我试图用keras模型训练一个unet，而在训练第一个时期时我得到了类型错误：
TypeError：无法腌制_thread.lock对象，请参见下面的错误消息
我已经尝试降级tensorflow并在 ... tensorflow \ contrib \ legacy_seq2seq \ python \ ops 的 seq2seq.py 脚本中添加脚本类似的问题，但这并不能解决我的问题。
我的代码：
def train_network():
conf = get_train_conf()
epochs = conf['epochs']
batch_size = conf['batch_size']
train_path = get_data_path('train')
validation_path = get_data_path('valid')
print('data input paths are:\n {}\n and:\n {}'.format(train_path,
validation_path))
print('counting samples..')
print('number of train, validation samples are:')
print(count_samples(train_path), count_samples(validation_path))
# train data
image_generator_train =
image.ImageDataGenerator(**conf['augmentation_args'])
mask_generator_train =
image.ImageDataGenerator(**conf['augmentation_args'])
train_generator = get_generator(batch_size, train_path + '/unmasked',
True, image_generator_train, color_mode='grayscale')
train_generator_mask = get_generator(batch_size, train_path + '/masked',
True, mask_generator_train, color_mode=conf['mask_color_mode'])
train_gen = zip(train_generator, train_generator_mask)
num_train_samples = train_generator.n
# validation data - don't augment
image_generator_validation = image.ImageDataGenerator()
mask_generator_validation = image.ImageDataGenerator()
valid_generator = get_generator(batch_size, validation_path + '/unmasked',
True, image_generator_validation, color_mode='grayscale')
valid_generator_mask = get_generator(batch_size, validation_path +
'/masked', True, mask_generator_validation,
color_mode=conf['mask_color_mode'])
valid_gen = zip(valid_generator, valid_generator_mask)
num_valid_samples = valid_generator.n
steps_per_epoch, validation_steps = (conf['steps_per_epoch'],
conf['validation_steps'])
print('{} rounds of training will take place'.format(epochs))
print('{} training steps per round'.format(steps_per_epoch))
print('{} validation steps per round'.format(validation_steps))
print('(with a batch size of {})'.format(batch_size))
print('{} training samples will be generated per epoch'.format(batch_size
* steps_per_epoch))
print('{} validation samples will be generated per
epoch'.format(batch_size * validation_steps))
answer = input("would you like to continue? y/n?\n").lower()
if not answer.lower() in ('yes', 'y'):
return
model = None
print(conf)
if conf['train_from_checkpoint']:
print('loading model from last checkpoint')
model =
pw_evaluate.load_model_from_wts(pw_evaluate.get_latest_model_file_name())
else:
model = unet(classes=conf['num_classes'])
#model = get_simple_unet()
model.compile(
optimizer=keras.optimizers.Adam(
lr=conf['learning_rate']),
loss=conf['loss_function'],
metrics=conf['metrics'])
#model.compile(optimizer=keras.optimizers.Adam(2e-4),
loss=pw_loss.dice_coef_loss, metrics=conf['metrics'])
time_st = str(datetime.utcfromtimestamp(time.time()).strftime('%Y-%m-%d
%H:%M:%S'))
weight_saver = ModelCheckpoint('weights.' + time_st + '.{epoch:02d}-
{val_loss:.2f}.h5',
save_best_only=True,
save_weights_only=True,
verbose=1,
period=conf['period'])
hist = model.fit_generator(
train_gen,
validation_data=valid_gen,
validation_steps=validation_steps,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
callbacks=[weight_saver],
verbose=1,
use_multiprocessing=True)
plot_file_name = 'training_plot_' + time_st + '.pdf'
print('finished training, plotting history to:')
print(plot_file_name)
figures = [loss_figure(hist), accuracy_figure(hist)]
plot_history(plot_file_name, figures)
log_file_name = 'log_results_' + time_st + '.txt'
print('logging results to:')
print(log_file_name)
log_results(hist, log_file_name, conf)
完整的堆栈跟踪：
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\threading.py", line 917, in _bootstrap_inner
self.run()
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\threading.py", line 865, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\data_utils.py", line 666, in _run
with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\data_utils.py", line 661, in <lambda>
initargs=(seqs, self.random_seed))
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 119, in Pool
context=self.get_context())
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\pool.py", line 176, in __init__
self._repopulate_pool()
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\pool.py", line 241, in _repopulate_pool
w.start()
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
TypeError: can't pickle _thread.lock objects
Exception in thread Thread-29:
Traceback (most recent call last):
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\threading.py", line 917, in _bootstrap_inner
self.run()
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\threading.py", line 865, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\data_utils.py", line 666, in _run
with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\data_utils.py", line 661, in <lambda>
initargs=(seqs, self.random_seed))
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 119, in Pool
context=self.get_context())
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\pool.py", line 176, in __init__
self._repopulate_pool()
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\pool.py", line 241, in _repopulate_pool
w.start()
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
TypeError: can't pickle _thread.lock objects
Using TensorFlow backend.
Using TensorFlow backend.
2019-06-04 11:45:59.121653: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-06-04 11:45:59.122454: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
Traceback (most recent call last):
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 115, in _main
File "<string>", line 1, in <module>
self = reduction.pickle.load(from_parent)
EOFError: Ran out of input
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\esp13\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 115, in _main
self = reduction.pickle.load(from_parent)
EOFError: Ran out of input
答案 0 :(得分：0)
根据pickle _thread.lock方法中的use_multiprocessing=True参数指定了fit_generator()错误。可能的根本原因是使用了不是线程安全的生成器。因此，多重进程试图更改相同的数据。
通过设置use_multiprocessing=False禁用此功能有助于避免该错误。