使用pytorch编写一个详细的DnCNN代码，并详细讲解DnCNN步骤

深度残差卷积网络 (Deep Convolutional Neural Network, DnCNN) 是一种用于图像去噪的深度学习模型。在PyTorch中实现DnCNN，我们可以分为以下几个步骤：

1. 导入所需库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision.transforms as transforms

2. 定义基本模块：

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size, stride, padding)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        return out + residual

class DnCNN(nn.Module):
    def __init__(self, num_blocks=17, in_channels=1, out_channels=1, kernel_size=3):
        super(DnCNN, self).__init__()
        self.layers = []
        for _ in range(num_blocks):
            self.layers.append(ResidualBlock(in_channels, out_channels))
        self.net = nn.Sequential(*self.layers)
        
    def forward(self, x):
        return self.net(x)

3. 数据预处理和加载：

# 假设我们有加噪声的数据集
train_dataset = ... # 加载你的噪声数据集
transform = transforms.Compose([
    transforms.ToTensor(),
])
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, transform=transform)

4. 初始化模型、损失函数和优化器：

model = DnCNN() # 根据需要调整参数
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

5. 训练循环：

num_epochs = 100
for epoch in range(num_epochs):
    for i, (noisy_images, _) in enumerate(train_loader):
        noisy_images = noisy_images.to(device) # 设定设备
        clean_images = ... # 获取对应的干净图像（如果你有训练数据）

        # 前向传播
        output = model(noisy_images)
        loss = criterion(output, clean_images)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")