wehub-resource-sync
126 lines
5.2 KiB
Python
126 lines
5.2 KiB
Python
# Copyright 2020 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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"""Options for saving SavedModels."""
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from tensorflow.python.saved_model import save_options
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("saved_model.LoadOptions", v1=[])
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class LoadOptions(object):
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"""Options for loading a SavedModel.
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This function may be used in the `options` argument in functions that
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load a SavedModel (`tf.saved_model.load`, `tf.keras.models.load_model`).
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"""
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# Define object attributes in __slots__ for improved memory and performance.
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__slots__ = ("allow_partial_checkpoint", "experimental_io_device",
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"experimental_skip_checkpoint", "experimental_variable_policy",
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"experimental_load_function_aliases")
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def __init__(self,
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allow_partial_checkpoint=False,
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experimental_io_device=None,
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experimental_skip_checkpoint=False,
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experimental_variable_policy=None,
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experimental_load_function_aliases=False):
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"""Creates an object that stores options for SavedModel loading.
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*When to set `allow_partial_checkpoint=True`?*
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This can be used when loading a Keras model (`tf.keras.models.load_model`)
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with custom objects. When new variables are added to the custom object
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class, loading will fail the assertion check that all loaded variables have
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been restored, because the SavedModel checkpoint only contains the variables
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that were in original the custom object.
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See the following example:
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```
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class Custom(tf.keras.Model):
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def __init__(self):
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super(Custom, self).__init__()
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self.v = tf.Variable(...)
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def call(self, inputs):
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return ...
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model = Custom()
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model.save(...)
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```
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After saving, say that `Custom` is updated to include an additional
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variable.
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```
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class Custom(tf.keras.Model):
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def __init__(self):
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super(Custom, self).__init__()
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self.v = tf.Variable(...)
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self.w = tf.Variable(...)
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def call(self, inputs):
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return ...
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```
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`tf.keras.models.load_model(path, custom_objects={'Custom': Custom})` fails
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to load since `Custom.w` does not exist in the SavedModel checkpoint. To
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acknowledge that there are variables that are not restored from the
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checkpoint and successfully load the model, call:
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```
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tf.keras.models.load_model(
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path, custom_objects={'Custom': Custom},
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options=tf.saved_model.LoadOptions(allow_partial_checkpoint=True))
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```
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Args:
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allow_partial_checkpoint: bool. Defaults to `False`. When enabled, allows
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the SavedModel checkpoint to not entirely match the loaded object.
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experimental_io_device: string. Applies in a distributed setting.
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Tensorflow device to use to access the filesystem. If `None` (default)
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then for each variable the filesystem is accessed from the CPU:0 device
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of the host where that variable is assigned. If specified, the
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filesystem is instead accessed from that device for all variables.
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This is for example useful if you want to load from a local directory,
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such as "/tmp" when running in a distributed setting. In that case
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pass a device for the host where the "/tmp" directory is accessible.
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experimental_skip_checkpoint: bool. Defaults to `False`. If set to `True`,
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checkpoints will not be restored. Note that this in the majority of
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cases will generate an unusable model.
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experimental_variable_policy: string. The policy to apply to variables
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when loading. This is either a `saved_model.experimental.VariablePolicy`
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enum instance or one of its value strings (case is not important). See
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that enum documentation for details. A value of `None` corresponds to
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the default policy.
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experimental_load_function_aliases: bool. Defaults to `False`. If set to
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`True`, a `function_aliases` attribute will be added to the loaded
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SavedModel object.
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Example:
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load_options = tf.saved_model.LoadOptions(experimental_io_device=
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'/job:localhost')
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restoredmodel = tf.keras.models.load_model(saved_model_path,
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options=load_options)
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"""
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self.experimental_io_device = experimental_io_device
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self.allow_partial_checkpoint = allow_partial_checkpoint
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self.experimental_skip_checkpoint = experimental_skip_checkpoint
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self.experimental_variable_policy = (
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save_options.VariablePolicy.from_obj(experimental_variable_policy))
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self.experimental_load_function_aliases = experimental_load_function_aliases
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