Source code for torchvision.tv_tensors._mask
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
[docs]class Mask(TVTensor):
""":class:`torch.Tensor` subclass for segmentation and detection masks.
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``.
"""
def __new__(
cls,
data: Any,
*,
dtype: Optional[torch.dtype] = None,
device: Optional[Union[torch.device, str, int]] = None,
requires_grad: Optional[bool] = None,
) -> Mask:
if isinstance(data, PIL.Image.Image):
from torchvision.transforms.v2 import functional as F
data = F.pil_to_tensor(data)
tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
return tensor.as_subclass(cls)