Welcome to the Torchmetrics community! We’re building largest collection of native pytorch metrics, with the goal of reducing boilerplate and increasing reproducibility.
We are always looking for help implementing new features or fixing bugs.
If you find a bug please submit a github issue.
Make sure the title explains the issue.
Describe your setup, what you are trying to do, expected vs. actual behaviour. Please add configs and code samples.
Add details on how to reproduce the issue - a minimal test case is always best, colab is also great. Note, that the sample code shall be minimal and if needed with publicly available data.
Try to fix it or recommend a solution. We highly recommend to use test-driven approach:
Convert your minimal code example to a unit/integration test with assert on expected results.
Start by debugging the issue… You can run just this particular test in your IDE and draft a fix.
Verify that your test case fails on the master branch and only passes with the fix applied.
Submit a PR!
Note, even if you do not find the solution, sending a PR with a test covering the issue is a valid contribution and we can help you or finish it with you :]
Submit a github issue - describe what is the motivation of such feature (adding the use case or an example is helpful).
Let’s discuss to determine the feature scope.
Submit a PR! We recommend test driven approach to adding new features as well:
Write a test for the functionality you want to add.
Write the functional code until the test passes.
Add/update the relevant tests!
This PR is a good example for adding a new metric
Want to keep Torchmetrics healthy? Love seeing those green tests? So do we! How to we keep it that way? We write tests! We value tests contribution even more than new features. One of the core values of torchmetrics is that our users can trust our metric implementation. We can only guarantee this if our metrics are well tested.
To build the documentation locally, simply execute the following commands from project root (only for Unix):
make cleancleans repo from temp/generated files
make docsbuilds documentation under docs/build/html
make testruns all project’s tests with coverage
All added or edited code shall be the own original work of the particular contributor.
If you use some third-party implementation, all such blocks/functions/modules shall be properly referred and if
possible also agreed by code’s author. For example -
This code is inspired from http://....
In case you adding new dependencies, make sure that they are compatible with the actual Torchmetrics license
(ie. dependencies should be at least as permissive as the Torchmetrics license).
Use f-strings for output formation (except logging when we stay with lazy
logging.info("Hello %s!", name).
You can use
pre-committo make sure your code style is correct.
We are using Sphinx with Napoleon extension. Moreover, we set Google style to follow with type convention.
See following short example of a sample function taking one position string and optional
from typing import Optional def my_func(param_a: int, param_b: Optional[float] = None) -> str: """Sample function. Args: param_a: first parameter param_b: second parameter Return: sum of both numbers Example: Sample doctest example... >>> my_func(1, 2) 3 .. note:: If you want to add something. """ p = param_b if param_b else 0 return str(param_a + p)
When updating the docs make sure to build them first locally and visually inspect the html files (in the browser) for formatting errors. In certain cases, a missing blank line or a wrong indent can lead to a broken layout. Run these commands
docs/build/html/index.html in your browser.
You need to have LaTeX installed for rendering math equations. You can for example install TeXLive by doing one of the following:
on Ubuntu (Linux) run
apt-get install texliveor otherwise follow the instructions on the TeXLive website
use the RTD docker image
with PL used class meta you need to use python 3.7 or higher
When you send a PR the continuous integration will run tests and build the docs.
Local: Testing your work locally will help you speed up the process since it allows you to focus on particular (failing) test-cases. To setup a local development environment, install both local and test dependencies:
python -m pip install -r requirements/test.txt python -m pip install pre-commit
You can run the full test-case in your terminal via this make script:
make test # or natively python -m pytest torchmetrics tests
Note: if your computer does not have multi-GPU nor TPU these tests are skipped.
GitHub Actions: For convenience, you can also use your own GHActions building which will be triggered with each commit. This is useful if you do not test against all required dependency versions.