NuclearDataset train과정에서 오류가 발생합니다.

cpu사용량이 올라가다가 BlockingIOError: [Errno 11] Resource temporarily unavailable 가 발생합니다.GPU는 사용량은 없습니다. data augumentaion과정에서 발생하는 것 같습니다.

배치숫자를 1로 바꾸어서 해도 동일한 오류가 발생합니다. 어떤 이유일까요? 강의 도중에 augmenters가 무겁다고 하셨는데, 혹시 저기에 적용할만한 다른 img_aug를 추천해주실 수 있을까요?

감사합니다.

BlockingIOError                           Traceback (most recent call last)
<ipython-input-38-ce418a61f9ea> in <module>
      6             learning_rate=train_config.LEARNING_RATE,
      7             epochs=40, augmentation=img_aug,
----> 8             layers='all')

~/.conda/envs/tf113/lib/python3.6/site-packages/mask_rcnn-2.1-py3.6.egg/mrcnn/model.py in train(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation, custom_callbacks, no_augmentation_sources)
   2372             max_queue_size=100,
   2373             workers=workers,
-> 2374             use_multiprocessing=True,
   2375         )
   2376         self.epoch = max(self.epoch, epochs)

~/.conda/envs/tf113/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/.conda/envs/tf113/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1413             use_multiprocessing=use_multiprocessing,
   1414             shuffle=shuffle,
-> 1415             initial_epoch=initial_epoch)
   1416 
   1417     @interfaces.legacy_generator_methods_support

~/.conda/envs/tf113/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    157                     use_multiprocessing=use_multiprocessing,
    158                     wait_time=wait_time)
--> 159             enqueuer.start(workers=workers, max_queue_size=max_queue_size)
    160             output_generator = enqueuer.get()
    161         else:

~/.conda/envs/tf113/lib/python3.6/site-packages/keras/utils/data_utils.py in start(self, workers, max_queue_size)
    724                     thread = threading.Thread(target=self._data_generator_task)
    725                 self._threads.append(thread)
--> 726                 thread.start()
    727         except:
    728             self.stop()

~/.conda/envs/tf113/lib/python3.6/multiprocessing/process.py in start(self)
    103                'daemonic processes are not allowed to have children'
    104         _cleanup()
--> 105         self._popen = self._Popen(self)
    106         self._sentinel = self._popen.sentinel
    107         # Avoid a refcycle if the target function holds an indirect

~/.conda/envs/tf113/lib/python3.6/multiprocessing/context.py in _Popen(process_obj)
    221     @staticmethod
    222     def _Popen(process_obj):
--> 223         return _default_context.get_context().Process._Popen(process_obj)
    224 
    225 class DefaultContext(BaseContext):

~/.conda/envs/tf113/lib/python3.6/multiprocessing/context.py in _Popen(process_obj)
    275         def _Popen(process_obj):
    276             from .popen_fork import Popen
--> 277             return Popen(process_obj)
    278 
    279     class SpawnProcess(process.BaseProcess):

~/.conda/envs/tf113/lib/python3.6/multiprocessing/popen_fork.py in __init__(self, process_obj)
     17         util._flush_std_streams()
     18         self.returncode = None
---> 19         self._launch(process_obj)
     20 
     21     def duplicate_for_child(self, fd):

~/.conda/envs/tf113/lib/python3.6/multiprocessing/popen_fork.py in _launch(self, process_obj)
     64         code = 1
     65         parent_r, child_w = os.pipe()
---> 66         self.pid = os.fork()
     67         if self.pid == 0:
     68             try:

BlockingIOError: [Errno 11] Resource temporarily unavailable

:

Epoch 30/30
61/61 [==============================] - 22s 361ms/step - loss: 0.1355 - rpn_class_loss: 0.0026 - rpn_bbox_loss: 0.0274 - mrcnn_class_loss: 0.0214 - mrcnn_bbox_loss: 0.0211 - mrcnn_mask_loss: 0.0630 - val_loss: 0.6885 - val_rpn_class_loss: 0.0778 - val_rpn_bbox_loss: 0.3417 - val_mrcnn_class_loss: 0.0657 - val_mrcnn_bbox_loss: 0.0819 - val_mrcnn_mask_loss: 0.1214
time: 12min 58s