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YOLOV5_RESEARCH_PLUS CI CPU testing #1034

YOLOV5_RESEARCH_PLUS CI CPU testing

YOLOV5_RESEARCH_PLUS CI CPU testing #1034

Workflow file for this run

name: YOLOV5_RESEARCH_PLUS CI CPU testing
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
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', '$b':
# model = torch.hub.load('.', 'custom', path=path, source='local')
# EOF
# - name: Test pose
# shell: bash # for Windows compatibility
# run: |
# m=${{ matrix.model }}-pose.pt # official weights
# b=runs/train-pose/exp/weights/best.pt # best.pt checkpoint
# python pose/train.py --imgsz 32 --model $m --data mnist2560 --epochs 1 --kpt-label # train
# #python pose/val.py --imgsz 32 --weights $b --data ../datasets/mnist2560 # val
# #python pose/predict.py --imgsz 32 --weights $b --source ../datasets/mnist2560/test/7/60.png # predict
# python pose/detect.py --imgsz 32 --weights $m --source data/images/bus.jpg --kpt-label # 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