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# Copyright 2017 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.
# =============================================================================
"""A tool for cost analysis."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
from google.protobuf import message
from google.protobuf import text_format
from tensorflow.contrib.fused_conv.ops import gen_fused_conv2d_bias_activation_op # pylint: disable=unused-import
from tensorflow.core.framework import graph_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.grappler import cost_analyzer
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.platform import app
from tensorflow.python.platform import gfile
from tensorflow.python.training import saver
def get_metagraph():
"""Constructs and returns a MetaGraphDef from the input file."""
with gfile.GFile(FLAGS.input) as input_file:
input_data = input_file.read()
try:
saved_model = saved_model_pb2.SavedModel()
text_format.Merge(input_data, saved_model)
meta_graph = saved_model.meta_graphs[0]
except text_format.ParseError:
try:
saved_model.ParseFromString(input_data)
meta_graph = saved_model.meta_graphs[0]
except message.DecodeError:
try:
meta_graph = meta_graph_pb2.MetaGraphDef()
text_format.Merge(input_data, meta_graph)
except text_format.ParseError:
try:
meta_graph.ParseFromString(input_data)
except message.DecodeError:
try:
graph_def = graph_pb2.GraphDef()
text_format.Merge(input_data, graph_def)
except text_format.ParseError:
try:
graph_def.ParseFromString(input_data)
except message.DecodeError:
raise ValueError("Invalid input file.")
importer.import_graph_def(graph_def, name="")
graph = ops.get_default_graph()
meta_graph = saver.export_meta_graph(
graph_def=graph.as_graph_def(), graph=graph)
if FLAGS.fetch is not None:
fetch_collection = meta_graph_pb2.CollectionDef()
for fetch in FLAGS.fetch.split(","):
fetch_collection.node_list.value.append(fetch)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
return meta_graph
def main(_):
metagraph = get_metagraph()
rewriter_config = rewriter_config_pb2.RewriterConfig()
if FLAGS.rewriter_config is not None:
text_format.Merge(FLAGS.rewriter_config, rewriter_config)
optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, metagraph)
metagraph.graph_def.CopyFrom(optimized_graph)
report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report,
FLAGS.verbose)
print(report)
if FLAGS.memory_report:
report = cost_analyzer.GenerateMemoryReport(metagraph)
print(report)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input",
type=str,
default=None,
help="Input file path. Accept SavedModel, MetaGraphDef, and GraphDef in "
"either binary or text format.")
parser.add_argument(
"--fetch",
type=str,
default=None,
help="The names of the fetch node delimited by comma.")
parser.add_argument(
"--rewriter_config",
type=str,
default=None,
help="Configuration for the grappler optimizers, described as a "
"RewriterConfig protocol buffer. Usage example 1: "
"--rewriter_config='optimize_tensor_layout: true "
"disable_model_pruning: true'. Usage example 2: "
"--rewriter_config='optimizers: \"constfold\" optimizers: \"layout\"'")
parser.add_argument(
"--per_node_report",
action="store_true",
help="Generate per-node report. By default the report contains stats "
"aggregated on a per op type basis, per_node_report adds results "
"for each individual node to the report.")
parser.add_argument(
"--memory_report",
action="store_true",
help="Generate memory usage report.")
parser.add_argument(
"--verbose",
action="store_true",
help="Generate verbose reports. By default, succinct reports are used.")
FLAGS, unparsed = parser.parse_known_args()
app.run(main=main, argv=[sys.argv[0]] + unparsed)
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