circle loss代码实现_Focal loss的简单实现(二分类+多分类)

下面是 circle loss 和 focal loss 的简单实现（包括二分类和多分类）：

Circle Loss：

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

class CircleLoss(nn.Module):
    def __init__(self, m=0.25, s=30):
        super(CircleLoss, self).__init__()
        self.m = m
        self.s = s
        
    def forward(self, feats, labels):
        sim_mat = torch.matmul(feats, feats.t())
        mask = labels.expand(labels.size(0), labels.size(0)).t().eq(labels.expand(labels.size(0), labels.size(0)))
        pos_mask = mask.triu(diagonal=1)
        neg_mask = mask.logical_not().triu(diagonal=1)
        pos_sim = sim_mat[pos_mask]
        neg_sim = sim_mat[neg_mask]
        alpha_p = F.relu(-pos_sim.detach() + 1 + self.m)
        alpha_n = F.relu(neg_sim.detach() + self.m)
        delta_p = 1 - self.m
        delta_n = self.m
        logit_p = -self.s * alpha_p * (pos_sim - delta_p)
        logit_n = self.s * alpha_n * (neg_sim - delta_n)
        logit = torch.cat([logit_p, logit_n], dim=0)
        loss = F.softplus(torch.logsumexp(logit, dim=0))
        return loss

Focal Loss：

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

class FocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2, reduction='mean'):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction
        
    def forward(self, inputs, targets):
        BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1-pt)**self.gamma * BCE_loss
        if self.reduction == 'mean':
            return torch.mean(F_loss)
        elif self.reduction == 'sum':
            return torch.sum(F_loss)
        else:
            return F_loss

以上代码适用于 PyTorch 深度学习框架。