def predict(self, repeats=1):
        '''
        Args:
            repeats (int): repeats number for prediction
        Returns:
            result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
                            matix element:[class, score, x_min, y_min, x_max, y_max]
                            MaskRCNN's result include 'masks': np.ndarray:
                            shape: [N, im_h, im_w]
        '''
        # model prediction
        for i in range(repeats):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            output_tensor = self.predictor.get_output_handle(output_names[0])
            np_output = output_tensor.copy_to_cpu()
        result = dict(output=np_output)
        return result

        Args:
            repeats (int): repeats number for prediction
        Returns:
            result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
                            matix element:[class, score, x_min, y_min, x_max, y_max]
                            MaskRCNN's result include 'masks': np.ndarray:
                            shape: [N, im_h, im_w]
        
deploy/pipeline/pphuman/attr_infer.py