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# Copyright (c) 2018 Samsung Electronics Co., Ltd. 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.
# utility for nncc
import os
import sys
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.platform import gfile
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib
# --------
def file_validity_check(fn, ext_must_be=''):
''' check if file exist and file extention is corrent '''
if os.path.exists(fn) == False:
print("# error: file does not exist " + fn)
return False
if ext_must_be != '':
ext = os.path.splitext(fn)[1]
if ext[1:].lower(
) != ext_must_be: # ext contains , e.g., '.pb'. need to exclud '.'
print("# error: wrong extension {}. Should be {} ".format(ext, ext_must_be))
return False
return True
# --------
def importGraphIntoSession(sess, filename, graphNameAfterImporting):
# this should be called inside
# with tf.Session() as sess:
assert sess
(_, _, ext) = splitDirFilenameExt(filename)
if (ext.lower() == 'pb'):
with gfile.FastGFile(filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
elif (ext.lower() == 'pbtxt'):
with open(filename, 'r') as reader:
graph_def = tf.GraphDef()
text_format.Parse(reader.read(), graph_def)
else:
print("# Error: unknown extension - " + ext)
tf.import_graph_def(graph_def, name=graphNameAfterImporting)
# --------
def splitDirFilenameExt(path):
# in case of '/tmp/.ssh/my.key.dat'
# this returns ('/tmp/.ssh', 'my.key', 'dat')
directory = os.path.split(path)[0]
ext = os.path.splitext(path)[1][1:] # remove '.', e.g., '.dat' -> 'dat'
filename = os.path.splitext(os.path.split(path)[1])[0]
return (directory, filename, ext)
# --------
def convertPbtxt2Pb(pbtxtPath):
''' convert pbtxt file to pb file. e.g., /tmp/a.pbtxt --> /tmp/a.pb '''
with open(pbtxtPath) as f:
txt = f.read()
gdef = text_format.Parse(txt, tf.GraphDef())
(directory, filename, ext) = splitDirFilenameExt(pbtxtPath)
tf.train.write_graph(gdef, directory, filename + '.pb', as_text=False)
return os.path.join(directory, filename + '.pb')
# --------
def convertPb2Pbtxt(pbPath):
''' convert pb file to pbtxt file. e.g., /tmp/a.pb --> /tmp/a.pbtxt '''
(directory, filename, ext) = splitDirFilenameExt(pbPath)
with gfile.FastGFile(pbPath, 'rb') as f:
content = f.read()
graph_def = tf.GraphDef()
graph_def.ParseFromString(content)
tf.import_graph_def(graph_def, name='')
tf.train.write_graph(graph_def, directory, filename + '.pbtxt', as_text=True)
return os.path.join(directory, filename + '.pbtxt')
# --------
def savePbAndCkpt(sess, directory, fn_prefix):
''' save files related to session's graph into directory.
- fn_prefix.pb : binary protocol buffer file
- fn_prefix.pbtxt : text format of protocol buffer file
- fn_prefix.ckpt.* : checkpoing files contains values of variables
returns (path of pb file, path of pbtxt file, path of ckpt files)
'''
tf.train.write_graph(sess.graph_def, directory, fn_prefix + '.pb', as_text=False)
tf.train.write_graph(sess.graph_def, directory, fn_prefix + '.pbtxt', as_text=True)
# save a checkpoint file, which will store the above assignment
saver = tf.train.Saver()
saver.save(sess, os.path.join(directory, 'checkoiint', fn_prefix + '.ckpt'))
return (os.path.join(directory, fn_prefix + '.pb'),
os.path.join(directory, fn_prefix + '.pbtxt'),
os.path.join(directory, 'checkoiint', fn_prefix + '.ckpt'))
def optimizeGraph(input_graph_path, input_node_name, output_node_name):
''' this function calls optimize_for_inference of tensorflow and generates '*_optimized.pb'.
- input_graph_path : must be a path to pb file
- input_node_name : name of input operation node
- output_node_name : name of head(top) operation node
'''
(directory, fn, ext) = splitDirFilenameExt(input_graph_path)
output_optimized_graph_path = os.path.join(directory, fn + '_optimized.pb')
# Optimize for inference
input_graph_def = tf.GraphDef()
with tf.gfile.Open(input_graph_path, "rb") as f:
data = f.read()
input_graph_def.ParseFromString(data)
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def, input_node_name.split(","), output_node_name.split(","),
tf.float32.as_datatype_enum)
# Save the optimized graph
f = tf.gfile.FastGFile(output_optimized_graph_path, "w")
f.write(output_graph_def.SerializeToString())
return output_optimized_graph_path
# --------
def freezeGraph(input_graph_path, checkpoint_path, output_node_name):
''' this function calls freeze_grapy.py of tensorflow and generates '*_frozen.pb' and '*_frozen.pbtxt'.
- input_graph_path : must be a path to pb file
- checkpoint_path : path of *.ckpt, e.g., '/tmp/inception_v3/graph.ckpt'
- output_node_name : name of head(top) operation node
'''
input_saver_def_path = ""
input_binary = True
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
clear_devices = True
(directory, fn, ext) = splitDirFilenameExt(input_graph_path)
output_frozen_graph_path = os.path.join(directory, fn + '_frozen.pb')
if file_validity_check(input_graph_path, 'pb') == False:
print("Error: {} not found or not have pb extension".format(input_graph_path))
sys.exit(0)
freeze_graph.freeze_graph(input_graph_path, input_saver_def_path, input_binary,
checkpoint_path, output_node_name, restore_op_name,
filename_tensor_name, output_frozen_graph_path,
clear_devices, "")
pbtxtPath = convertPb2Pbtxt(output_frozen_graph_path)
return (output_frozen_graph_path, pbtxtPath)
# --------
def generateTensorboardLog(pbFiles, graphNames, directory):
''' Generate logs for tensorboard. after calling this, graph(s) can be viewed inside tensorboard.
This function creates a new Session(), so call this outside of 'with Session():'
parameters:
- pbFiles: if multiple graphs needs to be shown, pass the list of pb (or pbtxt) files
- directory: parent directory of '/.tensorboard' directory where log files are saved
how to run tensorboard:
$ tensorboard --logdir=directory_in_parameter
'''
assert len(pbFiles) == len(graphNames)
# without this, graph used previous session is reused : https://stackoverflow.com/questions/42706761/closing-session-in-tensorflow-doesnt-reset-graph
tf.reset_default_graph()
with tf.Session() as sess:
i = 0
for pbFile in pbFiles:
graphName = graphNames[i]
importGraphIntoSession(sess, pbFile, graphName)
i = i + 1
tbLogPath = directory
train_writer = tf.summary.FileWriter(tbLogPath)
train_writer.add_graph(sess.graph)
train_writer.flush()
train_writer.close()
return tbLogPath
#--------
def isScalar(x):
'''
keyword argument:
x - base_freezer.Tensor
'''
return (type(x.getShape()) == [])
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