Source code for kornia.utils.one_hot

from typing import Optional

import torch

[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"""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