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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A `Predictor` constructed from an `learn.python.estimator.Estimator`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.predictor import predictor
from tensorflow.python.estimator import model_fn
from tensorflow.python.framework import ops
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.training import monitored_session
def _get_signature_def(
serving_input_receiver, estimator, output_key=None):
"""Construct a `SignatureDef` proto."""
if output_key is None:
output_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
# pylint: disable=protected-access
estimator_spec = estimator.model_fn(
serving_input_receiver.features, None, model_fn.ModeKeys.PREDICT,
estimator.config)
# pylint: enable=protected-access
export_outputs = estimator_spec.export_outputs
export_output = export_outputs.get(output_key)
if export_output is None:
raise KeyError('output_key must be one of {}; got {}'.format(
export_outputs.keys(), output_key))
return export_output.as_signature_def(serving_input_receiver.receiver_tensors)
class CoreEstimatorPredictor(predictor.Predictor):
"""A `Predictor` constructed from an `learn.python.estimator.Estimator`."""
def __init__(self,
estimator,
serving_input_receiver_fn,
output_key=None,
graph=None,
config=None):
"""Initialize a `CoreEstimatorPredictor`.
Args:
estimator: an instance of `learn.python.estimator.Estimator`.
serving_input_receiver_fn: a function that takes no arguments and returns
an instance of `ServingInputReceiver` compatible with `estimator`.
output_key: Optional string specifying the export output to use. If
`None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
config: `ConfigProto` proto used to configure the session.
"""
self._graph = graph or ops.Graph()
with self._graph.as_default():
serving_input_receiver = serving_input_receiver_fn()
signature_def = _get_signature_def(
serving_input_receiver, estimator, output_key)
checkpoint_dir = estimator.model_dir
self._session = monitored_session.MonitoredSession(
session_creator=monitored_session.ChiefSessionCreator(
config=config,
checkpoint_dir=checkpoint_dir))
feed_tensor_info = signature_def.inputs
self._feed_tensors = {k: self._graph.get_tensor_by_name(v.name)
for k, v in feed_tensor_info.items()}
fetch_tensor_info = signature_def.outputs
self._fetch_tensors = {k: self._graph.get_tensor_by_name(v.name)
for k, v in fetch_tensor_info.items()}
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