deel-torchlip 1.0.4

PyTorch implementation for k-Lipschitz layers

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

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.

📚 Table of contents

• 📚 Table of contents
• 🔥 Tutorials
• 🚀 Quick Start
• 📦 What's Included
• 👍 Contributing
• 👀 See Also
• 🙏 Acknowledgments
• 👨‍🎓 Creator
• 🗞️ Citation
• 📝 License

🔥 Tutorials

We propose some tutorials to get familiar with the library and its API:

Tutorial	Description	Link
Wasserstein Tutorial	Get started with the basics of torchlip to compute wasserstein distance.
Binary classification	Learning binary robust classifier with deel-torchlip's API.
Tutorial Multiclass classification	Learning multiclass robust classifier with deel-torchlip's API.

🚀 Quick Start

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)
)

📦 What's Included

The deel-torchlip library proposes a list of 1-Lipschitz layers equivalent to torch.nn ones.

torch.nn	1-Lipschitz?	deel-torchlip equivalent	comments
torch.nn.Linear	no	.SpectralLinear .FrobeniusLinear	.SpectralLinear and .FrobeniusLinear are similar when there is a single output.
torch.nn.Conv2d	no	.SpectralConv2d .FrobeniusConv2d	.SpectralConv2d also implements Björck normalization.
torch.nn.Conv1d	no	.SpectralConv1d	.SpectralConv1d also implements Björck normalization.
MaxPooling GlobalMaxPooling	yes	n/a
torch.nn.AvgPool2d torch.nn.AdaptiveAvgPool2d	no	.ScaledAvgPool2d .ScaledAdaptiveAvgPool2d .ScaledL2NormPool2d .ScaledAdaptativeL2NormPool2d	The Lipschitz constant is bounded by sqrt(pool_h * pool_w).
Flatten	yes	n/a
torch.nn.ConvTranspose2d	no	.SpectralConvTranspose2d	.SpectralConvTranspose2d also implements Björck normalization.
torch.nn.BatchNorm1d torch.nn.BatchNorm2d torch.nn.BatchNorm3d	no	.BatchCentering	This layer apply a bias based on statistics on batch, but no normalization factor (1-Lipschitz).
torch.nn.LayerNorm	no	.LayerCentering	This layer apply a bias based on statistics on each sample, but no normalization factor (1-Lipschitz).
Residual connections	no	.LipResidual	Learn a factor for mixing residual and a 1-Lipschitz branch.
torch.nn.Dropout	no	None	The Lipschitz constant is bounded by the dropout factor.

The deel-torchlip library proposes a list of classification losses

Type	torch.nn	deel-torchlip equivalent	comments
Binary classification	torch.nn.BCEWithLogitsLoss	.HKRLoss	alpha: Regularization factor ([0,1]) between the hinge and the KR loss; min_margin: Minimal margin for the hinge loss.
Multiclass classification	torch.nn.CrossEntropyLoss	.HKRMulticlassLoss .SoftHKRMulticlassLoss .LseHKRMulticlassLoss	alpha: Regularization factor ([0,1]) between the hinge and the KR loss; min_margin: Minimal margin for the hinge loss. temperature for the softmax calculation

👍 Contributing

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

👀 See Also

More from the DEEL project:

• Xplique a Python library exclusively dedicated to explaining neural networks.
• deel-lip a Python library for training k-Lipschitz neural networks on TF.
• Influenciae Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
• oodeel a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
• DEEL White paper a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.

🙏 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.

👨‍🎓 Creators

Main contributors of the deel-torchlip library are:

• Franck Mamalet
• Corentin Friedrich
• Justin Plakoo
• Thibaut Boissin
• Mikael Capelle
• Mathieu Serrurier

🗞️ 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 :wink:

@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},
}

📝 License

The package is released under MIT license.