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”

Parameters:

boxes1 (torch.Tensor[M, 4]) – bounding boxes
boxes2 (torch.Tensor[N, 4]) – bounding boxes

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”

Parameters:

boxes1 (torch.Tensor[M, 4]) – bounding boxes
boxes2 (torch.Tensor[N, 4]) – bounding boxes

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)