          """
          for mask, face in zip(batch["prediction"], batch["detected_faces"]):
              face.add_mask(self._storage_name,
                            mask,
                            face.feed_matrix,
                            face.feed_interpolators[1],
                            storage_size=self._storage_size)
              face.feed = dict()
  
          logger.trace("Item out: %s", {key: val.shape if isinstance(val, np.ndarray) else val
                                        for key, val in batch.items()})
 
</s> remove             face.feed = dict()
 </s> add         del batch["feed_faces"] </s> remove         for mask, face in zip(batch["prediction"], batch["detected_faces"]):
 </s> add         for mask, face, feed_face in zip(batch["prediction"],
                                         batch["detected_faces"],
                                         batch["feed_faces"]): </s> remove         encodings = self.vgg_face.predict(detected_face.aligned_face)
 </s> add         feed = AlignedFace(detected_face.landmarks_xy, image=image, size=224, centering="legacy")
        encodings = self.vgg_face.predict(feed.face)
 </s> remove         """ Patch targets and prediction images into images of training image size.
 </s> add         """ Patch targets and prediction images into images of model output size. </s> remove         logger.debug({key: {k: len(v) for k, v in val.items()} for key, val in retval.items()})
 </s> add         logger.debug({key: {k: v if isinstance(v, float) else len(v)
                            for k, v in val.items()}
                      for key, val in retval.items()})
                          feed_face.adjusted_matrix,
                          feed_face.interpolators[1],
          """
          for mask, face in zip(batch["prediction"], batch["detected_faces"]):
              face.add_mask(self._storage_name,
                            mask,
                          feed_face.adjusted_matrix,
                          feed_face.interpolators[1],
                          feed_face.adjusted_matrix,
                          feed_face.interpolators[1],
                            storage_size=self._storage_size)
              face.feed = dict()
  
          logger.trace("Item out: %s", {key: val.shape if isinstance(val, np.ndarray) else val
                                        for key, val in batch.items()})
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 
plugins/extract/mask/_base.py