khVddlmZddlmZmZmZddlZddlZddl m Z Gdde Z y)) annotations)AnyOptionalUnionN)TVTensorc4eZdZdZdddd ddZy)Maska:class:`torch.Tensor` subclass for segmentation and detection masks with shape ``[..., H, W]``. Args: data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as well as PIL images. dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from ``data``. device (torch.device, optional): Desired device. If omitted and ``data`` is a :class:`torch.Tensor`, the device is taken from it. Otherwise, the mask is constructed on the CPU. requires_grad (bool, optional): Whether autograd should record operations. If omitted and ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``. Ndtypedevice requires_gradct|tjjrddlm}|j |}|j ||||}|j|S)Nr) functionalr ) isinstancePILImagetorchvision.transforms.v2r pil_to_tensor _to_tensor as_subclass)clsdatar r rFtensors X/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/tv_tensors/_mask.py__new__z Mask.__new__sO dCIIOO , A??4(DE&P]^!!#&&) rrr zOptional[torch.dtype]r z'Optional[Union[torch.device, str, int]]rzOptional[bool]returnr )__name__ __module__ __qualname____doc__rrrr r sG "(,:>(, ''% ' 8 ' & ' 'rr ) __future__rtypingrrr PIL.Imagertorch _tv_tensorrr r$rrr*s#"'' '8'r