              batch_src_pts = self._get_landmarks(filenames, batch, side)
  
          # Color augmentation before mask is added
          if self._training_opts["augment_color"]:
              batch = self._processing.color_adjust(batch)
  
          # Add mask to batch prior to transforms and warps
          if self._mask_class:
              batch = np.array([self._mask_class(src_pts, image, channels=4).mask
                                for src_pts, image in zip(batch_src_pts, batch)])
  
          # Random Transform and flip
          batch = self._processing.transform(batch)
          if not self._training_opts["no_flip"]:
              batch = self._processing.random_flip(batch)
 
</s> remove         # Color augmentation before mask is added
 </s> add         # Color Augmentation of the image only </s> remove         if self._mask_class or to_landmarks:
 </s> add         if self._training_opts["warp_to_landmarks"]: </s> remove         to_landmarks = self._training_opts["warp_to_landmarks"]
 </s> add  </s> remove         if self.use_mask:
            batch = [[batch["feed"], batch["masks"]], batch["targets"] + [batch["masks"]]]
        else:
            batch = [batch["feed"], batch["targets"]]
        self.generate_preview(do_preview)
        return batch
 </s> add         targets_use_mask = self.model.training_opts["replicate_input_mask"]
        model_inputs = batch["feed"] + batch["masks"] if self.use_mask else batch["feed"]
        model_targets = batch["targets"] + batch["masks"] if targets_use_mask else batch["targets"]
        return model_inputs, model_targets
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
              batch_src_pts = self._get_landmarks(filenames, batch, side)
  
          # Color augmentation before mask is added
          if self._training_opts["augment_color"]:
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
            batch[..., :3] = self._processing.color_adjust(batch[..., :3])
  
          # Random Transform and flip
          batch = self._processing.transform(batch)
          if not self._training_opts["no_flip"]:
              batch = self._processing.random_flip(batch)
keep keep keep keep replace replace replace replace replace replace keep keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
lib/training_data.py