Python signature_def_utils.predict_signature_def方法代码示例

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def export_h5_to_pb(path_to_h5, export_path):

    # Set the learning phase to Test since the model is already trained.
    K.set_learning_phase(0)

    # Load the Keras model
    keras_model = load_model(path_to_h5)

    # Build the Protocol Buffer SavedModel at 'export_path'
    builder = saved_model_builder.SavedModelBuilder(export_path)

    # Create prediction signature to be used by TensorFlow Serving Predict API
    signature = predict_signature_def(inputs={"images": keras_model.input},
                                      outputs={"scores": keras_model.output})

    with K.get_session() as sess:
        # Save the meta graph and the variables
        builder.add_meta_graph_and_variables(sess=sess, tags=[tag_constants.SERVING],
                                         signature_def_map={"predict": signature})

    builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def export_h5_to_pb(path_to_h5, export_path):

    # Set the learning phase to Test since the model is already trained.
    K.set_learning_phase(0)

    # Load the Keras model
    keras_model = load_model(path_to_h5)

    # Build the Protocol Buffer SavedModel at 'export_path'
    builder = saved_model_builder.SavedModelBuilder(export_path)

    # Create prediction signature to be used by TensorFlow Serving Predict API
    signature = predict_signature_def(inputs={ "http": keras_model.input},
                                      outputs={"probability": keras_model.output})

    with K.get_session() as sess:
        # Save the meta graph and the variables
        builder.add_meta_graph_and_variables(sess=sess, tags=[tag_constants.SERVING],
                                         signature_def_map={"predict": signature})

    builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def as_signature_def(self, receiver_tensors):
    return signature_def_utils.predict_signature_def(receiver_tensors,
                                                     self.outputs) 

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def testPredictionSignatureDef(self):
    input1 = constant_op.constant("a", name="input-1")
    input2 = constant_op.constant("b", name="input-2")
    output1 = constant_op.constant("c", name="output-1")
    output2 = constant_op.constant("d", name="output-2")
    signature_def = signature_def_utils.predict_signature_def({
        "input-1": input1,
        "input-2": input2
    }, {"output-1": output1,
        "output-2": output2})

    self.assertEqual(signature_constants.PREDICT_METHOD_NAME,
                     signature_def.method_name)

    # Check inputs in signature def.
    self.assertEqual(2, len(signature_def.inputs))
    input1_tensor_info_actual = (signature_def.inputs["input-1"])
    self.assertEqual("input-1:0", input1_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, input1_tensor_info_actual.dtype)
    self.assertEqual(0, len(input1_tensor_info_actual.tensor_shape.dim))
    input2_tensor_info_actual = (signature_def.inputs["input-2"])
    self.assertEqual("input-2:0", input2_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, input2_tensor_info_actual.dtype)
    self.assertEqual(0, len(input2_tensor_info_actual.tensor_shape.dim))

    # Check outputs in signature def.
    self.assertEqual(2, len(signature_def.outputs))
    output1_tensor_info_actual = (signature_def.outputs["output-1"])
    self.assertEqual("output-1:0", output1_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, output1_tensor_info_actual.dtype)
    self.assertEqual(0, len(output1_tensor_info_actual.tensor_shape.dim))
    output2_tensor_info_actual = (signature_def.outputs["output-2"])
    self.assertEqual("output-2:0", output2_tensor_info_actual.name)
    self.assertEqual(types_pb2.DT_STRING, output2_tensor_info_actual.dtype)
    self.assertEqual(0, len(output2_tensor_info_actual.tensor_shape.dim)) 

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def export(self, last_checkpoint, output_dir):
    """Builds a prediction graph and xports the model.

    Args:
      last_checkpoint: Path to the latest checkpoint file from training.
      output_dir: Path to the folder to be used to output the model.
    """
    logging.info('Exporting prediction graph to %s', output_dir)
    with tf.Session(graph=tf.Graph()) as sess:
      # Build and save prediction meta graph and trained variable values.
      inputs, outputs = self.build_prediction_graph()
      signature_def_map = {
        'serving_default': signature_def_utils.predict_signature_def(inputs, outputs)
      }
      init_op = tf.global_variables_initializer()
      sess.run(init_op)
      self.restore_from_checkpoint(sess, self.inception_checkpoint_file,
                                   last_checkpoint)
      init_op_serving = control_flow_ops.group(
          variables.local_variables_initializer(),
          tf.tables_initializer())

      builder = saved_model_builder.SavedModelBuilder(output_dir)
      builder.add_meta_graph_and_variables(
          sess, [tag_constants.SERVING],
          signature_def_map=signature_def_map,
          legacy_init_op=init_op_serving)
      builder.save(False) 

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def build_standardized_signature_def(
    input_tensors, output_tensors, problem_type):
  """Build a SignatureDef using problem type and input and output Tensors.

  Note that this delegates the actual creation of the signatures to methods in
  //third_party/tensorflow/python/saved_model/signature_def_utils.py, which may
  assign names to the input and output tensors (depending on the problem type)
  that are standardized in the context of SavedModel.

  Args:
    input_tensors: a dict of string key to `Tensor`
    output_tensors: a dict of string key to `Tensor`
    problem_type: an instance of constants.ProblemType, specifying
      classification, regression, etc.

  Returns:
    A SignatureDef using SavedModel standard keys where possible.

  Raises:
    ValueError: if input_tensors or output_tensors is None or empty.
  """

  if not input_tensors:
    raise ValueError('input_tensors must be provided.')
  if not output_tensors:
    raise ValueError('output_tensors must be provided.')

  # Per-method signature_def functions will standardize the keys if possible
  if _is_classification_problem(problem_type, input_tensors, output_tensors):
    (_, examples), = input_tensors.items()
    classes = _get_classification_classes(output_tensors)
    scores = _get_classification_scores(output_tensors)
    if classes is None and scores is None:
      (_, classes), = output_tensors.items()
    return signature_def_utils.classification_signature_def(
        examples, classes, scores)
  elif _is_regression_problem(problem_type, input_tensors, output_tensors):
    (_, examples), = input_tensors.items()
    (_, predictions), = output_tensors.items()
    return signature_def_utils.regression_signature_def(examples, predictions)
  else:
    return signature_def_utils.predict_signature_def(
        input_tensors, output_tensors) 

# 需要导入模块: from tensorflow.python.saved_model import signature_def_utils [as 别名]
# 或者: from tensorflow.python.saved_model.signature_def_utils import predict_signature_def [as 别名]
def build_standardized_signature_def(
    input_tensors, output_tensors, problem_type):
  """Build a SignatureDef using problem type and input and output Tensors.

  Note that this delegates the actual creation of the signatures to methods in
  //third_party/tensorflow/python/saved_model/signature_def_utils.py, which may
  assign names to the input and output tensors (depending on the problem type)
  that are standardized in the context of SavedModel.

  Args:
    input_tensors: a dict of string key to `Tensor`
    output_tensors: a dict of string key to `Tensor`
    problem_type: an instance of constants.ProblemType, specifying
      classification, regression, etc.

  Returns:
    A SignatureDef using SavedModel standard keys where possible.

  Raises:
    ValueError: if input_tensors or output_tensors is None or empty.
  """

  if not input_tensors:
    raise ValueError('input_tensors must be provided.')
  if not output_tensors:
    raise ValueError('output_tensors must be provided.')

  # Per-method signature_def functions will standardize the keys if possible
  if _is_classification_problem(problem_type, input_tensors, output_tensors):
    (_, examples), = input_tensors.items()
    classes = output_tensors.get(prediction_key.PredictionKey.CLASSES)
    scores = _get_classification_scores(output_tensors)
    if classes is None and scores is None:
      (_, classes), = output_tensors.items()
    return signature_def_utils.classification_signature_def(
        examples, classes, scores)
  elif _is_regression_problem(problem_type, input_tensors, output_tensors):
    (_, examples), = input_tensors.items()
    (_, predictions), = output_tensors.items()
    return signature_def_utils.regression_signature_def(examples, predictions)
  else:
    return signature_def_utils.predict_signature_def(
        input_tensors, output_tensors)