工程 (六) ——PointNet点云分类_pointnet2

目录

一、 基本原理

二、工程目录

三、代码解析

3.1 分类

3.2 语义分割

四、测试运行

4.1 分类

4.2 语义分割

---

一、 基本原理

相比与pointnet，pointnet++是对点云逐层运用RNN最邻近收缩进行均匀降采样，加上上一层的特征传入PointNet

为不受坐标的影响，需要有Normalize步骤减去中心位置，以不受绝对距离的影响。比如一个人在1m和在20m都是一个人。

网络框架如下，pointnet++可以进行点云分类和语义分割的操作。

二、工程目录

• test和train：这一系列文件是pointnet++不同功能的测试和验证文件
• visualizer：是对点云分割结果的可视化
• data：放数据集的文件夹
• data_utils：对数据集的解析文件
• log：日志文件及各种功能网络模型的相关代码及预训练权重

 可以看到三个功能是分类、区域分割和语义分割，打开一个文件

 checkpoint：预训练权重文件

logs：训练日志文件

pointnet2_utils.py：网络相关的代码组件

pointnet2_cls_msg.py：网络模型文件与models下的一样

• models：各种网络模型

三、代码解析

3.1 分类

首先打开model/pointnet2_cls_ssg.py的一个分类网络模型的文件，代码解析如下，可以看到分类网络主要通过三个特征提取层PointNetSetAbstraction之后再通过全链接来达到分类的目的。

import torch.nn as nn
import torch.nn.functional as F
from pointnet2_utils import PointNetSetAbstraction
 
 
class get_model(nn.Module): #网络结构
    def __init__(self,num_class,normal_channel=True):
        super(get_model, self).__init__()
        in_channel = 6 if normal_channel else 3
        self.normal_channel = normal_channel
        # 三次特征提取特征层
        self.sa1 = PointNetSetAbstraction(npoint=512, radius=0.2, nsample=32, in_channel=in_channel, mlp=[64, 64, 128], group_all=False)
        self.sa2 = PointNetSetAbstraction(npoint=128, radius=0.4, nsample=64, in_channel=128 + 3, mlp=[128, 128, 256], group_all=False)
        self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=256 + 3, mlp=[256, 512, 1024], group_all=True)
        self.fc1 = nn.Linear(1024, 512) #全链接层
        '''
        BatchNorm1d小批量数据归一化方法
        1.加快网络的训练和收敛的速度
        2.控制梯度爆炸和梯度消失
        3.防止过拟合
        '''
        self.bn1 = nn.BatchNorm1d(512)
        '''
        nn.dropout()是为了防止或减轻过拟合而使用的函数，它一般用在全连接层
        Dropout就是在不同的训练过程中随机扔掉一部分神经元。
        也就是让某个神经元的激活值以一定的概率p
        让其停止工作，这次训练过程中不更新权值，也不参加神经网络的计算。
        '''
        self.drop1 = nn.Dropout(0.4)
        self.fc2 = nn.Linear(512, 256)
        self.bn2 = nn.BatchNorm1d(256)
        self.drop2 = nn.Dropout(0.4)
        self.fc3 = nn.Linear(256, num_class)
 
    def forward(self, xyz): #前向传播
*********************************
class get_loss(nn.Module): #损失函数
*********************************

PointNetSetAbstraction 的代码在pointnet2_untils.py中的PointNetSetAbstraction类中。

文件中有3个类

PointNetSetAbstraction，PointNetSetAbstractionMsg，PointNetFeaturePropagation分别代表不用网络模型所用到的层结构。文件中的其他函数为类中的相关函数

# PointNet网络，将每个区域的所有点变成一个特征 在输入网络之前，会把每个区域的坐标变成围绕中心点的相对坐标
class PointNetSetAbstraction(nn.Module):
    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)
        if points is not None:
            points = points.permute(0, 2, 1)
 
        if self.group_all:
            new_xyz, new_points = sample_and_group_all(xyz, points) #下采样特征点
        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]
        new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]
        for i, conv in enumerate(self.mlp_convs):
            bn = self.mlp_bns[i]
            new_points =  F.relu(bn(conv(new_points)))
 
        new_points = torch.max(new_points, 2)[0]
        new_xyz = new_xyz.permute(0, 2, 1)
        return new_xyz, new_points

使用sample_and_group以达到选取中心点分局部区域的目的

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
    fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint, C]  FPS法采样N个中心点
    new_xyz = index_points(xyz, fps_idx)
    idx = query_ball_point(radius, nsample, xyz, new_xyz) #根据N个中心点生成对应的局部区域
    grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]  
    grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C) #坐标归一化
 
    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

相关的采样点等函数代码如下

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
 
 
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
 
# FPS法采样N个中心点
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
    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) #采样点矩阵（B，npoint） npoint剩下的点
    distance = torch.ones(B, N).to(device) * 1e10 #采样点到所有点之间的距离（B，N）
    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) #最远点
    batch_indices = torch.arange(B, dtype=torch.long).to(device) #batch_size数组
    #寻找最远点
    for i in range(npoint): #把剩下的点进行循环
        centroids[:, i] = farthest #更新最远点
        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3) #取出最远点的坐标
        dist = torch.sum((xyz - centroid) ** 2, -1) #计算到最远点的欧式距离
        mask = dist < distance
        distance[mask] = dist[mask] #更新最远点
        farthest = torch.max(distance, -1)[1] #返回最远点索引
    return centroids
 
# 根据N个中心点生成对应的局部区域 这里使用到两个超参数 ，一个是每个区域中点的数量K，另一个是query的半径r。
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 = square_distance(new_xyz, xyz)
    group_idx[sqrdists > radius ** 2] = N
    group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]
    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])
    mask = group_idx == N
    group_idx[mask] = group_first[mask]
    return group_idx #返回若干个区域

3.2 语义分割

语义分割与分类的不同是除了将点云下采样得到特征向量后，还需要将点集上采样回原始点集数量，网络结构如下

class get_model(nn.Module):
    def __init__(self, num_classes):
        super(get_model, self).__init__()
        # 4次下采样提取特征层
        self.sa1 = PointNetSetAbstraction(1024, 0.1, 32, 9 + 3, [32, 32, 64], False)
        self.sa2 = PointNetSetAbstraction(256, 0.2, 32, 64 + 3, [64, 64, 128], False)
        self.sa3 = PointNetSetAbstraction(64, 0.4, 32, 128 + 3, [128, 128, 256], False)
        self.sa4 = PointNetSetAbstraction(16, 0.8, 32, 256 + 3, [256, 256, 512], False)
        # 4次上采样恢复点云
        self.fp4 = PointNetFeaturePropagation(768, [256, 256])
        self.fp3 = PointNetFeaturePropagation(384, [256, 256])
        self.fp2 = PointNetFeaturePropagation(320, [256, 128])
        self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])
        self.conv1 = nn.Conv1d(128, 128, 1)
        self.bn1 = nn.BatchNorm1d(128)
        self.drop1 = nn.Dropout(0.5)
        self.conv2 = nn.Conv1d(128, num_classes, 1)

PointNetSetAbstraction 与分类的代码功能一样，对于语义分割多了上采样层

PointNetFeaturePropagation使用了分层的差值方法，代码如下

class PointNetFeaturePropagation(nn.Module):
    def __init__(self, in_channel, mlp):
        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)
 
        points2 = points2.permute(0, 2, 1)
        B, N, C = xyz1.shape
        _, S, _ = xyz2.shape
 
        if S == 1:
            interpolated_points = points2.repeat(1, N, 1)
        else:
            dists = square_distance(xyz1, xyz2)
            dists, idx = dists.sort(dim=-1)
            dists, idx = dists[:, :, :3], idx[:, :, :3]  # [B, N, 3]
 
            dist_recip = 1.0 / (dists + 1e-8)
            norm = torch.sum(dist_recip, dim=2, keepdim=True)
            weight = dist_recip / norm
            interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)
 
        if points1 is not None:
            points1 = points1.permute(0, 2, 1)
            new_points = torch.cat([points1, interpolated_points], dim=-1)
        else:
            new_points = interpolated_points
 
        new_points = new_points.permute(0, 2, 1)
        for i, conv in enumerate(self.mlp_convs):
            bn = self.mlp_bns[i]
            new_points = F.relu(bn(conv(new_points)))
        return new_points

四、测试运行

4.1 分类

test_classification.py代码如下

"""
Author: Benny
Date: Nov 2019
"""
from data_utils.ModelNetDataLoader import ModelNetDataLoader
import argparse
import numpy as np
import os
import torch
import logging
from tqdm import tqdm
import sys
import importlib
 
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'models'))
 
 
def parse_args(): #初始化设置
    '''PARAMETERS'''
    parser = argparse.ArgumentParser('Testing')
    parser.add_argument('--use_cpu', action='store_true', default=False, help='use cpu mode')
    parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
    parser.add_argument('--batch_size', type=int, default=24, help='batch size in training')
    parser.add_argument('--num_category', default=40, type=int, choices=[10, 40],  help='training on ModelNet10/40')
    parser.add_argument('--num_point', type=int, default=1024, help='Point Number')
    parser.add_argument('--log_dir', type=str, required=True, help='Experiment root')
    parser.add_argument('--use_normals', action='store_true', default=False, help='use normals')
    parser.add_argument('--use_uniform_sample', action='store_true', default=False, help='use uniform sampiling')
    parser.add_argument('--num_votes', type=int, default=3, help='Aggregate classification scores with voting')
    return parser.parse_args()
 
 
def test(model, loader, num_class=40, vote_num=1): #5.测试
    mean_correct = []
    classifier = model.eval()
    class_acc = np.zeros((num_class, 3))
 
    for j, (points, target) in tqdm(enumerate(loader), total=len(loader)):
        if not args.use_cpu:
            points, target = points.cuda(), target.cuda()
 
        points = points.transpose(2, 1)
        vote_pool = torch.zeros(target.size()[0], num_class).cuda()
 
        for _ in range(vote_num):
            pred, _ = classifier(points)
            vote_pool += pred
        pred = vote_pool / vote_num
        pred_choice = pred.data.max(1)[1]
 
        for cat in np.unique(target.cpu()):
            classacc = pred_choice[target == cat].eq(target[target == cat].long().data).cpu().sum()
            class_acc[cat, 0] += classacc.item() / float(points[target == cat].size()[0])
            class_acc[cat, 1] += 1
        correct = pred_choice.eq(target.long().data).cpu().sum()
        mean_correct.append(correct.item() / float(points.size()[0]))
 
    class_acc[:, 2] = class_acc[:, 0] / class_acc[:, 1]
    class_acc = np.mean(class_acc[:, 2])
    instance_acc = np.mean(mean_correct)
    return instance_acc, class_acc
 
 
def main(args):
    def log_string(str):
        logger.info(str)
        print(str)
 
    '''HYPER PARAMETER''' #1.选择设备
    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
 
    '''CREATE DIR'''   #2.创建日志保存路径
    experiment_dir = 'log/classification/' + args.log_dir
 
    '''LOG'''
    args = parse_args()
    logger = logging.getLogger("Model")
    logger.setLevel(logging.INFO)
    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir) #将命令保存到这个文件
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(formatter)
    logger.addHandler(file_handler)
    log_string('PARAMETER ...')
    log_string(args)
 
    '''DATA LOADING'''  #3.数据加载
    log_string('Load dataset ...')
    data_path = 'data/modelnet40_normal_resampled/'
 
    test_dataset = ModelNetDataLoader(root=data_path, args=args, split='test', process_data=False)
    testDataLoader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=10)
 
    '''MODEL LOADING'''  #4.加载模型
    num_class = args.num_category
    model_name = os.listdir(experiment_dir + '/logs')[0].split('.')[0] #获取网络结构文件
    print(model_name)
    model = importlib.import_module(model_name) #加载网络
 
    classifier = model.get_model(num_class, normal_channel=args.use_normals)
    if not args.use_cpu:
        classifier = classifier.cuda()
 
    checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') #加载模型权重
    classifier.load_state_dict(checkpoint['model_state_dict'])
 
    with torch.no_grad():
        instance_acc, class_acc = test(classifier.eval(), testDataLoader, vote_num=args.num_votes, num_class=num_class) #进行测试
        log_string('Test Instance Accuracy: %f, Class Accuracy: %f' % (instance_acc, class_acc))
 
 
if __name__ == '__main__':
    args = parse_args()
    main(args)

分类官方使用的是ModelNet数据集，将下载好的 ModelNet40数据集放在./data/路径下

# ModelNet40
## Select different models in ./models 
 
## e.g., pointnet2_ssg without normal features
python train_classification.py --model pointnet2_cls_ssg --log_dir pointnet2_cls_ssg
python test_classification.py --log_dir pointnet2_cls_ssg
 
## e.g., pointnet2_ssg with normal features
python train_classification.py --model pointnet2_cls_ssg --use_normals --log_dir pointnet2_cls_ssg_normal
python test_classification.py --use_normals --log_dir pointnet2_cls_ssg_normal
 
## e.g., pointnet2_ssg with uniform sampling
python train_classification.py --model pointnet2_cls_ssg --use_uniform_sample --log_dir pointnet2_cls_ssg_fps
python test_classification.py --use_uniform_sample --log_dir pointnet2_cls_ssg_fps

--log_dir 后面跟的是网络模型的名称，也就是model文件夹下的文件。

4.2 语义分割

分割用的3D indoor数据集

Download 3D indoor parsing dataset (**S3DIS**) [here](http://buildingparser.stanford.edu/dataset.html)  and save in `data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/`.
```
cd data_utils
python collect_indoor3d_data.py
```
Processed data will save in `data/s3dis/stanford_indoor3d/`.
### Run
```
## Check model in ./models 
## e.g., pointnet2_ssg
python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg
python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual