  
          Returns
          -------
          dict:
              Includes the key `landmarks` if landmarks are required for training and the key `masks`
              if the masks are required for training. """
          retval = dict()
  
          get_masks = self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]
          if not self._model.command_line_arguments.warp_to_landmarks and not get_masks:
              return retval
 
</s> remove         get_masks = self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]
        if not self._model.command_line_arguments.warp_to_landmarks and not get_masks:
 </s> add         if not any([self._model.config["learn_mask"],
                    self._model.config["penalized_mask_loss"],
                    self._model.config["eye_multiplier"] > 1,
                    self._model.config["mouth_multiplier"] > 1,
                    self._model.command_line_arguments.warp_to_landmarks]): </s> remove         masks = targets[-1]
        for idx, tgt in enumerate(targets[:-1]):
 </s> add         if not self._model.config["penalized_mask_loss"] and additional_masks is None:
            logger.trace("No masks to compile. Returning targets")
            return targets

        if not self._model.config["penalized_mask_loss"] and additional_masks is not None:
            masks = additional_masks
        elif additional_masks is not None:
            masks = np.concatenate((masks, additional_masks), axis=-1)

        for idx, tgt in enumerate(targets): </s> remove         if get_masks:
 </s> add         if self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]: </s> remove         The target masks are returned as its own item and is the 4th channel of the final target
        output.
 </s> add         Returns
        -------
        dict:
            The targets and the masks separated into their own items. The targets are a list of
            3 channel, 4 dimensional :class:`numpy.ndarray` objects sized for each output from the
            model. The masks are a :class:`numpy.ndarray` of the final output size. Any additional
            masks(e.g. eye and mouth masks) will be collated together into a :class:`numpy.ndarray`
            of the final output size. The number of channels will be the number of additional
            masks available
            Includes the key `landmarks` if landmarks are required for training, `masks` if masks
            are required for training, `masks_eye` if eye masks are required and `masks_mouth` if
            mouth masks are required. """
  
          Returns
          -------
          dict:
            Includes the key `landmarks` if landmarks are required for training, `masks` if masks
            are required for training, `masks_eye` if eye masks are required and `masks_mouth` if
            mouth masks are required. """
            Includes the key `landmarks` if landmarks are required for training, `masks` if masks
            are required for training, `masks_eye` if eye masks are required and `masks_mouth` if
            mouth masks are required. """
          retval = dict()
  
          get_masks = self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]
          if not self._model.command_line_arguments.warp_to_landmarks and not get_masks:
              return retval
keep keep keep keep replace replace keep keep keep keep keep
 Add Mouth and Eye Priority to Loss options (#1054)

* Priority Training for Mouth and Eyes - Tensorflow

* Use chosen loss function for area multipliers

* loss multipliers for AMD

* Fix mask multipliers for plaid and roll PenalizedMaskLoss into LossWrapper

* losses_tf: roll PenalizedMaskLoss into LossWrapper 
plugins/train/trainer/_base.py