Convert Keras to TF Protobuf

# !/usr/bin/env python
"""
 Copyright (c) 2018 Intel Corporation
​
 Licensed under the Apache License, Version 2.0 (the 'License');
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
​
      http://www.apache.org/licenses/LICENSE-2.0
​
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an 'AS IS' BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

import os
import sys

import argparse
from pathlib import Path

import tensorflow as tf
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io

import tensorflow.keras.backend as K

def setKerasOptions():
    K._LEARNING_PHASE = tf.constant(0)
    K.set_learning_phase(False)
    K.set_learning_phase(0)
    K.set_image_data_format('channels_last')


def getInputParameters():
    parser = argparse.ArgumentParser()
    parser.add_argument('--input_model', '-m', required=True, type=str, help='Path to Keras model.')
    parser.add_argument('--num_outputs', '-no', required=False, type=int, help='Number of outputs. 1 by default.', default=1)

    return parser

def export_keras_to_tf(input_model, output_model, num_output):
    print('Loading Keras model: ', input_model)
    setKerasOptions()
    keras_model = tf.keras.models.load_model(input_model, compile=False)

    predictions = [None] * num_output
    prediction_node_names = [None] * num_output

    for i in range(num_output):
        prediction_node_names[i] = 'output_node' + str(i)
        predictions[i] = tf.identity(keras_model.outputs[i], name=prediction_node_names[i])

    sess = K.get_session()

    with sess.as_default():
        print(' --- Extracting default graph.')
        def_graph = sess.graph.as_graph_def()
        print(' --- Converting variables to constants.')
        constant_graph = tf.compat.v1.graph_util.convert_variables_to_constants(sess, def_graph, prediction_node_names)
        print(' --- Removing training nodes.')
        infer_graph = tf.compat.v1.graph_util.remove_training_nodes(constant_graph)

        graph_io.write_graph(infer_graph, '.', output_model, as_text=True)

def main():
    argv = getInputParameters().parse_args()

    input_model = argv.input_model
    num_output = argv.num_outputs
    output_model = str(Path(input_model).name) + '.pb'

    prediction_node_names = export_keras_to_tf(input_model, output_model, num_output)

    print('Output nodes are:', prediction_node_names)
    print('Saved as TF frozen model to: ', output_model)


if __name__ == '__main__':
  main()