                       self.__class__.__name__, {k: len(v) for k, v in images.items()}, batch_size,
                       config)
          self._model = model
          self._images = images
          self._config = config
          self._feeds = {side: self._load_generator(side, False).minibatch_ab()
                         for side in get_args(Literal["a", "b"])}
  
          self._display_feeds = dict(preview=self._set_preview_feed(), timelapse={})
 
</s> remove         self._target = {}
        self._samples = {}
        self._masks = {}

        self._feeds = {side: self._load_generator(idx).minibatch_ab(images[side], batch_size, side)
                       for idx, side in enumerate(("a", "b"))}
 </s> add         self._feeds = {side: self._load_generator(side, False).minibatch_ab()
                       for side in get_args(Literal["a", "b"])} </s> remove                      self.__class__.__name__, len(images), batch_size, config)
 </s> add                      self.__class__.__name__, {k: len(v) for k, v in images.items()}, batch_size,
                     config) </s> remove     def __init__(self, images, model, batch_size, config):
 </s> add     def __init__(self,
                 images: Dict[Literal["a", "b"], List[str]],
                 model: 'ModelBase',
                 batch_size: int,
                 config: ConfigType) -> None: </s> remove     def _load_generator(self, output_index):
 </s> add     def _load_generator(self,
                        side: Literal["a", "b"],
                        is_display: bool,
                        batch_size: Optional[int] = None,
                        images: Optional[List[str]] = None) -> DataGenerator: </s> remove             side_images = images[side] if images is not None else self._target[side]
            side_masks = masks[side] if masks is not None else self._masks[side]
            side_samples = samples[side] if samples is not None else self._samples[side]
            retval[side] = [side_samples[0:num_images],
                            side_images[0:num_images],
                            side_masks[0:num_images]]
 </s> add             retval[side] = [feed[side][0:num_images],
                            samples[side][0:num_images],
                            masks[side][0:num_images]]
        logger.debug("Compiled Samples: %s", {k: [i.shape for i in v] for k, v in retval.items()})
        self._batch_size = batch_size
                       self.__class__.__name__, {k: len(v) for k, v in images.items()}, batch_size,
                       config)
          self._model = model
          self._images = images
        self._batch_size = batch_size
          self._config = config
          self._feeds = {side: self._load_generator(side, False).minibatch_ab()
                         for side in get_args(Literal["a", "b"])}
  
          self._display_feeds = dict(preview=self._set_preview_feed(), timelapse={})
keep keep keep add keep keep keep keep keep
 Data Augmentation update (#1263)

- lib.detected_face
    - Subclass Masks for Landmark based masks
    - Add training mask propery + methods to DetectedFace
  - lib.training_training
    - subclass TrainingDataGenerator for training and preview data
    - Split cache into own module
    - Reduce thread count to 1 to prevent image corruption + data re-use
    - Process on largest model input/output size rather than stored image size
    - Size and crop masks during caching stage
    - Implement ring buffer for data flow
    - Fix preview reload bug
  - augmentation
    - typing
    - switch color aug order
    - better initialization
    - Fix warp + landmark warp to correctly apply at different image scales
    - Slightly improved warp caching
    - Don't store whether image is_preview. Handle all data as training images implicitly
  - plugins.trainer: Typing and fixes to work with trainingdata refactor 
plugins/train/trainer/_base.py