                               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")
                          batch_size = 1
                      predicted = self._predict(feed_faces, batch_size)
 
</s> remove                 detected_batch = [detected_face for item in batch
                                  for detected_face in item["detected_faces"]]
 </s> add                 feed_batch = [feed_face for item in batch
                              for feed_face in item["feed_faces"]] </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         preds = np.empty((self._loader.count, 512), dtype="float32")
        for idx, (filename, image) in enumerate(tqdm(self._loader.load(),
                                                     desc="Classifying Faces...",
                                                     total=self._loader.count)):
 </s> add         preds = []
        no_hash = 0
        for filename, image, hsh in tqdm(self._loader.load(),
                                         desc="Classifying Faces...",
                                         total=self._loader.count):
            if self._alignments is not None and self._alignments.version != 1.0:
                face = self._alignments.hashes_to_alignment.get(hsh)
                if face:
                    image = AlignedFace(face["landmarks_xy"],
                                        image=image,
                                        centering="legacy",
                                        size=self._vgg_face.input_size,
                                        is_aligned=True).face
                elif image.shape[0] != image.shape[1]:
                    logger.warning("Skipping image '%s' as it is not square (probably not a "
                                   "face)", filename)
                    continue
                else:  # Center crop the image and add count to warning count
                    center = image.shape[0] // 2
                    crop = slice(center - int(center * ratio), center + int(center * ratio))
                    image = image[crop, crop, :]
                    no_hash += 1

                    feed_faces = self._compile_feed_faces(feed_batch)
                               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(feed_batch)
                      batch_size = None
                      if is_amd and feed_faces.shape[0] != self._batchsize:
                          logger.verbose("Fallback to BS=1")
                          batch_size = 1
                      predicted = self._predict(feed_faces, batch_size)
keep keep keep keep 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