'Mongolian': 33,
'Aurek': 34,
'Angelic': 23,
'ULOG': 33,
'Oriya': 33,
'Avesta': 31,
'Tibetan': 43,
'Tengwar': 28,
'Keble': 25,
'Ge_ez': 31,
'Glagolitic': 46
}
model = VGG(n_layer='4+2', in_channels=1).to(device)
model.load_state_dict(torch.load(args.pretrain_dir), strict=False)
model.last = Identity()
init_feat_extractor = model
acc = {}
nmi = {}
ari = {}
for key, alphabetStr in omniglot_evaluation_alphabets_mapping.items():
runner_name = os.path.basename(__file__).split(".")[0]
model_dir = args.exp_root + '{}/{}'.format(runner_name,
args.subfolder_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
args.model_dir = model_dir + '/' + 'vgg6_{}.pth'.format(alphabetStr)
args.save_txt_path = args.exp_root + '{}/{}'.format(
runner_name, args.save_txt_name)
train_Dloader, eval_Dloader = omniglot_alphabet_func(
alphabet=alphabetStr, background=False,
num_workers=2)
testset = CIFAR10(root=args.dataset_root,
train=True,
download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=128,
shuffle=False,
num_workers=1)
state_dict_imagenet = torch.load(args.pretrain_dir)
del state_dict_imagenet['linear.0.weight']
del state_dict_imagenet['linear.0.bias']
model = ResNet(BasicBlock, [2, 2, 2, 2], args.n_clusters).to(device)
model.load_state_dict(state_dict_imagenet, strict=False)
model.linear = Identity()
init_acc, init_nmi, init_ari, init_centers, init_probs = init_prob_kmeans(
model, trainloader, args)
args.p_targets = target_distribution(init_probs)
model = ResNet(BasicBlock, [2, 2, 2, 2], args.n_clusters).to(device)
model.load_state_dict(state_dict_imagenet, strict=False)
model.center = Parameter(torch.Tensor(args.n_clusters, args.n_clusters))
model.center.data = torch.tensor(init_centers).float().to(device)
warmup_train(model, trainloader, testloader, args)
if args.DTC == 'Baseline':
Baseline_train(model, trainloader, testloader, args)
elif args.DTC == 'PI':
PI_train(model, trainloader_twice, testloader, args)
elif args.DTC == 'TE':