Awesome
SE3CNN
The group SE(3) is the group of 3 dimensional rotations and translations. This library aims to create SE(3) equivariant convolutional neural networks.
Example
import torch
from se3cnn import SE3Convolution
size = 32 # space size
scalar_field = torch.randn(1, 1, size, size, size) # [batch, _, x, y, z]
Rs_in = [(1, 0)] # 1 scalar field
Rs_out = [(1, 1)] # 1 vector field
conv = SE3Convolution(Rs_in, Rs_out, size=5)
# conv.weight.size() == [2] (2 radial degrees of freedom)
vector_field = conv(scalar_field) # [batch, vector component, x, y, z]
# vector_field.size() == [1, 3, 28, 28, 28]
Hierarchy
se3cnn
contains the libraryse3cnn/convolution.py
definesSE3Convolution
the main class of the libraryse3cnn/blocks
defines ways of introducing non linearity in an equivariant wayse3cnn/batchnorm.py
equivariant batch normalizationse3cnn/groupnorm.py
equivariant group normalizationse3cnn/dropout.py
equivariant dropout
experiments
contains experiments made with the libraryexamples
simple scripts
Dependencies
Usage
Install with
python setup.py install