Skip to content
CI

Custom Shufflenetv2 for extremely small and dense objects #256

Sign in to view logs

Custom Shufflenetv2 for extremely small and dense objects

Custom Shufflenetv2 for extremely small and dense objects #256

Workflow file for this run

.github/workflows/workflow.yml at 4e25651
name: CI
on:
push:
branches:
- main
pull_request:
jobs:
linter:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.8
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install flake8==5.0.4 isort==5.10.1
python -m pip install black==22.6.0
flake8 --version
- name: Lint
run: |
echo "Running isort"
isort --profile black .
echo "Running black"
black --check .
echo "Running flake8"
flake8 .
test_cpu:
runs-on: ubuntu-latest
strategy:
matrix:
torch: [1.10.1, 1.11.0, 1.12.1, 1.13.1]
include:
- torch: 1.10.1
torchvision: 0.11.2
- torch: 1.11.0
torchvision: 0.12.0
- torch: 1.12.1
torchvision: 0.13.1
- torch: 1.13.1
torchvision: 0.14.1
steps:
- name: Checkout
uses: actions/checkout@v2
- name: Set up Python 3.8
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
python -m pip install -U pip
python -m pip install ninja opencv-python-headless onnx pytest-xdist codecov
python -m pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
python -m pip install Cython termcolor numpy tensorboard pycocotools matplotlib pyaml opencv-python tqdm pytorch-lightning torchmetrics codecov flake8 pytest timm
python -m pip install -r requirements.txt
- name: Setup
run: rm -rf .eggs && python setup.py develop
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source nanodet -m pytest tests/
coverage xml
coverage report -m
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v2
if: matrix.torch == '1.12.1'
with:
file: ./coverage.xml
flags: unittests
env_vars: OS,PYTHON
name: codecov-umbrella
fail_ci_if_error: false
# test_cuda:
# runs-on: ubuntu-latest
# env:
# CUDA: 10.2.89-1
# CUDA_SHORT: 10.2
# UBUNTU_VERSION: ubuntu1804
# strategy:
# matrix:
# torch: [1.7.0, 1.8.0, 1.9.0]
# include:
# - torch: 1.7.0
# torchvision: 0.8.1
# - torch: 1.8.0
# torchvision: 0.9.0
# - torch: 1.9.0
# torchvision: 0.10.0
# steps:
# - name: Checkout
# uses: actions/checkout@v2
# - name: Set up Python 3.6
# uses: actions/setup-python@v2
# with:
# python-version: 3.6
# - name: Install CUDA
# run: |
# export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb
# wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER}
# sudo dpkg -i ${INSTALLER}
# wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub
# sudo apt-key add 7fa2af80.pub
# sudo apt update -qq
# sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-}
# sudo apt clean
# export CUDA_HOME=/usr/local/cuda-${CUDA_SHORT}
# export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH}
# export PATH=${CUDA_HOME}/bin:${PATH}
# - name: Install dependencies
# run: |
# python -m pip install -U pip
# python -m pip install ninja opencv-python-headless onnx pytest-xdist codecov
# python -m pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
# python -m pip install Cython termcolor numpy tensorboard pycocotools matplotlib pyaml opencv-python tqdm pytorch-lightning torchmetrics codecov flake8 pytest
# - name: Setup
# run: |
# rm -rf .eggs
# python setup.py check -m -s
# TORCH_CUDA_ARCH_LIST=7.0 pip install .
# - name: Run unittests and generate coverage report
# run: |
# coverage run --branch --source nanodet -m pytest tests/
# coverage xml
# coverage report -m
# - name: Upload coverage to Codecov
# uses: codecov/codecov-action@v1.0.10
# if: matrix.torch == '1.9.0'
# with:
# file: ./coverage.xml
# flags: unittests
# env_vars: OS,PYTHON
# name: codecov-umbrella
# fail_ci_if_error: false