BoundingBox

class torchvision.datapoints.BoundingBox(data: Any, *, format: Union[BoundingBoxFormat, str], spatial_size: Tuple[int, int], dtype: Optional[dtype] = None, device: Optional[Union[device, str, int]] = None, requires_grad: Optional[bool] = None)

[BETA] torch.Tensor subclass for bounding boxes.

Parameters:

• data – Any data that can be turned into a tensor with torch.as_tensor().

• format (BoundingBoxFormat, str) – Format of the bounding box.

• spatial_size (two-tuple of python:ints) – Height and width of the corresponding image or video.

• dtype (torch.dpython:type, optional) – Desired data type of the bounding box. If omitted, will be inferred from data.

• device (torch.device, optional) – Desired device of the bounding box. If omitted and data is a torch.Tensor, the device is taken from it. Otherwise, the bounding box is constructed on the CPU.

• requires_grad (bool, optional) – Whether autograd should record operations on the bounding box. If omitted and data is a torch.Tensor, the value is taken from it. Otherwise, defaults to False.

Examples using BoundingBox:

Datapoints FAQ

Datapoints FAQ

Getting started with transforms v2

Getting started with transforms v2

classmethod wrap_like(other: BoundingBox, tensor: Tensor, *, format: Optional[BoundingBoxFormat] = None, spatial_size: Optional[Tuple[int, int]] = None) → BoundingBox

Wrap a torch.Tensor as BoundingBox from a reference.

Parameters:

• other (BoundingBox) – Reference bounding box.

• tensor (Tensor) – Tensor to be wrapped as BoundingBox

• format (BoundingBoxFormat, str, optional) – Format of the bounding box. If omitted, it is taken from the reference.

• spatial_size (two-tuple of python:ints, optional) – Height and width of the corresponding image or video. If omitted, it is taken from the reference.