p y t o r c h 实 现 F o c a l L o s s pytorch实现Focal Loss pytorch实现FocalLoss
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class FocalLoss(nn.Module):
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.)
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()
batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p
if self.size_average:
loss = batch_loss.mean()
else:
loss = batch_loss.sum()
return loss