def predict(self, repeats=1):
        '''
        Args:
            repeats (int): repeat number for prediction
        Returns:
            results (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 results include 'masks': np.ndarray:
                            shape: [N, im_h, im_w]
        '''
        # model prediction
        np_heatmap, np_masks = None, None
        for i in range(repeats):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            heatmap_tensor = self.predictor.get_output_handle(output_names[0])
            np_heatmap = heatmap_tensor.copy_to_cpu()
            if self.pred_config.tagmap:
                masks_tensor = self.predictor.get_output_handle(output_names[1])
                heat_k = self.predictor.get_output_handle(output_names[2])
                inds_k = self.predictor.get_output_handle(output_names[3])
                np_masks = [
                    masks_tensor.copy_to_cpu(), heat_k.copy_to_cpu(),
                    inds_k.copy_to_cpu()
                ]
        result = dict(heatmap=np_heatmap, masks=np_masks)
        return result

        Args:
            repeats (int): repeat number for prediction
        Returns:
            results (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 results include 'masks': np.ndarray:
                            shape: [N, im_h, im_w]
        
deploy/python/keypoint_infer.py