107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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r"""Removes unneeded nodes from a GraphDef file.
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This script is designed to help streamline models, by taking the input and
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output nodes that will be used by an application and figuring out the smallest
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set of operations that are required to run for those arguments. The resulting
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minimal graph is then saved out.
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The advantages of running this script are:
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- You may be able to shrink the file size.
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- Operations that are unsupported on your platform but still present can be
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safely removed.
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The resulting graph may not be as flexible as the original though, since any
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input nodes that weren't explicitly mentioned may not be accessible any more.
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An example of command-line usage is:
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bazel build tensorflow/python/tools:strip_unused && \
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bazel-bin/tensorflow/python/tools/strip_unused \
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--input_graph=some_graph_def.pb \
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--output_graph=/tmp/stripped_graph.pb \
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--input_node_names=input0
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--output_node_names=softmax
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You can also look at strip_unused_test.py for an example of how to use it.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import sys
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from tensorflow.python.framework import dtypes
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from tensorflow.python.platform import app
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from tensorflow.python.tools import strip_unused_lib
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FLAGS = None
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def main(unused_args):
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strip_unused_lib.strip_unused_from_files(FLAGS.input_graph,
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FLAGS.input_binary,
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FLAGS.output_graph,
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FLAGS.output_binary,
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FLAGS.input_node_names,
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FLAGS.output_node_names,
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FLAGS.placeholder_type_enum)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.register('type', 'bool', lambda v: v.lower() == 'true')
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parser.add_argument(
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'--input_graph',
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type=str,
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default='',
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help='TensorFlow \'GraphDef\' file to load.')
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parser.add_argument(
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'--input_binary',
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nargs='?',
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const=True,
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type='bool',
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default=False,
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help='Whether the input files are in binary format.')
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parser.add_argument(
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'--output_graph',
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type=str,
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default='',
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help='Output \'GraphDef\' file name.')
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parser.add_argument(
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'--output_binary',
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nargs='?',
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const=True,
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type='bool',
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default=True,
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help='Whether to write a binary format graph.')
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parser.add_argument(
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'--input_node_names',
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type=str,
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default='',
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help='The name of the input nodes, comma separated.')
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parser.add_argument(
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'--output_node_names',
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type=str,
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default='',
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help='The name of the output nodes, comma separated.')
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parser.add_argument(
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'--placeholder_type_enum',
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type=int,
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default=dtypes.float32.as_datatype_enum,
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help='The AttrValue enum to use for placeholders.')
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FLAGS, unparsed = parser.parse_known_args()
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app.run(main=main, argv=[sys.argv[0]] + unparsed)
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