This repository holds the main python package for the GNN Tracking project. See the readme of the organization for an overview of the task. An early version of the pipeline implemented here is written up in this preprint. More resources are provided in the reading list here.
🔋 Batteries included: This repository implements a hole pipeline: from preprocessing to models, to the evaluation of the final performance metrics.
⚡ Built around pytorch lightning, our models are easy to train and to restore. By using hooks and callbacks, everything remains modular and maintainable.
✅ Tested: Most of the code is guaranteed to run
Install mamba or micromamba (installation instructions). Conda works as well, but will be slow to solve the environment, so it’s not recommended.
Set up your environment with one of the
environment/*.ymlfiles (see the readme in that folder)
pip3 install -e '.[testing,dev]'from this directory.
pytestfrom this directory to check if everything worked
For development: Install pre-commit hooks:
pre-commit install(from this directory)
A good place to get started are the demo notebooks. This package is versioned as .
🧰 Development guidelines#
If you open a PR and pre-commit fails for formatting, comment
to trigger a fixup commit from
To skip the slowest tests with
💚 Contributing, contact, citation#
A good place to start contributing are the issues marked with ‘good first issue’. It is always best to have the issue assigned to you before starting to work on it.
Core developers (emoji key):
Thanks also goes to these wonderful people:
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This project follows the all-contributors specification. Contributions of any kind welcome!