proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'lcnn9_mfcc':
model = model_.lcnn_9layers(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'lcnn29_mfcc':
model = model_.lcnn_29layers_v2(n_z=args.latent_size,
proj_size=len(list(
labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'TDNN':
model = model_.TDNN(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'TDNN_multipool':
model = model_.TDNN_multipool(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'lcnn9_mfcc':
model = model_.lcnn_9layers(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'lcnn29_mfcc':
model = model_.lcnn_29layers_v2(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'TDNN':
model = model_.TDNN(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'TDNN_multipool':
model = model_.ResNet_fb(n_z=args.latent_size, proj_size=None)
elif args.model == 'resnet_mfcc':
model = model_.ResNet_mfcc(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'lcnn9_mfcc':
model = model_.lcnn_9layers(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'lcnn29_mfcc':
model = model_.lcnn_29layers_v2(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'TDNN':
model = model_.TDNN(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'TDNN_multipool':
model = model_.TDNN_multipool(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'FTDNN':
model = model_.FTDNN(n_z=args.latent_size,
args = parser.parse_args()
if args.model == 'mfcc':
model = model_.cnn_lstm_mfcc(n_z=args.latent_size, ncoef=args.ncoef)
if args.model == 'fb':
model = model_.cnn_lstm_fb(n_z=args.latent_size)
elif args.model == 'resnet_fb':
model = model_.ResNet_fb(n_z=args.latent_size)
elif args.model == 'resnet_mfcc':
model = model_.ResNet_mfcc(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'lcnn9_mfcc':
model = model_.lcnn_9layers(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'lcnn29_mfcc':
model = model_.lcnn_29layers_v2(n_z=args.latent_size, ncoef=args.ncoef)
if args.pairwise:
if args.model == 'mfcc':
clone_model = model_.cnn_lstm_mfcc(n_z=args.latent_size,
ncoef=args.ncoef)
if args.model == 'fb':
clone_model = model_.cnn_lstm_fb(n_z=args.latent_size)
elif args.model == 'resnet_fb':
clone_model = model_.ResNet_fb(n_z=args.latent_size)
elif args.model == 'resnet_mfcc':
clone_model = model_.ResNet_mfcc(n_z=args.latent_size,
ncoef=args.ncoef)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.valid_batch_size,
shuffle=False,
worker_init_fn=set_np_randomseed)
else:
valid_loader = None
if args.model == 'lstm':
model = model_.cnn_lstm(nclasses=args.n_classes)
elif args.model == 'resnet':
model = model_.ResNet(nclasses=args.n_classes)
elif args.model == 'resnet_pca':
model = model_.ResNet_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_9':
model = model_.lcnn_9layers(nclasses=args.n_classes)
elif args.model == 'lcnn_29':
model = model_.lcnn_29layers_v2(nclasses=args.n_classes)
elif args.model == 'lcnn_9_pca':
model = model_.lcnn_9layers_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_29_pca':
model = model_.lcnn_29layers_v2_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_9_icqspec':
model = model_.lcnn_9layers_icqspec(nclasses=args.n_classes)
elif args.model == 'lcnn_9_prodspec':
model = model_.lcnn_9layers_prodspec(nclasses=args.n_classes)
elif args.model == 'lcnn_9_CC':
model = model_.lcnn_9layers_CC(nclasses=args.n_classes,
ncoef=args.ncoef,
init_coef=args.init_coef)
elif args.model == 'lcnn_29_CC':
if os.path.isfile(args.out_path):
os.remove(args.out_path)
print(args.out_path + ' Removed')
if args.cuda:
device = get_freer_gpu()
if args.model_la == 'lstm':
model_la = model_.cnn_lstm()
elif args.model_la == 'resnet':
model_la = model_.ResNet()
elif args.model_la == 'resnet_pca':
model_la = model_.ResNet_pca()
elif args.model_la == 'lcnn_9':
model_la = model_.lcnn_9layers()
elif args.model_la == 'lcnn_29':
model_la = model_.lcnn_29layers_v2()
elif args.model_la == 'lcnn_9_pca':
model_la = model_.lcnn_9layers_pca()
elif args.model_la == 'lcnn_29_pca':
model_la = model_.lcnn_29layers_v2_pca()
elif args.model_la == 'lcnn_9_icqspec':
model_la = model_.lcnn_9layers_icqspec()
elif args.model_la == 'lcnn_9_prodspec':
model_la = model_.lcnn_9layers_prodspec()
elif args.model_la == 'lcnn_9_CC':
model_la = model_.lcnn_9layers_CC(ncoef=args.ncoef_la)
elif args.model_la == 'lcnn_29_CC':
model_la = model_.lcnn_29layers_CC(ncoef=args.ncoef_la)
elif args.model_la == 'resnet_CC':
print(args.out_path + ' Removed')
print('Cuda Mode is: {}'.format(args.cuda))
print('Selected model is: {}'.format(args.model))
if args.cuda:
device = get_freer_gpu()
if args.model == 'lstm':
model = model_.cnn_lstm()
elif args.model == 'resnet':
model = model_.ResNet()
elif args.model == 'resnet_pca':
model = model_.ResNet_pca()
elif args.model == 'lcnn_9':
model = model_.lcnn_9layers()
elif args.model == 'lcnn_29':
model = model_.lcnn_29layers_v2()
elif args.model == 'lcnn_9_pca':
model = model_.lcnn_9layers_pca()
elif args.model == 'lcnn_29_pca':
model = model_.lcnn_29layers_v2_pca()
elif args.model == 'lcnn_9_icqspec':
model = model_.lcnn_9layers_icqspec()
elif args.model == 'lcnn_9_prodspec':
model = model_.lcnn_9layers_prodspec()
elif args.model == 'lcnn_9_CC':
model = model_.lcnn_9layers_CC(ncoef=args.ncoef,
init_coef=args.init_coef)
elif args.model == 'lcnn_29_CC':
model = model_.lcnn_29layers_CC(ncoef=args.ncoef,
