78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
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# Copyright 2021 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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"""Saving utilities to support Python's Pickle protocol."""
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import os
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import tempfile
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import tensorflow.compat.v2 as tf
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from keras.saving import saving_lib
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def deserialize_model_from_bytecode(serialized_model):
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"""Reconstruct a Model from the output of `serialize_model_as_bytecode`.
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Args:
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serialized_model: (bytes) return value from
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`serialize_model_as_bytecode`.
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Returns:
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Keras Model instance.
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"""
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# Note: we don't use a RAM path for this because zipfile cannot write
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# to such paths.
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temp_dir = tempfile.mkdtemp()
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try:
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filepath = os.path.join(temp_dir, "model.keras")
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with open(filepath, "wb") as f:
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f.write(serialized_model)
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# When loading, direct import will work for most custom objects
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# though it will require get_config() to be implemented.
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# Some custom objects (e.g. an activation in a Dense layer,
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# serialized as a string by Dense.get_config()) will require
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# a custom_object_scope.
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model = saving_lib.load_model(filepath, safe_mode=False)
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except Exception as e:
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raise e
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else:
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return model
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finally:
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tf.io.gfile.rmtree(temp_dir)
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def serialize_model_as_bytecode(model):
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"""Convert a Keras Model into a bytecode representation for pickling.
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Args:
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model: Keras Model instance.
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Returns:
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Tuple that can be read by `deserialize_from_bytecode`.
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"""
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# Note: we don't use a RAM path for this because zipfile cannot write
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# to such paths.
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temp_dir = tempfile.mkdtemp()
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try:
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filepath = os.path.join(temp_dir, "model.keras")
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saving_lib.save_model(model, filepath)
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with open(filepath, "rb") as f:
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data = f.read()
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except Exception as e:
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raise e
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else:
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return data
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finally:
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tf.io.gfile.rmtree(temp_dir)
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