使用上下文编码器加GAN网络，修复128*128并有随机掩膜的图片，生成器代码和判别器代码定义

以下是生成器和判别器代码的定义，用于修复128x128的图像并添加随机掩膜：

生成器代码：

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

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, 7, padding=3)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv4 = nn.Conv2d(64, 3, 7, padding=3)

        self.up1 = nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1)
        self.up2 = nn.ConvTranspose2d(32, 16, 4, stride=2, padding=1)
        self.up3 = nn.ConvTranspose2d(16, 3, 4, stride=2, padding=1)

    def forward(self, x, mask):
        x = x * mask
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = self.conv4(x)
    
        x = F.interpolate(x, scale_factor=2, mode='nearest')
        x = F.relu(self.up1(x))
        x = F.interpolate(x, scale_factor=2, mode='nearest')
        x = F.relu(self.up2(x))

        x = self.up3(x)
        x = x * (1 - mask) + x * mask
        return x

判别器代码：

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv5 = nn.Conv2d(512, 1, 3, padding=1)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = self.conv5(x)
        x = F.sigmoid(x)
        return x

这些代码中使用了卷积神经网络和反卷积神经网络，以及遮罩操作，来修复图像。其中生成器网络通过接受原始图像和随机遮罩作为输入，输出修复后的图像。判别器网络则用于判断输入的图像是否为原始图像。GAN网络通过两个网络的对抗学习，实现对于图像的修复效果。