YOLOv5 CI #611
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
# YOLOv5 Continuous Integration (CI) GitHub Actions tests | |
name: YOLOv5 CI | |
on: | |
push: | |
branches: [ master ] | |
pull_request: | |
branches: [ master ] | |
schedule: | |
- cron: '0 0 * * *' # runs at 00:00 UTC every day | |
jobs: | |
Benchmarks: | |
runs-on: ${{ matrix.os }} | |
strategy: | |
fail-fast: false | |
matrix: | |
os: [ ubuntu-latest ] | |
python-version: [ '3.9' ] # requires python<=3.9 | |
model: [ yolov5n ] | |
steps: | |
- uses: actions/checkout@v3 | |
- uses: actions/setup-python@v4 | |
with: | |
python-version: ${{ matrix.python-version }} | |
#- name: Cache pip | |
# uses: actions/cache@v3 | |
# with: | |
# path: ~/.cache/pip | |
# key: ${{ runner.os }}-Benchmarks-${{ hashFiles('requirements.txt') }} | |
# restore-keys: ${{ runner.os }}-Benchmarks- | |
- name: Install requirements | |
run: | | |
python -m pip install --upgrade pip wheel | |
pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu | |
python --version | |
pip --version | |
pip list | |
- name: Benchmark DetectionModel | |
run: | | |
python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29 | |
- name: Benchmark SegmentationModel | |
run: | | |
python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22 | |
- name: Test predictions | |
run: | | |
python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224 | |
python detect.py --weights ${{ matrix.model }}.onnx --img 320 | |
python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320 | |
python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224 | |
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: [ '3.10' ] | |
model: [ yolov5n ] | |
include: | |
- os: ubuntu-latest | |
python-version: '3.7' # '3.6.8' min | |
model: yolov5n | |
- os: ubuntu-latest | |
python-version: '3.8' | |
model: yolov5n | |
- os: ubuntu-latest | |
python-version: '3.9' | |
model: yolov5n | |
- os: ubuntu-latest | |
python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8 | |
model: yolov5n | |
torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/ | |
steps: | |
- uses: actions/checkout@v3 | |
- uses: actions/setup-python@v4 | |
with: | |
python-version: ${{ matrix.python-version }} | |
- name: Get cache dir | |
# https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow | |
id: pip-cache | |
run: echo "::set-output name=dir::$(pip cache dir)" | |
- name: Cache pip | |
uses: actions/cache@v3 | |
with: | |
path: ${{ steps.pip-cache.outputs.dir }} | |
key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }} | |
restore-keys: ${{ runner.os }}-${{ matrix.python-version }}-pip- | |
- name: Install requirements | |
run: | | |
python -m pip install --upgrade pip wheel | |
if [ "${{ matrix.torch }}" == "1.7.0" ]; then | |
pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu | |
else | |
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu | |
fi | |
shell: bash # for Windows compatibility | |
- name: Check environment | |
run: | | |
python -c "import utils; utils.notebook_init()" | |
echo "RUNNER_OS is ${{ runner.os }}" | |
echo "GITHUB_EVENT_NAME is ${{ github.event_name }}" | |
echo "GITHUB_WORKFLOW is ${{ github.workflow }}" | |
echo "GITHUB_ACTOR is ${{ github.actor }}" | |
echo "GITHUB_REPOSITORY is ${{ github.repository }}" | |
echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}" | |
python --version | |
pip --version | |
pip list | |
- name: Test detection | |
shell: bash # for Windows compatibility | |
run: | | |
# export PYTHONPATH="$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 mnist2560 --epochs 1 # train | |
python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist2560 # val | |
python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist2560/test/7/60.png # predict | |
python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict | |
python export.py --weights $b --img 64 --imgsz 224 --include torchscript # export | |
python - <<EOF | |
import torch | |
for path in '$m', '$b': | |
model = torch.hub.load('.', 'custom', path=path, source='local') | |
EOF |