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

以下是一个基于PyTorch框架的可见光和红外图像融合代码的例子。该代码使用了一个基于UNet的深度神经网络模型，将可见光图像和红外图像分别输入到两个分支中，然后将它们融合到一个输出图像中。此外，该代码还使用了L1损失函数来优化模型的训练过程。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvBlock, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.conv(x)
        return x

class UpConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UpConv, self).__init__()
        self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
        self.conv = ConvBlock(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        diffX = x2.size()[3] - x1.size()[3]
        diffY = x2.size()[2] - x1.size()[2]
        x1 = nn.functional.pad(x1, (diffX // 2, diffX - diffX // 2,
                                    diffY // 2, diffY - diffY // 2))
        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x

class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=3):
        super(UNet, self).__init__()

        self.down1 = ConvBlock(in_channels, 64)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.down2 = ConvBlock(64, 128)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.down3 = ConvBlock(128, 256)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.down4 = ConvBlock(256, 512)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottom = ConvBlock(512, 1024)

        self.up1 = UpConv(1024, 512)
        self.up2 = UpConv(512, 256)
        self.up3 = UpConv(256, 128)
        self.up4 = UpConv(128, 64)

        self.conv_last = nn.Conv2d(64, out_channels, kernel_size=1)

    def forward(self, x):
        x1 = self.down1(x)
        x2 = self.pool1(x1)

        x3 = self.down2(x2)
        x4 = self.pool2(x3)

        x5 = self.down3(x4)
        x6 = self.pool3(x5)

        x7 = self.down4(x6)
        x8 = self.pool4(x7)

        x9 = self.bottom(x8)

        x = self.up1(x9, x7)
        x = self.up2(x, x5)
        x = self.up3(x, x3)
        x = self.up4(x, x1)

        x = self.conv_last(x)
        return x

def train(model, train_loader, criterion, optimizer, device):
    model.train()
    train_loss = 0
    for batch_idx, (vis, ir) in enumerate(train_loader):
        vis, ir = vis.to(device), ir.to(device)
        optimizer.zero_grad()
        output = model(vis, ir)
        loss = criterion(output, vis)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()

    return train_loss / len(train_loader)

def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for vis, ir in test_loader:
            vis, ir = vis.to(device), ir.to(device)
            output = model(vis, ir)
            test_loss += criterion(output, vis).item()

    return test_loss / len(test_loader)

def run_training(train_loader, test_loader, num_epochs, learning_rate, device):
    model = UNet().to(device)
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    criterion = nn.L1Loss()

    for epoch in range(num_epochs):
        train_loss = train(model, train_loader, criterion, optimizer, device)
        test_loss = test(model, test_loader, criterion, device)
        print('Epoch: {}, Train Loss: {:.4f}, Test Loss: {:.4f}'.format(epoch+1, train_loss, test_loss))

if __name__ == '__main__':
    # Load the data and create the dataloaders
    # ...

    # Set the device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Train the model
    run_training(train_loader, test_loader, num_epochs=10, learning_rate=0.001, device=device)

请注意，该代码仅为示例，您需要根据您的实际数据和需求进行修改和调整。