使用PyTorch采用CIFAR10数据集基于卷积神经网络的图像去噪，噪声使用椒盐噪声

好的，下面是一个简单的示例代码，使用PyTorch实现基于卷积神经网络的图像去噪：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt

# 加载CIFAR10数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True,
                                 transform=transforms.Compose([
                                     transforms.ToTensor(),
                                     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                                 ]))

# 添加椒盐噪声
def add_noise(img, noise_level):
    noise = torch.randn(img.size()) * noise_level
    noisy_img = img + noise
    return noisy_img

# 定义卷积神经网络模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv4 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv5 = nn.Conv2d(64, 32, 3, padding=1)
        self.conv6 = nn.Conv2d(32, 3, 3, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(2, 2)
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = self.relu(self.conv4(x))
        x = self.upsample(self.relu(self.conv5(x)))
        x = self.upsample(self.relu(self.conv6(x)))
        return x

# 训练模型
def train(model, device, train_loader, optimizer, epoch, noise_level):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        noisy_data = add_noise(data, noise_level).to(device)
        optimizer.zero_grad()
        output = model(noisy_data)
        loss = nn.MSELoss()(output, data)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 测试模型
def test(model, device, test_loader, noise_level):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            noisy_data = add_noise(data, noise_level).to(device)
            output = model(noisy_data)
            test_loss += nn.MSELoss()(output, data).item() # sum up batch loss

    test_loss /= len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}\n'.format(test_loss))
    return test_loss

# 设置超参数
batch_size = 128
noise_level = 0.2
lr = 0.001
momentum = 0.9
epochs = 10
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# 加载训练集和测试集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True,
                                transform=transforms.Compose([
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                                ]))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)

# 初始化模型和优化器
model = CNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# 训练模型并测试
train_loss = []
test_loss = []
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch, noise_level)
    test_loss.append(test(model, device, test_loader, noise_level))
    train_loss.append(train_loss)

上述代码中，我们定义了一个卷积神经网络模型 `CNN`，并且定义了一个添加椒盐噪声的函数 `add_noise`。在训练过程中，我们使用 `add_noise` 函数添加椒盐噪声，并且使用均方误差作为损失函数。在测试过程中，我们同样使用 `add_noise` 函数添加椒盐噪声，并且计算测试集的均方误差。最后，我们使用随机梯度下降算法进行模型训练，并且打印训练过程中的损失值和测试集的均方误差。

注意，上述代码中的模型和超参数可能需要根据实际情况进行调整，以达到更好的去噪效果。