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# pylint: disable=g-bad-file-header
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
r"""Removes parts of a graph that are only needed for training.
There are several common transformations that can be applied to GraphDefs
created to train a model, that help reduce the amount of computation needed when
the network is used only for inference. These include:
- Removing training-only operations like checkpoint saving.
- Stripping out parts of the graph that are never reached.
- Removing debug operations like CheckNumerics.
- Folding batch normalization ops into the pre-calculated weights.
- Fusing common operations into unified versions.
This script takes either a frozen binary GraphDef file (where the weight
variables have been converted into constants by the freeze_graph script), or a
text GraphDef proto file (the weight variables are stored in a separate
checkpoint file), and outputs a new GraphDef with the optimizations applied.
If the input graph is a text graph file, make sure to include the node that
restores the variable weights in output_names. That node is usually named
"restore_all".
An example of command-line usage is:
bazel build tensorflow/python/tools:optimize_for_inference && \
bazel-bin/tensorflow/python/tools/optimize_for_inference \
--input=frozen_inception_graph.pb \
--output=optimized_inception_graph.pb \
--frozen_graph=True \
--input_names=Mul \
--output_names=softmax
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import graph_io
from tensorflow.python.platform import app
from tensorflow.python.platform import gfile
from tensorflow.python.tools import optimize_for_inference_lib
FLAGS = None
def main(unused_args):
if not gfile.Exists(FLAGS.input):
print("Input graph file '" + FLAGS.input + "' does not exist!")
return -1
input_graph_def = graph_pb2.GraphDef()
with gfile.Open(FLAGS.input, "rb") as f:
data = f.read()
if FLAGS.frozen_graph:
input_graph_def.ParseFromString(data)
else:
text_format.Merge(data.decode("utf-8"), input_graph_def)
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def,
FLAGS.input_names.split(","),
FLAGS.output_names.split(","),
FLAGS.placeholder_type_enum,
FLAGS.toco_compatible)
if FLAGS.frozen_graph:
f = gfile.FastGFile(FLAGS.output, "w")
f.write(output_graph_def.SerializeToString())
else:
graph_io.write_graph(output_graph_def,
os.path.dirname(FLAGS.output),
os.path.basename(FLAGS.output))
return 0
def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--input",
type=str,
default="",
help="TensorFlow \'GraphDef\' file to load.")
parser.add_argument(
"--output",
type=str,
default="",
help="File to save the output graph to.")
parser.add_argument(
"--input_names",
type=str,
default="",
help="Input node names, comma separated.")
parser.add_argument(
"--output_names",
type=str,
default="",
help="Output node names, comma separated.")
parser.add_argument(
"--frozen_graph",
nargs="?",
const=True,
type="bool",
default=True,
help="""\
If true, the input graph is a binary frozen GraphDef
file; if false, it is a text GraphDef proto file.\
""")
parser.add_argument(
"--placeholder_type_enum",
type=int,
default=dtypes.float32.as_datatype_enum,
help="The AttrValue enum to use for placeholders.")
parser.add_argument(
"--toco_compatible",
type=bool,
default=False,
help="""\
If true, only use ops compatible with Tensorflow
Lite Optimizing Converter.\
""")
return parser.parse_known_args()
if __name__ == "__main__":
FLAGS, unparsed = parse_args()
app.run(main=main, argv=[sys.argv[0]] + unparsed)
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