  
      def init_model(self):
          """ Initialize S3FD Model"""
          confidence = self.config["confidence"] / 100
          model_kwargs = dict(custom_objects=dict(O2K_Add=AddO2K,
                                                  O2K_Slice=SliceO2K,
                                                  O2K_Sum=SumO2K,
                                                  O2K_Sqrt=SqrtO2K,
                                                  O2K_Pow=PowO2K,
                                                  O2K_ConstantLayer=ConstantLayerO2K,
                                                  O2K_Div=DivO2K))
          self.model = S3fd(self.model_path,
                            model_kwargs,
                            self.config["allow_growth"],
                            self._exclude_gpus,
                            confidence)
 
</s> remove         return inputs[0] / inputs[1]


class S3fd(KSession):
    """ Keras Network """
    def __init__(self, model_path, model_kwargs, allow_growth, exclude_gpus, confidence):
        logger.debug("Initializing: %s: (model_path: '%s', model_kwargs: %s, allow_growth: %s, "
                     "exclude_gpus: %s, confidence: %s)", self.__class__.__name__, model_path,
                     model_kwargs, allow_growth, exclude_gpus, confidence)
        super().__init__("S3FD",
                         model_path,
                         model_kwargs=model_kwargs,
                         allow_growth=allow_growth,
                         exclude_gpus=exclude_gpus)
        self.load_model()
        self.confidence = confidence
        self.average_img = np.array([104.0, 117.0, 123.0])
        logger.debug("Initialized: %s", self.__class__.__name__)

    def prepare_batch(self, batch):
        """ Prepare a batch for prediction """
 </s> add  </s> remove         Assumes that the layer will be built to match that input shape provided.
 </s> add class S3fd(KSession):
    """ Keras Network """
    def __init__(self, model_path, model_kwargs, allow_growth, exclude_gpus, confidence):
        logger.debug("Initializing: %s: (model_path: '%s', model_kwargs: %s, allow_growth: %s, "
                     "exclude_gpus: %s, confidence: %s)", self.__class__.__name__, model_path,
                     model_kwargs, allow_growth, exclude_gpus, confidence)
        super().__init__("S3FD",
                         model_path,
                         model_kwargs=model_kwargs,
                         allow_growth=allow_growth,
                         exclude_gpus=exclude_gpus)
        self.define_model(self.model_definition)
        self.load_model_weights()
        self.confidence = confidence
        self.average_img = np.array([104.0, 117.0, 123.0])
        logger.debug("Initialized: %s", self.__class__.__name__) </s> remove         model_filename = "s3fd_keras_v1.h5"
 </s> add         model_filename = "s3fd_keras_v2.h5" </s> remove         """ Detect faces """
 </s> add         """ Process the output from the model to obtain faces

        Parameters
        ----------
        bounding_boxes_scales: list
            The output predictions from the S3FD model
        """ </s> remove                 score = ocls[0, 1, hindex, windex]
 </s> add                 score = ocls[0, hindex, windex, 1]
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
  
      def init_model(self):
          """ Initialize S3FD Model"""
          confidence = self.config["confidence"] / 100
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
        model_kwargs = dict(custom_objects=dict(L2Norm=L2Norm, SliceO2K=SliceO2K))
          self.model = S3fd(self.model_path,
                            model_kwargs,
                            self.config["allow_growth"],
                            self._exclude_gpus,
                            confidence)
keep keep keep keep replace replace replace replace replace replace replace keep keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py