  
              if batch:
                  logger.trace("Batching to predictor. Frames: %s, Faces: %s",
                               len(batch), faces_seen)
                  detected_batch = [detected_face for item in batch
                                    for detected_face in item["detected_faces"]]
                  if faces_seen != 0:
                      feed_faces = self._compile_feed_faces(detected_batch)
                      batch_size = None
                      if is_amd and feed_faces.shape[0] != self._batchsize:
                          logger.verbose("Fallback to BS=1")
 
</s> remove                     feed_faces = self._compile_feed_faces(detected_batch)
 </s> add                     feed_faces = self._compile_feed_faces(feed_batch) </s> remove         logger.trace("Input batch shape: %s, side: %s", batch.shape, side)
 </s> add         logger.trace("Input filenames: %s, batch shape: %s, side: %s",
                     filenames, batch.shape, side) </s> remove             self.set_destination_filename()
            self.final_alignments.save()
 </s> add             self._set_destination_filename()
            self._final_alignments.save() </s> remove         if (isinstance(self._images, np.ndarray) and
                self._visible_grid.shape[-1] != self._images.shape[-1]):
 </s> add         if (isinstance(self._images, np.ndarray) and isinstance(self._visible_grid, np.ndarray)
                and self._visible_grid.shape[-1] != self._images.shape[-1]):
                feed_batch = [feed_face for item in batch
                              for feed_face in item["feed_faces"]]
  
              if batch:
                  logger.trace("Batching to predictor. Frames: %s, Faces: %s",
                               len(batch), faces_seen)
                feed_batch = [feed_face for item in batch
                              for feed_face in item["feed_faces"]]
                feed_batch = [feed_face for item in batch
                              for feed_face in item["feed_faces"]]
                  if faces_seen != 0:
                      feed_faces = self._compile_feed_faces(detected_batch)
                      batch_size = None
                      if is_amd and feed_faces.shape[0] != self._batchsize:
                          logger.verbose("Fallback to BS=1")
keep keep keep keep 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 
scripts/convert.py