12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758 |
- #! /usr/bin/python
- # -*- encoding: utf-8 -*-
- # Adapted from https://github.com/wujiyang/Face_Pytorch (Apache License)
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import time, pdb, numpy, math
- from utils import accuracy
- class LossFunction(nn.Module):
- def __init__(self, nOut, nClasses, margin=0.3, scale=15, easy_margin=False, **kwargs):
- super(LossFunction, self).__init__()
- self.test_normalize = True
-
- self.m = margin
- self.s = scale
- self.in_feats = nOut
- self.weight = torch.nn.Parameter(torch.FloatTensor(nClasses, nOut), requires_grad=True)
- self.ce = nn.CrossEntropyLoss()
- nn.init.xavier_normal_(self.weight, gain=1)
- self.easy_margin = easy_margin
- self.cos_m = math.cos(self.m)
- self.sin_m = math.sin(self.m)
- # make the function cos(theta+m) monotonic decreasing while theta in [0°,180°]
- self.th = math.cos(math.pi - self.m)
- self.mm = math.sin(math.pi - self.m) * self.m
- print('Initialised AAMSoftmax margin %.3f scale %.3f'%(self.m,self.s))
- def forward(self, x, label=None):
- assert x.size()[0] == label.size()[0]
- assert x.size()[1] == self.in_feats
-
- # cos(theta)
- cosine = F.linear(F.normalize(x), F.normalize(self.weight))
- # cos(theta + m)
- sine = torch.sqrt((1.0 - torch.mul(cosine, cosine)).clamp(0, 1))
- phi = cosine * self.cos_m - sine * self.sin_m
- if self.easy_margin:
- phi = torch.where(cosine > 0, phi, cosine)
- else:
- phi = torch.where((cosine - self.th) > 0, phi, cosine - self.mm)
- #one_hot = torch.zeros(cosine.size(), device='cuda' if torch.cuda.is_available() else 'cpu')
- one_hot = torch.zeros_like(cosine)
- one_hot.scatter_(1, label.view(-1, 1), 1)
- output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
- output = output * self.s
- loss = self.ce(output, label)
- prec1 = accuracy(output.detach(), label.detach(), topk=(1,))[0]
- return loss, prec1
|