e3nn 0.6.0

Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.

Euclidean neural networks

Documentation | Code | CHANGELOG | Colab

The aim of this library is to help the development of E(3) equivariant neural networks. It contains fundamental mathematical operations such as tensor products and spherical harmonics.

import torch
from e3nn import o3

# Create a random array made of scalar (0e) and a vector (1o)
irreps_in = o3.Irreps("0e + 1o")
x = irreps_in.randn(-1)

# Apply a linear layer
irreps_out = o3.Irreps("2x0e + 2x1o")
linear = o3.Linear(irreps_in=irreps_in, irreps_out=irreps_out)
y = linear(x)

# Compute a tensor product with itself
tp = o3.FullTensorProduct(irreps_in1=irreps_in, irreps_in2=irreps_in)
z = tp(x, x)

# Optionally compile the tensor product
tp_pt2 = torch.compile(tp, fullgraph=True)
z_pt2 = tp_pt2(x, x) # Warning: First few calls might be slow due to compilation
torch.testing.assert_close(z, z_pt2)

Installation

Important: install pytorch and only then run the command

pip install --upgrade pip
pip install --upgrade e3nn

For details and optional dependencies, see INSTALL.md

Breaking changes

e3nn is under development. It is recommended to install using pip. The main branch is considered as unstable. The second version number is incremented every time a breaking change is made to the code.

0.(increment when backwards incompatible release).(increment for backwards compatible release)

Help

We are happy to help! The best way to get help on e3nn is to submit a Question or Bug Report.

Want to get involved? Great!

If you want to get involved in and contribute to the development, improvement, and application of e3nn, introduce yourself in the discussions.

Code of conduct

Our community abides by the Contributor Covenant Code of Conduct.

Citing

If you use e3nn in your research, please cite the following papers:

Euclidean Neural Networks:

• N. Thomas et al., "Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds" (2018). arXiv:1802.08219
• M. Weiler et al., "3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data" (2018). arXiv:1807.02547
• R. Kondor et al., "Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network" (2018). arXiv:1806.09231

e3nn:

• M. Geiger and T. Smidt, "e3nn: Euclidean Neural Networks" (2022). arXiv:2207.09453
• M. Geiger et al., "Euclidean neural networks: e3nn" (2022). Zenodo

For BibTeX entries, please refer to the CITATION.bib file in this repository.

Copyright

Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin and Kostiantyn Lapchevskyi. All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [e-mail].

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.