          self.scaling = scaling
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def show_sample(self):
          """ Display preview data """
          if len(self.images) != 2:
              logger.debug("Ping Pong training - Only one side trained. Aborting preview")
              return None
          logger.debug("Showing sample")
          feeds = dict()
 
</s> remove         self.coverage_ratio = coverage_ratio
        self.scaling = scaling
 </s> add         self._coverage_ratio = coverage_ratio
        self._scaling = scaling </s> remove         self.model = model
        self.use_mask = use_mask
 </s> add         self._model = model
        self._use_mask = use_mask </s> remove         """ Switch pingpong side """
 </s> add         """ Switch ping-pong training from one side of the model to the other """ </s> remove             self.samples = None
            self.target = None
 </s> add             self._samples = None
            self._target = None
            self._masks = None
        """ Compile a preview image.

        Returns
        -------
        :class:`numpy.ndarry`
            A compiled preview image ready for display or saving
        """
          self.scaling = scaling
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def show_sample(self):
        """ Compile a preview image.

        Returns
        -------
        :class:`numpy.ndarry`
            A compiled preview image ready for display or saving
        """
          if len(self.images) != 2:
              logger.debug("Ping Pong training - Only one side trained. Aborting preview")
              return None
          logger.debug("Showing sample")
          feeds = dict()
keep keep keep keep replace keep keep keep keep keep
 Smart Masks - Training Implementation (#914)

* Smart Masks - Training

- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code

* Update mask helptext in cli.py

* Fix Warp to Landmarks
Remove SHA1 hashing from training data

* Update mask training config

* Capture missing masks at training init

* lib.image.read_image_batch - Return filenames with batch for ordering

* scripts.train - Documentation

* plugins.train.trainer - documentation

* Ensure backward compatibility.
Fix convert for new predicted masks

* Update removed masks to components for legacy models. 
plugins/train/trainer/_base.py