Remove variables

datasets/LJSpeech.py

@@ -74,7 +74,7 @@ class LJSpeechDataset(Dataset):
 
     def get_dummy_data(self):
         r"""Get a dummy input for testing"""
-        return torch.autograd.Variable(torch.ones(16, 143)).type(torch.LongTensor)
+        return torch.ones(16, 143).type(torch.LongTensor)
 
     def collate_fn(self, batch):
         r"""

datasets/TWEB.py

@@ -72,10 +72,6 @@ class TWEBDataset(Dataset):
         sample = {'text': text, 'wav': wav, 'item_idx': self.frames[idx][0]}
         return sample
 
-    def get_dummy_data(self):
-        r"""Get a dummy input for testing"""
-        return torch.autograd.Variable(torch.ones(16, 143)).type(torch.LongTensor)
-
     def collate_fn(self, batch):
         r"""
             Perform preprocessing and create a final data batch:

layers/attention.py

@@ -1,5 +1,4 @@
 import torch
-from torch.autograd import Variable
 from torch import nn
 from torch.nn import functional as F
 

layers/custom_layers.py

@@ -1,6 +1,5 @@
 # coding: utf-8
 import torch
-from torch.autograd import Variable
 from torch import nn
 
 

layers/losses.py

@@ -1,6 +1,5 @@
 import torch
 from torch.nn import functional
-from torch.autograd import Variable
 from torch import nn
 
 
@@ -11,7 +10,6 @@ def _sequence_mask(sequence_length, max_len=None):
     batch_size = sequence_length.size(0)
     seq_range = torch.arange(0, max_len).long()
     seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
-    seq_range_expand = Variable(seq_range_expand)
     if sequence_length.is_cuda:
         seq_range_expand = seq_range_expand.cuda()
     seq_length_expand = (sequence_length.unsqueeze(1)