| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758 | #! /usr/bin/python# -*- encoding: utf-8 -*-# Adapted from https://github.com/wujiyang/Face_Pytorch (Apache License)import torchimport torch.nn as nnimport torch.nn.functional as Fimport time, pdb, numpy, mathfrom utils import accuracyclass 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
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