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

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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。

用上下文编码加GAN修复128*128带有随机掩膜区域的图像，提供生成器与判别器的代码

好的，我理解您的问题。为了回答您的问题，我需要先解释一下上下文编码和GAN修复的概念。

上下文编码通常是指将原始的输入数据转化为一种更高级的表示形式，这种形式可以帮助模型更好地理解输入数据的结构和语义信息。在图像处理中，上下文编码可以通过卷积神经网络来实现。GAN修复则是一种用于图像修复的技术，它使用生成器和判别器两个网络结构，通过训练生成器来修复图像中的缺陷区域，并通过判别器来判断修复结果的真实性。

下面是一份用上下文编码加GAN修复128*128带有随机掩膜区域的图像的生成器与判别器的代码：

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__()
        
        # Encoder
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.conv6 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.conv7 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        
        # Decoder
        self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.deconv2 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
        self.deconv3 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
        self.deconv4 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
        self.deconv5 = nn.ConvTranspose2d(1024, 256, kernel_size=4, stride=2, padding=1)
        self.deconv6 = nn.ConvTranspose2d(512, 128, kernel_size=4, stride=2, padding=1)
        self.deconv7 = nn.ConvTranspose2d(256, 64, kernel_size=4, stride=2, padding=1)
        self.deconv8 = nn.ConvTranspose2d(128, 3, kernel_size=4, stride=2, padding=1)
        
    def forward(self, x):
        # Encoder
        e1 = F.leaky_relu(self.conv1(x), negative_slope=0.2)
        e2 = F.leaky_relu(self.conv2(e1), negative_slope=0.2)
        e3 = F.leaky_relu(self.conv3(e2), negative_slope=0.2)
        e4 = F.leaky_relu(self.conv4(e3), negative_slope=0.2)
        e5 = F.leaky_relu(self.conv5(e4), negative_slope=0.2)
        e6 = F.leaky_relu(self.conv6(e5), negative_slope=0.2)
        e7 = F.leaky_relu(self.conv7(e6), negative_slope=0.2)
        e8 = F.leaky_relu(self.conv8(e7), negative_slope=0.2)
        
        # Decoder
        d1 = F.relu(self.deconv1(e8))
        d1 = F.dropout(d1, 0.5, training=True)
        d2 = F.relu(self.deconv2(torch.cat([d1, e7], dim=1)))
        d2 = F.dropout(d2, 0.5, training=True)
        d3 = F.relu(self.deconv3(torch.cat([d2, e6], dim=1)))
        d3 = F.dropout(d3, 0.5, training=True)
        d4 = F.relu(self.deconv4(torch.cat([d3, e5], dim=1)))
        d4 = F.dropout(d4, 0.5, training=True)
        d5 = F.relu(self.deconv5(torch.cat([d4, e4], dim=1)))
        d6 = F.relu(self.deconv6(torch.cat([d5, e3], dim=1)))
        d7 = F.relu(self.deconv7(torch.cat([d6, e2], dim=1)))
        d8 = torch.tanh(self.deconv8(torch.cat([d7, e1], dim=1)))
        return d8

判别器代码：

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

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
        self.conv5 = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1)
        
    def forward(self, x):
        x = F.leaky_relu(self.conv1(x), negative_slope=0.2)
        x = F.leaky_relu(self.conv2(x), negative_slope=0.2)
        x = F.leaky_relu(self.conv3(x), negative_slope=0.2)
        x = F.leaky_relu(self.conv4(x), negative_slope=0.2)
        x = torch.sigmoid(self.conv5(x))
        return x

注意：这只是一个代码示例，并不是一个完整的可运行代码。您需要使用这个示例代码作为起点，并根据您的数据集和实际需求进行适当的修改。