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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.
# ==============================================================================
"""Factory functions for `Predictor`s."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.predictor import contrib_estimator_predictor
from tensorflow.contrib.predictor import core_estimator_predictor
from tensorflow.contrib.predictor import saved_model_predictor
from tensorflow.python.estimator import estimator as core_estimator
def from_contrib_estimator(estimator,
prediction_input_fn,
input_alternative_key=None,
output_alternative_key=None,
graph=None):
"""Constructs a `Predictor` from a `tf.contrib.learn.Estimator`.
Args:
estimator: an instance of `tf.contrib.learn.Estimator`.
prediction_input_fn: a function that takes no arguments and returns an
instance of `InputFnOps`.
input_alternative_key: Optional. Specify the input alternative used for
prediction.
output_alternative_key: Specify the output alternative used for
prediction. Not needed for single-headed models but required for
multi-headed models.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
Returns:
An initialized `Predictor`.
Raises:
TypeError: if `estimator` is a core `Estimator` instead of a contrib
`Estimator`.
"""
if isinstance(estimator, core_estimator.Estimator):
raise TypeError('Espected estimator to be of type '
'tf.contrib.learn.Estimator, but got type '
'tf.python.estimator.Estimator. You likely want to call '
'from_estimator.')
return contrib_estimator_predictor.ContribEstimatorPredictor(
estimator,
prediction_input_fn,
input_alternative_key=input_alternative_key,
output_alternative_key=output_alternative_key,
graph=graph)
def from_estimator(estimator,
serving_input_receiver_fn,
output_key=None,
graph=None):
"""Constructs a `Predictor` from a `tf.python.estimator.Estimator`.
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.
Returns:
An initialized `Predictor`.
Raises:
TypeError: if `estimator` is a contrib `Estimator` instead of a core
`Estimator`.
"""
if isinstance(estimator, estimator.Estimator):
raise TypeError('Espected estimator to be of type '
'tf.python.estimator.Estimator, but got type '
'tf.contrib.learn.Estimator. You likely want to call '
'from_contrib_estimator.')
return core_estimator_predictor.CoreEstimatorPredictor(
estimator, serving_input_receiver_fn, output_key=output_key, graph=graph)
def from_saved_model(export_dir,
signature_def_key=None,
signature_def=None,
tags=None,
graph=None):
"""Constructs a `Predictor` from a `SavedModel` on disk.
Args:
export_dir: a path to a directory containing a `SavedModel`.
signature_def_key: Optional string specifying the signature to use. If
`None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
`signature_def_key` and `signature_def`
signature_def: A `SignatureDef` proto specifying the inputs and outputs
for prediction. Only one of `signature_def_key` and `signature_def`
should be specified.
tags: Optional. Tags that will be used to retrieve the correct
`SignatureDef`. Defaults to `DEFAULT_TAGS`.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
Returns:
An initialized `Predictor`.
Raises:
ValueError: More than one of `signature_def_key` and `signature_def` is
specified.
"""
return saved_model_predictor.SavedModelPredictor(
export_dir,
signature_def_key=signature_def_key,
signature_def=signature_def,
tags=tags,
graph=graph)
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