cudnn.benchmark = True
CKP_DIR = args.ckp_dir
LOG_DIR = args.log_dir + '/run-test_{}-n{}-lr{}-wd{}-m{}-embeddings{}-msceleb-alpha10' \
.format(args.optimizer, args.n_triplets, args.lr, args.wd,
args.margin, args.embedding_size)
# create logger
logger = Logger(LOG_DIR)
# Define visulaize SummaryWriter instance
writer = SummaryWriter('Log/amsoftmax_res10', comment='margin0.3')
kwargs = {'num_workers': 0, 'pin_memory': True} if args.cuda else {}
if args.cos_sim:
l2_dist = nn.CosineSimilarity(dim=1, eps=1e-6)
else:
l2_dist = PairwiseDistance(2)
voxceleb, voxceleb_dev = wav_list_reader(args.dataroot)
if args.makemfb:
# pbar = tqdm(voxceleb)
for datum in voxceleb:
mk_MFB(
(args.dataroot + '/voxceleb1_wav/' + datum['filename'] + '.wav'))
print("Complete convert")
if args.mfb:
transform = transforms.Compose([
concateinputfromMFB(),
# truncatedinputfromMFB(),
totensor()
])
# order to prevent any memory allocation on unused GPUs
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
args.cuda = not args.no_cuda and torch.cuda.is_available()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.multiprocessing.set_sharing_strategy('file_system')
if args.cuda:
cudnn.benchmark = True
# Define visulaize SummaryWriter instance
kwargs = {'num_workers': args.nj, 'pin_memory': False} if args.cuda else {}
l2_dist = nn.CosineSimilarity(
dim=1, eps=1e-6) if args.cos_sim else PairwiseDistance(2)
if args.input_length == 'var':
transform = transforms.Compose([
# concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, remove_vad=False),
varLengthFeat(remove_vad=args.remove_vad),
to2tensor()
])
transform_T = transforms.Compose([
# concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, input_per_file=args.test_input_per_file, remove_vad=False),
varLengthFeat(remove_vad=args.remove_vad),
to2tensor()
])
elif args.input_length == 'fix':
transform = transforms.Compose(
[concateinputfromMFB(remove_vad=args.remove_vad),
# Define visulaize SummaryWriter instance
writer = SummaryWriter(logdir=args.check_path, filename_suffix='_first')
sys.stdout = NewLogger(osp.join(args.check_path, 'log.txt'))
kwargs = {'num_workers': args.nj, 'pin_memory': True} if args.cuda else {}
if not os.path.exists(args.check_path):
os.makedirs(args.check_path)
opt_kwargs = {'lr': args.lr,
'lr_decay': args.lr_decay,
'weight_decay': args.weight_decay,
'dampening': args.dampening,
'momentum': args.momentum}
l2_dist = nn.CosineSimilarity(dim=1, eps=1e-6) if args.cos_sim else PairwiseDistance(2)
if args.acoustic_feature == 'fbank':
transform = transforms.Compose([
concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, remove_vad=False),
# varLengthFeat(),
to2tensor()
])
transform_T = transforms.Compose([
concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, input_per_file=args.test_input_per_file, remove_vad=False),
# varLengthFeat(),
to2tensor()
])
else:
transform = transforms.Compose([