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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, device='cuda:0'):
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
self.device = device
def forward(self, inputs, targets):
N = inputs.size(0)
C = inputs.size(1)
P = F.softmax(inputs, dim=1)
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()
self.alpha = self.alpha.to(self.device)
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
# import torch
# import torch.nn as nn
#
#
# class FocalLoss(nn.Module):
#
# def __init__(self, gamma=0, eps=1e-7):
# super(FocalLoss, self).__init__()
# self.gamma = gamma
# self.eps = eps
# self.ce = torch.nn.CrossEntropyLoss()
#
# def forward(self, input, target):
# logp = self.ce(input, target)
# p = torch.exp(-logp)
# loss = (1 - p) ** self.gamma * logp
# return loss.mean() |