使用pytorch编写一个DnCNN

深度卷积神经网络 (Deep Convolutional Neural Network, DnCNN) 是一种用于超分辨率图像恢复的经典模型，它利用了深度学习的强大能力来提升低分辨率图像的质量。在PyTorch中编写一个简单的DnCNN主要包括以下几个步骤：

1. **导入所需库**:

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

2. **定义网络结构**:
DnCNN通常包括卷积层、非线性激活函数（如ReLU）、以及残差连接。这里是一个基础版本的三层DnCNN示例：

   class DnCNN(nn.Module):
       def __init__(self, in_channels, out_channels, kernel_size=3, padding=1):
           super(DnCNN, self).__init__()
           self.conv_blocks = nn.Sequential(
               nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
               nn.ReLU(inplace=True),
               nn.Conv2d(out_channels, out_channels, kernel_size, padding=padding),
               nn.ReLU(inplace=True),
               nn.Conv2d(out_channels, out_channels, kernel_size, padding=padding)
           )
           self.output_layer = nn.Conv2d(out_channels, out_channels, kernel_size=1)

       def forward(self, x):
           residual = x
           x = self.conv_blocks(x)
           x += residual  # 残差连接
           return self.output_layer(F.relu(x))

3. **实例化并训练网络**:

   model = DnCNN(in_channels=3, out_channels=3)  # 对于RGB图像，in_channels通常是3
   criterion = nn.MSELoss()
   optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

4. **训练循环**:

   for epoch in range(num_epochs):
       for inputs, targets in dataloader:
           outputs = model(inputs)
           loss = criterion(outputs, targets)
           optimizer.zero_grad()
           loss.backward()
           optimizer.step()

5. **保存和加载模型**:

   torch.save(model.state_dict(), 'dncnn.pth')
   model.load_state_dict(torch.load('dncnn.pth'))