def test_graph_model_to_saved_model_accepts_signature_key_map(self):
"""graph_model_to_saved_model should accept signature key map"""
model_dir = testutils.get_path_to(testutils.MULTI_HEAD_PATH)
export_dir = tempfile.mkdtemp(suffix='.saved_model')
try:
tags = [tf.saved_model.SERVING]
signature_map = {
'': {api.SIGNATURE_OUTPUTS: ['Identity']},
'debug': {api.SIGNATURE_OUTPUTS: ['Identity', 'Identity_1']}}
signature_key = api.RenameMap([
('Identity', 'output'), ('Identity_1', 'autoencoder_output')
])
api.graph_model_to_saved_model(model_dir, export_dir,
tags=tags,
signature_def_map=signature_map,
signature_key_map=signature_key)
# try and load the model
meta_graph_def = load_meta_graph(export_dir, tags)
# the signatures should contain the renamed keys
for signature in meta_graph_def.signature_def.values():
self.assertIn('output', signature.outputs)
self.assertEqual(signature.outputs['output'].name,
'Identity:0')
signature = meta_graph_def.signature_def['debug']
self.assertIn('autoencoder_output', signature.outputs)
self.assertEqual(signature.outputs['autoencoder_output'].name,
'Identity_1:0')
finally:
if os.path.exists(export_dir):
shutil.rmtree(export_dir)
def test_graph_model_to_saved_model_accepts_signature_map(self):
"""graph_model_to_saved_model should accept signature map"""
model_dir = testutils.get_path_to(testutils.MULTI_HEAD_PATH)
export_dir = tempfile.mkdtemp(suffix='.saved_model')
try:
tags = [tf.saved_model.SERVING]
signature_map = {
'': {api.SIGNATURE_OUTPUTS: ['Identity']},
'debug': {api.SIGNATURE_OUTPUTS: ['Identity', 'Identity_1']}}
api.graph_model_to_saved_model(model_dir, export_dir,
tags=tags,
signature_def_map=signature_map)
# try and load the model
meta_graph_def = load_meta_graph(export_dir, tags)
self.assertIsNotNone(meta_graph_def)
# we want both signatures to be present
self.assertEqual(len(meta_graph_def.signature_def), 2)
# the signatures should be valid
for signature in meta_graph_def.signature_def.values():
self.assertTrue(is_valid_signature(signature))
# the default signature should have one output
default_key = tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY
self.assertEqual(
len(meta_graph_def.signature_def[default_key].outputs), 1)
# debug signature should be present and contain two outputs
self.assertIn('debug', meta_graph_def.signature_def.keys())
self.assertEqual(
len(meta_graph_def.signature_def['debug'].outputs), 2)
finally:
if os.path.exists(export_dir):
shutil.rmtree(export_dir)
def test_graph_model_to_saved_model(self):
"""graph_model_to_saved_model should save valid SavedModel"""
model_dir = testutils.get_path_to(testutils.SIMPLE_MODEL_PATH_NAME)
export_dir = tempfile.mkdtemp(suffix='.saved_model')
try:
tags = ['serving_default']
api.graph_model_to_saved_model(model_dir, export_dir, tags=tags)
self.assertTrue(os.path.exists(export_dir))
# must be valid model; tf.saved_model.contains_saved_model is
# insufficient
imported = tf.saved_model.load(export_dir, tags=tags)
self.assertIsNotNone(imported.graph)
finally:
if os.path.exists(export_dir):
shutil.rmtree(export_dir)
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_saved_model(self):
"""graph_model_to_saved_model should save valid SavedModel"""
model_dir = testutils.get_path_to(testutils.PRELU_MODEL_PATH)
export_dir = tempfile.mkdtemp(suffix='.saved_model')
try:
tags = [tf.saved_model.SERVING]
api.graph_model_to_saved_model(model_dir, export_dir, tags=tags)
self.assertTrue(os.path.exists(export_dir))
self.assertTrue(tf.saved_model.contains_saved_model(export_dir))
# try and load the model
meta_graph_def = load_meta_graph(export_dir, tags)
self.assertIsNotNone(meta_graph_def)
# we also want a signature to be present
self.assertEqual(len(meta_graph_def.signature_def), 1)
# the signatures should be valid
self.assertTrue(
is_valid_signature(
list(meta_graph_def.signature_def.values())[0]))
finally:
if os.path.exists(export_dir):
shutil.rmtree(export_dir)
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")
