ctc fix..

train.py

@@ -132,14 +132,13 @@ def train(opt):
         if 'CTC' in opt.Prediction:
             preds = model(image, text).log_softmax(2)
             preds_size = torch.IntTensor([preds.size(1)] * batch_size)
-            # permute 'preds' to use CTCloss format
-            cost = criterion(preds.permute(1, 0, 2), text.to(device), preds_size.to(device), length.to(device)) # For PyTorch 1.3.0
+            preds = preds.permute(1, 0, 2)
 
-            # (ctc_a) For PyTorch 1.2.0. To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss
+            # (ctc_a) For PyTorch 1.2.0 and 1.3.0. To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss
             # https://github.com/jpuigcerver/PyLaia/issues/16
-            # torch.backends.cudnn.enabled = False
-            # cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device))
-            # torch.backends.cudnn.enabled = True
+            torch.backends.cudnn.enabled = False
+            cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device))
+            torch.backends.cudnn.enabled = True
 
             # # (ctc_b) To reproduce our pretrained model / paper, use our previous code (below code) instead of (ctc_a).
             # # With PyTorch 1.2.0, the below code occurs NAN, so you may use PyTorch 1.1.0.