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Source code for catalyst.contrib.nn.criterion.focal

from functools import partial

from torch.nn.modules.loss import _Loss

from catalyst.utils import metrics


[docs]class FocalLossBinary(_Loss): """Compute focal loss for binary classification problem. It has been proposed in `Focal Loss for Dense Object Detection`_ paper. @TODO: Docs (add `Example`). Contribution is welcome. .. _Focal Loss for Dense Object Detection: https://arxiv.org/abs/1708.02002 """
[docs] def __init__( self, ignore: int = None, reduced: bool = False, gamma: float = 2.0, alpha: float = 0.25, threshold: float = 0.5, reduction: str = "mean", ): """@TODO: Docs. Contribution is welcome.""" super().__init__() self.ignore = ignore if reduced: self.loss_fn = partial( metrics.reduced_focal_loss, gamma=gamma, threshold=threshold, reduction=reduction, ) else: self.loss_fn = partial( metrics.sigmoid_focal_loss, gamma=gamma, alpha=alpha, reduction=reduction, )
[docs] def forward(self, logits, targets): """ Args: logits: [bs; ...] targets: [bs; ...] @TODO: Docs. Contribution is welcome. """ targets = targets.view(-1) logits = logits.view(-1) if self.ignore is not None: # Filter predictions with ignore label from loss computation not_ignored = targets != self.ignore logits = logits[not_ignored] targets = targets[not_ignored] loss = self.loss_fn(logits, targets) return loss
[docs]class FocalLossMultiClass(FocalLossBinary): """Compute focal loss for multi-class problem. Ignores targets having -1 label. It has been proposed in `Focal Loss for Dense Object Detection`_ paper. @TODO: Docs (add `Example`). Contribution is welcome. .. _Focal Loss for Dense Object Detection: https://arxiv.org/abs/1708.02002 """
[docs] def forward(self, logits, targets): """ Args: logits: [bs; num_classes; ...] targets: [bs; ...] @TODO: Docs. Contribution is welcome. """ num_classes = logits.size(1) loss = 0 targets = targets.view(-1) logits = logits.view(-1, num_classes) # Filter anchors with -1 label from loss computation if self.ignore is not None: not_ignored = targets != self.ignore for cls in range(num_classes): cls_label_target = (targets == (cls + 0)).long() cls_label_input = logits[..., cls] if self.ignore is not None: cls_label_target = cls_label_target[not_ignored] cls_label_input = cls_label_input[not_ignored] loss += self.loss_fn(cls_label_input, cls_label_target) return loss
# @TODO: check # class FocalLossMultiLabel(_Loss): # """Compute focal loss for multi-label problem. # Ignores targets having -1 label. # # It has been proposed in `Focal Loss for Dense Object Detection`_ paper. # # @TODO: Docs (add `Example`). Contribution is welcome. # # .. _Focal Loss for Dense Object Detection: # https://arxiv.org/abs/1708.02002 # """ # # def forward(self, logits, targets): # """ # Args: # logits: [bs; num_classes] # targets: [bs; num_classes] # """ # num_classes = logits.size(1) # loss = 0 # # for cls in range(num_classes): # # Filter anchors with -1 label from loss computation # if cls == self.ignore: # continue # # cls_label_target = targets[..., cls].long() # cls_label_input = logits[..., cls] # # loss += self.loss_fn(cls_label_input, cls_label_target) # # return loss __all__ = ["FocalLossBinary", "FocalLossMultiClass"]