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Euclidean Neural Networks
Python Cuda C++
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README.md

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.

Image and Point

The code is separated in two parts:

  • image for volumetric data [1]
  • point for point cloud data [2]

Example

Image

import torch
from se3cnn.image.convolution 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]

Point

from functools import partial
import torch
from se3cnn.point.radial import CosineBasisModel
from se3cnn.point.kernel import Kernel
from se3cnn.point.operations import Convolution
from se3cnn.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)

# Use the kernel to define a convolution operation
C = partial(Convolution, K)


Rs_in = [(1, 0)]  # one scalar
Rs_out = [(1, l) for l in range(10)]
conv = C(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)

plt.figure(figsize=(4, 4))
plot_sh_signal(features[:, 0], n=50)
plt.gca().view_init(azim=0, elev=45)

Hierarchy

  • se3cnn contains the library
    • se3cnn/SO3.py defines all the needed mathematical functions
    • se3cnn/image contains the code specific to voxels
    • se3cnn/point contains the code specific to points
    • se3cnn/non_linearities non linearities working for both point and voxel code
  • examples simple scripts and experiments

Installation

  1. install pytorch
  2. pip install git+https://github.com/AMLab-Amsterdam/lie_learn
  3. pip install git+https://github.com/mariogeiger/se3cnn

Usage

Install with

python setup.py install

Citing

DOI

@misc{mario_geiger_2019_3348277,
  author       = {Mario Geiger and
                  Tess Smidt and
                  Wouter Boomsma and
                  Maurice Weiler and
                  Michał Tyszkiewicz and
                  Jes Frellsen and
                  Benjamin K. Miller},
  title        = {mariogeiger/se3cnn: Point cloud support},
  month        = jul,
  year         = 2019,
  doi          = {10.5281/zenodo.3348277},
  url          = {https://doi.org/10.5281/zenodo.3348277}
}
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