unipercept 5.1.3

UniPecept: A unified framework for perception tasks focusing on research applications that require a high degree of flexibility and customization.

UniPercept

Installation

This package requires at least Python 3.11 and PyTorch 2.1. Once you have created an environment with these dependencies, we can proceed to install unipercept using one of three installation methods.

Stable release (recommended)

You can install the latest stable release from PyPI via

pip install unipercept

Master branch

To install the latest version, which is not guaranteed to be stable, install from GitHub using

pip install git+https://github.com/kurt-stolle/unipercept.git

Developers

If your use-case requires changes to our codebase, we recommend that you first fork this repository and download your own fork locally. Assuming you have the GitHub CLI installed, you can clone your fork with

gh repo clone unipercept

Then, you can proceed to install the package in editable mode by running

pip install --editable unipercept

You are invited to share your improvements to the codebase through a pull request on this repository. Before making a pull request, please ensure your changes follow our code guidelines by running pre-commit before adding your files into a Git commit.

Training and evaluation

Models can be trained and evalurated from the CLI or through the Python API.

CLI

To train a model with the CLI:

unicli train --config <config path>

Without a <config name>, an interactive prompt will be started to assist in finding a configuration file.

Developer guidelines

All tests can ran via python -m pytest. However, we also provide a make directive that uses pytorch-xdist to speed up the process:

make test

You may need to tune the parameters if memory problems arise during testing.

Similarly, benchmarks are implemented using pytest-benchmark. To run them, use:

make benchmark

Coverage reports are built using pytest-cov, and executed using:

make coverage

Last, we largely follow the same principles and methods as Transformers uses for testing. For more information on using pytest for automated testing, check out their documentation.

Acknowledgements

We would like to express our gratitude to the developers of the following open-source projects, which have significantly contributed to the success of our work:

• PyTorch: An open-source machine learning framework that accelerates the path from research prototyping to production deployment.
• Detectron2: A platform for object detection and segmentation built on PyTorch. We liberally use the packages and code from this project.
• PyTorch3D: A library on which we base our camera projection from 2D to 3D space.
• Panoptic FCN: An implementation of the Panoptic FCN method for panoptic segmentation tasks.
• ViP-DeepLab: The baseline implementation for the depth-aware video panoptic segmentation task.
• Panoptic Depth: A repository that implements the instance (de)normalization procedure that significantly improves depth estimation for things.

The Unified Perception implementation contains extracts of the above repositories that have been edited to suit the specific needs of this project. Whenever possible, the original libraries are used instead.

License

This repository is released under the MIT License. For more information, please refer to the LICENSE file.