pred, 1, 0).reshape(num_classes, -1) y = torch.transpose(y, 1, 0).reshape(num_classes, -1) # 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 actual_positives = y.sum(dim=0) if correct.sum() == 0: true_positives = torch.zeros_like(actual_positives) else: true_positives = correct.sum(dim=0) if self._type == "multilabel": if not self._average: self._true_positives = torch.cat([self._true_positives, true_positives], dim=0) # type: torch.Tensor self._positives = torch.cat([self._positives, actual_positives], dim=0) # type: torch.Tensor else: self._true_positives += torch.sum(true_positives / (actual_positives + self.eps)) self._positives += len(actual_positives) else: self._true_positives += true_positives self._positives += actual_positives ignite-0.4.3/ignite/metrics/root_mean_squared_error.py000066400000000000000000000013421401006565000232110ustar00rootroot00000000000000import 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 \left(y_{i} - x_{i} \right)^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) or `{'y_pred': y_pred, 'y': y}`. """ def compute(self) -> Union[torch.Tensor, float]: mse = super(RootMeanSquaredError, self).compute() return math.sqrt(mse) ignite-0.4.3/ignite/metrics/running_average.py000066400000000000000000000125421401006565000214470ustar00rootroot00000000000000from typing import Callable, Optional, Sequence, Union, cast import torch import ignite.distributed as idist from ignite.engine import Engine, Events from ignite.metrics.metric import EpochWise, Metric, MetricUsage, reinit__is_reduced, sync_all_reduce __all__ = ["RunningAverage"] class RunningAverage(Metric): """Compute running average of a metric or the output of process function. Args: src (Metric or None): input source: an instance of :class:`~ignite.metrics.Metric` or None. The latter corresponds to `engine.state.output` which holds the output of process function. alpha (float, optional): running average decay factor, default 0.98 output_transform (callable, optional): 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 (boolean, optional): whether the running average should be reset after each epoch (defaults to True). device (str or torch.device, optional): specifies which device updates are accumulated on. Should be None when ``src`` is an instance of :class:`~ignite.metrics.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. Examples: .. code-block:: python alpha = 0.98 acc_metric = RunningAverage(Accuracy(output_transform=lambda x: [x[1], x[2]]), alpha=alpha) acc_metric.attach(trainer, 'running_avg_accuracy') avg_output = RunningAverage(output_transform=lambda x: x[0], alpha=alpha) avg_output.attach(trainer, 'running_avg_loss') @trainer.on(Events.ITERATION_COMPLETED) def log_running_avg_metrics(engine): print("running avg accuracy:", engine.state.metrics['running_avg_accuracy']) print("running avg loss:", engine.state.metrics['running_avg_loss']) """ required_output_keys = None def __init__( self, src: Optional[Metric] = None, alpha: float = 0.98, output_transform: Optional[Callable] = None, epoch_bound: bool = True, device: Optional[Union[str, torch.device]] = None, ): 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.") if device is not None: raise ValueError("Argument device should be None if src is a Metric.") self.src = src self._get_src_value = self._get_metric_value setattr(self, "iteration_completed", self._metric_iteration_completed) 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._get_src_value = self._get_output_value setattr(self, "update", self._output_update) if device is None: device = torch.device("cpu") self.alpha = alpha self.epoch_bound = epoch_bound super(RunningAverage, self).__init__(output_transform=output_transform, device=device) # type: ignore[arg-type] @reinit__is_reduced def reset(self) -> None: self._value = None # type: Optional[Union[float, torch.Tensor]] @reinit__is_reduced def update(self, output: Sequence) -> None: # Implement abstract method pass def compute(self) -> Union[torch.Tensor, float]: if self._value is None: self._value = self._get_src_value() else: self._value = self._value * self.alpha + (1.0 - self.alpha) * self._get_src_value() return self._value def attach(self, engine: Engine, name: str, _usage: Union[str, MetricUsage] = EpochWise()) -> None: if self.epoch_bound: # restart average every epoch engine.add_event_handler(Events.EPOCH_STARTED, self.started) # compute metric engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed) # apply running average engine.add_event_handler(Events.ITERATION_COMPLETED, self.completed, name) def _get_metric_value(self) -> Union[torch.Tensor, float]: return self.src.compute() @sync_all_reduce("src") def _get_output_value(self) -> Union[torch.Tensor, float]: # we need to compute average instead of sum produced by @sync_all_reduce("src") output = cast(Union[torch.Tensor, float], self.src) / idist.get_world_size() return output def _metric_iteration_completed(self, engine: Engine) -> None: self.src.started(engine) self.src.iteration_completed(engine) @reinit__is_reduced def _output_update(self, output: Union[torch.Tensor, float]) -> None: if isinstance(output, torch.Tensor): output = output.detach().to(self._device, copy=True) self.src = output # type: ignore[assignment] ignite-0.4.3/ignite/metrics/ssim.py000066400000000000000000000171361401006565000172540ustar00rootroot00000000000000from typing import Callable, 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 Structual Similarity Index Measure Args: data_range (int or float): Range of the image. Typically, ``1.0`` or ``255``. kernel_size (int or list or tuple of int): Size of the kernel. Default: (11, 11) sigma (float or list or tuple of float): Standard deviation of the gaussian kernel. Argument is used if ``gaussian=True``. Default: (1.5, 1.5) k1 (float): Parameter of SSIM. Default: 0.01 k2 (float): Parameter of SSIM. Default: 0.03 gaussian (bool): ``True`` to use gaussian kernel, ``False`` to use uniform kernel output_transform (callable, optional): 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 (str or torch.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. Example: 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, ...}``. ``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. .. code-block:: python def process_function(engine, batch): # ... return y_pred, y engine = Engine(process_function) metric = SSIM(data_range=1.0) metric.attach(engine, "ssim") .. versionadded:: 0.4.2 """ 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"), ): if isinstance(kernel_size, int): self.kernel_size = [kernel_size, kernel_size] # type: Sequence[int] 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 = [sigma, sigma] # type: Sequence[float] 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) self.gaussian = gaussian 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 = self._gaussian_or_uniform_kernel(kernel_size=self.kernel_size, sigma=self.sigma) @reinit__is_reduced def reset(self) -> None: # Not a tensor because batch size is not known in advance. self._sum_of_batchwise_ssim = 0.0 # type: Union[float, torch.Tensor] self._num_examples = 0 self._kernel = self._gaussian_or_uniform_kernel(kernel_size=self.kernel_size, sigma=self.sigma) def _uniform(self, kernel_size: int) -> torch.Tensor: max, min = 2.5, -2.5 ksize_half = (kernel_size - 1) * 0.5 kernel = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, device=self._device) for i, j in enumerate(kernel): if min <= j <= max: kernel[i] = 1 / (max - min) else: kernel[i] = 0 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}." ) channel = y_pred.size(1) if len(self._kernel.shape) < 4: self._kernel = self._kernel.expand(channel, 1, -1, -1).to(device=y_pred.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") input_list = torch.cat([y_pred, y, y_pred * y_pred, y * y, y_pred * y]) outputs = F.conv2d(input_list, self._kernel, groups=channel) output_list = [outputs[x * y_pred.size(0) : (x + 1) * y_pred.size(0)] for x in range(len(outputs))] 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_batchwise_ssim += torch.mean(ssim_idx, (1, 2, 3), dtype=torch.float64).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_batchwise_ssim", "_num_examples") def compute(self) -> torch.Tensor: if self._num_examples == 0: raise NotComputableError("SSIM must have at least one example before it can be computed.") return torch.sum(self._sum_of_batchwise_ssim / self._num_examples) # type: ignore[arg-type] ignite-0.4.3/ignite/metrics/top_k_categorical_accuracy.py000066400000000000000000000032151401006565000236150ustar00rootroot00000000000000from 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)`` or ``{'y_pred': y_pred, 'y': y}``. """ def __init__( self, k: int = 5, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device) 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-0.4.3/ignite/utils.py000066400000000000000000000164701401006565000157730ustar00rootroot00000000000000import collections.abc as collections import functools import logging import random import sys import warnings from typing import Any, Callable, Dict, Optional, TextIO, Tuple, Type, TypeVar, Union, cast import torch __all__ = ["convert_tensor", "apply_to_tensor", "apply_to_type", "to_onehot", "setup_logger", "manual_seed"] def convert_tensor( input_: 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.""" 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(input_, _func) def apply_to_tensor( input_: 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. """ return apply_to_type(input_, torch.Tensor, func) def apply_to_type( input_: 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 a object of `input_type` or mapping, or sequence of objects of `input_type`. """ if isinstance(input_, input_type): return func(input_) if isinstance(input_, (str, bytes)): return input_ if isinstance(input_, collections.Mapping): return cast(Callable, type(input_))( {k: apply_to_type(sample, input_type, func) for k, sample in input_.items()} ) if isinstance(input_, tuple) and hasattr(input_, "_fields"): # namedtuple return cast(Callable, type(input_))(*(apply_to_type(sample, input_type, func) for sample in input_)) if isinstance(input_, collections.Sequence): return cast(Callable, type(input_))([apply_to_type(sample, input_type, func) for sample in input_]) raise TypeError((f"input must contain {input_type}, dicts or lists; found {type(input_)}")) 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`. .. 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] = None, 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, ) -> logging.Logger: """Setups logger: name, level, format etc. Args: name (str, optional): new name for the logger. If None, the standard logger is used. level (int): logging level, e.g. CRITICAL, ERROR, WARNING, INFO, DEBUG. stream (TextIO, optional): logging stream. If None, the standard stream is used (sys.stderr). format (str): logging format. By default, `%(asctime)s %(name)s %(levelname)s: %(message)s`. filepath (str, optional): Optional logging file path. If not None, logs are written to the file. distributed_rank (int, optional): Optional, rank in distributed configuration to avoid logger setup for workers. If None, distributed_rank is initialized to the rank of process. Returns: logging.Logger For example, to 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 # ... .. versionchanged:: 0.4.3 Added ``stream`` parameter. """ logger = logging.getLogger(name) # 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 # Remove previous handlers if logger.hasHandlers(): for h in list(logger.handlers): logger.removeHandler(h) formatter = logging.Formatter(format) if distributed_rank is None: import ignite.distributed as idist distributed_rank = idist.get_rank() if distributed_rank > 0: logger.addHandler(logging.NullHandler()) else: logger.setLevel(level) ch = logging.StreamHandler(stream=stream) ch.setLevel(level) ch.setFormatter(formatter) logger.addHandler(ch) if filepath is not None: fh = logging.FileHandler(filepath) fh.setLevel(level) fh.setFormatter(formatter) logger.addHandler(fh) 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 (int): Random state seed .. versionchanged:: 0.4.3 Added ``torch.cuda.manual_seed_all(seed)``. """ random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) 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) 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 ignite-0.4.3/mypy.ini000066400000000000000000000030121401006565000144650ustar00rootroot00000000000000[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-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_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-ignite.contrib.handlers.custom_events] ignore_errors = Trueignite-0.4.3/pyproject.toml000066400000000000000000000007241401006565000157110ustar00rootroot00000000000000[tool.black] line-length = 120 target-version = ['py36', 'py37', 'py38'] 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 ) ''' ignite-0.4.3/requirements-dev.txt000066400000000000000000000003761401006565000170400ustar00rootroot00000000000000# Tests numpy pytest codecov pytest-cov pytest-xdist dill # Test contrib dependencies tqdm scikit-learn matplotlib tensorboardX visdom polyaxon-client wandb mlflow neptune-client tensorboard pynvml clearml scikit-image # Examples dependencies pandas gym ignite-0.4.3/setup.cfg000066400000000000000000000013231401006565000146120ustar00rootroot00000000000000[metadata] license_file = 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 [flake8] max-line-length = 120 ignore = E722,F401,E203,E231,F841,W503,F403,E402 [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-0.4.3/setup.py000066400000000000000000000021771401006565000145130ustar00rootroot00000000000000import 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") VERSION = find_version("ignite", "__init__.py") requirements = [ "torch>=1.3,<2", ] setup( # Metadata name="pytorch-ignite", version=VERSION, author="PyTorch Core Team", author_email="soumith@pytorch.org", 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.*",)), zip_safe=True, install_requires=requirements, ) ignite-0.4.3/tests/000077500000000000000000000000001401006565000141345ustar00rootroot00000000000000ignite-0.4.3/tests/__init__.py000066400000000000000000000000421401006565000162410ustar00rootroot00000000000000# Needed to collect coverage data ignite-0.4.3/tests/ignite/000077500000000000000000000000001401006565000154135ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/__init__.py000066400000000000000000000000421401006565000175200ustar00rootroot00000000000000# Needed to collect coverage data ignite-0.4.3/tests/ignite/base/000077500000000000000000000000001401006565000163255ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/base/__init__.py000066400000000000000000000000001401006565000204240ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/base/test_mixins.py000066400000000000000000000001651401006565000212470ustar00rootroot00000000000000from ignite.base import Serializable def test_load_state_dict(): s = Serializable() s.load_state_dict({}) ignite-0.4.3/tests/ignite/conftest.py000066400000000000000000000164341401006565000176220ustar00rootroot00000000000000import shutil import sys import tempfile import pytest import torch import torch.distributed as dist @pytest.fixture() def dirname(): path = tempfile.mkdtemp() yield path shutil.rmtree(path) @pytest.fixture() def get_rank_zero_dirname(dirname): def func(): import ignite.distributed as idist zero_rank_dirname = idist.all_gather(dirname)[0] return zero_rank_dirname yield func @pytest.fixture() def local_rank(worker_id): """ use a different account in each xdist worker """ import os 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() def world_size(): import os 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(): import os 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 dist_info["backend"] == "nccl": torch.cuda.set_device(lrank) return {"local_rank": lrank, "world_size": dist_info["world_size"], "rank": dist_info["rank"]} def _destroy_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()) 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): import os import time 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 os.path.exists(port_file): 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) print(local_rank, "Port:", free_port) 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) print(local_rank, "Port:", free_port) 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=60), } 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): import os 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 dist_info["backend"] == "nccl": 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): import os 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): import os assert "MASTER_ADDR" in os.environ assert "MASTER_PORT" in os.environ 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 os 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) 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, np=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 ignite-0.4.3/tests/ignite/contrib/000077500000000000000000000000001401006565000170535ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/contrib/__init__.py000066400000000000000000000000201401006565000211540ustar00rootroot00000000000000# coding: utf-8 ignite-0.4.3/tests/ignite/contrib/conftest.py000066400000000000000000000023601401006565000212530ustar00rootroot00000000000000import random import pytest @pytest.fixture() def visdom_offline_logfile(dirname): import os log_file = os.path.join(dirname, "logs.visdom") yield log_file vd_hostname = None vd_port = None vd_server_process = None @pytest.fixture() def visdom_server(): # Start Visdom server once and stop it with visdom_server_stop global vd_hostname, vd_port, vd_server_process if vd_server_process is None: import subprocess import time from visdom import Visdom from visdom.server import download_scripts download_scripts() vd_hostname = "localhost" vd_port = random.randint(8089, 8887) try: vis = Visdom(server=vd_hostname, port=vd_port, raise_exceptions=True) except ConnectionError: pass vd_server_process = subprocess.Popen( ["python", "-m", "visdom.server", "--hostname", vd_hostname, "-port", str(vd_port)] ) time.sleep(5) vis = Visdom(server=vd_hostname, port=vd_port) assert vis.check_connection() vis.close() yield (vd_hostname, vd_port) @pytest.fixture() def visdom_server_stop(): yield None import time vd_server_process.kill() time.sleep(2) ignite-0.4.3/tests/ignite/contrib/engines/000077500000000000000000000000001401006565000205035ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/contrib/engines/__init__.py000066400000000000000000000000001401006565000226020ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/contrib/engines/test_common.py000066400000000000000000000511771401006565000234170ustar00rootroot00000000000000import os import sys from unittest.mock import MagicMock, call import pytest import torch import torch.nn as nn from torch.utils.data.distributed import DistributedSampler import ignite.contrib.handlers as handlers import ignite.distributed as idist 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 ): lr = 0.01 step_size = 100 gamma = 0.5 num_iters = 100 num_epochs = 10 model = DummyModel().to(device) if distributed and "cuda" in device: 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.contrib.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.contrib.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 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 / step_size ), f"{optimizer.param_groups[0]['lr']} vs {lr * gamma ** (num_iters * num_epochs / 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) with pytest.warns(UserWarning, match=r"Argument device is unused and deprecated"): setup_common_training_handlers(trainer, device="cpu") 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]}" 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): trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) model = DummyModel() acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.5, 0.6, 0.61, 0.7, 0.5] @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]} 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"} def test_gen_save_best_models_by_val_score(): trainer = Engine(lambda e, b: None) evaluator = Engine(lambda e, b: None) model = DummyModel() acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.5, 0.6, 0.61, 0.7, 0.5] @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]} 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, ) def test_add_early_stopping_by_val_score(): trainer = Engine(lambda e, b: None) 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]} 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 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": os.path.join(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": os.path.join(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": os.path.join(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": os.path.join(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": os.path.join(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={"offline_mode": True}, 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={"offline_mode": True}, 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_gpu(dirname, distributed_context_single_node_nccl): local_rank = distributed_context_single_node_nccl["local_rank"] device = f"cuda:{local_rank}" _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_cpu(dirname, distributed_context_single_node_gloo): device = "cpu" 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_cpu(dirname, distributed_context_multi_node_gloo): device = "cpu" 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_gpu(dirname, distributed_context_multi_node_nccl): local_rank = distributed_context_multi_node_nccl["local_rank"] rank = distributed_context_multi_node_nccl["rank"] device = f"cuda:{local_rank}" _test_setup_common_training_handlers(dirname, device, rank=rank, local_rank=local_rank, distributed=True) test_add_early_stopping_by_val_score() ignite-0.4.3/tests/ignite/contrib/engines/test_tbptt.py000066400000000000000000000074651401006565000232650ustar00rootroot00000000000000# 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 Tbptt_Events, create_supervised_tbptt_trainer 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-0.4.3/tests/ignite/contrib/handlers/000077500000000000000000000000001401006565000206535ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/contrib/handlers/__init__.py000066400000000000000000000006221401006565000227640ustar00rootroot00000000000000class 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-0.4.3/tests/ignite/contrib/handlers/conftest.py000066400000000000000000000025121401006565000230520ustar00rootroot00000000000000from unittest.mock import Mock import numpy as np import pytest import torch @pytest.fixture() def norm_mock(): def norm(x): return np.linalg.norm(x) 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): 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 return model return get_dummy_model ignite-0.4.3/tests/ignite/contrib/handlers/test_base_logger.py000066400000000000000000000214401401006565000245360ustar00rootroot00000000000000import math from unittest.mock import MagicMock, call import pytest import torch from ignite.contrib.handlers import CustomPeriodicEvent from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler from ignite.engine import Engine, Events, EventsList, State from tests.ignite.contrib.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) 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 or output_transform 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(engine=engine) assert metrics == true_metrics # 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(engine=engine) assert metrics == {"a": 0} # Only output as "output" handler = DummyOutputHandler("tag", metric_names=None, output_transform=lambda x: x) metrics = handler._setup_output_metrics(engine=engine) assert metrics == {"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(engine=engine) assert metrics == {"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(engine=engine) assert metrics == {"a": 0, "b": 1, "loss": engine.state.output} # All metrics handler = DummyOutputHandler("tag", metric_names="all", output_transform=None) metrics = handler._setup_output_metrics(engine=engine) assert metrics == true_metrics 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 def test_attach(): n_epochs = 5 data = list(range(50)) def _test(event, n_calls): 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) trainer.run(data, max_epochs=n_epochs) if isinstance(event, EventsList): events = [e for e in event] else: events = [event] calls = [call(trainer, logger, e) for e in events] mock_log_handler.assert_has_calls(calls) assert mock_log_handler.call_count == n_calls _test(Events.ITERATION_STARTED, len(data) * n_epochs) _test(Events.ITERATION_COMPLETED, len(data) * n_epochs) _test(Events.EPOCH_STARTED, n_epochs) _test(Events.EPOCH_COMPLETED, n_epochs) _test(Events.STARTED, 1) _test(Events.COMPLETED, 1) _test(Events.ITERATION_STARTED(every=10), len(data) // 10 * n_epochs) _test(Events.STARTED | Events.COMPLETED, 2) 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 with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): n_iterations = 10 cpe1 = CustomPeriodicEvent(n_iterations=n_iterations) n = len(data) * n_epochs / n_iterations nf = math.floor(n) ns = nf + 1 if nf < n else nf _test(cpe1.Events.ITERATIONS_10_STARTED, ns, cpe1) _test(cpe1.Events.ITERATIONS_10_COMPLETED, nf, cpe1) with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): n_iterations = 15 cpe2 = CustomPeriodicEvent(n_iterations=n_iterations) n = len(data) * n_epochs / n_iterations nf = math.floor(n) ns = nf + 1 if nf < n else nf _test(cpe2.Events.ITERATIONS_15_STARTED, ns, cpe2) _test(cpe2.Events.ITERATIONS_15_COMPLETED, nf, cpe2) with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): n_custom_epochs = 2 cpe3 = CustomPeriodicEvent(n_epochs=n_custom_epochs) n = n_epochs / n_custom_epochs nf = math.floor(n) ns = nf + 1 if nf < n else nf _test(cpe3.Events.EPOCHS_2_STARTED, ns, cpe3) _test(cpe3.Events.EPOCHS_2_COMPLETED, nf, cpe3) def test_as_context_manager(): n_epochs = 5 data = list(range(50)) class _DummyLogger(DummyLogger): def __init__(self, writer): self.writer = writer def close(self): self.writer.close() def _test(event, n_calls): 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() _test(Events.ITERATION_STARTED, len(data) * n_epochs) _test(Events.ITERATION_COMPLETED, len(data) * n_epochs) _test(Events.EPOCH_STARTED, n_epochs) _test(Events.EPOCH_COMPLETED, n_epochs) _test(Events.STARTED, 1) _test(Events.COMPLETED, 1) _test(Events.ITERATION_STARTED(every=10), len(data) // 10 * n_epochs) ignite-0.4.3/tests/ignite/contrib/handlers/test_clearml_logger.py000066400000000000000000001026551401006565000252530ustar00rootroot00000000000000import math import os from collections import defaultdict from unittest.mock import ANY, MagicMock, Mock, call import clearml import pytest import torch from clearml.binding.frameworks import WeightsFileHandler from clearml.model import Framework import ignite.distributed as idist from ignite.contrib.handlers.clearml_logger import ( ClearMLLogger, ClearMLSaver, GradsHistHandler, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, WeightsHistHandler, WeightsScalarHandler, global_step_from_engine, ) from ignite.contrib.handlers.trains_logger import TrainsLogger, TrainsSaver from ignite.engine import Engine, Events, State from ignite.handlers import Checkpoint def test_trains_logger_alias(dirname): with pytest.warns(UserWarning, match="ClearMLSaver: running in bypass mode"): TrainsLogger.set_bypass_mode(True) logger = TrainsLogger(output_uri=dirname) assert isinstance(logger, ClearMLLogger) 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)], 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"ClearMLLogger 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, ) 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_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(): 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(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_frozen_layers(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = WeightsScalarHandler(model) 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_has_calls( [ call(title="weights_norm/fc2", series="weight", iteration=5, value=12.0), call(title="weights_norm/fc2", series="bias", iteration=5, value=math.sqrt(12.0)), ], any_order=True, ) with pytest.raises(AssertionError): mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="weights_norm/fc1", series="weight", iteration=5, value=12.0), call(title="weights_norm/fc1", series="bias", iteration=5, value=math.sqrt(12.0)), ], any_order=True, ) assert mock_logger.clearml_logger.report_scalar.call_count == 2 def test_weights_hist_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): WeightsHistHandler(None) 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_frozen_layers(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = WeightsHistHandler(model) 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_has_calls( [ call(title="weights_fc2", hist_data=ANY, series="weight", step=5), call(title="weights_fc2", hist_data=ANY, series="bias", step=5), ], any_order=True, ) with pytest.raises(AssertionError): mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="weights_fc1", hist_data=ANY, series="weight", step=5), call(title="weights_fc1", 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(): 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(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_frozen_layers(dummy_model_factory, norm_mock): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = GradsScalarHandler(model, reduction=norm_mock) 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) mock_logger.clearml_logger.report_scalar.assert_has_calls( [ call(title="grads_norm/fc2", value=ANY, series="weight", iteration=mock_engine.state.epoch), call(title="grads_norm/fc2", value=ANY, series="bias", iteration=mock_engine.state.epoch), ], any_order=True, ) with pytest.raises(AssertionError): mock_logger.clearml_logger.report_scalar.assert_has_calls( [call(title="grads_norm/fc1", value=ANY, iteration=5), call("grads_norm/fc1", ANY, 5)], any_order=True ) assert mock_logger.clearml_logger.report_scalar.call_count == 2 assert norm_mock.call_count == 2 def test_grads_hist_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): GradsHistHandler(None) 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_frozen_layers(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = GradsHistHandler(model) 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) assert mock_logger.grad_helper.add_histogram.call_count == 2 mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="grads_fc2", hist_data=ANY, series="weight", step=5), call(title="grads_fc2", hist_data=ANY, series="bias", step=5), ], any_order=True, ) with pytest.raises(AssertionError): mock_logger.grad_helper.add_histogram.assert_has_calls( [ call(title="grads_fc1", hist_data=ANY, series="weight", step=5), call(title="grads_fc1", hist_data=ANY, series="bias", step=5), ], any_order=True, ) 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) 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) 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_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 = Mock(return_value=object()) 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 = Mock(return_value=object()) 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 = Mock(return_value=object()) 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 = os.path.join(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_cpu(distributed_context_single_node_gloo): _test_save_model_optimizer_lr_scheduler_with_state_dict("cpu") _test_save_model_optimizer_lr_scheduler_with_state_dict("cpu", 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_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("cpu", 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-0.4.3/tests/ignite/contrib/handlers/test_custom_events.py000066400000000000000000000116541401006565000251710ustar00rootroot00000000000000import math import pytest from ignite.contrib.handlers.custom_events import CustomPeriodicEvent from ignite.engine import Engine def test_bad_input(): with pytest.warns(DeprecationWarning, match=r"CustomPeriodicEvent is deprecated"): with pytest.raises(TypeError, match="Argument n_iterations should be an integer"): CustomPeriodicEvent(n_iterations="a") with pytest.raises(ValueError, match="Argument n_iterations should be positive"): CustomPeriodicEvent(n_iterations=0) with pytest.raises(TypeError, match="Argument n_iterations should be an integer"): CustomPeriodicEvent(n_iterations=10.0) with pytest.raises(TypeError, match="Argument n_epochs should be an integer"): CustomPeriodicEvent(n_epochs="a") with pytest.raises(ValueError, match="Argument n_epochs should be positive"): CustomPeriodicEvent(n_epochs=0) with pytest.raises(TypeError, match="Argument n_epochs should be an integer"): CustomPeriodicEvent(n_epochs=10.0) with pytest.raises(ValueError, match="Either n_iterations or n_epochs should be defined"): CustomPeriodicEvent() with pytest.raises(ValueError, match="Either n_iterations or n_epochs should be defined"): CustomPeriodicEvent(n_iterations=1, n_epochs=2) def test_new_events(): def update(*args, **kwargs): pass with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): engine = Engine(update) cpe = CustomPeriodicEvent(n_iterations=5) cpe.attach(engine) assert hasattr(cpe, "Events") assert hasattr(cpe.Events, "ITERATIONS_5_STARTED") assert hasattr(cpe.Events, "ITERATIONS_5_COMPLETED") assert engine._allowed_events[-2] == getattr(cpe.Events, "ITERATIONS_5_STARTED") assert engine._allowed_events[-1] == getattr(cpe.Events, "ITERATIONS_5_COMPLETED") with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): cpe = CustomPeriodicEvent(n_epochs=5) cpe.attach(engine) assert hasattr(cpe, "Events") assert hasattr(cpe.Events, "EPOCHS_5_STARTED") assert hasattr(cpe.Events, "EPOCHS_5_COMPLETED") assert engine._allowed_events[-2] == getattr(cpe.Events, "EPOCHS_5_STARTED") assert engine._allowed_events[-1] == getattr(cpe.Events, "EPOCHS_5_COMPLETED") def test_integration_iterations(): def _test(n_iterations, max_epochs, n_iters_per_epoch): def update(*args, **kwargs): pass engine = Engine(update) with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): cpe = CustomPeriodicEvent(n_iterations=n_iterations) cpe.attach(engine) data = list(range(n_iters_per_epoch)) custom_period = [0] n_calls_iter_started = [0] n_calls_iter_completed = [0] event_started = getattr(cpe.Events, "ITERATIONS_{}_STARTED".format(n_iterations)) @engine.on(event_started) def on_my_event_started(engine): assert (engine.state.iteration - 1) % n_iterations == 0 custom_period[0] += 1 custom_iter = getattr(engine.state, "iterations_{}".format(n_iterations)) assert custom_iter == custom_period[0] n_calls_iter_started[0] += 1 event_completed = getattr(cpe.Events, "ITERATIONS_{}_COMPLETED".format(n_iterations)) @engine.on(event_completed) def on_my_event_ended(engine): assert engine.state.iteration % n_iterations == 0 custom_iter = getattr(engine.state, "iterations_{}".format(n_iterations)) assert custom_iter == custom_period[0] n_calls_iter_completed[0] += 1 engine.run(data, max_epochs=max_epochs) n = len(data) * max_epochs / n_iterations nf = math.floor(n) assert custom_period[0] == n_calls_iter_started[0] assert n_calls_iter_started[0] == nf + 1 if nf < n else nf assert n_calls_iter_completed[0] == nf _test(3, 5, 16) _test(4, 5, 16) _test(5, 5, 16) _test(300, 50, 1000) def test_integration_epochs(): def update(*args, **kwargs): pass engine = Engine(update) n_epochs = 3 with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"): cpe = CustomPeriodicEvent(n_epochs=n_epochs) cpe.attach(engine) data = list(range(16)) custom_period = [1] @engine.on(cpe.Events.EPOCHS_3_STARTED) def on_my_epoch_started(engine): assert (engine.state.epoch - 1) % n_epochs == 0 assert engine.state.epochs_3 == custom_period[0] @engine.on(cpe.Events.EPOCHS_3_COMPLETED) def on_my_epoch_ended(engine): assert engine.state.epoch % n_epochs == 0 assert engine.state.epochs_3 == custom_period[0] custom_period[0] += 1 engine.run(data, max_epochs=10) assert custom_period[0] == 4 ignite-0.4.3/tests/ignite/contrib/handlers/test_lr_finder.py000066400000000000000000000175711401006565000242430ustar00rootroot00000000000000import copy import matplotlib import pytest import torch from torch import nn from torch.optim import SGD from ignite.contrib.handlers import FastaiLRFinder from ignite.engine import create_supervised_trainer 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): super(DummyModel, self).__init__() self.net = nn.Linear(1, 1) def forward(self, x): return self.net(x) @pytest.fixture def model(): model = DummyModel() yield model @pytest.fixture def optimizer(model): yield SGD(model.parameters(), lr=1e-4, momentum=0.9) @pytest.fixture def to_save(model, optimizer): yield {"model": model, "optimizer": optimizer} @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 dataloader(): yield torch.rand(100, 2, 1) 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) as f: pass with pytest.raises(TypeError, match=r"Object should have `state_dict` method"): with lr_finder.attach(dummy_engine, to_save={1: 2}) as f: pass with pytest.raises(ValueError, match=r"Mapping to_save should contain 'optimizer' key"): with lr_finder.attach(dummy_engine, to_save={"model": model}) as f: 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) as f: 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) as f: 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) as f: pass with pytest.raises(ValueError, match="if provided, num_iter should be positive"): with lr_finder.attach(dummy_engine, to_save=to_save, num_iter=0) as f: 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) 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_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() assert init_model_sd == model.state_dict() 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))]) 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) def test_detach_terminates(lr_finder, to_save, dummy_engine, dataloader, recwarn): with lr_finder.attach(dummy_engine, to_save, end_lr=100, 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 assert len(recwarn) == 0 def test_lr_suggestion(lr_finder, to_save, dummy_engine, dataloader): with lr_finder.attach(dummy_engine, to_save) as trainer_with_finder: trainer_with_finder.run(dataloader) assert 1e-4 <= lr_finder.lr_suggestion() <= 10 def test_plot(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.plot() lr_finder.plot(skip_end=0) def test_no_matplotlib(no_site_packages, lr_finder): with pytest.raises(RuntimeError, match=r"This method requires matplotlib to be installed"): lr_finder.plot() ignite-0.4.3/tests/ignite/contrib/handlers/test_mlflow_logger.py000066400000000000000000000271731401006565000251350ustar00rootroot00000000000000import os import sys from unittest.mock import MagicMock, call import pytest import torch from ignite.contrib.handlers.mlflow_logger import ( MLflowLogger, OptimizerParamsHandler, OutputHandler, global_step_from_engine, ) from ignite.engine import Engine, Events, State 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_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)], 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=os.path.join(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=os.path.join(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(os.path.join(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=os.path.join(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(os.path.join(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 i, 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=os.path.join(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.fixture def no_site_packages(): import sys mlflow_client_modules = {} for k in sys.modules: if "mlflow" in k: mlflow_client_modules[k] = sys.modules[k] for k in mlflow_client_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 mlflow_client_modules: sys.modules[k] = mlflow_client_modules[k] def test_no_mlflow_client(no_site_packages): with pytest.raises(RuntimeError, match=r"This contrib module requires mlflow to be installed."): MLflowLogger() ignite-0.4.3/tests/ignite/contrib/handlers/test_neptune_logger.py000066400000000000000000000440111401006565000253010ustar00rootroot00000000000000import math import os import sys import warnings from unittest.mock import ANY, MagicMock, call import pytest import torch import ignite.distributed as idist from ignite.contrib.handlers.neptune_logger import ( GradsScalarHandler, NeptuneLogger, NeptuneSaver, OptimizerParamsHandler, OutputHandler, WeightsScalarHandler, global_step_from_engine, ) from ignite.engine import Engine, Events, State from ignite.handlers.checkpoint import Checkpoint 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)], lr=0.01) wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr") mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.iteration = 123 wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metric.assert_called_once_with("lr/group_0", y=0.01, x=123) wrapper = OptimizerParamsHandler(optimizer, param_name="lr", tag="generator") mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metric.assert_called_once_with("generator/lr/group_0", y=0.01, x=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(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) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = 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_metric.assert_called_once_with("tag/output", y=12345, x=123) wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x}) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) mock_logger.log_metric.assert_called_once_with("another_tag/loss", y=12345, x=123) def test_output_handler_metric_names(): wrapper = OutputHandler("tag", metric_names=["a", "b"]) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = 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.log_metric.call_count == 2 mock_logger.log_metric.assert_has_calls([call("tag/a", y=12.23, x=5), call("tag/b", y=23.45, x=5),], any_order=True) wrapper = OutputHandler("tag", metric_names=["a",]) mock_engine = MagicMock() mock_logger.log_metric = 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=NeptuneLogger) mock_logger.log_metric = MagicMock() wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metric.call_count == 4 mock_logger.log_metric.assert_has_calls( [ call("tag/a/0", y=0.0, x=5), call("tag/a/1", y=1.0, x=5), call("tag/a/2", y=2.0, x=5), call("tag/a/3", y=3.0, x=5), ], any_order=True, ) wrapper = OutputHandler("tag", metric_names=["a", "c"]) mock_engine = MagicMock() mock_logger.log_metric = MagicMock() mock_engine.state = State(metrics={"a": 55.56, "c": "Some text"}) mock_engine.state.iteration = 7 mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = MagicMock() with pytest.warns(UserWarning): wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED) assert mock_logger.log_metric.call_count == 1 mock_logger.log_metric.assert_has_calls([call("tag/a", y=55.56, x=7),], any_order=True) # all metrics wrapper = OutputHandler("tag", metric_names="all") mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = 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.log_metric.call_count == 2 mock_logger.log_metric.assert_has_calls([call("tag/a", y=12.23, x=5), call("tag/b", y=23.45, x=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=NeptuneLogger) mock_logger.log_metric = 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_metric.call_count == 3 mock_logger.log_metric.assert_has_calls( [call("tag/a", y=12.23, x=5), call("tag/b", y=23.45, x=5), call("tag/loss", y=12345, x=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=NeptuneLogger) 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=NeptuneLogger) mock_logger.log_metric = 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_metric.call_count == 1 mock_logger.log_metric.assert_has_calls( [call("tag/loss", y=mock_engine.state.output, x=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_metric.call_count == 2 mock_logger.log_metric.assert_has_calls( [call("tag/loss", y=mock_engine.state.output, x=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=NeptuneLogger) mock_logger.log_metric = 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.log_metric.call_count == 1 mock_logger.log_metric.assert_has_calls([call("tag/loss", y=12345, x=10)]) 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) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = 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.log_metric.call_count == 4 mock_logger.log_metric.assert_has_calls( [ call(tag_prefix + "weights_norm/fc1/weight", y=0.0, x=5), call(tag_prefix + "weights_norm/fc1/bias", y=0.0, x=5), call(tag_prefix + "weights_norm/fc2/weight", y=12.0, x=5), call(tag_prefix + "weights_norm/fc2/bias", y=math.sqrt(12.0), x=5), ], any_order=True, ) _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) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = MagicMock() mock_engine = MagicMock() mock_engine.state = State() mock_engine.state.epoch = 5 wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED) mock_logger.log_metric.assert_has_calls( [call("weights_norm/fc2/weight", y=12.0, x=5), call("weights_norm/fc2/bias", y=math.sqrt(12.0), x=5),], any_order=True, ) with pytest.raises(AssertionError): mock_logger.log_metric.assert_has_calls( [call("weights_norm/fc1/weight", y=12.0, x=5), call("weights_norm/fc1/bias", y=math.sqrt(12.0), x=5),], any_order=True, ) assert mock_logger.log_metric.call_count == 2 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) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = 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.log_metric.assert_has_calls( [ call(tag_prefix + "grads_norm/fc1/weight", y=ANY, x=5), call(tag_prefix + "grads_norm/fc1/bias", y=ANY, x=5), call(tag_prefix + "grads_norm/fc2/weight", y=ANY, x=5), call(tag_prefix + "grads_norm/fc2/bias", y=ANY, x=5), ], any_order=True, ) assert mock_logger.log_metric.call_count == 4 assert norm_mock.call_count == 4 _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) mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_metric = 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) mock_logger.log_metric.assert_has_calls( [call("grads_norm/fc2/weight", y=ANY, x=5), call("grads_norm/fc2/bias", y=ANY, x=5),], any_order=True ) with pytest.raises(AssertionError): mock_logger.log_metric.assert_has_calls( [call("grads_norm/fc1/weight", y=ANY, x=5), call("grads_norm/fc1/bias", y=ANY, x=5),], any_order=True ) assert mock_logger.log_metric.call_count == 2 assert norm_mock.call_count == 2 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(offline_mode=True) def dummy_handler(engine, logger, event_name): global_step = engine.state.get_event_attrib_value(event_name) logger.log_metric("test_value", global_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(offline_mode=True) 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.log_metric("test_value", global_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.log_artifact = MagicMock() model = torch.nn.Module() to_save_serializable = {"model": model} saver = NeptuneSaver(mock_logger) fname = os.path.join(dirname, "test.pt") saver(to_save_serializable, fname) assert mock_logger.log_artifact.call_count == 1 def _test_neptune_saver_integration(device): model = torch.nn.Module().to(device) to_save_serializable = {"model": model} mock_logger = None if idist.get_rank() == 0: mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_artifact = MagicMock() mock_logger.delete_artifacts = MagicMock() saver = NeptuneSaver(mock_logger) checkpoint = Checkpoint(to_save=to_save_serializable, save_handler=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 idist.get_rank() == 0: assert mock_logger.log_artifact.call_count == 2 assert mock_logger.delete_artifacts.call_count == 1 def test_neptune_saver_integration(): _test_neptune_saver_integration("cpu") def test_neptune_saver_non_serializable(): mock_logger = MagicMock(spec=NeptuneLogger) mock_logger.log_artifact = MagicMock() to_save_non_serializable = {"model": lambda x: x} 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.log_artifact.call_count == 0 @pytest.fixture def no_site_packages(): neptune_client_modules = {} for k in sys.modules: if "neptune" in k: neptune_client_modules[k] = sys.modules[k] for k in neptune_client_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 neptune_client_modules: sys.modules[k] = neptune_client_modules[k] def test_no_neptune_client(no_site_packages): with pytest.raises(RuntimeError, match=r"This contrib module requires neptune-client to be installed."): NeptuneLogger() @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_cpu(distributed_context_single_node_gloo): _test_neptune_saver_integration("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_gpu(distributed_context_single_node_nccl): device = idist.device() _test_neptune_saver_integration(device) ignite-0.4.3/tests/ignite/contrib/handlers/test_param_scheduler.py000066400000000000000000001357511401006565000254360ustar00rootroot00000000000000from unittest.mock import MagicMock import numpy as np import pytest import torch from torch.optim.lr_scheduler import ExponentialLR, StepLR from ignite.contrib.handlers.param_scheduler import ( ConcatScheduler, CosineAnnealingScheduler, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, ParamScheduler, PiecewiseLinear, create_lr_scheduler_with_warmup, ) from ignite.engine import Engine, Events from tests.ignite.contrib.handlers import MockFP16DeepSpeedZeroOptimizer try: from torch.optim.lr_scheduler import MultiplicativeLR except ImportError: has_multiplicative_lr = False else: from distutils.version import LooseVersion # https://github.com/pytorch/pytorch/issues/32756 has_multiplicative_lr = LooseVersion(torch.__version__) >= LooseVersion("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(): 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) 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) for _ in range(2): lrs = [] trainer.run([0] * 9, max_epochs=2) 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, 0.8, 0.6, 0.4, 0.2, 0.0, 0.2, 0.4, # 0.6, 0.8, ], ) ) 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) # With float cycle_size optimizer = torch.optim.SGD([tensor], lr=0) scheduler = LinearCyclicalScheduler( optimizer, "lr", start_value=1.2, end_value=0.2, cycle_size=10.00000012, cycle_mult=1.0 ) 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] * 9, max_epochs=2) assert lrs == list( map( pytest.approx, [ # Cycle 1 1.2, 1.0, 0.8, 0.6, 0.4, 0.2, 0.4, 0.6, 0.8, 1.0, # Cycle 2 1.2, 1.0, 0.8, 0.6, 0.4, 0.2, 0.4, 0.6, # 0.8, 1.0, ], ) ) 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]) def test_cosine_annealing_scheduler(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) scheduler = CosineAnnealingScheduler(optimizer, "lr", 0, 1, 10) state_dict = scheduler.state_dict() data = [0] * 9 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 ) 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) for _ in range(2): lrs = [] trainer.run(data, max_epochs=max_epochs) assert lrs == list( map( pytest.approx, [ 0.0, 0.02447174185242318, 0.09549150281252627, 0.20610737385376332, 0.3454915028125263, 0.5, 0.6545084971874737, 0.7938926261462365, 0.9045084971874737, 0.9755282581475768, 0.0, 0.02447174185242318, 0.09549150281252627, 0.20610737385376332, 0.3454915028125263, 0.5, 0.6545084971874737, 0.7938926261462365, # 0.9045084971874737, 0.9755282581475768 ], ) ) 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) state_dict = concat_scheduler.state_dict() assert state_dict["durations"] == durations assert state_dict["_current_duration"] == durations[0] 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] 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) def test_concat_scheduler_two_schedulers(): tensor = torch.zeros([1], requires_grad=True) optimizer = torch.optim.SGD([tensor], lr=0) def _test(duration_vals_as_np_int): 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 _test(duration_vals_as_np_int=False) _test(duration_vals_as_np_int=True) 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(): with pytest.raises( TypeError, match=r"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler" ): LRScheduler(123) with pytest.raises( TypeError, match=r"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler" ): LRScheduler.simulate_values(1, None) def test_lr_scheduler(): def _test(torch_lr_scheduler_cls, **kwargs): tensor = torch.zeros([1], requires_grad=True) optimizer1 = torch.optim.SGD([tensor], lr=0.01) optimizer2 = torch.optim.SGD([tensor], lr=0.01) opt_state_dict1 = optimizer1.state_dict() opt_state_dict2 = optimizer2.state_dict() torch_lr_scheduler1 = torch_lr_scheduler_cls(optimizer=optimizer1, **kwargs) scheduler = LRScheduler(torch_lr_scheduler1) state_dict1 = scheduler.state_dict() torch_lr_scheduler2 = torch_lr_scheduler_cls(optimizer=optimizer2, **kwargs) state_dict2 = torch_lr_scheduler2.state_dict() def dummy_update(engine, batch): optimizer1.step() optimizer2.step() trainer = Engine(dummy_update) @trainer.on(Events.ITERATION_STARTED) def save_lr(engine): lrs.append(optimizer1.param_groups[0]["lr"]) @trainer.on(Events.ITERATION_STARTED) def save_true_lr(engine): lrs_true.append(optimizer2.param_groups[0]["lr"]) @trainer.on(Events.ITERATION_COMPLETED) def torch_lr_scheduler_step(engine): torch_lr_scheduler2.step() trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler) for _ in range(2): lrs = [] lrs_true = [] data = [0] * 10 max_epochs = 2 trainer.run(data, max_epochs=max_epochs) assert lrs_true == pytest.approx(lrs), f"{_}: {lrs_true} ({len(lrs_true)}) vs {lrs} ({len(lrs)})" optimizer1.load_state_dict(opt_state_dict1) scheduler.load_state_dict(state_dict1) optimizer2.load_state_dict(opt_state_dict2) torch_lr_scheduler2.load_state_dict(state_dict2) optimizer3 = torch.optim.SGD([tensor], lr=0.01) torch_lr_scheduler3 = torch_lr_scheduler_cls(optimizer=optimizer3, **kwargs) simulated_values = LRScheduler.simulate_values( num_events=len(data) * max_epochs, lr_scheduler=torch_lr_scheduler3 ) assert lrs == pytest.approx([v for i, v in simulated_values]) _test(StepLR, step_size=5, gamma=0.5) _test(ExponentialLR, gamma=0.78) if has_multiplicative_lr: _test(MultiplicativeLR, lr_lambda=lambda epoch: 0.95) 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)]) def test_piecewiselinear(): def _test(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) _test(milestones_as_np_int=True) _test(milestones_as_np_int=False) def test_simulate_and_plot_values(): import matplotlib matplotlib.use("Agg") def _test(scheduler_cls, **scheduler_kwargs): optimizer = None event = Events.ITERATION_STARTED if scheduler_cls == LRScheduler: optimizer = scheduler_kwargs["lr_scheduler"].optimizer event = Events.ITERATION_COMPLETED 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(event, 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]) if scheduler_cls == LRScheduler or scheduler_cls == ConcatScheduler: # As internal state of torch lr scheduler has been changed the following checks will fail return # 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)], ) def test_create_lr_scheduler_with_warmup(): 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, ) def _test(lr_scheduler, optimizer, warmup_start_value, warmup_end_value, warmup_duration, warmup_end_next_value): num_iterations = 10 max_epochs = 20 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, ) if warmup_end_value is None: warmup_end_value = optimizer.param_groups[0]["lr"] 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 i, 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( 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) 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) _test(torch_lr_scheduler, optimizer, 0.01, 0.05, 10, 0.2) optimizer = torch.optim.SGD([t1], lr=0.2) torch_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.98) _test(torch_lr_scheduler, optimizer, 0.01, 0.05, 2, 0.2) # B) 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) _test(torch_lr_scheduler, optimizer, 0.01, 0.2, 10, 0.2 * 0.98) optimizer = torch.optim.SGD([t1], lr=0.2) torch_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.98) _test(torch_lr_scheduler, optimizer, 0.01, 0.2, 2, 0.2 * 0.98) # C) lr_scheduler start_value != warmup_end_value t1 = torch.zeros([1], requires_grad=True) 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 ) _test(lr_scheduler, optimizer, 0.01, 0.05, 10, 0.8) 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 ) _test(lr_scheduler, optimizer, 0.01, 0.05, 2, 0.8) # D) lr_scheduler start_value == warmup_end_value t1 = torch.zeros([1], requires_grad=True) 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 ) _test(lr_scheduler, optimizer, 0.01, 0.8, 10, 0.8 - (0.8 / 5.0)) 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 ) _test(lr_scheduler, optimizer, 0.01, 0.8, 2, 0.8 - (0.8 / 5.0)) # E) warmup_end_value is None: fall back to case B) optimizer = torch.optim.SGD([t1], lr=0.2) torch_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.98) _test(torch_lr_scheduler, optimizer, 0.01, None, 10, 0.2 * 0.98) def test_create_lr_scheduler_with_warmup_on_combined_scheduler(): # Test with a complex scheduler def _test(save_history): 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) _test(save_history=False) _test(save_history=True) 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(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", {})]}) def test_param_group_scheduler(): def _test(lr_schedulers, save_lr): 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) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) data = [0] * num_iterations for _ in range(2): lrs = [] trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr, lrs) trainer.run(data, max_epochs=max_epochs) trainer.remove_event_handler(save_lr, Events.ITERATION_COMPLETED) 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]) 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 ) def save_lr_one_optimizer(engine, lrs): lrs.append((optimizer.param_groups[0]["lr"], optimizer.param_groups[1]["lr"])) _test([lr_scheduler1, lr_scheduler2], save_lr_one_optimizer) t1 = torch.zeros([1], requires_grad=True) optimizer_1 = torch.optim.SGD(params=[t1], lr=0.1) t2 = torch.zeros([1], requires_grad=True) 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) def save_lr_mutliple_optimizers(engine, lrs): lrs.append((optimizer_1.param_groups[0]["lr"], optimizer_2.param_groups[0]["lr"])) _test([lr_scheduler1, lr_scheduler2], save_lr_mutliple_optimizers) def test_scheduler_with_param_groups(): def _test(lr_scheduler, optimizer): 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) 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 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10) _test(lr_scheduler, optimizer) lr_scheduler = PiecewiseLinear( optimizer, "lr", milestones_values=[(5, 0.5), (15, 1.0), (25, 0.0), (35, 1.0), (40, 0.5)] ) _test(lr_scheduler, optimizer) lr_scheduler = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10) _test(lr_scheduler, optimizer) torch_lr_scheduler = ExponentialLR(optimizer, gamma=0.98) _test(LRScheduler(torch_lr_scheduler), optimizer) torch_lr_scheduler = StepLR(optimizer, step_size=50, gamma=0.5) _test(LRScheduler(torch_lr_scheduler), optimizer) 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,],) ) ignite-0.4.3/tests/ignite/contrib/handlers/test_polyaxon_logger.py000066400000000000000000000236131401006565000255010ustar00rootroot00000000000000import os from unittest.mock import MagicMock, call import pytest import torch from ignite.contrib.handlers.polyaxon_logger import ( OptimizerParamsHandler, OutputHandler, PolyaxonLogger, global_step_from_engine, ) from ignite.engine import Engine, Events, State 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_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)], 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) 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.fixture def no_site_packages(): import sys polyaxon_client_modules = {} for k in sys.modules: if "polyaxon" in k: polyaxon_client_modules[k] = sys.modules[k] for k in polyaxon_client_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 polyaxon_client_modules: sys.modules[k] = polyaxon_client_modules[k] def test_no_polyaxon_client(no_site_packages): with pytest.raises(RuntimeError, match=r"This contrib module requires polyaxon-client to be installed"): PolyaxonLogger() ignite-0.4.3/tests/ignite/contrib/handlers/test_stores.py000066400000000000000000000025571401006565000236140ustar00rootroot00000000000000import pytest from ignite.contrib.handlers import EpochOutputStore from ignite.engine.engine import Engine, Events @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_tranform(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) ignite-0.4.3/tests/ignite/contrib/handlers/test_tensorboard_logger.py000066400000000000000000000526701401006565000261570ustar00rootroot00000000000000import math import os from unittest.mock import ANY, MagicMock, call, patch import pytest import torch from ignite.contrib.handlers.tensorboard_logger import ( GradsHistHandler, GradsScalarHandler, OptimizerParamsHandler, OutputHandler, TensorboardLogger, WeightsHistHandler, WeightsScalarHandler, global_step_from_engine, ) from ignite.engine import Engine, Events, State 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_optimizer_params(): optimizer = torch.optim.SGD([torch.Tensor(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) 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(): 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(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", math.sqrt(12.0), 5), ], any_order=True, ) _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) 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_has_calls( [call("weights_norm/fc2/weight", 12.0, 5), call("weights_norm/fc2/bias", math.sqrt(12.0), 5),], any_order=True ) with pytest.raises(AssertionError): mock_logger.writer.add_scalar.assert_has_calls( [call("weights_norm/fc1/weight", 12.0, 5), call("weights_norm/fc1/bias", math.sqrt(12.0), 5),], any_order=True, ) assert mock_logger.writer.add_scalar.call_count == 2 def test_weights_hist_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): WeightsHistHandler(None) 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_frozen_layers(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = WeightsHistHandler(model) 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_has_calls( [ call(tag="weights/fc2/weight", values=ANY, global_step=5), call(tag="weights/fc2/bias", values=ANY, global_step=5), ], any_order=True, ) with pytest.raises(AssertionError): mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="weights/fc1/weight", values=ANY, global_step=5), call(tag="weights/fc1/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(): 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(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_frozen_layers(dummy_model_factory, norm_mock): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = GradsScalarHandler(model, reduction=norm_mock) 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) mock_logger.writer.add_scalar.assert_has_calls( [call("grads_norm/fc2/weight", ANY, 5), call("grads_norm/fc2/bias", ANY, 5),], any_order=True ) with pytest.raises(AssertionError): mock_logger.writer.add_scalar.assert_has_calls( [call("grads_norm/fc1/weight", ANY, 5), call("grads_norm/fc1/bias", ANY, 5),], any_order=True ) assert mock_logger.writer.add_scalar.call_count == 2 assert norm_mock.call_count == 2 def test_grads_hist_handler_wrong_setup(): with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"): GradsHistHandler(None) 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_frozen_layers(dummy_model_factory): model = dummy_model_factory(with_grads=True, with_frozen_layer=True) wrapper = GradsHistHandler(model) 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) assert mock_logger.writer.add_histogram.call_count == 2 mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="grads/fc2/weight", values=ANY, global_step=5), call(tag="grads/fc2/bias", values=ANY, global_step=5), ], any_order=True, ) with pytest.raises(AssertionError): mock_logger.writer.add_histogram.assert_has_calls( [ call(tag="grads/fc1/weight", values=ANY, global_step=5), call(tag="grads/fc1/bias", values=ANY, global_step=5), ], any_order=True, ) 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(RuntimeError, match=r"This contrib module requires either tensorboardX or torch"): TensorboardLogger(log_dir=None) ignite-0.4.3/tests/ignite/contrib/handlers/test_time_profilers.py000066400000000000000000000746411401006565000253230ustar00rootroot00000000000000import os import sys import time import pytest from pytest import approx from ignite.contrib.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler from ignite.engine import Engine, EventEnum, Events 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_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 = os.path.join(dirname, "test_log.csv") profiler.write_results(fp) assert os.path.isfile(fp) 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 = os.path.join(dirname, "test_log.csv") profiler.write_results(fp) assert os.path.isfile(fp) 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-0.4.3/tests/ignite/contrib/handlers/test_tqdm_logger.py000066400000000000000000000374031401006565000245770ustar00rootroot00000000000000# -*- coding: utf-8 -*- import sys import time from distutils.version import LooseVersion import numpy as np import pytest import torch from ignite.contrib.handlers import CustomPeriodicEvent, ProgressBar from ignite.engine import Engine, Events from ignite.handlers import 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 LooseVersion(tqdm.__version__) def update_fn(engine, batch): a = 1 engine.state.metrics["a"] = a return a def test_pbar(capsys): n_epochs = 2 loader = [1, 2] engine = Engine(update_fn) pbar = ProgressBar() 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() < LooseVersion("4.49.0"): expected = "Epoch [2/2]: [1/2] 50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ , a=1 [00:00<00:00]" else: expected = "Epoch [2/2]: [1/2] 50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ , a=1 [00:00 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) ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_mean_absolute_relative_error.py000066400000000000000000000044141401006565000322370ustar00rootroot00000000000000import torch from pytest import approx, raises from ignite.contrib.metrics.regression import MeanAbsoluteRelativeError from ignite.exceptions import NotComputableError def test_wrong_input_shapes(): m = MeanAbsoluteRelativeError() with raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with raises(ValueError): m.update((torch.rand(4,), torch.rand(4, 1, 2))) 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): m.update((a, ground_truth)) def test_zero_sample(): m = MeanAbsoluteRelativeError() with raises(NotComputableError): m.compute() ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_mean_error.py000066400000000000000000000032461401006565000264500ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.contrib.metrics.regression import MeanError from ignite.exceptions import NotComputableError def test_zero_div(): m = MeanError() with pytest.raises(NotComputableError): m.compute() def test_wrong_input_shapes(): m = MeanError() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): m.update((torch.rand(4,), torch.rand(4, 1, 2))) 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) ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_mean_normalized_bias.py000066400000000000000000000037611401006565000304630ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.contrib.metrics.regression import MeanNormalizedBias from ignite.exceptions import NotComputableError def test_zero_div(): m = MeanNormalizedBias() with pytest.raises(NotComputableError): m.compute() def test_zero_gt(): a = np.random.randn(4) ground_truth = np.zeros(4) m = MeanNormalizedBias() with pytest.raises(NotComputableError): m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) def test_wrong_input_shapes(): m = MeanNormalizedBias() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): m.update((torch.rand(4,), torch.rand(4, 1, 2))) 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) ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_median_absolute_error.py000066400000000000000000000053171401006565000306640ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.contrib.metrics.regression import MedianAbsoluteError from ignite.engine import Engine def test_wrong_input_shapes(): m = MedianAbsoluteError() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): 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_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_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 idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MedianAbsoluteError(output_transform=lambda x: (x[1], x[2])) 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) ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_median_absolute_percentage_error.py000066400000000000000000000057251401006565000330640ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.contrib.metrics.regression import MedianAbsolutePercentageError from ignite.engine import Engine def test_wrong_input_shapes(): m = MedianAbsolutePercentageError() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): 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_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_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 idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MedianAbsolutePercentageError(output_transform=lambda x: (x[1], x[2])) 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) ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_median_relative_absolute_error.py000066400000000000000000000057071401006565000325620ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.contrib.metrics.regression import MedianRelativeAbsoluteError from ignite.engine import Engine def test_wrong_input_shapes(): m = MedianRelativeAbsoluteError() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): 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 idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = MedianRelativeAbsoluteError(output_transform=lambda x: (x[1], x[2])) 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) ignite-0.4.3/tests/ignite/contrib/metrics/regression/test_r2_score.py000066400000000000000000000132411401006565000260310ustar00rootroot00000000000000import os import numpy as np import pytest import torch from sklearn.metrics import r2_score import ignite.distributed as idist from ignite.contrib.metrics.regression import R2Score from ignite.engine import Engine def test_wrong_input_shapes(): m = R2Score() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): m.update((torch.rand(4,), torch.rand(4, 1, 2))) 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_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) 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 idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = R2Score(output_transform=lambda x: (x[1], x[2])) 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) 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() assert r2_score(np_y, np_y_pred) == pytest.approx(res, abs=tol) for _ in range(3): _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_gpu(distributed_context_single_node_nccl): device = torch.device(f"cuda:{distributed_context_single_node_nccl['local_rank']}") _test_distrib_compute(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _test_distrib_compute(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) @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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _test_distrib_compute(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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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, tol=1e-3) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute(device, tol=1e-3) @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-0.4.3/tests/ignite/contrib/metrics/regression/test_wave_hedges_distance.py000066400000000000000000000030271401006565000304470ustar00rootroot00000000000000import numpy as np import pytest import torch from ignite.contrib.metrics.regression import WaveHedgesDistance def test_wrong_input_shapes(): m = WaveHedgesDistance() with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4, 1))) with pytest.raises(ValueError): m.update((torch.rand(4, 1), torch.rand(4, 1, 2))) with pytest.raises(ValueError): m.update((torch.rand(4, 1, 2), torch.rand(4,))) with pytest.raises(ValueError): m.update((torch.rand(4,), torch.rand(4, 1, 2))) 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) ignite-0.4.3/tests/ignite/contrib/metrics/test_average_precision.py000066400000000000000000000057531401006565000256310ustar00rootroot00000000000000import numpy as np import torch from sklearn.metrics import average_precision_score from ignite.contrib.metrics import AveragePrecision from ignite.engine import Engine def test_ap_score(): size = 100 np_y_pred = np.random.rand(size, 5) np_y = np.random.randint(0, 2, size=(size, 5), dtype=np.long) np_ap = average_precision_score(np_y, np_y_pred) ap_metric = AveragePrecision() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) ap_metric.reset() ap_metric.update((y_pred, y)) ap = ap_metric.compute() assert ap == np_ap def test_ap_score_2(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) np_y[size // 2 :] = 1 np.random.shuffle(np_y) np_ap = average_precision_score(np_y, np_y_pred) ap_metric = AveragePrecision() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) ap_metric.reset() n_iters = 10 batch_size = size // n_iters for i in range(n_iters): idx = i * batch_size ap_metric.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) ap = ap_metric.compute() assert ap == np_ap def test_integration_ap_score_with_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) np_y[size // 2 :] = 1 np.random.shuffle(np_y) np_ap = average_precision_score(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) ap_metric = AveragePrecision(output_transform=lambda x: (x[1], x[2])) ap_metric.attach(engine, "ap") data = list(range(size // batch_size)) ap = engine.run(data, max_epochs=1).metrics["ap"] assert ap == np_ap def test_integration_ap_score_with_activated_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y_pred_softmax = torch.softmax(torch.from_numpy(np_y_pred), dim=1).numpy() np_y = np.zeros((size,), dtype=np.long) np_y[size // 2 :] = 1 np.random.shuffle(np_y) np_ap = average_precision_score(np_y, np_y_pred_softmax) 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) ap_metric = AveragePrecision(output_transform=lambda x: (torch.softmax(x[1], dim=1), x[2])) ap_metric.attach(engine, "ap") data = list(range(size // batch_size)) ap = engine.run(data, max_epochs=1).metrics["ap"] assert ap == np_ap ignite-0.4.3/tests/ignite/contrib/metrics/test_gpu_info.py000066400000000000000000000121261401006565000237420ustar00rootroot00000000000000import sys from unittest.mock import Mock, patch import pytest import torch from ignite.contrib.metrics import GpuInfo from ignite.engine import Engine, State python_below_36 = (sys.version[0] == "3" and int(sys.version[2]) < 6) or int(sys.version[0]) < 2 @pytest.fixture def no_site_packages(): import sys import pynvml assert "pynvml" in sys.modules pynvml_module = sys.modules["pynvml"] del sys.modules["pynvml"] 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["pynvml"] = pynvml_module @pytest.mark.skipif(python_below_36, reason="No pynvml for python < 3.6") def test_no_pynvml_package(no_site_packages): with pytest.raises(RuntimeError, match="This contrib module requires pynvml to be installed."): GpuInfo() @pytest.mark.skipif(python_below_36 or torch.cuda.is_available(), reason="No pynvml for python < 3.6") 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(python_below_36 or not (torch.cuda.is_available()), reason="No pynvml for python < 3.6 and 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.contrib.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-0.4.3/tests/ignite/contrib/metrics/test_precision_recall_curve.py000066400000000000000000000076571401006565000266720ustar00rootroot00000000000000import numpy as np import pytest import torch from sklearn.metrics import precision_recall_curve from ignite.contrib.metrics.precision_recall_curve import PrecisionRecallCurve from ignite.engine import Engine from ignite.metrics.epoch_metric import EpochMetricWarning def test_precision_recall_curve(): size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) 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() assert np.array_equal(precision, sk_precision) assert np.array_equal(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,), dtype=np.long) 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"] assert np.array_equal(precision, sk_precision) assert np.array_equal(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,), dtype=np.long) 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"] assert np.array_equal(precision, sk_precision) assert np.array_equal(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) ignite-0.4.3/tests/ignite/contrib/metrics/test_roc_auc.py000066400000000000000000000070241401006565000235500ustar00rootroot00000000000000import numpy as np import pytest import torch from sklearn.metrics import roc_auc_score from ignite.contrib.metrics import ROC_AUC from ignite.engine import Engine from ignite.metrics.epoch_metric import EpochMetricWarning def test_roc_auc_score(): size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) np_y[size // 2 :] = 1 np_roc_auc = roc_auc_score(np_y, np_y_pred) roc_auc_metric = ROC_AUC() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) roc_auc_metric.reset() roc_auc_metric.update((y_pred, y)) roc_auc = roc_auc_metric.compute() assert roc_auc == np_roc_auc def test_roc_auc_score_2(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) np_y[size // 2 :] = 1 np.random.shuffle(np_y) np_roc_auc = roc_auc_score(np_y, np_y_pred) roc_auc_metric = ROC_AUC() y_pred = torch.from_numpy(np_y_pred) y = torch.from_numpy(np_y) roc_auc_metric.reset() n_iters = 10 batch_size = size // n_iters for i in range(n_iters): idx = i * batch_size roc_auc_metric.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) roc_auc = roc_auc_metric.compute() assert roc_auc == np_roc_auc def test_integration_roc_auc_score_with_output_transform(): np.random.seed(1) size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) np_y[size // 2 :] = 1 np.random.shuffle(np_y) np_roc_auc = roc_auc_score(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_auc_metric = ROC_AUC(output_transform=lambda x: (x[1], x[2])) roc_auc_metric.attach(engine, "roc_auc") data = list(range(size // batch_size)) roc_auc = engine.run(data, max_epochs=1).metrics["roc_auc"] assert roc_auc == np_roc_auc def test_integration_roc_auc_score_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,), dtype=np.long) np_y[size // 2 :] = 1 np.random.shuffle(np_y) np_roc_auc = roc_auc_score(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_auc_metric = ROC_AUC(output_transform=lambda x: (torch.sigmoid(x[1]), x[2])) roc_auc_metric.attach(engine, "roc_auc") data = list(range(size // batch_size)) roc_auc = engine.run(data, max_epochs=1).metrics["roc_auc"] assert roc_auc == np_roc_auc 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) ignite-0.4.3/tests/ignite/contrib/metrics/test_roc_curve.py000066400000000000000000000070021401006565000241200ustar00rootroot00000000000000import numpy as np import pytest import torch from sklearn.metrics import roc_curve from ignite.contrib.metrics.roc_auc import RocCurve from ignite.engine import Engine from ignite.metrics.epoch_metric import EpochMetricWarning def test_roc_curve(): size = 100 np_y_pred = np.random.rand(size, 1) np_y = np.zeros((size,), dtype=np.long) 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,), dtype=np.long) 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,), dtype=np.long) 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) ignite-0.4.3/tests/ignite/distributed/000077500000000000000000000000001401006565000177355ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/distributed/__init__.py000066400000000000000000000000001401006565000220340ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/distributed/check_idist_parallel.py000066400000000000000000000063011401006565000244340ustar00rootroot00000000000000import 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() if __name__ == "__main__": """ Usage: - No distributed configuration: ``` python tests/ignite/distributed/check_idist_parallel.py ``` - Launch 4 procs using gloo backend with `torch.distributed.launch` ``` python -m torch.distributed.launch --nproc_per_node=4 --use_env \ tests/ignite/distributed/check_idist_parallel.py --backend=gloo ``` - Launch 2 procs in 2 nodes using gloo backend with `torch.distributed.launch`: ``` bash -c "python -m torch.distributed.launch --nnodes=2 --node_rank=0 \ --master_addr=localhost --master_port=3344 --nproc_per_node=2 --use_env \ tests/ignite/distributed/check_idist_parallel.py --backend=gloo &" \ && bash -c "python -m torch.distributed.launch --nnodes=2 --node_rank=1 \ --master_addr=localhost --master_port=3344 --nproc_per_node=2 --use_env \ 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) args = parser.parse_args() config = {"model": "resnet18", "lr": 0.01} 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, ) with idist.Parallel(backend=args.backend, **dist_config) as parallel: parallel.run(training, config, a=1, b=2) ignite-0.4.3/tests/ignite/distributed/comp_models/000077500000000000000000000000001401006565000222365ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/distributed/comp_models/__init__.py000066400000000000000000000000001401006565000243350ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/distributed/comp_models/test_base.py000066400000000000000000000025401401006565000245620ustar00rootroot00000000000000import pytest import torch from ignite.distributed.comp_models.base import ComputationModel, _SerialModel 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" 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) model._do_all_reduce(torch.tensor(1)) model._do_all_gather(torch.tensor(1)) model._do_broadcast(torch.tensor(1), src=0) model.barrier() def test__encode_str__decode_str(): device = torch.device("cpu") s = "test-abcedfg" encoded_s = ComputationModel._encode_str(s, device) 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 ignite-0.4.3/tests/ignite/distributed/comp_models/test_horovod.py000066400000000000000000000165321401006565000253360ustar00rootroot00000000000000import 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-0.4.3/tests/ignite/distributed/comp_models/test_native.py000066400000000000000000000255201401006565000251410ustar00rootroot00000000000000import 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 _NativeDistModel @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"] 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(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) 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["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_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): _test__native_dist_model_create_from_backend_no_dist("gloo", "cpu") _test__native_dist_model_create_from_context_no_dist("gloo", "cpu") @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): _test__native_dist_model_create_from_backend_no_dist("nccl", "cuda:0") _test__native_dist_model_create_from_context_no_dist("nccl", "cuda:0") @pytest.mark.distributed def test__native_dist_model_create_dist_gloo_1(local_rank, world_size): _test__native_dist_model_create_from_backend_dist(local_rank, local_rank, world_size, "gloo", "cpu") @pytest.mark.distributed def test__native_dist_model_create_dist_gloo_2(local_rank, world_size): _test__native_dist_model_create_from_context_dist(local_rank, local_rank, world_size, "gloo", "cpu") @pytest.mark.distributed @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test__native_dist_model_create_dist_nccl_1(local_rank, world_size): _test__native_dist_model_create_from_backend_dist(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): 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 if backend == "nccl": assert _model.device() == torch.device(f"{device}:{local_rank}") elif backend == "gloo": assert _model.device() == torch.device(device) def _test__native_dist_model_spawn(backend, num_workers_per_machine, device, **spawn_kwargs): _NativeDistModel.spawn( _test_dist_spawn_fn, args=(backend, num_workers_per_machine, device), kwargs_dict={}, backend=backend, nproc_per_node=num_workers_per_machine, **spawn_kwargs, ) @pytest.mark.distributed @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test__native_dist_model_spawn_gloo(): _test__native_dist_model_spawn("gloo", num_workers_per_machine=4, device="cpu") _test__native_dist_model_spawn("gloo", num_workers_per_machine=4, device="cpu", start_method="fork") @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") def test__native_dist_model_spawn_nccl(): _test__native_dist_model_spawn("nccl", num_workers_per_machine=torch.cuda.device_count(), device="cuda") ignite-0.4.3/tests/ignite/distributed/comp_models/test_xla.py000066400000000000000000000145201401006565000244350ustar00rootroot00000000000000import 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, Events 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 Parallel, delayed 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-0.4.3/tests/ignite/distributed/test_auto.py000066400000000000000000000202011401006565000223110ustar00rootroot00000000000000import 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.distributed import DistributedSampler from torch.utils.data.sampler import RandomSampler, WeightedRandomSampler import ignite.distributed as idist from ignite.distributed.auto import DistributedProxySampler, auto_dataloader, auto_model, auto_optim def _test_auto_dataloader(ws, nproc, batch_size, num_workers=1, sampler_name=None, dl_type=DataLoader): data = torch.rand(100, 3, 12, 12) if sampler_name is None: sampler = None elif sampler_name == "WeightedRandomSampler": sampler = WeightedRandomSampler(weights=torch.ones(100), num_samples=100) 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}" dataloader = auto_dataloader( data, batch_size=batch_size, num_workers=num_workers, sampler=sampler, shuffle=sampler is None ) 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 ws < 2: sampler_type = RandomSampler if sampler is None else type(sampler) assert isinstance(dataloader.sampler, sampler_type) else: sampler_type = DistributedSampler if sampler is None else DistributedProxySampler assert isinstance(dataloader.sampler, sampler_type) if isinstance(dataloader, DataLoader): assert dataloader.pin_memory == ("cuda" in idist.device().type) 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 device 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 == device for p in model.parameters()] ), f"{[p.device.type for p in model.parameters()]} vs {device}" 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 device) 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") 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_model_optimizer(1, "cpu") @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_model_optimizer(ws, "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_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") device = "cuda" _test_auto_model_optimizer(ws, 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_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_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) 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(): import torch from torch.utils.data import WeightedRandomSampler 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}" ignite-0.4.3/tests/ignite/distributed/test_launcher.py000066400000000000000000000203741401006565000231550ustar00rootroot00000000000000import os import subprocess import sys from pathlib import Path import pytest import torch import ignite.distributed as idist from ignite.distributed.utils import has_hvd_support, has_native_dist_support, has_xla_support @pytest.fixture() def exec_filepath(): fp = Path(__file__).parent / "check_idist_parallel.py" assert fp.exists() yield fp.as_posix() def execute(cmd): import ignite env = dict(os.environ) 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()) 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(fp, backend, nprocs): # python -m torch.distributed.launch --nproc_per_node=nprocs --use_env \ # tests/ignite/distributed/check_idist_parallel.py --backend=backend cmd = [ sys.executable, "-m", "torch.distributed.launch", f"--nproc_per_node={nprocs}", "--use_env", 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_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") def test_check_idist_parallel_torch_launch_n_procs_gloo(exec_filepath): _test_check_idist_parallel_torch_launch(exec_filepath, "gloo", 4) @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.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_check_idist_parallel_torch_launch_n_procs_nccl(exec_filepath): _test_check_idist_parallel_torch_launch(exec_filepath, "nccl", torch.cuda.device_count()) 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") def test_check_idist_parallel_spawn_n_procs_gloo(exec_filepath): _test_check_idist_parallel_spawn(exec_filepath, "gloo", 4) @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") def test_check_idist_parallel_spawn_n_procs_nccl(exec_filepath): _test_check_idist_parallel_spawn(exec_filepath, "nccl", torch.cuda.device_count()) @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): assert 0 <= index < ws assert ws == idist.get_world_size() assert device in idist.device().type @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_idist_parallel_gloo(): with idist.Parallel(backend="gloo", nproc_per_node=4) as parallel: parallel.run(_test_func, ws=4, device="cpu") @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") def test_idist_parallel_gloo_nprocs(local_rank, world_size): os.environ["RANK"] = str(local_rank) with idist.Parallel(backend="gloo") as parallel: parallel.run(_test_func, ws=world_size, device="cpu") @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.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_parallel_nccl(): with idist.Parallel(backend="nccl", nproc_per_node=torch.cuda.device_count()) as parallel: parallel.run(_test_func, ws=torch.cuda.device_count(), device="cuda") @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.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_idist_parallel_nccl_nprocs(local_rank, world_size): os.environ["RANK"] = str(local_rank) with idist.Parallel(backend="nccl") as parallel: parallel.run(_test_func, ws=world_size, device="cuda") @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc") def test_idist_parallel_no_dist(): device = "cuda" if torch.cuda.is_available() else "cpu" with idist.Parallel(backend=None) as parallel: parallel.run(_test_func, ws=1, device=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 has_xla_support, reason="Skip if no PyTorch XLA package") def test_idist_parallel_spawn_params(): 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-0.4.3/tests/ignite/distributed/utils/000077500000000000000000000000001401006565000210755ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/distributed/utils/__init__.py000066400000000000000000000161331401006565000232120ustar00rootroot00000000000000import 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): assert idist.backend() == backend, f"{idist.backend()} vs {backend}" this_device = idist.device() assert isinstance(this_device, torch.device) if backend in ("nccl", "horovod") and "cuda" in this_device.type: true_device = torch.device(f"{true_device}:{local_rank}") assert this_device == true_device, f"{this_device} vs {true_device}" elif backend in ("gloo", "horovod"): assert this_device == torch.device(true_device) 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() 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_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() t = torch.tensor(idist.get_rank(), device=device) res = idist.all_reduce(t) assert res.item() == sum([i for i in range(idist.get_world_size())]) 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_gather(device): res = torch.tensor(idist.all_gather(10), device=device) true_res = torch.tensor([10,] * idist.get_world_size(), device=device) assert (res == true_res).all() t = torch.tensor(idist.get_rank(), device=device) res = idist.all_gather(t) true_res = torch.tensor([i for i in range(idist.get_world_size())], device=device) assert (res == true_res).all() x = "test-test" if idist.get_rank() == 0: x = "abc" res = idist.all_gather(x) true_res = ["abc",] + ["test-test"] * (idist.get_world_size() - 1) assert res == true_res base_x = "x" * 1026 x = base_x if idist.get_rank() == 0: x = "abc" if idist.get_rank() > 0: with pytest.warns(UserWarning, match=r"is larger than 1024 and thus will be truncated"): res = idist.all_gather(x) else: res = idist.all_gather(x) true_res = ["abc",] + [base_x[:1024]] * (idist.get_world_size() - 1) assert res == true_res t = torch.arange(100, device=device).reshape(4, 25) * (idist.get_rank() + 1) in_dtype = t.dtype res = idist.all_gather(t) assert res.shape == (idist.get_world_size() * 4, 25) assert res.dtype == in_dtype true_res = torch.zeros(idist.get_world_size() * 4, 25, device=device) for i in range(idist.get_world_size()): true_res[i * 4 : (i + 1) * 4, ...] = torch.arange(100, device=device).reshape(4, 25) * (i + 1) assert (res == true_res).all() if idist.get_world_size() > 1: with pytest.raises(TypeError, match=r"Unhandled input type"): idist.all_reduce([0, 1, 2]) def _test_distrib_broadcast(device): rank = idist.get_rank() ws = idist.get_world_size() for src in range(ws): d = 10 if rank == src else 0 res = idist.broadcast(d, src=src) true_res = 10 assert res == true_res if rank == src: t = torch.tensor([1.2345, 2.3456], dtype=torch.float, device=device) else: t = torch.empty(2, device=device) res = idist.broadcast(t, src=src) true_res = torch.tensor([1.2345, 2.3456], dtype=torch.float, device=device) assert (res == true_res).all(), f"{res} vs {true_res}" if rank == src: t = "test-abcdefg" else: t = "" res = idist.broadcast(t, src=src) true_res = "test-abcdefg" assert res == true_res if rank == src: t = torch.arange(100, device=device).reshape(4, 25) * (src + 1) else: t = torch.empty(4, 25, dtype=torch.long, device=device) in_dtype = torch.long res = idist.broadcast(t, src) assert res.shape == (4, 25) assert res.dtype == in_dtype true_res = torch.arange(100, device=device).reshape(4, 25) * (src + 1) assert (res == true_res).all() if idist.get_world_size() > 1: with pytest.raises(TypeError, match=r"Unhandled input type"): idist.broadcast([0, 1, 2], 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_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.data = 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-0.4.3/tests/ignite/distributed/utils/test_horovod.py000066400000000000000000000210541401006565000241700ustar00rootroot00000000000000import 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_all_gather, _test_distrib_all_reduce, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_config, _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): from ignite.distributed.comp_models.horovod import _HorovodDistModel 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) @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) @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) 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 j 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-0.4.3/tests/ignite/distributed/utils/test_native.py000066400000000000000000000245711401006565000240050ustar00rootroot00000000000000import os import pytest import torch import torch.distributed as dist import ignite.distributed as idist from ignite.distributed.utils import has_native_dist_support from tests.ignite.distributed.utils import ( _test_distrib_all_gather, _test_distrib_all_reduce, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_config, _test_distrib_one_rank_only, _test_distrib_one_rank_only_with_engine, _test_sync, ) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_native_distrib_single_node_launch_tool_gloo(local_rank, world_size): import os from datetime import timedelta timeout = timedelta(seconds=20) rank = local_rank os.environ["RANK"] = f"{rank}" idist.initialize("gloo", timeout=timeout) _test_distrib_config(local_rank, "gloo", world_size, "cpu", rank) idist.finalize() @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_native_distrib_single_node_launch_tool_nccl(local_rank, world_size): import os rank = local_rank os.environ["RANK"] = f"{rank}" idist.initialize("nccl") _test_distrib_config(local_rank, "nccl", world_size, "cuda", rank) idist.finalize() @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_native_distrib_single_node_spawn_gloo(): from datetime import timedelta timeout = timedelta(seconds=20) world_size = 4 idist.spawn( "gloo", _test_distrib_config, args=("gloo", world_size, "cpu"), nproc_per_node=world_size, 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") def test_native_distrib_single_node_spawn_nccl(): world_size = torch.cuda.device_count() idist.spawn("nccl", _test_distrib_config, args=("nccl", world_size, "cuda"), nproc_per_node=world_size) @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) 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"] _test_idist_methods_in_native_context("gloo", "cpu", 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"] _test_idist_methods_in_native_context("nccl", "cuda", 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"] _test_idist_methods_in_native_context_set_local_rank("gloo", "cpu", 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"] _test_idist_methods_in_native_context_set_local_rank("nccl", "cuda", 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_all_reduce_nccl(distributed_context_single_node_nccl): device = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _test_distrib_all_reduce(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 = "cpu" _test_distrib_all_reduce(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_gather_nccl(distributed_context_single_node_nccl): device = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _test_distrib_all_gather(device) @pytest.mark.distributed @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support") def test_idist_all_gather_gloo(distributed_context_single_node_gloo): device = "cpu" _test_distrib_all_gather(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 = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _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 = "cpu" _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 = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _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 = "cpu" _test_distrib_barrier(device) def _test_idist_methods_overhead(ok_factor): import time n = 100000 m = 5 t2 = 0.0 t1 = 0.0 for j 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 = "cpu" _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 = f"cuda:{local_rank}" _test_distrib_one_rank_only(device=device) _test_distrib_one_rank_only_with_engine(device=device) ignite-0.4.3/tests/ignite/distributed/utils/test_serial.py000066400000000000000000000034061401006565000237700ustar00rootroot00000000000000import torch import ignite.distributed as idist from tests.ignite.distributed.utils import _sanity_check, _test_sync def test_no_distrib(capsys): assert idist.backend() is None if torch.cuda.is_available(): assert idist.device().type == "cuda" 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] 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_all_reduce_no_dist(): assert idist.all_reduce(10) == 10 def test_idist_all_gather_no_dist(): assert idist.all_gather(10) == [10] assert (idist.all_gather(torch.tensor(10)) == torch.tensor(10)).all() ignite-0.4.3/tests/ignite/distributed/utils/test_xla.py000066400000000000000000000202041401006565000232700ustar00rootroot00000000000000import 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_reduce, _test_distrib_barrier, _test_distrib_broadcast, _test_distrib_config, _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) def _test_idist_all_reduce_xla_in_child_proc(index): device = idist.device() _test_distrib_all_reduce(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_all_gather_xla(): device = idist.device() _test_distrib_all_gather(device) def _test_idist_all_gather_xla_in_child_proc(index): device = idist.device() _test_distrib_all_gather(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-0.4.3/tests/ignite/engine/000077500000000000000000000000001401006565000166605ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/engine/__init__.py000066400000000000000000000045671401006565000210050ustar00rootroot00000000000000import 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-0.4.3/tests/ignite/engine/test_create_supervised.py000066400000000000000000000132001401006565000240010ustar00rootroot00000000000000import os from distutils.version import LooseVersion from typing import Optional import pytest import torch from pytest import approx from torch.nn import Linear from torch.nn.functional import mse_loss from torch.optim import SGD import ignite.distributed as idist from ignite.engine import create_supervised_evaluator, create_supervised_trainer from ignite.metrics import MeanSquaredError def _test_create_supervised_trainer( model_device: Optional[str] = None, trainer_device: Optional[str] = None, trace: bool = False ): model = Linear(1, 1) if model_device: model.to(model_device) model.weight.data.zero_() model.bias.data.zero_() optimizer = SGD(model.parameters(), 0.1) if trace: example_input = torch.randn(1, 1) model = torch.jit.trace(model, example_input) trainer = create_supervised_trainer(model, optimizer, mse_loss, device=trainer_device) x = torch.tensor([[1.0], [2.0]]) y = torch.tensor([[3.0], [5.0]]) data = [(x, y)] assert model.weight.data[0, 0].item() == approx(0.0) assert model.bias.item() == approx(0.0) if model_device == trainer_device or ((model_device == "cpu") ^ (trainer_device == "cpu")): state = trainer.run(data) assert state.output == approx(17.0) assert model.weight.data[0, 0].item() == approx(1.3) assert model.bias.item() == approx(0.8) else: if LooseVersion(torch.__version__) >= LooseVersion("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"is on CPU, but expected them to be on GPU"): trainer.run(data) def _test_create_supervised_evaluator( model_device: Optional[str] = None, evaluator_device: Optional[str] = None, trace: bool = False ): model = Linear(1, 1) if model_device: model.to(model_device) model.weight.data.zero_() model.bias.data.zero_() if trace: example_input = torch.randn(1, 1) model = torch.jit.trace(model, example_input) evaluator = create_supervised_evaluator(model, device=evaluator_device) 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")): state = evaluator.run(data) y_pred, y = state.output 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.weight.data[0, 0].item() == approx(0.0) assert model.bias.item() == approx(0.0) else: if LooseVersion(torch.__version__) >= LooseVersion("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"is on CPU, but expected them to be on GPU"): evaluator.run(data) def test_create_supervised_trainer(): _test_create_supervised_trainer() def test_create_supervised_trainer_with_cpu(): _test_create_supervised_trainer(trainer_device="cpu") def test_create_supervised_trainer_traced_with_cpu(): _test_create_supervised_trainer(trainer_device="cpu", trace=True) @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(model_device=model_device, trainer_device=trainer_device) @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(model_device=model_device, trainer_device=trainer_device) @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(trainer_device="cuda") def test_create_supervised_evaluator(): _test_create_supervised_evaluator() def test_create_supervised_evaluator_on_cpu(): _test_create_supervised_evaluator(evaluator_device="cpu") def test_create_supervised_evaluator_traced_on_cpu(): _test_create_supervised_evaluator(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) @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") def test_create_supervised_evaluator_with_metrics(): model = Linear(1, 1) model.weight.data.zero_() model.bias.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-0.4.3/tests/ignite/engine/test_custom_events.py000066400000000000000000000377221401006565000232020ustar00rootroot00000000000000from 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 CallableEvents, CallableEventWithFilter, EventEnum, EventsList def test_deprecated_callable_events_class(): engine = Engine(lambda engine, batch: 0) with pytest.warns(DeprecationWarning, match=r"Class ignite\.engine\.events\.CallableEvents is deprecated"): class CustomEvents(CallableEvents, Enum): TEST_EVENT = "test_event" with pytest.raises(TypeError, match=r"Value at \d of event_names should be a str or EventEnum"): engine.register_events(*CustomEvents) 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(): with pytest.raises(ValueError, match=r"Only one of the input arguments should be specified"): Events.ITERATION_STARTED() with pytest.raises(ValueError, match=r"Only one of the input arguments should be specified"): Events.ITERATION_STARTED(event_filter="123", every=12) 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"but will be called with"): Events.ITERATION_STARTED(event_filter=lambda x: x) def test_callable_events(): assert isinstance(Events.ITERATION_STARTED.value, str) def foo(engine, event): return True ret = Events.ITERATION_STARTED(event_filter=foo) assert isinstance(ret, CallableEventWithFilter) assert ret == Events.ITERATION_STARTED assert ret.filter == foo assert isinstance(Events.ITERATION_STARTED.value, str) # assert ret in Events assert Events.ITERATION_STARTED.name in f"{ret}" # assert ret in State.event_to_attr ret = Events.ITERATION_STARTED(every=10) assert isinstance(ret, CallableEventWithFilter) assert ret == Events.ITERATION_STARTED assert ret.filter is not None # assert ret in Events assert Events.ITERATION_STARTED.name in f"{ret}" # assert ret in State.event_to_attr ret = Events.ITERATION_STARTED(once=10) assert isinstance(ret, CallableEventWithFilter) assert ret == Events.ITERATION_STARTED assert ret.filter is not None # assert ret in Events assert Events.ITERATION_STARTED.name in f"{ret}" # assert ret in State.event_to_attr def _attach(e1, e2): assert id(e1) != id(e2) _attach(Events.ITERATION_STARTED(every=10), Events.ITERATION_COMPLETED(every=10)) 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) 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() def test_once_event_filter_with_engine(): def _test(event_name, event_attr): engine = Engine(lambda e, b: b) once = 2 counter = [ 0, ] num_calls = [ 0, ] @engine.on(event_name(once=once)) def assert_once(engine): assert getattr(engine.state, event_attr) == once num_calls[0] += 1 @engine.on(event_name) def assert_(engine): counter[0] += 1 assert getattr(engine.state, event_attr) == counter[0] d = list(range(100)) engine.run(d, max_epochs=5) assert num_calls[0] == 1 _test(Events.ITERATION_STARTED, "iteration") _test(Events.ITERATION_COMPLETED, "iteration") _test(Events.EPOCH_STARTED, "epoch") _test(Events.EPOCH_COMPLETED, "epoch") 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 device, 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_cpu(distributed_context_single_node_gloo): _test_every_event_filter_with_engine() _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") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_gpu(distributed_context_single_node_nccl): device = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _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]) ignite-0.4.3/tests/ignite/engine/test_deterministic.py000066400000000000000000000725131401006565000231440ustar00rootroot00000000000000import os import random import sys from unittest.mock import patch import numpy as np import pytest import torch import torch.nn as nn from torch.utils.data import DataLoader import ignite.distributed as idist from ignite.engine import Events from ignite.engine.deterministic import ( DeterministicEngine, ReproducibleBatchSampler, keep_random_state, update_dataloader, ) from ignite.utils import manual_seed from tests.ignite.engine import BatchChecker, setup_sampler 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(): import torch from torch.utils.data import DataLoader 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 device, 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 device, 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 device, 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 device, 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_gpu(distributed_context_single_node_nccl): device = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _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_cpu(distributed_context_single_node_gloo): device = "cpu" _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("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_cpu(distributed_context_multi_node_gloo): device = "cpu" _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_gpu(distributed_context_multi_node_nccl): device = f"cuda:{distributed_context_multi_node_nccl['local_rank']}" _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(): import torch from torch.utils.data import DataLoader from ignite.engine import DeterministicEngine, Events 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 = True from torch.optim import SGD from torch.utils.data import DataLoader 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 = os.path.join(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 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): 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) ignite-0.4.3/tests/ignite/engine/test_engine.py000066400000000000000000000656311401006565000215510ustar00rootroot00000000000000import os import time from unittest.mock import MagicMock, Mock, call 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, get_iterable_dataset def test_terminate(): 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(): 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(): engine = Engine(lambda e, b: None) def data(): pass with pytest.raises(TypeError, match=r"Argument data should be iterable"): engine.run(data) def test_current_epoch_counter_increases_every_epoch(): engine = Engine(MagicMock(return_value=1)) max_epochs = 5 counter = EpochCounter() engine.add_event_handler(Events.EPOCH_STARTED, counter) state = engine.run([1, 2], max_epochs=max_epochs) assert state.epoch == max_epochs counter.current_epoch_count = 1 state = engine.run([1, 2], max_epochs=max_epochs) assert state.epoch == max_epochs def test_current_iteration_counter_increases_every_iteration(): batches = [1, 2, 3] engine = Engine(MagicMock(return_value=1)) max_epochs = 5 counter = IterationCounter() engine.add_event_handler(Events.ITERATION_STARTED, counter) state = engine.run(batches, max_epochs=max_epochs) assert state.iteration == max_epochs * len(batches) counter.current_iteration_count = 1 state = engine.run(batches, max_epochs=max_epochs) assert state.iteration == max_epochs * len(batches) def test_stopping_criterion_is_max_epochs(): engine = Engine(MagicMock(return_value=1)) max_epochs = 5 state = engine.run([1], max_epochs=max_epochs) assert state.epoch == max_epochs def test_terminate_at_end_of_epoch_stops_run(): 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([1], max_epochs=max_epochs) assert state.epoch == last_epoch_to_run assert engine.should_terminate def test_terminate_at_start_of_epoch_stops_run_after_completing_iteration(): max_epochs = 5 epoch_to_terminate_on = 3 batches_per_epoch = [1, 2, 3] 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(batches_per_epoch, max_epochs=max_epochs) # epoch is not completed so counter is not incremented assert state.epoch == epoch_to_terminate_on assert engine.should_terminate # completes first iteration assert state.iteration == ((epoch_to_terminate_on - 1) * len(batches_per_epoch)) + 1 def test_terminate_stops_run_mid_epoch(): num_iterations_per_epoch = 10 iteration_to_stop = num_iterations_per_epoch + 3 engine = Engine(MagicMock(return_value=1)) def start_of_iteration_handler(engine): if engine.state.iteration == iteration_to_stop: engine.terminate() engine.add_event_handler(Events.ITERATION_STARTED, start_of_iteration_handler) state = engine.run(data=[None] * num_iterations_per_epoch, max_epochs=3) # 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 / num_iterations_per_epoch) # it starts from 0 def test_terminate_epoch_stops_mid_epoch(): num_iterations_per_epoch = 10 iteration_to_stop = num_iterations_per_epoch + 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=[None] * num_iterations_per_epoch, max_epochs=max_epochs) # completes the iteration but doesn't increment counter (this happens just before a new iteration starts) true_value = num_iterations_per_epoch * (max_epochs - 1) + iteration_to_stop % num_iterations_per_epoch assert state.iteration == true_value 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 def test_iteration_events_are_fired(): max_epochs = 5 num_batches = 3 data = _create_mock_data_loader(max_epochs, num_batches) 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) assert iteration_started.call_count == num_batches * max_epochs assert iteration_complete.call_count == num_batches * max_epochs expected_calls = [] for i 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 def test_last_event_name(): 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 engine.run([0, 1]) assert engine.last_event_name == Events.COMPLETED def test_reset_should_terminate(): 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(): 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(): 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(): 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(): 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(): 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 len(engine._init_iter) == 1 and engine._init_iter[0] == 10 # assert engine._dataloader_len == len(data) def test_run_asserts(): engine = Engine(lambda e, b: 1) with pytest.raises(ValueError, match=r"Input data has zero size. Please provide non-empty data"): engine.run([]) with pytest.warns(UserWarning, match="Argument seed is deprecated"): engine.run([0, 1, 2, 3, 4], seed=1234) def test_state_get_event_attrib_value(): 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 def test_time_stored_in_state(): def _test(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 ) _test(list(range(100)), max_epochs=2, epoch_length=100) _test(list(range(200)), max_epochs=2, epoch_length=100) _test(list(range(200)), max_epochs=5, epoch_length=100) def _test_check_triggered_events(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, Events.GET_BATCH_COMPLETED: max_epochs * epoch_length, 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(): # tests issue https://github.com/pytorch/ignite/issues/818 _test_check_triggered_events(list(range(10)), max_epochs=4, epoch_length=10) _test_check_triggered_events(list(range(100)), max_epochs=5, epoch_length=100) _test_check_triggered_events(list(range(100)), max_epochs=5, epoch_length=50, exp_iter_stops=50 * 5 // 100) _test_check_triggered_events(list(range(100)), max_epochs=5, epoch_length=150, exp_iter_stops=150 * 5 // 100) def test_run_check_triggered_events_list(): _test_run_check_triggered_events() def _test_run_check_triggered_events_on_iterator(): def infinite_data_iterator(): while True: for i in range(100): yield i _test_check_triggered_events(infinite_data_iterator(), max_epochs=5, epoch_length=100, exp_iter_stops=0) _test_check_triggered_events(infinite_data_iterator(), max_epochs=5, epoch_length=50, exp_iter_stops=0) _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 _test_check_triggered_events(limited_data_iterator(), max_epochs=1, epoch_length=100, exp_iter_stops=0) _test_check_triggered_events(limited_data_iterator(), max_epochs=10, epoch_length=10, exp_iter_stops=0) # These tests will fail with pytest.raises(AssertionError): with pytest.warns(UserWarning, match=r"Data iterator can not provide data anymore"): _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"): _test_check_triggered_events(limited_data_iterator(), max_epochs=3, epoch_length=75) with pytest.raises(AssertionError): with pytest.warns(UserWarning, match=r"Data iterator can not provide data anymore"): _test_check_triggered_events(limited_data_iterator(), max_epochs=1, epoch_length=101) def test_run_check_triggered_events_on_iterator(): _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_gpu(distributed_context_single_node_nccl): _test_run_check_triggered_events_on_iterator() _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_cpu(distributed_context_single_node_gloo): _test_run_check_triggered_events_on_iterator() _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_cpu(distributed_context_multi_node_gloo): _test_run_check_triggered_events_on_iterator() _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_gpu(distributed_context_multi_node_nccl): _test_run_check_triggered_events_on_iterator() _test_run_check_triggered_events() def test_engine_random_state(): 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(): # 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(): # 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(): # 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(): # 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(): # 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(): # 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(): # 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(): # 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 # # # # # 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(): # 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 # # # # # 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(): # 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) ignite-0.4.3/tests/ignite/engine/test_engine_state_dict.py000066400000000000000000000234331401006565000237460ustar00rootroot00000000000000import os from 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 larger than 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 = os.path.join(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 os.path.exists(fp) 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 larger than 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-0.4.3/tests/ignite/engine/test_event_handlers.py000066400000000000000000000411401401006565000232720ustar00rootroot00000000000000import functools import gc from unittest.mock import MagicMock, call, create_autospec 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) def test_event_removable_handle(): # Removable handle removes event from engine. engine = DummyEngine() handler = create_autospec(spec=lambda x: None) assert not hasattr(handler, "_parent") removable_handle = engine.add_event_handler(Events.STARTED, handler) assert engine.has_event_handler(handler, Events.STARTED) engine.run(1) handler.assert_called_once_with(engine) removable_handle.remove() assert not engine.has_event_handler(handler, Events.STARTED) # Second engine pass does not fire handle again. engine.run(1) handler.assert_called_once_with(engine) # Removable handle can be used as a context manager handler = create_autospec(spec=lambda x: None) with engine.add_event_handler(Events.STARTED, handler): assert engine.has_event_handler(handler, Events.STARTED) engine.run(1) assert not engine.has_event_handler(handler, Events.STARTED) handler.assert_called_once_with(engine) engine.run(1) handler.assert_called_once_with(engine) # Removeable handle only effects a single event registration handler = MagicMock(spec_set=True) with engine.add_event_handler(Events.STARTED, handler): with engine.add_event_handler(Events.COMPLETED, handler): assert engine.has_event_handler(handler, Events.STARTED) assert engine.has_event_handler(handler, Events.COMPLETED) assert engine.has_event_handler(handler, Events.STARTED) assert not engine.has_event_handler(handler, Events.COMPLETED) assert not engine.has_event_handler(handler, Events.STARTED) assert not engine.has_event_handler(handler, Events.COMPLETED) # Removeable handle is re-enter and re-exitable handler = MagicMock(spec_set=True) remove = engine.add_event_handler(Events.STARTED, handler) with remove: with remove: assert engine.has_event_handler(handler, Events.STARTED) assert not engine.has_event_handler(handler, Events.STARTED) assert not engine.has_event_handler(handler, Events.STARTED) # 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.STARTED, _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_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 == {} 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-0.4.3/tests/ignite/handlers/000077500000000000000000000000001401006565000172135ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/handlers/__init__.py000066400000000000000000000000421401006565000213200ustar00rootroot00000000000000# Needed to collect coverage data ignite-0.4.3/tests/ignite/handlers/test_checkpoint.py000066400000000000000000001554461401006565000227720ustar00rootroot00000000000000import os import warnings from collections import OrderedDict from unittest.mock import MagicMock import pytest import torch import torch.nn as nn from pkg_resources import parse_version import ignite.distributed as idist from ignite.engine import Engine, Events, State from ignite.handlers import Checkpoint, DiskSaver, EarlyStopping, ModelCheckpoint, global_step_from_engine 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) 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") model = DummyModel() to_save = {"model": model} with pytest.raises(TypeError, match=r"Argument `save_handler` should be callable"): Checkpoint(to_save, 12, "prefix") with pytest.raises( ValueError, match=r"If `score_name` is provided, then `score_function` should be also provided." ): Checkpoint(to_save, lambda x: x, score_name="acc") 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.warns(UserWarning, match=r"Argument archived is deprecated"): Checkpoint(to_save, lambda x: x, score_function=lambda e: 123, score_name="acc", archived=True) with pytest.raises(ValueError, match=r"Cannot have key 'checkpointer' if `include_self` is True"): Checkpoint({"checkpointer": model}, lambda x: x, include_self=True) def test_checkpoint_score_function_wrong_output(): model = DummyModel() 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) def test_checkpoint_default(): def _test(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" model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") 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") def test_checkpoint_include_self_state_dict(): def _test(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 model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") 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") def test_checkpoint_with_dp(): model = DummyModel() 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) def test_checkpoint_with_global_step_transform(): def _test(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" for prefix in ["", "dummytask"]: model = DummyModel() to_save = {"model": model} _test(prefix, to_save, model.state_dict(), "model") model = DummyModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) to_save = {"model": model, "optimizer": optimizer} _test(prefix, to_save, {"model": model.state_dict(), "optimizer": optimizer.state_dict()}, "checkpoint") def test_checkpoint_with_score_function(): def _test(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" model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") 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") def test_checkpoint_with_score_name_and_function(): def _test(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" model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") 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") 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(): def _test(to_save, obj, name): 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" model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") def test_checkpoint_with_score_name_and_function_and_trainer_epoch(): def _test(to_save, obj, name): 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" model = DummyModel() to_save = {"model": model} _test(to_save, model.state_dict(), "model") 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 = os.path.join(dirname, "existing_dir") nonempty = os.path.join(dirname, "nonempty") os.makedirs(existing) os.makedirs(nonempty) with open(os.path.join(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"Argument save_interval is deprecated and should be None"): ModelCheckpoint(existing, _PREFIX, save_interval=42) with pytest.raises(ValueError, match=r"Directory path '\S+' is not found"): ModelCheckpoint(os.path.join(dirname, "non_existing_dir"), _PREFIX, create_dir=False) with pytest.raises(ValueError, match=r"Argument save_as_state_dict is deprecated and should be True"): ModelCheckpoint(existing, _PREFIX, create_dir=False, save_as_state_dict=False) with pytest.raises(ValueError, match=r"If `score_name` is provided, then `score_function` "): ModelCheckpoint(existing, _PREFIX, create_dir=False, score_name="test") with pytest.raises(TypeError, match=r"global_step_transform should be a function"): ModelCheckpoint(existing, _PREFIX, create_dir=False, global_step_transform=1234) with pytest.warns(UserWarning, match=r"Argument archived is deprecated"): ModelCheckpoint(existing, _PREFIX, create_dir=False, archived=True) 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, str) assert os.path.join(dirname, _PREFIX) in fname assert os.path.exists(fname) loaded_objects = torch.load(fname) assert loaded_objects == model.state_dict() def test_model_checkpoint_simple_recovery_from_existing_non_empty(dirname): def _test(ext, require_empty): previous_fname = os.path.join(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) model = DummyModel() to_save = {"model": model} h(engine, to_save) fname = h.last_checkpoint ext = ".pt" assert isinstance(fname, str) assert os.path.join(dirname, f"{_PREFIX}_model_{1}{ext}") == fname assert os.path.exists(fname) assert os.path.exists(previous_fname) loaded_objects = torch.load(fname) assert loaded_objects == model.state_dict() os.remove(fname) _test(".txt", require_empty=True) _test(".pt", require_empty=False) 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"Unable to save checkpoint, save_handler should be DiskSaver, got {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_existance(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 = os.path.join(saver.dirname, fname) assert os.path.exists(fp) == expected if expected: saver.remove(fname) _test_existance(atomic=False, _to_save=to_save_serializable, expected=True) _test_existance(atomic=False, _to_save=to_save_non_serializable, expected=True) _test_existance(atomic=True, _to_save=to_save_serializable, expected=True) _test_existance(atomic=True, _to_save=to_save_non_serializable, expected=False) @pytest.mark.skipif( parse_version(torch.__version__) < parse_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 = os.path.join(saver.dirname, fname) assert os.path.exists(fp) 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 = os.path.join(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, 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() print(loss.device, y.device, x.device) 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 on_zero_rank) or (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 = os.path.join(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, 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 = os.path.join(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 == "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 dictionary"): Checkpoint.load_objects({}, []) with pytest.raises(TypeError, match=r"should have `load_state_dict` method"): Checkpoint.load_objects({"a": None}, {"a": None}) model = DummyModel() to_load = {"model": model, "another_model": model} with pytest.raises(ValueError, match=r"from `to_load` is not found in the checkpoint"): 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() 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: 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, str) assert os.path.join(dirname, _PREFIX) in fname assert os.path.exists(fname) loaded_objects = torch.load(fname) Checkpoint.load_objects(to_save, loaded_objects) os.remove(fname) # 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, str) assert os.path.join(dirname, _PREFIX) in fname assert os.path.exists(fname) loaded_objects = torch.load(fname) to_load = {"model": to_save["model"]} Checkpoint.load_objects(to_load, loaded_objects) os.remove(fname) # 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, str) assert os.path.join(dirname, _PREFIX) in fname assert os.path.exists(fname) loaded_objects = torch.load(fname) Checkpoint.load_objects(to_save, loaded_objects) 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) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UserWarning) Checkpoint.load_objects(to_load_single_object, loaded_checkpoint, strict=False, blah="blah") 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 = os.path.join(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) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_cpu(distributed_context_single_node_gloo, get_rank_zero_dirname): device = torch.device("cpu") dirname = get_rank_zero_dirname() _test_save_model_optimizer_lr_scheduler_with_state_dict(device, os.path.join(dirname, "1")) _test_save_model_optimizer_lr_scheduler_with_state_dict(device, os.path.join(dirname, "2"), on_zero_rank=True) _test_checkpoint_with_ddp(device) _test_checkpoint_load_objects_ddp(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_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, os.path.join(dirname, "1")) _test_save_model_optimizer_lr_scheduler_with_state_dict("cpu", os.path.join(dirname, "2"), on_zero_rank=True) _test_checkpoint_with_ddp(device=device) _test_checkpoint_load_objects_ddp(device=device) 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, str) assert os.path.join(dirname, _PREFIX) in fname assert os.path.exists(fname) 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(), os.path.join(dirname, "1")) _test_save_model_optimizer_lr_scheduler_with_state_dict(idist.device(), os.path.join(dirname, "2")) def _test_tpu_saves_to_cpu_nprocs(index, dirname): device = idist.device() _test_tpu_saves_to_cpu(device, os.path.join(dirname, "1")) _test_save_model_optimizer_lr_scheduler_with_state_dict(device, os.path.join(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 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, 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(): def _test( to_save, filename_prefix="", score_function=None, score_name=None, global_step_transform=None, filename_pattern=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 model = DummyModel() to_save = {"model": model} assert _test(to_save) == "model_203.pt" assert _test(to_save, "best") == "best_model_203.pt" assert _test(to_save, score_function=lambda e: e.state.score) == "model_0.9999.pt" res = _test(to_save, score_function=lambda e: e.state.score, global_step_transform=lambda e, _: e.state.epoch) assert res == "model_12_0.9999.pt" assert _test(to_save, score_function=lambda e: e.state.score, score_name="acc") == "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, ) assert res == "model_12_acc=0.9999.pt" assert _test(to_save, "best", score_function=lambda e: e.state.score) == "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 ) assert res == "best_model_12_0.9999.pt" res = _test(to_save, "best", score_function=lambda e: e.state.score, score_name="acc") 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, ) assert res == "best_model_12_acc=0.9999.pt" pattern = "{name}.{ext}" assert _test(to_save, filename_pattern=pattern) == "model.pt" pattern = "chk-{name}--{global_step}.{ext}" assert _test(to_save, to_save, filename_pattern=pattern) == "chk-model--203.pt" pattern = "chk-{filename_prefix}--{name}--{global_step}.{ext}" assert _test(to_save, "best", filename_pattern=pattern) == "chk-best--model--203.pt" pattern = "chk-{name}--{score}.{ext}" assert _test(to_save, score_function=lambda e: e.state.score, filename_pattern=pattern) == "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, ) 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) 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, ) 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) 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, ) 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) 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, ) 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, ) 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) == "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) 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 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 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 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_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 ignite-0.4.3/tests/ignite/handlers/test_early_stopping.py000066400000000000000000000260571401006565000236750ustar00rootroot00000000000000import 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): import torch.distributed as dist 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] dist.all_reduce(v) v /= dist.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): import torch.distributed as dist from ignite.metrics import Accuracy rank = dist.get_rank() ws = dist.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=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_gpu(local_rank, distributed_context_single_node_nccl): device = f"cuda:{local_rank}" _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_cpu(local_rank, distributed_context_single_node_gloo): device = "cpu" _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("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_cpu(distributed_context_multi_node_gloo): device = "cpu" _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_gpu(distributed_context_multi_node_nccl): device = f"cuda:{distributed_context_multi_node_nccl['local_rank']}" _test_distrib_with_engine_early_stopping(device) _test_distrib_integration_engine_early_stopping(device) ignite-0.4.3/tests/ignite/handlers/test_handlers.py000066400000000000000000000011001401006565000224140ustar00rootroot00000000000000from 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-0.4.3/tests/ignite/handlers/test_terminate_on_nan.py000066400000000000000000000062711401006565000241520ustar00rootroot00000000000000import numpy as np import torch from ignite.engine import Engine, Events, State from ignite.handlers import TerminateOnNan def test_terminate_on_nan_and_inf(): torch.manual_seed(12) def update_fn(engine, batch): pass trainer = Engine(update_fn) trainer.state = State() h = TerminateOnNan() trainer.state.output = 1.0 h(trainer) assert not trainer.should_terminate trainer.state.output = torch.tensor(123.45) h(trainer) assert not trainer.should_terminate trainer.state.output = torch.asin(torch.randn(10,)) h(trainer) assert trainer.should_terminate trainer.should_terminate = False trainer.state.output = np.array([1.0, 2.0]) h._output_transform = lambda x: x.tolist() h(trainer) assert not trainer.should_terminate h._output_transform = lambda x: x trainer.state.output = torch.asin(torch.randn(4, 4)) h(trainer) assert trainer.should_terminate trainer.should_terminate = False trainer.state.output = (10.0, 1.0 / torch.randint(0, 2, size=(4,)).type(torch.float), 1.0) h(trainer) assert trainer.should_terminate trainer.should_terminate = False trainer.state.output = (1.0, torch.tensor(1.0), "abc") h(trainer) assert not trainer.should_terminate trainer.state.output = 1.0 / torch.randint(0, 2, size=(4, 4)).type(torch.float) h(trainer) assert trainer.should_terminate trainer.should_terminate = False trainer.state.output = (float("nan"), 10.0) h(trainer) assert trainer.should_terminate trainer.should_terminate = False trainer.state.output = float("inf") h(trainer) assert trainer.should_terminate trainer.should_terminate = False trainer.state.output = [float("nan"), 10.0] h(trainer) assert trainer.should_terminate trainer.should_terminate = False 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-0.4.3/tests/ignite/handlers/test_time_limit.py000066400000000000000000000020061401006565000227560ustar00rootroot00000000000000import 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 test_terminate_on_time_limit(): def _train_func(engine, batch): time.sleep(1) def _test(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) _test(20, 10) _test(5, 10) ignite-0.4.3/tests/ignite/handlers/test_timing.py000066400000000000000000000023501401006565000221130ustar00rootroot00000000000000import 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)) def _equal(lhs, rhs): return round(lhs, 1) == round(rhs, 1) assert _equal(t_total.value(), (2 * n_iter * sleep_t)) assert _equal(t_batch.value(), (sleep_t)) assert _equal(t_train.value(), (n_iter * sleep_t)) t_total.reset() assert _equal(t_total.value(), 0.0) ignite-0.4.3/tests/ignite/metrics/000077500000000000000000000000001401006565000170615ustar00rootroot00000000000000ignite-0.4.3/tests/ignite/metrics/__init__.py000066400000000000000000000000421401006565000211660ustar00rootroot00000000000000# Needed to collect coverage data ignite-0.4.3/tests/ignite/metrics/test_accumulation.py000066400000000000000000000414301401006565000231600ustar00rootroot00000000000000import 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.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 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) def test_integration(): def _test(metric_cls, true_result_fn): size = 100 custom_variable = 10.0 + 5.0 * torch.rand(size, 12) 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]) custom_var_mean.attach(engine, "agg_custom_var") state = engine.run([0] * size) np.testing.assert_almost_equal( state.metrics["agg_custom_var"].numpy(), true_result_fn(custom_variable), decimal=5 ) size = 100 custom_variable = 10.0 + 5.0 * torch.rand(size) 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]) 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)) def _mean(y_true): return y_true.mean(dim=0).numpy() _test(Average, _mean) _test(GeometricAverage, _geom_mean) 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 _test_distrib_integration(device): def _test(metric_cls, true_result_fn, metric_device, tol=1e-5): size = 100 custom_variable = 10.0 + 5.0 * torch.rand(size, 12, 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) np.testing.assert_almost_equal( state.metrics["agg_custom_var"].cpu().numpy(), true_result_fn(custom_variable), 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) def _mean(y_true): y_true = idist.all_reduce(y_true) return y_true.mean(dim=0).cpu().numpy() / idist.get_world_size() def _geom_mean(y_true): log_y_true = torch.log(y_true) log_y_true = idist.all_reduce(log_y_true) np_t = log_y_true.cpu().numpy() return np.exp(np.mean(np_t, axis=0) / idist.get_world_size()) metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(Average, _mean, metric_device) _test(GeometricAverage, _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}" @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_gpu(distributed_context_single_node_nccl): device = torch.device(f"cuda:{distributed_context_single_node_nccl['local_rank']}") _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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _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("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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 = 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_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.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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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" 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) ignite-0.4.3/tests/ignite/metrics/test_accuracy.py000066400000000000000000001016531401006565000222720ustar00rootroot00000000000000import os import pytest import torch from sklearn.metrics import accuracy_score import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import Accuracy torch.manual_seed(12) def test_no_update(): acc = Accuracy() with pytest.raises(NotComputableError): acc.compute() def test__check_shape(): acc = Accuracy() with pytest.raises(ValueError): 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): 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): acc._check_shape((torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 5)).long())) def test_binary_wrong_inputs(): acc = Accuracy() with pytest.raises(ValueError): # 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): # 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): # incompatible shapes acc.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible shapes acc.update((torch.randint(0, 2, size=(10, 5, 6)).long(), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes acc.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5, 6)).long())) def test_binary_input_N(): # Binary accuracy on input of shape (N, 1) or (N, ) def _test(): acc = Accuracy() y_pred = torch.randint(0, 2, size=(10,)).long() y = torch.randint(0, 2, size=(10,)).long() 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()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100,)).long() y = torch.randint(0, 2, size=(100,)).long() n_iters = 16 batch_size = y.shape[0] // n_iters + 1 for i in range(n_iters): idx = i * batch_size acc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) 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()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_binary_input_NL(): # Binary accuracy on input of shape (N, L) def _test(): acc = Accuracy() y_pred = torch.randint(0, 2, size=(10, 5)).long() y = torch.randint(0, 2, size=(10, 5)).long() 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()) acc.reset() y_pred = torch.randint(0, 2, size=(10, 1, 5)).long() y = torch.randint(0, 2, size=(10, 1, 5)).long() 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()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100, 8)).long() y = torch.randint(0, 2, size=(100, 8)).long() n_iters = 16 batch_size = y.shape[0] // n_iters + 1 for i in range(n_iters): idx = i * batch_size acc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size])) 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()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_binary_input_NHW(): # Binary accuracy on input of shape (N, H, W, ...) def _test(): acc = Accuracy() y_pred = torch.randint(0, 2, size=(4, 12, 10)).long() y = torch.randint(0, 2, size=(4, 12, 10)).long() 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()) acc.reset() y_pred = torch.randint(0, 2, size=(4, 1, 12, 10)).long() y = torch.randint(0, 2, size=(4, 1, 12, 10)).long() 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()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100, 8, 8)).long() y = torch.randint(0, 2, size=(100, 8, 8)).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])) 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()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_binary_as_multiclass_input(): # Binary accuracy on input of shape (N, 1, ...) def _test(): acc = Accuracy() y_pred = torch.randint(0, 2, size=(4, 1)).long() y = torch.randint(0, 2, size=(4,)).long() 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()) acc.reset() y_pred = torch.randint(0, 2, size=(4, 1, 12)).long() y = torch.randint(0, 2, size=(4, 12)).long() 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()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100, 1, 8, 8)).long() y = torch.randint(0, 2, size=(100, 8, 8)).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])) 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()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() 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())) def test_multiclass_input_N(): # Multiclass input data of shape (N, ) and (N, C) def _test(): acc = Accuracy() y_pred = torch.rand(10, 4) y = torch.randint(0, 4, size=(10,)).long() 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()) acc.reset() y_pred = torch.rand(10, 10, 1) y = torch.randint(0, 18, size=(10, 1)).long() 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()) acc.reset() y_pred = torch.rand(10, 18) y = torch.randint(0, 18, size=(10,)).long() 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()) acc.reset() y_pred = torch.rand(4, 10) y = torch.randint(0, 10, size=(4,)).long() 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()) # 2-classes acc.reset() y_pred = torch.rand(4, 2) y = torch.randint(0, 2, size=(4,)).long() 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()) # Batched Updates acc.reset() y_pred = torch.rand(100, 5) y = torch.randint(0, 5, size=(100,)).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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() assert acc._type == "multiclass" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_multiclass_input_NL(): # Multiclass input data of shape (N, L) and (N, C, L) def _test(): acc = Accuracy() y_pred = torch.rand(10, 4, 5) y = torch.randint(0, 4, size=(10, 5)).long() 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()) acc.reset() y_pred = torch.rand(4, 10, 5) y = torch.randint(0, 10, size=(4, 5)).long() 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()) # Batched Updates acc.reset() y_pred = torch.rand(100, 9, 7) y = torch.randint(0, 9, size=(100, 7)).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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() assert acc._type == "multiclass" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_multiclass_input_NHW(): # Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...) def _test(): acc = Accuracy() y_pred = torch.rand(4, 5, 12, 10) y = torch.randint(0, 5, size=(4, 12, 10)).long() 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()) acc.reset() y_pred = torch.rand(4, 5, 10, 12, 8) y = torch.randint(0, 5, size=(4, 10, 12, 8)).long() 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()) # Batched Updates acc.reset() y_pred = torch.rand(100, 3, 8, 8) y = torch.randint(0, 3, size=(100, 8, 8)).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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() assert acc._type == "multiclass" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() 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())) def test_multilabel_input_N(): # Multilabel input data of shape (N, C, ...) and (N, C, ...) def _test(): acc = Accuracy(is_multilabel=True) y_pred = torch.randint(0, 2, size=(10, 4)) y = torch.randint(0, 2, size=(10, 4)).long() acc.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y.numpy() assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) acc.reset() y_pred = torch.randint(0, 2, size=(50, 7)).long() y = torch.randint(0, 2, size=(50, 7)).long() acc.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y.numpy() assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100, 4)) y = torch.randint(0, 2, size=(100, 4)).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])) np_y = y.numpy() np_y_pred = y_pred.numpy() assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_multilabel_input_NL(): # Multilabel input data of shape (N, C, L, ...) and (N, C, L, ...) def _test(): acc = Accuracy(is_multilabel=True) y_pred = torch.randint(0, 2, size=(10, 4, 5)) y = torch.randint(0, 2, size=(10, 4, 5)).long() acc.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) # (N, C, L, ...) -> (N * L * ..., C) np_y = to_numpy_multilabel(y) # (N, C, L, ...) -> (N * L ..., C) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) acc.reset() y_pred = torch.randint(0, 2, size=(4, 10, 8)).long() y = torch.randint(0, 2, size=(4, 10, 8)).long() acc.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) # (N, C, L, ...) -> (N * L * ..., C) np_y = to_numpy_multilabel(y) # (N, C, L, ...) -> (N * L ..., C) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100, 4, 5)) y = torch.randint(0, 2, size=(100, 4, 5)).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])) np_y_pred = to_numpy_multilabel(y_pred) # (N, C, L, ...) -> (N * L * ..., C) np_y = to_numpy_multilabel(y) # (N, C, L, ...) -> (N * L ..., C) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() def test_multilabel_input_NHW(): # Multilabel input data of shape (N, C, H, W, ...) and (N, C, H, W, ...) def _test(): acc = Accuracy(is_multilabel=True) y_pred = torch.randint(0, 2, size=(4, 5, 12, 10)) y = torch.randint(0, 2, size=(4, 5, 12, 10)).long() acc.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) # (N, C, H, W, ...) -> (N * H * W ..., C) np_y = to_numpy_multilabel(y) # (N, C, H, W, ...) -> (N * H * W ..., C) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) acc.reset() y_pred = torch.randint(0, 2, size=(4, 10, 12, 8)).long() y = torch.randint(0, 2, size=(4, 10, 12, 8)).long() acc.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) # (N, C, H, W, ...) -> (N * H * W ..., C) np_y = to_numpy_multilabel(y) # (N, C, H, W, ...) -> (N * H * W ..., C) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # Batched Updates acc.reset() y_pred = torch.randint(0, 2, size=(100, 5, 12, 10)) y = torch.randint(0, 2, size=(100, 5, 12, 10)).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])) np_y_pred = to_numpy_multilabel(y_pred) # (N, C, L, ...) -> (N * L * ..., C) np_y = to_numpy_multilabel(y) # (N, C, L, ...) -> (N * L ..., C) assert acc._type == "multilabel" assert isinstance(acc.compute(), float) assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test() 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}" # 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" n = acc._num_examples res = acc.compute() assert n * idist.get_world_size() == 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}" # 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" n = acc._num_examples res = acc.compute() assert n * idist.get_world_size() == 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 * idist.get_world_size() == 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}" # 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" n = acc._num_examples res = acc.compute() assert n * idist.get_world_size() == 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): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 s = 16 n_classes = 10 offset = n_iters * s y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) 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) 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) def _test_distrib_integration_multilabel(device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) def _test(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 s = 16 n_classes = 10 offset = n_iters * s y_true = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 8, 10)).to(device) y_preds = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 8, 10)).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, ...], y_true[i * s + rank * offset : (i + 1) * s + rank * offset, ...], ) 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) 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}" @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_gpu(distributed_context_single_node_nccl): device = torch.device(f"cuda:{distributed_context_single_node_nccl['local_rank']}") _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(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_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) @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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _test_distrib_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(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_multilabel_input_NHW(device) _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(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) @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-0.4.3/tests/ignite/metrics/test_confusion_matrix.py000066400000000000000000000620231401006565000240640ustar00rootroot00000000000000import 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, mIoU from ignite.metrics.confusion_matrix import DiceCoefficient, cmAccuracy, cmPrecision, cmRecall torch.manual_seed(12) def test_no_update(): cm = ConfusionMatrix(10) with pytest.raises(NotComputableError): 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) def test_multiclass_input_N(): # Multiclass input data of shape (N, ) def _test_N(): num_classes = 4 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(10, num_classes) y = torch.randint(0, num_classes, size=(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()) num_classes = 10 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(4, num_classes) y = torch.randint(0, num_classes, size=(4,)).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()) # 2-classes num_classes = 2 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(4, num_classes) y = torch.randint(0, num_classes, size=(4,)).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()) # Batched Updates num_classes = 5 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(100, num_classes) y = torch.randint(0, num_classes, size=(100,)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() assert np.all(confusion_matrix(np_y, np_y_pred, labels=list(range(num_classes))) == cm.compute().numpy()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test_N() def test_multiclass_input_NL(): # Multiclass input data of shape (N, L) def _test_NL(): num_classes = 4 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(10, num_classes, 5) y = torch.randint(0, num_classes, size=(10, 5)).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()) num_classes = 10 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(4, num_classes, 5) y = torch.randint(0, num_classes, size=(4, 5)).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()) # Batched Updates num_classes = 9 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(100, num_classes, 7) y = torch.randint(0, num_classes, size=(100, 7)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() assert np.all(confusion_matrix(np_y, np_y_pred, labels=list(range(num_classes))) == cm.compute().numpy()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test_NL() def test_multiclass_input_NHW(): # Multiclass input data of shape (N, H, W, ...) def _test_NHW(): num_classes = 5 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(4, num_classes, 12, 10) y = torch.randint(0, num_classes, 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()) num_classes = 5 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(4, num_classes, 10, 12, 8) y = torch.randint(0, num_classes, size=(4, 10, 12, 8)).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()) # Batched Updates num_classes = 3 cm = ConfusionMatrix(num_classes=num_classes) y_pred = torch.rand(100, num_classes, 8, 8) y = torch.randint(0, num_classes, size=(100, 8, 8)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().argmax(axis=1).ravel() assert np.all(confusion_matrix(np_y, np_y_pred, labels=list(range(num_classes))) == cm.compute().numpy()) # check multiple random inputs as random exact occurencies are rare for _ in range(10): _test_NHW() 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.int) y_true[1:11, 1:11] = 1 y_true[15:25, 15:25] = 2 y_pred = np.zeros((30, 30), dtype=np.int) 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="ignore_index should be non-negative integer"): IoU(cm, ignore_index=-1) with pytest.raises(ValueError, match="ignore_index should be non-negative integer"): IoU(cm, ignore_index="a") with pytest.raises(ValueError, match="ignore_index should be non-negative integer"): IoU(cm, ignore_index=10) with pytest.raises(ValueError, match="ignore_index should be non-negative integer"): IoU(cm, ignore_index=11) def test_iou(): def _test(average=None): 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_}" _test() _test(average="samples") 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="ignore_index should be non-negative integer"): DiceCoefficient(cm, ignore_index=-1) with pytest.raises(ValueError, match="ignore_index should be non-negative integer"): DiceCoefficient(cm, ignore_index="a") with pytest.raises(ValueError, match="ignore_index should be non-negative integer"): DiceCoefficient(cm, ignore_index=10) with pytest.raises(ValueError, match="ignore_index should be non-negative integer"): 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.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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_dill.py000066400000000000000000000013131401006565000214140ustar00rootroot00000000000000import 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-0.4.3/tests/ignite/metrics/test_epoch_metric.py000066400000000000000000000205321401006565000231350ustar00rootroot00000000000000import os import 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) # Target is not binary with pytest.raises(ValueError, match=r"Targets should be binary"): output = (torch.rand(4, 3), torch.randint(0, 5, size=(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(device=None): if device is None: device = idist.device() if idist.device().type != "xla" else "cpu" rank = idist.get_rank() torch.manual_seed(12) n_iters = 60 s = 16 n_classes = 7 offset = n_iters * s y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),), device=device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes, device=device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) engine = Engine(update) def assert_data_fn(all_preds, all_targets): assert all_preds.equal(y_preds), f"{all_preds.shape} vs {y_preds.shape}" assert all_targets.equal(y_true), f"{all_targets.shape} vs {y_true.shape}" 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) assert engine.state.metrics["epm"] == (y_preds.argmax(dim=1) == y_true).sum().item() @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_gpu(distributed_context_single_node_nccl): _test_distrib_integration(device="cuda") @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_cpu(distributed_context_single_node_gloo): _test_distrib_integration(device="cpu") @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_distrib_integration() def _test_distrib_xla_nprocs(index): _test_distrib_integration() @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.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_distrib_integration, (None,), np=nproc, do_init=True) ignite-0.4.3/tests/ignite/metrics/test_fbeta.py000066400000000000000000000173241401006565000215620ustar00rootroot00000000000000import 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=True) Fbeta(1.0, precision=p) with pytest.raises(ValueError, match=r"Input recall metric should have average=False"): r = Recall(average=True) 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 test_integration(): def _test(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) if isinstance(state.metrics["f2"], torch.Tensor): np.testing.assert_allclose(f2_true, state.metrics["f2"].numpy()) else: assert f2_true == pytest.approx(state.metrics["f2"]), f"{f2_true} vs {state.metrics['f2']}" _test(None, None, False, output_transform=None) _test(None, None, True, output_transform=None) def output_transform(output): return output["y_pred"], output["y"] _test(None, None, False, output_transform=output_transform) _test(None, None, True, output_transform=output_transform) precision = Precision(average=False) recall = Recall(average=False) _test(precision, recall, False, None) _test(precision, recall, True, None) def _test_distrib_integration(device): rank = idist.get_rank() torch.manual_seed(12) def _test(p, r, average, n_epochs, metric_device): n_iters = 60 s = 16 n_classes = 7 offset = n_iters * s y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) 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) 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_frequency.py000066400000000000000000000105401401006565000224730ustar00rootroot00000000000000import 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, 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 assert ( estimated_wps * lower_bound_factor < wps <= estimated_wps ), f"{e.state.iteration}: {estimated_wps * lower_bound_factor} < {wps} < {estimated_wps}" data = [[i] * batch_size for i in range(0, total_tokens, batch_size)] max_epochs = 1 if idist.model_name() != "horovod-dist" else 2 engine.run(data, max_epochs=2) @pytest.mark.skipif(sys.platform.startswith("darwin"), reason="Skip on MacOS") @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-0.4.3/tests/ignite/metrics/test_loss.py000066400000000000000000000165711401006565000214640ustar00rootroot00000000000000import os import pytest import torch from numpy.testing import assert_almost_equal from torch import nn from torch.nn.functional import nll_loss import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import Loss def test_zero_div(): loss = Loss(nll_loss) with pytest.raises(NotComputableError): loss.compute() def test_compute(): loss = Loss(nll_loss) y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]).log() y = torch.tensor([2, 2]).long() loss.update((y_pred, y)) assert_almost_equal(loss.compute(), 1.1512925625) y_pred = torch.tensor([[0.1, 0.3, 0.6], [0.6, 0.2, 0.2], [0.2, 0.7, 0.1]]).log() y = torch.tensor([2, 0, 2]).long() loss.update((y_pred, y)) assert_almost_equal(loss.compute(), 1.1253643036) # average def test_compute_on_criterion(): loss = Loss(nn.NLLLoss()) y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]).log() y = torch.tensor([2, 2]).long() loss.update((y_pred, y)) assert_almost_equal(loss.compute(), 1.1512925625) y_pred = torch.tensor([[0.1, 0.3, 0.6], [0.6, 0.2, 0.2], [0.2, 0.7, 0.1]]).log() y = torch.tensor([2, 0, 2]).long() loss.update((y_pred, y)) assert_almost_equal(loss.compute(), 1.1253643036) # average def test_non_averaging_loss(): loss = Loss(nn.NLLLoss(reduction="none")) y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]).log() y = torch.tensor([2, 2]).long() with pytest.raises(ValueError): loss.update((y_pred, y)) def test_kwargs_loss(): loss = Loss(nll_loss) y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]).log() y = torch.tensor([2, 2]).long() loss.update((y_pred, y, {"weight": torch.tensor([0, 0, 0], dtype=torch.float)})) assert_almost_equal(loss.compute(), 0) def test_reset(): loss = Loss(nll_loss) y_pred = torch.tensor([[0.1, 0.3, 0.6], [0.6, 0.2, 0.2]]).log() y = torch.tensor([2, 0]).long() loss.update((y_pred, y)) loss.compute() loss.reset() with pytest.raises(NotComputableError): loss.compute() def _test_distrib_compute_on_criterion(device): def _test(metric_device): criterion = nn.NLLLoss().to(device) loss = Loss(criterion, device=metric_device) y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]], device=device).log() y = torch.tensor([2, 2], device=device).long() loss.update((y_pred, y)) n = loss._num_examples assert n == len(y) res = loss.compute() assert n * idist.get_world_size() == 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 = torch.tensor([[0.1, 0.3, 0.6], [0.6, 0.2, 0.2], [0.2, 0.7, 0.1]], device=device).log() y = torch.tensor([2, 0, 2], device=device).long() loss.update((y_pred, y)) n = loss._num_examples res = loss.compute() assert n * idist.get_world_size() == 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()) _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: 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 = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]).log() y = torch.tensor([2, 2]).long() 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 = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]], requires_grad=True).log() y = torch.tensor([2, 2]).long() 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _test_distrib_compute_on_criterion(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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _test_distrib_compute_on_criterion(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_compute_on_criterion, (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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _test_distrib_compute_on_criterion(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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _test_distrib_compute_on_criterion(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_compute_on_criterion(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_compute_on_criterion(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-0.4.3/tests/ignite/metrics/test_mean_absolute_error.py000066400000000000000000000132241401006565000245230ustar00rootroot00000000000000import os import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import MeanAbsoluteError def test_zero_div(): mae = MeanAbsoluteError() with pytest.raises(NotComputableError): mae.compute() def test_compute(): mae = MeanAbsoluteError() y_pred = torch.Tensor([[2.0], [-2.0]]) y = torch.zeros(2) mae.update((y_pred, y)) assert isinstance(mae.compute(), float) assert mae.compute() == 2.0 mae.reset() y_pred = torch.Tensor([[3.0], [-3.0]]) y = torch.zeros(2) mae.update((y_pred, y)) assert isinstance(mae.compute(), float) assert mae.compute() == 3.0 def _test_distrib_integration(device): import numpy as np from ignite.engine import Engine rank = idist.get_rank() n_iters = 80 s = 50 offset = n_iters * s y_true = torch.arange(0, offset * idist.get_world_size(), dtype=torch.float).to(device) y_preds = torch.ones(offset * idist.get_world_size(), dtype=torch.float).to(device) def update(engine, i): return ( y_preds[i * s + offset * rank : (i + 1) * s + offset * rank], y_true[i * s + offset * rank : (i + 1) * s + offset * rank], ) def _test(metric_device): engine = Engine(update) m = MeanAbsoluteError(device=metric_device) m.attach(engine, "mae") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_mean_pairwise_distance.py000066400000000000000000000141601401006565000251710ustar00rootroot00000000000000import os import pytest import torch from pytest import approx import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import MeanPairwiseDistance def test_zero_div(): mpd = MeanPairwiseDistance() with pytest.raises(NotComputableError): mpd.compute() def test_compute(): mpd = MeanPairwiseDistance() y_pred = torch.Tensor([[3.0, 4.0], [-3.0, -4.0]]) y = torch.zeros(2, 2) mpd.update((y_pred, y)) assert isinstance(mpd.compute(), float) assert mpd.compute() == approx(5.0) mpd.reset() y_pred = torch.Tensor([[4.0, 4.0, 4.0, 4.0], [-4.0, -4.0, -4.0, -4.0]]) y = torch.zeros(2, 4) mpd.update((y_pred, y)) assert isinstance(mpd.compute(), float) assert mpd.compute() == approx(8.0) def _test_distrib_integration(device): import numpy as np from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) n_iters = 100 s = 50 offset = n_iters * s y_true = torch.rand(offset * idist.get_world_size(), 10).to(device) y_preds = torch.rand(offset * idist.get_world_size(), 10).to(device) def update(engine, i): return ( y_preds[i * s + offset * rank : (i + 1) * s + offset * rank, ...], y_true[i * s + offset * rank : (i + 1) * s + offset * rank, ...], ) def _test(metric_device): engine = Engine(update) m = MeanPairwiseDistance(device=metric_device) m.attach(engine, "mpwd") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) 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 * s : (i + 1) * s, ...], y_preds[i * s : (i + 1) * s, ...], 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_mean_squared_error.py000066400000000000000000000133421401006565000243520ustar00rootroot00000000000000import os import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import MeanSquaredError def test_zero_div(): mse = MeanSquaredError() with pytest.raises(NotComputableError): mse.compute() def test_compute(): mse = MeanSquaredError() y_pred = torch.Tensor([[2.0], [-2.0]]) y = torch.zeros(2) mse.update((y_pred, y)) assert isinstance(mse.compute(), float) assert mse.compute() == 4.0 mse.reset() y_pred = torch.Tensor([[3.0], [-3.0]]) y = torch.zeros(2) mse.update((y_pred, y)) assert isinstance(mse.compute(), float) assert mse.compute() == 9.0 def _test_distrib_integration(device, tol=1e-6): import numpy as np from ignite.engine import Engine rank = idist.get_rank() n_iters = 100 s = 10 offset = n_iters * s y_true = torch.arange(0, offset * idist.get_world_size(), dtype=torch.float).to(device) y_preds = torch.ones(offset * idist.get_world_size(), dtype=torch.float).to(device) def update(engine, i): return ( y_preds[i * s + offset * rank : (i + 1) * s + offset * rank], y_true[i * s + offset * rank : (i + 1) * s + offset * rank], ) def _test(metric_device): engine = Engine(update) m = MeanSquaredError(device=metric_device) m.attach(engine, "mse") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_metric.py000066400000000000000000000614121401006565000217610ustar00rootroot00000000000000import numbers import os import sys from unittest.mock import MagicMock import numpy as np import pytest import torch 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 ConfusionMatrix, Precision, Recall from ignite.metrics.metric import BatchFiltered, BatchWise, EpochWise, Metric, reinit__is_reduced 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 # __div__, only applicable to python2 if sys.version_info[0] < 3: m0_div_m1 = m0.__div__(m1) m0.update([1, 10, 100]) m1.update([1, 10, 100]) assert m0_div_m1.compute() == 0 m0.update([2, 20, 200]) m1.update([2, 20, 200]) assert m0_div_m1.compute() == 0 m2_div_2 = m2.__div__(2) m2.update([1, 10, 100]) assert m2_div_2.compute() == 50 m2_div_2 = 200 / m2 m2.update([1, 10, 100]) assert m2_div_2.compute() == 2 # __truediv__ 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_distrib_sync_all_reduce_decorator(device): from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce class DummyMetric(Metric): @reinit__is_reduced def reset(self): 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 @sync_all_reduce("a", "b", "c", "n") 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() @reinit__is_reduced def update(self, output): self.n += 1 self.c += 1.23456 self.a += 10.0 self.b -= 5.0 metric_device = device if torch.device(device).type != "xla" else "cpu" m = DummyMetric(device=metric_device) m.update(None) m.compute() # check if can call compute multiple times without all reduce invocation 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") def test_distrib_gpu(distributed_context_single_node_nccl): device = f"cuda:{distributed_context_single_node_nccl['local_rank']}" _test_distrib_sync_all_reduce_decorator(device) @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_cpu(distributed_context_single_node_gloo): device = "cpu" _test_distrib_sync_all_reduce_decorator(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 = "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) @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_cpu(distributed_context_multi_node_gloo): device = "cpu" _test_distrib_sync_all_reduce_decorator(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_gpu(distributed_context_multi_node_nccl): device = f"cuda:{distributed_context_multi_node_nccl['local_rank']}" _test_distrib_sync_all_reduce_decorator(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) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_sync_all_reduce_decorator(device) _test_creating_on_xla_fails(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"} 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 'EpochWise.usage_name' or 'BatchWise.usage_name'"): m.attach(engine, "dummy", usage="fake") def test_epochwise_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) def test(usage): engine = Engine(lambda e, b: b) m = MyMetric() 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) test("epoch_wise") test(EpochWise.usage_name) test(EpochWise()) def test_batchwise_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) def test(usage): engine = Engine(lambda e, b: b) m = MyMetric() 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) test("batch_wise") test(BatchWise.usage_name) test(BatchWise()) 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) ignite-0.4.3/tests/ignite/metrics/test_metrics_lambda.py000066400000000000000000000360501401006565000234440ustar00rootroot00000000000000import 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 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_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) def Fbeta(r, p, beta): return torch.mean((1 + beta ** 2) * p * r / (beta ** 2 * p + r)).item() F1 = MetricsLambda(Fbeta, recall, precision, 1) 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="macro") 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}" assert f1_true == approx(state.metrics["f1"]), f"{f1_true} vs {state.metrics['f1']}" def test_integration_ingredients_not_attached(): 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) 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") data = list(range(n_iters)) state = evaluator.run(data, max_epochs=1) f1_true = f1_score(y_true, np.argmax(y_pred, axis=-1), average="macro") assert f1_true == approx(state.metrics["f1"]), f"{f1_true} vs {state.metrics['f1']}" 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() np.random.seed(12) n_iters = 10 batch_size = 10 n_classes = 10 def _test(metric_device): y_true = np.arange(0, n_iters * batch_size * idist.get_world_size(), dtype="int64") % n_classes y_pred = 0.2 * np.random.rand(n_iters * batch_size * idist.get_world_size(), n_classes) for i in range(n_iters * batch_size * idist.get_world_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 = y_true.reshape(n_iters * idist.get_world_size(), batch_size) y_pred = y_pred.reshape(n_iters * idist.get_world_size(), batch_size, n_classes) def update_fn(engine, i): y_true_batch = y_true[i + rank * n_iters, ...] y_pred_batch = y_pred[i + rank * n_iters, ...] return torch.from_numpy(y_pred_batch), torch.from_numpy(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) assert "f1" in state.metrics assert "ff1" in state.metrics f1_true = f1_score(y_true.ravel(), np.argmax(y_pred.reshape(-1, n_classes), axis=-1), average="macro") assert f1_true == approx(state.metrics["f1"]) assert 1.0 + f1_true == approx(state.metrics["ff1"]) for _ in range(3): _test("cpu") if device.type != "xla": _test(idist.device()) def _test_distrib_metrics_on_diff_devices(device): n_classes = 10 n_iters = 12 s = 16 offset = n_iters * s rank = idist.get_rank() y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) precision = Precision(average=False, device="cpu") recall = Recall(average=False, device=device) custom_metric = precision * recall engine = Engine(update) custom_metric.attach(engine, "custom_metric") data = list(range(n_iters)) engine.run(data, max_epochs=2) @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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(local_rank, distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_precision.py000066400000000000000000001151031401006565000224660ustar00rootroot00000000000000import os import warnings import pytest import torch from sklearn.exceptions import UndefinedMetricWarning from sklearn.metrics import precision_score import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import Precision torch.manual_seed(12) def test_no_update(): precision = Precision() with pytest.raises(NotComputableError): precision.compute() precision = Precision(is_multilabel=True, average=True) with pytest.raises(NotComputableError): precision.compute() def test_binary_wrong_inputs(): pr = Precision() with pytest.raises(ValueError): # y has not only 0 or 1 values pr.update((torch.randint(0, 2, size=(10,)).long(), torch.arange(0, 10).long())) with pytest.raises(ValueError): # y_pred values are not thresholded to 0, 1 values pr.update((torch.rand(10,), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes pr.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible shapes pr.update((torch.randint(0, 2, size=(10, 5, 6)).long(), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes pr.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5, 6)).long())) def test_binary_input_N(): # Binary accuracy on input of shape (N, 1) or (N, ) def _test(average): pr = Precision(average=average) y_pred = torch.randint(0, 2, size=(10,)) y = torch.randint(0, 2, size=(10,)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) pr.reset() y_pred = torch.randint(0, 2, size=(10,)) y = torch.randint(0, 2, size=(10,)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) pr.reset() y_pred = torch.Tensor([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.51]) y_pred = torch.round(y_pred) y = torch.randint(0, 2, size=(10,)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) # Batched Updates pr.reset() y_pred = torch.randint(0, 2, size=(100,)) y = torch.randint(0, 2, size=(100,)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) for _ in range(5): _test(average=True) _test(average=False) def test_binary_input_NL(): # Binary accuracy on input of shape (N, L) def _test(average): pr = Precision(average=average) y_pred = torch.randint(0, 2, size=(10, 5)) y = torch.randint(0, 2, size=(10, 5)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) pr.reset() y_pred = torch.randint(0, 2, size=(10, 1, 5)) y = torch.randint(0, 2, size=(10, 1, 5)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) # Batched Updates pr.reset() y_pred = torch.randint(0, 2, size=(100, 1, 5)) y = torch.randint(0, 2, size=(100, 1, 5)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) for _ in range(5): _test(average=True) _test(average=False) def test_binary_input_NHW(): # Binary accuracy on input of shape (N, H, W) def _test(average): pr = Precision(average=average) y_pred = torch.randint(0, 2, size=(10, 12, 10)) y = torch.randint(0, 2, size=(10, 12, 10)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) pr.reset() y_pred = torch.randint(0, 2, size=(10, 1, 12, 10)) y = torch.randint(0, 2, size=(10, 1, 12, 10)).long() pr.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) # Batched Updates pr.reset() y_pred = torch.randint(0, 2, size=(100, 12, 10)) y = torch.randint(0, 2, size=(100, 12, 10)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert pr._type == "binary" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() assert precision_score(np_y, np_y_pred, average="binary") == pytest.approx(pr_compute) for _ in range(5): _test(average=True) _test(average=False) def test_multiclass_wrong_inputs(): pr = Precision() with pytest.raises(ValueError): # incompatible shapes pr.update((torch.rand(10, 5, 4), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes pr.update((torch.rand(10, 5, 6), torch.randint(0, 5, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible shapes pr.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long())) pr = Precision(average=True) 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())) 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())) pr = Precision(average=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())) 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())) def test_multiclass_input_N(): # Multiclass input data of shape (N, ) and (N, C) def _test(average): pr = Precision(average=average) y_pred = torch.rand(20, 6) y = torch.randint(0, 6, size=(20,)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) pr.reset() y_pred = torch.rand(10, 4) y = torch.randint(0, 4, size=(10,)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) # 2-classes pr.reset() y_pred = torch.rand(10, 2) y = torch.randint(0, 2, size=(10,)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) # Batched Updates pr.reset() y_pred = torch.rand(100, 3) y = torch.randint(0, 3, size=(100,)).long() batch_size = 16 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])) num_classes = y_pred.shape[1] np_y = y.numpy().ravel() np_y_pred = y_pred.argmax(dim=1).numpy().ravel() assert pr._type == "multiclass" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) for _ in range(5): _test(average=True) _test(average=False) def test_multiclass_input_NL(): # Multiclass input data of shape (N, L) and (N, C, L) def _test(average): pr = Precision(average=average) y_pred = torch.rand(10, 5, 8) y = torch.randint(0, 5, size=(10, 8)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) pr.reset() y_pred = torch.rand(15, 10, 8) y = torch.randint(0, 10, size=(15, 8)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) # Batched Updates pr.reset() y_pred = torch.rand(100, 8, 12) y = torch.randint(0, 8, size=(100, 12)).long() batch_size = 16 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])) num_classes = y_pred.shape[1] np_y = y.numpy().ravel() np_y_pred = y_pred.argmax(dim=1).numpy().ravel() assert pr._type == "multiclass" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) for _ in range(5): _test(average=True) _test(average=False) def test_multiclass_input_NHW(): # Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...) def _test(average): pr = Precision(average=average) y_pred = torch.rand(10, 5, 18, 16) y = torch.randint(0, 5, size=(10, 18, 16)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) pr.reset() y_pred = torch.rand(10, 7, 20, 12) y = torch.randint(0, 7, size=(10, 20, 12)).long() 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 isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) # Batched Updates pr.reset() y_pred = torch.rand(100, 8, 12, 14) y = torch.randint(0, 8, size=(100, 12, 14)).long() batch_size = 16 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])) num_classes = y_pred.shape[1] np_y = y.numpy().ravel() np_y_pred = y_pred.argmax(dim=1).numpy().ravel() assert pr._type == "multiclass" assert isinstance(pr.compute(), float if average else torch.Tensor) pr_compute = pr.compute() if average else pr.compute().numpy() sk_average_parameter = "macro" if average else None 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) for _ in range(5): _test(average=True) _test(average=False) def test_multilabel_wrong_inputs(): pr = Precision(average=True, is_multilabel=True) with pytest.raises(ValueError): # incompatible shapes pr.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible y_pred pr.update((torch.rand(10, 5), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible y pr.update((torch.randint(0, 5, size=(10, 5, 6)), torch.rand(10))) 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())) 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_input_NC(): def _test(average): pr = Precision(average=average, is_multilabel=True) y_pred = torch.randint(0, 2, size=(20, 5)) y = torch.randint(0, 2, size=(20, 5)).long() pr.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y.numpy() assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) pr.reset() y_pred = torch.randint(0, 2, size=(10, 4)) y = torch.randint(0, 2, size=(10, 4)).long() pr.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y.numpy() assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) # Batched Updates pr.reset() y_pred = torch.randint(0, 2, size=(100, 4)) y = torch.randint(0, 2, size=(100, 4)).long() batch_size = 16 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])) np_y = y.numpy() np_y_pred = y_pred.numpy() assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) for _ in range(5): _test(average=True) _test(average=False) pr1 = Precision(is_multilabel=True, average=True) pr2 = Precision(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() pr1.update((y_pred, y)) pr2.update((y_pred, y)) assert pr1.compute() == pytest.approx(pr2.compute().mean().item()) def test_multilabel_input_NCL(): def _test(average): pr = Precision(average=average, is_multilabel=True) y_pred = torch.randint(0, 2, size=(10, 5, 10)) y = torch.randint(0, 2, size=(10, 5, 10)).long() pr.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) pr.reset() y_pred = torch.randint(0, 2, size=(15, 4, 10)) y = torch.randint(0, 2, size=(15, 4, 10)).long() pr.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) # Batched Updates pr.reset() y_pred = torch.randint(0, 2, size=(100, 4, 12)) y = torch.randint(0, 2, size=(100, 4, 12)).long() batch_size = 16 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])) np_y = to_numpy_multilabel(y) np_y_pred = to_numpy_multilabel(y_pred) assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) for _ in range(5): _test(average=True) _test(average=False) pr1 = Precision(is_multilabel=True, average=True) pr2 = Precision(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() pr1.update((y_pred, y)) pr2.update((y_pred, y)) assert pr1.compute() == pytest.approx(pr2.compute().mean().item()) def test_multilabel_input_NCHW(): def _test(average): pr = Precision(average=average, is_multilabel=True) y_pred = torch.randint(0, 2, size=(10, 5, 18, 16)) y = torch.randint(0, 2, size=(10, 5, 18, 16)).long() pr.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) pr.reset() 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)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) # Batched Updates pr.reset() y_pred = torch.randint(0, 2, size=(100, 5, 12, 14)) y = torch.randint(0, 2, size=(100, 5, 12, 14)).long() batch_size = 16 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])) np_y = to_numpy_multilabel(y) np_y_pred = to_numpy_multilabel(y_pred) assert pr._type == "multilabel" pr_compute = pr.compute() if average else pr.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert precision_score(np_y, np_y_pred, average="samples") == pytest.approx(pr_compute) for _ in range(5): _test(average=True) _test(average=False) pr1 = Precision(is_multilabel=True, average=True) pr2 = Precision(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() pr1.update((y_pred, y)) pr2.update((y_pred, y)) assert pr1.compute() == pytest.approx(pr2.compute().mean().item()) def test_incorrect_type(): # Tests changing of type during training def _test(average): pr = Precision(average=average) y_pred = torch.softmax(torch.rand(4, 4), dim=1) y = torch.ones(4).long() pr.update((y_pred, y)) y_pred = torch.randint(0, 2, size=(4,)) y = torch.ones(4).long() with pytest.raises(RuntimeError): pr.update((y_pred, y)) _test(average=True) _test(average=False) pr1 = Precision(is_multilabel=True, average=True) pr2 = Precision(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() pr1.update((y_pred, y)) pr2.update((y_pred, y)) assert pr1.compute() == pytest.approx(pr2.compute().mean().item()) def test_incorrect_y_classes(): def _test(average): pr = Precision(average=average) 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)) _test(average=True) _test(average=False) def _test_distrib_integration_multiclass(device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) def _test(average, n_epochs, metric_device): n_iters = 60 s = 16 n_classes = 7 offset = n_iters * s y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) engine = Engine(update) pr = Precision(average=average, device=metric_device) pr.attach(engine, "pr") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "pr" in engine.state.metrics res = engine.state.metrics["pr"] if isinstance(res, torch.Tensor): assert res.device == metric_device res = res.cpu().numpy() true_res = precision_score( y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), average="macro" if average else None ) assert pytest.approx(res) == true_res metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for _ in range(2): for metric_device in metric_devices: _test(average=True, n_epochs=1, metric_device=metric_device) _test(average=True, n_epochs=2, metric_device=metric_device) _test(average=False, n_epochs=1, metric_device=metric_device) _test(average=False, n_epochs=2, metric_device=metric_device) def _test_distrib_integration_multilabel(device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) def _test(average, n_epochs, metric_device): n_iters = 60 s = 16 n_classes = 7 offset = n_iters * s y_true = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 6, 8)).to(device) y_preds = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 6, 8)).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, ...], y_true[i * s + rank * offset : (i + 1) * s + rank * offset, ...], ) engine = Engine(update) pr = Precision(average=average, is_multilabel=True) pr.attach(engine, "pr") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "pr" in engine.state.metrics 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 with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) true_res = precision_score( to_numpy_multilabel(y_true), to_numpy_multilabel(y_preds), average="samples" if average else None ) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for _ in range(2): for metric_device in metric_devices: _test(average=True, n_epochs=1, metric_device=metric_device) _test(average=True, n_epochs=2, metric_device=metric_device) if idist.get_world_size() > 1: with pytest.warns( RuntimeWarning, match="Precision/Recall metrics do not work in distributed setting when " "average=False and is_multilabel=True", ): pr = Precision(average=False, is_multilabel=True) y_pred = torch.randint(0, 2, size=(4, 3, 6, 8)) y = torch.randint(0, 2, size=(4, 3, 6, 8)).long() pr.update((y_pred, y)) pr_compute1 = pr.compute() pr_compute2 = pr.compute() assert len(pr_compute1) == 4 * 6 * 8 assert (pr_compute1 == pr_compute2).all() def _test_distrib_accumulator_device(device): # Binary accuracy on input of shape (N, 1) or (N, ) def _test(average, metric_device): pr = Precision(average=average, device=metric_device) assert pr._device == metric_device # 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._true_positives.device == metric_device ), f"{type(pr._true_positives.device)}:{pr._true_positives.device} vs {type(metric_device)}:{metric_device}" assert ( pr._positives.device == metric_device ), f"{type(pr._positives.device)}:{pr._positives.device} vs {type(metric_device)}:{metric_device}" metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(True, metric_device=metric_device) _test(False, metric_device=metric_device) def _test_distrib_multilabel_accumulator_device(device): # Multiclass input data of shape (N, ) and (N, C) def _test(average, metric_device): pr = Precision(is_multilabel=True, average=average, device=metric_device) assert pr._device == metric_device assert ( pr._true_positives.device == metric_device ), f"{type(pr._true_positives.device)}:{pr._true_positives.device} vs {type(metric_device)}:{metric_device}" assert ( pr._positives.device == metric_device ), f"{type(pr._positives.device)}:{pr._positives.device} vs {type(metric_device)}:{metric_device}" 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._true_positives.device == metric_device ), f"{type(pr._true_positives.device)}:{pr._true_positives.device} vs {type(metric_device)}:{metric_device}" assert ( pr._positives.device == metric_device ), f"{type(pr._positives.device)}:{pr._positives.device} vs {type(metric_device)}:{metric_device}" metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(True, metric_device=metric_device) _test(False, 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_cpu(local_rank, distributed_context_single_node_gloo): device = torch.device("cpu") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_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_multilabel, (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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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-0.4.3/tests/ignite/metrics/test_psnr.py000066400000000000000000000260561401006565000214650ustar00rootroot00000000000000import os import 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.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))) def _test_psnr(y_pred, y, data_range, device): psnr = PSNR(data_range=data_range, device=device) psnr.update((y_pred, y)) 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 torch.gt(psnr_compute, 0.0) assert isinstance(psnr_compute, torch.Tensor) assert psnr_compute.dtype == torch.float64 assert psnr_compute.device == torch.device(device) assert np.allclose(psnr_compute.numpy(), np_psnr / np_y.shape[0]) def test_psnr(): device = idist.device() # test for float manual_seed(42) y_pred = torch.rand(8, 3, 28, 28, device=device) y = y_pred * 0.8 data_range = (y.max() - y.min()).cpu().item() _test_psnr(y_pred, y, y.max() - y.min(), device) # test for YCbCr manual_seed(42) y_pred = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=device) y = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=device) data_range = (y.max() - y.min()).cpu().item() _test_psnr(y_pred, y, data_range, device) # test for uint8 manual_seed(42) y_pred = torch.randint(0, 256, (4, 3, 16, 16), dtype=torch.uint8, device=device) y = (y_pred * 0.8).to(torch.uint8) data_range = (y.max() - y.min()).cpu().item() _test_psnr(y_pred, y, data_range, device) # test with NHW shape manual_seed(42) y_pred = torch.rand(8, 28, 28, device=device) y = y_pred * 0.8 data_range = (y.max() - y.min()).cpu().item() _test_psnr(y_pred, y, data_range, device) def _test( y_pred, y, data_range, metric_device, n_iters, s, offset, rank, atol, output_transform=lambda x: x, compute_y_channel=False, ): from ignite.engine import Engine def update(engine, i): return ( y_pred[i * s + offset * rank : (i + 1) * s + offset * rank], y[i * s + offset * rank : (i + 1) * s + offset * rank], ) engine = Engine(update) PSNR(data_range=data_range, output_transform=output_transform, device=metric_device).attach(engine, "psnr") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) result = engine.state.metrics["psnr"] assert result > 0.0 assert "psnr" in engine.state.metrics 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) def _test_distrib_integration(device, atol=1e-8): rank = idist.get_rank() n_iters = 100 s = 10 offset = n_iters * s # test for float manual_seed(42) y_pred = torch.rand(offset * idist.get_world_size(), 3, 28, 28, device=device) y = y_pred * 0.65 data_range = (y.max() - y.min()).cpu().item() _test(y_pred, y, data_range, "cpu", n_iters, s, offset, rank, atol=atol) # test for YCbCr manual_seed(42) y_pred = torch.randint(16, 236, (offset * idist.get_world_size(), 1, 12, 12), dtype=torch.uint8, device=device) cbcr_pred = torch.randint(16, 241, (offset * idist.get_world_size(), 2, 12, 12), dtype=torch.uint8, device=device) y = torch.randint(16, 236, (offset * idist.get_world_size(), 1, 12, 12), dtype=torch.uint8, device=device) cbcr = torch.randint(16, 241, (offset * idist.get_world_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) data_range = (y[:, 0, ...].max() - y[:, 0, ...].min()).cpu().item() _test( y_pred=y_pred, y=y, data_range=data_range, metric_device="cpu", n_iters=n_iters, s=s, offset=offset, rank=rank, atol=atol, output_transform=lambda x: (x[0][:, 0, ...], x[1][:, 0, ...]), compute_y_channel=True, ) # test for uint8 manual_seed(42) y_pred = torch.randint(0, 256, (offset * idist.get_world_size(), 3, 16, 16), device=device, dtype=torch.uint8) y = (y_pred * 0.65).to(torch.uint8) data_range = (y.max() - y.min()).cpu().item() _test(y_pred, y, data_range, "cpu", n_iters, s, offset, rank, atol=atol) # test with NHW shape manual_seed(42) y_pred = torch.rand(offset * idist.get_world_size(), 28, 28, device=device) y = y_pred * 0.8 data_range = (y.max() - y.min()).cpu().item() _test(y_pred, y, data_range, "cpu", n_iters, s, offset, rank, atol=atol) if torch.device(device).type != "xla": manual_seed(42) y_pred = torch.rand(offset * idist.get_world_size(), 3, 28, 28, device=device) y = y_pred * 0.65 data_range = (y.max() - y.min()).cpu().item() _test(y_pred, y, data_range, idist.device(), n_iters, s, offset, rank, atol=atol) # test for YCbCr manual_seed(42) y_pred = torch.randint(16, 236, (offset * idist.get_world_size(), 1, 12, 12), dtype=torch.uint8, device=device) cbcr_pred = torch.randint( 16, 241, (offset * idist.get_world_size(), 2, 12, 12), dtype=torch.uint8, device=device ) y = torch.randint(16, 236, (offset * idist.get_world_size(), 1, 12, 12), dtype=torch.uint8, device=device) cbcr = torch.randint(16, 241, (offset * idist.get_world_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) data_range = (y[:, 0, ...].max() - y[:, 0, ...].min()).cpu().item() _test( y_pred=y_pred, y=y, data_range=data_range, metric_device=idist.device(), n_iters=n_iters, s=s, offset=offset, rank=rank, atol=atol, output_transform=lambda x: (x[0][:, 0, ...], x[1][:, 0, ...]), compute_y_channel=True, ) manual_seed(42) y_pred = torch.randint(0, 256, (offset * idist.get_world_size(), 3, 16, 16), device=device, dtype=torch.uint8) y = (y_pred * 0.65).to(torch.uint8) data_range = (y.max() - y.min()).cpu().item() _test(y_pred, y, data_range, idist.device(), n_iters, s, offset, rank, atol=atol) # test with NHW shape manual_seed(42) y_pred = torch.rand(offset * idist.get_world_size(), 28, 28, device=device) y = y_pred * 0.8 data_range = (y.max() - y.min()).cpu().item() _test(y_pred, y, data_range, idist.device(), n_iters, s, offset, rank, atol=atol) def _test_distrib_accumulator_device(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}" @pytest.mark.distributed @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support") def test_distrib_cpu(distributed_context_single_node_gloo): device = "cpu" _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") @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU") def test_distrib_gpu(local_rank, distributed_context_single_node_nccl): device = f"cuda:{local_rank}" _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("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_cpu(distributed_context_multi_node_gloo): device = "cpu" _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_gpu(distributed_context_multi_node_nccl): device = f"cuda:{distributed_context_multi_node_nccl['local_rank']}" _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) @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_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) ignite-0.4.3/tests/ignite/metrics/test_recall.py000066400000000000000000001144341401006565000217430ustar00rootroot00000000000000import os import warnings import pytest import torch from sklearn.exceptions import UndefinedMetricWarning from sklearn.metrics import recall_score import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import Recall torch.manual_seed(12) def test_no_update(): recall = Recall() with pytest.raises(NotComputableError): recall.compute() recall = Recall(is_multilabel=True, average=True) with pytest.raises(NotComputableError): recall.compute() def test_binary_wrong_inputs(): re = Recall() with pytest.raises(ValueError): # y has not only 0 or 1 values re.update((torch.randint(0, 2, size=(10,)), torch.arange(0, 10).long())) with pytest.raises(ValueError): # y_pred values are not thresholded to 0, 1 values re.update((torch.rand(10, 1), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible shapes re.update((torch.randint(0, 2, size=(10, 5, 6)), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10, 5, 6)).long())) def test_binary_input_N(): # Binary accuracy on input of shape (N, 1) or (N, ) def _test(average): re = Recall(average=average) y_pred = torch.randint(0, 2, size=(10,)) y = torch.randint(0, 2, size=(10,)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) re.reset() y_pred = torch.randint(0, 2, size=(10,)) y = torch.randint(0, 2, size=(10,)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) re.reset() y_pred = torch.Tensor([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.51]) y_pred = torch.round(y_pred) y = torch.randint(0, 2, size=(10,)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) # Batched Updates re.reset() y_pred = torch.randint(0, 2, size=(100,)) y = torch.randint(0, 2, size=(100,)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) for _ in range(5): _test(average=True) _test(average=False) def test_binary_input_NL(): # Binary accuracy on input of shape (N, L) def _test(average): re = Recall(average=average) y_pred = torch.randint(0, 2, size=(10, 5)) y = torch.randint(0, 2, size=(10, 5)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) re.reset() y_pred = torch.randint(0, 2, size=(10, 1, 5)) y = torch.randint(0, 2, size=(10, 1, 5)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) # Batched Updates re.reset() y_pred = torch.randint(0, 2, size=(100, 5)) y = torch.randint(0, 2, size=(100, 5)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) for _ in range(5): _test(average=True) _test(average=False) def test_binary_input_NHW(): # Binary accuracy on input of shape (N, H, W) def _test(average): re = Recall(average=average) y_pred = torch.randint(0, 2, size=(10, 12, 10)) y = torch.randint(0, 2, size=(10, 12, 10)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) re.reset() y_pred = torch.randint(0, 2, size=(10, 1, 12, 10)) y = torch.randint(0, 2, size=(10, 1, 12, 10)).long() re.update((y_pred, y)) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) # Batched Updates re.reset() y_pred = torch.randint(0, 2, size=(100, 12, 10)) y = torch.randint(0, 2, size=(100, 12, 10)).long() batch_size = 16 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])) np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() assert re._type == "binary" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() assert recall_score(np_y, np_y_pred, average="binary") == pytest.approx(re_compute) for _ in range(5): _test(average=True) _test(average=False) def test_multiclass_wrong_inputs(): re = Recall() with pytest.raises(ValueError): # incompatible shapes re.update((torch.rand(10, 5, 4), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible shapes re.update((torch.rand(10, 5, 6), torch.randint(0, 5, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible shapes re.update((torch.rand(10), torch.randint(0, 5, size=(10, 5, 6)).long())) re = Recall(average=True) 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())) 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())) re = Recall(average=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())) 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())) def test_multiclass_input_N(): # Multiclass input data of shape (N, ) and (N, C) def _test(average): re = Recall(average=average) y_pred = torch.rand(20, 6) y = torch.randint(0, 6, size=(20,)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) re.reset() y_pred = torch.rand(10, 4) y = torch.randint(0, 4, size=(10,)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) # 2-classes re.reset() y_pred = torch.rand(10, 2) y = torch.randint(0, 2, size=(10,)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) # Batched Updates re.reset() y_pred = torch.rand(100, 3) y = torch.randint(0, 3, size=(100,)).long() batch_size = 16 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])) num_classes = y_pred.shape[1] np_y = y.numpy().ravel() np_y_pred = y_pred.argmax(dim=1).numpy().ravel() assert re._type == "multiclass" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) for _ in range(5): _test(average=True) _test(average=False) def test_multiclass_input_NL(): # Multiclass input data of shape (N, L) and (N, C, L) def _test(average): re = Recall(average=average) y_pred = torch.rand(10, 5, 8) y = torch.randint(0, 5, size=(10, 8)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) re.reset() y_pred = torch.rand(15, 10, 8) y = torch.randint(0, 10, size=(15, 8)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) # Batched Updates re.reset() y_pred = torch.rand(100, 8, 12) y = torch.randint(0, 8, size=(100, 12)).long() batch_size = 16 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])) num_classes = y_pred.shape[1] np_y = y.numpy().ravel() np_y_pred = y_pred.argmax(dim=1).numpy().ravel() assert re._type == "multiclass" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) for _ in range(5): _test(average=True) _test(average=False) def test_multiclass_input_NHW(): # Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...) def _test(average): re = Recall(average=average) y_pred = torch.rand(10, 5, 18, 16) y = torch.randint(0, 5, size=(10, 18, 16)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) re.reset() y_pred = torch.rand(10, 7, 20, 12) y = torch.randint(0, 7, size=(10, 20, 12)).long() 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 isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) # Batched Updates re.reset() y_pred = torch.rand(100, 10, 12, 14) y = torch.randint(0, 10, size=(100, 12, 14)).long() batch_size = 16 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])) num_classes = y_pred.shape[1] np_y = y.numpy().ravel() np_y_pred = y_pred.argmax(dim=1).numpy().ravel() assert re._type == "multiclass" assert isinstance(re.compute(), float if average else torch.Tensor) re_compute = re.compute() if average else re.compute().numpy() sk_average_parameter = "macro" if average else None 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) for _ in range(5): _test(average=True) _test(average=False) def test_multilabel_wrong_inputs(): re = Recall(average=True, is_multilabel=True) with pytest.raises(ValueError): # incompatible shapes re.update((torch.randint(0, 2, size=(10,)), torch.randint(0, 2, size=(10,)).long())) with pytest.raises(ValueError): # incompatible y_pred re.update((torch.rand(10, 5), torch.randint(0, 2, size=(10, 5)).long())) with pytest.raises(ValueError): # incompatible y re.update((torch.randint(0, 5, size=(10, 5, 6)), torch.rand(10))) 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())) 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_input_NC(): def _test(average): re = Recall(average=average, is_multilabel=True) y_pred = torch.randint(0, 2, size=(20, 5)) y = torch.randint(0, 2, size=(20, 5)).long() re.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) re.reset() y_pred = torch.randint(0, 2, size=(10, 4)) y = torch.randint(0, 2, size=(10, 4)).long() re.update((y_pred, y)) np_y_pred = y_pred.numpy() np_y = y.numpy() assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) # Batched Updates re.reset() y_pred = torch.randint(0, 2, size=(100, 4)) y = torch.randint(0, 2, size=(100, 4)).long() batch_size = 16 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])) np_y = y.numpy() np_y_pred = y_pred.numpy() assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) for _ in range(5): _test(average=True) _test(average=False) re1 = Recall(is_multilabel=True, average=True) re2 = Recall(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4)) y = torch.randint(0, 2, size=(10, 4)).long() re1.update((y_pred, y)) re2.update((y_pred, y)) assert re1.compute() == pytest.approx(re2.compute().mean().item()) def test_multilabel_input_NCL(): def _test(average): re = Recall(average=average, is_multilabel=True) y_pred = torch.randint(0, 2, size=(10, 5, 10)) y = torch.randint(0, 2, size=(10, 5, 10)).long() re.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) re.reset() y_pred = torch.randint(0, 2, size=(15, 4, 10)) y = torch.randint(0, 2, size=(15, 4, 10)).long() re.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) # Batched Updates re.reset() y_pred = torch.randint(0, 2, size=(100, 4, 12)) y = torch.randint(0, 2, size=(100, 4, 12)).long() batch_size = 16 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])) np_y = to_numpy_multilabel(y) np_y_pred = to_numpy_multilabel(y_pred) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) for _ in range(5): _test(average=True) _test(average=False) re1 = Recall(is_multilabel=True, average=True) re2 = Recall(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20)) y = torch.randint(0, 2, size=(10, 4, 20)).long() re1.update((y_pred, y)) re2.update((y_pred, y)) assert re1.compute() == pytest.approx(re2.compute().mean().item()) def test_multilabel_input_NCHW(): def _test(average): re = Recall(average=average, is_multilabel=True) y_pred = torch.randint(0, 2, size=(10, 5, 18, 16)) y = torch.randint(0, 2, size=(10, 5, 18, 16)).long() re.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) re.reset() y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() re.update((y_pred, y)) np_y_pred = to_numpy_multilabel(y_pred) np_y = to_numpy_multilabel(y) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) # Batched Updates re.reset() y_pred = torch.randint(0, 2, size=(100, 5, 12, 14)) y = torch.randint(0, 2, size=(100, 5, 12, 14)).long() batch_size = 16 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])) np_y = to_numpy_multilabel(y) np_y_pred = to_numpy_multilabel(y_pred) assert re._type == "multilabel" re_compute = re.compute() if average else re.compute().mean().item() with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) assert recall_score(np_y, np_y_pred, average="samples") == pytest.approx(re_compute) for _ in range(5): _test(average=True) _test(average=False) re1 = Recall(is_multilabel=True, average=True) re2 = Recall(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() re1.update((y_pred, y)) re2.update((y_pred, y)) assert re1.compute() == pytest.approx(re2.compute().mean().item()) def test_incorrect_type(): # Tests changing of type during training def _test(average): re = Recall(average=average) y_pred = torch.softmax(torch.rand(4, 4), dim=1) y = torch.ones(4).long() re.update((y_pred, y)) y_pred = torch.zeros(4,) y = torch.ones(4).long() with pytest.raises(RuntimeError): re.update((y_pred, y)) _test(average=True) _test(average=False) re1 = Recall(is_multilabel=True, average=True) re2 = Recall(is_multilabel=True, average=False) y_pred = torch.randint(0, 2, size=(10, 4, 20, 23)) y = torch.randint(0, 2, size=(10, 4, 20, 23)).long() re1.update((y_pred, y)) re2.update((y_pred, y)) assert re1.compute() == pytest.approx(re2.compute().mean().item()) def test_incorrect_y_classes(): def _test(average): re = Recall(average=average) 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)) _test(average=True) _test(average=False) def _test_distrib_integration_multiclass(device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) def _test(average, n_epochs, metric_device): n_iters = 60 s = 16 n_classes = 7 offset = n_iters * s y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) engine = Engine(update) re = Recall(average=average, device=metric_device) re.attach(engine, "re") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "re" in engine.state.metrics res = engine.state.metrics["re"] if isinstance(res, torch.Tensor): assert res.device == metric_device res = res.cpu().numpy() true_res = recall_score( y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), average="macro" if average else None ) assert pytest.approx(res) == true_res metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for _ in range(2): for metric_device in metric_devices: _test(average=True, n_epochs=1, metric_device=metric_device) _test(average=True, n_epochs=2, metric_device=metric_device) _test(average=False, n_epochs=1, metric_device=metric_device) _test(average=False, n_epochs=2, metric_device=metric_device) def _test_distrib_integration_multilabel(device): from ignite.engine import Engine rank = idist.get_rank() torch.manual_seed(12) def _test(average, n_epochs, metric_device): n_iters = 60 s = 16 n_classes = 7 offset = n_iters * s y_true = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 6, 8)).to(device) y_preds = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 6, 8)).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, ...], y_true[i * s + rank * offset : (i + 1) * s + rank * offset, ...], ) engine = Engine(update) re = Recall(average=average, is_multilabel=True, device=metric_device) re.attach(engine, "re") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "re" in engine.state.metrics 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 with warnings.catch_warnings(): warnings.simplefilter("ignore", category=UndefinedMetricWarning) true_res = recall_score( to_numpy_multilabel(y_true), to_numpy_multilabel(y_preds), average="samples" if average else None ) assert pytest.approx(res) == true_res metric_devices = ["cpu"] if device.type != "xla": metric_devices.append(idist.device()) for _ in range(2): for metric_device in metric_devices: _test(average=True, n_epochs=1, metric_device=metric_device) _test(average=True, n_epochs=2, metric_device=metric_device) if idist.get_world_size() > 1: with pytest.warns( RuntimeWarning, match="Precision/Recall metrics do not work in distributed setting when " "average=False and is_multilabel=True", ): re = Recall(average=False, is_multilabel=True) y_pred = torch.randint(0, 2, size=(4, 3, 6, 8)) y = torch.randint(0, 2, size=(4, 3, 6, 8)).long() re.update((y_pred, y)) re_compute1 = re.compute() re_compute2 = re.compute() assert len(re_compute1) == 4 * 6 * 8 assert (re_compute1 == re_compute2).all() def _test_distrib_accumulator_device(device): # Binary accuracy on input of shape (N, 1) or (N, ) def _test(average, metric_device): re = Recall(average=average, device=metric_device) assert re._device == metric_device # 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._true_positives.device == metric_device ), f"{type(re._true_positives.device)}:{re._true_positives.device} vs {type(metric_device)}:{metric_device}" assert ( re._positives.device == metric_device ), f"{type(re._positives.device)}:{re._positives.device} vs {type(metric_device)}:{metric_device}" metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(True, metric_device=metric_device) _test(False, metric_device=metric_device) def _test_distrib_multilabel_accumulator_device(device): # Multiclass input data of shape (N, ) and (N, C) def _test(average, metric_device): re = Recall(is_multilabel=True, average=average, device=metric_device) assert re._device == metric_device assert ( re._true_positives.device == metric_device ), f"{type(re._true_positives.device)}:{re._true_positives.device} vs {type(metric_device)}:{metric_device}" assert ( re._positives.device == metric_device ), f"{type(re._positives.device)}:{re._positives.device} vs {type(metric_device)}:{metric_device}" 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._true_positives.device == metric_device ), f"{type(re._true_positives.device)}:{re._true_positives.device} vs {type(metric_device)}:{metric_device}" assert ( re._positives.device == metric_device ), f"{type(re._positives.device)}:{re._positives.device} vs {type(metric_device)}:{metric_device}" metric_devices = [torch.device("cpu")] if device.type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: _test(True, metric_device=metric_device) _test(False, 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_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_multilabel_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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration_multiclass(device) _test_distrib_integration_multilabel(device) _test_distrib_accumulator_device(device) _test_distrib_multilabel_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-0.4.3/tests/ignite/metrics/test_root_mean_squared_error.py000066400000000000000000000111161401006565000254120ustar00rootroot00000000000000import os import pytest import torch import ignite.distributed as idist from ignite.exceptions import NotComputableError from ignite.metrics import RootMeanSquaredError def test_zero_div(): rmse = RootMeanSquaredError() with pytest.raises(NotComputableError): rmse.compute() def test_compute(): rmse = RootMeanSquaredError() y_pred = torch.Tensor([[2.0], [-2.0]]) y = torch.zeros(2) rmse.update((y_pred, y)) assert isinstance(rmse.compute(), float) assert rmse.compute() == 2.0 rmse.reset() y_pred = torch.Tensor([[3.0], [-3.0]]) y = torch.zeros(2) rmse.update((y_pred, y)) assert isinstance(rmse.compute(), float) assert rmse.compute() == 3.0 def _test_distrib_integration(device, tol=1e-6): import numpy as np from ignite.engine import Engine rank = idist.get_rank() n_iters = 100 s = 10 offset = n_iters * s y_true = torch.arange(0, offset * idist.get_world_size(), dtype=torch.float).to(device) y_preds = (rank + 1) * torch.ones(offset, dtype=torch.float).to(device) def update(engine, i): return y_preds[i * s : (i + 1) * s], y_true[i * s + offset * rank : (i + 1) * s + offset * rank] def _test(metric_device): engine = Engine(update) m = RootMeanSquaredError(device=metric_device) m.attach(engine, "rmse") data = list(range(n_iters)) engine.run(data=data, max_epochs=1) assert "rmse" in engine.state.metrics res = engine.state.metrics["rmse"] y_preds_full = [] for i in range(idist.get_world_size()): y_preds_full.append((i + 1) * torch.ones(offset)) y_preds_full = torch.stack(y_preds_full).to(device).flatten() true_res = np.sqrt(np.mean(np.square((y_true - y_preds_full).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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(local_rank, distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/metrics/test_running_average.py000066400000000000000000000417731401006565000236600ustar00rootroot00000000000000import os from functools import partial 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 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") def test_integration(): n_iters = 100 batch_size = 10 n_classes = 10 y_true_batch_values = iter(np.random.randint(0, n_classes, size=(n_iters, batch_size))) y_pred_batch_values = iter(np.random.rand(n_iters, batch_size, n_classes)) loss_values = iter(range(n_iters)) def update_fn(engine, batch): loss_value = next(loss_values) y_true_batch = next(y_true_batch_values) y_pred_batch = next(y_pred_batch_values) return loss_value, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) trainer = Engine(update_fn) alpha = 0.98 acc_metric = RunningAverage(Accuracy(output_transform=lambda x: [x[1], x[2]]), alpha=alpha) acc_metric.attach(trainer, "running_avg_accuracy") avg_output = RunningAverage(output_transform=lambda x: x[0], alpha=alpha) avg_output.attach(trainer, "running_avg_output") running_avg_acc = [ None, ] @trainer.on(Events.ITERATION_COMPLETED) def manual_running_avg_acc(engine): _, y_pred, y = engine.state.output indices = torch.max(y_pred, 1)[1] correct = torch.eq(indices, y).view(-1) num_correct = torch.sum(correct).item() num_examples = correct.shape[0] batch_acc = num_correct * 1.0 / 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.EPOCH_STARTED) def running_avg_output_init(engine): engine.state.running_avg_output = None @trainer.on(Events.ITERATION_COMPLETED) def running_avg_output_update(engine): if engine.state.running_avg_output is None: engine.state.running_avg_output = engine.state.output[0] else: engine.state.running_avg_output = ( engine.state.running_avg_output * alpha + (1.0 - alpha) * engine.state.output[0] ) @trainer.on(Events.ITERATION_COMPLETED) def assert_equal_running_avg_acc_values(engine): 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.on(Events.ITERATION_COMPLETED) def assert_equal_running_avg_output_values(engine): assert ( engine.state.running_avg_output == engine.state.metrics["running_avg_output"] ), f"{engine.state.running_avg_output} vs {engine.state.metrics['running_avg_output']}" np.random.seed(10) running_avg_acc = [ None, ] n_iters = 10 batch_size = 10 n_classes = 10 data = list(range(n_iters)) loss_values = iter(range(n_iters)) y_true_batch_values = iter(np.random.randint(0, n_classes, size=(n_iters, batch_size))) y_pred_batch_values = iter(np.random.rand(n_iters, batch_size, n_classes)) trainer.run(data, max_epochs=1) running_avg_acc = [ None, ] n_iters = 10 batch_size = 10 n_classes = 10 data = list(range(n_iters)) loss_values = iter(range(n_iters)) y_true_batch_values = iter(np.random.randint(0, n_classes, size=(n_iters, batch_size))) y_pred_batch_values = iter(np.random.rand(n_iters, batch_size, n_classes)) trainer.run(data, max_epochs=1) def test_epoch_unbound(): n_iters = 10 n_epochs = 3 batch_size = 10 n_classes = 10 data = list(range(n_iters)) loss_values = iter(range(n_epochs * n_iters)) y_true_batch_values = iter(np.random.randint(0, n_classes, size=(n_epochs * n_iters, batch_size))) y_pred_batch_values = iter(np.random.rand(n_epochs * n_iters, batch_size, n_classes)) def update_fn(engine, batch): loss_value = next(loss_values) y_true_batch = next(y_true_batch_values) y_pred_batch = next(y_pred_batch_values) return loss_value, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) trainer = Engine(update_fn) alpha = 0.98 acc_metric = RunningAverage(Accuracy(output_transform=lambda x: [x[1], x[2]]), alpha=alpha, epoch_bound=False) acc_metric.attach(trainer, "running_avg_accuracy") avg_output = RunningAverage(output_transform=lambda x: x[0], alpha=alpha, epoch_bound=False) avg_output.attach(trainer, "running_avg_output") running_avg_acc = [None] @trainer.on(Events.STARTED) def running_avg_output_init(engine): engine.state.running_avg_output = None @trainer.on(Events.ITERATION_COMPLETED, running_avg_acc) def manual_running_avg_acc(engine, running_avg_acc): _, y_pred, y = engine.state.output indices = torch.max(y_pred, 1)[1] correct = torch.eq(indices, y).view(-1) num_correct = torch.sum(correct).item() num_examples = correct.shape[0] batch_acc = num_correct * 1.0 / 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 running_avg_output_update(engine): if engine.state.running_avg_output is None: engine.state.running_avg_output = engine.state.output[0] else: engine.state.running_avg_output = ( engine.state.running_avg_output * alpha + (1.0 - alpha) * engine.state.output[0] ) @trainer.on(Events.ITERATION_COMPLETED) def assert_equal_running_avg_acc_values(engine): 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.on(Events.ITERATION_COMPLETED) def assert_equal_running_avg_output_values(engine): assert ( engine.state.running_avg_output == engine.state.metrics["running_avg_output"] ), f"{engine.state.running_avg_output} vs {engine.state.metrics['running_avg_output']}" trainer.run(data, max_epochs=3) def test_multiple_attach(): 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) @trainer.on(Events.ITERATION_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) 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 def _test_distrib_on_output(device): rank = idist.get_rank() n_iters = 10 n_epochs = 3 batch_size = 10 # Data per rank data = list(range(n_iters)) k = n_epochs * batch_size * n_iters all_loss_values = torch.arange(0, k * idist.get_world_size(), dtype=torch.float64).to(device) loss_values = iter(all_loss_values[k * rank : k * (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 = idist.device() if torch.device(device).type != "xla" else "cpu" avg_output = RunningAverage(output_transform=lambda x: x, alpha=alpha, epoch_bound=False, device=metric_device) avg_output.attach(trainer, "running_avg_output") @trainer.on(Events.STARTED) def running_avg_output_init(engine): engine.state.running_avg_output = None @trainer.on(Events.ITERATION_COMPLETED) def running_avg_output_update(engine): i = engine.state.iteration - 1 o = sum([all_loss_values[i + j * k] for j 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(Events.ITERATION_COMPLETED) def assert_equal_running_avg_output_values(engine): it = engine.state.iteration assert engine.state.running_avg_output == pytest.approx( 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) def _test_distrib_on_metric(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(output_transform=lambda x: [x[0], x[1]], device=metric_device), alpha=alpha, epoch_bound=False ) acc_metric.attach(trainer, "running_avg_accuracy") running_avg_acc = [ None, ] true_acc_metric = Accuracy(device=metric_device) @trainer.on(Events.ITERATION_COMPLETED) def manual_running_avg_acc(engine): i = engine.state.iteration - 1 true_acc_metric.reset() for j in range(idist.get_world_size()): output = ( torch.from_numpy(all_y_pred_batch_values[j, i, :, :]), torch.from_numpy(all_y_true_batch_values[j, i, :]), ) true_acc_metric.update(output) 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): 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_distrib_accumulator_device(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}" @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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _test_distrib_on_output(device) _test_distrib_on_metric(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_cpu(distributed_context_single_node_gloo): device = torch.device("cpu") _test_distrib_on_output(device) _test_distrib_on_metric(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_on_output, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_on_metric, (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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _test_distrib_on_output(device) _test_distrib_on_metric(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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _test_distrib_on_output(device) _test_distrib_on_metric(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_on_output(device) _test_distrib_on_metric(device) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_on_output(device) _test_distrib_on_metric(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-0.4.3/tests/ignite/metrics/test_ssim.py000066400000000000000000000221121401006565000214430ustar00rootroot00000000000000import os 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() def _test_ssim(y_pred, y, data_range, kernel_size, sigma, gaussian, use_sample_covariance, device): atol = 7e-5 ssim = SSIM(data_range=data_range, sigma=sigma, device=device) ssim.update((y_pred, y)) ignite_ssim = ssim.compute() skimg_pred = y_pred.permute(0, 2, 3, 1).cpu().numpy() skimg_y = skimg_pred * 0.8 skimg_ssim = ski_ssim( skimg_pred, skimg_y, win_size=kernel_size, sigma=sigma, multichannel=True, gaussian_weights=gaussian, data_range=data_range, use_sample_covariance=use_sample_covariance, ) assert isinstance(ignite_ssim, torch.Tensor) assert ignite_ssim.dtype == torch.float64 assert ignite_ssim.device == torch.device(device) assert np.allclose(ignite_ssim.numpy(), skimg_ssim, atol=atol) def test_ssim(): device = "cuda" if torch.cuda.is_available() else "cpu" y_pred = torch.rand(8, 3, 224, 224, device=device) y = y_pred * 0.8 _test_ssim( y_pred, y, data_range=1.0, kernel_size=7, sigma=1.5, gaussian=False, use_sample_covariance=True, device=device ) y_pred = torch.rand(12, 3, 28, 28, device=device) y = y_pred * 0.8 _test_ssim( y_pred, y, data_range=1.0, kernel_size=11, sigma=1.5, gaussian=True, use_sample_covariance=False, device=device ) def _test_distrib_integration(device, tol=1e-4): from ignite.engine import Engine rank = idist.get_rank() n_iters = 100 s = 10 offset = n_iters * s def _test(metric_device): y_pred = torch.rand(offset * idist.get_world_size(), 3, 28, 28, dtype=torch.float, device=device) y = y_pred * 0.65 def update(engine, i): return ( y_pred[i * s + offset * rank : (i + 1) * s + offset * rank], y[i * s + offset * rank : (i + 1) * s + offset * rank], ) 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) assert "ssim" in engine.state.metrics res = engine.state.metrics["ssim"] np_pred = y_pred.permute(0, 2, 3, 1).cpu().numpy() np_true = np_pred * 0.65 true_res = ski_ssim( np_pred, np_true, win_size=11, sigma=1.5, multichannel=True, gaussian_weights=True, data_range=1.0, use_sample_covariance=False, ) 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.permute(0, 2, 3, 1).cpu().numpy() np_true = np_pred * 0.65 true_res = ski_ssim(np_pred, np_true, win_size=7, multichannel=True, gaussian_weights=False, data_range=1.0) assert pytest.approx(res, abs=tol) == true_res _test("cpu") if torch.device(device).type != "xla": _test(idist.device()) def _test_distrib_accumulator_device(device): metric_devices = [torch.device("cpu")] if torch.device(device).type != "xla": metric_devices.append(idist.device()) for metric_device in metric_devices: ssim = SSIM(data_range=1.0, device=metric_device) for dev in [ssim._device, ssim._kernel.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_batchwise_ssim.device assert dev == metric_device, f"{type(dev)}:{dev} 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_gpu(local_rank, distributed_context_single_node_nccl): device = f"cuda:{local_rank}" _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_cpu(distributed_context_single_node_gloo): device = "cpu" _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("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed") def test_multinode_distrib_cpu(distributed_context_multi_node_gloo): device = "cpu" _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_gpu(distributed_context_multi_node_nccl): device = f"cuda:{distributed_context_multi_node_nccl['local_rank']}" _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-3) _test_distrib_accumulator_device(device) def _test_distrib_xla_nprocs(index): device = idist.device() _test_distrib_integration(device, tol=1e-3) _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.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_integration, (device,), np=nproc, do_init=True) gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True) ignite-0.4.3/tests/ignite/metrics/test_top_k_categorical_accuracy.py000066400000000000000000000152441401006565000260230ustar00rootroot00000000000000import 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): 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 rank = idist.get_rank() torch.manual_seed(12) def _test(n_epochs, metric_device): n_iters = 100 s = 16 n_classes = 10 offset = n_iters * s y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device) y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :], y_true[i * s + rank * offset : (i + 1) * s + rank * offset], ) 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) 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()) for _ in range(3): 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_gpu(local_rank, distributed_context_single_node_nccl): device = torch.device(f"cuda:{local_rank}") _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_cpu(local_rank, distributed_context_single_node_gloo): device = torch.device("cpu") _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_cpu(distributed_context_multi_node_gloo): device = torch.device("cpu") _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_gpu(distributed_context_multi_node_nccl): device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}") _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-0.4.3/tests/ignite/test_utils.py000066400000000000000000000151011401006565000201620ustar00rootroot00000000000000import logging import os import sys import warnings from collections import namedtuple import pytest import torch from ignite.engine import Engine, Events from ignite.utils import convert_tensor, deprecated, 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) 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 = os.path.join(dirname, "log") def _test(stream): trainer.logger = setup_logger("trainer", stream=stream, filepath=fp) evaluator.logger = setup_logger("evaluator", stream=stream, filepath=fp) 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 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\n .. 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\n .. 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(): func_check_warning() assert func_check_warning() == 24 with warnings.catch_warnings(record=True) as w: # Cause all warnings to always be triggered. warnings.simplefilter("always") # Trigger a warning. func_check_warning() # Verify some things assert len(w) == 1 assert issubclass(w[-1].category, DeprecationWarning) assert ( "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." in str(w[-1].message) ) # 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." ) ignite-0.4.3/tests/run_cpu_tests.sh000066400000000000000000000012711401006565000173660ustar00rootroot00000000000000#!/bin/bash set -xeu if [ "${SKIP_DISTRIB_TESTS:-0}" -eq "1" ]; then skip_distrib_opt=(-m "not distributed and not tpu and not multinode_distributed") else skip_distrib_opt=(-m "") fi CUDA_VISIBLE_DEVICES="" pytest --tx 4*popen//python=python --cov ignite --cov-report term-missing --cov-report xml -vvv tests "${skip_distrib_opt[@]}" # https://pubs.opengroup.org/onlinepubs/009695399/utilities/xcu_chap02.html#tag_02_06_02 if [ "${SKIP_DISTRIB_TESTS:-0}" -eq "1" ]; then exit 0 fi export WORLD_SIZE=2 CUDA_VISIBLE_DEVICES="" pytest --cov ignite --cov-append --cov-report term-missing --cov-report xml --dist=each --tx $WORLD_SIZE*popen//python=python tests -m distributed -vvv ignite-0.4.3/tests/run_gpu_tests.sh000066400000000000000000000012061401006565000173700ustar00rootroot00000000000000#!/bin/bash if [ -z "$1" ]; then ngpus=1 else ngpus=$1 fi set -xeu pytest --cov ignite --cov-report term-missing --cov-report xml -vvv tests/ -k 'on_cuda' # https://pubs.opengroup.org/onlinepubs/009695399/utilities/xcu_chap02.html#tag_02_06_02 if [ "${SKIP_DISTRIB_TESTS:-0}" -eq "1" ]; then exit 0 fi pytest --cov ignite --cov-append --cov-report term-missing --cov-report xml -vvv tests/ -m distributed if [ ${ngpus} -gt 1 ]; then export WORLD_SIZE=${ngpus} pytest --cov ignite --cov-append --cov-report term-missing --cov-report xml --dist=each --tx ${WORLD_SIZE}*popen//python=python tests -m distributed -vvv fi ignite-0.4.3/tests/run_multinode_tests_in_docker.sh000066400000000000000000000026171401006565000226210ustar00rootroot00000000000000#!/bin/bash # Tests configuration: export nnodes=2 export nproc_per_node=4 export gpu=0 # 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="pytorch/pytorch:latest" install_test_requirements="pip install mock pytest pytest-xdist scikit-learn" cmd="pytest --dist=each --tx $nproc_per_node*popen//python=python3.6 tests -m multinode_distributed -vvv $@" export MASTER_ADDR=node0 export MASTER_PORT=9999 network=tempnet # Create user bridge network docker network create --driver bridge $network if [ $gpu == 1 ]; then env_multinode_option="-e GPU_MULTINODE_DISTRIB=1" else env_multinode_option="-e MULTINODE_DISTRIB=1" fi for i in $(seq 0 $((nnodes - 1)) ) do echo "Start Node $i" node_name="node$i" is_detached="-d" if [ $i == $((nnodes - 1)) ]; then is_detached="" fi export node_id=$i docker run --rm $is_detached \ -v $PWD:/workspace $env_multinode_option \ --env nnodes \ --env nproc_per_node \ --env node_id \ --env MASTER_ADDR \ --env MASTER_PORT \ --name $node_name \ --network $network \ $docker_image /bin/bash -c "$install_test_requirements && $cmd" done sleep 5 docker network rm $network ignite-0.4.3/tests/run_tpu_tests.sh000066400000000000000000000003531401006565000174070ustar00rootroot00000000000000#!/bin/bash set -xeu pytest --cov ignite --cov-report term-missing --cov-report xml tests/ -vvv -m tpu if [ -z ${NUM_TPU_WORKERS+x} ]; then export NUM_TPU_WORKERS=1 pytest --cov ignite --cov-report xml tests/ -vvv -m tpu fi

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