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36c95ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from typing import Optional
import torch
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"""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)
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(f"Input labels type is not a torch.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 = torch.zeros((shape[0], num_classes) + shape[1:], device=device, dtype=dtype)
return one_hot.scatter_(1, labels.unsqueeze(1), 1.0) + eps
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