如何在OpenCV中加载带有tensorflow后端的Keras模型构建

tensorFlowSession = K.get_session()
tf.saved_model.simple_save(tensorFlowSession, newpath + "/TensorFlow", inputs={"x": x}, outputs={"y": y})
tf.train.write_graph(tensorFlowSession.graph_def,newpath + "/TensorFlow",  "trainGraph_def.pbtxt")

然后我尝试在python中使用opencv加载保存的模型,我从python中的opencv开始,但是在c++中使用opencv时遇到了类似的错误。

net = cv.dnn.readNet(newpath + '/TensorFlow/' + 'saved_model.pb', newpath + '/TensorFlow/' + 'trainGraph.pbtxt')

问题是opencv无法加载tensorflow图,我得到一个错误-

Saving and loading a tensorflow graph using opencv 应该很直截了当,我在这里遗漏了什么?见附件我的代码。

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
import datetime
import cv2 as cv
from pathlib import Path
import numpy as np
from os import listdir
from os.path import isfile, join
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

batch_size = 128
num_classes = 10
epochs = 1

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# Save as tensor flow graph
# Save the model to pd file
exportModelDir = '/home/iaiai/Documents/Shahar/DataSets/TrainedNetworks/MnistKeras/'
newpath = exportModelDir + str(datetime.datetime.now())
print ("output dir", newpath)
if not os.path.exists(newpath):
    os.makedirs(newpath)

x = tf.placeholder(dtype=tf.float32, shape=input_shape)
y = tf.placeholder(dtype=tf.float32, shape=num_classes)
tensorFlowSession = K.get_session()
tf.saved_model.simple_save(tensorFlowSession, newpath + "/TensorFlow", inputs={"x": x}, outputs={"y": y})

# with tf.Session() as sess:
#     tf.train.Saver(tf.trainable_variables()).save(sess, newpath + "/TensorFlow/" + 'saveTrainableVariables')
if not os.path.exists(newpath + "/TensorFlow/" + 'saveTrainableVariables/'):
    os.makedirs(newpath + "/TensorFlow/" + 'saveTrainableVariables/')
tf.train.Saver(tf.trainable_variables()).save(tensorFlowSession, newpath + "/TensorFlow/saveTrainableVariables/" + 'saveTrainableVariables')

# Write graph in tensorflow format
tf.train.write_graph(tensorFlowSession.graph_def,newpath + "/TensorFlow", "trainGraph_def.pbtxt")
tf.train.write_graph(tensorFlowSession.graph,newpath + "/TensorFlow", "trainGraph.pbtxt")
tf.train.write_graph(tensorFlowSession.graph_def,newpath + "/TensorFlow", "trainGraph_def_notAsText.pbtxt", False)

# now try to load the tensorflow graph from opencv python module
net = cv.dnn.readNet(newpath + '/TensorFlow/' + 'saved_model.pb', newpath + '/TensorFlow/' + 'trainGraph.pbtxt')

    [libprotobuf ERROR /io/opencv/3rdparty/protobuf/src/google/protobuf/wire_format_lite.cc:629] String field 'tensorflow.FunctionDef.Node.ret' contains invalid UTF-8 data when parsing a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. 
Traceback (most recent call last):
  File "/snap/pycharm-community/79/helpers/pydev/pydevd.py", line 1664, in <module>
    main()
  File "/snap/pycharm-community/79/helpers/pydev/pydevd.py", line 1658, in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "/snap/pycharm-community/79/helpers/pydev/pydevd.py", line 1068, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "/snap/pycharm-community/79/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/home/iaiai/Documents/Shahar/Project2/MnistKerasExport/mnist_cnn.py", line 106, in <module>
    net = cv.dnn.readNet(newpath + '/TensorFlow/' + 'saved_model.pb', newpath + '/TensorFlow/' + 'trainGraph.pbtxt')
cv2.error: OpenCV(3.4.2) /io/opencv/modules/dnn/src/tensorflow/tf_io.cpp:44: error: (-2:Unspecified error) FAILED: ReadProtoFromBinaryFile(param_file, param). Failed to parse GraphDef file: /home/iaiai/Documents/Shahar/DataSets/TrainedNetworks/MnistKeras/2018-09-06 08:35:34.025216/TensorFlow/saved_model.pb in function 'ReadTFNetParamsFromBinaryFileOrDie'