generalized_box_iou_loss

torchvision.ops.generalized_box_iou_loss(boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = 'none', eps: float = 1e-07) → torch.Tensor

Original implementation from https://github.com/facebookresearch/fvcore/blob/bfff2ef/fvcore/nn/giou_loss.py

Gradient-friendly IoU loss with an additional penalty that is non-zero when the boxes do not overlap and scales with the size of their smallest enclosing box. 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.

Parameters

• boxes1 (Tensor[N, 4] or Tensor[4]) – first set of boxes

• boxes2 (Tensor[N, 4] or Tensor[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

Reference:

Hamid Rezatofighi et. al: Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression: https://arxiv.org/abs/1902.09630