          mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
          return mask
  
      @property
      def has_predicted_mask(self):
          """ Return whether this model has a predicted mask """
          return bool(self.model.state.config.get("learn_mask", False))
  
      @staticmethod
      def get_batchsize(queue_size):
          """ Get the batchsize """
          logger.debug("Getting batchsize")
 
</s> remove     def input_mask(self):
        """ Return the input mask """
        mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
        return mask
 </s> add     def _input_size(self):
        """ int: The size in pixels of the Faceswap model input. """
        return self._model.input_shape[0] </s> remove     def get_batchsize(queue_size):
        """ Get the batchsize """
 </s> add     def _get_batchsize(queue_size):
        """ Get the batch size for feeding the model.

        Sets the batch size to 1 if inference is being run on CPU, otherwise the minimum of the
        :attr:`self._queue_size` and 16.

        Returns
        -------
        int
            The batch size that the model is to be fed at.
        """ </s> remove         """ Return the model output size """
        return self.model.output_shape[0]
 </s> add         """ int: The size in pixels of the Faceswap model output. """
        return self._model.output_shape[0] </s> remove     def input_size(self):
        """ Return the model input size """
        return self.model.input_shape[0]
 </s> add     def has_predicted_mask(self):
        """ bool: ``True`` if the model was trained to learn a mask, otherwise ``False``. """
        return bool(self._model.state.config.get("learn_mask", False)) </s> remove     def get_mask(self, detected_face, predicted_mask):
        """ Return the mask from lib/model/masks and intersect with box """
 </s> add         Parameters
        ----------
        detected_face: :class:`lib.faces_detect.DetectedFace`
            The DetectedFace object as returned from :class:`scripts.convert.Predictor`.
        predicted_mask: :class:`numpy.ndarray`
            The predicted mask as output from the Faceswap Model if the model was trained
            with a mask, otherwise ``None``

        Returns
        -------
        :class:`numpy.ndarray`
            The mask sized to Faceswap model output with any requested blurring applied.
        """
    def _input_mask(self):
        """ :class:`numpy.ndarray`: A dummy mask for inputting to the model. """
        mask = np.zeros((1, ) + self._model.state.mask_shapes[0], dtype="float32")
        return mask
          mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
          return mask
  
      @property
    def _input_mask(self):
        """ :class:`numpy.ndarray`: A dummy mask for inputting to the model. """
        mask = np.zeros((1, ) + self._model.state.mask_shapes[0], dtype="float32")
        return mask
    def _input_mask(self):
        """ :class:`numpy.ndarray`: A dummy mask for inputting to the model. """
        mask = np.zeros((1, ) + self._model.state.mask_shapes[0], dtype="float32")
        return mask
    def _input_mask(self):
        """ :class:`numpy.ndarray`: A dummy mask for inputting to the model. """
        mask = np.zeros((1, ) + self._model.state.mask_shapes[0], dtype="float32")
        return mask
  
      @staticmethod
      def get_batchsize(queue_size):
          """ Get the batchsize """
          logger.debug("Getting batchsize")
keep keep keep keep replace replace 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