4d6f8114b0
Allows users to hook into TF's object-based checkpointing with arbitrary Python state. Doing this now for the TF Agents migration. This is a reasonable place to cutoff and expose a public API; the NumPy stuff can be copied out of contrib. Experimental for now pending Trackable being exposed, since it may make more sense to combine the Variable/Tensor saving API with the non-Tensor Python state API. PiperOrigin-RevId: 235579652
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
"""Utilities for including Python state in TensorFlow checkpoints."""
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# Copyright 2018 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import abc
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import functools
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import six
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from tensorflow.python.training.tracking import base
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("train.experimental.PythonState")
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@six.add_metaclass(abc.ABCMeta)
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class PythonState(base.Trackable):
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"""A mixin for putting Python state in an object-based checkpoint.
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This is an abstract class which allows extensions to TensorFlow's object-based
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checkpointing (see `tf.train.Checkpoint`). For example a wrapper for NumPy
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arrays:
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```python
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import io
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import numpy
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class NumpyWrapper(tf.train.experimental.PythonState):
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def __init__(self, array):
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self.array = array
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def serialize(self):
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string_file = io.BytesIO()
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try:
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numpy.save(string_file, self.array, allow_pickle=False)
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serialized = string_file.getvalue()
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finally:
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string_file.close()
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return serialized
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def deserialize(self, string_value):
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string_file = io.BytesIO(string_value)
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try:
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self.array = numpy.load(string_file, allow_pickle=False)
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finally:
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string_file.close()
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```
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Instances of `NumpyWrapper` are checkpointable objects, and will be saved and
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restored from checkpoints along with TensorFlow state like variables.
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```python
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root = tf.train.Checkpoint(numpy=NumpyWrapper(numpy.array([1.])))
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save_path = root.save(prefix)
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root.numpy.array *= 2.
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assert [2.] == root.numpy.array
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root.restore(save_path)
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assert [1.] == root.numpy.array
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```
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"""
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@abc.abstractmethod
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def serialize(self):
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"""Callback to serialize the object. Returns a string."""
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@abc.abstractmethod
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def deserialize(self, string_value):
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"""Callback to deserialize the object."""
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def _gather_saveables_for_checkpoint(self):
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"""Specify callbacks for saving and restoring `array`."""
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return {
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"py_state": functools.partial(
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base.PythonStringStateSaveable,
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state_callback=self.serialize,
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restore_callback=self.deserialize)
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}
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