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，最后展示图片，完整代码

以下是使用PyTorch基于卷积神经网络的图像去噪代码，其中使用CIFAR10数据集和椒盐噪声：

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

# 定义卷积神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 4 * 4, 256)
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = self.pool(torch.relu(self.conv3(x)))
        x = x.view(-1, 64 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义椒盐噪声函数
def add_noise(img):
    noise = torch.randn(img.size()) * 0.1
    noisy_img = img + noise
    return torch.clamp(noisy_img, 0, 1)

# 加载CIFAR10数据集并将训练集和测试集转换为tensor格式
transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(lambda x: add_noise(x))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)

# 定义模型、损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 200 == 199:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('Finished Training')

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

# 展示去噪前后的图片
dataiter = iter(testloader)
images, labels = dataiter.next()

noisy_images = add_noise(images)
outputs = net(noisy_images)

fig, axs = plt.subplots(2, 10, figsize=(20, 4))
for i in range(10):
    axs[0, i].imshow(np.transpose(images[i], (1, 2, 0)))
    axs[1, i].imshow(np.transpose(outputs[i].detach().numpy(), (1, 2, 0)))

plt.show()

代码中使用了一个包含三个卷积层和两个全连接层的卷积神经网络模型，训练了10个epoch，并在测试集上进行了测试。在展示去噪前后的图片时，我们将展示10张测试集中的图片。