写一段基于pytorch的深度学习图像融合代码

以下是一个基于 PyTorch 的图像融合的示例代码，它将两张大小相同的图像进行融合：

import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image

# 定义数据集
class ImageDataset(torch.utils.data.Dataset):
    def __init__(self, img_path1, img_path2):
        self.img_path1 = img_path1
        self.img_path2 = img_path2
        
        self.transform = transforms.Compose([
            transforms.Resize((256, 256)),
            transforms.ToTensor(),
            transforms.Normalize((0.5,), (0.5,))
        ])
        
    def __getitem__(self, index):
        img1 = Image.open(self.img_path1)
        img2 = Image.open(self.img_path2)
        
        img1 = self.transform(img1)
        img2 = self.transform(img2)
        
        return img1, img2
    
    def __len__(self):
        return 1

# 定义模型
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.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        
    def forward(self, img1, img2):
        x = torch.cat([img1, img2], dim=1)
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.relu(self.conv3(x))
        x = self.conv4(x)
        return x

# 训练数据集路径
img_path1 = "/path/to/image1.jpg"
img_path2 = "/path/to/image2.jpg"

# 定义数据加载器
dataset = ImageDataset(img_path1, img_path2)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=1)

# 定义模型、损失函数和优化器
model = FusionNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    running_loss = 0.0
    for data in dataloader:
        img1, img2 = data
        optimizer.zero_grad()
        output = model(img1, img2)
        loss = criterion(output, img1)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch [%d/%d], Loss: %.4f' % (epoch+1, num_epochs, running_loss))
    
# 图像融合
model.eval()
with torch.no_grad():
    img1 = Image.open(img_path1)
    img2 = Image.open(img_path2)
    img1 = dataset.transform(img1).unsqueeze(0)
    img2 = dataset.transform(img2).unsqueeze(0)
    output = model(img1, img2)
    output = output.squeeze(0)
    output = output.permute(1, 2, 0)
    output = (output + 1) / 2.0
    output = output.detach().numpy() * 255.0
    output = output.astype('uint8')
    output = Image.fromarray(output)
    output.save("/path/to/fused_image.jpg")

这个示例代码中，我们首先定义了一个数据集类 `ImageDataset`，它将两张图片进行数据预处理，并返回给训练器。接着，我们定义了一个融合网络 `FusionNet`，它包含了四个卷积层和一个 ReLU 激活函数。在训练过程中，我们使用均方误差损失函数和 Adam 优化器进行训练。最后，我们使用训练好的模型将两张输入图像进行融合，并将输出保存为一张新的图像。