YOLOV7改进-添加EIoU,SIoU,AlphaIoU,FocalEIoU

在YoloV7中添加EIoU,SIoU,AlphaIoU,FocalEIoU.

yolov7中box_iou其默认用的是CIoU，其中代码还带有GIoU，DIoU, AlphaIoU，文件路径：utils/general.py，函数名为：bbox_iou

defbbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
    box2 = box2.T

    # Get the coordinates of bounding boxesif x1y1x2y2:# x1, y1, x2, y2 = box1
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]else:# transform from xywh to xyxy
        b1_x1, b1_x2 = box1[0]- box1[2]/2, box1[0]+ box1[2]/2
        b1_y1, b1_y2 = box1[1]- box1[3]/2, box1[1]+ box1[3]/2
        b2_x1, b2_x2 = box2[0]- box2[2]/2, box2[0]+ box2[2]/2
        b2_y1, b2_y2 = box2[1]- box2[3]/2, box2[1]+ box2[3]/2# Intersection area
    inter =(torch.min(b1_x2, b2_x2)- torch.max(b1_x1, b2_x1)).clamp(0)* \
            (torch.min(b1_y2, b2_y2)- torch.max(b1_y1, b2_y1)).clamp(0)# Union Area
    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
    union = w1 * h1 + w2 * h2 - inter + eps

    iou = inter / union

    if GIoU or DIoU or CIoU:
        cw = torch.max(b1_x2, b2_x2)- torch.min(b1_x1, b2_x1)# convex (smallest enclosing box) width
        ch = torch.max(b1_y2, b2_y2)- torch.min(b1_y1, b2_y1)# convex heightif CIoU or DIoU:# Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            c2 = cw **2+ ch **2+ eps  # convex diagonal squared
            rho2 =((b2_x1 + b2_x2 - b1_x1 - b1_x2)**2+(b2_y1 + b2_y2 - b1_y1 - b1_y2)**2)/4# center distance squaredif DIoU:return iou - rho2 / c2  # DIoUelif CIoU:# https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
                v =(4/ math.pi **2)* torch.pow(torch.atan(w2 /(h2 + eps))- torch.atan(w1 /(h1 + eps)),2)with torch.no_grad():
                    alpha = v /(v - iou +(1+ eps))return iou -(rho2 / c2 + v * alpha)# CIoUelse:# GIoU https://arxiv.org/pdf/1902.09630.pdf
            c_area = cw * ch + eps  # convex areareturn iou -(c_area - union)/ c_area  # GIoUelse:return iou  # IoU

我们可以看到函数顶部，有GIoU，DIoU，CIoU的bool参数可以选择，如果全部为False的时候，其会返回最普通的Iou，如果其中一个为True的时候，即返回设定为True的那个Iou。

那么重点来了，我们怎么在这个函数里面添加EIoU,SIoU,AlphaIoU,FocalEIoU呢？

我们只需要把上面提及到的这个函数替换成以下，代码出自：github链接，这个github上还有一些yolov5的改进源码和一些常用的脚本，有兴趣可以去看看，请各位也帮忙点个star支持下，谢谢！

defbbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, SIoU=False, EIoU=False, Focal=False, alpha=1, gamma=0.5, eps=1e-7):# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
    box2 = box2.T

    # Get the coordinates of bounding boxesif x1y1x2y2:# x1, y1, x2, y2 = box1
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]else:# transform from xywh to xyxy
        b1_x1, b1_x2 = box1[0]- box1[2]/2, box1[0]+ box1[2]/2
        b1_y1, b1_y2 = box1[1]- box1[3]/2, box1[1]+ box1[3]/2
        b2_x1, b2_x2 = box2[0]- box2[2]/2, box2[0]+ box2[2]/2
        b2_y1, b2_y2 = box2[1]- box2[3]/2, box2[1]+ box2[3]/2# Intersection area
    inter =(torch.min(b1_x2, b2_x2)- torch.max(b1_x1, b2_x1)).clamp(0)* \
            (torch.min(b1_y2, b2_y2)- torch.max(b1_y1, b2_y1)).clamp(0)# Union Area
    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
    union = w1 * h1 + w2 * h2 - inter + eps

    # IoU# iou = inter / union # ori iou
    iou = torch.pow(inter/(union + eps), alpha)# alpha iouif CIoU or DIoU or GIoU or EIoU or SIoU:
        cw = b1_x2.maximum(b2_x2)- b1_x1.minimum(b2_x1)# convex (smallest enclosing box) width
        ch = b1_y2.maximum(b2_y2)- b1_y1.minimum(b2_y1)# convex heightif CIoU or DIoU or EIoU or SIoU:# Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            c2 =(cw **2+ ch **2)** alpha + eps  # convex diagonal squared
            rho2 =(((b2_x1 + b2_x2 - b1_x1 - b1_x2)**2+(b2_y1 + b2_y2 - b1_y1 - b1_y2)**2)/4)** alpha  # center dist ** 2if CIoU:# https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
                v =(4/ math.pi **2)*(torch.atan(w2 / h2)- torch.atan(w1 / h1)).pow(2)with torch.no_grad():
                    alpha_ciou = v /(v - iou +(1+ eps))if Focal:return iou -(rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha)), torch.pow(inter/(union + eps), gamma)# Focal_CIoUelse:return iou -(rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha))# CIoUelif EIoU:
                rho_w2 =((b2_x2 - b2_x1)-(b1_x2 - b1_x1))**2
                rho_h2 =((b2_y2 - b2_y1)-(b1_y2 - b1_y1))**2
                cw2 = torch.pow(cw **2+ eps, alpha)
                ch2 = torch.pow(ch **2+ eps, alpha)if Focal:return iou -(rho2 / c2 + rho_w2 / cw2 + rho_h2 / ch2), torch.pow(inter/(union + eps), gamma)# Focal_EIouelse:return iou -(rho2 / c2 + rho_w2 / cw2 + rho_h2 / ch2)# EIouelif SIoU:# SIoU Loss https://arxiv.org/pdf/2205.12740.pdf
                s_cw =(b2_x1 + b2_x2 - b1_x1 - b1_x2)*0.5+ eps
                s_ch =(b2_y1 + b2_y2 - b1_y1 - b1_y2)*0.5+ eps
                sigma = torch.pow(s_cw **2+ s_ch **2,0.5)
                sin_alpha_1 = torch.abs(s_cw)/ sigma
                sin_alpha_2 = torch.abs(s_ch)/ sigma
                threshold =pow(2,0.5)/2
                sin_alpha = torch.where(sin_alpha_1 > threshold, sin_alpha_2, sin_alpha_1)
                angle_cost = torch.cos(torch.arcsin(sin_alpha)*2- math.pi /2)
                rho_x =(s_cw / cw)**2
                rho_y =(s_ch / ch)**2
                gamma = angle_cost -2
                distance_cost =2- torch.exp(gamma * rho_x)- torch.exp(gamma * rho_y)
                omiga_w = torch.abs(w1 - w2)/ torch.max(w1, w2)
                omiga_h = torch.abs(h1 - h2)/ torch.max(h1, h2)
                shape_cost = torch.pow(1- torch.exp(-1* omiga_w),4)+ torch.pow(1- torch.exp(-1* omiga_h),4)if Focal:return iou - torch.pow(0.5*(distance_cost + shape_cost)+ eps, alpha), torch.pow(inter/(union + eps), gamma)# Focal_SIouelse:return iou - torch.pow(0.5*(distance_cost + shape_cost)+ eps, alpha)# SIouif Focal:return iou - rho2 / c2, torch.pow(inter/(union + eps), gamma)# Focal_DIoUelse:return iou - rho2 / c2  # DIoU
        c_area = cw * ch + eps  # convex areaif Focal:return iou - torch.pow((c_area - union)/ c_area + eps, alpha), torch.pow(inter/(union + eps), gamma)# Focal_GIoU https://arxiv.org/pdf/1902.09630.pdfelse:return iou - torch.pow((c_area - union)/ c_area + eps, alpha)# GIoU https://arxiv.org/pdf/1902.09630.pdfif Focal:return iou, torch.pow(inter/(union + eps), gamma)# Focal_IoUelse:return iou  # IoU

注意事项

1. 我认为Focal_EIoU的思想是可以用作与其他IoU的变种，因此我对里面所有的IoU都支持Focal_EIoU的思想，只需要设定Focal参数为True即可，我自己测试的过程中，除了Focal_SIoU出现loss为inf之外，其他的都正常，不过这个不同的数据集可能出现不一样，具体可以自行测试下。
2. gamma参数是Focal_EIoU中的gamma参数，一般就是为0.5，有需要可以自行更改。
3. alpha参数为AlphaIoU中的alpha参数，默认为1,1的意思就是跟正常的IoU一样，如果想采用AlphaIoU的话，论文alpha默认值为3。(比如我不想使用AlphaIoU的特性，我就把alpha设置为1就可以，如果我想使用AlphaIoU的特性，我可以设置alpha为3)。
4. 跟Focal_EIoU一样，我认为AlphaIoU的思想同样可以用在其他的IoU变种上，简单来说就是如果你设置了alpha为3，其他IoU设定的参数(GIoU，DIoU，CIoU，EIoU，SIoU)为False的时候，那就是AlphaIoU，如果你设置了alpha为3，CIoU为True的时候，那就是AlphaCIoU，效果的话就因数据集和模型而已，具体可以自行测试下。
5. 想用那个IoU变种，就直接设置参数为True即可。
6. AlphaIoU理论上与Focal_EIoU没有直接的冲突，但是作者这边没有详细测试过，这两者一起用会是什么效果，有兴趣可以自行测试下。

除了以上这个函数替换，还需要在utils/loss.py中ComputeLoss Class中的__call__和ComputeLossOTA Class中的__call__函数中修改一下：

原本的__call__函数如下：


主要对上述两个红框部分替换为以下代码：

iftype(iou)istuple:
    lbox +=(iou[1].detach()*(1- iou[0])).mean()
    iou = iou[0]else:
    lbox +=(1.0- iou).mean()# iou loss

原因是因为yolov7中的yaml配置文件有一个loss_ota的参数会选择采用哪一个Loss(ComputeLoss,ComputeLossOTA)，为了避免有一个不记得修改，就两个都一起修改即可。

最后修改参数就在调用bbox_iou中进行修改即可，比如上面的代码就是使用了CIoU，如果你想使用Focal_EIoU那么你可以修改为下：

iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, EIoU=True, Focal=True)

最后希望这篇文章可以帮助到大家。博文求点赞，github求star，谢谢啦！