Structure 如何在DCGAN中增加图像的大小_Structure_Generator_Generative Adversarial Network_Discriminator_High Resolution

Structure 如何在DCGAN中增加图像的大小

Structure 如何在DCGAN中增加图像的大小,structure,generator,generative-adversarial-network,discriminator,high-resolution,Structure,Generator,Generative Adversarial Network,Discriminator,High Resolution,我使用DCGAN来合成医学图像。然而，目前，Img_的大小是64，这是太低的分辨率如何更改生成器和鉴别器以使其具有512*512高分辨率下面是我的代码 # Root directory for dataset dataroot = "./Image/Knee/" # Number of workers for dataloader workers = 4 # Batch size during training batch_size = 128 # Spatial

我使用DCGAN来合成医学图像。然而，目前，Img_的大小是64，这是太低的分辨率

如何更改生成器和鉴别器以使其具有512*512高分辨率

下面是我的代码

# Root directory for dataset
dataroot = "./Image/Knee/"

# Number of workers for dataloader
workers = 4

# Batch size during training
batch_size = 128

# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 64

# 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 = 64

# Size of feature maps in discriminator
ndf = 64

# Number of training epochs
num_epochs = 500

# 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 = 2

# We can use an image folder dataset the way we have it setup.
# Create the dataset
dataset = 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)),
                           ]))
# Create the dataloader
dataloader = 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 > 0) else "cpu")

# Plot some training images
real_batch = next(iter(dataloader))
plt.figure(figsize=(8,8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(utils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))

这是生成器代码

# Generator Code

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            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()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        return self.main(input)
    
# Create the generator
netG = Generator(ngpu).to(device)

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

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


print(device.type)
print(ngpu)
# Print the model
print(netG)

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 32 x 32
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 16 x 16
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 8 x 8
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 4 x 4
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    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 > 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))

这是鉴别器代码

# Generator Code

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            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()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        return self.main(input)
    
# Create the generator
netG = Generator(ngpu).to(device)

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

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


print(device.type)
print(ngpu)
# Print the model
print(netG)

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 32 x 32
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 16 x 16
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 8 x 8
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 4 x 4
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    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 > 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))

我用了基本的DCGAN。我想更改网络结构以生成512*512高分辨率图像

这是损失函数和优化器

# 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))

这是培训代码

# Training Loop

# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0
num_epochs = 500
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, device=device)
        # Forward pass real batch through D
        output = netD(real_cpu).view(-1)
        # Calculate loss on all-real batc
        output=output.float()
        label =label.float()
        #print(output.shape)
        #print(label.shape)
        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)
        print(fake.detach())
        # 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
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        # Add the gradients from the all-real and all-fake 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(utils.make_grid(fake, padding=2, normalize=True))
            
        iters += 1

如果我使用你给我的答案代码，我会收到下面的错误信息

运行时错误：输入类型（torch.cuda.FloatTensor）和权重类型（torch.FloatTensor）应该相同

请您再看一遍好吗？

DCGAN的生成器和鉴别器的示例代码处理图像大小
（3，512，512）
：
产出：

Generator( (main): Sequential( (0): ConvTranspose2d(100, 1024, kernel_size=(4, 4), stride=(1, 1), bias=False) (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) (3): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace=True) (6): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) (9): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (10): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU(inplace=True) (12): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): ReLU(inplace=True) (15): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (16): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (17): ReLU(inplace=True) (18): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (19): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (20): ReLU(inplace=True) (21): ConvTranspose2d(16, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (22): Tanh() ) ) Discriminator( (main): Sequential( (0): Conv2d(3, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): LeakyReLU(negative_slope=0.2, inplace=True) (2): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): LeakyReLU(negative_slope=0.2, inplace=True) (5): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): LeakyReLU(negative_slope=0.2, inplace=True) (8): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (10): LeakyReLU(negative_slope=0.2, inplace=True) (11): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (13): LeakyReLU(negative_slope=0.2, inplace=True) (14): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (15): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (16): LeakyReLU(negative_slope=0.2, inplace=True) (17): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (18): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (19): LeakyReLU(negative_slope=0.2, inplace=True) (20): Conv2d(1024, 1, kernel_size=(4, 4), stride=(1, 1), bias=False) (21): Sigmoid() ) ) Outputs size: torch.Size([128, 3, 512, 512]) torch.Size([128, 1, 1, 1])

注意上面每个Conv2d和ConvTranspose2d中的输出通道数只是一个例子，它是处理图像大小的DCGAN的生成器和鉴别器示例代码
（3，512，512）
：
产出：

Generator( (main): Sequential( (0): ConvTranspose2d(100, 1024, kernel_size=(4, 4), stride=(1, 1), bias=False) (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) (3): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace=True) (6): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) (9): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (10): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU(inplace=True) (12): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): ReLU(inplace=True) (15): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (16): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (17): ReLU(inplace=True) (18): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (19): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (20): ReLU(inplace=True) (21): ConvTranspose2d(16, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (22): Tanh() ) ) Discriminator( (main): Sequential( (0): Conv2d(3, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): LeakyReLU(negative_slope=0.2, inplace=True) (2): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): LeakyReLU(negative_slope=0.2, inplace=True) (5): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): LeakyReLU(negative_slope=0.2, inplace=True) (8): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (10): LeakyReLU(negative_slope=0.2, inplace=True) (11): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (13): LeakyReLU(negative_slope=0.2, inplace=True) (14): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (15): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (16): LeakyReLU(negative_slope=0.2, inplace=True) (17): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (18): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (19): LeakyReLU(negative_slope=0.2, inplace=True) (20): Conv2d(1024, 1, kernel_size=(4, 4), stride=(1, 1), bias=False) (21): Sigmoid() ) ) Outputs size: torch.Size([128, 3, 512, 512]) torch.Size([128, 1, 1, 1])

注意上面每个Conv2d和ConvTranspose2d中的输出通道数只是一个示例，这是您可以用来获得更好性能的超参数
您只需在生成器的末尾再给三个
ConvTranspose2d
层，在开始时给鉴别器再给三个
Conv2d
，使用
stride=2
我应该为gnererator最后一层中的所有3个使用nn.Conv2d（ndf*8,1,4,1,0，bias=False）吗？nn.Conv2d（nc，ndf，4，2，1，bias=False）怎么样？在鉴别器中？你能帮我输入参数吗？我真的非常需要帮助。谢谢我建议您查看的文档，为了更好地理解这些功能，这次我可以帮助您，因为您说您确实需要帮助，但是您下次尝试修改网络时可能会遇到类似的问题非常感谢。我真的很感激。另外，我也会查看文档。没问题：）如果你认为我的答案解决了你的问题，那么把它标记为答案，这样其他有同样问题的人就会知道，祝你玩得愉快。你只需在生成器的末尾再给三个
ConvTranspose2d
层，在开始时给鉴别器再给三个
Conv2d
，带
stride=2
的鉴别器，我是否应该对gnererator最后一层的所有3个使用nn.Conv2d（ndf*8,1,4,1,0，bias=False）？nn.Conv2d（nc，ndf，4，2，1，bias=False）怎么样？在鉴别器中？你能帮我输入参数吗？我真的非常需要帮助。谢谢我建议您查看的文档，为了更好地理解这些功能，这次我可以帮助您，因为您说您确实需要帮助，但是您下次尝试修改网络时可能会遇到类似的问题非常感谢。我真的很感激。如果你认为我的答案解决了你的问题，那么把它标记为答案，这样其他有同样问题的人就会知道，祝你有一个愉快的一天。