def test(model, epoch, writer, xvector_dir):
this_xvector_dir = "%s/test/epoch_%s" % (xvector_dir, epoch)
extract_loader = torch.utils.data.DataLoader(extract_dir,
batch_size=1,
shuffle=False,
**kwargs)
verification_extract(extract_loader, model, this_xvector_dir, epoch)
verify_dir = ScriptVerifyDataset(dir=args.test_dir,
trials_file=args.trials,
xvectors_dir=this_xvector_dir,
loader=read_vec_flt)
verify_loader = torch.utils.data.DataLoader(verify_dir,
batch_size=128,
shuffle=False,
**kwargs)
eer, eer_threshold, mindcf_01, mindcf_001 = verification_test(
test_loader=verify_loader,
dist_type=('cos' if args.cos_sim else 'l2'),
log_interval=args.log_interval,
xvector_dir=this_xvector_dir,
epoch=epoch)
print(
'\33[91mTest ERR: {:.4f}%, Threshold: {:.4f}, mindcf-0.01: {:.4f}, mindcf-0.001: {:.4f}.\33[0m\n'
.format(100. * eer, eer_threshold, mindcf_01, mindcf_001))
writer.add_scalar('Test/EER', 100. * eer, epoch)
writer.add_scalar('Test/Threshold', eer_threshold, epoch)
writer.add_scalar('Test/mindcf-0.01', mindcf_01, epoch)
writer.add_scalar('Test/mindcf-0.001', mindcf_001, epoch)
def valid_test(train_extract_loader, model, epoch, xvector_dir):
# switch to evaluate mode
model.eval()
this_xvector_dir = "%s/train/epoch_%s" % (xvector_dir, epoch)
verification_extract(train_extract_loader, model, this_xvector_dir, epoch)
verify_dir = ScriptVerifyDataset(dir=args.train_test_dir,
trials_file=args.train_trials,
xvectors_dir=this_xvector_dir,
loader=read_vec_flt)
verify_loader = torch.utils.data.DataLoader(verify_dir,
batch_size=128,
shuffle=False,
**kwargs)
eer, eer_threshold, mindcf_01, mindcf_001 = verification_test(
test_loader=verify_loader,
dist_type=('cos' if args.cos_sim else 'l2'),
log_interval=args.log_interval,
xvector_dir=this_xvector_dir,
epoch=epoch)
print('Test Epoch {}:\n\33[91mTrain EER: {:.4f}%, Threshold: {:.4f}, ' \
'mindcf-0.01: {:.4f}, mindcf-0.001: {:.4f}.'.format(epoch,
100. * eer,
eer_threshold,
mindcf_01,
mindcf_001))
writer.add_scalar('Train/EER', 100. * eer, epoch)
writer.add_scalar('Train/Threshold', eer_threshold, epoch)
writer.add_scalar('Train/mindcf-0.01', mindcf_01, epoch)
writer.add_scalar('Train/mindcf-0.001', mindcf_001, epoch)
torch.cuda.empty_cache()
def main():
# Views the training images and displays the distance on anchor-negative and anchor-positive
# test_display_triplet_distance = False
# print the experiment configuration
print('\nCurrent time is \33[91m{}\33[0m.'.format(str(time.asctime())))
print('Parsed options: {}'.format(vars(args)))
# print('Number of Speakers: {}.\n'.format(train_dir.num_spks))
# instantiate model and initialize weights
kernel_size = args.kernel_size.split(',')
kernel_size = [int(x) for x in kernel_size]
padding = [int((x - 1) / 2) for x in kernel_size]
kernel_size = tuple(kernel_size)
padding = tuple(padding)
channels = args.channels.split(',')
channels = [int(x) for x in channels]
model_kwargs = {'embedding_size': args.embedding_size,
'resnet_size': args.resnet_size,
'inst_norm': args.inst_norm,
'input_dim': args.feat_dim,
'fast': args.fast,
'num_classes': train_dir.num_spks,
'alpha': args.alpha,
'channels': channels,
'stride': args.stride,
'avg_size': args.avg_size,
'time_dim': args.time_dim,
'encoder_type': args.encoder_type,
'kernel_size': kernel_size,
'padding': padding,
'dropout_p': args.dropout_p}
print('Model options: {}'.format(model_kwargs))
if args.valid or args.extract:
model = create_model(args.model, **model_kwargs)
if args.loss_type == 'asoft':
model.classifier = AngleLinear(in_features=args.embedding_size, out_features=train_dir.num_spks, m=args.m)
elif args.loss_type == 'amsoft':
model.classifier = AdditiveMarginLinear(feat_dim=args.embedding_size, n_classes=train_dir.num_spks)
assert os.path.isfile(args.resume)
print('=> loading checkpoint {}'.format(args.resume))
checkpoint = torch.load(args.resume)
# start_epoch = checkpoint['epoch']
filtered = {k: v for k, v in checkpoint['state_dict'].items() if 'num_batches_tracked' not in k}
# model_dict = model.state_dict()
# model_dict.update(filtered)
model.load_state_dict(filtered)
#
try:
model.dropout.p = args.dropout_p
except:
pass
start = args.start_epoch
print('Epoch is : ' + str(start))
if args.cuda:
model.cuda()
# train_loader = torch.utils.data.DataLoader(train_dir, batch_size=args.batch_size, shuffle=True, **kwargs)
if args.valid:
valid_loader = torch.utils.data.DataLoader(valid_dir, batch_size=args.test_batch_size, shuffle=False,
**kwargs)
valid(valid_loader, model)
if args.extract:
verify_loader = torch.utils.data.DataLoader(verfify_dir, batch_size=args.test_batch_size, shuffle=False,
**kwargs)
extract(verify_loader, model, args.xvector_dir)
file_loader = read_vec_flt
test_dir = ScriptVerifyDataset(dir=args.test_dir, trials_file=args.trials,
xvectors_dir=args.xvector_dir, loader=file_loader)
test_loader = torch.utils.data.DataLoader(test_dir, batch_size=args.test_batch_size * 64, shuffle=False, **kwargs)
test(test_loader)
# train_loader = torch.utils.data.DataLoader(train_dir, batch_size=args.batch_size, shuffle=True, **kwargs)
if args.valid:
valid_loader = torch.utils.data.DataLoader(valid_dir, batch_size=args.test_batch_size, shuffle=False,
**kwargs)
valid(valid_loader, model)
del train_dir # , valid_dir
print('Memery Usage: %.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
if args.extract:
verify_loader = torch.utils.data.DataLoader(verfify_dir, batch_size=args.test_batch_size, shuffle=False,
**kwargs)
extract(verify_loader, model, args.xvector_dir)
file_loader = read_vec_flt
test_dir = ScriptVerifyDataset(dir=args.test_dir, trials_file=args.trials, xvectors_dir=args.xvector_dir,
loader=file_loader)
test_loader = torch.utils.data.DataLoader(test_dir, batch_size=args.test_batch_size * 64, shuffle=False, **kwargs)
test(test_loader)
# python TrainAndTest/Spectrogram/train_surescnn10_kaldi.py > Log/SuResCNN10/spect_161/
# test easy spectrogram soft 161 vox1
# Test ERR is 1.6076%, Threshold is 0.31004807353019714
# mindcf-0.01 0.2094, mindcf-0.001 0.3767.
# test hard spectrogram soft 161 vox1
# Test ERR is 2.9182%, Threshold is 0.35036733746528625
# mindcf-0.01 0.3369, mindcf-0.001 0.5494.