pix2pix伪代码

以下是Pix2Pix的伪代码：

# 定义输入图像和目标图像
input_images = ...
target_images = ...

# 定义生成器和判别器的网络结构
generator = ...
discriminator = ...

# 定义优化器和损失函数
optimizer_G = ...
optimizer_D = ...
criterion_GAN = ...
criterion_L1 = ...

# 训练生成器和判别器
for epoch in range(num_epochs):
    for i, (input_image, target_image) in enumerate(zip(input_images, target_images)):
        
        # 训练判别器
        optimizer_D.zero_grad()
        fake_target = generator(input_image)
        real_loss_D = criterion_GAN(discriminator(input_image, target_image), True)
        fake_loss_D = criterion_GAN(discriminator(input_image, fake_target.detach()), False)
        loss_D = (real_loss_D + fake_loss_D) / 2
        loss_D.backward()
        optimizer_D.step()
        
        # 训练生成器
        optimizer_G.zero_grad()
        fake_target = generator(input_image)
        loss_GAN_G = criterion_GAN(discriminator(input_image, fake_target), True)
        loss_L1 = criterion_L1(fake_target, target_image)
        loss_G = loss_GAN_G + lambda_L1 * loss_L1
        loss_G.backward()
        optimizer_G.step()

其中，`generator` 是生成目标图像的生成器网络，`discriminator` 是对输入图像和目标图像进行判别的判别器网络。`criterion_GAN` 是对抗损失函数，`criterion_L1` 是L1损失函数。在训练过程中，首先训练判别器，然后训练生成器，其中损失函数包括对抗损失和L1损失。