removed two deprecated function calls, added __name__ check to addres…

…s multithreading bug in dataloader (pytorch#414)

vae/main.py

@@ -22,7 +22,6 @@
 args = parser.parse_args()
 args.cuda = not args.no_cuda and torch.cuda.is_available()
 
-
 torch.manual_seed(args.seed)
 
 device = torch.device("cuda" if args.cuda else "cpu")
@@ -61,7 +60,7 @@ def reparameterize(self, mu, logvar):
 
     def decode(self, z):
         h3 = F.relu(self.fc3(z))
-        return F.sigmoid(self.fc4(h3))
+        return torch.sigmoid(self.fc4(h3))
 
     def forward(self, x):
         mu, logvar = self.encode(x.view(-1, 784))
@@ -75,7 +74,7 @@ def forward(self, x):
 
 # Reconstruction + KL divergence losses summed over all elements and batch
 def loss_function(recon_x, x, mu, logvar):
-    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), size_average=False)
+    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')
 
     # see Appendix B from VAE paper:
     # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
@@ -125,12 +124,12 @@ def test(epoch):
     test_loss /= len(test_loader.dataset)
     print('====> Test set loss: {:.4f}'.format(test_loss))
 
-
-for epoch in range(1, args.epochs + 1):
-    train(epoch)
-    test(epoch)
-    with torch.no_grad():
-        sample = torch.randn(64, 20).to(device)
-        sample = model.decode(sample).cpu()
-        save_image(sample.view(64, 1, 28, 28),
-                   'results/sample_' + str(epoch) + '.png')
+if __name__ == "__main__":
+    for epoch in range(1, args.epochs + 1):
+        train(epoch)
+        test(epoch)
+        with torch.no_grad():
+            sample = torch.randn(64, 20).to(device)
+            sample = model.decode(sample).cpu()
+            save_image(sample.view(64, 1, 28, 28),
+                       'results/sample_' + str(epoch) + '.png')