接受N级score，bbox_pred，anchor和image_shape作为输入，通过anchor和框的偏移(bbox_pred)得到proposal，然后对这些proposal做NMS，最后选出前num个。
二、执行步骤- 将每级score，bbox_pred，anchor按照score从大到小排序，并选择前nms_pre个(一般为1000)，共N*nms_pre个。通过anchor和框的偏移(bbox_pred)得到proposal去除框大小为负数的框，并且对于每级的proposal，加上一个足够大的offset，使得每级的框之间不会有重叠，将多分类NMS转成单分类NMS将N级score和proposal整合在一起，按照score从大到小排序做NMS取前num个，并且给proposal减去之前加上的offset
#路径：mmdetection/mmdet/models/dense_heads/cascade_rpn_head.py:StageCascadeRPNHead::_get_bboxes_single
level_ids = []
mlvl_scores = []
mlvl_bbox_preds = []
mlvl_valid_anchors = []
for idx in range(len(cls_scores)): #len(cls_scores)表示是N级cascade
rpn_cls_score = cls_scores[idx] #每级的score
rpn_bbox_pred = bbox_preds[idx] #每级的bbox_preds
assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
#每级score的shape是(num_anchors * num_classes, H, W)，bbox_preds的shape是(num_anchors * 4, H, W)
rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
if self.use_sigmoid_cls: #对score做二分类，用sigmoid
rpn_cls_score = rpn_cls_score.reshape(-1)
scores = rpn_cls_score.sigmoid()
else: #对score做二分类，用softmax
rpn_cls_score = rpn_cls_score.reshape(-1, 2)
# We set FG labels to [0, num_class-1] and BG label to
# num_class in RPN head since mmdet v2.5, which is unified to
# be consistent with other head since mmdet v2.0. In mmdet v2.0
# to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
scores = rpn_cls_score.softmax(dim=1)[:, 0]
rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
anchors = mlvl_anchors[idx]
if 0 < nms_pre < scores.shape[0]:
# sort is faster than topk
# _, topk_inds = scores.topk(cfg.nms_pre)
ranked_scores, rank_inds = scores.sort(descending=True)
topk_inds = rank_inds[:nms_pre]
scores = ranked_scores[:nms_pre]
rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
anchors = anchors[topk_inds, :]
mlvl_scores.append(scores)
mlvl_bbox_preds.append(rpn_bbox_pred)
mlvl_valid_anchors.append(anchors)
level_ids.append(
scores.new_full((scores.size(0), ), idx, dtype=torch.long))
scores = torch.cat(mlvl_scores)
anchors = torch.cat(mlvl_valid_anchors)
rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
proposals = self.bbox_coder.decode( #通过anchor和框的偏移(bbox_pred)得到proposal
anchors, rpn_bbox_pred, max_shape=img_shape)
ids = torch.cat(level_ids)
if cfg.min_bbox_size >= 0: #去除大小为负数的框
w = proposals[:, 2] - proposals[:, 0]
h = proposals[:, 3] - proposals[:, 1]
valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
if not valid_mask.all():
proposals = proposals[valid_mask]
scores = scores[valid_mask]
ids = ids[valid_mask]
#NMS操作
if proposals.numel() > 0:
dets, _ = batched_nms(proposals, scores, ids, cfg.nms)
else:
return proposals.new_zeros(0, 5)
#取前max_per_img个
return dets[:cfg.max_per_img]
四，cpu源码解析
Tensor *output = ctx->Output(0, {num, 5});//指定0号输出的尺寸是(num,5)，num是框的数量，前四个是框的坐标，最后一个是框的得分
float* output_ptr = output->template MutableData();//获取输出指针
float* score_ptr = new float[level*nms_pre];//level是级数，nms_pre是每级保留的框数
memset(score_ptr, 0, sizeof(float)*level*nms_pre);//有个级不足nms_pre个框，将多余的框的分数置零
float* score_sorted_ptr = new float[level*nms_pre];//排序后分数保存的地址
float* bbox_pred = new float[level*nms_pre*4]; //bbox_pred，每个坐标都对应一个，一个框有4个坐标
float* anchor = new float[level*nms_pre*4]; //anchor的坐标，一个框有4个
float* proposal_ptr = new float[level*nms_pre*4]; //偏移后proposal的坐标，一个框有4个
float* proposal_sorted_ptr = new float[level*nms_pre*4]; //排序后proposal的坐标
//step1 整合并排序N级score，bbox_pred和anchor
vector vec_thread;
for(int i=0; i(i)->template Data();
const float* input_bbox = Input(i+level)->template Data();
const float* input_anchor = Input(i+level*2)->template Data();
//对score进行排序，并且取前nms_pre个
vector vec_node;
vec_node.resize(Input(i).Size());//排序所有的score
vector sorted_id = SortedIdx(input_score, vec_node, nms_pre);
}
typedef struct{
float key;
int value;
}KeyValuePair;
bool compareNode(KeyValuePair node1, KeyValuePair node2) return node1.key>node2.key;
vector SortedIdx(float* input_score, vector& vec_node, int nms_pre){
for(int i=0; i sorted_id(nms_pre);
for(int i=0; i
