U-net实现RGB图像重构高光谱图像代码

以下是一个简单的U-Net实现RGB图像重构高光谱图像的代码示例，使用的是PyTorch框架：

import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    """双层卷积模块"""
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

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

class Up(nn.Module):
    """上采样模块"""
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
        self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

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

class UNet(nn.Module):
    """U-Net模型"""
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv1 = DoubleConv(in_channels, 64)
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = DoubleConv(64, 128)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = DoubleConv(128, 256)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4 = DoubleConv(256, 512)
        self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5 = DoubleConv(512, 1024)
        self.up1 = Up(1024, 512)
        self.up2 = Up(512, 256)
        self.up3 = Up(256, 128)
        self.up4 = Up(128, 64)
        self.out = nn.Conv2d(64, out_channels, kernel_size=1)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.maxpool1(x1)
        x2 = self.conv2(x2)
        x3 = self.maxpool2(x2)
        x3 = self.conv3(x3)
        x4 = self.maxpool3(x3)
        x4 = self.conv4(x4)
        x5 = self.maxpool4(x4)
        x5 = self.conv5(x5)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        x = self.out(x)
        return x

使用U-Net模型进行RGB图像重构高光谱图像的训练和测试代码示例：

import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from dataset import CustomDataset

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor()
])
dataset = CustomDataset(transform=transform)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)

# 定义U-Net模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = UNet(in_channels=3, out_channels=30).to(device)

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

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    for inputs, targets in train_loader:
        inputs, targets = inputs.to(device), targets.to(device)

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

    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
test_input = torch.rand(1, 3, 256, 256).to(device)
with torch.no_grad():
    test_output = model(test_input)
print(test_output.shape)

需要注意的是，以上代码中使用的`CustomDataset`是自定义的数据集类，需要根据实际情况进行修改。同时，由于高光谱图像的通道数较多，为了简化示例代码，将输出通道数设置为30，实际应用中需要根据数据集的实际情况进行调整。