Contributing¶

TorchUncertainty is in early development stage. We are looking for contributors to help us build a comprehensive library for uncertainty quantification in PyTorch.

We are particularly open to any comment that you would have on this project. Specifically, we are open to changing these guidelines as the project evolves.

The scope of TorchUncertainty¶

TorchUncertainty can host any method - if possible linked to a paper - and roughly contained in the following fields:

Uncertainty quantification in general, including Bayesian deep learning, Monte Carlo dropout, ensemble methods, etc.
Out-of-distribution detection methods
Applications (e.g. object detection, segmentation, etc.)

Common guidelines¶

Clean development install of TorchUncertainty¶

If you are interested in contributing to torch_uncertainty, we first advise you to follow the following steps to reproduce a clean development environment ensuring continuous integration does not break.

Check that you have PyTorch already installed on your system
Clone the repository
Install torch-uncertainty in editable mode with the dev packages: python3 -m pip install -e .[dev]
Install pre-commit hooks with pre-commit install

Build the documentation locally¶

To build the documentation, reinstall TorchUncertainty with the packages of the docs group:

python3 -m pip install -e .[dev,docs]

Then navigate to ./docs and build the documentation with:

make html

Optionally, specify html-noplot instead of html to avoid running the tutorials.

Guidelines¶

Commits

We use ruff for code formatting, linting, and imports (as a drop-in replacement for black, isort, and flake8). The pre-commit hooks will ensure that your code is properly formatted and linted before committing.

Please ensure that the tests are passing on your machine before pushing on a PR. This will avoid multiplying the number featureless commits. To do this, run, at the root of the folder:

python3 -m pytest tests

Try to include an emoji at the start of each commit message following the suggestions from this page.

Pull requests

To make your changes, create a branch on a personal fork and create a PR when your contribution is mostly finished or if you need help.

Check that your PR complies with the following conditions:

The name of your branch is not main nor dev (see issue #58)
Your PR does not reduce the code coverage
Your code is documented: the function signatures are typed, and the main functions have clear docstrings
Your code is mostly original, and the parts coming from licensed sources are explicitly stated as such
If you implement a method, please add a reference to the corresponding paper in the references page.
Also, remember to add TorchUncertainty to the list of libraries implementing this reference on PapersWithCode.

If you need help to implement a method, increase the coverage, or solve ruff-raised errors, create the PR with the need-help flag and explain your problems in the comments. A maintainer will do their best to help you.

Datasets & Datamodules¶

We intend to include datamodules for the most popular datasets only.

Post-processing methods¶

For now, we intend to follow scikit-learn style API for post-processing methods (except that we use a validation dataset instead of a numpy array). You may get inspiration from the already implemented temperature-scaling.

License¶

If you feel that the current license is an obstacle to your contribution, let us know, and we may reconsider. However, the models’ weights hosted on Hugging Face are likely to remain Apache 2.0.