TypeError:无法腌制_thread.RLock对象 [英] TypeError: can't pickle _thread.RLock objects
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问题描述
在此处检查所有关于Stackoverflow的答案后: TypeError:无法腌制_thread.lock对象，我还没有找到为什么这种方法不起作用或我的问题是什么.
After checking all the existing answers on Stackoverflow here: Checkpointing keras model: TypeError: can't pickle _thread.lock objects and here: TypeError: can't pickle _thread.lock objects, I haven't found out why this won't work or what's wrong in my case.
我正在使用Python3.这是我的模型构建函数:
I am using Python 3. Here is my model building functions:
def upsample_and_concat(x1,x2,output_channels,in_channels,layer):
pool_size = 2
deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02))
deconvtf=tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1])
deconv_output = tf.concat([deconvtf, x2], 3)
deconv_output.set_shape([None, None, None, output_channels * 2])
return deconv_output
def Depth_to_space_tf(input):
return tf.depth_to_space(input, 2)
def build_model():
inputs=keras.layers.Input(shape=(None, None, 4))
conv1=keras.layers.Conv2D(filters=256,kernel_size=(3,3),strides=(1,1),padding='same',name='conv1_1')(inputs)
conv1=keras.layers.LeakyReLU(alpha=0.2,name='conv1_1_relu')(conv1)
conv1=keras.layers.Conv2D(filters=256,kernel_size=(3,3),strides=(1,1),padding='same',name='conv1_2')(conv1)
conv1=keras.layers.LeakyReLU(alpha=0.2,name='conv1_2_relu')(conv1)
pool1=keras.layers.MaxPooling2D(pool_size=(2,2),padding="same",name='pool1')(conv1)
conv2=keras.layers.Conv2D(filters=512,kernel_size=(3,3),strides=(1,1),padding='same',name='conv2_1')(pool1)
conv2=keras.layers.LeakyReLU(alpha=0.2,name='conv2_1_relu')(conv2)
conv2=keras.layers.Conv2D(filters=512,kernel_size=(3,3),strides=(1,1),padding='same',name='conv2_2')(conv2)
conv2=keras.layers.LeakyReLU(alpha=0.2,name='conv2_2_relu')(conv2)
up6=keras.layers.core.Lambda(upsample_and_concat,arguments={'x2':conv1,'output_channels':256,'in_channels':512,'layer':'upsample_concat_1'},name='upsample_concat_1')(conv2)
conv6=keras.layers.Conv2D(filters=256,kernel_size=(3,3),strides=(1,1),padding='same',name='conv6_1')(up6)
conv6=keras.layers.LeakyReLU(alpha=0.2,name='conv6_1_relu')(conv6)
conv6=keras.layers.Conv2D(filters=256,kernel_size=(3,3),strides=(1,1),padding='same',name='conv6_2')(conv6)
conv6=keras.layers.LeakyReLU(alpha=0.2,name='conv6_2_relu')(conv6)
conv7=keras.layers.Conv2D(filters=12,kernel_size=(1,1),strides=(1,1),name='conv10')(conv6)
predictions = keras.layers.core.Lambda(Depth_to_space_tf,name='depth_to_space')(conv7)
model = keras.models.Model(inputs=inputs, outputs=predictions)
return model
这是我的数据加载器代码:
Here is my data loader code:
class DataLoader(keras.utils.Sequence):
def __init__(self,params,data):
self.epochs=params.epochs
self.input_dir=params.input_dir
self.gt_dir=params.gt_dir
self.train_ids=data.train_ids
self.shuffled_ids=np.random.permutation(len(self.train_ids))
for index, val in np.ndenumerate(self.shuffled_ids):
print ('index:{}, image:{}'.format(index[0], val))
self.epoch_counter=0
self.ps=params.ps
def on_epoch_end(self):
self.shuffled_ids=np.random.permutation(len(self.train_ids))
print("in on epoch end")
def __len__(self):
'Denotes the number of batches per epoch'
return len(self.train_ids)
def __getitem__(self, ind):
'Generates data containing batch_size samples'
train_id = self.train_ids[self.shuffled_ids[ind]]
in_files = glob.glob(self.input_dir + '%05d_00*.ARW' % train_id)
in_path = in_files[np.random.randint(0, len(in_files))]
in_fn = os.path.basename(in_path)
gt_files = glob.glob(self.gt_dir + '%05d_00*.ARW' % train_id)
gt_path = gt_files[0]
gt_fn = os.path.basename(gt_path)
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure / in_exposure, 300)
st = time.time()
raw = rawpy.imread(in_path)
input_images = np.expand_dims(pack_raw(raw), axis=0) * ratio
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(use_camera_wb=True,
half_size=False,
no_auto_bright=True, output_bps=16)
gt_images = np.expand_dims(np.float32(im / 65535.0),axis=0)
H = input_images.shape[1]
W = input_images.shape[2]
xx = np.random.randint(0, W - self.ps)
yy = np.random.randint(0, H - self.ps)
input_patch = input_images[:, yy:yy + self.ps, xx:xx + self.ps, :]
gt_patch = gt_images[:, yy * 2:yy * 2 + self.ps * 2, xx * 2:xx * 2 + self.ps * 2, :]
if np.random.randint(2) == 1:
input_patch = np.flip(input_patch, axis=1)
gt_patch = np.flip(gt_patch, axis=1)
if np.random.randint(2) == 1:
input_patch = np.flip(input_patch, axis=2)
gt_patch = np.flip(gt_patch, axis=2)
if np.random.randint(2) == 1:
input_patch = np.transpose(input_patch, (0, 2, 1, 3))
gt_patch = np.transpose(gt_patch, (0, 2, 1, 3))\
input_patch = np.minimum(input_patch, 1.0)
return (input_patch,gt_patch)
在model.fit_generator中将dataloader类的实例与
when using the instance of the dataloader class in model.fit_generator with
callbacks = [
ModelCheckpoint(filepath=save_fname, monitor='loss', verbose=1, save_best_only=True)
]
当模型尝试将检查点保存到文件时，我恰好收到以下错误:
I get the below error exactly when the model tries to save the checkpoint to the file:
TypeError:无法腌制_thread.RLock对象
TypeError: can't pickle _thread.RLock objects
完整堆栈跟踪:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-52-fde8b84b2b2d> in <module>()
1 """ ================ TRAIN THE MODEL ================ """
2 steps_per_epoch=1
----> 3 model_history=train_generator(model,dataloader,params.epochs,steps_per_epoch,save_fname)
32 frames
<ipython-input-48-2a9e71ef3338> in train_generator(model, data_gen, epochs, steps_per_epoch, save_fname)
11 max_queue_size=20,
12 workers=7,
---> 13 use_multiprocessing=True
14 )
15 print("Model Training completed!!! Jay Yogeshwar!!!")
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/usr/local/lib/python3.6/dist-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)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 @interfaces.legacy_generator_methods_support
/usr/local/lib/python3.6/dist-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)
249 break
250
--> 251 callbacks.on_epoch_end(epoch, epoch_logs)
252 epoch += 1
253 if callback_model.stop_training:
/usr/local/lib/python3.6/dist-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
77 logs = logs or {}
78 for callback in self.callbacks:
---> 79 callback.on_epoch_end(epoch, logs)
80
81 def on_batch_begin(self, batch, logs=None):
/usr/local/lib/python3.6/dist-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
444 self.model.save_weights(filepath, overwrite=True)
445 else:
--> 446 self.model.save(filepath, overwrite=True)
447 else:
448 if self.verbose > 0:
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer)
1088 raise NotImplementedError
1089 from ..models import save_model
-> 1090 save_model(self, filepath, overwrite, include_optimizer)
1091
1092 def save_weights(self, filepath, overwrite=True):
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in save_model(model, filepath, overwrite, include_optimizer)
380
381 try:
--> 382 _serialize_model(model, f, include_optimizer)
383 finally:
384 if opened_new_file:
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in _serialize_model(model, f, include_optimizer)
81 model_config = {}
82 model_config['class_name'] = model.__class__.__name__
---> 83 model_config['config'] = model.get_config()
84 model_config = json.dumps(model_config, default=get_json_type)
85 model_config = model_config.encode('utf-8')
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in get_config(self)
929 model_outputs.append([layer.name, new_node_index, tensor_index])
930 config['output_layers'] = model_outputs
--> 931 return copy.deepcopy(config)
932
933 @classmethod
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_list(x, memo, deepcopy)
213 append = y.append
214 for a in x:
--> 215 append(deepcopy(a, memo))
216 return y
217 d[list] = _deepcopy_list
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/usr/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/usr/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/usr/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
167 reductor = getattr(x, "__reduce_ex__", None)
168 if reductor:
--> 169 rv = reductor(4)
170 else:
171 reductor = getattr(x, "__reduce__", None)
TypeError: can't pickle _thread.RLock objects
我是python和keras的新手，无法弄清楚这里出了什么问题.就其他解释而言，在保存模型时无法序列化某些内容.但我无法理解此处无法序列化的内容.
I am new to python and keras and not able to figure out what's wrong here. As far as other explanations, it's failing to serialize something while saving the model. But I am not able to understand what is failing to serialize here.
推荐答案
我认为TFer2正确.问题在于TensorFlow模型无法通过酸洗在本地进行序列化.某处(我认为它在您的回调中) deepcopy 在模型上被调用.要确认这一点，您可以尝试应用此修补程序，或者改进的版本此处.
I think TFer2 got it right. The issue is that TensorFlow models are not natively serializable by pickling. Somewhere (I think it's in your callback) deepcopy is being called on a Model. To confirm this, you can try to apply this hotfix, or an improved version here.
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