shadow1229/SE3Transformer

fork of huhlim/SE3Transformer, which is another fork of NVIDIA's SE(3)-Transformer implementation with modifications

SE3Transformer

It is a fork of NVIDIA's SE(3)-Transformer implementation. I made some minor modifications, including

• removal of torch.cuda.nvtx.nvtx_range
• addition of the nonlinearity argument to NormSE3, SE3Transformer, and so on.
• addition of some basic network implementations using SE(3)-Transformer.

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Installation

for CPU only

pip install git+http://github.com/huhlim/SE3Transformer

for CUDA (GPU) usage

1. Install DGL library with CUDA support

# This is an example with cudatoolkit=11.3.
# Set a proper cudatoolkit version that is compatible with your CUDA drivier and DGL library.
conda install dgl -c dglteam/label/cu113
# or
pip install dgl -f https://data.dgl.ai/wheels/cu113/repo.html

2. Install this package

pip install git+http://github.com/huhlim/SE3Transformer

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Code Snippets

• se3_transformer.LinearModule: LinearSE3 and NormSE3 https://github.com/huhlim/SE3Transformer/blob/b74f7079cf6694fdf9b0185ce15ba4ed6e1ec747/se3_transformer/snippets.py#L14-L64
• se3_transformer.InteractionModule: A wrapper of SE3Transformer https://github.com/huhlim/SE3Transformer/blob/b74f7079cf6694fdf9b0185ce15ba4ed6e1ec747/se3_transformer/snippets.py#L67-L118

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Usage

• LinearModule + InteractionModule https://github.com/huhlim/SE3Transformer/blob/b74f7079cf6694fdf9b0185ce15ba4ed6e1ec747/example/example.py#L1-L84 In this example,
  • A fully connected graph is created with random coordinates
  • Input features: 8 scalars and 4 vectors
  • Output features: 2 scalars and 1 vector
  • LinearModule: two LinearSE3 with NormSE3, returns 16 scalars and 8 vectors.
  • InteractionModule: two layers of attention blocks with two heads, takes the output of the LinearModule as node_feats and no edge_feats.