cuequivariance-torch

import torch from cuequivariance.modules import SO3Linear # Ensure CUDA is available if not torch.cuda.is_available(): raise RuntimeError("CUDA is not available. cu-equivariance-torch requires a CUDA-enabled PyTorch installation with a GPU.") # Define device device = torch.device("cuda") # Example: SO(3) Linear layer for rotation matrices # Input dimensions: (batch, features_in, 3, 3) where the last two dimensions represent a 3x3 matrix batch_size = 4 features_in = 16 features_out = 32 # Create a dummy input tensor on the CUDA device # For SO3Linear, the input tensor's last two dimensions are treated as matrix components. # The layer handles the equivariant operations. input_data = torch.randn(batch_size, features_in, 3, 3, device=device) # Instantiate an SO(3) Linear layer and move it to the CUDA device so3_linear_layer = SO3Linear(features_in, features_out).to(device) # Pass the input through the layer output_data = so3_linear_layer(input_data) print(f"Input shape: {input_data.shape}, device: {input_data.device}") print(f"Output shape: {output_data.shape}, device: {output_data.device}") # Expected output shape: (batch_size, features_out, 3, 3)