def _init_from_metadata(cls, metadata):
"""Create revived layer from metadata stored in the SavedModel proto."""
init_args = dict(
name=metadata['name'],
trainable=metadata['trainable'])
if metadata.get('dtype') is not None:
init_args['dtype'] = metadata['dtype']
if metadata.get('batch_input_shape') is not None:
init_args['batch_input_shape'] = metadata['batch_input_shape']
revived_obj = cls(**init_args)
with utils.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
revived_obj._expects_training_arg = metadata['expects_training_arg']
config = metadata.get('config')
if generic_utils.validate_config(config):
revived_obj._config = config
if metadata.get('input_spec') is not None:
revived_obj.input_spec = recursively_deserialize_keras_object(
metadata['input_spec'],
module_objects={'InputSpec': input_spec.InputSpec})
if metadata.get('activity_regularizer') is not None:
revived_obj.activity_regularizer = regularizers.deserialize(
metadata['activity_regularizer'])
if metadata.get('_is_feature_layer') is not None:
revived_obj._is_feature_layer = metadata['_is_feature_layer']
if metadata.get('stateful') is not None:
revived_obj.stateful = metadata['stateful']
# pylint:enable=protected-access
return revived_obj, _revive_setter
def _maybe_add_serialized_attributes(layer, metadata):
# Store attributes revived from SerializedAttributes in a un-tracked
# dictionary. The attributes are the ones listed in CommonEndpoints or
# "keras_api" for keras-specific attributes.
if not hasattr(layer, '_serialized_attributes'):
with utils.no_automatic_dependency_tracking_scope(layer):
layer._serialized_attributes = {'metadata': metadata} # pylint: disable=protected-access
def _set_network_attributes_from_metadata(revived_obj):
"""Sets attributes recorded in the metadata."""
with utils.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
metadata = revived_obj._serialized_attributes['metadata']
if metadata.get('dtype') is not None:
revived_obj._set_dtype_policy(metadata['dtype'])
revived_obj._trainable = metadata['trainable']
def _restore_child_layer_functions(original_fns):
"""Restores attributes replaced with `_replace_child_layer_functions`."""
for child_layer, fns in original_fns.items():
with utils.no_automatic_dependency_tracking_scope(child_layer):
for fn_name, fn in fns.items():
try:
setattr(child_layer, fn_name, fn) # pylint: disable=protected-access
except AttributeError:
pass # In the case of _activity_regularizer, setting the attribute
def replace_metric_functions(child_layer, serialized_fns):
"""Replaces metric functions with wrapped functions."""
original_fns[child_layer] = {
"__call__": child_layer.__call__,
"result": child_layer.result,
"update_state": child_layer.update_state,
}
with utils.no_automatic_dependency_tracking_scope(child_layer):
child_layer.__call__ = serialized_fns["__call__"]
child_layer.result = serialized_fns["result"]
child_layer.update_state = serialized_fns["update_state"]
def replace_metric_functions(child_layer, serialized_fns):
"""Replaces metric functions with wrapped functions."""
original_fns[child_layer] = {
'__call__': child_layer.__call__,
'result': child_layer.result,
'update_state': child_layer.update_state
}
with utils.no_automatic_dependency_tracking_scope(child_layer):
child_layer.__call__ = serialized_fns['__call__']
child_layer.result = serialized_fns['result']
child_layer.update_state = serialized_fns['update_state']
def _restore_child_layer_functions(original_fns):
"""Restores attributes replaced with `_replace_child_layer_functions`."""
for child_layer, fns in original_fns.items():
with utils.no_automatic_dependency_tracking_scope(child_layer):
for fn_name, fn in fns.items():
try:
setattr(child_layer, fn_name, fn)
except AttributeError:
# In the case of _activity_regularizer, setting the
# attribute may be disallowed.
pass
def _reset_layer_losses(parent_layer):
"""Resets losses of layer and its sublayers, and returns original losses."""
losses_dict = {}
for layer in utils.list_all_layers_and_sublayers(parent_layer):
losses_dict[layer] = {
"losses": layer._losses[:],
"eager_losses": layer._eager_losses[:],
}
with utils.no_automatic_dependency_tracking_scope(layer):
layer._losses = []
layer._eager_losses = []
return losses_dict
def _init_from_metadata(cls, metadata):
"""Revives the saved InputLayer from the Metadata."""
init_args = dict(
name=metadata['name'],
dtype=metadata['dtype'],
sparse=metadata['sparse'],
ragged=metadata['ragged'],
batch_input_shape=metadata['batch_input_shape'])
revived_obj = cls(**init_args)
with utils.no_automatic_dependency_tracking_scope(revived_obj):
revived_obj._config = metadata['config'] # pylint:disable=protected-access
return revived_obj, setattr
def replace_layer_functions(child_layer, serialized_fns):
"""Replaces layer call and activity regularizer with wrapped functions."""
original_fns[child_layer] = {
'call': child_layer.call,
'_activity_regularizer': child_layer._activity_regularizer
}
with utils.no_automatic_dependency_tracking_scope(child_layer):
try:
child_layer._activity_regularizer = serialized_fns.get(
'activity_regularizer_fn')
except AttributeError:
# Some layers have an unsettable activity regularizer.
pass
child_layer.call = utils.use_wrapped_call(
child_layer,
serialized_fns['call_and_return_conditional_losses'],
default_training_value=False)
def _init_from_metadata(cls, metadata):
"""Create revived network from metadata stored in the SavedModel proto."""
revived_obj = cls(name=metadata['name'])
# Store attributes revived from SerializedAttributes in a un-tracked
# dictionary. The attributes are the ones listed in CommonEndpoints or
# "keras_api" for keras-specific attributes.
with utils.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
revived_obj._expects_training_arg = metadata['expects_training_arg']
config = metadata.get('config')
if generic_utils.validate_config(config):
revived_obj._config = config
if metadata.get('activity_regularizer') is not None:
revived_obj.activity_regularizer = regularizers.deserialize(
metadata['activity_regularizer'])
# pylint:enable=protected-access
return revived_obj, _revive_setter # pylint:disable=protected-access
def _restore_layer_losses(losses_dict):
for layer in losses_dict:
with utils.no_automatic_dependency_tracking_scope(layer):
layer._losses = losses_dict[layer]["losses"]
layer._eager_losses = losses_dict[layer]["eager_losses"]
