def convert_to_frozen_pb(self):
tflite_convertor = CustomTocoConverter()
# converting to frozen graph
tflite_convertor.set_config_for_frozen_graph(
input_dir_path=self._save_dir,
input_pb_name=self._pb_filename,
input_ckpt_name=self._ckpt_filename,
output_dir_path=self._save_dir,
output_node_names=self._output_node_name)
tflite_convertor.convert_to_frozen_graph()
logging.info('[TfliteSaverHook] Frozen graph is successfully created.')
def convert_to_tflite(module_name, pbsavedir, pbfilename, ckptfilename,
output_node_name, input_shape):
# check tflite compatability
print('------------------------------------------------')
print('[tfTest] tflite compatability check')
tflitedir = getcwd() + '/tflite_files/'
if not tf.gfile.Exists(tflitedir):
tf.gfile.MakeDirs(tflitedir)
tflitefilename = module_name + '.tflite'
tflite_convertor = TFliteConvertor()
#output_node_name = 'unittest0/' + module_name + '/' + expected_output_name
# converting to frozen graph
tflite_convertor.set_config_for_frozen_graph(
input_dir_path=pbsavedir,
input_pb_name=pbfilename,
input_ckpt_name='ckpt/' + ckptfilename,
output_dir_path=pbsavedir,
output_node_names=output_node_name)
tflite_convertor.convert_to_frozen_graph()
print("Your tflite at %s" % tflitedir + tflitefilename)
# converting to tfilte graph
tflite_convertor.set_config_for_tflite(
input_dir_path=pbsavedir,
output_dir_path=tflitedir,
input_pb_file='frozen_' + pbfilename,
output_tflite_file=tflitefilename,
input_shape=str(input_shape[0]) + ',' + str(input_shape[1]) + ',' +
str(input_shape[2]) + ',' + str(input_shape[3]),
output_array=output_node_name,
tf_src_dir_path=PATH_TENSORFLOW_SRC)
# frozen grpah to tflite conversion
tflite_convertor.convert_to_tflite_from_frozen_graph()
print('[tftest] tflite compatability check successful')
def convert_to_frozen_pb(module_name, pbsavedir, pbfilename, ckptfilename,
output_node_name, input_shape):
print('------------------------------------------------')
print('[tfTest] frozen pb conversion')
tflitedir = getcwd() + '/tflite_files/'
if not tf.gfile.Exists(tflitedir):
tf.gfile.MakeDirs(tflitedir)
tflitefilename = module_name + '.tflite'
tflite_convertor = TFliteConvertor()
#output_node_name = 'unittest0/' + module_name + '/' + expected_output_name
# converting to frozen graph
tflite_convertor.set_config_for_frozen_graph(
input_dir_path=pbsavedir,
input_pb_name=pbfilename,
input_ckpt_name='ckpt/' + ckptfilename,
output_dir_path=pbsavedir,
output_node_names=output_node_name)
tflite_convertor.convert_to_frozen_graph()
print('[tftest] frozen graph is successfully generated.')
# your dir for exporting tflite file
output_tflite_path = getcwd() + '/pb_and_ckpt/lenet5/tflite_out/'
# your dir path for tensorflow source
# where you need to fork tensorflow repo
#
PATH_TENSORFLOW_SRC = '/Users/jwkangmacpro2/SourceCodes/tensorflow/'
# The output/input node names are obtained from Tensorboard
output_node_names = 'model_out/Softmax'
input_node_names = 'input'
# input placeholder shape
input_shape_str = '1,28,28,1'
tflite_convertor = TFliteConvertor()
# tflite config
tflite_convertor.set_config_for_tflite(
input_dir_path=input_frozen_pb_path,
output_dir_path=output_tflite_path,
input_pb_file='frozen_tf_graph_def_lenet5.pb',
output_tflite_file='tflite_lenet5.tflite',
inference_type='FLOAT',
input_shape=input_shape_str,
input_array=input_node_names,
output_array=output_node_names,
tf_src_dir_path=PATH_TENSORFLOW_SRC)
# frozen grpah to tflite conversion
tflite_convertor.convert_to_tflite_from_frozen_graph()
author: Jaewook Kang
date : 2018 Apr
'''
import sys
from os import getcwd
# for lenet5
input_model_path = getcwd()+'/pb_and_ckpt/lenet5/'
output_model_path = getcwd()+'/pb_and_ckpt/lenet5/frozen_pb_out/'
# The output node name is from Tensorboard
output_node_names = 'model_out/Softmax'
sys.path.insert(0, input_model_path)
sys.path.insert(0, getcwd()+'/convertor/')
from tflite_convertor import TFliteConvertor
tflite_convertor = TFliteConvertor()
tflite_convertor.set_config_for_frozen_graph(input_dir_path=input_model_path+'runtrain-20180613101503/',
input_pb_name='tf_graph_def_lenet5.pb',
input_ckpt_name='lenet5_model_variable.ckpt',
output_dir_path=output_model_path,
output_node_names=output_node_names)
tflite_convertor.convert_to_frozen_graph()
author: Jaewook Kang and Doyoung Kwak
date : 2018 June
'''
import sys
from os import getcwd
# for dont be turtle proj
input_model_path = getcwd() + '/pb_and_ckpt/turtle/'
output_model_path = getcwd() + '/pb_and_ckpt/turtle/frozen_pb_out/'
# The output node name is from Tensorboard
output_node_names = 'HG/last5/Sigmoid'
sys.path.insert(0, input_model_path)
sys.path.insert(0, getcwd() + '/convertor/')
from tflite_convertor import TFliteConvertor
tflite_convertor = TFliteConvertor()
tflite_convertor.set_config_for_frozen_graph(
input_dir_path=input_model_path + 'runtrain-20180613-yglee/',
input_pb_name='net.pb',
input_ckpt_name='net.ckpt',
output_dir_path=output_model_path,
output_node_names=output_node_names)
tflite_convertor.convert_to_frozen_graph()