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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

Formatting

The torchvision code is formatted by black, and checked against pep8 compliance with flake8. Instead of relying directly on black however, we rely on ufmt, for compatibility reasons with Facebook internal infrastructure.

To format your code, install ufmt with pip install ufmt==1.3.2 black==21.9b0 usort==0.6.4 and use e.g.:

ufmt format torchvision

For the vast majority of cases, this is all you should need to run. For the formatting to be a bit faster, you can also choose to only apply ufmt to the files that were edited in your PR with e.g.:

ufmt format `git diff main --name-only`

Similarly, you can check for flake8 errors with flake8 torchvision, although they should be fairly rare considering that most of the errors are automatically taken care of by ufmt already.

Pre-commit hooks

For convenience and purely optionally, you can rely on pre-commit hooks which will run both ufmt and flake8 prior to every commit.

First install the pre-commit package with pip install pre-commit, and then run pre-commit install at the root of the repo for the hooks to be set up - that's it.

Feel free to read the pre-commit docs to learn more and improve your workflow. You'll see for example that pre-commit run --all-files will run both ufmt and flake8 without the need for you to commit anything, and that the --no-verify flag can be added to git commit to temporarily deactivate the hooks.

Type annotations

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 architecture or improved model weights

Please refer to the guidelines in Contributing to Torchvision - Models.

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.