深度学习图像修复pytorch代码

深度学习图像修复通常使用生成式对抗网络 (GANs) 或者基于自编码器的模型，如PixelCNN、CycleGAN等。在PyTorch中，你可以采用U-Net这样的卷积神经网络架构来进行图像修复任务。下面是一个简单的U-Net示例代码片段：

import torch
import torch.nn as nn

# 定义U-Net结构
class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=3, depth=5, start_filts=64):
        super(UNet, self).__init__()
        
        # 编码部分
        encoder_down = []
        for _ in range(depth):
            encoder_down.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, start_filts * (2 ** (_ + 1)), kernel_size=4, stride=2, padding=1),
                    nn.BatchNorm2d(start_filts * (2 ** (_ + 1))),
                    nn.ReLU(True)
                )
            )
            in_channels = start_filts * (2 ** (_ + 1))

        # 中间层
        self.center = nn.Sequential(
            nn.Conv2d(in_channels, start_filts * (2 ** (depth + 1)), kernel_size=3, padding=1),
            nn.BatchNorm2d(start_filts * (2 ** (depth + 1))),
            nn.ReLU(True)
        )

        # 解码部分
        decoder_up = []
        for _ in range(depth):
            decoder_up.append(
                nn.Sequential(
                    nn.ConvTranspose2d(start_filts * (2 ** (depth - _)), start_filts * (2 ** (_ + 1)) // 2, kernel_size=4, stride=2, padding=1),
                    nn.BatchNorm2d(start_filts * (2 ** (_ + 1)) // 2),
                    nn.ReLU(True),
                    nn.Conv2d(start_filts * (2 ** (_ + 1)) // 2, start_filts * (2 ** (_ + 1)) // 2, kernel_size=3, padding=1),
                    nn.BatchNorm2d(start_filts * (2 ** (_ + 1)) // 2),
                    nn.ReLU(True)
                )
            )

        decoder_up.append(nn.ConvTranspose2d(start_filts, out_channels, kernel_size=4, stride=2, padding=1))
        self.decoder_up = nn.ModuleList(decoder_up)

        self.encoder_down = nn.ModuleList(encoder_down)

    def forward(self, x):
        encoder_outputs = []
        for enc in self.encoder_down:
            x = enc(x)
            encoder_outputs.append(x)

        center = self.center(x)
        decoder_output = self.decoder_up[0](center)

        for i in range(1, len(self.decoder_up)):
            decoder_output = torch.cat([decoder_output, encoder_outputs.pop()], dim=1)
            decoder_output = self.decoder_up[i](decoder_output)

        return decoder_output

# 使用示例
model = UNet()
input_image = torch.randn(1, 3, 256, 256)
output = model(input_image)