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h5_converter.py
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h5_converter.py
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# coding=utf-8
'''
* Copyright 2018 Canaan Inc.
*
* 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 keras.models
import tensorflow as tf
import tempfile
from keras import backend as K
from tensorflow.python.framework import graph_io
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
"""
Freezes the state of a session into a prunned computation graph.
Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
prunned so subgraphs that are not neccesary to compute the requested
outputs are removed.
@param session The TensorFlow session to be frozen.
@param keep_var_names A list of variable names that should not be frozen,
or None to freeze all the variables in the graph.
@param output_names Names of the relevant graph outputs.
@param clear_devices Remove the device directives from the graph for better portability.
@return The frozen graph definition.
"""
from tensorflow.python.framework.graph_util import convert_variables_to_constants
graph = session.graph
with graph.as_default():
freeze_var_names = None #list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
# output_names += [v.op.name for v in tf.global_variables()]
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
def convert(h5_in):
pb_out = tempfile.mktemp('.pb')
*pb_path_list, pb_name = pb_out.split('/')
pb_path = '/'.join(pb_path_list)
K.set_learning_phase(0)
net_model = keras.models.load_model(h5_in, custom_objects={'tf': tf})
frozen_graph = freeze_session(K.get_session(), output_names=[net_model.output.op.name])
graph_io.write_graph(frozen_graph, pb_path, pb_name, as_text=False)
tf.reset_default_graph()
return pb_out