Python signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY属性代码示例

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def __init__(self):

        model_path = os.environ.get('MODEL_PATH', '/model')

        self.sess = tf.Session(graph=tf.Graph())
        saved_metagraphdef = tf.saved_model.loader.load(self.sess,
                [tag_constants.SERVING], model_path)

        self.inputs_tensor_info = signature_def_utils.get_signature_def_by_key(
                saved_metagraphdef,
                signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY).inputs
        outputs_tensor_info = signature_def_utils.get_signature_def_by_key(
                saved_metagraphdef,
                signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY).outputs

        self.output_tensor_keys_sorted = sorted(outputs_tensor_info.keys())
        self.output_tensor_names_sorted = [
           outputs_tensor_info[tensor_key].name
           for tensor_key in self.output_tensor_keys_sorted
           ] 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [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()
      init_op = tf.global_variables_initializer()
      sess.run(init_op)
      self.restore_from_checkpoint(sess, self.inception_checkpoint_file,
                                   last_checkpoint)
      signature_def = build_signature(inputs=inputs, outputs=outputs)
      signature_def_map = {
          signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_def
      }
      builder = saved_model_builder.SavedModelBuilder(output_dir)
      builder.add_meta_graph_and_variables(
          sess,
          tags=[tag_constants.SERVING],
          signature_def_map=signature_def_map)
      builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def export_model(self, model_dir, global_step_val, last_checkpoint):
    """Exports the model so that it can used for batch predictions."""

    with self.graph.as_default():
      with tf.Session() as session:
        session.run(tf.global_variables_initializer())
        self.saver.restore(session, last_checkpoint)

        signature = signature_def_utils.build_signature_def(
            inputs=self.inputs,
            outputs=self.outputs,
            method_name=signature_constants.PREDICT_METHOD_NAME)

        signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: 
                         signature}

        model_builder = saved_model_builder.SavedModelBuilder(model_dir)
        model_builder.add_meta_graph_and_variables(session,
            tags=[tag_constants.SERVING],
            signature_def_map=signature_map,
            clear_devices=True)
        model_builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def to_savedmodel(model, export_path):
    """Convert the Keras HDF5 model into TensorFlow SavedModel."""

    builder = saved_model_builder.SavedModelBuilder(export_path)

    signature = predict_signature_def(
        inputs={'input': model.inputs[0]}, outputs={'income': model.outputs[0]})

    with K.get_session() as sess:
        builder.add_meta_graph_and_variables(
            sess=sess,
            tags=[tag_constants.SERVING],
            signature_def_map={
                signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
            })
        builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def build_all_signature_defs(input_alternatives, output_alternatives,
                             actual_default_output_alternative_key):
  """Build `SignatureDef`s from all pairs of input and output alternatives."""

  signature_def_map = {
      ('%s:%s' % (input_key, output_key or 'None')):
      build_standardized_signature_def(
          inputs, outputs, problem_type)
      for input_key, inputs in input_alternatives.items()
      for output_key, (problem_type, outputs)
      in output_alternatives.items()}

  # Add the default SignatureDef
  default_inputs = input_alternatives.get(DEFAULT_INPUT_ALTERNATIVE_KEY)
  if not default_inputs:
    raise ValueError('A default input_alternative must be provided.')
    # default_inputs = input_alternatives[FEATURES_INPUT_ALTERNATIVE_KEY]
  # default outputs are guaranteed to exist above
  (default_problem_type, default_outputs) = (
      output_alternatives[actual_default_output_alternative_key])
  signature_def_map[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = (
      build_standardized_signature_def(
          default_inputs, default_outputs, default_problem_type))

  return signature_def_map 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def export_model(self, model_dir, global_step_val, last_checkpoint):
    """Exports the model so that it can used for batch predictions."""

    with self.graph.as_default():
      with tf.Session() as session:
        session.run(tf.global_variables_initializer())
        self.saver.restore(session, last_checkpoint)

        signature = signature_def_utils.build_signature_def(
            inputs=self.inputs,
            outputs=self.outputs,
            method_name=signature_constants.PREDICT_METHOD_NAME)

        signature_map = {
            signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
        }

        model_builder = saved_model_builder.SavedModelBuilder(model_dir)
        model_builder.add_meta_graph_and_variables(
            session,
            tags=[tag_constants.SERVING],
            signature_def_map=signature_map,
            clear_devices=True)
        model_builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def export_model(self, model_dir, global_step_val, last_checkpoint):
    """Exports the model so that it can used for batch predictions."""

    with self.graph.as_default():
      with tf.Session() as session:
        session.run(tf.global_variables_initializer())
        self.saver.restore(session, last_checkpoint)

        signature = signature_def_utils.build_signature_def(
            inputs=self.inputs,
            outputs=self.outputs,
            method_name=signature_constants.PREDICT_METHOD_NAME)

        signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                         signature}

        model_builder = saved_model_builder.SavedModelBuilder(model_dir)
        model_builder.add_meta_graph_and_variables(session,
            tags=[tag_constants.SERVING],
            signature_def_map=signature_map,
            clear_devices=True)
        model_builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def save_signature(self, directory):

        signature = signature_def_utils.build_signature_def(
            inputs={
                'input':
                saved_model_utils.build_tensor_info(self.input),
                'dropout_rate':
                saved_model_utils.build_tensor_info(self.dropout_rate)
            },
            outputs={
                'output': saved_model_utils.build_tensor_info(self.output)
            },
            method_name=signature_constants.PREDICT_METHOD_NAME)
        signature_map = {
            signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
        }
        model_builder = saved_model_builder.SavedModelBuilder(directory)
        model_builder.add_meta_graph_and_variables(
            self.sess,
            tags=[tag_constants.SERVING],
            signature_def_map=signature_map,
            clear_devices=True)
        model_builder.save(as_text=False) 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def export_model(self, model_dir, global_step_val, last_checkpoint):
        """Exports the model so that it can used for batch predictions."""
        with self.graph.as_default():
            with tf.Session() as session:
                session.run(tf.global_variables_initializer())
                self.saver.restore(session, last_checkpoint)

                signature = signature_def_utils.build_signature_def(
                    inputs=self.inputs,
                    outputs=self.outputs,
                    method_name=signature_constants.PREDICT_METHOD_NAME)

                signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                                     signature}

                model_builder = saved_model_builder.SavedModelBuilder(model_dir)
                model_builder.add_meta_graph_and_variables(session,
                                                           tags=[tag_constants.SERVING],
                                                           signature_def_map=signature_map,
                                                           clear_devices=True)
                model_builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def export_model(self, model_dir, global_step_val, last_checkpoint):
    """Exports the model so that it can used for batch predictions."""

    with self.graph.as_default():
      with tf.Session(config=self.config) as session:
        session.run(tf.global_variables_initializer())
        self.saver.restore(session, last_checkpoint)

        signature = signature_def_utils.build_signature_def(
            inputs=self.inputs,
            outputs=self.outputs,
            method_name=signature_constants.PREDICT_METHOD_NAME)

        signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                         signature}

        model_builder = saved_model_builder.SavedModelBuilder(model_dir)
        model_builder.add_meta_graph_and_variables(session,
            tags=[tag_constants.SERVING],
            signature_def_map=signature_map,
            clear_devices=True)
        model_builder.save() 

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

    Args:
      last_checkpoint: The latest checkpoint 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.
      input_signatures, output_signatures = self.build_prediction_graph()
      # Remove this if once Tensorflow 0.12 is standard.
      try:
        init_op = tf.global_variables_initializer()
      except AttributeError:
        init_op = tf.initialize_all_variables()
      sess.run(init_op)
      trained_saver = tf.train.Saver()
      trained_saver.restore(sess, last_checkpoint)

      predict_signature_def = signature_def_utils.build_signature_def(
          input_signatures, output_signatures,
          signature_constants.PREDICT_METHOD_NAME)
      # Create a saver for writing SavedModel training checkpoints.
      build = builder.SavedModelBuilder(
          os.path.join(output_dir, 'saved_model'))
      build.add_meta_graph_and_variables(
          sess, [tag_constants.SERVING],
          signature_def_map={
              signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  predict_signature_def
          },
          assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS))
      build.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def export(model_version, model_dir, sess, inputs, y_op):
    """导出tensorflow_serving可用的模型（Saved Model方式）（推荐）
    prediction_signature必备的三个参数分别是输入inputs、输出outputs和方法名method_name，如果缺失方法名将会报错：“grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INTERNAL, details="Expected prediction signature method_name to be one of {tensorflow/serving/predict, tensorflow/serving/classify, tensorflow/serving/regress}. Was: ")”。每一个SavedModel关联着一个独立的checkpoint。每一个图元都绑定一个或多个标签，这些标签用来明确图元被加载的方式。标签只接受两种类型：serve或者train，保存时可以同时包含两个标签。其中tag_constants.SERVING = "serve"，tag_constants.TRAINING = "train"。模型用于TensorFlow Serving时，标签必须包含serve类型。如果标签只包含train类型，TensorFlow Serving加载模型时会报错：“E tensorflow_serving/core/aspired_versions_manager.cc:351] Servable {name: default version: 2} cannot be loaded: Not found: Could not find meta graph def matching supplied tags.”。定义signature_def_map时注意定义默认服务签名键，如果缺少则会报错：“grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.FAILED_PRECONDITION, details="Default serving signature key not found.")”。
    """
    if model_version <= 0:
        print('Please specify a positive value for version number.')
        sys.exit()

    path = os.path.dirname(os.path.abspath(model_dir))
    if os.path.isdir(path) == False:
        logging.warning('Path (%s) not exists, making directories...', path)
        os.makedirs(path)

    export_path = os.path.join(
        compat.as_bytes(model_dir),
        compat.as_bytes(str(model_version)))

    if os.path.isdir(export_path) == True:
        logging.warning('Path (%s) exists, removing directories...', export_path)
        shutil.rmtree(export_path)

    builder = saved_model_builder.SavedModelBuilder(export_path)
    tensor_info_x = utils.build_tensor_info(inputs)
    tensor_info_y = utils.build_tensor_info(y_op)

    prediction_signature = signature_def_utils.build_signature_def(
        inputs={'x': tensor_info_x},
        outputs={'y': tensor_info_y},
        method_name=signature_constants.PREDICT_METHOD_NAME)

    builder.add_meta_graph_and_variables(
        sess,
        [tag_constants.SERVING],
        signature_def_map={
            'predict_text': prediction_signature,
            signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature
        })

    builder.save() 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def log_saved_model(saved_model_path: Union[bytes, str],
                    global_step: int,
                    saved_model_load_fn: Callable):
    """
    Log all the saved models to mlflow

    Parameters
    ----------
    saved_model_path
        path to saved model
    global_step
        global step for saved model
    """
    # pylint: disable=unused-argument
    # saved_model_load_fn is coming from patch
    if mlflow.active_run() is None:
        _warn_about_no_run()
        return
    if isinstance(saved_model_path, bytes):
        saved_model_path = saved_model_path.decode()
    saved_model_tag = os.path.split(saved_model_path)[-1]
    artifact_path = os.path.join("models", saved_model_tag)
    mlflow_tf.log_model(
        tf_saved_model_dir=saved_model_path,
        tf_meta_graph_tags=[tag_constants.SERVING],
        tf_signature_def_key=
        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
        artifact_path=artifact_path)
    mlflow_artifacts_path = mlflow.get_artifact_uri()
    saved_model_artifact_path = os.path.join(
        mlflow_artifacts_path, artifact_path)
    project_utils.log_exported_model_info(
        saved_model_artifact_path, global_step) 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def _tf_load_model(sess, model_dir):
  """Load a tf model from model_dir, and return input/output alias maps."""

  meta_graph_pb = tf.saved_model.loader.load(
      sess=sess,
      tags=[tf.saved_model.tag_constants.SERVING],
      export_dir=model_dir)

  signature = meta_graph_pb.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
  input_alias_map = {friendly_name: tensor_info_proto.name
                     for (friendly_name, tensor_info_proto) in signature.inputs.items()}
  output_alias_map = {friendly_name: tensor_info_proto.name
                      for (friendly_name, tensor_info_proto) in signature.outputs.items()}

  return input_alias_map, output_alias_map 

# 需要导入模块: from tensorflow.python.saved_model import signature_constants [as 别名]
# 或者: from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY [as 别名]
def _load_tf_saved_model(path):
    try:
        import tensorflow as tf
        from tensorflow.python.training.tracking.tracking import AutoTrackable

        TF2 = tf.__version__.startswith('2')
    except ImportError:
        raise MissingDependencyException(
            "Tensorflow package is required to use TfSavedModelArtifact"
        )

    if TF2:
        return tf.saved_model.load(path)
    else:
        loaded = tf.compat.v2.saved_model.load(path)
        if isinstance(loaded, AutoTrackable) and not hasattr(loaded, "__call__"):
            logger.warning(
                '''Importing SavedModels from TensorFlow 1.x.
                `outputs = imported(inputs)` is not supported in bento service due to
                tensorflow API.

                Recommended usage:

                ```python
                from tensorflow.python.saved_model import signature_constants

                imported = tf.saved_model.load(path_to_v1_saved_model)
                wrapped_function = imported.signatures[
                    signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
                wrapped_function(tf.ones([]))
                ```

                See https://www.tensorflow.org/api_docs/python/tf/saved_model/load for
                details.
                '''
            )
        return loaded