diff --git a/dcgan/main.py b/dcgan/main.py
index 8847017453..a65874cb11 100644
--- a/dcgan/main.py
+++ b/dcgan/main.py
@@ -198,9 +198,6 @@ def forward(self, input):
     input, label = input.cuda(), label.cuda()
     noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
 
-input = Variable(input)
-label = Variable(label)
-noise = Variable(noise)
 fixed_noise = Variable(fixed_noise)
 
 # setup optimizer
@@ -216,21 +213,25 @@ def forward(self, input):
         netD.zero_grad()
         real_cpu, _ = data
         batch_size = real_cpu.size(0)
-        input.data.resize_(real_cpu.size()).copy_(real_cpu)
-        label.data.resize_(batch_size).fill_(real_label)
-
-        output = netD(input)
-        errD_real = criterion(output, label)
+        if opt.cuda:
+            real_cpu = real_cpu.cuda()
+        input.resize_as_(real_cpu).copy_(real_cpu)
+        label.resize_(batch_size).fill_(real_label)
+        inputv = Variable(input)
+        labelv = Variable(label)
+
+        output = netD(inputv)
+        errD_real = criterion(output, labelv)
         errD_real.backward()
         D_x = output.data.mean()
 
         # train with fake
-        noise.data.resize_(batch_size, nz, 1, 1)
-        noise.data.normal_(0, 1)
-        fake = netG(noise)
-        label.data.fill_(fake_label)
+        noise.resize_(batch_size, nz, 1, 1).normal_(0, 1)
+        noisev = Variable(noise)
+        fake = netG(noisev)
+        labelv = Variable(label.fill_(fake_label))
         output = netD(fake.detach())
-        errD_fake = criterion(output, label)
+        errD_fake = criterion(output, labelv)
         errD_fake.backward()
         D_G_z1 = output.data.mean()
         errD = errD_real + errD_fake
@@ -240,9 +241,9 @@ def forward(self, input):
         # (2) Update G network: maximize log(D(G(z)))
         ###########################
         netG.zero_grad()
-        label.data.fill_(real_label)  # fake labels are real for generator cost
+        labelv = Variable(label.fill_(real_label))  # fake labels are real for generator cost
         output = netD(fake)
-        errG = criterion(output, label)
+        errG = criterion(output, labelv)
         errG.backward()
         D_G_z2 = output.data.mean()
         optimizerG.step()