用DGCNN写一个点云配准模型，要具体的代码

好的，以下是一个使用DGCNN实现点云配准的简单示例代码：

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

class DGCNN(nn.Module):
    def __init__(self, num_points, num_features, num_transform_params):
        super(DGCNN, self).__init__()
        self.conv1 = nn.Conv1d(num_features, 64, 1)
        self.conv2 = nn.Conv1d(64 + num_features, 128, 1)
        self.conv3 = nn.Conv1d(128 + 64, 256, 1)
        self.conv4 = nn.Conv1d(256 + 128, 512, 1)
        self.fc1 = nn.Linear(512 + 256, 1024)
        self.fc2 = nn.Linear(1024, 512)
        self.fc3 = nn.Linear(512, num_transform_params)
        
    def forward(self, x):
        batch_size = x.size(0)
        num_points = x.size(1)
        x = x.transpose(2, 1)  # [B, C, N] -> [B, N, C]
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(torch.cat([x, x.transpose(2, 1).repeat(1, 1, num_points)], dim=1)))
        x = F.relu(self.conv3(torch.cat([x, x.transpose(2, 1).repeat(1, 1, num_points)], dim=1)))
        x = F.relu(self.conv4(torch.cat([x, x.transpose(2, 1).repeat(1, 1, num_points)], dim=1)))
        x = F.max_pool1d(x, num_points).squeeze(-1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        x = x.view(batch_size, -1)
        return x

def point_cloud_align(input_points, target_points, num_transform_params=12):
    model = DGCNN(num_points=input_points.size(1), num_features=input_points.size(2), num_transform_params=num_transform_params)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    criterion = nn.MSELoss()
    
    num_epochs = 10
    batch_size = input_points.size(0)
    
    for epoch in range(num_epochs):
        epoch_loss = 0.0
        for i in range(0, input_points.size(0), batch_size):
            optimizer.zero_grad()
            batch_input = input_points[i:i+batch_size]
            batch_target = target_points[i:i+batch_size]
            pred_params = model(batch_input)
            pred_transform = pred_params.view(-1, 4, 3)
            pred_points = torch.bmm(batch_input, pred_transform.transpose(2, 1))
            loss = criterion(pred_points, batch_target)
            loss.backward()
            optimizer.step()
            epoch_loss += loss.item()
        print("Epoch %d loss: %.4f" % (epoch+1, epoch_loss))
    
    return model

这个代码实现了一个简单的点云配准模型，使用了DGCNN网络架构。`DGCNN`类定义了DGCNN网络中的卷积层、池化层、全连接层等，其中最后一层全连接层输出点云的变换矩阵。`point_cloud_align`函数定义了点云配准的训练过程，包括模型的定义、损失函数的定义、优化器的定义以及迭代训练过程。具体来说，每次迭代中，将输入点云和目标点云配对作为模型的输入，计算模型输出的点云变换矩阵，并将其与目标点云的真实变换矩阵进行比较，计算损失函数并进行反向传播更新模型参数。最终返回训练好的模型，用于未知数据的点云配准。