                       for key, aligned in self._aligned_faces[lm_side].items()}
          closest_hashes = [self._cache["nearest_landmarks"].get(filename) for filename in filenames]
          if None in closest_hashes:
              closest_hashes = self._cache_closest_hashes(filenames, batch_src_points, landmarks)
  
          batch_dst_points = np.array([landmarks[choice(hsh)] for hsh in closest_hashes])
          logger.trace("Returning: (batch_dst_points: %s)", batch_dst_points.shape)
          return batch_dst_points
  
 
</s> remove             size = self._training_sizes[side]
            centering = "legacy" if self._alignments_version[side] == 1.0 else "head"
            logger.debug("side: %s, centering: %s", side, centering)
 </s> add  </s> remove                 face.load_aligned(None, size=size, centering=centering)
 </s> add                 face.aligned = AlignedFace(face.landmarks_xy,
                                           centering=self._config["centering"],
                                           size=size,
                                           is_aligned=True) </s> remove                            centering=centering,
 </s> add                            centering=self._config["centering"], </s> remove                      old_centering, image.shape, out.shape)
 </s> add                      self._centering, image.shape, out.shape)
            # Resize mismatched training image size landmarks
            sizes = {side: list(self._aligned_faces[side].values())[0].size
                     for side in self._aligned_faces}
            if len(set(sizes.values())) > 1:
                scale = sizes[side] / sizes[lm_side]
                landmarks = {key: lms * scale for key, lms in landmarks.items()}
                       for key, aligned in self._aligned_faces[lm_side].items()}
          closest_hashes = [self._cache["nearest_landmarks"].get(filename) for filename in filenames]
          if None in closest_hashes:
            # Resize mismatched training image size landmarks
            sizes = {side: list(self._aligned_faces[side].values())[0].size
                     for side in self._aligned_faces}
            if len(set(sizes.values())) > 1:
                scale = sizes[side] / sizes[lm_side]
                landmarks = {key: lms * scale for key, lms in landmarks.items()}
              closest_hashes = self._cache_closest_hashes(filenames, batch_src_points, landmarks)
  
          batch_dst_points = np.array([landmarks[choice(hsh)] for hsh in closest_hashes])
          logger.trace("Returning: (batch_dst_points: %s)", batch_dst_points.shape)
          return batch_dst_points
  
keep keep add keep keep keep keep keep keep
 Bugfix - Warp to landmarks 
lib/training_data.py