# Source code for torchvision.ops.diou_loss

```
from typing import Tuple
import torch
from ..utils import _log_api_usage_once
from ._utils import _loss_inter_union, _upcast_non_float
[docs]def distance_box_iou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Gradient-friendly IoU loss with an additional penalty that is non-zero when the
distance between boxes' centers isn't zero. Indeed, for two exactly overlapping
boxes, the distance IoU is the same as the IoU loss.
This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.
Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the
same dimensions.
Args:
boxes1 (Tensor[N, 4]): first set of boxes
boxes2 (Tensor[N, 4]): second set of boxes
reduction (string, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be
applied to the output. ``'mean'``: The output will be averaged.
``'sum'``: The output will be summed. Default: ``'none'``
eps (float, optional): small number to prevent division by zero. Default: 1e-7
Returns:
Tensor: Loss tensor with the reduction option applied.
Reference:
Zhaohui Zheng et. al: Distance Intersection over Union Loss:
https://arxiv.org/abs/1911.08287
"""
# Original Implementation from https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/losses.py
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(distance_box_iou_loss)
boxes1 = _upcast_non_float(boxes1)
boxes2 = _upcast_non_float(boxes2)
loss, _ = _diou_iou_loss(boxes1, boxes2, eps)
if reduction == "mean":
loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
elif reduction == "sum":
loss = loss.sum()
return loss
def _diou_iou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
eps: float = 1e-7,
) -> Tuple[torch.Tensor, torch.Tensor]:
intsct, union = _loss_inter_union(boxes1, boxes2)
iou = intsct / (union + eps)
# smallest enclosing box
x1, y1, x2, y2 = boxes1.unbind(dim=-1)
x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
xc1 = torch.min(x1, x1g)
yc1 = torch.min(y1, y1g)
xc2 = torch.max(x2, x2g)
yc2 = torch.max(y2, y2g)
# The diagonal distance of the smallest enclosing box squared
diagonal_distance_squared = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps
# centers of boxes
x_p = (x2 + x1) / 2
y_p = (y2 + y1) / 2
x_g = (x1g + x2g) / 2
y_g = (y1g + y2g) / 2
# The distance between boxes' centers squared.
centers_distance_squared = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2)
# The distance IoU is the IoU penalized by a normalized
# distance between boxes' centers squared.
loss = 1 - iou + (centers_distance_squared / diagonal_distance_squared)
return loss, iou
```