mmsegmentation/.circleci/test.yml

version: 2.1

# the default pipeline parameters, which will be updated according to
# the results of the path-filtering orb
parameters:
  lint_only:
    type: boolean
    default: true

jobs:
  lint:
    docker:
      - image: cimg/python:3.7.4
    steps:
      - checkout
      - run:
          name: Install pre-commit hook
          command: |
            pip install pre-commit
            pre-commit install            
      - run:
          name: Linting
          command: pre-commit run --all-files
      - run:
          name: Check docstring coverage
          command: |
            pip install interrogate
            interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 75 mmseg            
  build_cpu:
    parameters:
      # The python version must match available image tags in
      # https://circleci.com/developer/images/image/cimg/python
      python:
        type: string
      torch:
        type: string
      torchvision:
        type: string
    docker:
      - image: cimg/python:<< parameters.python >>
    resource_class: large
    steps:
      - checkout
      - run:
          name: Install Libraries
          command: |
            sudo apt-get update
            sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5            
      - run:
          name: Configure Python & pip
          command: |
            pip install --upgrade pip
            pip install wheel            
      - run:
          name: Install PyTorch
          command: |
            python -V
            pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html            
      - run:
          name: Install mmseg dependencies
          command: |
            pip install git+https://github.com/open-mmlab/mmengine.git@main
            pip install -U openmim
            mim install 'mmcv>=2.0.0rc1'
            pip install git+https://github.com/open-mmlab/mmclassification@dev-1.x
            pip install -r requirements/tests.txt -r requirements/optional.txt            
      - run:
          name: Build and install
          command: |
            pip install -e .            
      - run:
          name: Skip timm unittests and generate coverage report
          command: |
            python -m coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
            python -m coverage xml
            python -m coverage report -m            
  build_cuda:
    parameters:
      torch:
        type: string
      cuda:
        type: enum
        enum: ["10.1", "10.2", "11.1"]
      cudnn:
        type: integer
        default: 7
    machine:
      image: ubuntu-2004-cuda-11.4:202110-01
      # docker_layer_caching: true
    resource_class: gpu.nvidia.small
    steps:
      - checkout
      - run:
          # Cloning repos in VM since Docker doesn't have access to the private key
          name: Clone Repos
          command: |
            git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
            git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmclassification.git /home/circleci/mmclassification            
      - run:
          name: Build Docker image
          command: |
            docker build .circleci/docker -t mmseg:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
            docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmclassification:/mmclassification -w /mmseg --name mmseg mmseg:gpu            
      - run:
          name: Install mmseg dependencies
          command: |
            docker exec mmseg pip install -e /mmengine
            docker exec mmseg pip install -U openmim
            docker exec mmseg mim install 'mmcv>=2.0.0rc1'
            docker exec mmseg pip install -e /mmclassification
            docker exec mmseg pip install -r requirements/tests.txt -r requirements/optional.txt            
      - run:
          name: Build and install
          command: |
            docker exec mmseg pip install -e .            
      - run:
          name: Run unittests but skip timm unittests
          command: |
            docker exec mmseg pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py            
workflows:
  pr_stage_lint:
    when: << pipeline.parameters.lint_only >>
    jobs:
      - lint:
          name: lint
          filters:
            branches:
              ignore:
                - dev-1.x
                - 1.x
                - master
  pr_stage_test:
    when:
      not:
        << pipeline.parameters.lint_only >>
    jobs:
      - lint:
          name: lint
          filters:
            branches:
              ignore:
                - dev-1.x
                - 1.x
                - master
      - build_cpu:
          name: minimum_version_cpu
          torch: 1.6.0
          torchvision: 0.7.0
          python: 3.6.9  # The lowest python 3.6.x version available on CircleCI images
          requires:
            - lint
      - build_cpu:
          name: maximum_version_cpu
          torch: 1.12.1
          torchvision: 0.13.1
          python: 3.9.0
          requires:
            - minimum_version_cpu
      - hold:
          type: approval
          requires:
            - maximum_version_cpu
      - build_cuda:
          name: mainstream_version_gpu
          torch: 1.8.1
          # Use double quotation mark to explicitly specify its type
          # as string instead of number
          cuda: "10.2"
          requires:
            - hold
  merge_stage_test:
    when:
      not:
        << pipeline.parameters.lint_only >>
    jobs:
      - build_cuda:
          name: minimum_version_gpu
          torch: 1.6.0
          # Use double quotation mark to explicitly specify its type
          # as string instead of number
          cuda: "10.1"
          filters:
            branches:
              only:
                - dev-1.x
                - 1.x
                - master