YOLOv5改进、YOLOv7改进IoU损失函数：YOLOv7涨点Trick，改进添加SIoU损失函数、EIoU损失函数、GIoU损失函数、α-IoU损失函数

• 该教程为改进入门指南，属于《芒果书》系列，包含大量的原创首发改进方式, 所有文章都是全网首发原创改进内容

• 本篇文章 基于 YOLOv5、YOLOv7芒果改进YOLO系列：YOLOv7改进IoU损失函数：YOLOv7涨点Trick，改进添加SIoU损失函数、EIoU损失函数、GIoU损失函数、α-IoU损失函数、打造全新YOLOv7检测器。

• 重点：有不少同学已经反应有效涨点!!! 其他改进内容：CSDN原创YOLO进阶目录 | 《芒果改进YOLO进阶指南》推荐！

• 最全《芒果书》改进目录：YOLOv5改进、YOLOv7改进（芒果书系列）目录一览｜原创YOLO改进模型全系列目录 | 人工智能专家唐宇迪老师联袂推荐
  

解析｜YOLOv7网络模型源代码训练推理教程解析

• 手把手调参最新 YOLOv7 模型 推理部分（一）
• 手把手调参最新 YOLOv7 模型 训练部分（二）

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总结｜YOLO系列期刊创新点总结

• ☁️：国庆假期浏览了几十篇YOLO改进英文期刊，总结改进创新的一些相同点(期刊创新点持续更新)

• ☁️：国庆假期看了一系列图像分割Unet、DeepLabv3+改进期刊论文，总结了一些改进创新的技巧

核心代码改进

以下SIoU、EIoU、GIoU、α-IoU改进，代码均在博主开源的YOLOAir中有写

改进核心代码

在YOLOv5中，使用以下函数替换原有的utils/metrics.py文件中的bbox_iou函数

如果在YOLOv7中，使用以下函数替换原有的utils/general.py文件中的bbox_iou函数

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, EIoU=False, SIoU=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 boxes
    if 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 CIoU or DIoU or GIoU or EIoU or SIoU:
        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 height
        if CIoU or DIoU or EIoU or SIoU:  # 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 squared
            if DIoU: #DIoU
                return iou - rho2 / c2  # DIoU
            elif CIoU:  #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) - torch.atan(w1 / h1), 2)
                with torch.no_grad():
                    alpha = v / (v - iou + (1 + eps))
                return iou - (rho2 / c2 + v * alpha)  # CIoU  
            elif SIoU:# SIoU
                s_cw = (b2_x1 + b2_x2 - b1_x1 - b1_x2) * 0.5
                s_ch = (b2_y1 + b2_y2 - b1_y1 - b1_y2) * 0.5
                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)
                return iou - 0.5 * (distance_cost + shape_cost)
            else:# EIoU
                w_dis=torch.pow(b1_x2-b1_x1-b2_x2+b2_x1, 2)
                h_dis=torch.pow(b1_y2-b1_y1-b2_y2+b2_y1, 2)
                cw2=torch.pow(cw , 2)+eps
                ch2=torch.pow(ch , 2)+eps
                return iou-(rho2/c2+w_dis/cw2+h_dis/ch2)
        else:
            c_area = cw * ch + eps  # convex area
            return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
    return iou  # IoU

改进α-IoU核心代码

def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9):
    # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4
    box2 = box2.T

    # Get the coordinates of bounding boxes
    if 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

    # change iou into pow(iou+eps)
    # iou = inter / union
    iou = torch.pow(inter/union + eps, alpha)
    # beta = 2 * alpha
    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 height
        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            c2 = (cw ** 2 + ch ** 2) ** alpha + eps  # convex diagonal
            rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2)
            rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2)
            rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha  # center distance
            if DIoU:
                return iou - rho2 / c2  # DIoU
            elif 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) - torch.atan(w1 / h1), 2)
                with torch.no_grad():
                    alpha_ciou = v / ((1 + eps) - inter / union + v)
                # return iou - (rho2 / c2 + v * alpha_ciou)  # CIoU
                return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha))  # CIoU
        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
            # c_area = cw * ch + eps  # convex area
            # return iou - (c_area - union) / c_area  # GIoU
            c_area = torch.max(cw * ch + eps, union) # convex area
            return iou - torch.pow((c_area - union) / c_area + eps, alpha)  # GIoU
    else:
        return iou # torch.log(iou+eps) or iou

SIoU改进

参考上面的核心代码

将
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)
替换为
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, SIoU=True)

EIoU改进

参考上面的核心代码

将
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)
替换为
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, EIoU=True)

GIoU改进

参考上面的核心代码

将
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)
替换为
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, GIoU=True)

α-IoU改进

参考上面的核心代码

bbox_alpha_iou

将
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)
替换为
iou = bbox_alpha_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)

以上是yolov5的改进

yolov7 将 tbox[i] 改为 selected_tbox

比如 iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)
改为iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)

代码直接运行

python train.py cfg yolov7.yaml即可