binary_focal_loss.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class BinaryFocalLoss(nn.Module):
    def __init__(self, alpha=1, gamma=2, logits=True, reduce=True):
        super(BinaryFocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.logits = logits
        self.reduce = reduce

    def forward(self, inputs, targets):
        if self.logits:
            BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
        else:
            BCE_loss = F.binary_cross_entropy(inputs, targets, reduction='none')
        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1-pt)**self.gamma * BCE_loss

        if self.reduce:
            return torch.mean(F_loss)
        else:
            return F_loss