#! /usr/bin/python # -*- encoding: utf-8 -*- import torch import torch.nn as nn import torch.nn.functional as F import time, pdb, numpy from utils import accuracy class LossFunction(nn.Module): def __init__(self, init_w=10.0, init_b=-5.0, **kwargs): super(LossFunction, self).__init__() self.test_normalize = True self.w = nn.Parameter(torch.tensor(init_w)) self.b = nn.Parameter(torch.tensor(init_b)) self.criterion = torch.nn.CrossEntropyLoss() print('Initialised AngleProto') def forward(self, x, label=None): assert x.size()[1] >= 2 out_anchor = torch.mean(x[:,1:,:],1) out_positive = x[:,0,:] stepsize = out_anchor.size()[0] cos_sim_matrix = F.cosine_similarity(out_positive.unsqueeze(-1),out_anchor.unsqueeze(-1).transpose(0,2)) torch.clamp(self.w, 1e-6) cos_sim_matrix = cos_sim_matrix * self.w + self.b label = torch.from_numpy(numpy.asarray(range(0,stepsize))).cuda() nloss = self.criterion(cos_sim_matrix, label) prec1 = accuracy(cos_sim_matrix.detach(), label.detach(), topk=(1,))[0] return nloss, prec1