请用Pytorch框架给出基于深度学习的去噪实现这个实验的相关代码

好的，下面是一个简单的基于PyTorch框架的去噪实现代码：

import torch
import torch.nn as nn
import torch.optim as optim

class DnCNN(nn.Module):
    def __init__(self, num_layers=17, num_filters=64):
        super(DnCNN, self).__init__()

        layers = []
        layers.append(nn.Conv2d(1, num_filters, kernel_size=3, stride=1, padding=1, bias=False))
        layers.append(nn.ReLU(inplace=True))
        for i in range(num_layers - 2):
            layers.append(nn.Conv2d(num_filters, num_filters, kernel_size=3, stride=1, padding=1, bias=False))
            layers.append(nn.BatchNorm2d(num_filters, eps=0.0001, momentum=0.95))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(num_filters, 1, kernel_size=3, stride=1, padding=1, bias=False))
        self.dncnn = nn.Sequential(*layers)

    def forward(self, x):
        y = x
        out = self.dncnn(x)
        return y - out

def train_dncnn(model, train_loader, val_loader, epochs, lr, device):
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=lr)

    for epoch in range(epochs):
        model.train()
        train_loss = 0.0
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = data.to(device), target.to(device)
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()

        train_loss /= len(train_loader)

        model.eval()
        val_loss = 0.0
        with torch.no_grad():
            for batch_idx, (data, target) in enumerate(val_loader):
                data, target = data.to(device), target.to(device)
                output = model(data)
                loss = criterion(output, target)
                val_loss += loss.item()

            val_loss /= len(val_loader)

        print('Epoch: {}, Train Loss: {:.6f}, Val Loss: {:.6f}'.format(epoch+1, train_loss, val_loss))

这个代码实现了DnCNN模型，并提供了一个训练函数`train_dncnn`，可以用于训练模型。其中`train_loader`和`val_loader`是训练集和验证集的数据加载器，`epochs`是训练轮数，`lr`是学习率，`device`是设备（CPU或GPU）。在训练模型之后，可以使用训练好的模型对测试集进行去噪处理。