PointNet++S3DIS数据集训练报错记录

1.2022-05-27

This error occurs when I execute the train_semseg.py:

PS F:pointnet_pointnet2_pytorch-master> python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg PARAMETER ... Namespace(batch_size=16, decay_rate=0.0001, epoch=32, gpu='0', learning_rate=0.001, log_dir='pointnet2_sem_seg', lr_decay=0.7, model='pointnet2_sem_seg', npoint=4096, optimizer='Adam', step_size=10, test_area=5) 
start loading training data ... 100%|
████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 118/118 [00:09<00:00, 12.18it/s] [1.1122853 1.1530312 1. 2.2862618 2.3985515 2.3416872 1.6953672 2.051836 1.7089869 3.416529 1.840006 2.7374067 1.3777069]
Totally 28940 samples in train set. 
start loading test data ... 100%|
██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [00:04<00:00, 10.26it/s] [ 1.1516608 1.2053679 1. 11.941072 2.6087077 2.0597224 2.1135178 2.0812197 2.5563374 4.5242124 1.4960177 2.9274836 1.6089553] 
Totally 12881 samples in test set. 
The number of training data is: 28940 
The number of test data is: 12881 Use pretrain model
Learning rate:0.000700
 BN momentum updated to: 0.050000
 Traceback (most recent call last): 
 File "train_semseg.py", line 295, in main(args) File "train_semseg.py", line 181, in main for i, (points, target) in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9): 
File "F:miniconda3envspytorch_1.8_wshlibsite-packagestorchutilsdatadataloader.py", line 355, in iter return self._get_iterator() 
 File "F:miniconda3envspytorch_1.8_wshlibsite-packagestorchutilsdatadataloader.py", line 301, in _get_iterator return _MultiProcessingDataLoaderIter(self) File "F:miniconda3envspytorch_1.8_wshlibsite-packagestorchutilsdatadataloader.py", line 914, in init w.start() 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingprocess.py", line 121, in start self._popen = self._Popen(self) 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingcontext.py", line 224, in _Popen return _default_context.get_context().Process._Popen(process_obj) 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingcontext.py", line 326, in _Popen return Popen(process_obj) 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingpopen_spawn_win32.py", line 93, in init reduction.dump(process_obj, to_child) 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingreduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj)
 AttributeError: Can't pickle local object 'main..'
 Traceback (most recent call last): File "", line 1, in 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingspawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) 
 File "F:miniconda3envspytorch_1.8_wshlibmultiprocessingspawn.py", line 126, in _main self = reduction.pickle.load(from_parent)
 EOFError: Ran out of input

有没有可能是数据集过大的问题
“由于训练集和验证集数据量过大导致的下采样倍数不够。我觉得可以试一试通过.json文件减小训练集和验证集的数目”
删除掉部分数据集，训练数据的数量从原来的47600减少到28900，但还是同样报错。

2.2022-05-31

看到帖子说有可能是数据集的问题，将npy改成hdf5数据集试试
利用tensorflow版本的pointnet 运行sem_seg下的gen_indoor3d_util.py
报错1：运行gen_indoor3d_h5.py时出现报错：
File “F:pointnetv2-mastersem_segindoor3d_util.py”, line 126, in sample_data
return np.concatenate([data, dup_data], 0), range(N) + list(sample)
TypeError: unsupported operand type(s) for +: ‘range’ and ‘list’
前面的都没有问题，但是到F:pointnetv2-masterdatastanford_indoor3dArea_1_hallway_6.npy
解决：indoor3d_util.py
range(N)+list(sample) 改成list(range(N))
参考：https://blog.csdn.net/u014311125/article/details/122078418?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522165400530016782391848045%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=165400530016782391848045&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2_allfirst_rank_ecpm_v1~rank_v31_ecpm-3-122078418-null-null.142^v11control,157^v12control&utm_term=pointnet%2B%2B%E8%AE%AD%E7%BB%83S3DIS%E6%95%B0%E6%8D%AE&spm=1018.2226.3001.4187

报错2：ply_data_all_0.h5’, errno = 2, error message = ‘No such file or directory’, flags = 0, o_flags = 0
解决：data_prep_util.py
将h5_fout = h5py.File(h5_filename)改成h5_fout = h5py.File(h5_filename, ‘w’)

报错3：没有找到Area_5_hallway_6. npy
重新启用了 python collect_indoor3d_data.py ，还是没有生成，真是奇怪。
把meta下面的txt文件中含有Area_5_hallway_6的信息都删掉
emo了，好像又不知道怎么读入hdf5

最终解决：
参考：https://blog.csdn.net/qq_41212586/article/details/116498177

在错误定位的地方找到num_workers，将值改为0。另有一种情况是将use_multiprocessing的值置为False。

但是我的cpu处理器数量是16个，代码设置的是10个并没有超出啊
调整num_workers的数量为1，4，8，10，运行都会报错。但num_workers=0时，训练速度很慢，16个小时才13epoch。

2.2022-06-01

设置batch_size= 32,训练结果：

2022-06-02 00:32:40,447 - Model - INFO - **** Epoch 8 (32/32) ****
2022-06-02 00:32:40,447 - Model - INFO - Learning rate:0.000343
2022-06-02 01:19:00,425 - Model - INFO - Training mean loss: 0.130799
2022-06-02 01:19:00,426 - Model - INFO - Training accuracy: 0.952489
2022-06-02 01:19:00,529 - Model - INFO - ---- EPOCH 008 EVALUATION ----
2022-06-02 01:34:37,188 - Model - INFO - eval mean loss: 1.027795
2022-06-02 01:34:37,188 - Model - INFO - eval point avg class IoU: 0.519243
2022-06-02 01:34:37,189 - Model - INFO - eval point accuracy: 0.826248
2022-06-02 01:34:37,189 - Model - INFO - eval point avg class acc: 0.610861
2022-06-02 01:34:37,189 - Model - INFO - ------- IoU --------
class ceiling        weight: 0.091, IoU: 0.905 
class floor          weight: 0.199, IoU: 0.974 
class wall           weight: 0.165, IoU: 0.737 
class beam           weight: 0.279, IoU: 0.000 
class column         weight: 0.000, IoU: 0.053 
class window         weight: 0.019, IoU: 0.571 
class door           weight: 0.034, IoU: 0.128 
class table          weight: 0.030, IoU: 0.673 
class chair          weight: 0.039, IoU: 0.714 
class sofa           weight: 0.019, IoU: 0.423 
class bookcase       weight: 0.003, IoU: 0.620 
class board          weight: 0.110, IoU: 0.525 
class clutter        weight: 0.012, IoU: 0.427 

2022-06-02 01:34:37,190 - Model - INFO - Eval mean loss: 1.027795
2022-06-02 01:34:37,190 - Model - INFO - Eval accuracy: 0.826248
2022-06-02 01:34:37,190 - Model - INFO - Best mIoU: 0.521394

与原作者的对比：

2021-03-23 12:47:10,783 - Model - INFO - Eval mean loss: 0.939576
2021-03-23 12:47:10,783 - Model - INFO - Eval accuracy: 0.823115
2021-03-23 12:47:10,783 - Model - INFO - Best mIoU: 0.526403

PointNet++S3DIS数据集训练报错记录

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