focal_loss.py

# Copyright (c) Microsoft Corporation
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# MIT License
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import torch
import torch.nn as nn
import torch.nn.functional as F

class FocalLoss2d(nn.Module):

    def __init__(self, gamma=2, size_average=True):
        super(FocalLoss2d, self).__init__()
        self.gamma = gamma
        self.size_average = size_average


    def forward(self, logit, target, class_weight=None, type='sigmoid'):
        target = target.view(-1, 1).long()

        if type=='sigmoid':
            if class_weight is None:
                class_weight = [1]*2 #[0.5, 0.5]

            prob   = torch.sigmoid(logit)
            prob   = prob.view(-1, 1)
            prob   = torch.cat((1-prob, prob), 1)
            select = torch.FloatTensor(len(prob), 2).zero_().cuda()
            select.scatter_(1, target, 1.)

        elif  type=='softmax':
            B,C,H,W = logit.size()
            if class_weight is None:
                class_weight =[1]*C #[1/C]*C

            logit   = logit.permute(0, 2, 3, 1).contiguous().view(-1, C)
            prob    = F.softmax(logit,1)
            select  = torch.FloatTensor(len(prob), C).zero_().cuda()
            select.scatter_(1, target, 1.)

        class_weight = torch.FloatTensor(class_weight).cuda().view(-1,1)
        class_weight = torch.gather(class_weight, 0, target)

        prob       = (prob*select).sum(1).view(-1,1)
        prob       = torch.clamp(prob,1e-8,1-1e-8)
        batch_loss = - class_weight *(torch.pow((1-prob), self.gamma))*prob.log()

        if self.size_average:
            loss = batch_loss.mean()
        else:
            loss = batch_loss

        return loss


if __name__ == '__main__':
    L = FocalLoss2d()
    out = torch.randn(2, 3, 3).cuda()
    target = (torch.sigmoid(out) > 0.5).float()
    loss = L(out, target)
    print(loss)