fix encode with batch_max_length

test.py

@@ -85,7 +85,7 @@ def validation(model, criterion, evaluation_loader, converter, opt):
             length_for_pred = torch.cuda.IntTensor([opt.batch_max_length] * batch_size)
             text_for_pred = torch.cuda.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0)
 
-            text_for_loss, length_for_loss = converter.encode(labels)
+            text_for_loss, length_for_loss = converter.encode(labels, batch_max_length=opt.batch_max_length)
 
         start_time = time.time()
         if 'CTC' in opt.Prediction:

utils.py

@@ -15,7 +15,7 @@ class CTCLabelConverter(object):
 
         self.character = ['[blank]'] + dict_character  # dummy '[blank]' token for CTCLoss (index 0)
 
-    def encode(self, text):
+    def encode(self, text, batch_max_length=25):
         """convert text-label into text-index.
         input:
             text: text labels of each image. [batch_size]