首先，从github上下载源码

https://github.com/yanx27/Pointnet_Pointnet2_pytorch

也可以从百度网盘下载

链接：https://pan.baidu.com/s/1sgTYuqnBVC9p3bib450SOQ

提取码：gujd

再下载对应的测试数据

1. 分类数据modelnet40_normal_resampled下载，放在目录data/modelnet40_normal_resampled/中

链接：https://pan.baidu.com/s/1OSjVP_1cRr2zowsdyLoK1A

提取码：9r7l

2. 部件分割数据shapenetcore_partanno_segmentation_benchmark_v0_normal下载，放在目录data/shapenetcore_partanno_segmentation_benchmark_v0_normal/中

链接：https://pan.baidu.com/s/1VebwBPk6fEuAhff__T4Oag

提取码：h44m

3. 语义分割数据Stanford3dDataset_v1.2_Aligned_Version下载（数据太大，需要可以私信我），放在目录data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/中

修改源码

用Pycharm打开对应工程目录，运行一下train_classification.py，如果运行错误，则需要修改对应源码

然后就可以根据github上的说明进行测试啦

# 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

# ModelNet10
## Similar setting like ModelNet40, just using --num_category 10

## e.g., pointnet2_ssg without normal features
python train_classification.py --model pointnet2_cls_ssg --log_dir pointnet2_cls_ssg --num_category 10
python test_classification.py --log_dir pointnet2_cls_ssg --num_category 10

CUDA报错

任何的CUDA报错，都可以减小batch_size来解决

Pycharm参数设置

如果在Pycharm中直接运行py文件，设置参数的话，需要在IDE中进行，如下图所示

进入Edit Configurations之后，填写参数，每个参数用空格分开