EIOU损失函数的python代码

EIOU损失函数是一种用于目标检测任务的评价指标，用于衡量预测框与真实框之间的重叠程度。下面是一个简单的Python代码示例，用于计算EIOU损失函数：

import torch

def calculate_eiou_loss(pred_boxes, target_boxes):
    # 计算预测框和真实框的坐标
    pred_x1, pred_y1, pred_x2, pred_y2 = pred_boxes[:, 0], pred_boxes[:, 1], pred_boxes[:, 2], pred_boxes[:, 3]
    target_x1, target_y1, target_x2, target_y2 = target_boxes[:, 0], target_boxes[:, 1], target_boxes[:, 2], target_boxes[:, 3]

    # 计算预测框和真实的面积
    pred_area = (pred_x2 - pred_x1 + 1) * (pred_y2 - pred_y1 + 1)
    target_area = (target_x2 - target_x1 + 1) * (target_y2 - target_y1 + 1)

    # 计算交集框的坐标
    inter_x1 = torch.max(pred_x1, target_x1)
    inter_y1 = torch.max(pred_y1, target_y1)
    inter_x2 = torch.min(pred_x2, target_x2)
    inter_y2 = torch.min(pred_y2, target_y2)

    # 计算交集框的面积
    inter_area = torch.clamp(inter_x2 - inter_x1 + 1, min=0) * torch.clamp(inter_y2 - inter_y1 + 1, min=0)

    # 计算并集框的面积
    union_area = pred_area + target_area - inter_area

    # 计算EIOU损失
    eiou_loss = 1 - inter_area / union_area

    return eiou_loss.mean()

请注意，这只是一个简单的示例代码，具体实现可能会根据具体的需求和框架而有所不同。