wehub-resource-sync
88 lines
2.8 KiB
Python
88 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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import abc
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.trackable import base
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from tensorflow.python.util.tf_export import tf_export
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PYTHON_STATE = "py_state"
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@tf_export("train.experimental.PythonState")
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class PythonState(base.Trackable, metaclass=abc.ABCMeta):
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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 _serialize_to_tensors(self):
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"""Implements Trackable._serialize_to_tensors."""
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with ops.init_scope():
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value = constant_op.constant(self.serialize(), dtype=dtypes.string)
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return {PYTHON_STATE: value}
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