How can I improve my DCGAN?

username: GERMAIN_NICOLAS_MORA
cooked: <p>Hello, I have managed to generate a DCGAN for 128x128 images, however when I try to apply it to images with a size of 256x256, the network seems to fail and it generates 64 low quality.</p>
<p>I would like to know what is happening with my network or what am I doing wrong, I attach my code and the results</p>
<pre><code class="lang-auto"># direccion del directorio de entrenamiento
dataroot = "/content/dataset"


# Number of workers for dataloader
workers = 2

# Batch size during training
batch_size = 1

# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 256

# Number of channels in the training images. For color images this is 3
nc = 3

# Size of z latent vector (i.e. size of generator input)
nz = 100

# Size of feature maps in generator
ngf = 16

# Size of feature maps in discriminator
ndf = 16

# Number of training epochs
num_epochs = 50

# Learning rate for optimizers
lr = 0.0002

# Beta1 hyperparam for Adam optimizers
beta1 = 0.5

# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1

print("Dataset done")

#Creacion del bloque de entramieto 
#Configuracion del lote de entrenamiento 
dataset = torchvision.datasets.ImageFolder(root=dataroot,
                           transform=transforms.Compose([
                               transforms.Resize(image_size),
                               transforms.CenterCrop(image_size),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),]))
# creacion del un dataloader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                         shuffle=True, num_workers=workers)

# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu &gt; 0) else "cpu")

# Plot some training images
real_batch = next(iter(dataloader))
plt.figure(figsize=(4,4))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))

dataset_size = len(dataloader.dataset)
dataset_size

638

# Generator Code

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # state size. (ngf*32) x 4 x 4
            
            nn.ConvTranspose2d(nz, ngf * 32, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 32),
            nn.ReLU(True),

            # state size. (ngf*16) x 8 x 8
            nn.ConvTranspose2d(ngf * 32, ngf * 16, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 16),
            nn.ReLU(True),

            # state size. (ngf*8) x 16 x 16
            nn.ConvTranspose2d(ngf * 16, ngf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),

            # state size. (ngf*4) x 32 x 32
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),

            # state size. (ngf*2) x 32 x 32
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),

            # state size. (ngf*2) x 32 x 32
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),

            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            
        )


    def forward(self, input):
        return self.main(input)

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            
            # input is (nc) x 128 x 128
            nn.Conv2d(nc, ndf, 4, stride=2, padding=1, bias=False), 
            nn.LeakyReLU(0.2, inplace=True),

            # state size. (ndf) x 64 x 64
            nn.Conv2d(ndf, ndf * 2, 4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),

            # state size. (ndf*2) x 32 x 32
            nn.Conv2d(ndf * 2, ndf * 4, 4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),

            # state size. (ndf*4) x 16 x 16 
            nn.Conv2d(ndf * 4, ndf * 8, 4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),

            # state size. (ndf*8) x 8 x 8
            nn.Conv2d(ndf * 8, ndf * 16, 4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(ndf * 16),
            nn.LeakyReLU(0.2, inplace=True),

            # state size. (ndf*8) x 8 x 8
            nn.Conv2d(ndf * 16, ndf * 32, 4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(ndf * 32),
            nn.LeakyReLU(0.2, inplace=True),

            # state size. (ndf*16) x 4 x 4
            nn.Conv2d(ndf * 32, 1, 4, stride=1, padding=0, bias=False),
            nn.Sigmoid()
            # state size. 1
        )

    def forward(self, input):
        return self.main(input)

# Create the Discriminator
netD = Discriminator(ngpu).to(device)

# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu &gt; 1):
    netD = nn.DataParallel(netD, list(range(ngpu)))

# Apply the weights_init function to randomly initialize all weights
#  to mean=0, stdev=0.2.
netD.apply(weights_init)

# Print the model
print(netD)


# Initialize BCELoss function
criterion = nn.BCELoss()

# Create batch of latent vectors that we will use to visualize
#  the progression of the generator
fixed_noise = torch.randn(64, nz, 1, 1, device=device)

# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.

# Setup Adam optimizers for both G and D
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
0# Training Loop

# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
    # For each batch in the dataloader
    for i, data in enumerate(dataloader, 0):

        ############################
        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        ###########################
        ## Train with all-real batch
        netD.zero_grad()
        # Format batch
        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
        # Forward pass real batch through D
        output = netD(real_cpu).view(-1)
        # Calculate loss on all-real batch
        errD_real = criterion(output, label)
        # Calculate gradients for D in backward pass
        errD_real.backward()
        D_x = output.mean().item()

        ## Train with all-fake batch
        # Generate batch of latent vectors
        noise = torch.randn(b_size, nz, 1, 1, device=device)
        # Generate fake image batch with G
        fake = netG(noise)
        label.fill_(fake_label)
        # Classify all fake batch with D
        output = netD(fake.detach()).view(-1)
        # Calculate D's loss on the all-fake batch
        errD_fake = criterion(output, label)
        # Calculate the gradients for this batch, accumulated (summed) with previous gradients
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        # Compute error of D as sum over the fake and the real batches
        errD = errD_real + errD_fake
        # Update D
        optimizerD.step()

        ############################
        # (2) Update G network: maximize log(D(G(z)))
        ###########################
        netG.zero_grad()
        label.fill_(real_label)  # fake labels are real for generator cost
        # Since we just updated D, perform another forward pass of all-fake batch through D
        output = netD(fake).view(-1)
        # Calculate G's loss based on this output
        errG = criterion(output, label)
        # Calculate gradients for G
        errG.backward()
        D_G_z2 = output.mean().item()
        # Update G
        optimizerG.step()

        # Output training stats
        if i % 100 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        # Save Losses for plotting later
        G_losses.append(errG.item())
        D_losses.append(errD.item())

        # Check how the generator is doing by saving G's output on fixed_noise
        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

        iters += 1
</code></pre>
<p>Result</p>
<p><img src="https://discuss.pytorch.org/uploads/default/original/3X/0/6/0624a9c099f9e6387fdfa0efcef954077e30026d.png" alt="download" data-base62-sha1="SlpJNTn23KZ2bZkVfLxkl9kCBf" width="449" height="449"><br>
<img src="https://discuss.pytorch.org/uploads/default/original/3X/e/0/e08ae61bfa7a7983c68afed25be96c964a41062e.png" alt="downloada" data-base62-sha1="w2oxECqyM0N5NI5adCn8KaDvHQO" width="606" height="333"></p>