your focal loss is wrong? it seems little different with others, can you explain your code?

[Jack-zz-ze]

class FocalLoss(nn.Module): def init(self, alpha=1, gamma=2, logits=False, reduce=True): super(FocalLoss, 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, reduce=False)
    else:
        BCE_loss = F.binary_cross_entropy(inputs, targets, reduce=False)
    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

[Jack-zz-ze]

they just use forward function, and you use forward and backforward ,can you explain it

[CoinCheung]

Have you compared the outputs of the two implementations?

[Jack-zz-ze]

def forward(ctx, logits, label, alpha, gamma):         # logits = logits.float()

        probs = torch.sigmoid(logits)         coeff = (label - probs).abs().pow(gamma).neg_()         log_probs = torch.where(logits >= 0,                 F.softplus(logits, -1, 50),                 logits - F.softplus(logits, 1, 50))         log_1_probs = torch.where(logits >= 0,                 -logits + F.softplus(logits, -1, 50),                 -F.softplus(logits, 1, 50))         ce_term1 = logprobs.mul(label).mul_(alpha)         ce_term2 = log_1probs.mul(1. - label).mul_(1. - alpha)         ce = ceterm1.add(ce_term2)         loss = ce * coeff

        ctx.vars = (coeff, probs, ce, label, gamma, alpha)

        return loss

I don't observe which pt is. Can you write the formulas you want to achieve for each step？

---Original--- From: @.> Date: Wed, Aug 4, 2021 12:17 PM To: @.>; Cc: @.**@.>; Subject: Re: [CoinCheung/pytorch-loss] your focal loss is wrong? it seems little different with others, can you explain your code? (#27)

Have you compared the outputs of the two implementations?

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[CoinCheung]

If you find my focal loss is wrong, please post an example code to show the difference between the correct implementation, and I will see where problem is in my code.

[Jack-zz-ze]

1、focal_loss = -α(1-pt)*γ log(pt) 2、your code: log_probs = torch.where(logits >= 0, F.softplus(logits, -1, 50), logits - F.softplus(logits, 1, 50)) 3、Do you want to show log(pt), but F.softplus is(1/β)log(1+e^(βx)).
log(pt)=labellog(p)+(1-label)log(1-p)， but log_probs= -xlog(1+e^(-x)) + (1-x)[ x-log(1+e^(x)) ] is different

[noobgrow]

I have the same question, could you please the line? log_probs = torch.where(logits >= 0, F.softplus(logits, -1, 50), logits - F.softplus(logits, 1, 50))

[AwalkZY]

Just make some formula derivations, these implementations are totally the same and this version can be more stable. (Awesome work!) @noobgrow @Jack-zz-ze

[CoinCheung]

close this because this is not active anymore.