'fix_ctc_loss_issue'

test.py

@@ -92,7 +92,12 @@ def validation(model, criterion, evaluation_loader, converter, opt):
             # Calculate evaluation loss for CTC deocder.
             preds_size = torch.IntTensor([preds.size(1)] * batch_size)
             preds = preds.permute(1, 0, 2)  # to use CTCloss format
+
+            # 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_for_loss, preds_size, length_for_loss)
+            torch.backends.cudnn.enabled = True
 
             # Select max probabilty (greedy decoding) then decode index to character
             _, preds_index = preds.max(2)

train.py

@@ -128,9 +128,14 @@ def train(opt):
 
         if 'CTC' in opt.Prediction:
             preds = model(image, text).log_softmax(2)
-            preds_size = torch.IntTensor([preds.size(1)] * batch_size)
+            preds_size = torch.IntTensor([preds.size(1)] * batch_size).to(device)
             preds = preds.permute(1, 0, 2)  # to use CTCLoss format
+
+            # 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, preds_size, length)
+            torch.backends.cudnn.enabled = True
 
         else:
             preds = model(image, text[:, :-1]) # align with Attention.forward

utils.py

@@ -30,7 +30,7 @@ class CTCLabelConverter(object):
         text = ''.join(text)
         text = [self.dict[char] for char in text]
 
-        return (torch.IntTensor(text), torch.IntTensor(length))
+        return (torch.IntTensor(text).to(device), torch.IntTensor(length).to(device))
 
     def decode(self, text_index, length):
         """ convert text-index into text-label. """