ctc update

demo.py

@@ -56,8 +56,8 @@ def demo(opt):
 
                 # Select max probabilty (greedy decoding) then decode index to character
                 preds_size = torch.IntTensor([preds.size(1)] * batch_size)
-                _, preds_index = preds.permute(1, 0, 2).max(2)
-                preds_index = preds_index.transpose(1, 0).contiguous().view(-1)
+                _, preds_index = preds.max(2)
+                preds_index = preds_index.view(-1)
                 preds_str = converter.decode(preds_index.data, preds_size.data)
 
             else:

test.py

@@ -97,7 +97,7 @@ def validation(model, criterion, evaluation_loader, converter, opt):
 
             # Select max probabilty (greedy decoding) then decode index to character
             _, preds_index = preds.max(2)
-            preds_index = preds_index.transpose(1, 0).contiguous().view(-1)
+            preds_index = preds_index.view(-1)
             preds_str = converter.decode(preds_index.data, preds_size.data)
 
         else:

train.py

@@ -132,13 +132,14 @@ 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 = preds.permute(1, 0, 2)  # to use CTCLoss format
+            # 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
 
-            # (ctc_a) To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss
+            # (ctc_a) For PyTorch 1.2.0. 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.