Focal Loss 的Pytorch

torch focalloss
import torch
from torch.autograd import Variable


# class FocalLoss(torch.nn.Module):
#     def __init__(self, gamma=2):
#         super().__init__()
#         self.gamma = gamma
# 
#     def forward(self, log_pred_prob_onehot, target):
#         pred_prob_oh = torch.exp(log_pred_prob_onehot)
#         pt = Variable(pred_prob_oh.data.gather(1, target.data.view(-1, 1)), requires_grad=True)
#         modulator = (1 - pt) ** self.gamma
#         mce = modulator * (-torch.log(pt))
# 
#         return mce.mean()

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class FocalLoss(nn.Module):
    r"""
        This criterion is a implemenation of Focal Loss, which is proposed in 
        Focal Loss for Dense Object Detection.

            Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])

        The losses are averaged across observations for each minibatch.

        Args:
            alpha(1D Tensor, Variable) : the scalar factor for this criterion
            gamma(float, double) : gamma > 0; reduces the relative loss for well-classiﬁed examples (p > .5), 
                                   putting more focus on hard, misclassiﬁed examples
            size_average(bool): By default, the losses are averaged over observations for each minibatch.
                                However, if the field size_average is set to False, the losses are
                                instead summed for each minibatch.


    """
    def __init__(self, class_num, alpha=None, gamma=2, size_average=True):
        super(FocalLoss, self).__init__()
        if alpha is None:
            self.alpha = Variable(torch.ones(class_num, 1))
        else:
            if isinstance(alpha, Variable):
                self.alpha = alpha
            else:
                self.alpha = Variable(alpha)
        self.gamma = gamma
        self.class_num = class_num
        self.size_average = size_average

    def forward(self, inputs, targets):
        N = inputs.size(0)
        C = inputs.size(1)
        P = F.softmax(inputs)

        class_mask = inputs.data.new(N, C).fill_(0)
        class_mask = Variable(class_mask)
        ids = targets.view(-1, 1)
        class_mask.scatter_(1, ids.data, 1.)
        #print(class_mask)


        if inputs.is_cuda and not self.alpha.is_cuda:
            self.alpha = self.alpha.cuda()
        alpha = self.alpha[ids.data.view(-1)]

        probs = (P*class_mask).sum(1).view(-1,1)

        log_p = probs.log()
        #print('probs size= {}'.format(probs.size()))
        #print(probs)

        batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p 
        #print('-----bacth_loss------')
        #print(batch_loss)


        if self.size_average:
            loss = batch_loss.mean()
        else:
            loss = batch_loss.sum()
        return loss
if __name__ == '__main__':

    loss=FocalLoss()

    conf_mask = torch.FloatTensor([0.0, 1.0, 0.0, 1.0, 1.0])-1
    conf_data = torch.FloatTensor([-0.1, -0.9, 0.0, -0.2, -0.2])

    print(loss(conf_mask,conf_data))
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