<gh_stars>0
import math
import torch
import torch.nn as nn
from utils_frequent.IoUs.multi_iou_cal import MultiIoUCal
def diou_loss(pred, target, eps=1e-7):
    r"""`Implementation of Distance-IoU Loss: Faster and Better
    Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.

    Code is modified from https://github.com/Zzh-tju/DIoU.

    Args:
        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
            shape (n, 4).
        target (Tensor): Corresponding gt bboxes, shape (n, 4).
        eps (float): Eps to avoid log(0).
    Return:
        Tensor: Loss tensor.
    """
    # overlap
    lt = torch.max(pred[:, :2], target[:, :2])
    rb = torch.min(pred[:, 2:], target[:, 2:])
    wh = (rb - lt).clamp(min=0)
    overlap = wh[:, 0] * wh[:, 1]

    # union
    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
    union = ap + ag - overlap + eps

    # IoU
    ious = overlap / union

    # enclose area
    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)

    cw = enclose_wh[:, 0]
    ch = enclose_wh[:, 1]

    c2 = cw**2 + ch**2 + eps

    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
    b2_x1, b2_y1 = target[:, 0], target[:, 1]
    b2_x2, b2_y2 = target[:, 2], target[:, 3]

    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
    rho2 = left + right

    # DIoU
    dious = ious - rho2 / c2
    loss = 1 - dious
    return loss

class DIoULoss(nn.Module):

    def __init__(self, eps=1e-6):
        super(DIoULoss, self).__init__()
        self.eps = eps


    def forward(self,
                pred,
                target,
                ):
        loss = self.loss_weight * diou_loss(
            pred,
            target,
            eps=self.eps
            )
        return loss


import math
import torch
import torch.nn as nn
from utils_frequent.IoUs.multi_iou_cal import MultiIoUCal
def diou_loss(pred, target, eps=1e-7):
    r"""`Implementation of Distance-IoU Loss: Faster and Better
    Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.

    Code is modified from https://github.com/Zzh-tju/DIoU.

    Args:
        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
            shape (n, 4).
        target (Tensor): Corresponding gt bboxes, shape (n, 4).
        eps (float): Eps to avoid log(0).
    Return:
        Tensor: Loss tensor.
    """
    # overlap
    lt = torch.max(pred[:, :2], target[:, :2])
    rb = torch.min(pred[:, 2:], target[:, 2:])
    wh = (rb - lt).clamp(min=0)
    overlap = wh[:, 0] * wh[:, 1]

    # union
    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
    union = ap + ag - overlap + eps

    # IoU
    ious = overlap / union

    # enclose area
    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)

    cw = enclose_wh[:, 0]
    ch = enclose_wh[:, 1]

    c2 = cw**2 + ch**2 + eps

    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
    b2_x1, b2_y1 = target[:, 0], target[:, 1]
    b2_x2, b2_y2 = target[:, 2], target[:, 3]

    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
    rho2 = left + right

    # DIoU
    dious = ious - rho2 / c2
    loss = 1 - dious
    return loss

class DIoULoss(nn.Module):

    def __init__(self, eps=1e-6):
        super(DIoULoss, self).__init__()
        self.eps = eps


    def forward(self,
                pred,
                target,
                ):
        loss = self.loss_weight * diou_loss(
            pred,
            target,
            eps=self.eps
            )
        return loss
`Implementation of Distance-IoU Loss: Faster and Better     Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.      Code is modified from https://github.com/Zzh-tju/DIoU.      Args:         pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),             shape (n, 4).         target (Tensor): Corresponding gt bboxes, shape (n, 4).         eps (float): Eps to avoid log(0).     Return:         Tensor: Loss tensor. # overlap # union # IoU # enclose area # DIoU