          self.model.predict(placeholder)
  
      def process_input(self, batch):
          """ Compile the detected faces for prediction """
          mean = (0.485, 0.456, 0.406)
          std = (0.229, 0.224, 0.225)
  
          batch["feed"] = ((np.array([feed.face[..., :3]
                                      for feed in batch["feed_faces"]],
                                     dtype="float32") / 255.0) - mean) / std
          logger.trace("feed shape: %s", batch["feed"].shape)
 
</s> remove                              19)
 </s> add                              lbls) </s> remove         self._segment_indices = self._get_segment_indices()
 </s> add  </s> remove         model_filename = "bisnet_face_parsing_v1.h5"
 </s> add         model_filename = f"bisnet_face_parsing_v{'2' if self._is_faceswap else '1'}.h5"
        mean = (0.384, 0.314, 0.279) if self._is_faceswap else (0.485, 0.456, 0.406)
        std = (0.324, 0.286, 0.275) if self._is_faceswap else (0.229, 0.224, 0.225)
          self.model.predict(placeholder)
  
      def process_input(self, batch):
          """ Compile the detected faces for prediction """
        mean = (0.384, 0.314, 0.279) if self._is_faceswap else (0.485, 0.456, 0.406)
        std = (0.324, 0.286, 0.275) if self._is_faceswap else (0.229, 0.224, 0.225)
        mean = (0.384, 0.314, 0.279) if self._is_faceswap else (0.485, 0.456, 0.406)
        std = (0.324, 0.286, 0.275) if self._is_faceswap else (0.229, 0.224, 0.225)
  
          batch["feed"] = ((np.array([feed.face[..., :3]
                                      for feed in batch["feed_faces"]],
                                     dtype="float32") / 255.0) - mean) / std
          logger.trace("feed shape: %s", batch["feed"].shape)
keep keep keep keep replace replace keep keep keep keep keep
 Bisenet-FP - Improved weights for masker (#1210)

* Add option to load faceswap trained weights

* Switch to original weights if fs weights not present

* typofix 
plugins/extract/mask/bisenet_fp.py