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: """ # Original Implementation from 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


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