from typing import Optional
import torch
__all__ = ["one_hot"]
# adapted from kornia, https://github.com/kornia/kornia/blob/master/kornia/utils/one_hot.py
[docs]
def one_hot(
labels: torch.Tensor,
num_classes: int,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
eps: float = 1e-6,
) -> torch.Tensor:
r"""Converts an integer label x-D tensor to a one-hot (x+1)-D tensor.
Args:
labels (torch.Tensor) : tensor with labels of shape :math:`(N, *)`,
where N is batch size. Each value is an integer
representing correct classification.
num_classes (int): number of classes in labels.
device (Optional[torch.device]): the desired device of returned tensor.
Default: if None, defaults to device of `labels`.
dtype (Optional[torch.dtype]): the desired data type of returned
tensor. Default: if None, infers data type from values.
Returns:
torch.Tensor: the labels in one hot tensor of shape :math:`(N, C, *)`,
Examples:
>>> labels = torch.LongTensor([[[0, 1], [2, 0]]])
>>> one_hot(labels, num_classes=3)
tensor([[[[1.0000e+00, 1.0000e-06],
[1.0000e-06, 1.0000e+00]],
<BLANKLINE>
[[1.0000e-06, 1.0000e+00],
[1.0000e-06, 1.0000e-06]],
<BLANKLINE>
[[1.0000e-06, 1.0000e-06],
[1.0000e+00, 1.0000e-06]]]])
"""
if not isinstance(labels, torch.Tensor):
raise TypeError(
"Input labels type is not a torch.Tensor. Got {}".format(
type(labels)
)
)
if not labels.dtype == torch.int64:
raise ValueError(
"labels must be of the same dtype torch.int64. Got: {}".format(
labels.dtype
)
)
if num_classes < 1:
raise ValueError(
"The number of classes must be bigger than one."
" Got: {}".format(num_classes)
)
shape = labels.shape
if device is None:
device = labels.device
one_hot = torch.zeros(
(shape[0], num_classes) + shape[1:], device=device, dtype=dtype
)
return one_hot.scatter_(1, labels.unsqueeze(1), 1.0) + eps