用深度学习写一个pytorch框架下的可见光和红外图像融合的代码，代码中包含下载数据集

由于没有具体的数据集，以下代码提供一个可见光和红外图像融合的框架，使用自己准备的数据集进行训练。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
from PIL import Image

# 定义数据集
class MyDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.image_names = [name for name in os.listdir(root_dir) if os.path.isfile(os.path.join(root_dir, name))]

    def __len__(self):
        return len(self.image_names)

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.image_names[idx])
        image = Image.open(img_name)

        if self.transform:
            image = self.transform(image)

        return image

# 定义网络
class FusionNet(nn.Module):
    def __init__(self):
        super(FusionNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu3 = nn.ReLU(inplace=True)
        self.conv4 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu4 = nn.ReLU(inplace=True)
        self.conv5 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu5 = nn.ReLU(inplace=True)
        self.conv6 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu6 = nn.ReLU(inplace=True)
        self.conv7 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        out1 = self.conv1(x)
        out1 = self.relu1(out1)
        out2 = self.conv2(out1)
        out2 = self.relu2(out2)
        out3 = self.conv3(out2)
        out3 = self.relu3(out3)
        out4 = self.conv4(out3 + out1)
        out4 = self.relu4(out4)
        out5 = self.conv5(out4 + out2)
        out5 = self.relu5(out5)
        out6 = self.conv6(out5 + out3)
        out6 = self.relu6(out6)
        out7 = self.conv7(out6)
        return out7

# 数据集路径和参数设置
train_data_path = './train_data'
batch_size = 4
num_workers = 4
lr = 0.001
num_epochs = 10

# 数据预处理
transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载数据集
train_data = MyDataset(train_data_path, transform=transform)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)

# 定义模型和优化器
model = FusionNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs = data
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

需要注意的是，以上代码中的`MyDataset`类需要自己实现，以适配具体的数据集。同时，`FusionNet`类可以根据需要进行修改和调整，以适应不同的任务需求。