      @staticmethod
      def compile_feed_faces(detected_faces):
          """ Compile the faces for feeding into the predictor """
          logger.trace("Compiling feed face. Batchsize: %s", len(detected_faces))
          feed_faces = np.stack([detected_face.feed_face for detected_face in detected_faces])
          logger.trace("Compiled Feed faces. Shape: %s", feed_faces.shape)
          return feed_faces
  
      def predict(self, feed_faces, batch_size=None):
          """ Perform inference on the feed """
 
</s> remove         image = self._convert_color(input_image[..., :3])
 </s> add         image = self._convert_color(input_image) </s> remove         """ Set the batchsize for the given plugin based on given available vram """
 </s> add         """ Set the batch size for the given plugin based on given available vram """ </s> remove         The normalization method is dictated by the cli argument:
 </s> add         The normalization method is dictated by the command line argument: </s> remove         padding = int((size * (coverage_ratio - 0.625)) / 2)
 </s> add         adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
        padding = round((size * adjusted_ratio) / 2)
        feed_faces = np.stack([detected_face.feed_face[..., :3]
                               for detected_face in detected_faces]) / 255.0
      @staticmethod
      def compile_feed_faces(detected_faces):
          """ Compile the faces for feeding into the predictor """
          logger.trace("Compiling feed face. Batchsize: %s", len(detected_faces))
        feed_faces = np.stack([detected_face.feed_face[..., :3]
                               for detected_face in detected_faces]) / 255.0
          logger.trace("Compiled Feed faces. Shape: %s", feed_faces.shape)
          return feed_faces
  
      def predict(self, feed_faces, batch_size=None):
          """ Perform inference on the feed """
keep keep keep keep replace keep keep keep keep keep
 Smart Mask - Extract code review

- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format 
scripts/convert.py