def _add_children_recreated_from_config(self, obj, proto, node_id):
"""Recursively records objects recreated from config."""
# pylint: disable=protected-access
if node_id in self._traversed_nodes_from_config:
return
self._traversed_nodes_from_config.append(node_id)
obj._maybe_initialize_trackable()
if isinstance(obj, base_layer.Layer) and not obj.built:
metadata = json_utils.decode(proto.user_object.metadata)
self._try_build_layer(obj, node_id,
metadata.get('build_input_shape'))
# Create list of all possible children
children = []
# Look for direct children
for reference in proto.children:
obj_child = obj._lookup_dependency(reference.local_name)
children.append((obj_child, reference.node_id))
# Add metrics that may have been added to the layer._metrics list.
# This is stored in the SavedModel as layer.keras_api.layer_metrics in
# SavedModels created after Tf 2.2.
metric_list_node_id = self._search_for_child_node(
node_id, [constants.KERAS_ATTR, 'layer_metrics'],
raise_error=False)
if metric_list_node_id is not None and hasattr(obj, '_metrics'):
obj_metrics = {m.name: m for m in obj._metrics}
for reference in self._proto.nodes[metric_list_node_id].children:
metric = obj_metrics.get(reference.local_name)
if metric is not None:
children.append((metric, reference.node_id))
for (obj_child, child_id) in children:
child_proto = self._proto.nodes[child_id]
if not isinstance(obj_child, trackable.Trackable):
continue
if (child_proto.user_object.identifier
in revived_types.registered_identifiers()):
setter = revived_types.get_setter(child_proto.user_object)
elif obj_child._object_identifier in KERAS_OBJECT_IDENTIFIERS:
setter = _revive_setter
else:
setter = setattr
# pylint: enable=protected-access
if (child_id in self._nodes_recreated_from_config
and self._nodes_recreated_from_config[child_id][0]
is not obj_child):
# This means that the same trackable object is referenced by two
# different objects that were recreated from the config.
logging.warn(
'Looks like there is an object (perhaps variable or layer)'
' that is shared between different layers/models. This '
'may cause issues when restoring the variable values.'
'Object: {}'.format(obj_child))
self._nodes_recreated_from_config[child_id] = (
obj_child, self._config_node_setter(setter))
self._add_children_recreated_from_config(obj_child, child_proto,
child_id)
def _add_children_recreated_from_config(self, obj, proto, node_id):
"""Recursively records objects recreated from config."""
# pylint: disable=protected-access
if node_id in self._traversed_nodes_from_config:
return
self._traversed_nodes_from_config.append(node_id)
obj._maybe_initialize_trackable()
for reference in proto.children:
obj_child = obj._lookup_dependency(reference.local_name)
child_id = reference.node_id
child_proto = self._proto.nodes[child_id]
if not isinstance(obj_child, trackable.Trackable):
continue
if (child_proto.user_object.identifier
in revived_types.registered_identifiers()):
setter = revived_types.get_setter(child_proto.user_object)
elif obj_child._object_identifier in KERAS_OBJECT_IDENTIFIERS:
setter = _revive_setter
else:
setter = setattr
# pylint: enable=protected-access
if (child_id in self._nodes_recreated_from_config
and self._nodes_recreated_from_config[child_id][0]
is not obj_child):
# This means that the same trackable object is referenced by two
# different objects that were recreated from the config.
logging.warn(
'Looks like there is an object (perhaps variable or layer)'
' that is shared between different layers/models. This '
'may cause issues when restoring the variable values.'
'Object: {}'.format(obj_child))
self._nodes_recreated_from_config[child_id] = (
obj_child, self._config_node_setter(setter))
self._all_nodes_recreated_from_config.add(obj_child)
self._add_children_recreated_from_config(obj_child, child_proto,
child_id)
def _add_children_recreated_from_config(self, obj, proto, node_id):
"""Recursively records objects recreated from config."""
# pylint: disable=protected-access
if node_id in self._traversed_nodes_from_config:
return
self._traversed_nodes_from_config.append(node_id)
obj._maybe_initialize_trackable()
for reference in proto.children:
obj_child = obj._lookup_dependency(reference.local_name)
setter = setattr
if not isinstance(obj_child, trackable.Trackable):
continue
if obj_child._object_identifier in revived_types.registered_identifiers(
):
setter = lambda *unused: None
elif obj_child._object_identifier in KERAS_OBJECT_IDENTIFIERS:
metadata = self._proto.nodes[
reference.node_id].user_object.metadata
setter = _revive_setter
_add_serialized_attributes(obj_child, json.loads(metadata))
# pylint: enable=protected-access
if (reference.node_id in self._nodes_recreated_from_config
and self._nodes_recreated_from_config[reference.node_id][0]
is not obj_child):
# This means that the same trackable object is referenced by two
# different objects that were recreated from the config.
logging.warn(
'Looks like there is an object (perhaps variable or layer)'
' that is shared between different layers/models. This '
'may cause issues when training the model. Object: {}'.
format(obj_child))
self._nodes_recreated_from_config[
reference.node_id] = obj_child, setter
self._add_children_recreated_from_config(
obj_child, self._proto.nodes[reference.node_id],
reference.node_id)
def _add_children_recreated_from_config(self, obj, proto, node_id):
"""Recursively records objects recreated from config."""
# pylint: disable=protected-access
if node_id in self._traversed_nodes_from_config:
return
parent_path = self._node_paths[node_id]
self._traversed_nodes_from_config.add(node_id)
obj._maybe_initialize_trackable()
if isinstance(obj, base_layer.Layer) and not obj.built:
metadata = json_utils.decode(proto.user_object.metadata)
self._try_build_layer(obj, node_id, metadata.get('build_input_shape'))
# Create list of all possible children
children = []
# Look for direct children
for reference in proto.children:
obj_child = obj._lookup_dependency(reference.local_name)
children.append((obj_child, reference.node_id, reference.local_name))
# Add metrics that may have been added to the layer._metrics list.
# This is stored in the SavedModel as layer.keras_api.layer_metrics in
# SavedModels created after Tf 2.2.
metric_list_node_id = self._search_for_child_node(
node_id, [constants.KERAS_ATTR, 'layer_metrics'])
if metric_list_node_id is not None and hasattr(obj, '_metrics'):
obj_metrics = {m.name: m for m in obj._metrics}
for reference in self._proto.nodes[metric_list_node_id].children:
metric = obj_metrics.get(reference.local_name)
if metric is not None:
metric_path = '{}.layer_metrics.{}'.format(constants.KERAS_ATTR,
reference.local_name)
children.append((metric, reference.node_id, metric_path))
for (obj_child, child_id, child_name) in children:
child_proto = self._proto.nodes[child_id]
if not isinstance(obj_child, trackable.Trackable):
continue
if (child_proto.user_object.identifier in
revived_types.registered_identifiers()):
setter = revived_types.get_setter(child_proto.user_object)
elif obj_child._object_identifier in constants.KERAS_OBJECT_IDENTIFIERS:
setter = _revive_setter
else:
setter = setattr
# pylint: enable=protected-access
if child_id in self.loaded_nodes:
if self.loaded_nodes[child_id][0] is not obj_child:
# This means that the same trackable object is referenced by two
# different objects that were recreated from the config.
logging.warn('Looks like there is an object (perhaps variable or '
'layer) that is shared between different layers/models. '
'This may cause issues when restoring the variable '
'values. Object: {}'.format(obj_child))
continue
# Overwrite variable names with the ones saved in the SavedModel.
if (child_proto.WhichOneof('kind') == 'variable' and
child_proto.variable.name):
obj_child._handle_name = child_proto.variable.name + ':0' # pylint: disable=protected-access
if isinstance(obj_child, data_structures.TrackableDataStructure):
setter = lambda *args: None
child_path = '{}.{}'.format(parent_path, child_name)
self._node_paths[child_id] = child_path
self._add_children_recreated_from_config(
obj_child, child_proto, child_id)
self.loaded_nodes[child_id] = obj_child, setter
