monitor display

nnet/xvector.py

@@ -1935,6 +1935,7 @@ def new_train_epoch(model,
                 scaler.update()
             else:
                 loss.backward()
+                print("optimizer.step")
                 optimizer.step()
             running_loss += loss.item()
             accuracy += (torch.argmax(output.data, 1) == target).sum()
@@ -1964,6 +1965,7 @@ def new_train_epoch(model,
                 pickle.dump(data.cpu(), fh)
             import sys
             sys.exit()
+
         running_loss = 0.0
         scheduler.step()
     return model