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package_testing.yml
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name: package testing
on:
schedule:
- cron: '0 0 * * *' # Runs at 00:00 UTC every day
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
operating-system: [ubuntu-latest, windows-latest, macos-latest]
python-version: [3.8, 3.9, "3.10", "3.11"]
fail-fast: false
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Update pip
run: python -m pip install --upgrade pip
- name: Get pip cache dir
id: pip-cache
run: |
echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT
- name: Restore pip cache
uses: actions/cache@v3
with:
path: ${{ steps.pip-cache.outputs.dir }}
key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py') }}
restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
- name: Install latest YOLOv5 package
run: >
pip install --upgrade --force-reinstall yolov5
- name: Test with unittest
run: |
python -m unittest
- name: Test yolov5 train
shell: bash # for Windows compatibility
run: |
# donwload coco formatted testing data
python tests/download_test_coco_dataset.py
# train (dl base model from hf hub)
yolov5 train --data tests/data/coco_data.yaml --img 128 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 1 --device cpu --freeze 10
python yolov5/train.py --img 128 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 1 --device cpu
yolov5 train --img 128 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 1 --device cpu --freeze 10
yolov5 train --img 128 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 1 --device cpu --evolve 2
# train
yolov5 train --data tests/data/coco_data.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --freeze 10
python yolov5/train.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu
yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --freeze 10
yolov5 train --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --epochs 1 --device cpu --evolve 2
- name: Test yolov5 detect
shell: bash # for Windows compatibility
run: |
python yolov5/detect.py --weights yolov5/weights/yolov5n.pt --device cpu
yolov5 detect --weights yolov5/weights/yolov5n.pt --device cpu
python yolov5/detect.py --weights runs/train/exp/weights/last.pt --device cpu
yolov5 detect --weights runs/train/exp/weights/last.pt --device cpu
- name: Test yolov5 val
shell: bash # for Windows compatibility
run: |
python yolov5/val.py --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu
yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights yolov5/weights/yolov5n.pt --device cpu
python yolov5/val.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu
yolov5 val --data yolov5/data/coco128.yaml --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device cpu
- name: Test yolov5 export
shell: bash # for Windows compatibility
run: |
pip install onnx onnx-simplifier tensorflowjs
python yolov5/export.py --weights yolov5/weights/yolov5n.pt --device cpu --include torchscript,onnx,tflite
yolov5 export --weights yolov5/weights/yolov5n.pt --device cpu --simplify --include torchscript,onnx,saved_model,pb,tfjs
- name: Test yolov5 benchmarks
shell: bash # for Windows compatibility
run: |
yolov5 benchmarks --weights yolov5n.pt --img 128 --pt-only --device cpu
- name: Test yolov5 classify
shell: bash # for Windows compatibility
run: |
yolov5 classify train --img 128 --data mnist2560 --model yolov5n-cls.pt --epochs 1 --device cpu
yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu
yolov5 classify val --img 128 --data datasets/mnist2560 --weights yolov5n-cls.pt --device cpu
yolov5 classify predict --img 128 --weights yolov5n-cls.pt --device cpu
- name: Test yolov5 segment
shell: bash # for Windows compatibility
run: |
yolov5 segment train --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu
yolov5 segment train --img 128 --weights fcakyon/yolov5n-seg-v7.0 --epochs 1 --device cpu
# yolov5 segment val --img 128 --weights yolov5n-seg.pt --device cpu
yolov5 segment predict --img 128 --weights yolov5n-seg.pt --device cpu
- name: Test roboflow train
shell: bash # for Windows compatibility
run: |
yolov5 train --data https://universe.roboflow.com/gdit/aerial-airport/dataset/1 --weights yolov5/weights/yolov5n.pt --img 128 --epochs 1 --device cpu --roboflow_token ${{ secrets.ROBOFLOW_API_KEY }}
# yolov5 classify train --data https://universe.roboflow.com/carlos-gabriel-da-silva-machado-siwvs/turtles-i1tlr/dataset/1 --img 128 --model yolov5n-cls.pt --epochs 1 --device cpu --roboflow_token ${{ secrets.ROBOFLOW_API_KEY }}
yolov5 segment train --data https://universe.roboflow.com/aymane-outani-auooc/cable-fzjik/dataset/2 --img 128 --weights yolov5n-seg.pt --epochs 1 --device cpu --roboflow_token ${{ secrets.ROBOFLOW_API_KEY }}