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([