def __call__(self, results):
        """Call function to pad images, masks, semantic segmentation maps.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Updated result dict.
        """
        rand_ind = np.random.permutation(range(len(self.scales)))[0]
        rand_scale = self.scales[rand_ind]

        y_size = [int(img.shape[0] * rand_scale) for img in results['img']]
        x_size = [int(img.shape[1] * rand_scale) for img in results['img']]
        scale_factor = np.eye(4)
        scale_factor[0, 0] *= rand_scale
        scale_factor[1, 1] *= rand_scale
        results['img'] = [mmcv.imresize(img, (x_size[idx], y_size[idx]), return_scale=False) for idx, img in
                          enumerate(results['img'])]
        lidar2img = [scale_factor @ l2i for l2i in results['lidar2img']]
        results['lidar2img'] = lidar2img
        results['img_shape'] = [img.shape for img in results['img']]
        results['ori_shape'] = [img.shape for img in results['img']]

        return results
Call function to pad images, masks, semantic segmentation maps.
        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: Updated result dict.
        
projects/mmdet3d_plugin/datasets/pipelines/transform_3d.py