def __call__(self, results):
        """Call function to resize images, bounding boxes, masks, semantic
        segmentation map.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Resized results, 'im_shape', 'pad_shape', 'scale_factor', \
                'keep_ratio' keys are added into result dict.
        """
        if 'scale' not in results:
            if 'scale_factor' in results:
                img_shape = results['image'].shape[:2]
                scale_factor = results['scale_factor'][0]
                # assert isinstance(scale_factor, float)
                results['scale'] = [int(x * scale_factor)
                                    for x in img_shape][::-1]
            else:
                self._random_scale(results)
        else:
            if not self.override:
                assert 'scale_factor' not in results, (
                    'scale and scale_factor cannot be both set.')
            else:
                results.pop('scale')
                if 'scale_factor' in results:
                    results.pop('scale_factor')
                self._random_scale(results)

        self._resize_img(results)
        self._resize_bboxes(results)
        self._resize_masks(results)
        self._resize_seg(results)
        self._resize_keypoints(results)
        self._resize_areas(results)
        return results
Call function to resize images, bounding boxes, masks, semantic
        segmentation map.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Resized results, 'im_shape', 'pad_shape', 'scale_factor',                 'keep_ratio' keys are added into result dict.
        
ppdet/data/transform/keypoint_operators.py