92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
# Copyright 2017 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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"""SavedModel simple save functionality."""
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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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from tensorflow.python.framework import ops
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from tensorflow.python.saved_model import builder
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from tensorflow.python.saved_model import signature_constants
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from tensorflow.python.saved_model import signature_def_utils
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from tensorflow.python.saved_model import tag_constants
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=['saved_model.simple_save'])
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@deprecation.deprecated(
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None,
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'This function will only be available through the v1 compatibility '
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'library as tf.compat.v1.saved_model.simple_save.')
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def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None):
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"""Convenience function to build a SavedModel suitable for serving.
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In many common cases, saving models for serving will be as simple as:
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simple_save(session,
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export_dir,
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inputs={"x": x, "y": y},
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outputs={"z": z})
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Although in many cases it's not necessary to understand all of the many ways
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to configure a SavedModel, this method has a few practical implications:
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- It will be treated as a graph for inference / serving (i.e. uses the tag
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`saved_model.SERVING`)
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- The SavedModel will load in TensorFlow Serving and supports the
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[Predict
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API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
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To use the Classify, Regress, or MultiInference APIs, please
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use either
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[tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)
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or the lower level
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[SavedModel
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APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md).
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- Some TensorFlow ops depend on information on disk or other information
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called "assets". These are generally handled automatically by adding the
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assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that
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collection are exported; if you need more custom behavior, you'll need to
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use the
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[SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py).
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More information about SavedModel and signatures can be found here:
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https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md.
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Args:
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session: The TensorFlow session from which to save the meta graph and
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variables.
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export_dir: The path to which the SavedModel will be stored.
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inputs: dict mapping string input names to tensors. These are added
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to the SignatureDef as the inputs.
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outputs: dict mapping string output names to tensors. These are added
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to the SignatureDef as the outputs.
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legacy_init_op: Legacy support for op or group of ops to execute after the
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restore op upon a load.
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"""
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signature_def_map = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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signature_def_utils.predict_signature_def(inputs, outputs)
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}
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b = builder.SavedModelBuilder(export_dir)
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b.add_meta_graph_and_variables(
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session,
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tags=[tag_constants.SERVING],
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signature_def_map=signature_def_map,
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assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS),
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main_op=legacy_init_op,
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clear_devices=True)
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b.save()
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