def __init__(self, cls_ref, method_name, **kwargs):
self.cls_ref = cls_ref
self.method_name = method_name
self.cls_symbol = (
get_canonical_name_for_symbol(
self.cls_ref, add_prefix_to_v1_names=True) or
get_canonical_name_for_symbol(
self.cls_ref, api_name='keras', add_prefix_to_v1_names=True))
if 'name' not in kwargs:
kwargs['name'] = backend.unique_object_name(
'tf.' + self.cls_symbol + '.' + self.method_name,
zero_based=True,
avoid_observed_names=True)
kwargs['autocast'] = False
# Do not individually trace op layers in the SavedModel.
self._must_restore_from_config = True
super(ClassMethod, self).__init__(**kwargs)
# Preserve all argument data structures when saving/loading a config
# (e.g., don't unnest lists that contain one element)
self._preserve_input_structure_in_config = True
self._expects_training_arg = False
self._expects_mask_arg = False
def _make_unique_name(self, name_uid_map=None, avoid_names=None,
namespace='', zero_based=False):
base_name = base_layer.to_snake_case(self.__class__.__name__)
name = backend.unique_object_name(
base_name,
name_uid_map=name_uid_map,
avoid_names=avoid_names,
namespace=namespace,
zero_based=zero_based)
return (name, base_name)
def __init__(self, attr_name, **kwargs):
self.attr_name = attr_name
if 'name' not in kwargs:
kwargs['name'] = backend.unique_object_name(
'input.' + self.attr_name, zero_based=True, avoid_observed_names=True)
kwargs['autocast'] = False
# Do not individually trace op layers in the SavedModel.
self._must_restore_from_config = True
super(InstanceProperty, self).__init__(**kwargs)
# Preserve all argument data structures when saving/loading a config
# (e.g., don't unnest lists that contain one element)
self._preserve_input_structure_in_config = True
def __init__(self, function, **kwargs):
self.function = function
self.symbol = (
get_canonical_name_for_symbol(
self.function, add_prefix_to_v1_names=True) or
get_canonical_name_for_symbol(
self.function, api_name='keras', add_prefix_to_v1_names=True))
if 'name' not in kwargs:
# Generate a name.
# TFOpLambda layers avoid already-observed names,
# because users cannot easily control the generated names.
# Without this avoidance, users would be more likely to run
# into unavoidable duplicate layer name collisions.
# (For standard layers users could just set `name` when creating the
# layer to work around a collision, but they can't do that for
# auto-generated layers)
if self.symbol:
name = 'tf.' + self.symbol
else:
name = self.function.__name__
kwargs['name'] = backend.unique_object_name(
name, zero_based=True, avoid_observed_names=True)
kwargs['autocast'] = False
# Decorate the function to produce this layer's call method
def _call_wrapper(*args, **kwargs):
return self._call_wrapper(*args, **kwargs)
self.call = tf.__internal__.decorator.make_decorator(
function, _call_wrapper)
# Do not individually trace op layers in the SavedModel.
self._must_restore_from_config = True
super(TFOpLambda, self).__init__(**kwargs)
# Preserve all argument data structures when saving/loading a config
# (e.g., don't unnest lists that contain one element)
self._preserve_input_structure_in_config = True
# Warning on every invocation will be quite irksome in Eager mode.
self._already_warned = False
self._expects_training_arg = False
self._expects_mask_arg = False