EVOLUTION-MANAGER

Edit File: signature_def_utils_impl.py

# 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.
# ==============================================================================
"""SignatureDef utility functions implementation."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import utils_impl as utils
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export

@tf_export(
    v1=[
        'saved_model.build_signature_def',
        'saved_model.signature_def_utils.build_signature_def'
    ])
@deprecation.deprecated_endpoints(
    'saved_model.signature_def_utils.build_signature_def')
def build_signature_def(inputs=None, outputs=None, method_name=None):
  """Utility function to build a SignatureDef protocol buffer.

Args:
    inputs: Inputs of the SignatureDef defined as a proto map of string to
        tensor info.
    outputs: Outputs of the SignatureDef defined as a proto map of string to
        tensor info.
    method_name: Method name of the SignatureDef as a string.

Returns:
    A SignatureDef protocol buffer constructed based on the supplied arguments.
  """
  signature_def = meta_graph_pb2.SignatureDef()
  if inputs is not None:
    for item in inputs:
      signature_def.inputs[item].CopyFrom(inputs[item])
  if outputs is not None:
    for item in outputs:
      signature_def.outputs[item].CopyFrom(outputs[item])
  if method_name is not None:
    signature_def.method_name = method_name
  return signature_def

@tf_export(
    v1=[
        'saved_model.regression_signature_def',
        'saved_model.signature_def_utils.regression_signature_def'
    ])
@deprecation.deprecated_endpoints(
    'saved_model.signature_def_utils.regression_signature_def')
def regression_signature_def(examples, predictions):
  """Creates regression signature from given examples and predictions.

This function produces signatures intended for use with the TensorFlow Serving
  Regress API (tensorflow_serving/apis/prediction_service.proto), and so
  constrains the input and output types to those allowed by TensorFlow Serving.

Args:
    examples: A string `Tensor`, expected to accept serialized tf.Examples.
    predictions: A float `Tensor`.

Returns:
    A regression-flavored signature_def.

Raises:
    ValueError: If examples is `None`.
  """
  if examples is None:
    raise ValueError('Regression examples cannot be None.')
  if not isinstance(examples, ops.Tensor):
    raise ValueError('Regression examples must be a string Tensor.')
  if predictions is None:
    raise ValueError('Regression predictions cannot be None.')

input_tensor_info = utils.build_tensor_info(examples)
  if input_tensor_info.dtype != types_pb2.DT_STRING:
    raise ValueError('Regression examples must be a string Tensor.')
  signature_inputs = {signature_constants.REGRESS_INPUTS: input_tensor_info}

output_tensor_info = utils.build_tensor_info(predictions)
  if output_tensor_info.dtype != types_pb2.DT_FLOAT:
    raise ValueError('Regression output must be a float Tensor.')
  signature_outputs = {signature_constants.REGRESS_OUTPUTS: output_tensor_info}

signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.REGRESS_METHOD_NAME)

return signature_def

@tf_export(
    v1=[
        'saved_model.classification_signature_def',
        'saved_model.signature_def_utils.classification_signature_def'
    ])
@deprecation.deprecated_endpoints(
    'saved_model.signature_def_utils.classification_signature_def')
def classification_signature_def(examples, classes, scores):
  """Creates classification signature from given examples and predictions.

This function produces signatures intended for use with the TensorFlow Serving
  Classify API (tensorflow_serving/apis/prediction_service.proto), and so
  constrains the input and output types to those allowed by TensorFlow Serving.

Args:
    examples: A string `Tensor`, expected to accept serialized tf.Examples.
    classes: A string `Tensor`.  Note that the ClassificationResponse message
      requires that class labels are strings, not integers or anything else.
    scores: a float `Tensor`.

Returns:
    A classification-flavored signature_def.

Raises:
    ValueError: If examples is `None`.
  """
  if examples is None:
    raise ValueError('Classification examples cannot be None.')
  if not isinstance(examples, ops.Tensor):
    raise ValueError('Classification examples must be a string Tensor.')
  if classes is None and scores is None:
    raise ValueError('Classification classes and scores cannot both be None.')

input_tensor_info = utils.build_tensor_info(examples)
  if input_tensor_info.dtype != types_pb2.DT_STRING:
    raise ValueError('Classification examples must be a string Tensor.')
  signature_inputs = {signature_constants.CLASSIFY_INPUTS: input_tensor_info}

signature_outputs = {}
  if classes is not None:
    classes_tensor_info = utils.build_tensor_info(classes)
    if classes_tensor_info.dtype != types_pb2.DT_STRING:
      raise ValueError('Classification classes must be a string Tensor.')
    signature_outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES] = (
        classes_tensor_info)
  if scores is not None:
    scores_tensor_info = utils.build_tensor_info(scores)
    if scores_tensor_info.dtype != types_pb2.DT_FLOAT:
      raise ValueError('Classification scores must be a float Tensor.')
    signature_outputs[signature_constants.CLASSIFY_OUTPUT_SCORES] = (
        scores_tensor_info)

signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.CLASSIFY_METHOD_NAME)

return signature_def

@tf_export(
    v1=[
        'saved_model.predict_signature_def',
        'saved_model.signature_def_utils.predict_signature_def'
    ])
@deprecation.deprecated_endpoints(
    'saved_model.signature_def_utils.predict_signature_def')
def predict_signature_def(inputs, outputs):
  """Creates prediction signature from given inputs and outputs.

This function produces signatures intended for use with the TensorFlow Serving
  Predict API (tensorflow_serving/apis/prediction_service.proto). This API
  imposes no constraints on the input and output types.

Args:
    inputs: dict of string to `Tensor`.
    outputs: dict of string to `Tensor`.

Returns:
    A prediction-flavored signature_def.

Raises:
    ValueError: If inputs or outputs is `None`.
  """
  if inputs is None or not inputs:
    raise ValueError('Prediction inputs cannot be None or empty.')
  if outputs is None or not outputs:
    raise ValueError('Prediction outputs cannot be None or empty.')

signature_inputs = {key: utils.build_tensor_info(tensor)
                      for key, tensor in inputs.items()}
  signature_outputs = {key: utils.build_tensor_info(tensor)
                       for key, tensor in outputs.items()}

signature_def = build_signature_def(
      signature_inputs, signature_outputs,
      signature_constants.PREDICT_METHOD_NAME)

return signature_def

def supervised_train_signature_def(
    inputs, loss, predictions=None, metrics=None):
  return _supervised_signature_def(
      signature_constants.SUPERVISED_TRAIN_METHOD_NAME, inputs, loss=loss,
      predictions=predictions, metrics=metrics)

def supervised_eval_signature_def(
    inputs, loss, predictions=None, metrics=None):
  return _supervised_signature_def(
      signature_constants.SUPERVISED_EVAL_METHOD_NAME, inputs, loss=loss,
      predictions=predictions, metrics=metrics)

def _supervised_signature_def(
    method_name, inputs, loss=None, predictions=None,
    metrics=None):
  """Creates a signature for training and eval data.

This function produces signatures that describe the inputs and outputs
  of a supervised process, such as training or evaluation, that
  results in loss, metrics, and the like. Note that this function only requires
  inputs to be not None.

Args:
    method_name: Method name of the SignatureDef as a string.
    inputs: dict of string to `Tensor`.
    loss: dict of string to `Tensor` representing computed loss.
    predictions: dict of string to `Tensor` representing the output predictions.
    metrics: dict of string to `Tensor` representing metric ops.

Returns:
    A train- or eval-flavored signature_def.

Raises:
    ValueError: If inputs or outputs is `None`.
  """
  if inputs is None or not inputs:
    raise ValueError('{} inputs cannot be None or empty.'.format(method_name))

signature_inputs = {key: utils.build_tensor_info(tensor)
                      for key, tensor in inputs.items()}

signature_outputs = {}
  for output_set in (loss, predictions, metrics):
    if output_set is not None:
      sig_out = {key: utils.build_tensor_info(tensor)
                 for key, tensor in output_set.items()}
      signature_outputs.update(sig_out)

signature_def = build_signature_def(
      signature_inputs, signature_outputs, method_name)

return signature_def

@tf_export(
    v1=[
        'saved_model.is_valid_signature',
        'saved_model.signature_def_utils.is_valid_signature'
    ])
@deprecation.deprecated_endpoints(
    'saved_model.signature_def_utils.is_valid_signature')
def is_valid_signature(signature_def):
  """Determine whether a SignatureDef can be served by TensorFlow Serving."""
  if signature_def is None:
    return False
  return (_is_valid_classification_signature(signature_def) or
          _is_valid_regression_signature(signature_def) or
          _is_valid_predict_signature(signature_def))

def _is_valid_predict_signature(signature_def):
  """Determine whether the argument is a servable 'predict' SignatureDef."""
  if signature_def.method_name != signature_constants.PREDICT_METHOD_NAME:
    return False
  if not signature_def.inputs.keys():
    return False
  if not signature_def.outputs.keys():
    return False
  return True

def _is_valid_regression_signature(signature_def):
  """Determine whether the argument is a servable 'regress' SignatureDef."""
  if signature_def.method_name != signature_constants.REGRESS_METHOD_NAME:
    return False

if (set(signature_def.inputs.keys())
      != set([signature_constants.REGRESS_INPUTS])):
    return False
  if (signature_def.inputs[signature_constants.REGRESS_INPUTS].dtype !=
      types_pb2.DT_STRING):
    return False

if (set(signature_def.outputs.keys())
      != set([signature_constants.REGRESS_OUTPUTS])):
    return False
  if (signature_def.outputs[signature_constants.REGRESS_OUTPUTS].dtype !=
      types_pb2.DT_FLOAT):
    return False

return True

def _is_valid_classification_signature(signature_def):
  """Determine whether the argument is a servable 'classify' SignatureDef."""
  if signature_def.method_name != signature_constants.CLASSIFY_METHOD_NAME:
    return False

if (set(signature_def.inputs.keys())
      != set([signature_constants.CLASSIFY_INPUTS])):
    return False
  if (signature_def.inputs[signature_constants.CLASSIFY_INPUTS].dtype !=
      types_pb2.DT_STRING):
    return False

allowed_outputs = set([signature_constants.CLASSIFY_OUTPUT_CLASSES,
                         signature_constants.CLASSIFY_OUTPUT_SCORES])

if not signature_def.outputs.keys():
    return False
  if set(signature_def.outputs.keys()) - allowed_outputs:
    return False
  if (signature_constants.CLASSIFY_OUTPUT_CLASSES in signature_def.outputs
      and
      signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES].dtype
      != types_pb2.DT_STRING):
    return False
  if (signature_constants.CLASSIFY_OUTPUT_SCORES in signature_def.outputs
      and
      signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES].dtype !=
      types_pb2.DT_FLOAT):
    return False

return True

def op_signature_def(op, key):
  """Creates a signature def with the output pointing to an op.

Note that op isn't strictly enforced to be an Op object, and may be a Tensor.
  It is recommended to use the build_signature_def() function for Tensors.

Args:
    op: An Op (or possibly Tensor).
    key: Key to graph element in the SignatureDef outputs.

Returns:
    A SignatureDef with a single output pointing to the op.
  """
  # Use build_tensor_info_from_op, which creates a TensorInfo from the element's
  # name.
  return build_signature_def(outputs={key: utils.build_tensor_info_from_op(op)})

def load_op_from_signature_def(signature_def, key, import_scope=None):
  """Load an Op from a SignatureDef created by op_signature_def().

Args:
    signature_def: a SignatureDef proto
    key: string key to op in the SignatureDef outputs.
    import_scope: Scope used to import the op

Returns:
    Op (or possibly Tensor) in the graph with the same name as saved in the
      SignatureDef.

Raises:
    NotFoundError: If the op could not be found in the graph.
  """
  tensor_info = signature_def.outputs[key]
  try:
    # The init and train ops are not strictly enforced to be operations, so
    # retrieve any graph element (can be either op or tensor).
    return utils.get_element_from_tensor_info(
        tensor_info, import_scope=import_scope)
  except KeyError:
    raise errors.NotFoundError(
        None, None,
        'The {0} could not be found in the graph. Please make sure the '
        'SavedModel was created by the internal _SavedModelBuilder. If you '
        'are using the public API, please make sure the SignatureDef in the '
        'SavedModel does not contain the key "{0}".'.format(key))