          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):
          """ Perform inference on the feed """
          logger.trace("Predicting: Batchsize: %s", len(feed_faces))
          feed = [feed_faces]
          if self.has_predicted_mask:
              feed.append(np.repeat(self.input_mask, feed_faces.shape[0], axis=0))
 
</s> remove         predicted = self.predictor(feed)
 </s> add         predicted = self.predictor(feed, batch_size=batch_size) </s> remove                     predicted = self.predict(feed_faces)
 </s> add                     batch_size = None
                    if is_plaidml and feed_faces.shape[0] != self.batchsize:
                        logger.verbose("Fallback to BS=1")
                        batch_size = 1
                    predicted = self.predict(feed_faces, batch_size)
    def predict(self, feed_faces, batch_size=None):
          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 """
          logger.trace("Predicting: Batchsize: %s", len(feed_faces))
          feed = [feed_faces]
          if self.has_predicted_mask:
              feed.append(np.repeat(self.input_mask, feed_faces.shape[0], axis=0))
keep keep keep keep replace keep keep keep keep keep
 Added s3fd-amd + minor fixes (#837) 
scripts/convert.py