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Contributing to Torchvision

We want to make contributing to this project as easy and transparent as possible.

TL;DR

We appreciate all contributions. If you are interested in contributing to Torchvision, there are many ways to help out. Your contributions may fall into the following categories:

  • It helps the project if you could

    • Report issues you're facing
    • Give a 👍 on issues that others reported and that are relevant to you

  • Answering queries on the issue tracker, investigating bugs are very valuable contributions to the project.

  • You would like to improve the documentation. This is no less important than improving the library itself! If you find a typo in the documentation, do not hesitate to submit a GitHub pull request.

  • If you would like to fix a bug

  • If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Issues

We use GitHub issues to track public bugs. Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue.

Development installation

Install PyTorch Nightly

conda install pytorch -c pytorch-nightly
# or with pip (see https://pytorch.org/get-started/locally/)
# pip install numpy
# pip install --pre torch -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html

Install Torchvision

git clone https://github.com/pytorch/vision.git
cd vision
python setup.py develop
# or, for OSX
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop
# for C++ debugging, please use DEBUG=1
# DEBUG=1 python setup.py develop
pip install flake8 typing mypy pytest pytest-mock scipy

You may also have to install libpng-dev and libjpeg-turbo8-dev libraries:

conda install libpng jpeg

Development Process

If you plan to modify the code or documentation, please follow the steps below:

  1. Fork the repository and create your branch from main.
  2. If you have modified the code (new feature or bug-fix), please add unit tests.
  3. If you have changed APIs, update the documentation. Make sure the documentation builds.
  4. Ensure the test suite passes.
  5. Make sure your code passes the formatting checks (see below).

For more details about pull requests, please read GitHub's guides.

If you would like to contribute a new model, please see here.

If you would like to contribute a new dataset, please see here.

Code formatting and typing

Contributions should be compatible with Python 3.X versions and be compliant with PEP8. To check the codebase, please either run

pre-commit run --all-files

or run

pre-commit install

once to perform these checks automatically before every git commit. If pre-commit is not available you can install it with

pip install pre-commit

The codebase has type annotations, please make sure to add type hints if required. We use mypy tool for type checking:

mypy --config-file mypy.ini

Unit tests

If you have modified the code by adding a new feature or a bug-fix, please add unit tests for that. To run a specific test:

pytest test/<test-module.py> -vvv -k <test_myfunc>
# e.g. pytest test/test_transforms.py -vvv -k test_center_crop

If you would like to run all tests:

pytest test -vvv

Tests that require internet access should be in test/test_internet.py.

Documentation

Torchvision uses Google style for formatting docstrings. Length of line inside docstrings block must be limited to 120 characters.

Please, follow the instructions to build and deploy the documentation locally.

Install requirements

cd docs
pip install -r requirements.txt

Build

cd docs
make html

Then open docs/build/html/index.html in your favorite browser.

The docs are also automatically built when you submit a PR. The job that builds the docs is named build_docs. You can access the rendered docs by clicking on that job and then going to the "Artifacts" tab.

You can clean the built docs and re-start the build from scratch by doing make clean.

Building the example gallery - or not

When you run make html for the first time, all the examples in the gallery will be built. Subsequent builds should be faster, and will only build the examples that have been modified.

You can run make html-noplot to not build the examples at all. This is useful after a make clean to do some quick checks that are not related to the examples.

You can also choose to only build a subset of the examples by using the EXAMPLES_PATTERN env variable, which accepts a regular expression. For example EXAMPLES_PATTERN="transforms" make html will only build the examples with "transforms" in their name.

New model

More details on how to add a new model will be provided later. Please, do not send any PR with a new model without discussing it in an issue as, most likely, it will not be accepted.

New dataset

More details on how to add a new dataset will be provided later. Please, do not send any PR with a new dataset without discussing it in an issue as, most likely, it will not be accepted.

Pull Request

If all previous checks (flake8, mypy, unit tests) are passing, please send a PR. Submitted PR will pass other tests on different operation systems, python versions and hardwares.

For more details about pull requests workflow, please read GitHub's guides.

License

By contributing to Torchvision, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree.