''' This script converts a .h5 Keras model into a Tensorflow .pb file. Attribution: This script was adapted from https://github.com/amir-abdi/keras_to_tensorflow MIT License Copyright (c) 2017 bitbionic Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' import os import os.path as osp import argparse import tensorflow as tf from keras.models import load_model from keras import backend as K def convertGraph( modelPath, outdir, numoutputs, prefix, name): ''' Converts an HD5F file to a .pb file for use with Tensorflow. Args: modelPath (str): path to the .h5 file outdir (str): path to the output directory numoutputs (int): prefix (str): the prefix of the output aliasing name (str): Returns: None ''' #NOTE: If using Python > 3.2, this could be replaced with os.makedirs( name, exist_ok=True ) if not os.path.isdir(outdir): os.mkdir(outdir) K.set_learning_phase(0) net_model = load_model(modelPath) # Alias the outputs in the model - this sometimes makes them easier to access in TF pred = [None]*numoutputs pred_node_names = [None]*numoutputs for i in range(numoutputs): pred_node_names[i] = prefix+'_'+str(i) pred[i] = tf.identity(net_model.output[i], name=pred_node_names[i]) print('Output nodes names are: ', pred_node_names) sess = K.get_session() # Write the graph in human readable f = 'graph_def_for_reference.pb.ascii' tf.train.write_graph(sess.graph.as_graph_def(), outdir, f, as_text=True) print('Saved the graph definition in ascii format at: ', osp.join(outdir, f)) # Write the graph in binary .pb file from tensorflow.python.framework import graph_util from tensorflow.python.framework import graph_io constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names) graph_io.write_graph(constant_graph, outdir, name, as_text=False) print('Saved the constant graph (ready for inference) at: ', osp.join(outdir, name)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model','-m', dest='model', required=True, help='REQUIRED: The HDF5 Keras model you wish to convert to .pb') parser.add_argument('--numout','-n', type=int, dest='num_out', required=True, help='REQUIRED: The number of outputs in the model.') parser.add_argument('--outdir','-o', dest='outdir', required=False, default='./', help='The directory to place the output files - default("./")') parser.add_argument('--prefix','-p', dest='prefix', required=False, default='k2tfout', help='The prefix for the output aliasing - default("k2tfout")') parser.add_argument('--name', dest='name', required=False, default='output_graph.pb', help='The name of the resulting output graph - default("output_graph.pb")') args = parser.parse_args() convertGraph( args.model, args.outdir, args.num_out, args.prefix, args.name )