(pred, target, eps=1e-6)
return loss
IoU loss.

    Computing the IoU loss between a set of predicted bboxes and target bboxes.
    The loss is calculated as negative log of IoU.

    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.
IoU loss.
IoU loss .
def iou_loss(pred, target, eps=1e-6):
    """IoU loss.

    Computing the IoU loss between a set of predicted bboxes and target bboxes.
    The loss is calculated as negative log of IoU.

    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.
    """
    ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
    loss = -ious.log()
    return loss
def iou_loss ( pred , target , eps = 1e-6 ) : ious = bbox_overlaps ( pred , target , is_aligned = True ) . clamp ( min = eps ) loss = - ious . log ( ) return loss