                                         for k, v in retval.items()})
          return retval
  
      # <<< COLOR AUGMENTATION >>> #
      def color_adjust(self, batch):
          """ Perform color augmentation on the passed in batch.
  
          The color adjustment parameters are set in :file:`config.train.ini`
  
          Parameters
 
</s> remove     def transform(self, batch):
 </s> add     def transform(self, batch: np.ndarray): </s> remove         return batch
 </s> add  </s> remove         grids = np.mgrid[0:p_mx:complex(self._training_size), 0:p_mx:complex(self._training_size)]

        self._constants = dict(clahe_base_contrast=clahe_base_contrast,
                               tgt_slices=tgt_slices,
                               warp_mapx=warp_mapx,
                               warp_mapy=warp_mapy,
                               warp_pad=warp_pad,
                               warp_slices=warp_slices,
                               warp_lm_edge_anchors=edge_anchors,
                               warp_lm_grids=grids)
        self.initialized = True
        logger.debug("Initialized constants: %s", {k: str(v) if isinstance(v, np.ndarray) else v
                                                   for k, v in self._constants.items()})

    # <<< TARGET IMAGES >>> #
    def get_targets(self, batch):
        """ Returns the target images, and masks, if required.

        Parameters
        ----------
        batch: :class:`numpy.ndarray`
            This should be a 4+-dimensional array of training images in the format (`batchsize`,
            `height`, `width`, `channels`). Targets should be requested after performing image
            transformations but prior to performing warps.

            The 4th channel should be the mask. Any channels above the 4th should be any additional
            masks that are requested.

        Returns
        -------
        dict
            The following keys will be within the returned dictionary:

            * **targets** (`list`) - A list of 4-dimensional :class:`numpy.ndarray` s in the \
            order and size of each output of the model as defined in :attr:`output_shapes`. The \
            format of these arrays will be (`batchsize`, `height`, `width`, `3`). **NB:** \
            masks are not included in the `targets` list. If masks are to be included in the \
            output they will be returned as their own item from the `masks` key.

            * **masks** (:class:`numpy.ndarray`) - A 4-dimensional array containing the target \
            masks in the format (`batchsize`, `height`, `width`, `1`).
        """
        logger.trace("Compiling targets: batch shape: %s", batch.shape)
        slices = self._constants["tgt_slices"]
        target_batch = [np.array([cv2.resize(image[slices, slices, :],
                                             (size, size),
                                             cv2.INTER_AREA)
                                  for image in batch], dtype='float32') / 255.
                        for size in self._output_sizes]
        logger.trace("Target image shapes: %s",
                     [tgt_images.shape for tgt_images in target_batch])

        retval = self._separate_target_mask(target_batch)
        logger.trace("Final targets: %s",
                     {k: v.shape if isinstance(v, np.ndarray) else [img.shape for img in v]
                      for k, v in retval.items()})
        return retval

    @staticmethod
    def _separate_target_mask(target_batch):
        """ Return the batch and the batch of final masks

        Parameters
        ----------
        target_batch: list
            List of 4 dimension :class:`numpy.ndarray` objects resized the model outputs.
            The 4th channel of the array contains the face mask, any additional channels after
            this are additional masks (e.g. eye mask and mouth mask)

        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
        """
        logger.trace("target_batch shapes: %s", [tgt.shape for tgt in target_batch])
        retval = dict(targets=[batch[..., :3] for batch in target_batch],
                      masks=target_batch[-1][..., 3][..., None])
        if target_batch[-1].shape[-1] > 4:
            retval["additional_masks"] = target_batch[-1][..., 4:]
        logger.trace("returning: %s", {k: v.shape if isinstance(v, np.ndarray) else [tgt.shape
                                                                                     for tgt in v]
                                       for k, v in retval.items()})
 </s> add         grids = np.mgrid[0: p_mx: complex(self._processing_size),  # type: ignore
                         0: p_mx: complex(self._processing_size)]  # type: ignore
        retval = AugConstants(clahe_base_contrast=max(2, self._processing_size // 128),
                              clahe_chance=int(self._config.get("color_clahe_chance", 50)) / 100,
                              clahe_max_size=int(self._config.get("color_clahe_max_size", 4)),
                              lab_adjust=lab_adjust,
                              transform_rotation=int(self._config.get("rotation_range", 10)),
                              transform_zoom=int(self._config.get("zoom_amount", 5)) / 100,
                              transform_shift=tform_shift,
                              warp_maps=np.stack((warp_mapx, warp_mapy), axis=1),
                              warp_pad=(warp_pad, warp_pad),
                              warp_slices=slice(warp_pad // 10, -warp_pad // 10),
                              warp_lm_edge_anchors=edge_anchors,
                              warp_lm_grids=grids)
        logger.debug("Initialized constants: %s", retval) </s> remove         logger.trace("Randomly transformed image")
        return batch
 </s> add         logger.trace("Randomly transformed image")  # type: ignore </s> remove     def random_flip(self, batch):
 </s> add     def random_flip(self, batch: np.ndarray):
    def color_adjust(self, batch: np.ndarray) -> np.ndarray:
                                         for k, v in retval.items()})
          return retval
  
      # <<< COLOR AUGMENTATION >>> #
    def color_adjust(self, batch: np.ndarray) -> np.ndarray:
          """ Perform color augmentation on the passed in batch.
  
          The color adjustment parameters are set in :file:`config.train.ini`
  
          Parameters
keep keep keep keep replace 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 
lib/training/augmentation.py