尝试保存我的keras模型时收到错误消息：TypeError: can't pickle _thread.RLock objects。我相信这与我正在使用的lambda层有关，但是我不确定要修复哪些层。
到目前为止（在代码中看到），我已经尝试按照以下问题的答案3进行操作：Checkpointing keras model: TypeError: can't pickle _thread.lock objects
我这样做是通过从lambda中提取参数来避免它们引起问题。但是，这似乎无法解决该错误。我正在Windows 10，使用anaconda的python 3.7上运行。
这是我要保存的模型：
import tensorflow as tf
from keras.layers import Lambda, Add, Multiply, Conv2D, Input
import keras.backend as K
import functools
import keras
from keras.models import Model
def resBlock_Keras(x, channels=64, kernel_size=[3,3], scale=1):
tmp = Conv2D(channels, kernel_size, activation='relu', padding='same')(x)
tmp = Conv2D(channels, kernel_size, padding='same')(tmp)
multiply_by_scale = functools.partial(
tf.multiply,
scale
)
add_input = functools.partial(
tf.add,
x
)
tmp = Lambda(lambda q: multiply_by_scale(q))(tmp)
return Lambda(lambda q: add_input(q))(tmp)
def PS_Keras(X, r, color=False):
if color:
split = functools.partial(
tf.split,
num_or_size_splits=3,
axis=3
)
concat = functools.partial(
tf.concat,
axis=3
)
Xc = Lambda(lambda x: split(x))(X)
shifts = [_phase_shift_keras(x, r) for x in Xc]
X = Lambda(lambda q: concat(q))(shifts)
else:
X = _phase_shift_keras(X, r)
return X
def _phase_shift_keras(I, r):
bsize, a, b, c = I.get_shape().as_list()
bsize = K.shape(I)[0] # Handling Dimension(None) type for undefined batch dim
X = K.reshape(I, [bsize, a, b, c//(r*r),r, r]) # bsize, a, b, c/(r*r), r, r
X = K.permute_dimensions(X, (0, 1, 2, 5, 4, 3)) # bsize, a, b, r, r, c/(r*r)
#Keras backend does not support tf.split, so in future versions this could be nicer
X = [X[:,i,:,:,:,:] for i in range(a)] # a, [bsize, b, r, r, c/(r*r)
X = K.concatenate(X, 2) # bsize, b, a*r, r, c/(r*r)
X = [X[:,i,:,:,:] for i in range(b)] # b, [bsize, r, r, c/(r*r)
X = K.concatenate(X, 2) # bsize, a*r, b*r, c/(r*r)
return X
def upsample_keras(x,scale=2,features=64,activation='relu'):
assert scale in [2,3,4]
x = Conv2D(features,[3,3],activation=activation,padding='same')(x)
if scale == 2:
ps_features = 3*(scale**2)
x = Conv2D(ps_features,[3,3],activation=activation,padding='same')(x)
ps = functools.partial(
PS_Keras,
r=2,
color=True
)
x = Lambda(lambda q: ps(q))(x)
elif scale == 3:
ps_features =3*(scale**2)
x = Conv2D(ps_features,[3,3],activation=activation,padding='same')(x)
ps = functools.partial(
PS_Keras,
r=3,
color=True
)
x = Lambda(lambda q: ps(q))(x)
elif scale == 4:
ps_features = 3*(2**2)
ps = functools.partial(
PS_Keras,
r=2,
color=True
)
for i in range(2):
x = Conv2D(ps_features,[3,3],activation=activation,padding='same')(x)
x = Lambda(lambda q: ps(q))(x)
return x
def subtract_mean(x):
return tf.subtract(x,127.0)
def build_model(img_size=32,num_layers=32,feature_size=64,scale=2,output_channels=3):
MEAN_PIXEL = 127.0
MIN_PIXEL = 0.0
MAX_PIXEL = 255.0
scaling_factor = 0.1
img_size = img_size
scale=scale
input = Input(shape=(img_size,img_size,output_channels))
curr_layer = Lambda(lambda x: subtract_mean(x))(input)
first_conv = Conv2D(feature_size, [3,3], padding='same')(curr_layer)
curr_layer = first_conv
for i in range(num_layers):
curr_layer = resBlock_Keras(curr_layer, feature_size, scale=scaling_factor)
curr_layer = Conv2D(feature_size, [3,3],padding='same')(curr_layer)
curr_layer = Add()([curr_layer, first_conv])
clip_by_value = functools.partial(
tf.clip_by_value,
clip_value_min=MIN_PIXEL,
clip_value_max=MAX_PIXEL)
upsample = functools.partial(
upsample_keras,
scale = scale,
features = feature_size,
activation=None
)
curr_layer = Lambda(lambda x: upsample(x))(curr_layer)
output = Lambda(lambda x: clip_by_value(x))(curr_layer)
model = Model(inputs=input, outputs=output)
model.compile(loss='mae',optimizer='adam')
model.summary()
model.save("model.h5")
build_model()
这是我收到的完整堆栈跟踪记录：
Traceback (most recent call last):
File "tmp.py", line 125, in <module>
build_model()
File "tmp.py", line 123, in build_model
model.save("model.h5")
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\site-packages\keras\engine\network.py", line 1090, in save
save_model(self, filepath, overwrite, include_optimizer)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\site-packages\keras\engine\saving.py", line 382, in save_model
_serialize_model(model, f, include_optimizer)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\site-packages\keras\engine\saving.py", line 83, in _serialize_model
model_config['config'] = model.get_config()
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\site-packages\keras\engine\network.py", line 931, in get_config
return copy.deepcopy(config)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 240, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 215, in _deepcopy_list
append(deepcopy(a, memo))
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 240, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 240, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 280, in _reconstruct
state = deepcopy(state, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 220, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 280, in _reconstruct
state = deepcopy(state, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 240, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 280, in _reconstruct
state = deepcopy(state, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 240, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 280, in _reconstruct
state = deepcopy(state, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 150, in deepcopy
y = copier(x, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 240, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\pwatm\Anaconda3\envs\gpu\lib\copy.py", line 169, in deepcopy
rv = reductor(4)
TypeError: can't pickle _thread.RLock objects