def deserialize_keras_model(config_json,
weight_data=None,
use_unique_name_scope=False):
"""Deserialize a Keras Model from buffers or file-like objects.
Args:
config: content of the JSON containing model topology and weights manifest,
in Keras (i.e., tfjs-layers) format. It can be one of the following
types:
- A JSON object, i.e., a `dict`.
- A `str` or `buffer`, in which case it will be parsed as a JSON object.
- A `file` object or `file`-like object containing the JSON, in which
case it will be read with the `read()` method and the content parsed
as a JSON object.
weight_data: a `list` of `buffer`s or a `list` of `file`-like objects
(e.g., `io.BytesIO`) containing the binary weight values.
If `None`, the weights of the model will not be loaded (i.e., only the
topology of the model will be loaded).
use_unique_name_scope: Use a unique ID as the name scope for the loaded
model. This may facilitate loading of multiple Keras models in the
same TensorFlow Graph or Session context. Default: `False`.
"""
if isinstance(config_json, (six.string_types, bytes)):
config_json = json.loads(tf.compat.as_text(config_json))
elif not isinstance(config_json, dict):
config_json = json.load(config_json)
_check_config_json(config_json)
model_topology_json = config_json['modelTopology']
weight_entries = None
if weight_data:
weights_manifest = _get_weights_manifest_from_config_json(config_json)
if not isinstance(weight_data, list):
raise ValueError('weight_data must be a list, but is %s' %
type(weight_data))
if hasattr(weight_data[0], 'read'):
# weight_data is a list of file-like objects.
weight_data = [d.read() for d in weight_data]
weight_entries = read_weights.decode_weights(weights_manifest,
weight_data,
flatten=True)
return _deserialize_keras_model(
model_topology_json,
weight_entries=weight_entries,
use_unique_name_scope=use_unique_name_scope)
def deserialize_keras_model(config_json,
weight_data=None,
use_unique_name_scope=False):
"""Deserialize a Keras Model from buffers or file-like objects.
Args:
config: content of the JSON containing model topology and weights manifest,
in Keras (i.e., tfjs-layers) format. It can be one of the following
types:
- A JSON object, i.e., a `dict`.
- A `str` or `buffer`, in which case it will be parsed as a JSON object.
- A `file` object or `file`-like object containing the JSON, in which
case it will be read with the `read()` method and the content parsed
as a JSON object.
weight_data: a `list` of `buffer`s or a `list` of `file`-like objects
(e.g., `io.BytesIO`) containing the binary weight values.
If `None`, the weights of the model will not be loaded (i.e., only the
topology of the model will be loaded).
use_unique_name_scope: Use a unique ID as the name scope for the loaded
model. This may facilitate loading of multiple Keras models in the
same TensorFlow Graph or Session context. Default: `False`.
"""
if isinstance(config_json, (six.string_types, bytes)):
config_json = json.loads(tf.compat.as_text(config_json))
elif not isinstance(config_json, dict):
config_json = json.load(config_json)
_check_config_json(config_json)
model_topology_json = config_json['modelTopology']
weight_entries = None
if weight_data:
weights_manifest = _get_weights_manifest_from_config_json(config_json)
if not isinstance(weight_data, list):
raise ValueError(
'weight_data must be a list, but is %s' % type(weight_data))
if hasattr(weight_data[0], 'read'):
# weight_data is a list of file-like objects.
weight_data = [d.read() for d in weight_data]
weight_entries = read_weights.decode_weights(weights_manifest,
weight_data,
flatten=True)
return _deserialize_keras_model(model_topology_json,
weight_entries=weight_entries,
use_unique_name_scope=use_unique_name_scope)
def load_keras_model(config_json_path,
weights_path_prefix=None,
weights_data_buffers=None,
load_weights=True,
use_unique_name_scope=False):
"""Load a Keras Model from TensorFlow.js-format artifacts from file system
Args:
config_json_path: Path to the TensorFlow.js-format JSON file that includes
the model topology and weights manifest.
weights_path_prefix: Optional path prefix for the weights files.
If not specified (`None`), will assume the prefix is the same directory
as the dirname of `config_json_path`.
weights_data_buffers: A buffer of a `list` of buffers containing the weight
values concatenated and sharded in the order as specified by the
weights manifest at `config_json_path`. This argument is mutually
exclusive with `weights_path_prefix`.
load_weights: Whether the weights are to be loaded according
to the weights manifest at `config_json_path`. Default: `True`.
use_unique_name_scope: Use a unique ID as the name scope for the loaded
model. This may facilitate loading of multiple Keras models in the
same TensorFlow Graph or Session context. Default: `False`.
Returns:
The loaded instance of `tf.keras.Model`.
Raises:
TypeError, if the format of the JSON content of `config_json_path` has an
invalid format.
KeyError, if required keys do not exist in the JSON content of
`config_json_path`.
ValueError, if both `weights_data_buffers` and `weights_path_prefix` are
provided.
"""
if weights_data_buffers and weights_path_prefix:
raise ValueError(
'The arguments weights_data_buffers and weights_path_prefix are '
'mutually exclusive and should not be both specified.')
with open(config_json_path, 'rt') as f:
config_json = json.load(f)
_check_config_json(config_json)
weight_entries = None
if load_weights:
weights_manifest = _get_weights_manifest_from_config_json(config_json)
if not weights_data_buffers and not weights_path_prefix:
weights_path_prefix = os.path.dirname(
os.path.realpath(config_json_path))
if not os.path.isdir(weights_path_prefix):
raise ValueError(
'Weights path prefix is not an existing directory: %s' %
weights_path_prefix)
if weights_path_prefix:
weight_entries = read_weights.read_weights(weights_manifest,
weights_path_prefix,
flatten=True)
else:
weight_entries = read_weights.decode_weights(weights_manifest,
weights_data_buffers,
flatten=True)
return _deserialize_keras_model(config_json['modelTopology'],
weight_entries=weight_entries,
use_unique_name_scope=use_unique_name_scope)
def load_keras_model(config_json_path,
weights_path_prefix=None,
weights_data_buffers=None,
load_weights=True,
use_unique_name_scope=False):
"""Load a Keras Model from TensorFlow.js-format artifacts.
Args:
config_json_path: Path to the TensorFlow.js-format JSON file that includes
the model topology and weights manifest.
weights_path_prefix: Optional path prefix for the weights files.
If not specified (`None`), will assume the prefix is the same directory
as the dirname of `config_json_path`.
weights_data_buffers: A buffer of a `list` of buffers containing the weight
values concatenated and sharded in the order as specified by the
weights manifest at `config_json_path`. This argument is mutually
exclusive with `weights_path_prefix`.
load_weights: Whether the weights are to be loaded according
to the weights manifest at `config_json_path`. Default: `True`.
use_unique_name_scope: Use a unique ID as the name scope for the loaded
model. This may facilitate loading of multiple Keras models in the
same TensorFlow Graph or Session context. Default: `False`.
Returns:
The loaded instance of `keras.Model`.
Raises:
TypeError, if the format of the JSON content of `config_json_path` has an
invalid format.
KeyError, if required keys do not exist in the JSON content of
`config_json_path`.
ValueError, if both `weights_data_buffers` and `weights_path_prefix` are
provided.
"""
with open(config_json_path, 'rt') as f:
model_and_weights_manifest = json.load(f)
if not isinstance(model_and_weights_manifest, dict):
raise TypeError(
'The JSON content of %s is required to be a `dict`, but found %s' %
(config_json_path, type(model_and_weights_manifest)))
if 'modelTopology' not in model_and_weights_manifest:
raise KeyError(
'Field "modelTopology" is missing from the JSON content in %s' %
config_json_path)
model_json = model_and_weights_manifest['modelTopology']
if 'model_config' in model_json:
model_json = model_json['model_config']
unique_name_scope = uuid.uuid4().hex if use_unique_name_scope else None
with tf.name_scope(unique_name_scope):
model = keras.models.model_from_json(json.dumps(model_json))
if load_weights:
if 'weightsManifest' not in model_and_weights_manifest:
raise KeyError(
'Field "weightsManifest" is missing from the JSON content in %s'
% config_json_path)
weights_manifest = model_and_weights_manifest['weightsManifest']
if weights_data_buffers:
if weights_path_prefix:
raise ValueError(
'The arguments weights_data_buffers and weights_path_prefix are '
'mutually exclusive and should not be both specified.')
weight_entries = read_weights.decode_weights(weights_manifest,
weights_data_buffers,
flatten=True)
else:
weight_names = [
keras_h5_conversion.normalize_weight_name(
w.name[len(unique_name_scope) +
1:]) if use_unique_name_scope else
keras_h5_conversion.normalize_weight_name(w.name[:-2])
for w in model.weights
]
if not weights_path_prefix:
weights_path_prefix = os.path.dirname(config_json_path)
if not os.path.isdir(weights_path_prefix):
raise ValueError(
'Weights path prefix is not an existing directory: %s' %
weights_path_prefix)
weight_entries = read_weights.read_weights(weights_manifest,
weights_path_prefix,
flatten=True)
weights_dict = dict()
for weight_entry in weight_entries:
weights_dict[weight_entry['name']] = weight_entry['data']
weights_list = []
for weight_name in weight_names:
weights_list.append(weights_dict[weight_name])
model.set_weights(weights_list)
return model
def load_keras_model(config_json_path,
weights_path_prefix=None,
weights_data_buffers=None,
load_weights=True,
use_unique_name_scope=False):
"""Load a Keras Model from TensorFlow.js-format artifacts from file system
Args:
config_json_path: Path to the TensorFlow.js-format JSON file that includes
the model topology and weights manifest.
weights_path_prefix: Optional path prefix for the weights files.
If not specified (`None`), will assume the prefix is the same directory
as the dirname of `config_json_path`.
weights_data_buffers: A buffer of a `list` of buffers containing the weight
values concatenated and sharded in the order as specified by the
weights manifest at `config_json_path`. This argument is mutually
exclusive with `weights_path_prefix`.
load_weights: Whether the weights are to be loaded according
to the weights manifest at `config_json_path`. Default: `True`.
use_unique_name_scope: Use a unique ID as the name scope for the loaded
model. This may facilitate loading of multiple Keras models in the
same TensorFlow Graph or Session context. Default: `False`.
Returns:
The loaded instance of `keras.Model`.
Raises:
TypeError, if the format of the JSON content of `config_json_path` has an
invalid format.
KeyError, if required keys do not exist in the JSON content of
`config_json_path`.
ValueError, if both `weights_data_buffers` and `weights_path_prefix` are
provided.
"""
if weights_data_buffers and weights_path_prefix:
raise ValueError(
'The arguments weights_data_buffers and weights_path_prefix are '
'mutually exclusive and should not be both specified.')
with open(config_json_path, 'rt') as f:
config_json = json.load(f)
_check_config_json(config_json)
weight_entries = None
if load_weights:
weights_manifest = _get_weights_manifest_from_config_json(config_json)
if not weights_data_buffers and not weights_path_prefix:
weights_path_prefix = os.path.dirname(
os.path.realpath(config_json_path))
if not os.path.isdir(weights_path_prefix):
raise ValueError(
'Weights path prefix is not an existing directory: %s' %
weights_path_prefix)
if weights_path_prefix:
weight_entries = read_weights.read_weights(weights_manifest,
weights_path_prefix,
flatten=True)
else:
weight_entries = read_weights.decode_weights(weights_manifest,
weights_data_buffers,
flatten=True)
return _deserialize_keras_model(config_json['modelTopology'],
weight_entries=weight_entries,
use_unique_name_scope=use_unique_name_scope)