pointnet 代码复现

Posted 2022-08-01 jiangxiaoju

文章目录

• 一、环境准备
• 二、数据集准备
• 三、训练模型
• 四、实验结果

一、环境准备

根据作者描述使用python2.7+tensorflow1.0.1+cuda8.0。在Ubuntu16.04中复现。

1. 配置cuda8.0+cudnn5.1

具体过程可参考网上教程

2. 配置python2.7+tensorflow1.0.1、

使用conda创建虚拟环境便于管理

conda create -n pointnet python=2.7 anaconda

进入虚拟环境。

source activate pointnet

安装tensorlfow1.0.1。

pip install tensorflow-gpu==1.0.1

如果网不好建议切换清华源下载。

pip install tensorflow-gpu==1.0.1 -i https://pypi.tuna.tsinghua.edu.cn/simple

3. 一些必要的库

sudo apt-get install libhdf5-dev
sudo pip install h5py

如果你是使用conda虚拟环境，安装h5py可用下面代码

pip install h5py

二、数据集准备

代码一共有三个部分，分别是分类网络，和两个分割网络。每个部分都有各自对应的数据集

1. 点云分类数据集

代码检测不到数据会自动下载。

2. part_seg部分的数据集

cd part_seg
sh download_data.sh

3. sem_seg部分的数据集

cd sem_seg
sh download_data.sh

三、训练模型

三个模型均运行train.py文件即可

python train.py

分类网络，经过以上训练，可以评估模型并输出一些错误案例的可视化图像。

python python evaluate.py --visu

四、实验结果

1. 分类网络

训练log

**** EPOCH 249 ****
----0-----
mean loss: 0.073358
accuracy: 0.976074
----1-----
mean loss: 0.078584
accuracy: 0.966912
----2-----
mean loss: 0.067263
accuracy: 0.978516
----3-----
mean loss: 0.074200
accuracy: 0.979004
----4-----
mean loss: 0.075995
accuracy: 0.975098
----0-----
----1-----
eval mean loss: 0.550484
eval accuracy: 0.878247
eval avg class acc: 0.845228

test结果

Namespace(batch_size=4, dump_dir='dump', gpu=0, model='pointnet_cls', model_path='log/model.ckpt', num_point=1024, visu=True)
Model restored.
----0----
----1----
eval mean loss: 0.551200
eval accuracy: 0.878849
eval avg class acc: 0.847773
  airplane:	1.000
   bathtub:	0.860
       bed:	0.970
     bench:	0.700
 bookshelf:	0.910
    bottle:	0.940
      bowl:	0.950
       car:	0.990
     chair:	0.990
      cone:	0.900
       cup:	0.700
   curtain:	0.850
      desk:	0.826
      door:	0.850
   dresser:	0.686
flower_pot:	0.250
 glass_box:	0.960
    guitar:	1.000
  keyboard:	1.000
      lamp:	0.800
    laptop:	1.000
    mantel:	0.950
   monitor:	0.950
night_stand:	0.709
    person:	0.950
     piano:	0.870
     plant:	0.770
     radio:	0.750
range_hood:	0.920
      sink:	0.650
      sofa:	0.950
    stairs:	0.900
     stool:	0.800
     table:	0.810
      tent:	0.950
    toilet:	0.970
  tv_stand:	0.780
      vase:	0.750
  wardrobe:	0.600
      xbox:	0.750

一些可视化效果.

2. 部分分割网络

训练log

>>> Training for the epoch 199/200 ...
Loading train file 
/pointnet/pointnet/part_seg/./hdf5_data/ply_data_train0.h5
	Training Total Mean_loss: 0.082099
		Training Label Mean_loss: 3.379165
		Training Label Accuracy: 0.050293
		Training Seg Mean_loss: 0.082023
		Training Seg Accuracy: 0.968230
Loading train file 
/pointnet/pointnet/part_seg/./hdf5_data/ply_data_train5.h5
	Training Total Mean_loss: 0.081096
		Training Label Mean_loss: 3.370121
		Training Label Accuracy: 0.055614
		Training Seg Mean_loss: 0.081020
		Training Seg Accuracy: 0.968714
Loading train file 
/pointnet/pointnet/part_seg/./hdf5_data/ply_data_train4.h5
	Training Total Mean_loss: 0.081863
		Training Label Mean_loss: 3.373602
		Training Label Accuracy: 0.058594
		Training Seg Mean_loss: 0.081786
		Training Seg Accuracy: 0.968368
Loading train file 
/pointnet/pointnet/part_seg/./hdf5_data/ply_data_train3.h5
	Training Total Mean_loss: 0.081906
		Training Label Mean_loss: 3.324682
		Training Label Accuracy: 0.056152
		Training Seg Mean_loss: 0.081837
		Training Seg Accuracy: 0.968433
Loading train file 
/pointnet/pointnet/part_seg/./hdf5_data/ply_data_train2.h5
	Training Total Mean_loss: 0.080512
		Training Label Mean_loss: 3.353689
		Training Label Accuracy: 0.060547
		Training Seg Mean_loss: 0.080434
		Training Seg Accuracy: 0.968954
Loading train file 
/pointnet/pointnet/part_seg/./hdf5_data/ply_data_train1.h5
	Training Total Mean_loss: 0.081348
		Training Label Mean_loss: 3.364903
		Training Label Accuracy: 0.059570
		Training Seg Mean_loss: 0.081274
		Training Seg Accuracy: 0.968459
Successfully store the checkpoint model into train_results/trained_models/epoch_200.ckpt

3.场景语义分割网络

训练log

**** EPOCH 049 ****
----
mean loss: 0.099005
accuracy: 0.964090
----
eval mean loss: 0.577622
eval accuracy: 0.877734
eval avg class acc: 0.754512