标签：group point xyz py shape points new self 逐行

继续巩固PointNet++代码的实现这篇博客，把代码逐行注释一遍！
point_util.py部分的python代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
from time import time
import numpy as np

def timeit(tag, t):
    print("{}: {}s".format(tag, time() - t))
    return time()
# 归一化点云，使用已centroid为中心的坐标，球半径为1
def pc_normalize(pc):
    l = pc.shape[0]
    centroid = np.mean(pc, axis=0)
    pc = pc - centroid
    m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
    pc = pc / m
    return pc
# 欧式距离
# 函数输入是两组点，N为第一组点src个数，M为第二组点dst个数，C为输入点的通道数（如果xyz时C=3）
# 函数返回的是两组点两两之间的欧式距离，即N*M矩阵
# 函数训练中数据以Mini-Batch的形式输入，所以一个Batch数量的维度为B
def square_distance(src, dst):
    """
    Calculate Euclid distance between each two points.

    src^T * dst = xn * xm + yn * ym + zn * zm；
    sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
    sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
    dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
         = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst

    Input:
        src: source points, [B, N, C]
        dst: target points, [B, M, C]
    Output:
        dist: per-point square distance, [B, N, M]
    """
    B, N, _ = src.shape
    _, M, _ = dst.shape
    dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
    dist += torch.sum(src ** 2, -1).view(B, N, 1)
    dist += torch.sum(dst ** 2, -1).view(B, 1, M)
    return dist

# 按照输入的点云数据和索引返回索引的点云数据
# 例如Points为B*2048*3点云，idx为[5.666，1000.2000]
# 则返回Batch中第5666，1000，2000个点组成的B*4*3的点云集
# 如果idx为一个[B,D1,''''DN],则它会按照idx中的纬度结构将其提取成[B，D1，‘’‘DN,C]
def index_points(points, idx):
    """

    Input:
        points: input points data, [B, N, C]
        idx: sample index data, [B, S]
    Return:
        new_points:, indexed points data, [B, S, C]
    """
    device = points.device
    B = points.shape[0]
    view_shape = list(idx.shape)
    view_shape[1:] = [1] * (len(view_shape) - 1)
    repeat_shape = list(idx.shape)
    repeat_shape[0] = 1
    batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
    new_points = points[batch_indices, idx, :]
    return new_points

# farthest_point_sample函数完成最远点采样
# 从一个输入点云中按照所需要的点的个数npoint采样出足够多的点
# 并且点与点的距离需要足够远
# 返回结果是npoint个采样点再原始点云中的索引
def farthest_point_sample(xyz, npoint):
    """
    Input:
        xyz: pointcloud data, [B, N, 3]
        npoint: number of samples
    Return:
        centroids: sampled pointcloud index, [B, npoint]
    """
    device = xyz.device
    B, N, C = xyz.shape
    # 初始化一个centrdis矩阵，用于存储npoint个采样点的索引位置，大小为B*npoint
    # 其中B为BatchSize的个数
    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)#8*512
    #distance矩阵（B*N）用来记录batch中所有点到某一个点的距离，初始化值很大，后面会迭代更新
    distance = torch.ones(B, N).to(device) * 1e10 #8*1024
    # farthest表示当前最远的点,也是随机初始化,范围为0-N,初始化B个;每个batch都随机有一个初始化最远点
    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)#batch里每个样本随机初始化一个最远点的索引
    # batch_indices初始化为0-(B-1)的数组
    batch_indices = torch.arange(B, dtype=torch.long).to(device)
    for i in range(npoint):
        # 假设当前采样点centroids为当前的最远点farthest
        centroids[:, i] = farthest #第一个采样点选随机初始化的索引
        # 取出该中心点centroid的坐标
        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)#得到当前采样点的坐标 B*3
        # 取出该中心点centroid点的欧式距离,存到dist矩阵中
        dist = torch.sum((xyz - centroid) ** 2, -1)#计算当前采样点与其他点的距离
        # 建立一个mask,如果dist中的元素小于distance矩阵中保存的距离值,则更新distance中的对应值
        # 随着迭代的继续,distance矩阵中的值会慢慢变小
        # 其相当于记录着某个batch中每个点距离所有已出现的采样点的最小距离
        mask = dist < distance#选择距离最近的来更新距离（更新维护这个表）
        distance[mask] = dist[mask]#从distance矩阵中去除最远的点为farthest,继续下一轮迭代
        farthest = torch.max(distance, -1)[1]#重新计算得到最远点索引（在更新的表中选择距离最大的那个点）
    return centroids
# query_ball_point函数用于寻找球形邻域中的点
# 输入中radius为球形邻域的半径，nsample为每个邻域中要采样的点
# new_xyz为centroids点的数据，xyz为所有的点云数据
# 输出为每个样本的每个球形邻域的nsample个采样点集的索引【B，S,nsample】

def query_ball_point(radius, nsample, xyz, new_xyz):
    """
    Input:
        radius: local region radius
        nsample: max sample number in local region
        xyz: all points, [B, N, 3]
        new_xyz: query points, [B, S, 3]
    Return:
        group_idx: grouped points index, [B, S, nsample]
    """
    device = xyz.device
    B, N, C = xyz.shape
    _, S, _ = new_xyz.shape
    group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
    # sqrdists:[B,S,N]记录S个中心点（new_xyz）与所有点（xyz）之间的欧氏距离
    sqrdists = square_distance(new_xyz, xyz)#得到B N M （就是N个点中每一个和M中每一个的欧氏距离）
    group_idx[sqrdists > radius ** 2] = N #找到距离大于给定半径的设置成一个N值（1024）索引
    group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]#做升序排序，后面的都是大的值（1024）
    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])#如果半径内的点没那么多，就直接用第一个点来代替了。。。
    # 找到group_idx中值等于N的点
    mask = group_idx == N
    # 将这些点的值替换为第一个点的值
    group_idx[mask] = group_first[mask]
    return group_idx
# Sampling+Grouping主要用于将整个点云分散成局部的group
# 对于每一个group都可以用PointNet单独的提取局部的全局特征
# Sampling+Grouping分成了sampl_and_group和sampl_and_group_all两个函数
# 其区别在于sample_and_group_all直接将所有点作为一个group
# 例如：
# 512=npoint:poins sampled in farthest point sampling
# 0.2=radius:search radius in local region
# 32=nsample:how many points in each local region

def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False):
    """
    Input:
        npoint:
        radius:
        nsample:
        xyz: input points position data, [B, N, 3]
        points: input points data, [B, N, D]
    Return:
        new_xyz: sampled points position data, [B, npoint, nsample, 3]
        new_points: sampled points data, [B, npoint, nsample, 3+D]
    """
    B, N, C = xyz.shape
    S = npoint
    # 从原点云通过最远点采样挑出的采样点作为new_xyz：
    # 先用farhest_point_sample函数实现最远点采样得到采样点的索引
    # 再通过index_points将这些点的从原始点中挑出来，作为new_xyz
    fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint, C]
    torch.cuda.empty_cache()
    new_xyz = index_points(xyz, fps_idx) #中心点
    torch.cuda.empty_cache()
    # idx:[B,npoint,nsample]，代表npoint个球形区域中每个区域的nsample个采样点的索引
    idx = query_ball_point(radius, nsample, xyz, new_xyz)
    torch.cuda.empty_cache()
    grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]
    torch.cuda.empty_cache()
    grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)
    torch.cuda.empty_cache()
    # 如果每个点上有新的特征维度，则拼接新的特征与旧的特征，否则直接返回旧的特征
    # 注：用于拼接点的特征数据和点坐标数据
    if points is not None:
        grouped_points = index_points(points, idx)
        new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) # [B, npoint, nsample, C+D]
    else:
        new_points = grouped_xyz_norm
    if returnfps:
        return new_xyz, new_points, grouped_xyz, fps_idx
    else:
        return new_xyz, new_points

# sample_and_group_all直接将所有点作为一个group；n_point=1
def sample_and_group_all(xyz, points):
    """
    Input:
        xyz: input points position data, [B, N, 3]
        points: input points data, [B, N, D]
    Return:
        new_xyz: sampled points position data, [B, 1, 3]
        new_points: sampled points data, [B, 1, N, 3+D]
    """
    device = xyz.device
    B, N, C = xyz.shape
    new_xyz = torch.zeros(B, 1, C).to(device)
    grouped_xyz = xyz.view(B, 1, N, C)
    if points is not None:
        new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1)
    else:
        new_points = grouped_xyz
    return new_xyz, new_points

#PointNetSetAbstraction类实现普通的SetAbstraciton：
# 然后通过sample_and_group的操作形成局部group
# 然后对局部group中的每一个点做MLP操作，最后进行局部最大池化，得到局部的全局特征
class PointNetSetAbstraction(nn.Module):
    # 例如：npoint=128,radius=0.4,nsample=64,in_channle=128+3,mlp=[128,128,256],group_all=False
    # 128=npoint:points sampled in farthest point sampling
    # 0.4=radius:search radius in local region
    # 64=nsample:how many points inn each local region
    # [128,128,256]=output size for MLP on each point
    def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):
        super(PointNetSetAbstraction, self).__init__()
        self.npoint = npoint
        self.radius = radius
        self.nsample = nsample
        self.mlp_convs = nn.ModuleList()
        self.mlp_bns = nn.ModuleList()
        last_channel = in_channel
        for out_channel in mlp:
            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))
            self.mlp_bns.append(nn.BatchNorm2d(out_channel))
            last_channel = out_channel
        self.group_all = group_all

    def forward(self, xyz, points):
        """
        Input:
            xyz: input points position data, [B, C, N]
            points: input points data, [B, D, N]
        Return:
            new_xyz: sampled points position data, [B, C, S]
            new_points_concat: sample points feature data, [B, D', S]
        """
        xyz = xyz.permute(0, 2, 1)
        # print(xyz.shape)
        if points is not None:
            points = points.permute(0, 2, 1)
        # print(points.shape)
        # 形成局部的group
        if self.group_all:
            new_xyz, new_points = sample_and_group_all(xyz, points)
        else:
            new_xyz, new_points = sample_and_group(self.npoint, self.radius, self.nsample, xyz, points)
        # new_xyz: sampled points position data, [B, npoint, C]
        # new_points: sampled points data, [B, npoint, nsample, C+D]
        # print(new_points.shape)
        new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]
        # 以下是pointnet操作：
        # 对局部group中每一个点做MLP操作:
        # 利用1*1的2d卷积相当于把每个group当成一个通道，共npoint个通道
        # 对[C+D，nsample]的维度上做逐像素的卷积，结果相当于对单个c+D维度做1d的卷积
        # print(new_points.shape)
        for i, conv in enumerate(self.mlp_convs):
            bn = self.mlp_bns[i]
            new_points =  F.relu(bn(conv(new_points)))
        # print(new_points.shape)
        # 最后进行局部的最大池化，得到局部的全局特征
        new_points = torch.max(new_points, 2)[0]
        # print(new_points.shape)
        new_xyz = new_xyz.permute(0, 2, 1)
        # print(new_xyz.shape)
        return new_xyz, new_points

#PointNetSetAbstractionMsg类实现MSG方法的Set Abstraction:
#这里radius_list输入的是一个list，例如[0.1，0.2,0.4]
#对于不同的半径做ball query，将不同半径下的点云特征保存在new_points_list中，最后再拼接到一起
class PointNetSetAbstractionMsg(nn.Module):
    def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list):
        super(PointNetSetAbstractionMsg, self).__init__()
        self.npoint = npoint
        self.radius_list = radius_list
        self.nsample_list = nsample_list
        self.conv_blocks = nn.ModuleList()
        self.bn_blocks = nn.ModuleList()
        for i in range(len(mlp_list)):
            convs = nn.ModuleList()
            bns = nn.ModuleList()
            last_channel = in_channel + 3
            for out_channel in mlp_list[i]:
                convs.append(nn.Conv2d(last_channel, out_channel, 1))
                bns.append(nn.BatchNorm2d(out_channel))
                last_channel = out_channel
            self.conv_blocks.append(convs)
            self.bn_blocks.append(bns)

    def forward(self, xyz, points):
        """
        Input:
            xyz: input points position data, [B, C, N]
            points: input points data, [B, D, N]
        Return:
            new_xyz: sampled points position data, [B, C, S]
            new_points_concat: sample points feature data, [B, D', S]
        """
        xyz = xyz.permute(0, 2, 1) #就是坐标点位置特征
        # print(xyz.shape)
        if points is not None:
            points = points.permute(0, 2, 1) ##就是额外提取的特征，第一次的时候就是那个法向量特征
        # print(points.shape)
        B, N, C = xyz.shape
        S = self.npoint
        # 最远点采样
        new_xyz = index_points(xyz, farthest_point_sample(xyz, S))#采样后的点
        print(new_xyz.shape)
        # 将不同半径下的点云特征保存在new_points_list
        new_points_list = []
        for i, radius in enumerate(self.radius_list):
            K = self.nsample_list[i]
            # query_ball_point函数用于寻找球形邻域中的点
            group_idx = query_ball_point(radius, K, xyz, new_xyz)#返回的是索引
            # 按照输入的点云数据和索引返回索引的点云数据
            grouped_xyz = index_points(xyz, group_idx)#得到各个组中实际点
            grouped_xyz -= new_xyz.view(B, S, 1, C)#去mean new_xyz相当于簇的中心点
            if points is not None:
                grouped_points = index_points(points, group_idx)
                # 拼接点云特征数据和点坐标数据
                grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1)
                # print(grouped_points.shape)
            else:
                grouped_points = grouped_xyz

            grouped_points = grouped_points.permute(0, 3, 2, 1)  # [B, D, K, S]
            # print(grouped_points.shape)
            for j in range(len(self.conv_blocks[i])):
                conv = self.conv_blocks[i][j]
                bn = self.bn_blocks[i][j]
                grouped_points =  F.relu(bn(conv(grouped_points)))
            # print(grouped_points.shape)
            # 最大池化，获得局部区域的全局特征
            new_points = torch.max(grouped_points, 2)[0]  # [B, D', S] 就是pointnet里的maxpool操作
            # print(new_points.shape)
            new_points_list.append(new_points) #不同半径下的点云特征的列表

        new_xyz = new_xyz.permute(0, 2, 1)
        # 拼接不同半径下的点云特征
        new_points_concat = torch.cat(new_points_list, dim=1)
        # print(new_points_concat.shape)
        return new_xyz, new_points_concat

#Feature Propagation的实现主要通过线性差值和MLP完成
# 当点的个数只有一个的时候，采用repeat直接复制成N个点
# 当点的个数大于一个的时候，采用线性差值的方式进行上采样
# 拼接上下采样对应点的SA的特征，再对拼接后的每一个点做一个MLP
class PointNetFeaturePropagation(nn.Module):
    def __init__(self, in_channel, mlp):    #例如in_channel=384，mlp=[256,128]
        super(PointNetFeaturePropagation, self).__init__()
        self.mlp_convs = nn.ModuleList()
        self.mlp_bns = nn.ModuleList()
        last_channel = in_channel
        for out_channel in mlp:
            self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))
            self.mlp_bns.append(nn.BatchNorm1d(out_channel))
            last_channel = out_channel

    def forward(self, xyz1, xyz2, points1, points2):
        """
        Input:
            xyz1: input points position data, [B, C, N]
            xyz2: sampled input points position data, [B, C, S]
            points1: input points data, [B, D, N]
            points2: input points data, [B, D, S]
        Return:
            new_points: upsampled points data, [B, D', N]
        """
        xyz1 = xyz1.permute(0, 2, 1)
        xyz2 = xyz2.permute(0, 2, 1)
        # print(xyz1.shape)
        # print(xyz2.shape)

        points2 = points2.permute(0, 2, 1)
        # print(points2.shape)
        B, N, C = xyz1.shape
        _, S, _ = xyz2.shape

        if S == 1:
            # 当点的个数只有一个的时候，采用repeat直接复制成N个点
            interpolated_points = points2.repeat(1, N, 1)
            # print(interpolated_points.shape)
        else:
            # 当点的个数大于一个的时候，采用线性差值的方式进行上采样
            dists = square_distance(xyz1, xyz2)
            # print(dists.shape)
            dists, idx = dists.sort(dim=-1)
            dists, idx = dists[:, :, :3], idx[:, :, :3]  # [B, N, 3]

            dist_recip = 1.0 / (dists + 1e-8)#距离越远的点权重越小
            norm = torch.sum(dist_recip, dim=2, keepdim=True)
            weight = dist_recip / norm#对于每一个点的权重再做一个全局的归一化
            # print(weight.shape)
            # print(index_points(points2, idx).shape)
            # 获得插值点
            interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)
            # print(interpolated_points.shape)

        if points1 is not None:
            points1 = points1.permute(0, 2, 1)
            # 拼接上下采样前对应点SA层的特征
            new_points = torch.cat([points1, interpolated_points], dim=-1)
        else:
            new_points = interpolated_points
        # print(new_points.shape)
        new_points = new_points.permute(0, 2, 1)
        # print(new_points.shape)
        for i, conv in enumerate(self.mlp_convs):
            bn = self.mlp_bns[i]
            new_points = F.relu(bn(conv(new_points)))
        # print(new_points.shape)
        return new_points

标签：group,point,xyz,py,shape,points,new,self,逐行
来源： https://blog.csdn.net/weixin_45947476/article/details/121436828

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。