                  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)
                      predicted = self.predict(feed_faces)
                  else:
                      predicted = list()
  
                  self.queue_out_frames(batch, predicted)
  
 
</s> remove         predicted = self.predictor(feed)
 </s> add         predicted = self.predictor(feed, batch_size=batch_size) </s> remove     def predict(self, feed_faces):
 </s> add     def predict(self, feed_faces, batch_size=None): </s> remove             passed_angles = [int(angle)
                             for angle in rotation.split(",")]
 </s> add             passed_angles = [
                int(angle)
                for angle in rotation.split(",")
                if int(angle) != 0
            ]
                    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)
                  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_plaidml and feed_faces.shape[0] != self.batchsize:
                        logger.verbose("Fallback to BS=1")
                        batch_size = 1
                    predicted = self.predict(feed_faces, batch_size)
                  else:
                      predicted = list()
  
                  self.queue_out_frames(batch, predicted)
  
keep keep keep keep replace keep keep keep keep keep
 Added s3fd-amd + minor fixes (#837) 
scripts/convert.py