The group E(3) is the group of 3 dimensional rotations, translations and mirror. This library aims to create E(3) equivariant convolutional neural networks.
from functools import partial
import torch
from e3nn.radial import CosineBasisModel
from e3nn.kernel import Kernel
from e3nn.point.operations import Convolution
from e3nn.util.plot import plot_sh_signal
import matplotlib.pyplot as plt
# Radial model: R -> R^d
# Projection on cos^2 basis functions followed by a fully connected network
RadialModel = partial(CosineBasisModel, max_radius=3.0, number_of_basis=3, h=100, L=1, act=torch.relu)
# 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)
# Define input and output representations
Rs_in = [(1, 0)] # one scalar
Rs_out = [(1, l) for l in range(10)]
# Use the kernel to define a convolution operation
conv = Convolution(K, Rs_in, Rs_out)
n = 3 # number of points
features = torch.ones(1, n, 1)
geometry = torch.randn(1, n, 3)
features = conv(features, geometry)e3nncontains the librarye3nn/o3.pydefines all the needed mathematical functionse3nn/imagecontains voxels linear operationse3nn/pointcontains points linear operationse3nn/non_linearitiesnon linearities operations
examplessimple scripts and experiments
- install pytorch
pip install git+https://github.com/AMLab-Amsterdam/lie_learnpip install git+https://github.com/e3nn/e3nn
@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}
}