383 lines
14 KiB
Python
383 lines
14 KiB
Python
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# 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"""Converts checkpoint variables into Const ops in a standalone GraphDef file.
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This script is designed to take a GraphDef proto, a SaverDef proto, and a set of
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variable values stored in a checkpoint file, and output a GraphDef with all of
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the variable ops converted into const ops containing the values of the
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variables.
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It's useful to do this when we need to load a single file in C++, especially in
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environments like mobile or embedded where we may not have access to the
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RestoreTensor ops and file loading calls that they rely on.
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An example of command-line usage is:
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bazel build tensorflow/python/tools:freeze_graph && \
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bazel-bin/tensorflow/python/tools/freeze_graph \
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--input_graph=some_graph_def.pb \
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--input_checkpoint=model.ckpt-8361242 \
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--output_graph=/tmp/frozen_graph.pb --output_node_names=softmax
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You can also look at freeze_graph_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 google.protobuf import text_format
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from tensorflow.core.framework import graph_pb2
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from tensorflow.core.protobuf import saver_pb2
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from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
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from tensorflow.python import pywrap_tensorflow
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from tensorflow.python.client import session
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from tensorflow.python.framework import graph_util
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from tensorflow.python.framework import importer
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from tensorflow.python.platform import app
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from tensorflow.python.platform import gfile
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from tensorflow.python.saved_model import loader
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from tensorflow.python.saved_model import tag_constants
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from tensorflow.python.tools import saved_model_utils
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from tensorflow.python.training import saver as saver_lib
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def freeze_graph_with_def_protos(input_graph_def,
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input_saver_def,
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input_checkpoint,
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output_node_names,
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restore_op_name,
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filename_tensor_name,
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output_graph,
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clear_devices,
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initializer_nodes,
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variable_names_whitelist="",
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variable_names_blacklist="",
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input_meta_graph_def=None,
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input_saved_model_dir=None,
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saved_model_tags=None,
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checkpoint_version=saver_pb2.SaverDef.V2):
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"""Converts all variables in a graph and checkpoint into constants."""
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del restore_op_name, filename_tensor_name # Unused by updated loading code.
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# 'input_checkpoint' may be a prefix if we're using Saver V2 format
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if (not input_saved_model_dir and
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not saver_lib.checkpoint_exists(input_checkpoint)):
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print("Input checkpoint '" + input_checkpoint + "' doesn't exist!")
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return -1
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if not output_node_names:
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print("You need to supply the name of a node to --output_node_names.")
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return -1
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# Remove all the explicit device specifications for this node. This helps to
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# make the graph more portable.
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if clear_devices:
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if input_meta_graph_def:
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for node in input_meta_graph_def.graph_def.node:
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node.device = ""
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elif input_graph_def:
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for node in input_graph_def.node:
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node.device = ""
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if input_graph_def:
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_ = importer.import_graph_def(input_graph_def, name="")
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with session.Session() as sess:
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if input_saver_def:
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saver = saver_lib.Saver(
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saver_def=input_saver_def, write_version=checkpoint_version)
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saver.restore(sess, input_checkpoint)
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elif input_meta_graph_def:
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restorer = saver_lib.import_meta_graph(
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input_meta_graph_def, clear_devices=True)
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restorer.restore(sess, input_checkpoint)
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if initializer_nodes:
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sess.run(initializer_nodes.replace(" ", "").split(","))
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elif input_saved_model_dir:
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if saved_model_tags is None:
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saved_model_tags = []
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loader.load(sess, saved_model_tags, input_saved_model_dir)
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else:
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var_list = {}
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reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
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var_to_shape_map = reader.get_variable_to_shape_map()
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for key in var_to_shape_map:
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try:
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tensor = sess.graph.get_tensor_by_name(key + ":0")
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except KeyError:
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# This tensor doesn't exist in the graph (for example it's
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# 'global_step' or a similar housekeeping element) so skip it.
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continue
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var_list[key] = tensor
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saver = saver_lib.Saver(
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var_list=var_list, write_version=checkpoint_version)
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saver.restore(sess, input_checkpoint)
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if initializer_nodes:
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sess.run(initializer_nodes.replace(" ", "").split(","))
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variable_names_whitelist = (
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variable_names_whitelist.replace(" ", "").split(",")
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if variable_names_whitelist else None)
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variable_names_blacklist = (
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variable_names_blacklist.replace(" ", "").split(",")
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if variable_names_blacklist else None)
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if input_meta_graph_def:
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output_graph_def = graph_util.convert_variables_to_constants(
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sess,
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input_meta_graph_def.graph_def,
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output_node_names.replace(" ", "").split(","),
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variable_names_whitelist=variable_names_whitelist,
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variable_names_blacklist=variable_names_blacklist)
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else:
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output_graph_def = graph_util.convert_variables_to_constants(
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sess,
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input_graph_def,
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output_node_names.replace(" ", "").split(","),
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variable_names_whitelist=variable_names_whitelist,
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variable_names_blacklist=variable_names_blacklist)
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# Write GraphDef to file if output path has been given.
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if output_graph:
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with gfile.GFile(output_graph, "wb") as f:
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f.write(output_graph_def.SerializeToString())
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return output_graph_def
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def _parse_input_graph_proto(input_graph, input_binary):
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"""Parser input tensorflow graph into GraphDef proto."""
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if not gfile.Exists(input_graph):
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print("Input graph file '" + input_graph + "' does not exist!")
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return -1
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input_graph_def = graph_pb2.GraphDef()
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mode = "rb" if input_binary else "r"
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with gfile.FastGFile(input_graph, mode) as f:
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if input_binary:
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input_graph_def.ParseFromString(f.read())
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else:
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text_format.Merge(f.read(), input_graph_def)
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return input_graph_def
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def _parse_input_meta_graph_proto(input_graph, input_binary):
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"""Parser input tensorflow graph into MetaGraphDef proto."""
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if not gfile.Exists(input_graph):
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print("Input meta graph file '" + input_graph + "' does not exist!")
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return -1
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input_meta_graph_def = MetaGraphDef()
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mode = "rb" if input_binary else "r"
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with gfile.FastGFile(input_graph, mode) as f:
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if input_binary:
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input_meta_graph_def.ParseFromString(f.read())
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else:
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text_format.Merge(f.read(), input_meta_graph_def)
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print("Loaded meta graph file '" + input_graph)
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return input_meta_graph_def
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def _parse_input_saver_proto(input_saver, input_binary):
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"""Parser input tensorflow Saver into SaverDef proto."""
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if not gfile.Exists(input_saver):
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print("Input saver file '" + input_saver + "' does not exist!")
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return -1
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mode = "rb" if input_binary else "r"
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with gfile.FastGFile(input_saver, mode) as f:
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saver_def = saver_pb2.SaverDef()
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if input_binary:
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saver_def.ParseFromString(f.read())
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else:
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text_format.Merge(f.read(), saver_def)
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return saver_def
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def freeze_graph(input_graph,
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input_saver,
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input_binary,
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input_checkpoint,
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output_node_names,
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restore_op_name,
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filename_tensor_name,
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output_graph,
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clear_devices,
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initializer_nodes,
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variable_names_whitelist="",
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variable_names_blacklist="",
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input_meta_graph=None,
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input_saved_model_dir=None,
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saved_model_tags=tag_constants.SERVING,
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checkpoint_version=saver_pb2.SaverDef.V2):
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"""Converts all variables in a graph and checkpoint into constants."""
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input_graph_def = None
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if input_saved_model_dir:
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input_graph_def = saved_model_utils.get_meta_graph_def(
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input_saved_model_dir, saved_model_tags).graph_def
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elif input_graph:
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input_graph_def = _parse_input_graph_proto(input_graph, input_binary)
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input_meta_graph_def = None
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if input_meta_graph:
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input_meta_graph_def = _parse_input_meta_graph_proto(
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input_meta_graph, input_binary)
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input_saver_def = None
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if input_saver:
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input_saver_def = _parse_input_saver_proto(input_saver, input_binary)
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freeze_graph_with_def_protos(
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input_graph_def,
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input_saver_def,
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input_checkpoint,
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output_node_names,
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restore_op_name,
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filename_tensor_name,
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output_graph,
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clear_devices,
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initializer_nodes,
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variable_names_whitelist,
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variable_names_blacklist,
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input_meta_graph_def,
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input_saved_model_dir,
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saved_model_tags.replace(" ", "").split(","),
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checkpoint_version=checkpoint_version)
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def main(unused_args, flags):
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if flags.checkpoint_version == 1:
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checkpoint_version = saver_pb2.SaverDef.V1
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elif flags.checkpoint_version == 2:
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checkpoint_version = saver_pb2.SaverDef.V2
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else:
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print("Invalid checkpoint version (must be '1' or '2'): %d" %
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flags.checkpoint_version)
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return -1
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freeze_graph(flags.input_graph, flags.input_saver, flags.input_binary,
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flags.input_checkpoint, flags.output_node_names,
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flags.restore_op_name, flags.filename_tensor_name,
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flags.output_graph, flags.clear_devices, flags.initializer_nodes,
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flags.variable_names_whitelist, flags.variable_names_blacklist,
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flags.input_meta_graph, flags.input_saved_model_dir,
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flags.saved_model_tags, checkpoint_version)
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def run_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_saver",
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type=str,
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default="",
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help="TensorFlow saver file to load.")
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parser.add_argument(
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"--input_checkpoint",
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type=str,
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default="",
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help="TensorFlow variables file to load.")
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parser.add_argument(
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"--checkpoint_version",
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type=int,
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default=2,
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help="Tensorflow variable file 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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"--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_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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"--restore_op_name",
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type=str,
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default="save/restore_all",
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help="""\
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The name of the master restore operator. Deprecated, unused by updated \
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loading code.
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""")
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parser.add_argument(
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"--filename_tensor_name",
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type=str,
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default="save/Const:0",
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help="""\
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The name of the tensor holding the save path. Deprecated, unused by \
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updated loading code.
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""")
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parser.add_argument(
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"--clear_devices",
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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 remove device specifications.")
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parser.add_argument(
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"--initializer_nodes",
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type=str,
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default="",
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help="Comma separated list of initializer nodes to run before freezing.")
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parser.add_argument(
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"--variable_names_whitelist",
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type=str,
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default="",
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help="""\
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Comma separated list of variables to convert to constants. If specified, \
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only those variables will be converted to constants.\
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""")
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parser.add_argument(
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"--variable_names_blacklist",
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type=str,
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default="",
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help="""\
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Comma separated list of variables to skip converting to constants.\
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""")
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parser.add_argument(
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"--input_meta_graph",
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type=str,
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default="",
|
||
|
|
help="TensorFlow \'MetaGraphDef\' file to load.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--input_saved_model_dir",
|
||
|
|
type=str,
|
||
|
|
default="",
|
||
|
|
help="Path to the dir with TensorFlow \'SavedModel\' file and variables.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--saved_model_tags",
|
||
|
|
type=str,
|
||
|
|
default="serve",
|
||
|
|
help="""\
|
||
|
|
Group of tag(s) of the MetaGraphDef to load, in string format,\
|
||
|
|
separated by \',\'. For tag-set contains multiple tags, all tags \
|
||
|
|
must be passed in.\
|
||
|
|
""")
|
||
|
|
flags, unparsed = parser.parse_known_args()
|
||
|
|
|
||
|
|
my_main = lambda unused_args: main(unused_args, flags)
|
||
|
|
app.run(main=my_main, argv=[sys.argv[0]] + unparsed)
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
run_main()
|