FocusiOU损失函数代码

### FocusIoU Loss Function 实现

FocusIoU 是一种改进版的 IoU 损失函数，在处理物体检测任务中的边界框回归时表现出色。该损失函数不仅考虑了交并比(IoU)，还引入了额外的因素来提升模型性能。

下面是一个 Python 版本的 FocusIoU 损失函数实现：

import torch
import torch.nn.functional as F

def focus_iou_loss(pred_boxes, target_boxes):
    """
    计算两个边界框之间的FocusIoU损失
    
    参数:
        pred_boxes (Tensor): 预测边框，形状为[N, 4] 
        target_boxes (Tensor): 目标边框，形状为[N, 4]

    返回:
        Tensor: 平均FocusIoU损失值
    """

    # 获取预测和目标边框坐标
    pred_x1 = pred_boxes[:, 0]
    pred_y1 = pred_boxes[:, 1]
    pred_x2 = pred_boxes[:, 2]
    pred_y2 = pred_boxes[:, 3]

    target_x1 = target_boxes[:, 0]
    target_y1 = target_boxes[:, 1]
    target_x2 = target_boxes[:, 2]
    target_y2 = target_boxes[:, 3]

    # 计算面积
    area_pred = (pred_x2 - pred_x1) * (pred_y2 - pred_y1)
    area_target = (target_x2 - target_x1) * (target_y2 - target_y1)

    # 找到重叠区域的最大最小坐标
    inter_xmin = torch.max(pred_x1, target_x1)
    inter_ymin = torch.max(pred_y1, target_y1)
    inter_xmax = torch.min(pred_x2, target_x2)
    inter_ymax = torch.min(pred_y2, target_y2)

    # 判断是否有交叉部分
    inter_area = torch.clamp(inter_xmax - inter_xmin, min=0) * \
                 torch.clamp(inter_ymax - inter_ymin, min=0)

    union_area = area_pred + area_target - inter_area

    iou = inter_area / union_area

    giou_term = ((inter_xmax - inter_xmin) * (inter_ymax - inter_ymin)) / union_area

    focus_iou = iou - (giou_term ** 2)

    return 1 - focus_iou.mean()

此代码实现了 FocusIoU 损失函数，其中包含了对边界框坐标的解析以及 IoU 和 GIoU 组件的计算[^1]。