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

这里提供一个基于卷积神经网络的可见光和红外图像融合的代码，使用PyTorch框架实现。

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import cv2

然后，我们定义一个卷积神经网络模型：

class FusionNet(nn.Module):
    def __init__(self):
        super(FusionNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv6 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv10 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)
        self.conv11 = nn.Conv2d(1024, 1024, kernel_size=3, padding=1)

        self.upconv1 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.upconv2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.upconv3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.upconv4 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.upconv5 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2)
        self.finalconv = nn.Conv2d(32, 3, kernel_size=3, padding=1)

        self.relu = nn.ReLU()

    def forward(self, x):
        x1 = self.relu(self.conv1(x))
        x2 = self.relu(self.conv2(x1))
        x3 = self.relu(self.conv3(x2))
        x4 = self.relu(self.conv4(x3))
        x5 = self.relu(self.conv5(x4))
        x6 = self.relu(self.conv6(x5))
        x7 = self.relu(self.conv7(x6))
        x8 = self.relu(self.conv8(x7))
        x9 = self.relu(self.conv9(x8))
        x10 = self.relu(self.conv10(x9))
        x11 = self.relu(self.conv11(x10))

        y = self.relu(self.upconv1(x11))
        y = torch.cat((y, x9), dim=1)
        y = self.relu(self.upconv2(y))
        y = torch.cat((y, x7), dim=1)
        y = self.relu(self.upconv3(y))
        y = torch.cat((y, x5), dim=1)
        y = self.relu(self.upconv4(y))
        y = torch.cat((y, x3), dim=1)
        y = self.relu(self.upconv5(y))
        y = torch.cat((y, x1), dim=1)
        y = self.finalconv(y)
        return y

该模型包括11个卷积层和5个反卷积层，其中前10个卷积层和5个反卷积层用于特征提取和上采样，最后一个卷积层用于输出融合后的图像。我们使用ReLU作为激活函数。

接下来，我们定义训练和测试函数：

def train(model, train_loader, criterion, optimizer, device):
    model.train()
    train_loss = 0
    for batch_idx, (visible, infrared, target) in enumerate(train_loader):
        visible, infrared, target = visible.to(device), infrared.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(torch.cat((visible, infrared), dim=1))
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    train_loss /= len(train_loader)
    return train_loss

def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for batch_idx, (visible, infrared, target) in enumerate(test_loader):
            visible, infrared, target = visible.to(device), infrared.to(device), target.to(device)
            output = model(torch.cat((visible, infrared), dim=1))
            loss = criterion(output, target)
            test_loss += loss.item()
    test_loss /= len(test_loader)
    return test_loss

在训练过程中，我们需要定义损失函数和优化器。这里我们使用均方误差作为损失函数，使用Adam作为优化器：

criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

最后，我们训练模型并保存结果：

# 训练模型
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: {:.6f} Test Loss: {:.6f}'.format(epoch+1, train_loss, test_loss))

# 保存模型
torch.save(model.state_dict(), 'fusion_model.pth')

完整代码如下：