  
      # << INTERNAL METHODS >> #
      def _validate_samples(self, data):
          """ Ensures that the total number of images within :attr:`images` is greater or equal to
          the selected :attr:`batchsize`. Raises an exception if this is not the case. """
          length = len(data)
          msg = ("Number of images is lower than batch-size (Note that too few "
 
</s> remove             loss = self.model.predictors[self.side].train_on_batch(x=model_inputs, y=model_targets)
 </s> add             loss = self.model.predictors[self.side].train_on_batch(*batch) </s> remove                    "\nIf you are using 'warp to landmarks' then every "
 </s> add                    "\nIf you are training with a mask or using 'warp to landmarks' then every " </s> remove         model_inputs, model_targets = self.get_next()
 </s> add         batch = self.get_next(do_preview) </s> remove         config item ``warp_to_landmarks`` is ``True``. If the landmarks for an image cannot be
        found, then an error is raised. """
 </s> add         config item ``warp_to_landmarks`` is ``True`` or if :attr:`mask_type` is not ``None``. If
        the landmarks for an image cannot be found, then an error is raised. """
    def _set__mask_class(self):
        """ Returns the correct mask class from :mod:`lib`.model.masks` as defined in the
        :attr:`mask_type` parameter. """
        mask_type = self._training_opts.get("mask_type", None)
        if mask_type:
            logger.debug("Mask type: '%s'", mask_type)
            _mask_class = getattr(masks, mask_type)
        else:
            _mask_class = None
        logger.debug("Mask class: %s", _mask_class)
        return _mask_class

  
      # << INTERNAL METHODS >> #
    def _set__mask_class(self):
        """ Returns the correct mask class from :mod:`lib`.model.masks` as defined in the
        :attr:`mask_type` parameter. """
        mask_type = self._training_opts.get("mask_type", None)
        if mask_type:
            logger.debug("Mask type: '%s'", mask_type)
            _mask_class = getattr(masks, mask_type)
        else:
            _mask_class = None
        logger.debug("Mask class: %s", _mask_class)
        return _mask_class

      def _validate_samples(self, data):
          """ Ensures that the total number of images within :attr:`images` is greater or equal to
          the selected :attr:`batchsize`. Raises an exception if this is not the case. """
          length = len(data)
          msg = ("Number of images is lower than batch-size (Note that too few "
keep add keep keep keep keep keep
 Serialize masks to alignments file

- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version 
lib/training_data.py