YOLOv3 CI #2411
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| # Ultralytics YOLOv3 🚀, AGPL-3.0 license | |
| # YOLOv3 Continuous Integration (CI) GitHub Actions tests | |
| name: YOLOv3 CI | |
| on: | |
| push: | |
| branches: [master] | |
| pull_request: | |
| branches: [master] | |
| schedule: | |
| - cron: "0 0 * * *" # runs at 00:00 UTC every day | |
| workflow_dispatch: | |
| jobs: | |
| Tests: | |
| timeout-minutes: 60 | |
| runs-on: ${{ matrix.os }} | |
| strategy: | |
| fail-fast: false | |
| matrix: | |
| os: [ubuntu-latest, windows-latest] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049 | |
| python-version: ["3.11"] | |
| model: [yolov5n] | |
| include: | |
| - os: ubuntu-latest | |
| python-version: "3.8" # '3.6.8' min | |
| model: yolov5n | |
| - os: ubuntu-latest | |
| python-version: "3.9" | |
| model: yolov5n | |
| - os: ubuntu-latest | |
| python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8 | |
| model: yolov5n | |
| torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/ | |
| steps: | |
| - uses: actions/checkout@v4 | |
| - uses: actions/setup-python@v5 | |
| with: | |
| python-version: ${{ matrix.python-version }} | |
| cache: "pip" # caching pip dependencies | |
| - name: Install requirements | |
| run: | | |
| python -m pip install --upgrade pip wheel | |
| torch="" | |
| if [ "${{ matrix.torch }}" == "1.8.0" ]; then | |
| torch="torch==1.8.0 torchvision==0.9.0" | |
| fi | |
| pip install -r requirements.txt $torch --extra-index-url https://download.pytorch.org/whl/cpu | |
| shell: bash # for Windows compatibility | |
| - name: Check environment | |
| run: | | |
| yolo checks | |
| pip list | |
| - name: Test detection | |
| shell: bash # for Windows compatibility | |
| run: | | |
| # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories | |
| m=${{ matrix.model }} # official weights | |
| b=runs/train/exp/weights/best # best.pt checkpoint | |
| python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train | |
| for d in cpu; do # devices | |
| for w in $m $b; do # weights | |
| python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val | |
| python detect.py --imgsz 64 --weights $w.pt --device $d # detect | |
| done | |
| done | |
| python hubconf.py --model $m # hub | |
| # python models/tf.py --weights $m.pt # build TF model | |
| python models/yolo.py --cfg $m.yaml # build PyTorch model | |
| python export.py --weights $m.pt --img 64 --include torchscript # export | |
| python - <<EOF | |
| import torch | |
| im = torch.zeros([1, 3, 64, 64]) | |
| for path in '$m', '$b': | |
| model = torch.hub.load('.', 'custom', path=path, source='local') | |
| print(model('data/images/bus.jpg')) | |
| model(im) # warmup, build grids for trace | |
| torch.jit.trace(model, [im]) | |
| EOF | |
| - name: Test segmentation | |
| shell: bash # for Windows compatibility | |
| run: | | |
| m=${{ matrix.model }}-seg # official weights | |
| b=runs/train-seg/exp/weights/best # best.pt checkpoint | |
| python segment/train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train | |
| python segment/train.py --imgsz 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device cpu # train | |
| for d in cpu; do # devices | |
| for w in $m $b; do # weights | |
| python segment/val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val | |
| python segment/predict.py --imgsz 64 --weights $w.pt --device $d # predict | |
| python export.py --weights $w.pt --img 64 --include torchscript --device $d # export | |
| done | |
| done | |
| - name: Test classification | |
| shell: bash # for Windows compatibility | |
| run: | | |
| m=${{ matrix.model }}-cls.pt # official weights | |
| b=runs/train-cls/exp/weights/best.pt # best.pt checkpoint | |
| python classify/train.py --imgsz 32 --model $m --data mnist160 --epochs 1 # train | |
| python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist160 # val | |
| python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist160/test/7/60.png # predict | |
| python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict | |
| python export.py --weights $b --img 64 --include torchscript # export | |
| python - <<EOF | |
| import torch | |
| for path in '$m', '$b': | |
| model = torch.hub.load('.', 'custom', path=path, source='local') | |
| EOF | |
| Summary: | |
| runs-on: ubuntu-latest | |
| needs: [Tests] # Add job names that you want to check for failure | |
| if: always() # This ensures the job runs even if previous jobs fail | |
| steps: | |
| - name: Check for failure and notify | |
| if: (needs.Tests.result == 'failure' || needs.Tests.result == 'cancelled') && github.repository == 'ultralytics/yolov3' && (github.event_name == 'schedule' || github.event_name == 'push') | |
| uses: slackapi/[email protected] | |
| with: | |
| payload: | | |
| {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"} | |
| env: | |
| SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }} |