e3nn
E(3) is the Euclidean group in dimension 3. That is the group of rotations, translations and mirror.
e3nn is a pytorch library that aims to create E(3) equivariant neural networks.
Example
from functools import partial
import torch
from e3nn.non_linearities.rescaled_act import swish
from e3nn.radial import GaussianRadialModel
from e3nn.kernel import Kernel
from e3nn.point.operations import Convolution
from e3nn.non_linearities.norm import Norm
from e3nn import rs
# Define the input and output representations
Rs_in = [(1, 0), (2, 1)] # Input = One scalar plus two vectors
Rs_out = [(1, 1)] # Output = One single vector
# Radial model: R+ -> R^d
RadialModel = partial(GaussianRadialModel, max_radius=3.0, number_of_basis=3, h=100, L=1, act=swish)
# kernel: composed on a radial part that contains the learned parameters
# and an angular part given by the spherical hamonics and the Clebsch-Gordan coefficients
K = partial(Kernel, RadialModel=RadialModel, normalization='norm')
# Use the kernel to define a convolution operation
C = partial(Convolution, K)
# Create the convolution module
conv = C(Rs_in, Rs_out)
# Module to compute the norm of each irreducible component
norm = Norm(Rs_out, normalization='norm')
n = 5 # number of input points
features = rs.randn(1, n, Rs_in, normalization='norm', requires_grad=True)
in_geometry = torch.randn(1, n, 3)
out_geometry = torch.zeros(1, 1, 3) # One point at the origin
norm(conv(features, in_geometry, out_geometry)).backward()
print(features)
print(features.grad)
Hierarchy
e3nncontains the librarye3nn/o3.pyO(3) irreducible representationse3nn/rsh.pyreal spherical harmonicse3nn/rs.pygeometrical tensor representationse3nn/imagecontains voxels linear operationse3nn/pointcontains points linear operationse3nn/non_linearitiesnon linearities operations
examplessimple scripts and experiments
Installation
pip install git+https://github.com/e3nn/e3nn
To get the CUDA kernels read the instructions in INSTALL.md.
Citing
@software{e3nn_2020_3723557,
author = {Mario Geiger and
Tess Smidt and
Benjamin K. Miller and
Wouter Boomsma and
Kostiantyn Lapchevskyi and
Maurice Weiler and
Michał Tyszkiewicz and
Jes Frellsen},
title = {github.com/e3nn/e3nn},
month = mar,
year = 2020,
publisher = {Zenodo},
version = {v0.3-alpha},
doi = {10.5281/zenodo.3723557},
url = {https://doi.org/10.5281/zenodo.3723557}
}
2 maintainers
Metadata
2 maintainers
Release files for e3nn 0.0.0
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