用pytouch编写DnCNN网络

好的，下面是用PyTorch编写DnCNN网络的步骤：

1. 导入必要的库和模块：

import torch
import torch.nn as nn

2. 定义DnCNN网络的类，包括卷积层、残差块和反卷积层：

class DnCNN(nn.Module):
    def __init__(self, channels, num_of_layers=17):
        super(DnCNN, self).__init__()
        kernel_size = 3
        padding = 1
        features = 64
        layers = []
        layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
        layers.append(nn.ReLU(inplace=True))
        for i in range(num_of_layers - 2):
            layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
            layers.append(nn.BatchNorm2d(features))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False))
        self.dncnn = nn.Sequential(*layers)
    def forward(self, x):
        out = self.dncnn(x)
        return out

3. 实例化DnCNN网络：

channels = 3 # 输入和输出的通道数
num_of_layers = 17 # 残差块的数量
model = DnCNN(channels, num_of_layers)

4. 定义损失函数和优化器：

criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

5. 训练模型：

for epoch in range(num_epochs):
    for i, data in enumerate(train_loader):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

6. 测试模型：

with torch.no_grad():
    for data in test_loader:
        inputs, labels = data
        outputs = model(inputs)
        loss = criterion(outputs, labels)