def find_top_rrpn_proposals(
    proposals,
    pred_objectness_logits,
    image_sizes,
    nms_thresh,
    pre_nms_topk,
    post_nms_topk,
    min_box_size,
    training,
):
    """
    For each feature map, select the `pre_nms_topk` highest scoring proposals,
    apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk`
    highest scoring proposals among all the feature maps if `training` is True,
    otherwise, returns the highest `post_nms_topk` scoring proposals for each
    feature map.

    Args:
        proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5).
            All proposal predictions on the feature maps.
        pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A).
        image_sizes (list[tuple]): sizes (h, w) for each image
        nms_thresh (float): IoU threshold to use for NMS
        pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS.
            When RRPN is run on multiple feature maps (as in FPN) this number is per
            feature map.
        post_nms_topk (int): number of top k scoring proposals to keep after applying NMS.
            When RRPN is run on multiple feature maps (as in FPN) this number is total,
            over all feature maps.
        min_box_size(float): minimum proposal box side length in pixels (absolute units wrt
            input images).
        training (bool): True if proposals are to be used in training, otherwise False.
            This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..."
            comment.

    Returns:
        proposals (list[Instances]): list of N Instances. The i-th Instances
            stores post_nms_topk object proposals for image i.
    """
    num_images = len(image_sizes)
    device = proposals[0].device

    # 1. Select top-k anchor for every level and every image
    topk_scores = []  # #lvl Tensor, each of shape N x topk
    topk_proposals = []
    level_ids = []  # #lvl Tensor, each of shape (topk,)
    batch_idx = torch.arange(num_images, device=device)
    for level_id, proposals_i, logits_i in zip(
        itertools.count(), proposals, pred_objectness_logits
    ):
        Hi_Wi_A = logits_i.shape[1]
        if isinstance(Hi_Wi_A, torch.Tensor):  # it's a tensor in tracing
            num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk)
        else:
            num_proposals_i = min(Hi_Wi_A, pre_nms_topk)

        topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)

        # each is N x topk
        topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 5

        topk_proposals.append(topk_proposals_i)
        topk_scores.append(topk_scores_i)
        level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device))

    # 2. Concat all levels together
    topk_scores = cat(topk_scores, dim=1)
    topk_proposals = cat(topk_proposals, dim=1)
    level_ids = cat(level_ids, dim=0)

    # 3. For each image, run a per-level NMS, and choose topk results.
    results = []
    for n, image_size in enumerate(image_sizes):
        boxes = RotatedBoxes(topk_proposals[n])
        scores_per_img = topk_scores[n]
        valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
        if not valid_mask.all():
            boxes = boxes[valid_mask]
            scores_per_img = scores_per_img[valid_mask]
        boxes.clip(image_size)

        # filter empty boxes
        keep = boxes.nonempty(threshold=min_box_size)
        lvl = level_ids
        if _is_tracing() or keep.sum().item() != len(boxes):
            boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], level_ids[keep])

        keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh)
        # In Detectron1, there was different behavior during training vs. testing.
        # (https://github.com/facebookresearch/Detectron/issues/459)
        # During training, topk is over the proposals from *all* images in the training batch.
        # During testing, it is over the proposals for each image separately.
        # As a result, the training behavior becomes batch-dependent,
        # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size.
        # This bug is addressed in Detectron2 to make the behavior independent of batch size.
        keep = keep[:post_nms_topk]

        res = Instances(image_size)
        res.proposal_boxes = boxes[keep]
        res.objectness_logits = scores_per_img[keep]
        results.append(res)
    return results

    For each feature map, select the `pre_nms_topk` highest scoring proposals,
    apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk`
    highest scoring proposals among all the feature maps if `training` is True,
    otherwise, returns the highest `post_nms_topk` scoring proposals for each
    feature map.

    Args:
        proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5).
            All proposal predictions on the feature maps.
        pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A).
        image_sizes (list[tuple]): sizes (h, w) for each image
        nms_thresh (float): IoU threshold to use for NMS
        pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS.
            When RRPN is run on multiple feature maps (as in FPN) this number is per
            feature map.
        post_nms_topk (int): number of top k scoring proposals to keep after applying NMS.
            When RRPN is run on multiple feature maps (as in FPN) this number is total,
            over all feature maps.
        min_box_size(float): minimum proposal box side length in pixels (absolute units wrt
            input images).
        training (bool): True if proposals are to be used in training, otherwise False.
            This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..."
            comment.

    Returns:
        proposals (list[Instances]): list of N Instances. The i-th Instances
            stores post_nms_topk object proposals for image i.
    
video_chat_with_StableLM/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py