Continuous Regular Group Convolutions (WIP 👷‍♀️👷‍♂️)

This package implements a Pytorch framework for group convolutions that are easy to use and implement in existing Pytorch modules. The package offers premade modules for E3 and SE3 convolutions, as well as basic operations such as pooling and normalization for $\mathbb{R}^n \rtimes H$ input. The method is explained in the paper Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis, accepted at MICCAI 2023 (see reference below).

Installation from Source

Download gconv and save to a directory. Then from that directory run the following command:

pip install -e gconv

Getting Started

The gconv modules are as straightforward to use as any regular Pytorch convolution module. The only difference is the output consisting of both the feature maps, as well as the group elements on which they are defined. See the example below:

import torch                                                                        # 1
import gconv.gnn as gnn                                                             # 2
                                                                                    # 3
x1 = torch.randn(16, 3, 28, 28, 28)                                                 # 4
                                                                                    # 5
lifting_layer = gnn.GLiftingConvSE3(in_channels=3, out_channels=16, kernel_size=5)  # 6
gconv_layer = gnn.GSeparableConvSE3(in_channels=16, out_channels=32, kernel_size=5) # 7
                                                                                    # 8
pool = gnn.GAvgGlobalPool()                                                         # 9
                                                                                    # 10
x2, H1 = lifting_layer(x1)                                                          # 11
x3, H2 = gconv_layer(x2, H1)                                                        # 12
                                                                                    # 13
y = pool(x3, H2)                                                                    # 14

In line 5, a random batch of three-channel $\mathbb{R}^3$ volumes is created. In line 6, the $\mathbb{R}^3$ is lifted to $\text{SE}(3) = \mathbb{R}^3 \rtimes \text{SO}(3)$. In line 7, an $\text{SE}(3)$ convolution is performed. In line 14, a global pooling is performed, resulting in $\text{SE}(3)$ invariant features.

Furthermore, gconv offers all the necessary tools to build fully custom group convolutions. All that is required is implementing 5 (or less, depending on the type of convolution) group ops! For more details on how to implement custom group convolutions, see gconv_tutorial.ipynb.

Requirements:

python >= 3.10
torch
tqdm

Reference:

Paper accepted at MICCAI 2023.

@misc{kuipers2023regular,
      title={Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis}, 
      author={Thijs P. Kuipers and Erik J. Bekkers},
      year={2023},
      eprint={2306.13960},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}