          self._config = config
          self._model_input_size = model_input_size
          self._model_output_shapes = model_output_shapes
          self._training_opts = training_opts
          self._landmarks = self._training_opts.get("landmarks", None)
          self._nearest_landmarks = {}
  
          # Batchsize and processing class are set when this class is called by a batcher
 
</s> remove         elif serializer not in ("json", "pickle", "yaml"):
            raise ValueError("Error: {} is not a valid serializer. Use "
                             "'json', 'pickle' or 'yaml'")
 </s> add  </s> remove         self._file_extension = "p"
 </s> add         self._file_extension = "pickle" </s> remove         retval = opts["warp_to_landmarks"]
 </s> add         retval = bool(opts.get("mask_type", None) or opts["warp_to_landmarks"])
        self._mask_class = self._set__mask_class()
          self._config = config
          self._model_input_size = model_input_size
          self._model_output_shapes = model_output_shapes
          self._training_opts = training_opts
        self._mask_class = self._set__mask_class()
          self._landmarks = self._training_opts.get("landmarks", None)
          self._nearest_landmarks = {}
  
          # Batchsize and processing class are set when this class is called by a batcher
keep keep keep add 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