写一个上下文信息的图像修复的pytorch代码

这里提供一个简单的上下文信息的图像修复的PyTorch代码，其中使用了卷积神经网络和自编码器。

首先，导入需要用到的库和模块：

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

然后，定义一个卷积神经网络，用于提取图像特征：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        return x

接着，定义一个自编码器，用于学习图像的重构：

class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(64 * 32 * 32, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 128)
        )

        self.decoder = nn.Sequential(
            nn.Linear(128, 256),
            nn.ReLU(),
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, 64 * 32 * 32),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

然后，定义一个训练函数，用于训练模型并保存模型参数：

def train(model, train_loader, num_epochs=10, learning_rate=0.001):
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    
    for epoch in range(num_epochs):
        running_loss = 0.0
        for data in train_loader:
            img, _ = data
            img = img.cuda()
            img = img.view(img.size(0), -1)
            encoded_img = model.encoder(img)
            decoded_img = model.decoder(encoded_img)
            loss = criterion(decoded_img, img)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print('Epoch [%d], Loss: %.4f' % (epoch+1, running_loss/len(train_loader)))
    
    torch.save(model.state_dict(), 'model.pth')

最后，定义一个测试函数，用于加载模型参数并进行图像修复：

def test(model, test_loader):
    model.load_state_dict(torch.load('model.pth'))
    model.eval()
    
    with torch.no_grad():
        for data in test_loader:
            img, _ = data
            img = img.cuda()
            img = img.view(img.size(0), -1)
            encoded_img = model.encoder(img)
            decoded_img = model.decoder(encoded_img)
            decoded_img = decoded_img.view(decoded_img.size(0), 3, 32, 32)
            img = img.view(img.size(0), 3, 32, 32)
            for i in range(decoded_img.size(0)):
                plt.figure()
                plt.subplot(1, 2, 1)
                plt.imshow(np.transpose(img.cpu().numpy()[i], (1, 2, 0)))
                plt.subplot(1, 2, 2)
                plt.imshow(np.transpose(decoded_img.cpu().numpy()[i], (1, 2, 0)))
                plt.show()

使用上述函数进行训练和测试：

train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=64, shuffle=False)

cnn = CNN()
cnn.cuda()
autoencoder = Autoencoder()
autoencoder.cuda()

train(cnn, train_loader)
train(autoencoder, train_loader)

test(cnn, test_loader)
test(autoencoder, test_loader)

其中，train_data和test_data是训练集和测试集的数据集合，可通过torchvision.datasets模块中的CIFAR10数据集获取。