deel-torchlip is an open source Python API to build and train Lipschitz neural networks. It is built on top of PyTorch.

deel-torchlip provides:

• Easy-to-use Lipschitz layers -- deel-torchlip layers are custom PyTorch layers and are very user-friendly. No need to be an expert in Lipschitz networks!
• Custom losses for robustness -- The provided losses help improving adversarial robustness in classification tasks by increasing margins between outputs of the network (see our paper for more information).
• Certified robustness -- One main advantage of Lipschitz networks is the costless computation of certificates ensuring that there is no adversarial attacks smaller than these certified radii of robustness.

For TensorFlow/Keras users, we released the deel-lip package offering a similar implementation based on Keras.

Install

The latest release can be installed using pip. The torch package will also be installed as a dependency. If torch is already present, be sure that the version is compatible with the deel-torchlip version.

$ pip install deel-torchlip

Usage

Creating a Lipschitz network is similar to building a PyTorch model: standard layers are replaced with their Lipschitz counterparts from deel-torchlip. PyTorch layers that are already Lipschitz can still be used in Lipschitz networks, such as torch.nn.ReLU() or torch.nn.Flatten().

import torch
from deel import torchlip

# Build a Lipschitz network with 4 layers, that can be used in a training loop,
# like any torch.nn.Sequential network
model = torchlip.Sequential(
    torchlip.SpectralConv2d(
        in_channels=3, out_channels=16, kernel_size=(3, 3), padding="same"
    ),
    torchlip.GroupSort2(),
    torch.nn.Flatten(),
    torchlip.SpectralLinear(15544, 64)
)

See the full documentation for a complete API description and for our tutorials to get started.

Citation

This library was built to support the work presented in our CVPR 2021 paper Achieving robustness in classification using optimal transport with Hinge regularization. If you use our library for your work, please cite our paper 😉

@misc{2006.06520,
Author = {Mathieu Serrurier and Franck Mamalet and Alberto González-Sanz and Thibaut Boissin and Jean-Michel Loubes and Eustasio del Barrio},
Title = {Achieving robustness in classification using optimal transport with hinge regularization},
Year = {2020},
Eprint = {arXiv:2006.06520},
}

Contributions

Contributions are welcome! You can open an issue or fork this repository and propose a pull-request. The development environment with all required dependencies should be installed by running:

$ make prepare-dev

Code formatting and linting are performed with black and flake8. Tests are run with pytest. These three commands are gathered in:

$ make test

Finally, commits should respect pre-commit hooks. To be sure that your code changes are accepted, you can run the following target:

$ make check_all

License

Copyright IRT Antoine de Saint Exupéry et Université Paul Sabatier Toulouse III - All rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry, CRIAQ and ANITI - https://www.deel.ai/

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Acknowledgments

This project received funding from the French "Investing for the Future – PIA3" program within the Artificial and Natural Intelligence Toulouse Institute (ANITI). The authors gratefully acknowledge the support of the DEEL project.