From 91ac9d48f1b05431a5faa4a3b16e74f6fa34b868 Mon Sep 17 00:00:00 2001
From: Eren Golge <egolge@mozilla.com>
Date: Wed, 25 Apr 2018 08:00:19 -0700
Subject: [PATCH] Remove variables

---
 datasets/LJSpeech.py    | 2 +-
 datasets/TWEB.py        | 4 ----
 layers/attention.py     | 1 -
 layers/custom_layers.py | 1 -
 layers/losses.py        | 2 --
 5 files changed, 1 insertion(+), 9 deletions(-)

diff --git a/datasets/LJSpeech.py b/datasets/LJSpeech.py
index 898b88bf..aea6575d 100644
--- a/datasets/LJSpeech.py
+++ b/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"""
diff --git a/datasets/TWEB.py b/datasets/TWEB.py
index af070381..eeae551a 100644
--- a/datasets/TWEB.py
+++ b/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:
diff --git a/layers/attention.py b/layers/attention.py
index 3bab1ad6..51d3542a 100644
--- a/layers/attention.py
+++ b/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
 
diff --git a/layers/custom_layers.py b/layers/custom_layers.py
index c7337e71..e7f52d7c 100644
--- a/layers/custom_layers.py
+++ b/layers/custom_layers.py
@@ -1,6 +1,5 @@
 # coding: utf-8
 import torch
-from torch.autograd import Variable
 from torch import nn
 
 
diff --git a/layers/losses.py b/layers/losses.py
index 3e8376a4..9e467ef8 100644
--- a/layers/losses.py
+++ b/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)