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MultiThreadedAugmenter issues #94
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I use the MultiThreadedAugmenter to apply transform, but suffer problems. How can I solve this? I wish the library can be modify to be compatiable with the transform interface of pytorch, so that it can be applied easily. |
Hi, can you please post the entire error message? There are parts missing, all I see is the workers telling me that something happened but the important bit isn't there. |
ok, thanks for you reply, I will give it a try. If there is still a problem, I will return the full error message.
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Yinglin Zhang
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***@***.***
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签名由网易邮箱大师定制
On 3/28/2022 15:48,Fabian ***@***.***> wrote:
Hi, can you please post the entire error message? There are parts missing, all I see is the workers telling me that something happened but the important bit isn't there.
Simple way to debug would be to use SingleThreadedAugmenter for development. It will give clearer error messages. Once that works you can just replace it with MultiThreadedAugmenter and it will work
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You are receiving this because you authored the thread.Message ID: ***@***.***>
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does it work now? |
Problem:
Traceback (most recent call last):
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/site-packages/batchgenerators/dataloading/multi_threaded_augmenter.py", line 36, in producer
data_loader.set_thread_id(thread_id)
AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute 'set_thread_id'
Exception in thread Thread-2:
Traceback (most recent call last):
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/site-packages/batchgenerators/dataloading/multi_threaded_augmenter.py", line 92, in results_loop
raise RuntimeError("Abort event was set. So someone died and we should end this madness. \nIMPORTANT: "
RuntimeError: Abort event was set. So someone died and we should end this madness.
IMPORTANT: This is not the actual error message! Look further up to see what caused the error. Please also check whether your RAM was full
Traceback (most recent call last):
File "/data/home/zhangyinglin/workspace/pytorch-unet-cornea/transform.py", line 126, in
multithreaded_generator.next()
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/site-packages/batchgenerators/dataloading/multi_threaded_augmenter.py", line 182, in next
return self.next()
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/site-packages/batchgenerators/dataloading/multi_threaded_augmenter.py", line 206, in next
item = self.__get_next_item()
File "/data/home/zhangyinglin/anaconda3/lib/python3.7/site-packages/batchgenerators/dataloading/multi_threaded_augmenter.py", line 190, in __get_next_item
raise RuntimeError("MultiThreadedAugmenter.abort_event was set, something went wrong. Maybe one of "
RuntimeError: MultiThreadedAugmenter.abort_event was set, something went wrong. Maybe one of your workers crashed. This is not the actual error message! Look further up your stdout to see what caused the error. Please also check whether your RAM was full
Process finished with exit code 1
Code as follow:
from batchgenerators.transforms.spatial_transforms import SpatialTransform, MirrorTransform
from batchgenerators.dataloading.nondet_multi_threaded_augmenter import NonDetMultiThreadedAugmenter
from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
from batchgenerators.dataloading.data_loader import DataLoaderFromDataset
from batchgenerators.transforms.abstract_transforms import Compose
import numpy as np
from utils.dataset import BasicDataset
from torch.utils.data import DataLoader
import os
import matplotlib.pyplot as plt
-------------------------------------------------------
data_path = '/data2/imed-data/zhangyinglin/Other_Dataset/'
data_name = 'Ciliary_split2'
train_dir = os.path.join(data_path, data_name, '', 'train/')
val_dir = os.path.join(data_path, data_name, '', 'val/')
final_weight = os.path.join(data_path, data_name, '', 'weights/final/')
body_weight = os.path.join(data_path, data_name, '', 'weights/body/')
canny_weight = os.path.join(data_path, data_name, '', 'weights/edge/')
dir_mask = os.path.join(data_path, data_name, "", "final_mask/")
canny_mask = os.path.join(data_path, data_name, "", "canny_mask/")
body_mask = os.path.join(data_path, data_name, "", "body_mask/")
img_size = 192
img_scale = 1
-------------------------------------------------------
transform = []
params = {'do_elastic': False,
'elastic_deform_alpha': (0., 200.),
'elastic_deform_sigma': (9., 13.),
'rotation_x': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),
'rotation_y': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),
'rotation_z': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),
'rotation_p_per_axis': 1,
'do_scaling': True,
'scale_range': (0.7, 1.4),
'border_mode_data': 'constant',
'random_crop': False,
'p_eldef': 0.2,
'p_scale': 0.2,
'p_rot': 0.2,
'independent_scale_factor_for_each_axis': 1,
'patch_size': np.asarray([192, 192])
}
transform.append(SpatialTransform(
patch_size=params['patch_size'], patch_center_dist_from_border=None,
do_elastic_deform=False, alpha=params['elastic_deform_alpha'],
sigma=params['elastic_deform_sigma'],
do_rotation=True, angle_x=params["rotation_x"], angle_y=params["rotation_y"],
angle_z=params["rotation_z"], p_rot_per_axis=params["rotation_p_per_axis"],
do_scale=params["do_scaling"], scale=params["scale_range"],
border_mode_data=params["border_mode_data"], border_cval_data=0, order_data=3,
border_mode_seg="constant", border_cval_seg=-1,
order_seg=1, random_crop=params["random_crop"], data_key='data', label_key='seg', p_el_per_sample=params["p_eldef"],
p_scale_per_sample=params["p_scale"], p_rot_per_sample=params["p_rot"],
independent_scale_for_each_axis=params["independent_scale_factor_for_each_axis"]
))
if name == 'main':
tr_transform = Compose(transform)
train_loader = DataLoader(train, batch_size=1, shuffle=True, num_workers=4, pin_memory=True)
batchgen = iter(train_loader)
multithreaded_generator = MultiThreadedAugmenter(batchgen, tr_transform, 1, 1, seeds=None)
multithreaded_generator.next()
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