点云分割-CPC算法的使用代码

CPC (Contrastive Predictive Coding) 是一种自监督学习算法，已经被广泛应用于点云分割任务中。下面是使用 CPC 算法进行点云分割的示例代码，仅供参考：

# 导入相关库
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
from sklearn.neighbors import KDTree
import open3d as o3d

# 定义 CPC 算法模型
class CPCModel(nn.Module):
    def __init__(self, input_shape, hidden_size, output_size):
        super(CPCModel, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv1d(input_shape[1], hidden_size, 1),
            nn.BatchNorm1d(hidden_size),
            nn.ReLU(),
            nn.Conv1d(hidden_size, hidden_size, 1),
            nn.BatchNorm1d(hidden_size),
            nn.ReLU(),
            nn.Conv1d(hidden_size, hidden_size, 1),
            nn.BatchNorm1d(hidden_size),
            nn.ReLU(),
            nn.Conv1d(hidden_size, hidden_size, 1),
            nn.BatchNorm1d(hidden_size),
            nn.ReLU(),
            nn.Conv1d(hidden_size, output_size, 1),
            nn.BatchNorm1d(output_size)
        )

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

# 定义数据集类
class PointCloudDataset(torch.utils.data.Dataset):
    def __init__(self, point_clouds):
        self.point_clouds = point_clouds
        self.kdtrees = [KDTree(pc) for pc in point_clouds]

    def __len__(self):
        return len(self.point_clouds)

    def __getitem__(self, idx):
        pc = self.point_clouds[idx]
        kdtree = self.kdtrees[idx]

        # 随机选择两个点
        idx1, idx2 = np.random.choice(len(pc), size=2, replace=False)

        # 获取两个点周围的点
        _, idxs1 = kdtree.query(pc[idx1], k=64)
        _, idxs2 = kdtree.query(pc[idx2], k=64)

        # 将两个点及其周围的点组成一个 batch
        batch = np.concatenate([pc[idx1][None], pc[idx2][None], pc[idxs1], pc[idxs2]], axis=0)
        return batch.T

# 加载点云数据并创建数据集实例
point_clouds = [o3d.io.read_point_cloud(f"point_cloud_{i}.ply").xyz for i in range(10)]
dataset = PointCloudDataset(point_clouds)

# 创建数据加载器
batch_size = 32
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 创建 CPC 模型和优化器
input_shape = (batch_size, 6, 64)
hidden_size = 128
output_size = 128
model = CPCModel(input_shape, hidden_size, output_size)
optimizer = torch.optim.Adam(model.parameters())

# 训练 CPC 模型
num_epochs = 10
for epoch in range(num_epochs):
    for batch in loader:
        batch = batch.float().transpose(1, 2)
        preds = model(batch)
        loss = -(preds[:, :, :-1] * preds[:, :, 1:]).sum(dim=-1).mean()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch + 1}/{num_epochs}, loss={loss:.4f}")
    
# 使用 CPC 模型进行点云分割
for i, pc in enumerate(point_clouds):
    # 将点云转换成 tensor
    pc = torch.tensor(pc).float()[None].transpose(1, 2)

    # 获取点云中每个点的特征向量
    features = model.encoder(pc).transpose(1, 2)

    # 对特征向量进行聚类
    kmeans = KMeans(n_clusters=2)
    labels = kmeans.fit_predict(features)

    # 将聚类结果可视化
    colors = np.zeros((len(pc[0]), 3))
    colors[labels == 0] = [1, 0, 0]
    colors[labels == 1] = [0, 1, 0]
    pcd = o3d.geometry.PointCloud()
    pcd.points = o3d.utility.Vector3dVector(pc[0].numpy())
    pcd.colors = o3d.utility.Vector3dVector(colors)
    o3d.visualization.draw_geometries([pcd])

需要注意的是，上述代码中使用了 Open3D 库进行点云的读取和可视化，可以根据需求进行替换。此外，为了简化示例代码，上述代码中使用了 KMeans 算法进行聚类，实际应用中应根据需求选择适合的聚类算法。