torch-uncertainty 0.1.0

A PyTorch Library for benchmarking and leveraging efficient predictive uncertainty quantification techniques.

Torch Uncertainty

Torch Uncertainty is a package designed to help you leverage uncertainty quantification techniques and make your neural networks more reliable. It is based on PyTorch Lightning to handle multi-GPU training and inference and automatic logging through tensorboard.

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This package provides a multi-level API, including:

• ready-to-train baselines on research datasets, such as CIFAR and ImageNet
• baselines available for training on your datasets
• layers available for use in your networks

Installation

The package can be installed from PyPI or from source.

From PyPI (available soon)

Install the package via pip: pip install torch-uncertainty

From source

Installing Poetry

Installation guidelines for poetry are available on https://python-poetry.org/docs/. They boil down to executing the following command:

curl -sSL https://install.python-poetry.org | python3 -

Installing the package

Clone the repository:

git clone https://github.com/ENSTA-U2IS/torch-uncertainty.git

Create a new conda environment and activate it with:

conda create -n uncertainty && conda activate uncertainty

Install the package using poetry:

poetry install torch-uncertainty or, for development, poetry install torch-uncertainty --with dev

Depending on your system, you may encounter errors. If so, kill the process and add PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring at the beginning of every poetry install commands.

Contributing

In case that you would like to contribute, install from source and add the pre-commit hooks with pre-commit install

Getting Started and Documentation

Please find the documentation at torch-uncertainty.github.io.

A quickstart is available at torch-uncertainty.github.io/quickstart.

Implemented baselines

To date, the following baselines are implemented:

• Deep Ensembles
• Masksembles
• Packed-Ensembles

Awesome Torch repositories

You may find a lot of information about modern uncertainty estimation techniques on the Awesome Uncertainty in Deep Learning.

References