我这里PointNet++的代码用的是pytorch版本的，链接为 https://github.com/yanx27/Pointnet2_pytorch

将自己的数据集格式修改为和modelnet40_normal_resampled数据集格式一样。

       由于源码中测试脚本只是输出了测试数据集的分类精确度，且测试数据集同样的是有标签的，没有模型验证脚本，由于个人实验需要，希望当模型训练完成后能用自己的无标签数据输入后输出类别去检测模型的分类效果，因此根据模型测试脚本，修改了一下代码，可以实现输入一个无标签的数据，从而输出分类结果以及可视化，从而更直观的验证模型训练的准确度。 

 代码如下，其中可视化部分参考这位博主的文章 pointconv pytorch modelnet40 点云分类结果可视化_对象被抛出的博客-CSDN博客_modelnet40可视化

from data_utils.ModelNetDataLoader_my import ModelNetDataLoader
import argparse
import numpy as np
import os
import torch
import logging
from tqdm import tqdm
import sys
import importlib
import matplotlib.pyplot as pltBASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'models'))def pc_normalize(pc):  #点云数据归一化centroid = np.mean(pc, axis=0)pc = pc - centroidm = np.max(np.sqrt(np.sum(pc**2, axis=1)))pc = pc / mreturn pc
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(torch.cuda.is_available())
def parse_args():'''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=4, help='batch size in training')parser.add_argument('--num_category', default=10, type=int, choices=[10, 40],  help='training on ModelNet10/40')parser.add_argument('--num_point', type=int, default=10000, help='Point Number')parser.add_argument('--log_dir', type=str, default='pointnet2_cls_msg', 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()
#加载数据集
dataset='/home/niu/mysubject/Pointnet_Pointnet2_pytorch-master/evalset/aaa_1.txt'
pcdataset = np.loadtxt(dataset, delimiter=' ').astype(np.float32)#数据读取，我的数据是三个维度，数据之间是空格，如果是逗号修改一下即可
point_set = pcdataset[0:10000, :] #我的输入数据设置为原始数据中10000个点
point_set[:, 0:3] = pc_normalize(point_set[:, 0:3]) #归一化数据
point_set = point_set[:, 0:3] 
point_set = point_set.transpose(1,0)#将数据由N*C转换为C*N
#print(point_set.shape)
point_set = point_set.reshape(1, 3, 10000)
n_points = point_set
point_set = torch.as_tensor(point_set)#需要将数据格式变为张量，不然会报错
point_set = point_set.cuda()
#print(point_set.shape)
#print(point_set.shape)
#分类测试函数
def test(model,point_set, num_class=10, vote_num=1):#mean_correct = []classifier = model.eval()class_acc = np.zeros((num_class, 3))vote_pool = torch.zeros(1, 10).cuda()for _ in range(vote_num):pred, _ = classifier(point_set)print(pred)vote_pool += predpred = vote_pool / vote_num# 对预测结果每行取最大值得到分类pred_choice = pred.data.max(1)[1]print(pred_choice)#可视化file_dir = '/home/niu/mysubject/Pointnet_Pointnet2_pytorch-master/visualizer'save_name_prefix = 'pred'draw(n_points[:, 0, :], n_points[:, 1, :], n_points[:, 2, :], save_name_prefix, file_dir, color=pred_choice)return pred_choice
#定义可视化函数
def draw(x, y, z, name, file_dir, color=None):"""绘制单个样本的三维点图"""if color is None:for i in range(len(x)):ax = plt.subplot(projection='3d')  # 创建一个三维的绘图工程save_name = name + '-{}.png'.format(i)save_name = os.path.join(file_dir,save_name)ax.scatter(x[i], y[i], z[i],s=0.1, c='r')ax.set_zlabel('Z')  # 坐标轴ax.set_ylabel('Y')ax.set_xlabel('X')plt.draw()plt.savefig(save_name)# plt.show()else:colors = ['red', 'blue', 'green', 'yellow', 'orange', 'tan', 'orangered', 'lightgreen', 'coral', 'aqua']for i in range(len(x)):ax = plt.subplot(projection='3d')  # 创建一个三维的绘图工程save_name = name + '-{}-{}.png'.format(i, color[i])save_name = os.path.join(file_dir,save_name)ax.scatter(x[i], y[i], z[i],s=0.1, c=colors[color[i]])ax.set_zlabel('Z')  # 坐标轴ax.set_ylabel('Y')ax.set_xlabel('X')plt.draw()plt.savefig(save_name)# plt.show()def main(args):def log_string(str):logger.info(str)print(str)'''HYPER PARAMETER'''os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu'''CREATE DIR'''experiment_dir = '/home/niu/mysubject/Pointnet_Pointnet2_pytorch-master/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)file_handler.setLevel(logging.INFO)file_handler.setFormatter(formatter)logger.addHandler(file_handler)log_string('PARAMETER ...')log_string(args)'''num_class = args.num_category#选择模型model_name = os.listdir(experiment_dir + '/logs')[0].split('.')[0]model = importlib.import_module(model_name)classifier = model.get_model(num_class, normal_channel=args.use_normals)if not args.use_cpu:classifier = classifier.cuda()#选择训练好的.pth文件checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth')classifier.load_state_dict(checkpoint['model_state_dict'])#预测分类with torch.no_grad():pred_choice = test(classifier.eval(), point_set, vote_num=args.num_votes, num_class=num_class)#log_string('pred_choice: %f' % (pred_choice))if __name__ == '__main__':args = parse_args()main(args)

用自己的数据集训练完成后，测试代码中修改训练好的.pth文件。根据自己的数据格式修改自己对应的参数以及数据集路径运行即可

分类输出结果：

输出为分类的数据类别3

 可视化结果保存在visualizer文件下，可视化结果：