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#!/usr/bin/python
# 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.
import os
import sys
import platform
import tensorflow as tf
import argparse
import base_freezer as base
import model_freezer_util as util
class Gen(base.BaseFreezer):
'''
class to generate tflite file for TOPK
'''
def __init__(self, path):
super(self.__class__, self).__init__(path)
def getOutputDirectory(self):
return os.path.join(self.root_output_path,
'topk') # the root path of generated files
def getTestCases(self):
'''
this returns a hash of test case (= set of input type), for example:
[1.2, -2.3] : two input, both are scalar. one is 1.2, another is -2.3
[[5,3], [5,4,3]] : two input, both are shapes. one is [5.3], another is [5,4,3]
test name (key of hash) is used as
- prefix of file name to be generated
- output node name pf graph
'''
return {
"topk_2d": [
base.Tensor(shape=[2, 3], dtype=tf.float32),
base.Tensor(shape=[], const_val=2, dtype=tf.int32)
],
"topk_3d": [
base.Tensor(shape=[2, 3, 4], dtype=tf.float32),
base.Tensor(shape=[], const_val=2, dtype=tf.int32)
],
}
def buildModel(self, sess, test_case_tensor, tc_name):
'''
please, refer to the comment in MUL_gen.py to see how to rewrite this method
'''
input_list = []
# ------ modify below for your model FROM here -------#
x_tensor = self.createTFInput(test_case_tensor[0], input_list)
y_tensor = self.createTFInput(test_case_tensor[1], input_list)
# defining output node and input list
output_node = tf.nn.top_k(
x_tensor,
y_tensor, # add your input here
name=tc_name) # do not modify name
# ------ modify UNTIL here for your model -------#
# Note if don't have any CONST value, creating checkpoint file fails.
# The next lines insert such (CONST) to prevent such error.
# So, Graph.pb/pbtxt contains this garbage info,
# but this garbage info will be removed in Graph_frozen.pb/pbtxt
garbage = tf.get_variable(
"garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer())
init_op = tf.global_variables_initializer()
garbage_value = [0]
sess.run(tf.assign(garbage, garbage_value))
sess.run(init_op)
# ------ modify appropriate return value -------#
# returning (input_node_list, output_node_list)
return (input_list, [output_node])
'''
How to run
$ chmod +x tools/tensorflow_model_freezer/sample/name_of_this_file.py
$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ \
tools/tensorflow_model_freezer/sample/name_of_this_file.py \
~/temp # directory where model files are saved
'''
# --------
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Converted Tensorflow model in python to frozen model.')
parser.add_argument(
"out_dir",
help=
"directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored."
)
args = parser.parse_args()
root_output_path = args.out_dir
Gen(root_output_path).createSaveFreezeModel()
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