Neural network 当使用多个GPU进行训练时,加载预训练模型失败
我已经训练了一个网络模型,并通过Neural network 当使用多个GPU进行训练时,加载预训练模型失败,neural-network,deep-learning,keras,backend,Neural Network,Deep Learning,Keras,Backend,我已经训练了一个网络模型,并通过checkpoint=ModelCheckpoint(filepath='weights.hdf5')回调保存了它的权重和体系结构。在培训期间,我通过调用下面的函数来使用多个GPU: def make_parallel(model, gpu_count): def get_slice(data, idx, parts): shape = tf.shape(data) size = tf.concat([ shape[:1]
checkpoint=ModelCheckpoint(filepath='weights.hdf5')
回调保存了它的权重和体系结构。在培训期间,我通过调用下面的函数来使用多个GPU:def make_parallel(model, gpu_count):
def get_slice(data, idx, parts):
shape = tf.shape(data)
size = tf.concat([ shape[:1] // parts, shape[1:] ],axis=0)
stride = tf.concat([ shape[:1] // parts, shape[1:]*0 ],axis=0)
start = stride * idx
return tf.slice(data, start, size)
outputs_all = []
for i in range(len(model.outputs)):
outputs_all.append([])
#Place a copy of the model on each GPU, each getting a slice of the batch
for i in range(gpu_count):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i) as scope:
inputs = []
#Slice each input into a piece for processing on this GPU
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)
inputs.append(slice_n)
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
#Save all the outputs for merging back together later
for l in range(len(outputs)):
outputs_all[l].append(outputs[l])
# merge outputs on CPU
with tf.device('/cpu:0'):
merged = []
for outputs in outputs_all:
merged.append(merge(outputs, mode='concat', concat_axis=0))
return Model(input=model.inputs, output=merged)
使用测试代码:from keras.models import Model, load_model
import numpy as np
import tensorflow as tf
model = load_model('cpm_log/deneme.hdf5')
x_test = np.random.randint(0, 255, (1, 368, 368, 3))
output = model.predict(x = x_test, batch_size=1)
print output[4].shape
我得到的错误如下:
Traceback (most recent call last):
File "cpm_test.py", line 5, in <module>
model = load_model('cpm_log/Jun5_1000/deneme.hdf5')
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 240, in load_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 301, in model_from_config
return layer_module.deserialize(config, custom_objects=custom_objects)
File "/usr/local/lib/python2.7/dist-packages/keras/layers/__init__.py", line 46, in deserialize
printable_module_name='layer')
File "/usr/local/lib/python2.7/dist-packages/keras/utils/generic_utils.py", line 140, in deserialize_keras_object
list(custom_objects.items())))
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2378, in from_config
process_layer(layer_data)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2373, in process_layer
layer(input_tensors[0], **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 578, in __call__
output = self.call(inputs, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/layers/core.py", line 659, in call
return self.function(inputs, **arguments)
File "/home/muhammed/DEV_LIBS/developments/mocap/pose_estimation/training/cpm/multi_gpu.py", line 12, in get_slice
def get_slice(data, idx, parts):
NameError: global name 'tf' is not defined
回溯(最近一次呼叫最后一次):
文件“cpm_test.py”,第5行,在
模型=负载模型('cpm\u log/Jun5\u 1000/deneme.hdf5')
文件“/usr/local/lib/python2.7/dist-packages/keras/models.py”,第240行,在load\u模型中
模型=来自配置的模型(模型配置,自定义对象=自定义对象)
文件“/usr/local/lib/python2.7/dist-packages/keras/models.py”,第301行,模型配置中的
返回层\模块。反序列化(配置,自定义\对象=自定义\对象)
文件“/usr/local/lib/python2.7/dist-packages/keras/layers/_-init__.py”,第46行,反序列化
可打印\u模块\u name='layer')
文件“/usr/local/lib/python2.7/dist packages/keras/utils/generic_utils.py”,第140行,反序列化_keras_对象
列表(自定义对象.项())
文件“/usr/local/lib/python2.7/dist packages/keras/engine/topology.py”,第2378行,from_config
处理层(层数据)
文件“/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py”,第2373行,进程层中
图层(输入_张量[0],**kwargs)
文件“/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py”,第578行,在调用中__
输出=自调用(输入,**kwargs)
文件“/usr/local/lib/python2.7/dist packages/keras/layers/core.py”,第659行,在调用中
返回self.function(输入,**参数)
文件“/home/muhammed/DEV_LIBS/developments/mocap/pose_assessment/training/cpm/multi_gpu.py”,第12行,在get_切片中
def get_切片(数据、idx、零件):
NameError:未定义全局名称“tf”
通过检查错误输出,我确定问题在于并行化代码。然而,我无法解决这个问题 您可能需要使用
自定义对象
来启用模型加载
import tensorflow as tf
model = load_model('model.h5', custom_objects={'tf': tf,})
如果在
get_slice
定义的开头添加import tensorflow as tf
,会发生什么情况?它已被添加。一切都没有改变。