def test_convert_hub_module(self):
self._create_hub_module()
tf_saved_model_conversion_v2.convert_tf_hub_module(
os.path.join(self._tmp_dir, HUB_MODULE_DIR),
os.path.join(self._tmp_dir, SAVED_MODEL_DIR), 'default')
weights = [{
'paths': ['group1-shard1of1.bin'],
'weights': [{
'shape': [2],
'name': 'module/Variable',
'dtype': 'float32'
}]
}]
tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
# Check model.json and weights manifest.
with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
model_json = json.load(f)
self.assertTrue(model_json['modelTopology'])
weights_manifest = model_json['weightsManifest']
self.assertEqual(weights_manifest, weights)
self.assertTrue(
glob.glob(os.path.join(self._tmp_dir, SAVED_MODEL_DIR,
'group*-*')))
def test_convert_hub_module_v2(self):
self._create_saved_model()
module_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
tf_saved_model_conversion_v2.convert_tf_hub_module(
module_path, tfjs_path, "serving_default", "serve")
# Check model.json and weights manifest.
with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
model_json = json.load(f)
self.assertTrue(model_json['modelTopology'])
self.assertIsNot(model_json['modelTopology']['versions'], None)
signature = model_json['userDefinedMetadata']['signature']
self.assertIsNot(signature, None)
self.assertIsNot(signature['inputs'], None)
self.assertIsNot(signature['outputs'], None)
weights_manifest = model_json['weightsManifest']
self.assertCountEqual(weights_manifest[0]['paths'],
['group1-shard1of1.bin'])
self.assertIn('weights', weights_manifest[0])
self.assertTrue(
glob.glob(os.path.join(self._tmp_dir, SAVED_MODEL_DIR,
'group*-*')))
def test_convert_hub_module_v1_sharded(self):
self._create_hub_module()
module_path = os.path.join(self._tmp_dir, HUB_MODULE_DIR)
tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
# Do initial conversion without sharding.
tf_saved_model_conversion_v2.convert_tf_hub_module(
module_path, tfjs_path)
weight_files = glob.glob(os.path.join(tfjs_path, 'group*.bin'))
# Get size of weights in bytes after graph optimizations.
optimized_total_weight = sum(
[os.path.getsize(f) for f in weight_files])
# Due to the shard size, there ought to be 3 shards after conversion.
weight_shard_size_bytes = int(optimized_total_weight * 0.4)
tfjs_path = os.path.join(self._tmp_dir, 'sharded_model')
# Convert Hub model again with shard argument set.
tf_saved_model_conversion_v2.convert_tf_hub_module(
module_path,
tfjs_path,
weight_shard_size_bytes=weight_shard_size_bytes)
weight_files = sorted(glob.glob(os.path.join(tfjs_path, 'group*.bin')))
self.assertEqual(len(weight_files), 3)
weight_file_sizes = [os.path.getsize(f) for f in weight_files]
self.assertEqual(sum(weight_file_sizes), optimized_total_weight)
self.assertEqual(weight_file_sizes[0], weight_file_sizes[1])
self.assertLess(weight_file_sizes[2], weight_file_sizes[0])
def test_convert_hub_module_v2(self):
self._create_saved_model()
module_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
tf_saved_model_conversion_v2.convert_tf_hub_module(
module_path, tfjs_path, "serving_default", "serve")
weights = [{
'paths': ['group1-shard1of1.bin'],
'weights': [{
'shape': [],
'name': 'StatefulPartitionedCall/mul',
'dtype': 'float32'
}]
}]
# Check model.json and weights manifest.
with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
model_json = json.load(f)
self.assertTrue(model_json['modelTopology'])
weights_manifest = model_json['weightsManifest']
self.assertEqual(weights_manifest, weights)
self.assertTrue(
glob.glob(os.path.join(self._tmp_dir, SAVED_MODEL_DIR,
'group*-*')))
def test_convert_hub_module_v1_with_metadata(self):
self._create_hub_module()
module_path = os.path.join(self._tmp_dir, HUB_MODULE_DIR)
tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
metadata_json = {'a': 1}
tf_saved_model_conversion_v2.convert_tf_hub_module(
module_path, tfjs_path, metadata={'key': metadata_json})
# Check model.json and weights manifest.
with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
model_json = json.load(f)
self.assertEqual(metadata_json, model_json['userDefinedMetadata']['key'])
def test_convert_hub_module_v1(self):
self._create_hub_module()
module_path = os.path.join(self._tmp_dir, HUB_MODULE_DIR)
tfjs_path = os.path.join(self._tmp_dir, SAVED_MODEL_DIR)
tf_saved_model_conversion_v2.convert_tf_hub_module(module_path, tfjs_path)
# Check model.json and weights manifest.
with open(os.path.join(tfjs_path, 'model.json'), 'rt') as f:
model_json = json.load(f)
self.assertTrue(model_json['modelTopology'])
weights_manifest = model_json['weightsManifest']
self.assertCountEqual(weights_manifest[0]['paths'],
['group1-shard1of1.bin'])
self.assertIn('weights', weights_manifest[0])
self.assertTrue(
glob.glob(
os.path.join(self._tmp_dir, SAVED_MODEL_DIR, 'group*-*')))
def convert(arguments):
args = get_arg_parser().parse_args(arguments)
if args.show_version:
print('\ntensorflowjs %s\n' % version.version)
print('Dependency versions:')
print(' keras %s' % keras.__version__)
print(' tensorflow %s' % tf.__version__)
return
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.')
weight_shard_size_bytes = 1024 * 1024 * 4
if args.weight_shard_size_bytes:
if args.output_format != common.TFJS_LAYERS_MODEL:
raise ValueError(
'The --weight_shard_size_bytes flag is only supported under '
'output_format=tfjs_layers_model.')
weight_shard_size_bytes = args.weight_shard_size_bytes
if args.input_path is None:
raise ValueError('Error: The input_path argument must be set. '
'Run with --help flag for usage information.')
input_format, output_format = _standardize_input_output_formats(
args.input_format, args.output_format)
quantization_dtype = (
quantization.QUANTIZATION_BYTES_TO_DTYPES[args.quantization_bytes]
if args.quantization_bytes else None)
if (args.signature_name and input_format
not in (common.TF_SAVED_MODEL, common.TF_HUB_MODEL)):
raise ValueError(
'The --signature_name flag is applicable only to "tf_saved_model" and '
'"tf_hub" input format, but the current input format is '
'"%s".' % input_format)
# TODO(cais, piyu): More conversion logics can be added as additional
# branches below.
if (input_format == common.KERAS_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_keras_h5_to_tfjs_layers_model_conversion(
args.input_path,
output_dir=args.output_path,
quantization_dtype=quantization_dtype,
split_weights_by_layer=args.split_weights_by_layer)
elif (input_format == common.KERAS_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
dispatch_keras_h5_to_tfjs_graph_model_conversion(
args.input_path,
output_dir=args.output_path,
quantization_dtype=quantization_dtype,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops)
elif (input_format == common.KERAS_SAVED_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_keras_saved_model_to_tensorflowjs_conversion(
args.input_path,
args.output_path,
quantization_dtype=quantization_dtype,
split_weights_by_layer=args.split_weights_by_layer)
elif (input_format == common.TF_SAVED_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_saved_model(
args.input_path,
args.output_path,
signature_def=args.signature_name,
saved_model_tags=args.saved_model_tags,
quantization_dtype=quantization_dtype,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops)
elif (input_format == common.TF_HUB_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_hub_module(
args.input_path,
args.output_path,
args.signature_name,
args.saved_model_tags,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.KERAS_MODEL):
dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
args.output_path)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.KERAS_SAVED_MODEL):
dispatch_tensorflowjs_to_keras_saved_model_conversion(
args.input_path, args.output_path)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_tensorflowjs_to_tensorflowjs_conversion(
args.input_path,
args.output_path,
quantization_dtype=_parse_quantization_bytes(
args.quantization_bytes),
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
args.input_path,
args.output_path,
quantization_dtype=_parse_quantization_bytes(
args.quantization_bytes),
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops)
else:
raise ValueError(
'Unsupported input_format - output_format pair: %s - %s' %
(input_format, output_format))
def convert(arguments):
args = get_arg_parser().parse_args(arguments)
if args.show_version:
print('\ntensorflowjs %s\n' % version.version)
print('Dependency versions:')
print(' keras %s' % tf.keras.__version__)
print(' tensorflow %s' % tf.__version__)
return
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.input_path is None:
raise ValueError('Error: The input_path argument must be set. '
'Run with --help flag for usage information.')
input_format, output_format = _standardize_input_output_formats(
args.input_format, args.output_format)
weight_shard_size_bytes = 1024 * 1024 * 4
if args.weight_shard_size_bytes is not None:
if (output_format
not in (common.TFJS_LAYERS_MODEL, common.TFJS_GRAPH_MODEL)):
raise ValueError(
'The --weight_shard_size_bytes flag is only supported when '
'output_format is tfjs_layers_model or tfjs_graph_model.')
if not (isinstance(args.weight_shard_size_bytes, int)
and args.weight_shard_size_bytes > 0):
raise ValueError(
'Expected weight_shard_size_bytes to be a positive integer, '
'but got %s' % args.weight_shard_size_bytes)
weight_shard_size_bytes = args.weight_shard_size_bytes
quantization_dtype_map = _parse_quantization_dtype_map(
args.quantize_float16, args.quantize_uint8, args.quantize_uint16,
args.quantization_bytes)
if (not args.output_node_names and input_format == common.TF_FROZEN_MODEL):
raise ValueError(
'The --output_node_names flag is required for "tf_frozen_model"')
if (args.signature_name and input_format
not in (common.TF_SAVED_MODEL, common.TF_HUB_MODEL)):
raise ValueError(
'The --signature_name flag is applicable only to "tf_saved_model" and '
'"tf_hub" input format, but the current input format is '
'"%s".' % input_format)
if (args.control_flow_v2 and output_format != common.TFJS_GRAPH_MODEL):
raise ValueError(
'The --control_flow_v2 flag is applicable only to "tfjs_graph_model" '
'as output format, but the current output format '
'is "%s"' % input_format, output_format)
# TODO(cais, piyu): More conversion logics can be added as additional
# branches below.
if (input_format == common.KERAS_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_keras_h5_to_tfjs_layers_model_conversion(
args.input_path,
output_dir=args.output_path,
quantization_dtype_map=quantization_dtype_map,
split_weights_by_layer=args.split_weights_by_layer,
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == common.KERAS_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
dispatch_keras_h5_to_tfjs_graph_model_conversion(
args.input_path,
output_dir=args.output_path,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2)
elif (input_format == common.KERAS_SAVED_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_keras_saved_model_to_tensorflowjs_conversion(
args.input_path,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
split_weights_by_layer=args.split_weights_by_layer,
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == common.TF_SAVED_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_saved_model(
args.input_path,
args.output_path,
signature_def=args.signature_name,
saved_model_tags=args.saved_model_tags,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2)
elif (input_format == common.TF_HUB_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_hub_module(
args.input_path,
args.output_path,
signature=args.signature_name,
saved_model_tags=args.saved_model_tags,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.KERAS_MODEL):
dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
args.output_path)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.KERAS_SAVED_MODEL):
dispatch_tensorflowjs_to_keras_saved_model_conversion(
args.input_path, args.output_path)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_tensorflowjs_to_tensorflowjs_conversion(
args.input_path,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
args.input_path,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == common.TF_FROZEN_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_frozen_model(
args.input_path,
args.output_node_names,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes)
else:
raise ValueError(
'Unsupported input_format - output_format pair: %s - %s' %
(input_format, output_format))
def _dispatch_converter(input_format, output_format, args,
quantization_dtype_map, weight_shard_size_bytes,
metadata_map):
# TODO(cais, piyu): More conversion logics can be added as additional
# branches below.
if (input_format == common.KERAS_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_keras_h5_to_tfjs_layers_model_conversion(
args.input_path,
output_dir=args.output_path,
quantization_dtype_map=quantization_dtype_map,
split_weights_by_layer=args.split_weights_by_layer,
weight_shard_size_bytes=weight_shard_size_bytes,
metadata=metadata_map)
elif (input_format == common.KERAS_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
dispatch_keras_h5_to_tfjs_graph_model_conversion(
args.input_path,
output_dir=args.output_path,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2,
experiments=args.experiments,
metadata=metadata_map)
elif (input_format == common.KERAS_SAVED_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_keras_saved_model_to_tensorflowjs_conversion(
args.input_path,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
split_weights_by_layer=args.split_weights_by_layer,
weight_shard_size_bytes=weight_shard_size_bytes,
metadata=metadata_map)
elif (input_format == common.TF_SAVED_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_saved_model(
args.input_path,
args.output_path,
signature_def=args.signature_name,
saved_model_tags=args.saved_model_tags,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2,
experiments=args.experiments,
metadata=metadata_map)
elif (input_format == common.TF_HUB_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_hub_module(
args.input_path,
args.output_path,
signature=args.signature_name,
saved_model_tags=args.saved_model_tags,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2,
experiments=args.experiments,
metadata=metadata_map)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.KERAS_MODEL):
dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
args.output_path)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.KERAS_SAVED_MODEL):
dispatch_tensorflowjs_to_keras_saved_model_conversion(
args.input_path, args.output_path)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.TFJS_LAYERS_MODEL):
dispatch_tensorflowjs_to_tensorflowjs_conversion(
args.input_path,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == common.TFJS_LAYERS_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
args.input_path,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
control_flow_v2=args.control_flow_v2,
experiments=args.experiments,
metadata=metadata_map)
elif (input_format == common.TF_FROZEN_MODEL
and output_format == common.TFJS_GRAPH_MODEL):
tf_saved_model_conversion_v2.convert_tf_frozen_model(
args.input_path,
args.output_node_names,
args.output_path,
quantization_dtype_map=quantization_dtype_map,
skip_op_check=args.skip_op_check,
strip_debug_ops=args.strip_debug_ops,
weight_shard_size_bytes=weight_shard_size_bytes,
experiments=args.experiments,
metadata=metadata_map)
else:
raise ValueError(
'Unsupported input_format - output_format pair: %s - %s' %
(input_format, output_format))
def main():
FLAGS = setup_arguments()
if FLAGS.show_version:
print('\ntensorflowjs %s\n' % version.version)
print('Dependency versions:')
print(' keras %s' % keras.__version__)
print(' tensorflow %s' % tf.__version__)
return
weight_shard_size_bytes = 1024 * 1024 * 4
if FLAGS.weight_shard_size_bytes:
if FLAGS.output_format != 'tfjs_layers_model':
raise ValueError(
'The --weight_shard_size_byte flag is only supported under '
'output_format=tfjs_layers_model.')
weight_shard_size_bytes = FLAGS.weight_shard_size_bytes
if FLAGS.input_path is None:
raise ValueError('Error: The input_path argument must be set. '
'Run with --help flag for usage information.')
input_format, output_format = _standardize_input_output_formats(
FLAGS.input_format, FLAGS.output_format)
quantization_dtype = (
quantization.QUANTIZATION_BYTES_TO_DTYPES[FLAGS.quantization_bytes]
if FLAGS.quantization_bytes else None)
if (FLAGS.signature_name
and input_format not in ('tf_saved_model', 'tf_hub')):
raise ValueError(
'The --signature_name flag is applicable only to "tf_saved_model" and '
'"tf_hub" input format, but the current input format is '
'"%s".' % input_format)
# TODO(cais, piyu): More conversion logics can be added as additional
# branches below.
if input_format == 'keras' and output_format == 'tfjs_layers_model':
dispatch_keras_h5_to_tfjs_layers_model_conversion(
FLAGS.input_path,
output_dir=FLAGS.output_path,
quantization_dtype=quantization_dtype,
split_weights_by_layer=FLAGS.split_weights_by_layer)
elif input_format == 'keras' and output_format == 'tfjs_graph_model':
dispatch_keras_h5_to_tfjs_graph_model_conversion(
FLAGS.input_path,
output_dir=FLAGS.output_path,
quantization_dtype=quantization_dtype,
skip_op_check=FLAGS.skip_op_check,
strip_debug_ops=FLAGS.strip_debug_ops)
elif (input_format == 'keras_saved_model'
and output_format == 'tfjs_layers_model'):
dispatch_keras_saved_model_to_tensorflowjs_conversion(
FLAGS.input_path,
FLAGS.output_path,
quantization_dtype=quantization_dtype,
split_weights_by_layer=FLAGS.split_weights_by_layer)
elif (input_format == 'tf_saved_model'
and output_format == 'tfjs_graph_model'):
tf_saved_model_conversion_v2.convert_tf_saved_model(
FLAGS.input_path,
FLAGS.output_path,
signature_def=FLAGS.signature_name,
saved_model_tags=FLAGS.saved_model_tags,
quantization_dtype=quantization_dtype,
skip_op_check=FLAGS.skip_op_check,
strip_debug_ops=FLAGS.strip_debug_ops)
elif (input_format == 'tf_hub' and output_format == 'tfjs_graph_model'):
tf_saved_model_conversion_v2.convert_tf_hub_module(
FLAGS.input_path,
FLAGS.output_path,
FLAGS.signature_name,
FLAGS.saved_model_tags,
skip_op_check=FLAGS.skip_op_check,
strip_debug_ops=FLAGS.strip_debug_ops)
elif (input_format == 'tfjs_layers_model' and output_format == 'keras'):
dispatch_tensorflowjs_to_keras_h5_conversion(FLAGS.input_path,
FLAGS.output_path)
elif (input_format == 'tfjs_layers_model'
and output_format == 'keras_saved_model'):
dispatch_tensorflowjs_to_keras_saved_model_conversion(
FLAGS.input_path, FLAGS.output_path)
elif (input_format == 'tfjs_layers_model'
and output_format == 'tfjs_layers_model'):
dispatch_tensorflowjs_to_tensorflowjs_conversion(
FLAGS.input_path,
FLAGS.output_path,
quantization_dtype=_parse_quantization_bytes(
FLAGS.quantization_bytes),
weight_shard_size_bytes=weight_shard_size_bytes)
elif (input_format == 'tfjs_layers_model'
and output_format == 'tfjs_graph_model'):
dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
FLAGS.input_path,
FLAGS.output_path,
quantization_dtype=_parse_quantization_bytes(
FLAGS.quantization_bytes),
skip_op_check=FLAGS.skip_op_check,
strip_debug_ops=FLAGS.strip_debug_ops)
else:
raise ValueError(
'Unsupported input_format - output_format pair: %s - %s' %
(input_format, output_format))
