holocron.ops

holocron.ops implements operators that are specific for Computer Vision.

Note

Those operators currently do not support TorchScript.

Boxes

holocron.ops.box_diou(boxes1, boxes2)[source]

Computes the Distance-IoU loss as described in “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression”.

The loss is defined as follows:

\[\mathcal{L}_{DIoU} = 1 - IoU + \frac{\rho^2(b, b^{GT})}{c^2}\]

where \(IoU\) is the Intersection over Union, \(b\) and \(b^{GT}\) are the centers of the box and the ground truth box respectively, \(c\) c is the diagonal length of the smallest enclosing box covering the two boxes, and \(\rho(.)\) is the Euclidean distance.

Parameters:
Returns:

Distance-IoU loss

Return type:

torch.Tensor[M, N]

holocron.ops.box_ciou(boxes1, boxes2)[source]

Computes the Complete IoU loss as described in “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression”.

The loss is defined as follows:

\[\mathcal{L}_{CIoU} = 1 - IoU + \frac{\rho^2(b, b^{GT})}{c^2} + \alpha v\]

where \(IoU\) is the Intersection over Union, \(b\) and \(b^{GT}\) are the centers of the box and the ground truth box respectively, \(c\) c is the diagonal length of the smallest enclosing box covering the two boxes, \(\rho(.)\) is the Euclidean distance, \(\alpha\) is a positive trade-off parameter, and \(v\) is the aspect ratio consistency.

More specifically:

\[v = \frac{4}{\pi^2} \Big(\arctan{\frac{w^{GT}}{h^{GT}}} - \arctan{\frac{w}{h}}\Big)^2\]

and

\[\alpha = \frac{v}{(1 - IoU) + v}\]
Parameters:
Returns:

Complete IoU loss

Return type:

torch.Tensor[M, N]

Example

>>> import torch
>>> from holocron.ops.boxes import box_ciou
>>> boxes1 = torch.tensor([[0, 0, 100, 100], [100, 100, 200, 200]], dtype=torch.float32)
>>> boxes2 = torch.tensor([[50, 50, 150, 150]], dtype=torch.float32)
>>> box_ciou(boxes1, boxes2)