写一个DNN的毫米波雷达数据与图像数据融合的代码

以下是一个基于 PyTorch 的毫米波雷达数据与图像数据融合的 DNN 代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class RadarImageFusionNet(nn.Module):
    def __init__(self):
        super(RadarImageFusionNet, self).__init__()
        self.conv1_radar = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.conv2_radar = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.conv3_radar = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.pool_radar = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1_radar = nn.Linear(in_features=128 * 7 * 7, out_features=512)
        
        self.conv1_image = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.conv2_image = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.conv3_image = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.pool_image = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1_image = nn.Linear(in_features=128 * 7 * 7, out_features=512)
        
        self.fc2 = nn.Linear(in_features=1024, out_features=256)
        self.fc3 = nn.Linear(in_features=256, out_features=2)

    def forward(self, radar_data, image_data):
        x_radar = F.relu(self.conv1_radar(radar_data))
        x_radar = self.pool_radar(F.relu(self.conv2_radar(x_radar)))
        x_radar = self.pool_radar(F.relu(self.conv3_radar(x_radar)))
        x_radar = x_radar.view(-1, 128 * 7 * 7)
        x_radar = F.relu(self.fc1_radar(x_radar))
        
        x_image = F.relu(self.conv1_image(image_data))
        x_image = self.pool_image(F.relu(self.conv2_image(x_image)))
        x_image = self.pool_image(F.relu(self.conv3_image(x_image)))
        x_image = x_image.view(-1, 128 * 7 * 7)
        x_image = F.relu(self.fc1_image(x_image))
        
        x = torch.cat((x_radar, x_image), dim=1)
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

这个模型包含两个分支，一个用于处理毫米波雷达数据，另一个用于处理图像数据。每个分支都包含三个卷积层和一个全连接层，最后将两个分支的输出拼接在一起，再经过两个全连接层产生最终的输出。在 forward 方法中，输入的 radar_data 和 image_data 分别经过雷达分支和图像分支处理，并将两个分支的输出拼接在一起。