216 lines
8.4 KiB
Python
216 lines
8.4 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras initializer serialization / deserialization."""
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import threading
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import tensorflow.compat.v2 as tf
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from keras.initializers import initializers
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from keras.initializers import initializers_v1
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from keras.saving.legacy import serialization as legacy_serialization
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from keras.utils import generic_utils
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from keras.utils import tf_inspect as inspect
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# isort: off
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from tensorflow.python import tf2
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from tensorflow.python.ops import init_ops
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from tensorflow.python.util.tf_export import keras_export
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# LOCAL.ALL_OBJECTS is meant to be a global mutable. Hence we need to make it
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# thread-local to avoid concurrent mutations.
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LOCAL = threading.local()
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def populate_deserializable_objects():
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"""Populates dict ALL_OBJECTS with every built-in initializer."""
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global LOCAL
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if not hasattr(LOCAL, "ALL_OBJECTS"):
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LOCAL.ALL_OBJECTS = {}
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LOCAL.GENERATED_WITH_V2 = None
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if (
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LOCAL.ALL_OBJECTS
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and LOCAL.GENERATED_WITH_V2 == tf.__internal__.tf2.enabled()
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):
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# Objects dict is already generated for the proper TF version:
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# do nothing.
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return
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LOCAL.ALL_OBJECTS = {}
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LOCAL.GENERATED_WITH_V2 = tf.__internal__.tf2.enabled()
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# Compatibility aliases (need to exist in both V1 and V2).
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LOCAL.ALL_OBJECTS["ConstantV2"] = initializers.Constant
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LOCAL.ALL_OBJECTS["GlorotNormalV2"] = initializers.GlorotNormal
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LOCAL.ALL_OBJECTS["GlorotUniformV2"] = initializers.GlorotUniform
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LOCAL.ALL_OBJECTS["HeNormalV2"] = initializers.HeNormal
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LOCAL.ALL_OBJECTS["HeUniformV2"] = initializers.HeUniform
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LOCAL.ALL_OBJECTS["IdentityV2"] = initializers.Identity
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LOCAL.ALL_OBJECTS["LecunNormalV2"] = initializers.LecunNormal
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LOCAL.ALL_OBJECTS["LecunUniformV2"] = initializers.LecunUniform
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LOCAL.ALL_OBJECTS["OnesV2"] = initializers.Ones
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LOCAL.ALL_OBJECTS["OrthogonalV2"] = initializers.Orthogonal
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LOCAL.ALL_OBJECTS["RandomNormalV2"] = initializers.RandomNormal
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LOCAL.ALL_OBJECTS["RandomUniformV2"] = initializers.RandomUniform
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LOCAL.ALL_OBJECTS["TruncatedNormalV2"] = initializers.TruncatedNormal
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LOCAL.ALL_OBJECTS["VarianceScalingV2"] = initializers.VarianceScaling
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LOCAL.ALL_OBJECTS["ZerosV2"] = initializers.Zeros
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# Out of an abundance of caution we also include these aliases that have
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# a non-zero probability of having been included in saved configs in the
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# past.
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LOCAL.ALL_OBJECTS["glorot_normalV2"] = initializers.GlorotNormal
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LOCAL.ALL_OBJECTS["glorot_uniformV2"] = initializers.GlorotUniform
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LOCAL.ALL_OBJECTS["he_normalV2"] = initializers.HeNormal
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LOCAL.ALL_OBJECTS["he_uniformV2"] = initializers.HeUniform
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LOCAL.ALL_OBJECTS["lecun_normalV2"] = initializers.LecunNormal
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LOCAL.ALL_OBJECTS["lecun_uniformV2"] = initializers.LecunUniform
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if tf.__internal__.tf2.enabled():
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# For V2, entries are generated automatically based on the content of
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# initializers.py.
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v2_objs = {}
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base_cls = initializers.Initializer
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generic_utils.populate_dict_with_module_objects(
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v2_objs,
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[initializers],
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obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls),
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)
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for key, value in v2_objs.items():
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LOCAL.ALL_OBJECTS[key] = value
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# Functional aliases.
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LOCAL.ALL_OBJECTS[generic_utils.to_snake_case(key)] = value
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else:
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# V1 initializers.
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v1_objs = {
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"Constant": tf.compat.v1.constant_initializer,
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"GlorotNormal": tf.compat.v1.glorot_normal_initializer,
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"GlorotUniform": tf.compat.v1.glorot_uniform_initializer,
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"Identity": tf.compat.v1.initializers.identity,
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"Ones": tf.compat.v1.ones_initializer,
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"Orthogonal": tf.compat.v1.orthogonal_initializer,
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"VarianceScaling": tf.compat.v1.variance_scaling_initializer,
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"Zeros": tf.compat.v1.zeros_initializer,
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"HeNormal": initializers_v1.HeNormal,
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"HeUniform": initializers_v1.HeUniform,
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"LecunNormal": initializers_v1.LecunNormal,
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"LecunUniform": initializers_v1.LecunUniform,
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"RandomNormal": initializers_v1.RandomNormal,
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"RandomUniform": initializers_v1.RandomUniform,
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"TruncatedNormal": initializers_v1.TruncatedNormal,
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}
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for key, value in v1_objs.items():
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LOCAL.ALL_OBJECTS[key] = value
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# Functional aliases.
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LOCAL.ALL_OBJECTS[generic_utils.to_snake_case(key)] = value
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# More compatibility aliases.
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LOCAL.ALL_OBJECTS["normal"] = LOCAL.ALL_OBJECTS["random_normal"]
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LOCAL.ALL_OBJECTS["uniform"] = LOCAL.ALL_OBJECTS["random_uniform"]
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LOCAL.ALL_OBJECTS["one"] = LOCAL.ALL_OBJECTS["ones"]
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LOCAL.ALL_OBJECTS["zero"] = LOCAL.ALL_OBJECTS["zeros"]
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# For backwards compatibility, we populate this file with the objects
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# from ALL_OBJECTS. We make no guarantees as to whether these objects will
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# using their correct version.
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populate_deserializable_objects()
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globals().update(LOCAL.ALL_OBJECTS)
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# Utility functions
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@keras_export("keras.initializers.serialize")
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def serialize(initializer, use_legacy_format=False):
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if use_legacy_format:
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return legacy_serialization.serialize_keras_object(initializer)
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# To be replaced by new serialization_lib
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return legacy_serialization.serialize_keras_object(initializer)
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@keras_export("keras.initializers.deserialize")
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def deserialize(config, custom_objects=None, use_legacy_format=False):
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"""Return an `Initializer` object from its config."""
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populate_deserializable_objects()
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if use_legacy_format:
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return legacy_serialization.deserialize_keras_object(
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config,
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module_objects=LOCAL.ALL_OBJECTS,
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custom_objects=custom_objects,
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printable_module_name="initializer",
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)
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# To be replaced by new serialization_lib
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return legacy_serialization.deserialize_keras_object(
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config,
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module_objects=LOCAL.ALL_OBJECTS,
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custom_objects=custom_objects,
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printable_module_name="initializer",
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)
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@keras_export("keras.initializers.get")
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def get(identifier):
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"""Retrieve a Keras initializer by the identifier.
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The `identifier` may be the string name of a initializers function or class
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(case-sensitively).
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>>> identifier = 'Ones'
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>>> tf.keras.initializers.deserialize(identifier)
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<...keras.initializers.initializers.Ones...>
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You can also specify `config` of the initializer to this function by passing
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dict containing `class_name` and `config` as an identifier. Also note that
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the `class_name` must map to a `Initializer` class.
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>>> cfg = {'class_name': 'Ones', 'config': {}}
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>>> tf.keras.initializers.deserialize(cfg)
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<...keras.initializers.initializers.Ones...>
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In the case that the `identifier` is a class, this method will return a new
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instance of the class by its constructor.
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Args:
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identifier: String or dict that contains the initializer name or
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configurations.
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Returns:
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Initializer instance base on the input identifier.
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Raises:
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ValueError: If the input identifier is not a supported type or in a bad
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format.
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"""
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if identifier is None:
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return None
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if isinstance(identifier, dict):
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use_legacy_format = "module" not in identifier
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return deserialize(identifier, use_legacy_format=use_legacy_format)
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elif isinstance(identifier, str):
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identifier = str(identifier)
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return deserialize(identifier)
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elif callable(identifier):
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if inspect.isclass(identifier):
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identifier = identifier()
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return identifier
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else:
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raise ValueError(
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"Could not interpret initializer identifier: " + str(identifier)
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)
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