| from typing import List, Optional | |
| import torch | |
| from ._functional import soft_tversky_score | |
| from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE | |
| from .dice import DiceLoss | |
| __all__ = ["TverskyLoss", "TverskyLossFocal"] | |
| class TverskyLoss(DiceLoss): | |
| """Implementation of Tversky loss for image segmentation task. | |
| Where TP and FP is weighted by alpha and beta params. | |
| With alpha == beta == 0.5, this loss becomes equal DiceLoss. | |
| It supports binary, multiclass and multilabel cases | |
| Args: | |
| mode: Metric mode {'binary', 'multiclass', 'multilabel'} | |
| classes: Optional list of classes that contribute in loss computation; | |
| By default, all channels are included. | |
| log_loss: If True, loss computed as ``-log(tversky)`` otherwise ``1 - tversky`` | |
| from_logits: If True assumes input is raw logits | |
| smooth: | |
| ignore_index: Label that indicates ignored pixels (does not contribute to loss) | |
| eps: Small epsilon for numerical stability | |
| alpha: Weight constant that penalize model for FPs (False Positives) | |
| beta: Weight constant that penalize model for FNs (False Positives) | |
| gamma: Constant that squares the error function. Defaults to ``1.0`` | |
| Return: | |
| loss: torch.Tensor | |
| """ | |
| def __init__( | |
| self, | |
| mode: str, | |
| classes: List[int] = None, | |
| log_loss: bool = False, | |
| from_logits: bool = True, | |
| smooth: float = 0.0, | |
| ignore_index: Optional[int] = None, | |
| eps: float = 1e-7, | |
| alpha: float = 0.5, | |
| beta: float = 0.5, | |
| gamma: float = 1.0, | |
| ): | |
| assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} | |
| super().__init__(mode, classes, log_loss, from_logits, smooth, ignore_index, eps) | |
| self.alpha = alpha | |
| self.beta = beta | |
| self.gamma = gamma | |
| def aggregate_loss(self, loss): | |
| return loss.mean() ** self.gamma | |
| def compute_score(self, output, target, smooth=0.0, eps=1e-7, dims=None) -> torch.Tensor: | |
| return soft_tversky_score(output, target, self.alpha, self.beta, smooth, eps, dims) | |