  
          if self._model.config["eye_multiplier"] > 1:
              retval["masks_eye"] = alignments.masks_eye
  
          if self._model.config["mouth_multiplier"] > 1:
              retval["masks_mouth"] = alignments.masks_mouth
  
          logger.debug({key: {k: len(v) for k, v in val.items()} for key, val in retval.items()})
          return retval
  
 
</s> remove         if self._model.config["eye_multiplier"] > 1:
 </s> add         if penalized_loss and self._model.config["eye_multiplier"] > 1: </s> remove         if self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]:
 </s> add         if self._model.config["learn_mask"] or penalized_loss: </s> remove                     self._model.config["penalized_mask_loss"],
 </s> add                     penalized_loss, </s> remove             logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
            masks = np.array([self._get_mask(item[side][filename], size)
                              for filename in filenames], dtype=batch.dtype)
            masks = self._resize_masks(size, masks)
 </s> add             if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks)
        if penalized_loss and self._model.config["mouth_multiplier"] > 1:
  
          if self._model.config["eye_multiplier"] > 1:
              retval["masks_eye"] = alignments.masks_eye
  
        if penalized_loss and self._model.config["mouth_multiplier"] > 1:
              retval["masks_mouth"] = alignments.masks_mouth
  
          logger.debug({key: {k: len(v) for k, v in val.items()} for key, val in retval.items()})
          return retval
  
keep keep keep keep replace keep keep keep keep keep
 - Bugfix: Training - Disable loss multipliers if penalized loss not selected
- Training: Half mouth/eye multiplier defaults
- GUI: Remove analysis callback from convert tab 
plugins/train/trainer/_base.py