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name: YOLOv5 CI |
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on: |
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push: |
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branches: [ master ] |
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pull_request: |
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branches: [ master ] |
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schedule: |
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- cron: '0 0 * * *' |
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jobs: |
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Benchmarks: |
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runs-on: ${{ matrix.os }} |
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strategy: |
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fail-fast: false |
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matrix: |
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os: [ ubuntu-latest ] |
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python-version: [ '3.10' ] |
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model: [ yolov5n ] |
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steps: |
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- uses: actions/checkout@v3 |
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- uses: actions/setup-python@v4 |
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with: |
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python-version: ${{ matrix.python-version }} |
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cache: 'pip' |
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- name: Install requirements |
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run: | |
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python -m pip install --upgrade pip wheel |
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pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu |
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python --version |
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pip --version |
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pip list |
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- name: Benchmark DetectionModel |
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run: | |
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python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29 |
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- name: Benchmark SegmentationModel |
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run: | |
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python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22 |
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- name: Test predictions |
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run: | |
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python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224 |
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python detect.py --weights ${{ matrix.model }}.onnx --img 320 |
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python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320 |
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python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224 |
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Tests: |
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timeout-minutes: 60 |
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runs-on: ${{ matrix.os }} |
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strategy: |
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fail-fast: false |
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matrix: |
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os: [ ubuntu-latest, windows-latest ] |
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python-version: [ '3.10' ] |
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model: [ yolov5n ] |
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include: |
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- os: ubuntu-latest |
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python-version: '3.7' |
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model: yolov5n |
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- os: ubuntu-latest |
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python-version: '3.8' |
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model: yolov5n |
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- os: ubuntu-latest |
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python-version: '3.9' |
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model: yolov5n |
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- os: ubuntu-latest |
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python-version: '3.8' |
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model: yolov5n |
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torch: '1.7.0' |
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steps: |
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- uses: actions/checkout@v3 |
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- uses: actions/setup-python@v4 |
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with: |
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python-version: ${{ matrix.python-version }} |
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cache: 'pip' |
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- name: Install requirements |
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run: | |
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python -m pip install --upgrade pip wheel |
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if [ "${{ matrix.torch }}" == "1.7.0" ]; then |
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pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu |
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else |
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pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu |
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fi |
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shell: bash |
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- name: Check environment |
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run: | |
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python -c "import utils; utils.notebook_init()" |
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echo "RUNNER_OS is ${{ runner.os }}" |
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echo "GITHUB_EVENT_NAME is ${{ github.event_name }}" |
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echo "GITHUB_WORKFLOW is ${{ github.workflow }}" |
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echo "GITHUB_ACTOR is ${{ github.actor }}" |
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echo "GITHUB_REPOSITORY is ${{ github.repository }}" |
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echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}" |
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python --version |
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pip --version |
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pip list |
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- name: Test detection |
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shell: bash |
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run: | |
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# export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories |
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m=${{ matrix.model }} # official weights |
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b=runs/train/exp/weights/best # best.pt checkpoint |
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python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train |
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for d in cpu; do # devices |
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for w in $m $b; do # weights |
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python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val |
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python detect.py --imgsz 64 --weights $w.pt --device $d # detect |
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done |
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done |
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python hubconf.py --model $m # hub |
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# python models/tf.py --weights $m.pt # build TF model |
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python models/yolo.py --cfg $m.yaml # build PyTorch model |
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python export.py --weights $m.pt --img 64 --include torchscript # export |
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python - <<EOF |
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import torch |
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im = torch.zeros([1, 3, 64, 64]) |
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for path in '$m', '$b': |
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model = torch.hub.load('.', 'custom', path=path, source='local') |
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print(model('data/images/bus.jpg')) |
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model(im) # warmup, build grids for trace |
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torch.jit.trace(model, [im]) |
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EOF |
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- name: Test segmentation |
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shell: bash |
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run: | |
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m=${{ matrix.model }}-seg # official weights |
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b=runs/train-seg/exp/weights/best # best.pt checkpoint |
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python segment/train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train |
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python segment/train.py --imgsz 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device cpu # train |
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for d in cpu; do # devices |
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for w in $m $b; do # weights |
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python segment/val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val |
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python segment/predict.py --imgsz 64 --weights $w.pt --device $d # predict |
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python export.py --weights $w.pt --img 64 --include torchscript --device $d # export |
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done |
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done |
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- name: Test classification |
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shell: bash |
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run: | |
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m=${{ matrix.model }}-cls.pt # official weights |
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b=runs/train-cls/exp/weights/best.pt # best.pt checkpoint |
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python classify/train.py --imgsz 32 --model $m --data mnist160 --epochs 1 # train |
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python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist160 # val |
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python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist160/test/7/60.png # predict |
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python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict |
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python export.py --weights $b --img 64 --include torchscript # export |
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python - <<EOF |
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import torch |
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for path in '$m', '$b': |
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model = torch.hub.load('.', 'custom', path=path, source='local') |
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EOF |
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