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- #! /usr/bin/python
- # -*- encoding: utf-8 -*-
- ## Fast re-implementation of the GE2E loss (https://arxiv.org/abs/1710.10467)
- ## Numerically checked against https://github.com/cvqluu/GE2E-Loss
- 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 GE2E')
- def forward(self, x, label=None):
- assert x.size()[1] >= 2
- gsize = x.size()[1]
- centroids = torch.mean(x, 1)
- stepsize = x.size()[0]
- cos_sim_matrix = []
- for ii in range(0,gsize):
- idx = [*range(0,gsize)]
- idx.remove(ii)
- exc_centroids = torch.mean(x[:,idx,:], 1)
- cos_sim_diag = F.cosine_similarity(x[:,ii,:],exc_centroids)
- cos_sim = F.cosine_similarity(x[:,ii,:].unsqueeze(-1),centroids.unsqueeze(-1).transpose(0,2))
- cos_sim[range(0,stepsize),range(0,stepsize)] = cos_sim_diag
- cos_sim_matrix.append(torch.clamp(cos_sim,1e-6))
- cos_sim_matrix = torch.stack(cos_sim_matrix,dim=1)
- 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.view(-1,stepsize), torch.repeat_interleave(label,repeats=gsize,dim=0).cuda())
- prec1 = accuracy(cos_sim_matrix.view(-1,stepsize).detach(), torch.repeat_interleave(label,repeats=gsize,dim=0).detach(), topk=(1,))[0]
- return nloss, prec1
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