用python写一个基于Transformer的高光谱和LiDAR特征融合分类

好，这是一个比较复杂的任务，需要用到一些机器学习和深度学习的相关技术。下面是一个基于Transformer的高光谱和LiDAR特征融合分类的Python示例代码，供您参考：

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

class TransformerEncoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads):
        super(TransformerEncoder, self).__init__()
        self.self_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.feed_forward = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.ReLU())
        self.layer_norm1 = nn.LayerNorm(hidden_size)
        self.layer_norm2 = nn.LayerNorm(hidden_size)
        self.dropout1 = nn.Dropout(p=0.1)
        self.dropout2 = nn.Dropout(p=0.1)
        self.num_layers = num_layers

    def forward(self, x):
        for i in range(self.num_layers):
            residual = x
            x = self.layer_norm1(x)
            x, _ = self.self_attn(x, x, x)
            x = self.dropout1(x)
            x += residual
            residual = x
            x = self.layer_norm2(x)
            x = self.feed_forward(x)
            x = self.dropout2(x)
            x += residual
        return x

class HSI_LiDAR_Transformer(nn.Module):
    def __init__(self, hsi_input_size, lidar_input_size, hidden_size, num_classes):
        super(HSI_LiDAR_Transformer, self).__init__()
        self.hsi_encoder = TransformerEncoder(hsi_input_size, hidden_size, num_layers=2, num_heads=4)
        self.lidar_encoder = TransformerEncoder(lidar_input_size, hidden_size, num_layers=2, num_heads=4)
        self.fc = nn.Linear(hidden_size * 2, num_classes)

    def forward(self, hsi, lidar):
        hsi = self.hsi_encoder(hsi)
        lidar = self.lidar_encoder(lidar)
        x = torch.cat((hsi, lidar), dim=1)
        x = self.fc(x)
        return x

# 训练和测试的代码
model = HSI_LiDAR_Transformer(hsi_input_size=256, lidar_input_size=64, hidden_size=128, num_classes=10)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 训练集和测试集，假设都已经准备好了
train_loader = ...
test_loader = ...

for epoch in range(10):
    model.train()
    for i, (hsi, lidar, label) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(hsi, lidar)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()

    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for i, (hsi, lidar, label) in enumerate(test_loader):
            output = model(hsi, lidar)
            _, predicted = torch.max(output.data, 1)
            total += label.size(0)
            correct += (predicted == label).sum().item()

    print('Epoch %d, Test Accuracy: %f' % (epoch, correct / total))

这段代码实现了一个基于Transformer的高光谱和LiDAR特征融合分类器，使用了PyTorch框架。在代码中，我们首先定义了一个TransformerEncoder类，它实现了一个Transformer的Encoder模块。然后我们定义了一个HSI_LiDAR_Transformer类，它使用两个TransformerEncoder对输入的高光谱和LiDAR特征进行编码，然后将编码后的结果进行拼接，并接上一个全连接层进行分类。最后我们使用Adam优化器和交叉熵损失函数来训练模型，并在测试集上进行测试。

需要注意的是，这只是一个示例代码，具体的实现细节还需要根据具体的数据集和任务进行调整。