          if self.has_predicted_mask:
              feed.append(np.repeat(self.input_mask, feed_faces.shape[0], axis=0))
          logger.trace("Input shape(s): %s", [item.shape for item in feed])
  
          predicted = self.predictor(feed)
          predicted = predicted if isinstance(predicted, list) else [predicted]
          logger.trace("Output shape(s): %s", [predict.shape for predict in predicted])
  
          predicted = self.filter_multi_out(predicted)
  
 
</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) </s> remove     def predict(self, feed_faces):
 </s> add     def predict(self, feed_faces, batch_size=None): </s> remove         dims = (int(width * scale), int(height * scale))

        if scale < 1.0:
            logger.trace("Resizing image from %sx%s to %s.",
                         width, height, "x".join(str(i) for i in dims))
 </s> add         if scale != 1.0:
            dims = (int(width * scale), int(height * scale))
            if scale < 1.0:
                logger.verbose("Resizing image from %sx%s to %s. Scale=%s",
                               width, height, "x".join(str(i) for i in dims), scale)
            image = cv2.resize(image, dims, interpolation=interpln)
        if pad_to:
            image = Detector.pad_image(image, pad_to)
        return image
        predicted = self.predictor(feed, batch_size=batch_size)
          if self.has_predicted_mask:
              feed.append(np.repeat(self.input_mask, feed_faces.shape[0], axis=0))
          logger.trace("Input shape(s): %s", [item.shape for item in feed])
  
        predicted = self.predictor(feed, batch_size=batch_size)
          predicted = predicted if isinstance(predicted, list) else [predicted]
          logger.trace("Output shape(s): %s", [predict.shape for predict in predicted])
  
          predicted = self.filter_multi_out(predicted)
  
keep keep keep keep replace keep keep keep keep keep
 Added s3fd-amd + minor fixes (#837) 
scripts/convert.py