def convert(arguments):
"""
Convert a TensorflowJS-model to a TensorFlow-model.
Args:
arguments: List of command-line arguments
"""
args = get_arg_parser().parse_args(arguments)
if args.show_version:
print("\ntfjs_graph_converter {}\n".format(version.VERSION))
print("Dependency versions:")
print(" tensorflow {}".format(tf.version.VERSION))
print(" tensorflowjs {}".format(tfjs.__version__))
return
def info(message, end=None):
if not args.silence:
print(message, end=end)
if not args.input_path:
raise ValueError(
"Missing input_path argument. For usage, use the --help flag.")
if not args.output_path:
raise ValueError(
"Missing output_path argument. For usage, use the --help flag.")
info("TensorFlow.js Graph Model Converter\n")
info("Graph model: {}".format(args.input_path))
info("Output: {}".format(args.output_path))
info("Target format: {}".format(args.output_format))
info("\nConverting....", end=" ")
start_time = time.perf_counter()
if args.output_format == common.CLI_FROZEN_MODEL:
api.graph_model_to_frozen_graph(args.input_path, args.output_path)
elif args.output_format == common.CLI_SAVED_MODEL:
api.graph_model_to_saved_model(args.input_path, args.output_path,
args.saved_model_tags)
else:
raise ValueError("Unsupported output format: {}".format(
args.output_format))
end_time = time.perf_counter()
info("Done.")
info("Conversion took {0:.3f}s".format(end_time - start_time))
return
def test_graph_model_to_frozen_graph(self):
"""graph_model_to_frozen_graph should save valid frozen graph model"""
try:
input_name = testutils.get_path_to(
testutils.SIMPLE_MODEL_PATH_NAME)
output_name = os.path.join(tempfile.gettempdir(), 'frozen.pb')
api.graph_model_to_frozen_graph(input_name, output_name)
# make sure the output file exists and isn't empty
self.assertTrue(os.path.exists(output_name))
self.assertGreater(os.stat(output_name).st_size, 256)
# file must be a valid protobuf message
with open(output_name, 'rb') as pb_file:
graph_def = testutils.GraphDef()
graph_def.ParseFromString(pb_file.read())
finally:
if os.path.exists(output_name):
os.remove(output_name)
def convert(arguments):
"""
Convert a TensorflowJS-model to a TensorFlow-model.
Args:
arguments: List of command-line arguments
"""
args = get_arg_parser().parse_args(arguments)
if args.show_version:
print(f"\ntfjs_graph_converter {version.VERSION}\n")
print("Dependency versions:")
print(f" tensorflow {tf.version.VERSION}")
print(f" tensorflowjs {tfjs.__version__}")
return
def info(message, end=None):
if not args.silence:
print(message, end=end, flush=True)
if not args.input_path:
raise ValueError(
"Missing input_path argument. For usage, use the --help flag.")
if not args.output_path:
raise ValueError(
"Missing output_path argument. For usage, use the --help flag.")
if args.output_format == common.CLI_SAVED_MODEL:
if args.signature_key is not None and args.outputs is None:
raise ValueError(f'--{common.CLI_SIGNATURE_KEY} requires '
f'--{common.CLI_OUTPUTS} to be specified')
if args.method_name is not None and args.outputs is None:
raise ValueError(f'--{common.CLI_METHOD_NAME} requires '
f'--{common.CLI_OUTPUTS} to be specified')
if args.rename is not None and args.outputs is None:
raise ValueError(f'--{common.CLI_RENAME} requires '
f'--{common.CLI_OUTPUTS} to be specified')
info("TensorFlow.js Graph Model Converter\n")
info(f"Graph model: {args.input_path}")
info(f"Output: {args.output_path}")
info(f"Target format: {args.output_format}")
info("\nConverting....", end=" ")
start_time = time.perf_counter()
if args.output_format == common.CLI_FROZEN_MODEL:
api.graph_model_to_frozen_graph(args.input_path, args.output_path,
args.compat_mode)
elif args.output_format == common.CLI_SAVED_MODEL:
api.graph_model_to_saved_model(
args.input_path,
args.output_path,
tags=args.saved_model_tags,
signature_def_map=_get_signature(args),
signature_key_map=_get_signature_keys(args),
compat_mode=args.compat_mode)
else:
raise ValueError(f"Unsupported output format: {args.output_format}")
end_time = time.perf_counter()
info("Done.")
info(f"Conversion took {end_time-start_time:.3f}s")