def giou_loss(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Calculates the generalized intersection over union loss.
It has been proposed in `Generalized Intersection over Union: A Metric and A
Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_.
Args:
preds: an Nx4 batch of prediction bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
target: an Mx4 batch of target bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
Example:
>>> import torch
>>> from pl_bolts.losses.object_detection import giou_loss
>>> preds = torch.tensor([[100, 100, 200, 200]])
>>> target = torch.tensor([[150, 150, 250, 250]])
>>> giou_loss(preds, target)
tensor([[1.0794]])
Returns:
GIoU loss in an NxM tensor containing the pairwise GIoU loss for every element in preds and target,
where N is the number of prediction bounding boxes and M is the number of target bounding boxes
"""
loss = 1 - giou(preds, target)
return loss
def test_giou_multi(preds, target, expected_giou):
torch.testing.assert_allclose(giou(preds, target), expected_giou)
def test_no_overlap(preds, target, expected_giou):
torch.testing.assert_allclose(giou(preds, target), expected_giou)
