基于pytorch写一个gan的损失函数

下面是一个基于PyTorch实现的GAN损失函数的示例代码：

import torch.nn.functional as F

def gan_loss_function(real_output, fake_output):
    # 计算生成器损失
    gen_loss = F.binary_cross_entropy_with_logits(fake_output, torch.ones_like(fake_output))
    
    # 计算判别器损失
    real_loss = F.binary_cross_entropy_with_logits(real_output, torch.ones_like(real_output))
    fake_loss = F.binary_cross_entropy_with_logits(fake_output, torch.zeros_like(fake_output))
    disc_loss = real_loss + fake_loss
    
    return gen_loss, disc_loss

其中，`real_output`是判别器对真实样本的输出，`fake_output`是判别器对生成样本的输出。`binary_cross_entropy_with_logits`函数是PyTorch中用于计算二元交叉熵损失的函数，可以方便地计算生成器损失和判别器损失。最终返回的是生成器损失和判别器损失。

相关问题

用pytorch写一个GAN代码

这里提供一个简单的GAN代码，使用PyTorch实现：```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.utils import save_image# 定义生成器
class Generator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Generator, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.tanh = nn.Tanh() def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.tanh(self.fc3(x))
return x# 定义判别器
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid() def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x# 定义超参数
batch_size = 100
input_size = 100
hidden_size = 256
output_size = 784
num_epochs = 200# 加载MNIST数据集
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(.5,), std=(.5,))
])
train_dataset = dset.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)# 初始化生成器和判别器
G = Generator(input_size, hidden_size, output_size)
D = Discriminator(output_size, hidden_size, 1)# 定义损失函数和优化器
criterion = nn.BCELoss()
G_optimizer = optim.Adam(G.parameters(), lr=.0002)
D_optimizer = optim.Adam(D.parameters(), lr=.0002)# 训练GAN模型
for epoch in range(num_epochs):
for i, (images, _) in enumerate(train_loader):
# 训练判别器
real_labels = torch.ones(batch_size, 1)
fake_labels = torch.zeros(batch_size, 1)
real_images = images.view(batch_size, -1)
z = torch.randn(batch_size, input_size)
fake_images = G(z) D_real_loss = criterion(D(real_images), real_labels)
D_fake_loss = criterion(D(fake_images), fake_labels)
D_loss = D_real_loss + D_fake_loss
D_optimizer.zero_grad()
D_loss.backward()
D_optimizer.step() # 训练生成器
z = torch.randn(batch_size, input_size)
fake_images = G(z)
G_loss = criterion(D(fake_images), real_labels)
G_optimizer.zero_grad()
G_loss.backward()
G_optimizer.step() # 打印损失值
if (i+1) % 100 == :
print('Epoch [{}/{}], Step [{}/{}], D_loss: {:.4f}, G_loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, len(train_loader), D_loss.item(), G_loss.item())) # 保存生成的图像
if (epoch+1) % 10 == :
fake_images = fake_images.view(batch_size, 1, 28, 28)
save_image(fake_images, './samples/sample_{}.png'.format(epoch+1))

提供self attention GAN的pytorch代码，同时将损失函数替换成WGAN—GP中的损失函数

以下是使用PyTorch实现Self-Attention GAN(SAGAN)并替换成WGAN-GP损失函数的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SelfAttention(nn.Module):
    def __init__(self, in_dim):
        super(SelfAttention, self).__init__()
        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = nn.Parameter(torch.zeros(1))
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        batch_size, c, h, w = x.size()
        proj_query = self.query_conv(x).view(batch_size, -1, h*w).permute(0, 2, 1)
        proj_key = self.key_conv(x).view(batch_size, -1, h*w)
        energy = torch.bmm(proj_query, proj_key)
        attention = self.softmax(energy)
        proj_value = self.value_conv(x).view(batch_size, -1, h*w)

        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(batch_size, c, h, w)

        out = self.gamma*out + x
        return out


class Generator(nn.Module):
    def __init__(self, z_dim=100, image_size=64, conv_dim=64):
        super(Generator, self).__init__()
        self.image_size = image_size
        self.fc = nn.Linear(z_dim, conv_dim*8*(image_size//16)**2)
        self.conv1 = nn.Conv2d(conv_dim*8, conv_dim*4, 3, 1, 1)
        self.conv2 = nn.Conv2d(conv_dim*4, conv_dim*2, 3, 1, 1)
        self.self_attention = SelfAttention(conv_dim*2)
        self.conv3 = nn.Conv2d(conv_dim*2, conv_dim, 3, 1, 1)
        self.conv4 = nn.Conv2d(conv_dim, 3, 3, 1, 1)
        self.bn1 = nn.BatchNorm2d(conv_dim*8)
        self.bn2 = nn.BatchNorm2d(conv_dim*4)
        self.bn3 = nn.BatchNorm2d(conv_dim*2)
        self.bn4 = nn.BatchNorm2d(conv_dim)

    def forward(self, z):
        x = self.fc(z)
        x = x.view(-1, 512, self.image_size//16, self.image_size//16)
        x = F.relu(self.bn1(x))
        x = F.interpolate(x, scale_factor=2)
        x = F.relu(self.bn2(self.conv1(x)))
        x = F.interpolate(x, scale_factor=2)
        x = F.relu(self.bn3(self.self_attention(self.conv2(x))))
        x = F.interpolate(x, scale_factor=2)
        x = F.relu(self.bn4(self.conv3(x)))
        x = torch.tanh(self.conv4(x))
        return x


class Discriminator(nn.Module):
    def __init__(self, image_size=64, conv_dim=64):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(3, conv_dim, 4, 2, 1)
        self.conv2 = nn.Conv2d(conv_dim, conv_dim*2, 4, 2, 1)
        self.conv3 = nn.Conv2d(conv_dim*2, conv_dim*4, 4, 2, 1)
        self.self_attention = SelfAttention(conv_dim*4)
        self.conv4 = nn.Conv2d(conv_dim*4, conv_dim*8, 4, 2, 1)
        self.conv5 = nn.Conv2d(conv_dim*8, 1, 4, 1, 0)
        self.bn1 = nn.BatchNorm2d(conv_dim)
        self.bn2 = nn.BatchNorm2d(conv_dim*2)
        self.bn3 = nn.BatchNorm2d(conv_dim*4)
        self.bn4 = nn.BatchNorm2d(conv_dim*8)

    def forward(self, x):
        x = F.leaky_relu(self.conv1(x), 0.1)
        x = F.leaky_relu(self.bn2(self.conv2(x)), 0.1)
        x = F.leaky_relu(self.bn3(self.self_attention(self.conv3(x))), 0.1)
        x = F.leaky_relu(self.bn4(self.conv4(x)), 0.1)
        x = self.conv5(x)
        return x.view(-1, 1)
        

# WGAN-GP loss
def wgan_gp_loss(discriminator, real_images, fake_images, batch_size, device):
    # Calculate critic scores for real images
    real_scores = discriminator(real_images)

    # Sample random points in the latent space
    z = torch.randn(batch_size, 100, device=device)

    # Generate fake images
    fake_images = fake_images.detach()
    fake_images = Generator(z)

    # Calculate critic scores for fake images
    fake_scores = discriminator(fake_images)

    # Compute the gradient penalty
    alpha = torch.rand(batch_size, 1, 1, 1, device=device)
    interpolated_images = (alpha * real_images + (1 - alpha) * fake_images).requires_grad_(True)
    interpolated_scores = discriminator(interpolated_images)
    gradients = torch.autograd.grad(outputs=interpolated_scores, inputs=interpolated_images,
                                    grad_outputs=torch.ones_like(interpolated_scores), create_graph=True, retain_graph=True)[0]
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()

    # Compute the Wasserstein distance
    wasserstein_distance = real_scores.mean() - fake_scores.mean()

    # Compute the loss for the discriminator
    d_loss = -wasserstein_distance + 10 * gradient_penalty

    # Compute the loss for the generator
    g_loss = -fake_scores.mean()

    return d_loss, g_loss

在训练GAN的过程中，使用wgan_gp_loss函数来替代原来的GAN损失函数。例如：

# 初始化模型和优化器
generator = Generator().to(device)
discriminator = Discriminator().to(device)
g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 训练GAN
for epoch in range(num_epochs):
    for i, (real_images, _) in enumerate(dataloader):
        real_images = real_images.to(device)
        batch_size = real_images.size(0)

        # 训练判别器
        discriminator.zero_grad()
        d_loss, _ = wgan_gp_loss(discriminator, real_images, generator, batch_size, device)
        d_loss.backward()
        d_optimizer.step()

        # 训练生成器
        generator.zero_grad()
        _, g_loss = wgan_gp_loss(discriminator, real_images, generator, batch_size, device)
        g_loss.backward()
        g_optimizer.step()

        if i % 100 == 0:
            print("Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}".format(
                epoch+1, num_epochs, i+1, len(dataloader), d_loss.item(), g_loss.item()))

    # 保存模型和图片
    with torch.no_grad():
        fake_images = generator(fixed_z)
        save_image(fake_images, "SAGAN_WGAN_GP_{}.png".format(epoch+1), nrow=8, normalize=True)
    torch.save(generator.state_dict(), "SAGAN_WGAN_GP_Generator_{}.ckpt".format(epoch+1))
    torch.save(discriminator.state_dict(), "SAGAN_WGAN_GP_Discriminator_{}.ckpt".format(epoch+1))