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from typing import Optional |
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from functools import partial |
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import torch |
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from torch.nn.modules.loss import _Loss |
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from ._functional import focal_loss_with_logits |
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from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE |
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__all__ = ["FocalLoss"] |
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class FocalLoss(_Loss): |
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def __init__( |
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self, |
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mode: str, |
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alpha: Optional[float] = None, |
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gamma: Optional[float] = 2.0, |
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ignore_index: Optional[int] = None, |
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reduction: Optional[str] = "mean", |
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normalized: bool = False, |
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reduced_threshold: Optional[float] = None, |
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): |
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"""Compute Focal loss |
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Args: |
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mode: Loss mode 'binary', 'multiclass' or 'multilabel' |
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alpha: Prior probability of having positive value in target. |
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gamma: Power factor for dampening weight (focal strength). |
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ignore_index: If not None, targets may contain values to be ignored. |
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Target values equal to ignore_index will be ignored from loss computation. |
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normalized: Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf). |
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reduced_threshold: Switch to reduced focal loss. Note, when using this mode you |
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should use `reduction="sum"`. |
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Shape |
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- **y_pred** - torch.Tensor of shape (N, C, H, W) |
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- **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) |
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Reference |
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https://github.com/BloodAxe/pytorch-toolbelt |
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""" |
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assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} |
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super().__init__() |
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self.mode = mode |
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self.ignore_index = ignore_index |
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self.focal_loss_fn = partial( |
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focal_loss_with_logits, |
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alpha=alpha, |
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gamma=gamma, |
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reduced_threshold=reduced_threshold, |
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reduction=reduction, |
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normalized=normalized, |
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) |
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def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: |
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if self.mode in {BINARY_MODE, MULTILABEL_MODE}: |
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y_true = y_true.view(-1) |
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y_pred = y_pred.view(-1) |
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if self.ignore_index is not None: |
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not_ignored = y_true != self.ignore_index |
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y_pred = y_pred[not_ignored] |
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y_true = y_true[not_ignored] |
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loss = self.focal_loss_fn(y_pred, y_true) |
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elif self.mode == MULTICLASS_MODE: |
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num_classes = y_pred.size(1) |
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loss = 0 |
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if self.ignore_index is not None: |
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not_ignored = y_true != self.ignore_index |
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for cls in range(num_classes): |
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cls_y_true = (y_true == cls).long() |
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cls_y_pred = y_pred[:, cls, ...] |
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if self.ignore_index is not None: |
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cls_y_true = cls_y_true[not_ignored] |
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cls_y_pred = cls_y_pred[not_ignored] |
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loss += self.focal_loss_fn(cls_y_pred, cls_y_true) |
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return loss |
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