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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 files for Squeeze
'''
def __init__(self, path):
super(self.__class__, self).__init__(path)
def getOutputDirectory(self):
return os.path.join(self.root_output_path,
'squeeze') # the root path of generated files
def getTestCases(self):
'''
this returns a a hash containg test cases.
key of return hash is test case name and
value of return hash is test is a list of input tensor metadata.
test name (key of hash) is used as
- prefix of file name to be generated (don't use white space or special characters)
- output node name pf graph
'''
return {
"squeeze_2d": [base.Tensor([1, 3])],
"squeeze_4d_1": [base.Tensor([1, 3, 2, 1])],
# squeeze with axis
"squeeze_4d_2": [base.Tensor([1, 3, 2, 1]),
[0]], # squeeze [1, 3, 2, 1] to [3, 2, 1]
"squeeze_4d_3": [base.Tensor([1, 3, 2, 1]),
[3]], # squeeze [1, 3, 2, 1] to [1, 3, 2]
"squeeze_4d_4": [base.Tensor([1, 3, 2, 1]),
[0, 3]] # squeeze [1, 3, 2, 1] to [3, 2]
}
def buildModel(self, sess, test_case_tensor, tc_name):
'''
This method is called per test case (defined by getTestCases()).
keyword argument:
test_case_tensor -- test case tensor metadata
For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] }
test_case_tensor is [base.Tensor([5]), base.Tensor([5])]
'''
input_list = []
# ------ modify below for your model FROM here -------#
x_tensor = self.createTFInput(test_case_tensor[0], input_list)
if len(test_case_tensor) == 2:
axis_tensor = test_case_tensor[1]
# defining output node = x_input * y_input
# and input list
if len(test_case_tensor) == 1:
output_node = tf.squeeze(input=x_tensor, name=tc_name) # do not modify name
else:
output_node = tf.squeeze(
input=x_tensor, axis=axis_tensor, 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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