168 lines
7.3 KiB
YAML
168 lines
7.3 KiB
YAML
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 Continuous Integration (CI) GitHub Actions tests
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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 * * *' # runs at 00:00 UTC every day
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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: [ '3.9' ] # requires python<=3.9
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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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#- name: Cache pip
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# uses: actions/cache@v3
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# with:
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# path: ~/.cache/pip
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# key: ${{ runner.os }}-Benchmarks-${{ hashFiles('requirements.txt') }}
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# restore-keys: ${{ runner.os }}-Benchmarks-
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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 ] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049
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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' # '3.6.8' min
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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' # torch 1.7.0 requires python >=3.6, <=3.8
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model: yolov5n
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torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/
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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: ${{ matrix.python-version }}
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- name: Get cache dir
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# https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
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id: pip-cache
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run: echo "::set-output name=dir::$(pip cache dir)"
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- name: Cache pip
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uses: actions/cache@v3
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with:
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path: ${{ steps.pip-cache.outputs.dir }}
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key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
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restore-keys: ${{ runner.os }}-${{ matrix.python-version }}-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 # for Windows compatibility
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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 ${{ github.repository_owner }}"
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python --version
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pip --version
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pip list
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- name: Test detection
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shell: bash # for Windows compatibility
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run: |
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# export PYTHONPATH="$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', '$b':
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model = torch.hub.load('.', 'custom', path=path, source='local')
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print(model('data/images/bus.jpg'))
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model(im) # warmup, build grids for trace
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torch.jit.trace(model, [im])
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EOF
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- name: Test segmentation
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shell: bash # for Windows compatibility
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run: |
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m=${{ 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 $d # export
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done
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done
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- name: Test classification
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shell: bash # for Windows compatibility
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run: |
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m=${{ 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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