| |
|
|
| import torch |
|
|
| from .base_data_element import BaseDataElement |
|
|
|
|
| class LabelData(BaseDataElement): |
| """Data structure for label-level annotations or predictions.""" |
|
|
| @staticmethod |
| def onehot_to_label(onehot: torch.Tensor) -> torch.Tensor: |
| """Convert the one-hot input to label. |
| |
| Args: |
| onehot (torch.Tensor, optional): The one-hot input. The format |
| of input must be one-hot. |
| |
| Returns: |
| torch.Tensor: The converted results. |
| """ |
| assert isinstance(onehot, torch.Tensor) |
| if (onehot.ndim == 1 and onehot.max().item() <= 1 |
| and onehot.min().item() >= 0): |
| return onehot.nonzero().squeeze(-1) |
| else: |
| raise ValueError( |
| 'input is not one-hot and can not convert to label') |
|
|
| @staticmethod |
| def label_to_onehot(label: torch.Tensor, num_classes: int) -> torch.Tensor: |
| """Convert the label-format input to one-hot. |
| |
| Args: |
| label (torch.Tensor): The label-format input. The format |
| of item must be label-format. |
| num_classes (int): The number of classes. |
| |
| Returns: |
| torch.Tensor: The converted results. |
| """ |
| assert isinstance(label, torch.Tensor) |
| onehot = label.new_zeros((num_classes, )) |
| assert max(label, default=torch.tensor(0)).item() < num_classes |
| onehot[label] = 1 |
| return onehot |
|
|
