add denormalization function

Author: yunjey

tutorials/10 - Generative Adversarial Network/main-gpu.py

@@ -11,8 +11,11 @@
         transforms.ToTensor(),
         transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
 
+def denorm(x):
+    return (x + 1) / 2
+
 # MNIST Dataset
-train_dataset = dsets.MNIST(root='../data/',
+train_dataset = dsets.MNIST(root='./data/',
                             train=True, 
                             transform=transform,
                             download=True)
@@ -66,16 +69,16 @@ def forward(self, x):
         # Build mini-batch dataset
         images = images.view(images.size(0), -1)
         images = Variable(images.cuda())
-        real_labels = Variable(torch.ones(images.size(0))).cuda()
-        fake_labels = Variable(torch.zeros(images.size(0))).cuda()
+        real_labels = Variable(torch.ones(images.size(0)).cuda())
+        fake_labels = Variable(torch.zeros(images.size(0)).cuda())
 
         # Train the discriminator
         discriminator.zero_grad()
         outputs = discriminator(images)
         real_loss = criterion(outputs, real_labels)
         real_score = outputs
 
-        noise = Variable(torch.randn(images.size(0), 128)).cuda()
+        noise = Variable(torch.randn(images.size(0), 128).cuda())
         fake_images = generator(noise)
         outputs = discriminator(fake_images.detach()) 
         fake_loss = criterion(outputs, fake_labels)
@@ -87,7 +90,7 @@ def forward(self, x):
 
         # Train the generator 
         generator.zero_grad()
-        noise = Variable(torch.randn(images.size(0), 128)).cuda()
+        noise = Variable(torch.randn(images.size(0), 128).cuda())
         fake_images = generator(noise)
         outputs = discriminator(fake_images)
         g_loss = criterion(outputs, real_labels)
@@ -98,13 +101,13 @@ def forward(self, x):
             print('Epoch [%d/%d], Step[%d/%d], d_loss: %.4f, g_loss: %.4f, ' 
                   'D(x): %.2f, D(G(z)): %.2f' 
                   %(epoch, 200, i+1, 600, d_loss.data[0], g_loss.data[0],
-                    real_score.cpu().data.mean(), fake_score.cpu().data.mean()))
+                    real_score.data.mean(), fake_score.cpu().data.mean()))
 
     # Save the sampled images
     fake_images = fake_images.view(fake_images.size(0), 1, 28, 28)
-    torchvision.utils.save_image(fake_images.data, 
+    torchvision.utils.save_image(denorm(fake_images.data), 
         './data/fake_samples_%d.png' %(epoch+1))
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')

tutorials/10 - Generative Adversarial Network/main.py

@@ -11,8 +11,11 @@
         transforms.ToTensor(),
         transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
 
+def denorm(x):
+    return (x + 1) / 2
+
 # MNIST Dataset
-train_dataset = dsets.MNIST(root='../data/',
+train_dataset = dsets.MNIST(root='./data/',
                             train=True, 
                             transform=transform,
                             download=True)
@@ -102,9 +105,9 @@ def forward(self, x):
 
     # Save the sampled images
     fake_images = fake_images.view(fake_images.size(0), 1, 28, 28)
-    torchvision.utils.save_image(fake_images.data, 
+    torchvision.utils.save_image(denorm(fake_images.data), 
         './data/fake_samples_%d.png' %(epoch+1))
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')

tutorials/11 - Deep Convolutional Generative Adversarial Network/main-gpu.py

@@ -12,8 +12,11 @@
         transforms.ToTensor(),
         transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
 
+def denorm(x):
+    return (x + 1) / 2
+
 # CIFAR-10 Dataset
-train_dataset = dsets.CIFAR10(root='../data/',
+train_dataset = dsets.CIFAR10(root='./data/',
                                train=True, 
                                transform=transform,
                                download=True)
@@ -126,9 +129,9 @@ def forward(self, x):
                     real_score.cpu().data.mean(), fake_score.cpu().data.mean()))
 
             # Save the sampled images
-            torchvision.utils.save_image(fake_images.data, 
+            torchvision.utils.save_image(denorm(fake_images.data), 
                 './data/fake_samples_%d_%d.png' %(epoch+1, i+1))
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')

tutorials/11 - Deep Convolutional Generative Adversarial Network/main.py

@@ -12,8 +12,11 @@
         transforms.ToTensor(),
         transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
 
+def denorm(x):
+    return (x + 1) / 2
+
 # CIFAR-10 Dataset
-train_dataset = dsets.CIFAR10(root='../data/',
+train_dataset = dsets.CIFAR10(root='./data/',
                                train=True, 
                                transform=transform,
                                download=True)
@@ -126,9 +129,9 @@ def forward(self, x):
                     real_score.data.mean(), fake_score.data.mean()))
 
             # Save the sampled images
-            torchvision.utils.save_image(fake_images.data, 
+            torchvision.utils.save_image(denorm(fake_images.data), 
                 './data/fake_samples_%d_%d.png' %(epoch+1, i+1))
 
 # Save the Models
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')