import torch
from kornia.core import Tensor, zeros
[docs]def one_hot(labels: Tensor, num_classes: int, device: torch.device, dtype: torch.dtype, eps: float = 1e-6) -> Tensor:
r"""Convert an integer label x-D tensor to a one-hot (x+1)-D tensor.
Args:
labels: tensor with labels of shape :math:`(N, *)`, where N is batch size.
Each value is an integer representing correct classification.
num_classes: number of classes in labels.
device: the desired device of returned tensor.
dtype: the desired data type of returned tensor.
Returns:
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, device=torch.device('cpu'), dtype=torch.int64)
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, Tensor):
raise TypeError(f"Input labels type is not a Tensor. Got {type(labels)}")
if not labels.dtype == torch.int64:
raise ValueError(f"labels must be of the same dtype torch.int64. Got: {labels.dtype}")
if num_classes < 1:
raise ValueError("The number of classes must be bigger than one." " Got: {}".format(num_classes))
shape = labels.shape
one_hot = zeros((shape[0], num_classes) + shape[1:], device=device, dtype=dtype)
return one_hot.scatter_(1, labels.unsqueeze(1), 1.0) + eps