          self.faces_count = 0
          self.verify_output = False
  
          if arguments.allow_growth:
              self.set_tf_allow_growth()
  
          self.model = self.load_model()
          self.output_indices = {"face": self.model.largest_face_index,
                                 "mask": self.model.largest_mask_index}
          self.predictor = self.model.converter(self.args.swap_model)
 
</s> remove         self.model = self.load_model()
        self.output_indices = {"face": self.model.largest_face_index,
                               "mask": self.model.largest_mask_index}
        self.predictor = self.model.converter(self.args.swap_model)
        self.queues = dict()
 </s> add     @property
    def thread(self):
        """ :class:`~lib.multithreading.MultiThread`: The thread that is running the prediction
        function from the Faceswap model. """
        return self._thread

    @property
    def in_queue(self):
        """ :class:`queue.Queue`: The input queue to the predictor. """
        return self._in_queue

    @property
    def out_queue(self):
        """ :class:`queue.Queue`: The output queue from the predictor. """
        return self._out_queue

    @property
    def faces_count(self):
        """ int: The total number of faces seen by the Predictor. """
        return self._faces_count </s> remove         self.batchsize = self.get_batchsize(queue_size)
        self.args = arguments
        self.in_queue = in_queue
        self.out_queue = queue_manager.get_queue("patch")
        self.serializer = get_serializer("json")
        self.faces_count = 0
        self.verify_output = False
 </s> add         self._batchsize = self._get_batchsize(queue_size)
        self._args = arguments
        self._in_queue = in_queue
        self._out_queue = queue_manager.get_queue("patch")
        self._serializer = get_serializer("json")
        self._faces_count = 0
        self._verify_output = False </s> remove         self.thread = MultiThread(self.predict_faces, thread_count=1)
        self.thread.start()
        logger.debug("Initialized %s: (out_queue: %s)", self.__class__.__name__, self.out_queue)
 </s> add     @property
    def verify_output(self):
        """ bool: ``True`` if multiple faces have been found in frames, otherwise ``False``. """
        return self._verify_output </s> remove                     self.verify_output = True
 </s> add                     self._verify_output = True </s> remove                 self.faces_count += faces_count
 </s> add                 self._faces_count += faces_count
            self._set_tf_allow_growth()

        self._model = self._load_model()
        self._output_indices = {"face": self._model.largest_face_index,
                                "mask": self._model.largest_mask_index}
        self._predictor = self._model.converter(self._args.swap_model)
        self._thread = self._launch_predictor()
        logger.debug("Initialized %s: (out_queue: %s)", self.__class__.__name__, self._out_queue)
          self.faces_count = 0
          self.verify_output = False
  
          if arguments.allow_growth:
            self._set_tf_allow_growth()

        self._model = self._load_model()
        self._output_indices = {"face": self._model.largest_face_index,
                                "mask": self._model.largest_mask_index}
        self._predictor = self._model.converter(self._args.swap_model)
        self._thread = self._launch_predictor()
        logger.debug("Initialized %s: (out_queue: %s)", self.__class__.__name__, self._out_queue)
  
          self.model = self.load_model()
          self.output_indices = {"face": self.model.largest_face_index,
                                 "mask": self.model.largest_mask_index}
          self.predictor = self.model.converter(self.args.swap_model)
keep keep keep keep replace keep keep keep keep keep
 Smart Masks to Convert (#957)

- scripts.convert - Use Smart Masks for Convert
    * Make on-the-fly conversion an explicit option

- Move BlurMask to lib.faces_detect

- tools.preview - Fix for smart masks
    * Subclass from tk.Tk
    * Options to lib.gui.control_helper
    *variable cleanup

- lib.logger - Demote more tensorflow deprecation messages

- Documentation:
    * lib.faces_detect.BlurMask
    * plugins.convert.mask
    * lib.convert
    * scripts.convert
    * scripts.fsmedia
    * tools.preview 
scripts/convert.py