parameters: (self, model_name, model_base_path, verbose=False)
docstring: Initialize the service.
          
      Args:
        model_name: The name of the model.
        model_base_path: The file path of the model.
      Return:
        None
docstring_summary: Initialize the service.
          
      Args:
        model_name: The name of the model.
        model_base_path: The file path of the model.
      Return:
        None
docstring_tokens: Initialize the service . Args : model_name : The name of the model . model_base_path : The file path of the model . Return : None
function: def __init__(self, model_name, model_base_path, verbose=False):
      """
      Initialize the service.
          
      Args:
        model_name: The name of the model.
        model_base_path: The file path of the model.
      Return:
        None
      """
      super(PytorchOnnxInferenceService, self).__init__()
  
      # Init onnx datatype map to numpy
      self.init_dtype_map()
  
      self.model_name = model_name
      self.model_base_path = model_base_path
      self.model_version_list = []
  
      self.model_version_dict = {}
      self.model_dict = {}
      self.executor_dict = {}
      self.model_graph_signature_dict = {}
  
      # This property is for index.html
      self.model_graph_signature = self.model_graph_signature_dict
  
      self.platform = "PyTorch_ONNX"
      self.verbose = verbose
  
      # Find available models
      model_path_list = []
      if os.path.isdir(self.model_base_path):
        for filename in os.path.listdir(self.model_base_path):
          if filename.endswith(".onnx"):
            path = os.path.join(self.model_base_path, filename)
            logging.info("Found onnx model: {}".format(path))
            model_path_list.append(path)
        if len(model_path_list) == 0:
          logging.error("No onnx model found in {}".format(self.model_base_path))
      elif os.path.isfile(self.model_base_path):
        logging.info("Found onnx model: {}".format(self.model_base_path))
        model_path_list.append(self.model_base_path)
      else:
        raise Exception("Invalid model_base_path: {}".format(self.model_base_path))
  
      # Load available models
      count = 1
      for model_path in model_path_list:
        try:
          version = str(count)
          model, executor, signature = self.load_model(model_path)
          logging.info("Load onnx model: {}, signature: {}".format(
            model_path, json.dumps(signature)))
          self.model_version_dict[version] = model_path
          self.model_dict[version] = model
          self.executor_dict[version] = executor
          self.model_graph_signature_dict[version] = signature
          self.model_version_list.append(version)
          count += 1
        except Exception as e:
          traceback.print_exc()
function_tokens: def __init__ ( self , model_name , model_base_path , verbose = False ) : super ( PytorchOnnxInferenceService , self ) . __init__ ( ) # Init onnx datatype map to numpy self . init_dtype_map ( ) self . model_name = model_name self . model_base_path = model_base_path self . model_version_list = [ ] self . model_version_dict = { } self . model_dict = { } self . executor_dict = { } self . model_graph_signature_dict = { } # This property is for index.html self . model_graph_signature = self . model_graph_signature_dict self . platform = "PyTorch_ONNX" self . verbose = verbose # Find available models model_path_list = [ ] if os . path . isdir ( self . model_base_path ) : for filename in os . path . listdir ( self . model_base_path ) : if filename . endswith ( ".onnx" ) : path = os . path . join ( self . model_base_path , filename ) logging . info ( "Found onnx model: {}" . format ( path ) ) model_path_list . append ( path ) if len ( model_path_list ) == 0 : logging . error ( "No onnx model found in {}" . format ( self . model_base_path ) ) elif os . path . isfile ( self . model_base_path ) : logging . info ( "Found onnx model: {}" . format ( self . model_base_path ) ) model_path_list . append ( self . model_base_path ) else : raise Exception ( "Invalid model_base_path: {}" . format ( self . model_base_path ) ) # Load available models count = 1 for model_path in model_path_list : try : version = str ( count ) model , executor , signature = self . load_model ( model_path ) logging . info ( "Load onnx model: {}, signature: {}" . format ( model_path , json . dumps ( signature ) ) ) self . model_version_dict [ version ] = model_path self . model_dict [ version ] = model self . executor_dict [ version ] = executor self . model_graph_signature_dict [ version ] = signature self . model_version_list . append ( version ) count += 1 except Exception as e : traceback . print_exc ( )