diff --git a/model.py b/model.py
old mode 100644
new mode 100755
index 148fee2..fd11717
--- a/model.py
+++ b/model.py
@@ -67,7 +67,7 @@ class Model(nn.Module):
         else:
             raise Exception('Prediction is neither CTC or Attn')
 
-    def forward(self, input, length, text, is_train=True):
+    def forward(self, input, text, is_train=True):
         """ Transformation stage """
         if not self.stages['Trans'] == "None":
             input = self.Transformation(input)
@@ -87,6 +87,6 @@ class Model(nn.Module):
         if self.stages['Pred'] == 'CTC':
             prediction = self.Prediction(contextual_feature.contiguous())
         else:
-            prediction = self.Prediction(contextual_feature.contiguous(), length, text, is_train)
+            prediction = self.Prediction(contextual_feature.contiguous(), text, is_train)
 
         return prediction
diff --git a/modules/prediction.py b/modules/prediction.py
old mode 100644
new mode 100755
index ee34b6d..20f9623
--- a/modules/prediction.py
+++ b/modules/prediction.py
@@ -19,11 +19,10 @@ class Attention(nn.Module):
         one_hot = one_hot.scatter_(1, input_char, 1)
         return one_hot
 
-    def forward(self, batch_H, length, text, is_train=True, batch_max_length=25):
+    def forward(self, batch_H, text, is_train=True, batch_max_length=25):
         """
         input:
             batch_H : contextual_feature H = hidden state of encoder. [batch_size x num_steps x num_classes]
-            length : the length of each label. train: [3, 7, ....], test: [25, 25, 25, ...] [batch_size]
             text : the text-index of each image. [batch_size x (max_length+1)]. +1 for [GO] token. text[:, 0] = [GO].
         output: probability distribution at each step [batch_size x num_steps x num_classes]
         """
diff --git a/test.py b/test.py
old mode 100644
new mode 100755
index 814e7a1..87a92b2
--- a/test.py
+++ b/test.py
@@ -85,7 +85,7 @@ def validation(model, criterion, evaluation_loader, converter, opt):
 
         start_time = time.time()
         if 'CTC' in opt.Prediction:
-            preds = model(image, length_for_pred, text_for_pred)
+            preds = model(image, text_for_pred)
             forward_time = time.time() - start_time
 
             # Calculate evaluation loss for CTC deocder.
@@ -99,7 +99,7 @@ def validation(model, criterion, evaluation_loader, converter, opt):
             sim_preds = converter.decode(preds.data, preds_size.data)
 
         else:
-            preds = model(image, length_for_pred, text_for_pred, is_train=False)
+            preds = model(image, text_for_pred, is_train=False)
             forward_time = time.time() - start_time
 
             preds = preds[:, :text_for_loss.shape[1] - 1, :]
diff --git a/train.py b/train.py
old mode 100644
new mode 100755
index a51065f..6629e49
--- a/train.py
+++ b/train.py
@@ -131,13 +131,13 @@ def train(opt):
         batch_size = image.size(0)
 
         if 'CTC' in opt.Prediction:
-            preds = model(image, length, text)
+            preds = model(image, text)
             preds_size = torch.IntTensor([preds.size(1)] * batch_size)
             preds = preds.permute(1, 0, 2)  # to use CTCLoss format
             cost = criterion(preds, text, preds_size, length) / batch_size
 
         else:
-            preds = model(image, length, text)
+            preds = model(image, text)
             target = text[:, 1:]  # without [GO] Symbol
             cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1))