def forward(self, x, label=None):
assert x.size()[1] == 2
out_anchor = F.normalize(x[:, 0, :], p=2, dim=1)
out_positive = F.normalize(x[:, 1, :], p=2, dim=1)
stepsize = out_anchor.size()[0]
output = -1 * (F.pairwise_distance(
out_anchor.unsqueeze(-1),
out_positive.unsqueeze(-1).transpose(0, 2))**2)
negidx = self.mineHardNegative(output.detach())
out_negative = out_positive[negidx, :]
labelnp = numpy.array([1] * len(out_positive) +
[0] * len(out_negative))
## calculate distances
pos_dist = F.pairwise_distance(out_anchor, out_positive)
neg_dist = F.pairwise_distance(out_anchor, out_negative)
## loss function
nloss = torch.mean(
F.relu(
torch.pow(pos_dist, 2) - torch.pow(neg_dist, 2) + self.margin))
scores = -1 * torch.cat([pos_dist, neg_dist],
dim=0).detach().cpu().numpy()
errors = tuneThresholdfromScore(scores, labelnp, [])
return nloss, errors[1]
def forward(self, x, label=None):
out_anchor = x[:, 0, :]
out_positive = x[:, 1, :]
stepsize = out_anchor.size()[0]
output = -1 * (F.pairwise_distance(
out_anchor.unsqueeze(-1).expand(-1, -1, stepsize),
out_positive.unsqueeze(-1).expand(-1, -1, stepsize).transpose(
0, 2))**2)
negidx = self.mineHardNegative(output.detach())
out_negative = out_positive[negidx, :]
labelnp = numpy.array([1] * len(out_positive) +
[0] * len(out_negative))
## calculate distances
pos_dist = F.pairwise_distance(out_anchor, out_positive)
neg_dist = F.pairwise_distance(out_anchor, out_negative)
## loss functions
if self.loss_func == 'contrastive':
nloss = torch.mean(
torch.cat([
torch.pow(pos_dist, 2),
torch.pow(F.relu(self.margin - neg_dist), 2)
],
dim=0))
elif self.loss_func == 'triplet':
nloss = torch.mean(
F.relu(
torch.pow(pos_dist, 2) - torch.pow(neg_dist, 2) +
self.margin))
scores = -1 * torch.cat([pos_dist, neg_dist],
dim=0).detach().cpu().numpy()
errors = tuneThresholdfromScore(scores, labelnp, [])
return nloss, errors[1]
s.loadParameters(args.initial_model)
print("Model %s loaded!" % args.initial_model)
for ii in range(0, it - 1):
if ii % args.test_interval == 0:
clr = s.updateLearningRate(args.lr_decay)
# ==================== EVAL ====================
if args.eval == True:
sc, lab = s.evaluateFromListSave(args.test_list,
print_interval=100,
feat_dir=feat_save_path,
test_path=args.test_path)
result = tuneThresholdfromScore(sc, lab, [1, 0.1])
print('EER %2.4f' % result[1])
quit()
scorefile = open(result_save_path + "/scores.txt", "a+")
for items in vars(args):
print(items,
vars(args)[items])
scorefile.write('%s %s\n' % (items, vars(args)[items]))
scorefile.flush()
# ==================== ASSERTION ====================
gsize_dict = {
