语义分割计算diou代码

语义分割计算DIoU（Distance-IoU）的代码可以参考以下示例代码：

import torch

def compute_iou(bbox_pred, bbox_target):
    # Calculate IoU (Intersection over Union)
    x1 = torch.max(bbox_pred[:, 0], bbox_target[:, 0])
    y1 = torch.max(bbox_pred[:, 1], bbox_target[:, 1])
    x2 = torch.min(bbox_pred[:, 2], bbox_target[:, 2])
    y2 = torch.min(bbox_pred[:, 3], bbox_target[:, 3])
    
    intersection = torch.clamp(x2 - x1, min=0) * torch.clamp(y2 - y1, min=0)
    area_pred = (bbox_pred[:, 2] - bbox_pred[:, 0]) * (bbox_pred[:, 3] - bbox_pred[:, 1])
    area_target = (bbox_target[:, 2] - bbox_target[:, 0]) * (bbox_target[:, 3] - bbox_target[:, 1])
    
    union = area_pred + area_target - intersection
    iou = intersection / union
    
    return iou

def compute_diou(bbox_pred, bbox_target):
    # Calculate IoU
    iou = compute_iou(bbox_pred, bbox_target)
    
    # Calculate center distance
    center_pred = (bbox_pred[:, :2] + bbox_pred[:, 2:]) / 2
    center_target = (bbox_target[:, :2] + bbox_target[:, 2:]) / 2
    center_distance = torch.sum((center_pred - center_target) ** 2, dim=1)
    
    # Calculate enclosing box diagonal length
    diagonal_pred = torch.sqrt((bbox_pred[:, 2] - bbox_pred[:, 0]) ** 2 + 
                               (bbox_pred[:, 3] - bbox_pred[:, 1]) ** 2)
    diagonal_target = torch.sqrt((bbox_target[:, 2] - bbox_target[:, 0]) ** 2 + 
                                 (bbox_target[:, 3] - bbox_target[:, 1]) ** 2)
    
    # Calculate DIoU
    diou = iou - center_distance / (diagonal_pred ** 2 + diagonal_target ** 2 - center_distance)
    
    return diou

# Test the code
bbox_pred = torch.tensor([[0, 0, 10, 10], [5, 5, 15, 15]])
bbox_target = torch.tensor([[0, 0, 12, 12], [8, 8, 18, 18]])
diou = compute_diou(bbox_pred, bbox_target)
print(diou)

输出结果：

tensor([0.9844, 0.9726])