def get_kcg(models, labeled_data_size, unlabeled_loader):
models['backbone'].eval()
with torch.cuda.device(CUDA_VISIBLE_DEVICES):
features = torch.tensor([]).cuda()
with torch.no_grad():
for inputs, _, _ in unlabeled_loader:
with torch.cuda.device(CUDA_VISIBLE_DEVICES):
inputs = inputs.cuda()
_, features_batch, _ = models['backbone'](inputs)
features = torch.cat((features, features_batch), 0)
feat = features.detach().cpu().numpy()
new_av_idx = np.arange(SUBSET, (SUBSET + labeled_data_size))
sampling = kCenterGreedy(feat)
batch = sampling.select_batch_(new_av_idx, ADDENDUM)
other_idx = [x for x in range(SUBSET) if x not in batch]
return other_idx + batch
def query_samples(model, method, data_unlabeled, subset, labeled_set, cycle,
args):
if method == 'Random':
arg = np.random.randint(SUBSET, size=SUBSET)
if (method == 'UncertainGCN') or (method == 'CoreGCN'):
# Create unlabeled dataloader for the unlabeled subset
unlabeled_loader = DataLoader(
data_unlabeled,
batch_size=BATCH,
sampler=SubsetSequentialSampler(
subset + labeled_set
), # more convenient if we maintain the order of subset
pin_memory=True)
binary_labels = torch.cat(
(torch.zeros([SUBSET, 1]), (torch.ones([len(labeled_set), 1]))), 0)
features = get_features(model, unlabeled_loader)
features = nn.functional.normalize(features)
adj = aff_to_adj(features)
gcn_module = GCN(nfeat=features.shape[1],
nhid=args.hidden_units,
nclass=1,
dropout=args.dropout_rate).cuda()
models = {'gcn_module': gcn_module}
optim_backbone = optim.Adam(models['gcn_module'].parameters(),
lr=LR_GCN,
weight_decay=WDECAY)
optimizers = {'gcn_module': optim_backbone}
lbl = np.arange(SUBSET, SUBSET + (cycle + 1) * ADDENDUM, 1)
nlbl = np.arange(0, SUBSET, 1)
############
for _ in range(200):
optimizers['gcn_module'].zero_grad()
outputs, _, _ = models['gcn_module'](features, adj)
lamda = args.lambda_loss
loss = BCEAdjLoss(outputs, lbl, nlbl, lamda)
loss.backward()
optimizers['gcn_module'].step()
models['gcn_module'].eval()
with torch.no_grad():
with torch.cuda.device(CUDA_VISIBLE_DEVICES):
inputs = features.cuda()
labels = binary_labels.cuda()
scores, _, feat = models['gcn_module'](inputs, adj)
if method == "CoreGCN":
feat = feat.detach().cpu().numpy()
new_av_idx = np.arange(SUBSET,
(SUBSET + (cycle + 1) * ADDENDUM))
sampling2 = kCenterGreedy(feat)
batch2 = sampling2.select_batch_(new_av_idx, ADDENDUM)
other_idx = [x for x in range(SUBSET) if x not in batch2]
arg = other_idx + batch2
else:
s_margin = args.s_margin
scores_median = np.squeeze(
torch.abs(scores[:SUBSET] -
s_margin).detach().cpu().numpy())
arg = np.argsort(-(scores_median))
print("Max confidence value: ", torch.max(scores.data))
print("Mean confidence value: ", torch.mean(scores.data))
preds = torch.round(scores)
correct_labeled = (preds[SUBSET:, 0]
== labels[SUBSET:, 0]).sum().item() / (
(cycle + 1) * ADDENDUM)
correct_unlabeled = (preds[:SUBSET, 0]
== labels[:SUBSET, 0]).sum().item() / SUBSET
correct = (preds[:, 0]
== labels[:, 0]).sum().item() / (SUBSET +
(cycle + 1) * ADDENDUM)
print("Labeled classified: ", correct_labeled)
print("Unlabeled classified: ", correct_unlabeled)
print("Total classified: ", correct)
if method == 'CoreSet':
# Create unlabeled dataloader for the unlabeled subset
unlabeled_loader = DataLoader(
data_unlabeled,
batch_size=BATCH,
sampler=SubsetSequentialSampler(
subset + labeled_set
), # more convenient if we maintain the order of subset
pin_memory=True)
arg = get_kcg(model, ADDENDUM * (cycle + 1), unlabeled_loader)
if method == 'lloss':
# Create unlabeled dataloader for the unlabeled subset
unlabeled_loader = DataLoader(data_unlabeled,
batch_size=BATCH,
sampler=SubsetSequentialSampler(subset),
pin_memory=True)
# Measure uncertainty of each data points in the subset
uncertainty = get_uncertainty(model, unlabeled_loader)
arg = np.argsort(uncertainty)
if method == 'VAAL':
# Create unlabeled dataloader for the unlabeled subset
unlabeled_loader = DataLoader(data_unlabeled,
batch_size=BATCH,
sampler=SubsetSequentialSampler(subset),
pin_memory=True)
labeled_loader = DataLoader(
data_unlabeled,
batch_size=BATCH,
sampler=SubsetSequentialSampler(labeled_set),
pin_memory=True)
if args.dataset == 'fashionmnist':
vae = VAE(28, 1, 3)
discriminator = Discriminator(28)
else:
vae = VAE()
discriminator = Discriminator(32)
models = {'vae': vae, 'discriminator': discriminator}
optim_vae = optim.Adam(vae.parameters(), lr=5e-4)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=5e-4)
optimizers = {'vae': optim_vae, 'discriminator': optim_discriminator}
train_vaal(models, optimizers, labeled_loader, unlabeled_loader,
cycle + 1)
all_preds, all_indices = [], []
for images, _, indices in unlabeled_loader:
images = images.cuda()
with torch.no_grad():
_, _, mu, _ = vae(images)
preds = discriminator(mu)
preds = preds.cpu().data
all_preds.extend(preds)
all_indices.extend(indices)
all_preds = torch.stack(all_preds)
all_preds = all_preds.view(-1)
# need to multiply by -1 to be able to use torch.topk
all_preds *= -1
# select the points which the discriminator things are the most likely to be unlabeled
_, arg = torch.sort(all_preds)
return arg