Skip to main content

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

Project description

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

  • e3nn contains the library
    • e3nn/o3.py O(3) irreducible representations
    • e3nn/rsh.py real spherical harmonics
    • e3nn/rs.py geometrical tensor representations
    • e3nn/image contains voxels linear operations
    • e3nn/point contains points linear operations
    • e3nn/non_linearities non linearities operations
  • examples simple scripts and experiments

Installation

pip install git+https://github.com/e3nn/e3nn

To get the CUDA kernels read the instructions in INSTALL.md.

Citing

DOI

@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}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

e3nn-0.0.0.tar.gz (607.5 kB view details)

Uploaded Source

Built Distribution

e3nn-0.0.0-py3-none-any.whl (701.7 kB view details)

Uploaded Python 3

File details

Details for the file e3nn-0.0.0.tar.gz.

File metadata

  • Download URL: e3nn-0.0.0.tar.gz
  • Upload date:
  • Size: 607.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.3

File hashes

Hashes for e3nn-0.0.0.tar.gz
Algorithm Hash digest
SHA256 f86909a2fdeee807024791258de13e7e8a8411fb0d489cc8f5519b3483aaea17
MD5 04c6049735a5c35411ca91ae5be30da4
BLAKE2b-256 0d002fce38a34891b8ce5a95905b8cac955bc168e7d7cf4d57a1456a5b29964b

See more details on using hashes here.

File details

Details for the file e3nn-0.0.0-py3-none-any.whl.

File metadata

  • Download URL: e3nn-0.0.0-py3-none-any.whl
  • Upload date:
  • Size: 701.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.3

File hashes

Hashes for e3nn-0.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 0a84b301f065ad371d608ce71d7ffde8bfdee7df9d3d3a532fa858f03b8d7e93
MD5 4a0e05168c8c6572eba2751544e51a46
BLAKE2b-256 ed3441ec81a75d116c5ccd327a8dade0f3fe114b78c6841001fcf030cd1b901f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page