              retval[side] = alignments_path
          logger.debug("Alignments paths: %s", retval)
          return retval
  
      def _get_landmarks(self):
          """ Pre-generate landmarks as they are needed for both warp to landmarks and eye/mouth
          masks.
  
          Returns
          -------
          dict
              The :class:`numpy.ndarray` aligned landmarks for keys "a" and "b"
 
</s> remove             The :class:`numpy.ndarray` aligned landmarks for keys "a" and "b"
 </s> add             The "a", "b" keys for each side, containing a sub-dictionary with the
            filename as key and :class:`lib.faces.detected_face.AlignedFace` object as value. </s> remove         retval = {side: self._transform_landmarks(side, detected_faces)
                  for side, detected_faces in self._detected_faces.items()}
        logger.trace(retval)
 </s> add         retval = dict()
        for side, detected_faces in self._detected_faces.items():
            centering = "legacy" if self._alignments_version[side] == 1.0 else "head"
            logger.debug("side: %s, centering: %s", side, centering)
            ret_side = dict()
            for fhash, face in detected_faces.items():
                face.load_aligned(None, size=self._training_size, centering=centering)
                for filename in self._hash_to_filenames(side, fhash):
                    ret_side[filename] = face.aligned
            retval[side] = ret_side </s> remove     # Get landmarks
 </s> add  </s> remove     def _transform_landmarks(self, side, detected_faces):
        """ Transform frame landmarks to their aligned face variant.

        Parameters
        ----------
        side: {"a" or "b"}
            The side currently being processed
        detected_faces: list
            A list of :class:`lib.faces_detect.DetectedFace` objects

        Returns
        -------
        dict
            The face filenames as keys with the aligned landmarks as value.
        """
        landmarks = dict()
        for face in detected_faces.values():
            face.load_aligned(None, size=self._training_size)
            for filename in self._hash_to_filenames(side, face.hash):
                landmarks[filename] = face.aligned_landmarks
        return landmarks

 </s> add  </s> remove     def landmarks(self):
        """ dict: The :class:`numpy.ndarray` aligned landmarks for keys "a" and "b" """
        return self._landmarks
 </s> add     def aligned_faces(self):
        """ dict: The "a", "b" keys for each side, containing a sub-dictionary with the
        filename as key and :class:`lib.faces.detected_face.aligned` object as value. """
        return self._aligned_faces

    @property
    def versions(self):
        """ dict: The "a", "b" keys for each side, with value being the alignment file version
        that provided the data. This is used to crop the faces correctly based on whether the
        extracted faces are legacy or full-head extracts. """
        return self._alignments_version
    def _get_aligned_faces(self):
        """ Pre-generate aligned faces as they are needed for all training functions.
              retval[side] = alignments_path
          logger.debug("Alignments paths: %s", retval)
          return retval
  
    def _get_aligned_faces(self):
        """ Pre-generate aligned faces as they are needed for all training functions.
    def _get_aligned_faces(self):
        """ Pre-generate aligned faces as they are needed for all training functions.
    def _get_aligned_faces(self):
        """ Pre-generate aligned faces as they are needed for all training functions.
  
          Returns
          -------
          dict
              The :class:`numpy.ndarray` aligned landmarks for keys "a" and "b"
keep keep keep keep replace replace replace keep keep keep keep keep
 Extract - Increase area and move centering (#1095)

* Extract
  - Implement aligner re-feeding
  - Add extract type to pipeline.ExtractMedia
  - Add pose annotation to debug
* Convert
  - implement centering
  - remove usage of feed and reference face properties
  - Remove distributed option from convert
  - Force update of alignments file on legacy receive
* Train
  - Resize preview image to model output size
  - Force legacy centering if centering does not exist in model's state file
  - Enable training on legacy face sets

* Alignments Tool
  - Update draw to include head/pose
  - Remove DFL drop + linting
  - Remove remove-frames job
  - remove align-eyes option
  - Update legacy masks to new extract type
  - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
  - Re-generate thumbnails on legacy upgrade
* Mask Tool
  - Update for new extract + bugfix full frame
* Manual Tool
  - Update to new extraction method
   - Disable legacy alignments,
   - extract box bugfix
   - extract faces - size to 512 and center on head
* Preview Tool
  - Display based on model centering
* Sort Tool
  - Use alignments for sort by face

* lib.aligner
  - Add Pose Class
  - Add AlignedFace Class
  - center _MEAN_FACE on x
  - Add meta information with versioning to alignments file
  - lib.aligner.get_align_matrix to use landmarks not face
  - Refactor aligned faces in lib.faces_detect
* lib.logger
  - larger file log padding
* lib.config
  - Fix global changeable_items
* lib.face_filter
  - Use new extracted face images
* lib.image
  - bump thumbnail default size to 96px 
plugins/train/trainer/_base.py