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

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        """

        _init_lazy_if_proper(results, self.lazy)
        if 'keypoint' in results:
            assert not self.lazy, ('Keypoint Augmentations are not compatible '
                                   'with lazy == True')

        if 'scale_factor' not in results:
            results['scale_factor'] = np.array([1, 1], dtype=np.float32)
        img_h, img_w = results['img_shape']

        if self.keep_ratio:
            new_w, new_h = mmcv.rescale_size((img_w, img_h), self.scale)
        else:
            new_w, new_h = self.scale
        self.scale_factor = np.array([new_w / img_w, new_h / img_h],
                                     dtype=np.float32)

        results['img_shape'] = (new_h, new_w)
        results['keep_ratio'] = self.keep_ratio
        results['scale_factor'] = results['scale_factor'] * self.scale_factor

        if not self.lazy:
            if 'imgs' in results:
                results['imgs'] = self._resize_imgs(results['imgs'], new_w,
                                                    new_h)
            if 'keypoint' in results:
                results['keypoint'] = self._resize_kps(results['keypoint'],
                                                       self.scale_factor)
        else:
            lazyop = results['lazy']
            if lazyop['flip']:
                raise NotImplementedError('Put Flip at last for now')
            lazyop['interpolation'] = self.interpolation

        if 'gt_bboxes' in results:
            assert not self.lazy
            results['gt_bboxes'] = self._box_resize(results['gt_bboxes'],
                                                    self.scale_factor)
            if 'proposals' in results and results['proposals'] is not None:
                assert results['proposals'].shape[1] == 4
                results['proposals'] = self._box_resize(
                    results['proposals'], self.scale_factor)

        return results
Performs the Resize augmentation.

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