def iou_loss(preds: Tensor, target: Tensor) -> Tensor:
"""Calculates the intersection over union loss.
Args:
preds: batch of prediction bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
target: 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 iou_loss
>>> preds = torch.tensor([[100, 100, 200, 200]])
>>> target = torch.tensor([[150, 150, 250, 250]])
>>> iou_loss(preds, target)
tensor([[0.8571]])
Returns:
IoU loss
"""
loss = 1 - iou(preds, target)
return loss
def test_iou_multi(preds, target, expected_iou):
torch.testing.assert_allclose(iou(preds, target), expected_iou)
def test_iou_no_overlap(preds, target, expected_iou):
torch.testing.assert_allclose(iou(preds, target), expected_iou)