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