def populate_deserializable_objects(): """Populates dict ALL_OBJECTS with every built-in layer. """ global LOCAL if not hasattr(LOCAL, 'ALL_OBJECTS'): LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = None if LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled(): # Objects dict is already generated for the proper TF version: # do nothing. return LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = tf2.enabled() base_cls = base_layer.Layer generic_utils.populate_dict_with_module_objects( LOCAL.ALL_OBJECTS, ALL_MODULES, obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls)) # Overwrite certain V1 objects with V2 versions if tf2.enabled(): generic_utils.populate_dict_with_module_objects( LOCAL.ALL_OBJECTS, ALL_V2_MODULES, obj_filter=lambda x: inspect.isclass(x) and issubclass( x, base_cls)) # These deserialization aliases are added for backward compatibility, # as in TF 1.13, "BatchNormalizationV1" and "BatchNormalizationV2" # were used as class name for v1 and v2 version of BatchNormalization, # respectively. Here we explicitly convert them to their canonical names. LOCAL.ALL_OBJECTS[ 'BatchNormalizationV1'] = batch_normalization_v1.BatchNormalization LOCAL.ALL_OBJECTS[ 'BatchNormalizationV2'] = batch_normalization.BatchNormalization # Prevent circular dependencies. from tensorflow.python.keras import models # pylint: disable=g-import-not-at-top LOCAL.ALL_OBJECTS['Input'] = input_layer.Input LOCAL.ALL_OBJECTS['InputSpec'] = input_spec.InputSpec LOCAL.ALL_OBJECTS['Functional'] = models.Functional LOCAL.ALL_OBJECTS['Model'] = models.Model LOCAL.ALL_OBJECTS['Sequential'] = models.Sequential # Merge layers, function versions. LOCAL.ALL_OBJECTS['add'] = merge.add LOCAL.ALL_OBJECTS['subtract'] = merge.subtract LOCAL.ALL_OBJECTS['multiply'] = merge.multiply LOCAL.ALL_OBJECTS['average'] = merge.average LOCAL.ALL_OBJECTS['maximum'] = merge.maximum LOCAL.ALL_OBJECTS['minimum'] = merge.minimum LOCAL.ALL_OBJECTS['concatenate'] = merge.concatenate LOCAL.ALL_OBJECTS['dot'] = merge.dot
def populate_deserializable_objects(): """Populates dict ALL_OBJECTS with every built-in layer. """ global LOCAL if not hasattr(LOCAL, 'ALL_OBJECTS'): LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = None if LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled(): # Objects dict is already generated for the proper TF version: # do nothing. return LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = tf2.enabled() base_cls = base_layer.Layer generic_utils.populate_dict_with_module_objects( LOCAL.ALL_OBJECTS, ALL_MODULES, obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls)) # Overwrite certain V1 objects with V2 versions if tf2.enabled(): generic_utils.populate_dict_with_module_objects( LOCAL.ALL_OBJECTS, ALL_V2_MODULES, obj_filter=lambda x: inspect.isclass(x) and issubclass( x, base_cls)) # These deserialization aliases are added for backward compatibility, # as in TF 1.13, "BatchNormalizationV1" and "BatchNormalizationV2" # were used as class name for v1 and v2 version of BatchNormalization, # respectively. Here we explicitly convert them to their canonical names. LOCAL.ALL_OBJECTS[ 'BatchNormalizationV1'] = normalization.BatchNormalization LOCAL.ALL_OBJECTS[ 'BatchNormalizationV2'] = normalization_v2.BatchNormalization # Prevent circular dependencies. from tensorflow.python.keras import models # pylint: disable=g-import-not-at-top from tensorflow.python.keras.premade.linear import LinearModel # pylint: disable=g-import-not-at-top from tensorflow.python.keras.premade.wide_deep import WideDeepModel # pylint: disable=g-import-not-at-top from tensorflow.python.feature_column import dense_features # pylint: disable=g-import-not-at-top from tensorflow.python.feature_column import sequence_feature_column as sfc # pylint: disable=g-import-not-at-top LOCAL.ALL_OBJECTS['Input'] = input_layer.Input LOCAL.ALL_OBJECTS['Network'] = models.Network LOCAL.ALL_OBJECTS['Model'] = models.Model LOCAL.ALL_OBJECTS['Sequential'] = models.Sequential LOCAL.ALL_OBJECTS['LinearModel'] = LinearModel LOCAL.ALL_OBJECTS['WideDeepModel'] = WideDeepModel LOCAL.ALL_OBJECTS['DenseFeatures'] = dense_features.DenseFeatures LOCAL.ALL_OBJECTS['SequenceFeatures'] = sfc.SequenceFeatures
def populate_deserializable_objects(): """Populates dict ALL_OBJECTS with every built-in layer. """ global LOCAL if not hasattr(LOCAL, 'ALL_OBJECTS'): LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = None if LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled(): # Objects dict is already generated for the proper TF version: # do nothing. return LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = tf2.enabled() base_cls = base_layer.Layer generic_utils.populate_dict_with_module_objects( LOCAL.ALL_OBJECTS, ALL_MODULES, obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls)) # Overwrite certain V1 objects with V2 versions if tf2.enabled(): generic_utils.populate_dict_with_module_objects( LOCAL.ALL_OBJECTS, ALL_V2_MODULES, obj_filter=lambda x: inspect.isclass(x) and issubclass( x, base_cls)) # Prevent circular dependencies. from tensorflow.python.keras import models # pylint: disable=g-import-not-at-top LOCAL.ALL_OBJECTS['Input'] = input_layer.Input LOCAL.ALL_OBJECTS['InputSpec'] = input_spec.InputSpec LOCAL.ALL_OBJECTS['Functional'] = models.Functional LOCAL.ALL_OBJECTS['Model'] = models.Model LOCAL.ALL_OBJECTS['Sequential'] = models.Sequential # Merge layers, function versions. LOCAL.ALL_OBJECTS['add'] = merge.add LOCAL.ALL_OBJECTS['subtract'] = merge.subtract LOCAL.ALL_OBJECTS['multiply'] = merge.multiply LOCAL.ALL_OBJECTS['average'] = merge.average LOCAL.ALL_OBJECTS['maximum'] = merge.maximum LOCAL.ALL_OBJECTS['minimum'] = merge.minimum LOCAL.ALL_OBJECTS['concatenate'] = merge.concatenate LOCAL.ALL_OBJECTS['dot'] = merge.dot
def populate_deserializable_objects(): """Populates dict ALL_OBJECTS with every built-in initializer. """ global LOCAL if not hasattr(LOCAL, 'ALL_OBJECTS'): LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = None if LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled(): # Objects dict is already generated for the proper TF version: # do nothing. return LOCAL.ALL_OBJECTS = {} LOCAL.GENERATED_WITH_V2 = tf2.enabled() # Compatibility aliases (need to exist in both V1 and V2). LOCAL.ALL_OBJECTS['ConstantV2'] = initializers_v2.Constant LOCAL.ALL_OBJECTS['GlorotNormalV2'] = initializers_v2.GlorotNormal LOCAL.ALL_OBJECTS['GlorotUniformV2'] = initializers_v2.GlorotUniform LOCAL.ALL_OBJECTS['HeNormalV2'] = initializers_v2.HeNormal LOCAL.ALL_OBJECTS['HeUniformV2'] = initializers_v2.HeUniform LOCAL.ALL_OBJECTS['IdentityV2'] = initializers_v2.Identity LOCAL.ALL_OBJECTS['LecunNormalV2'] = initializers_v2.LecunNormal LOCAL.ALL_OBJECTS['LecunUniformV2'] = initializers_v2.LecunUniform LOCAL.ALL_OBJECTS['OnesV2'] = initializers_v2.Ones LOCAL.ALL_OBJECTS['OrthogonalV2'] = initializers_v2.Orthogonal LOCAL.ALL_OBJECTS['RandomNormalV2'] = initializers_v2.RandomNormal LOCAL.ALL_OBJECTS['RandomUniformV2'] = initializers_v2.RandomUniform LOCAL.ALL_OBJECTS['TruncatedNormalV2'] = initializers_v2.TruncatedNormal LOCAL.ALL_OBJECTS['VarianceScalingV2'] = initializers_v2.VarianceScaling LOCAL.ALL_OBJECTS['ZerosV2'] = initializers_v2.Zeros # Out of an abundance of caution we also include these aliases that have # a non-zero probability of having been included in saved configs in the past. LOCAL.ALL_OBJECTS['glorot_normalV2'] = initializers_v2.GlorotNormal LOCAL.ALL_OBJECTS['glorot_uniformV2'] = initializers_v2.GlorotUniform LOCAL.ALL_OBJECTS['he_normalV2'] = initializers_v2.HeNormal LOCAL.ALL_OBJECTS['he_uniformV2'] = initializers_v2.HeUniform LOCAL.ALL_OBJECTS['lecun_normalV2'] = initializers_v2.LecunNormal LOCAL.ALL_OBJECTS['lecun_uniformV2'] = initializers_v2.LecunUniform if tf2.enabled(): # For V2, entries are generated automatically based on the content of # initializers_v2.py. v2_objs = {} base_cls = initializers_v2.Initializer generic_utils.populate_dict_with_module_objects( v2_objs, [initializers_v2], obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls)) for key, value in v2_objs.items(): LOCAL.ALL_OBJECTS[key] = value # Functional aliases. LOCAL.ALL_OBJECTS[generic_utils.to_snake_case(key)] = value else: # V1 initializers. v1_objs = { 'Constant': init_ops.Constant, 'GlorotNormal': init_ops.GlorotNormal, 'GlorotUniform': init_ops.GlorotUniform, 'Identity': init_ops.Identity, 'Ones': init_ops.Ones, 'Orthogonal': init_ops.Orthogonal, 'VarianceScaling': init_ops.VarianceScaling, 'Zeros': init_ops.Zeros, 'HeNormal': initializers_v1.HeNormal, 'HeUniform': initializers_v1.HeUniform, 'LecunNormal': initializers_v1.LecunNormal, 'LecunUniform': initializers_v1.LecunUniform, 'RandomNormal': initializers_v1.RandomNormal, 'RandomUniform': initializers_v1.RandomUniform, 'TruncatedNormal': initializers_v1.TruncatedNormal, } for key, value in v1_objs.items(): LOCAL.ALL_OBJECTS[key] = value # Functional aliases. LOCAL.ALL_OBJECTS[generic_utils.to_snake_case(key)] = value # More compatibility aliases. LOCAL.ALL_OBJECTS['normal'] = LOCAL.ALL_OBJECTS['random_normal'] LOCAL.ALL_OBJECTS['uniform'] = LOCAL.ALL_OBJECTS['random_uniform'] LOCAL.ALL_OBJECTS['one'] = LOCAL.ALL_OBJECTS['ones'] LOCAL.ALL_OBJECTS['zero'] = LOCAL.ALL_OBJECTS['zeros']