wehub-resource-sync
118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
# 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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"""Layer serialization/deserialization functions.
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"""
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# pylint: disable=wildcard-import
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# pylint: disable=unused-import
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import threading
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from tensorflow.python import tf2
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.keras.engine import input_layer
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from tensorflow.python.keras.engine import input_spec
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from tensorflow.python.keras.layers import advanced_activations
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from tensorflow.python.keras.layers import convolutional
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from tensorflow.python.keras.layers import convolutional_recurrent
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from tensorflow.python.keras.layers import core
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from tensorflow.python.keras.layers import dense_attention
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from tensorflow.python.keras.layers import embeddings
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from tensorflow.python.keras.layers import merge
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from tensorflow.python.keras.layers import pooling
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from tensorflow.python.keras.layers import recurrent
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from tensorflow.python.keras.layers import rnn_cell_wrapper_v2
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.keras.utils import tf_inspect as inspect
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ALL_MODULES = (base_layer, input_layer, advanced_activations, convolutional,
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convolutional_recurrent, core, dense_attention,
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embeddings, merge, pooling, recurrent)
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ALL_V2_MODULES = (rnn_cell_wrapper_v2,)
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# 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 layer.
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"""
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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 LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled():
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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 = tf2.enabled()
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base_cls = base_layer.Layer
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generic_utils.populate_dict_with_module_objects(
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LOCAL.ALL_OBJECTS,
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ALL_MODULES,
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obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls))
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# Overwrite certain V1 objects with V2 versions
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if tf2.enabled():
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generic_utils.populate_dict_with_module_objects(
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LOCAL.ALL_OBJECTS,
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ALL_V2_MODULES,
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obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls))
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# Prevent circular dependencies.
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from tensorflow.python.keras import models # pylint: disable=g-import-not-at-top
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LOCAL.ALL_OBJECTS['Input'] = input_layer.Input
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LOCAL.ALL_OBJECTS['InputSpec'] = input_spec.InputSpec
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LOCAL.ALL_OBJECTS['Functional'] = models.Functional
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LOCAL.ALL_OBJECTS['Model'] = models.Model
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LOCAL.ALL_OBJECTS['Sequential'] = models.Sequential
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# Merge layers, function versions.
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LOCAL.ALL_OBJECTS['add'] = merge.add
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LOCAL.ALL_OBJECTS['subtract'] = merge.subtract
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LOCAL.ALL_OBJECTS['multiply'] = merge.multiply
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LOCAL.ALL_OBJECTS['average'] = merge.average
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LOCAL.ALL_OBJECTS['maximum'] = merge.maximum
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LOCAL.ALL_OBJECTS['minimum'] = merge.minimum
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LOCAL.ALL_OBJECTS['concatenate'] = merge.concatenate
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LOCAL.ALL_OBJECTS['dot'] = merge.dot
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def serialize(layer):
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return generic_utils.serialize_keras_object(layer)
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def deserialize(config, custom_objects=None):
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"""Instantiates a layer from a config dictionary.
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Args:
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config: dict of the form {'class_name': str, 'config': dict}
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custom_objects: dict mapping class names (or function names)
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of custom (non-Keras) objects to class/functions
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Returns:
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Layer instance (may be Model, Sequential, Network, Layer...)
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"""
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populate_deserializable_objects()
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return generic_utils.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='layer')
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