Amend the decoder output (leave out the first all zero time step)

model/dcrnn_model.py

@@ -173,7 +173,8 @@ class DCRNNModel(BaseModel):
         context, _ = self.encoder(source, init_hidden_state)  # (num_layers, batch, outdim)
 
         outputs = self.decoder(target, context, teacher_forcing_ratio=teacher_forcing_ratio)
-        return outputs  # (seq_length+1, batch_size, num_nodes*output_dim)  (13, 50, 207*1)
+        # the elements of the first time step of the outputs are all zeros.
+        return outputs[1:, :, :]  # (seq_length, batch_size, num_nodes*output_dim)  (12, 50, 207*1)
 
     @property
     def batch_size(self):

trainer/dcrnn_trainer.py

@@ -122,8 +122,8 @@ class DCRNNTrainer(BaseTrainer):
                 data, target = data.to(self.device), target.to(self.device)
 
                 output = self.model(data, target, 0)
-                output = torch.transpose(output[1:].view(12, self.model.batch_size, self.model.num_nodes,
-                                                         self.model.output_dim), 0, 1)  # back to (50, 12, 207, 1)
+                output = torch.transpose(output.view(12, self.model.batch_size, self.model.num_nodes,
+                                                     self.model.output_dim), 0, 1)  # back to (50, 12, 207, 1)
 
                 loss = self.loss(output.cpu(), label)