def apply(self, sample, context=None):
        """
        Learning Data Augmentation Strategies for Object Detection, see https://arxiv.org/abs/1906.11172
        """
        im = sample['image']
        gt_bbox = sample['gt_bbox']
        if not isinstance(im, np.ndarray):
            raise TypeError("{}: image is not a numpy array.".format(self))
        if len(im.shape) != 3:
            raise ImageError("{}: image is not 3-dimensional.".format(self))
        if len(gt_bbox) == 0:
            return sample

        height, width, _ = im.shape
        norm_gt_bbox = np.ones_like(gt_bbox, dtype=np.float32)
        norm_gt_bbox[:, 0] = gt_bbox[:, 1] / float(height)
        norm_gt_bbox[:, 1] = gt_bbox[:, 0] / float(width)
        norm_gt_bbox[:, 2] = gt_bbox[:, 3] / float(height)
        norm_gt_bbox[:, 3] = gt_bbox[:, 2] / float(width)

        from .autoaugment_utils import distort_image_with_autoaugment
        im, norm_gt_bbox = distort_image_with_autoaugment(im, norm_gt_bbox,
                                                          self.autoaug_type)

        gt_bbox[:, 0] = norm_gt_bbox[:, 1] * float(width)
        gt_bbox[:, 1] = norm_gt_bbox[:, 0] * float(height)
        gt_bbox[:, 2] = norm_gt_bbox[:, 3] * float(width)
        gt_bbox[:, 3] = norm_gt_bbox[:, 2] * float(height)

        sample['image'] = im
        sample['gt_bbox'] = gt_bbox
        return sample

        Learning Data Augmentation Strategies for Object Detection, see https://arxiv.org/abs/1906.11172
        
ppdet/data/transform/operators.py