#!/usr/bin/env python
# -*- coding: utf-8 -*-
# --------------------------------------------------------
# Licensed under The MIT License [see LICENSE for details]
# Written by Chao CHEN (chaochancs@gmail.com)
# Created On: 2017-08-11
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
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) : 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): 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 = torch.ones(class_num, 1)
else:
self.alpha = alpha
self.gamma = gamma
self.class_num = class_num
self.size_average = size_average
def forward(self, inputs, targets):
N = inputs.size(0)
print(N)
C = inputs.size(1)
P = F.softmax(inputs)
class_mask = inputs.data.new(N, C).fill_(0)
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__":
alpha = torch.rand(21, 1)
print(alpha)
FL = FocalLoss(class_num=5, gamma=0 )
CE = nn.CrossEntropyLoss()
N = 4
C = 5
inputs = torch.rand(N, C, requires_grad=True)
targets = torch.LongTensor(N).random_(C)
inputs_fl = inputs.clone()
targets_fl = targets.clone()
inputs_ce = inputs.clone()
targets_ce = targets.clone()
print('----inputs----')
print(inputs)
print('---target-----')
print(targets)
fl_loss = FL(inputs_fl, targets_fl)
ce_loss = CE(inputs_ce, targets_ce)
print('ce = {}, fl ={}'.format(ce_loss.data[0], fl_loss.data[0]))
fl_loss.backward()
ce_loss.backward()
#print(inputs_fl.grad.data)
print(inputs_ce.grad.data)
