'Iter_%06d_model.ckpt' % total_iters))
torch.save(
{
'iters': total_iters,
'net_state_dict': margin.state_dict()
},
os.path.join(save_dir,
'Iter_%06d_margin.ckpt' % total_iters))
# evaluate accuracy
if total_iters % 3000 == 0:
model.eval()
for phase in ['LFW', 'CFP_FP', 'AgeDB30']:
featureLs, featureRs = getFeature(model,
dataloaders[phase],
device,
flip=args.flip)
ACCs, threshold = evaluation_10_fold(featureLs,
featureRs,
dataset[phase],
method=args.method)
print(
'Epoch {}/{},{} average acc:{:.4f} average threshold:{:.4f}'
.format(epoch, args.epoch - 1, phase,
np.mean(ACCs) * 100, np.mean(threshold)))
if best_acc[phase] <= np.mean(ACCs) * 100:
best_acc[phase] = np.mean(ACCs) * 100
best_iters[phase] = total_iters
with open(test_logging_file, 'a') as f:
f.write(
torch.save({
'iters': total_iters,
'net_state_dict': model.state_dict()},
os.path.join(save_dir, 'Iter_%06d_model.ckpt' % total_iters))
torch.save({
'iters': total_iters,
'net_state_dict': margin.state_dict()},
os.path.join(save_dir, 'Iter_%06d_margin.ckpt' % total_iters))
# evaluate accuracy
if total_iters % 3000 == 0:
model.eval()
for phase in ['LFW', 'CFP_FP', 'AgeDB30']:
featureLs, featureRs = getFeature(model,best_arc, dataloaders[phase], "cuda:1", flip = args.flip,)
ACCs, threshold = evaluation_10_fold(featureLs, featureRs, dataset[phase], method = args.method)
print('Epoch {}/{},{} average acc:{:.4f} average threshold:{:.4f}'
.format(epoch, args.epoch-1, phase, np.mean(ACCs) * 100, np.mean(threshold)))
if best_acc[phase] <= np.mean(ACCs) * 100:
best_acc[phase] = np.mean(ACCs) * 100
best_iters[phase] = total_iters
with open(test_logging_file, 'a') as f:
f.write('Epoch {}/{}, {} average acc:{:.4f} average threshold:{:.4f}'
.format(epoch, args.epoch-1, phase, np.mean(ACCs) * 100, np.mean(threshold))+'\n')
f.close()
model.train()
time_elapsed = time.time() - start