Source code for torchvision.ops.giou_loss
import torch
[docs]def generalized_box_iou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> 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.
Args:
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
"""
x1, y1, x2, y2 = boxes1.unbind(dim=-1)
x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
# Intersection keypoints
xkis1 = torch.max(x1, x1g)
ykis1 = torch.max(y1, y1g)
xkis2 = torch.min(x2, x2g)
ykis2 = torch.min(y2, y2g)
intsctk = torch.zeros_like(x1)
mask = (ykis2 > ykis1) & (xkis2 > xkis1)
intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk
iouk = intsctk / (unionk + eps)
# smallest enclosing box
xc1 = torch.min(x1, x1g)
yc1 = torch.min(y1, y1g)
xc2 = torch.max(x2, x2g)
yc2 = torch.max(y2, y2g)
area_c = (xc2 - xc1) * (yc2 - yc1)
miouk = iouk - ((area_c - unionk) / (area_c + eps))
loss = 1 - miouk
if reduction == "mean":
loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
elif reduction == "sum":
loss = loss.sum()
return loss