def __call__(self, results):
        """Performs the ThreeCrop augmentation.

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        """
        _init_lazy_if_proper(results, False)
        if 'gt_bboxes' in results or 'proposals' in results:
            warnings.warn('ThreeCrop cannot process bounding boxes')

        imgs = results['imgs']
        img_h, img_w = results['imgs'][0].shape[:2]

        crop_w, crop_h = self.crop_size
        assert crop_h == img_h or crop_w == img_w

        if crop_h == img_h:  # usually in this case
            w_step = (img_w - crop_w) // 2
            offsets = [
                (0, 0),             # left
                (2 * w_step, 0),    # right
                (w_step, 0),        # middle
            ]
        elif crop_w == img_w:
            h_step = (img_h - crop_h) // 2
            offsets = [
                (0, 0),             # top
                (0, 2 * h_step),    # down
                (0, h_step),        # middle
            ]

        cropped = []
        crop_bboxes = []
        for x_offset, y_offset in offsets:
            bbox = [x_offset, y_offset, x_offset + crop_w, y_offset + crop_h]
            crop = [
                img[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w]
                for img in imgs
            ]
            cropped.extend(crop)
            crop_bboxes.extend([bbox for _ in range(len(imgs))])

        crop_bboxes = np.array(crop_bboxes)
        results['imgs'] = cropped
        results['crop_bbox'] = crop_bboxes
        results['img_shape'] = results['imgs'][0].shape[:2]

        return results
Performs the ThreeCrop augmentation.

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        
EVA-01/video/datasets/pipeline.py