def __call__(self, results):
        """Performs the MultiScaleCrop 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')

        img_h, img_w = results['img_shape']
        base_size = min(img_h, img_w)
        crop_sizes = [int(base_size * s) for s in self.scales]

        candidate_sizes = []
        for i, h in enumerate(crop_sizes):
            for j, w in enumerate(crop_sizes):
                if abs(i - j) <= self.max_wh_scale_gap:
                    candidate_sizes.append([w, h])

        crop_size = random.choice(candidate_sizes)
        for i in range(2):
            if abs(crop_size[i] - self.input_size[i]) < 3:
                crop_size[i] = self.input_size[i]

        crop_w, crop_h = crop_size

        if self.random_crop:
            x_offset = random.randint(0, img_w - crop_w)
            y_offset = random.randint(0, img_h - crop_h)
        else:
            w_step = (img_w - crop_w) // 4
            h_step = (img_h - crop_h) // 4
            candidate_offsets = [
                (0, 0),                         # upper left
                (4 * w_step, 0),                # upper right
                (0, 4 * h_step),                # lower left
                (4 * w_step, 4 * h_step),       # lower right
                (2 * w_step, 2 * h_step),       # center
            ]
            if self.num_fixed_crops == 13:
                extra_candidate_offsets = [
                    (0, 2 * h_step),            # center left
                    (4 * w_step, 2 * h_step),   # center right
                    (2 * w_step, 4 * h_step),   # lower center
                    (2 * w_step, 0 * h_step),   # upper center
                    (1 * w_step, 1 * h_step),   # upper left quarter
                    (3 * w_step, 1 * h_step),   # upper right quarter
                    (1 * w_step, 3 * h_step),   # lower left quarter
                    (3 * w_step, 3 * h_step)    # lower right quarter
                ]
                candidate_offsets.extend(extra_candidate_offsets)
            x_offset, y_offset = random.choice(candidate_offsets)

        new_h, new_w = crop_h, crop_w

        crop_bbox = np.array(
            [x_offset, y_offset, x_offset + new_w, y_offset + new_h])
        results['crop_bbox'] = crop_bbox
        results['img_shape'] = (new_h, new_w)
        results['scales'] = self.scales

        if 'crop_quadruple' not in results:
            results['crop_quadruple'] = np.array(
                [0, 0, 1, 1],  # x, y, w, h
                dtype=np.float32)

        x_ratio, y_ratio = x_offset / img_w, y_offset / img_h
        w_ratio, h_ratio = new_w / img_w, new_h / img_h

        old_crop_quadruple = results['crop_quadruple']
        old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1]
        old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3]
        new_crop_quadruple = [
            old_x_ratio + x_ratio * old_w_ratio,
            old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio,
            h_ratio * old_x_ratio
        ]
        results['crop_quadruple'] = np.array(
            new_crop_quadruple, dtype=np.float32)

        if not self.lazy:
            if 'keypoint' in results:
                results['keypoint'] = self._crop_kps(results['keypoint'], crop_bbox)
            if 'imgs' in results:
                results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox)
        else:
            lazyop = results['lazy']
            if lazyop['flip']:
                raise NotImplementedError('Put Flip at last for now')

            # record crop_bbox in lazyop dict to ensure only crop once in Fuse
            lazy_left, lazy_top, lazy_right, lazy_bottom = lazyop['crop_bbox']
            left = x_offset * (lazy_right - lazy_left) / img_w
            right = (x_offset + new_w) * (lazy_right - lazy_left) / img_w
            top = y_offset * (lazy_bottom - lazy_top) / img_h
            bottom = (y_offset + new_h) * (lazy_bottom - lazy_top) / img_h
            lazyop['crop_bbox'] = np.array([(lazy_left + left), (lazy_top + top),
                                            (lazy_left + right), (lazy_top + bottom)], dtype=np.float32)

        if 'gt_bboxes' in results:
            assert not self.lazy
            results = self._all_box_crop(results, results['crop_bbox'])

        return results
Performs the MultiScaleCrop augmentation.

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