def _revive_layer_or_model_from_config(self, metadata, node_id):
"""Revives a layer/custom model from config; returns None if infeasible."""
# Check that the following requirements are met for reviving from config:
# 1. Object can be deserialized from config.
# 2. If the object needs to be built, then the build input shape can be
# found.
class_name = metadata.get('class_name')
config = metadata.get('config')
shared_object_id = metadata.get('shared_object_id')
must_restore_from_config = metadata.get('must_restore_from_config')
if not generic_utils.validate_config(config):
return None
try:
obj = layers_module.deserialize(
generic_utils.serialize_keras_class_and_config(
class_name, config, shared_object_id=shared_object_id))
except ValueError:
if must_restore_from_config:
raise RuntimeError(
'Unable to restore a layer of class {cls}. Layers of '
'class {cls} require that the class be provided to '
'the model loading code, either by registering the '
'class using @keras.utils.register_keras_serializable '
'on the class def and including that file in your '
'program, or by passing the class in a '
'keras.utils.CustomObjectScope that wraps this load '
'call.'.format(cls=class_name))
else:
return None
# Use the dtype, name, and trainable status. Often times these are not
# specified in custom configs, so retrieve their values from the metadata.
# pylint: disable=protected-access
obj._name = metadata['name']
if metadata.get('trainable') is not None:
obj.trainable = metadata['trainable']
if metadata.get('dtype') is not None:
obj._set_dtype_policy(metadata['dtype'])
if metadata.get('stateful') is not None:
obj.stateful = metadata['stateful']
# Restore model save spec for subclassed models. (layers do not store a
# SaveSpec)
if isinstance(obj, training_lib.Model):
save_spec = metadata.get('save_spec')
if save_spec is not None:
obj._set_save_spec(save_spec)
# pylint: enable=protected-access
build_input_shape = metadata.get('build_input_shape')
built = self._try_build_layer(obj, node_id, build_input_shape)
if not built:
# If the layer cannot be built, revive a custom layer instead.
return None
return obj
def _revive_metric_from_config(self, metadata):
"""Revives a metric object using the config saved in the metadata."""
class_name = tf.compat.as_str(metadata['class_name'])
config = metadata.get('config')
if not generic_utils.validate_config(config):
return None
try:
obj = metrics.deserialize(
generic_utils.serialize_keras_class_and_config(class_name, config))
except ValueError:
return None
build_input_shape = metadata.get('build_input_shape')
if build_input_shape is not None and hasattr(obj, '_build'):
obj._build(build_input_shape) # pylint: disable=protected-access
return obj
def _revive_layer_or_model_from_config(self, metadata, node_id):
"""Revives a layer/custom model from config; returns None if infeasible."""
# Check that the following requirements are met for reviving from config:
# 1. Object can be deserialized from config.
# 2. If the object needs to be built, then the build input shape can be
# found.
class_name = metadata.get('class_name')
config = metadata.get('config')
shared_object_id = metadata.get('shared_object_id')
must_restore_from_config = metadata.get('must_restore_from_config')
if not generic_utils.validate_config(config):
return None
try:
obj = layers_module.deserialize(
generic_utils.serialize_keras_class_and_config(
class_name, config, shared_object_id=shared_object_id))
except (TypeError, KeyError) as e:
# A name conflict has occurred. The `class_name` is in the Keras native
# framework; however, the value in the framework is different from the
# user's class definition which confuses the KerasObjectLoader.
builtin_layer = layers_module.get_builtin_layer(class_name)
if builtin_layer:
raise RuntimeError(
f'Unable to restore object of class \'{class_name}\' likely due to '
f'name conflict with built-in Keras class \'{builtin_layer}\'. To '
'override the built-in Keras definition of the object, decorate '
'your class with `@keras.utils.register_keras_serializable` and '
'include that file in your program, or pass your class in a '
'`keras.utils.CustomObjectScope` that wraps this load call.') from e
else:
raise
except ValueError as e:
if must_restore_from_config:
raise e
else:
return None
# Use the dtype, name, and trainable status. Often times these are not
# specified in custom configs, so retrieve their values from the metadata.
# pylint: disable=protected-access
obj._name = metadata['name']
if metadata.get('trainable') is not None:
obj.trainable = metadata['trainable']
if metadata.get('dtype') is not None:
obj._set_dtype_policy(metadata['dtype'])
if metadata.get('stateful') is not None:
obj.stateful = metadata['stateful']
# Restore model save spec for subclassed models. (layers do not store a
# SaveSpec)
if isinstance(obj, training_lib.Model):
full_save_spec = metadata.get('full_save_spec')
if full_save_spec is not None:
args_spec, kwargs_spec = full_save_spec
inputs_spec = args_spec.pop(0)
obj._set_save_spec(inputs_spec, args_spec, kwargs_spec)
# pylint: enable=protected-access
build_input_shape = metadata.get('build_input_shape')
built = self._try_build_layer(obj, node_id, build_input_shape)
if not built:
# If the layer cannot be built, revive a custom layer instead.
return None
return obj