cuequivariance

import torch from cuequivariance.modules import PointCloudTransformer # --- Runtime CUDA Check --- # cuequivariance requires a CUDA-enabled GPU and PyTorch compiled with CUDA support. if not torch.cuda.is_available(): raise RuntimeError( "CUDA is not available. cuequivariance requires a CUDA-enabled GPU and PyTorch " "compiled with CUDA support. Please check your PyTorch installation and CUDA setup." ) # Ensure PyTorch device is set to CUDA device = torch.device("cuda") # --- Example Data Generation --- batch_size = 1 num_points = 100 input_feature_dim = 64 output_feature_dim = 128 # Input points (batch_size, num_points, 3) - Represents 3D coordinates points = torch.randn(batch_size, num_points, 3, device=device) # Input features (batch_size, num_points, input_feature_dim) - Features associated with each point features = torch.randn(batch_size, num_points, input_feature_dim, device=device) # --- Initialize and Use a cuequivariance Module --- # PointCloudTransformer is an example module demonstrating 3D equivariant operations. transformer = PointCloudTransformer(input_feature_dim, output_feature_dim).to(device) # Perform the forward pass output_features = transformer(points, features) # --- Verify Output --- print(f"Input points shape: {points.shape}") print(f"Input features shape: {features.shape}") print(f"Output features shape: {output_features.shape}") assert output_features.shape == (batch_size, num_points, output_feature_dim) print("cuequivariance quickstart example ran successfully!")