change dataset.py to nbv_dataset.py

modules/transformer_seq_encoder.py

@@ -22,7 +22,6 @@ class TransformerSequenceEncoder(nn.Module):
         self.fc = nn.Linear(embed_dim, config["output_dim"])
 
     def encode_sequence(self, pts_embedding_list_batch, pose_embedding_list_batch):
-        # Combine features and pad sequences
         combined_features_batch = []
         lengths = []
 
@@ -36,16 +35,11 @@ class TransformerSequenceEncoder(nn.Module):
 
         combined_tensor = pad_sequence(combined_features_batch, batch_first=True)  # Shape: [batch_size, max_seq_len, embed_dim]
 
-        # Prepare mask for padding
         max_len = max(lengths)
         padding_mask = torch.tensor([([0] * length + [1] * (max_len - length)) for length in lengths], dtype=torch.bool).to(combined_tensor.device)
-        # Transformer encoding
+
         transformer_output = self.transformer_encoder(combined_tensor, src_key_padding_mask=padding_mask)
-
-        # Mean pooling
         final_feature = transformer_output.mean(dim=1)
-
-        # Fully connected layer
         final_output = self.fc(final_feature)
 
         return final_output