def testConvertBytes(self):
source, header = util.convert_bytes_to_c_source(
b"\x00\x01\x02\x23", "foo", 16, use_tensorflow_license=False)
self.assertTrue(
source.find("const unsigned char foo[] DATA_ALIGN_ATTRIBUTE = {"))
self.assertTrue(source.find(""" 0x00, 0x01,
0x02, 0x23,"""))
self.assertNotEqual(-1, source.find("const int foo_len = 4;"))
self.assertEqual(-1, source.find("/* Copyright"))
self.assertEqual(-1, source.find("#include " ""))
self.assertNotEqual(-1,
header.find("extern const unsigned char foo[];"))
self.assertNotEqual(-1, header.find("extern const int foo_len;"))
self.assertEqual(-1, header.find("/* Copyright"))
source, header = util.convert_bytes_to_c_source(
b"\xff\xfe\xfd\xfc",
"bar",
80,
include_guard="MY_GUARD",
include_path="my/guard.h",
use_tensorflow_license=True)
self.assertNotEqual(
-1,
source.find("const unsigned char bar[] DATA_ALIGN_ATTRIBUTE = {"))
self.assertNotEqual(-1, source.find(""" 0xff, 0xfe, 0xfd, 0xfc,"""))
self.assertNotEqual(-1, source.find("/* Copyright"))
self.assertNotEqual(-1, source.find("#include \"my/guard.h\""))
self.assertNotEqual(-1, header.find("#ifndef MY_GUARD"))
self.assertNotEqual(-1, header.find("#define MY_GUARD"))
self.assertNotEqual(-1, header.find("/* Copyright"))
def write_model(model_object, output_tflite_file, include_path, array_name):
"""Writes the tflite model, a python object, into the output file.
Args:
model_object: A tflite model as a python object
output_tflite_file: Full path name to the output tflite file.
include_path: Path to model header file
array_name: name of the array for .cc output
Raises:
ValueError: If file is not formatted in .cc or .tflite
"""
model_bytearray = _convert_model_from_object_to_bytearray(model_object)
if output_tflite_file.endswith('.cc'):
mode = 'w'
converted_model = convert_bytes_to_c_source(
data=model_bytearray,
array_name=array_name,
include_path=include_path,
use_tensorflow_license=True)[0]
elif output_tflite_file.endswith('.tflite'):
mode = 'wb'
converted_model = model_bytearray
else:
raise ValueError('File format not supported')
with open(output_tflite_file, mode) as output_file:
output_file.write(converted_model)
def main(_):
with open(FLAGS.input_tflite_file, "rb") as input_handle:
input_data = input_handle.read()
source, header = util.convert_bytes_to_c_source(
data=input_data,
array_name=FLAGS.array_variable_name,
max_line_width=FLAGS.line_width,
include_guard=FLAGS.include_guard,
include_path=FLAGS.include_path,
use_tensorflow_license=FLAGS.use_tensorflow_license)
with open(FLAGS.output_source_file, "w") as source_handle:
source_handle.write(source)
with open(FLAGS.output_header_file, "w") as header_handle:
header_handle.write(header)
def run_main(_):
"""Main in convert_file_to_c_source.py."""
parser = argparse.ArgumentParser(
description=("Command line tool to run TensorFlow Lite Converter."))
parser.add_argument(
"--input_tflite_file",
type=str,
help="Full filepath of the input TensorFlow Lite file.",
required=True)
parser.add_argument("--output_source_file",
type=str,
help="Full filepath of the output C source file.",
required=True)
parser.add_argument("--output_header_file",
type=str,
help="Full filepath of the output C header file.",
required=True)
parser.add_argument("--array_variable_name",
type=str,
help="Name to use for the C data array variable.",
required=True)
parser.add_argument("--line_width",
type=int,
help="Width to use for formatting.",
default=80)
parser.add_argument("--include_guard",
type=str,
help="Name to use for the C header include guard.",
default=None)
parser.add_argument(
"--include_path",
type=str,
help="Optional path to include in generated source file.",
default=None)
parser.add_argument(
"--use_tensorflow_license",
dest="use_tensorflow_license",
help=
"Whether to prefix the generated files with the TF Apache2 license.",
action="store_true")
parser.set_defaults(use_tensorflow_license=False)
flags, _ = parser.parse_known_args(args=sys.argv[1:])
with open(flags.input_tflite_file, "rb") as input_handle:
input_data = input_handle.read()
source, header = util.convert_bytes_to_c_source(
data=input_data,
array_name=flags.array_variable_name,
max_line_width=flags.line_width,
include_guard=flags.include_guard,
include_path=flags.include_path,
use_tensorflow_license=flags.use_tensorflow_license)
with open(flags.output_source_file, "w") as source_handle:
source_handle.write(source)
with open(flags.output_header_file, "w") as header_handle:
header_handle.write(header)
