目标检测的后处理阶段需要NMS算法。
NMS算法包括计算当前score最大框和其他框的IoU大小的步骤。
对于这个步骤，可以进行优化，改变IoU的计算方法。
目前，经典的IoU计算方式有GIoU、DIoU、CIoU。 我们可以用传统的NMS算法修改实现
CIoU NMS，CIoU NMS和CIoU NMS。
首先看看传统NMS算法的代码：
defori_NMS(self，scores，boxes，iou_thres ) :ifboxes.shape(0)==0:returntorch.zeros )，0，设备boxes [ :3 ] areas=(x2-x1 ) (Y2-Y1 ) () ) ) 652，) keep=[ ] while order.numel (0: iforrer xmin=torch.clamp (x1 [ order [ 1: ]，min=float ) x1[I] ) ymin=torch.clamp ) y1[order[1:]，min=float max=float(y2[I] ) inter_area=torch.clamp ) xmax-xmin，min=0.0 ) *torch.clamp ) ymax-ymin， min=0.0 ) iou=inter _ area/(areas [ I ] areas [ order [ 1: ] ]-inter _ area1e-16 ) mask_ind=) iou=iou returntorch.longtensor ) keep )传统的NMS算法在网上解密很多，简单来说就是接着选择剩下的score的最大边框，然后依次循环到最后。
接下来使用DIoU改造NMS算法。
# wzfadddiounmsdefdiou _ box _ NMS (self、scores、boxes、iou_thres ) 3360 ifboxes.shape [0]==03360返回跟踪存储、) keep=[ ] while order.numel (0: iforder.numel )==1:keep.append(order.item ) ) breakelse3360I=) xmin=torch.clamp (x1 [ order [ 1: ]，min=float ) x1[I] ) ymin=torch.clamp ) y1[order[1:]，min
torch.clamp(x2[order[1:]], max = float(x2[i])) ymax = torch.clamp(y2[order[1:]], max = float(y2[i])) inter_area = torch.clamp(xmax - xmin, min=0.0) * torch.clamp(ymax - ymin, min=0.0) iou = inter_area / (areas[i] + areas[order[1:]] - inter_area + 1e-16) # diou add center # inter_diag cxpreds = (x2[i] + x1[i]) / 2 cypreds = (y2[i] + y1[i]) / 2 cxbbox = (x2[order[1:]] + x1[order[1:]]) / 2 cybbox = (y1[order[1:]] + y2[order[1:]]) / 2 inter_diag = (cxbbox - cxpreds) ** 2 + (cybbox - cypreds) ** 2 # outer_diag ox1 = torch.min(x1[order[1:]], x1[i]) oy1 = torch.min(y1[order[1:]], y1[i]) ox2 = torch.max(x2[order[1:]], x2[i]) oy2 = torch.max(y2[order[1:]], y2[i]) outer_diag = (ox1 - ox2) ** 2 + (oy1 - oy2) ** 2 diou = iou - inter_diag / outer_diag diou = torch.clamp(diou, min=-1.0, max=1.0) # mask_ind = (iou <= iou_thres).nonzero().squeeze() mask_ind = (diou <= iou_thres).nonzero().squeeze() if mask_ind.numel() == 0: break order = order[mask_ind + 1] return torch.LongTensor(keep)关于DIoU,D是Distance的意思，也就是下面这张图。
DIoU Loss
图片是讲DIoU Loss，但是也可以帮助我们理解DIoU的计算方式。
其实从代码角度，我们可以发现，就是乘了一个系数。
接下来我们看GIoU。
其实GIoU是更早的论文了，先于DIoU和CIoU。
直接看下代码吧！~
# wzf add ciou def ciou_box_nms(self, scores, boxes, iou_thres): if boxes.shape[0] == 0: return torch.zeros(0,device=boxes.device).long() x1,y1,x2,y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:,3] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = torch.sort(scores, descending=True)[1] #(?,) keep =[] while order.numel() > 0: if order.numel() == 1: keep.append(order.item()) break else: i = order[0].item() keep.append(i) xmin = torch.clamp(x1[order[1:]], min = float(x1[i])) ymin = torch.clamp(y1[order[1:]], min = float(y1[i])) xmax = torch.clamp(x2[order[1:]], max = float(x2[i])) ymax = torch.clamp(y2[order[1:]], max = float(y2[i])) inter_area = torch.clamp(xmax - xmin, min=0.0) * torch.clamp(ymax - ymin, min=0.0) iou = inter_area / (areas[i] + areas[order[1:]] - inter_area + 1e-16) # ciou add center # inter_diag cxpreds = (x2[i] + x1[i]) / 2 cypreds = (y2[i] + y1[i]) / 2 cxbbox = (x2[order[1:]] + x1[order[1:]]) / 2 cybbox = (y1[order[1:]] + y2[order[1:]]) / 2 inter_diag = (cxbbox - cxpreds) ** 2 + (cybbox - cypreds) ** 2 # outer_diag ox1 = torch.min(x1[order[1:]], x1[i]) oy1 = torch.min(y1[order[1:]], y1[i]) ox2 = torch.max(x2[order[1:]], x2[i]) oy2 = torch.max(y2[order[1:]], y2[i]) outer_diag = (ox1 - ox2) ** 2 + (oy1 - oy2) ** 2 # ciou u = (inter_diag) / outer_diag w1 = x2[i] - x1[i] h1 = y2[i] - y1[i] w2 = x2[order[1:]] - x1[order[1:]] h2 = y2[order[1:]] - y1[order[1:]] with torch.no_grad(): arctan = torch.atan(w2 / h2) - torch.atan(w1 / h1) v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w2 / h2) - torch.atan(w1 / h1)), 2) S = 1 - iou alpha = v / (S + v) ciou = iou - (u + alpha * v) # end # mask_ind = (iou <= iou_thres).nonzero().squeeze() mask_ind = (ciou <= iou_thres).nonzero().squeeze() if mask_ind.numel() == 0: break order = order[mask_ind + 1] return torch.LongTensor(keep)CIoU，中的C就是Completely的意思，也就是考虑的东西更多也更加复杂。而且和DIoU是同一篇论文！
后续：关于NMS的改进里还有Soft-NMS，其中也是有IoU的计算的，我们也是可以进行修改得到Soft CIoU-NMS,Soft DIoU-NMS和Soft GIoU-NMS。
参考：
1、paper:Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
2、paper:Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression