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# cluster parameter initiate
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model.eval()
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targets = np.zeros(len(eval_loader.dataset))
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feats = np.zeros((len(eval_loader.dataset), 512))
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for _, (x, label, idx) in enumerate(eval_loader):
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x = x.to(device)
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feat = model(x)
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feat = feat.view(x.size(0), -1)
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idx = idx.data.cpu().numpy()
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feats[idx, :] = feat.data.cpu().numpy()
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targets[idx] = label.data.cpu().numpy()
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# evaluate clustering performance
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pca = PCA(n_components=args.n_clusters)
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feats = pca.fit_transform(feats)
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kmeans = KMeans(n_clusters=args.n_clusters, n_init=20)
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y_pred = kmeans.fit_predict(feats)
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acc, nmi, ari = cluster_acc(targets, y_pred), nmi_score(targets, y_pred), ari_score(targets, y_pred)
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print('Init acc {:.4f}, nmi {:.4f}, ari {:.4f}'.format(acc, nmi, ari))
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probs = feat2prob(torch.from_numpy(feats), torch.from_numpy(kmeans.cluster_centers_))
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return acc, nmi, ari, kmeans.cluster_centers_, probs
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def warmup_train(model, train_loader, eva_loader, args):
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optimizer = SGD(model.parameters(), lr=args.warmup_lr, momentum=args.momentum, weight_decay=args.weight_decay)
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for epoch in range(args.warmup_epochs):
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loss_record = AverageMeter()
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acc_record = AverageMeter()
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model.train()
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for batch_idx, (x, label, idx) in enumerate(tqdm(train_loader)):
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x = x.to(device)
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output = model(x)
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prob = feat2prob(output, model.center)
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loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
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loss_record.update(loss.item(), x.size(0))
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print('Warmup_train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
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_, _, _, probs= test(model, eva_loader, args, epoch)
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args.p_targets = target_distribution(probs)
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def Baseline_train(model, train_loader, eva_loader, args):
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optimizer = SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
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for epoch in range(args.epochs):
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loss_record = AverageMeter()
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acc_record = AverageMeter()
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model.train()
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for batch_idx, (x, _, idx) in enumerate(tqdm(train_loader)):
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x = x.to(device)
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output = model(x)
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prob = feat2prob(output, model.center)
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loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
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loss_record.update(loss.item(), x.size(0))
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
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_, _, _, probs= test(model, eva_loader, args, epoch)
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if epoch%args.update_interval == 0:
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print('updating target ...')
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args.p_targets = target_distribution(probs)
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torch.save(model.state_dict(), args.model_dir)
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print("model saved to {}.".format(args.model_dir))
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def PI_train(model, train_loader, eva_loader, args):
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optimizer = SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
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w = 0
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for epoch in range(args.epochs):
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loss_record = AverageMeter()
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acc_record = AverageMeter()
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model.train()
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w = args.rampup_coefficient * ramps.sigmoid_rampup(epoch, args.rampup_length)
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for batch_idx, ((x, x_bar), label, idx) in enumerate(tqdm(train_loader)):
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x, x_bar = x.to(device), x_bar.to(device)
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feat = model(x)
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feat_bar = model(x_bar)
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prob = feat2prob(feat, model.center)
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prob_bar = feat2prob(feat_bar, model.center)
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sharp_loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
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consistency_loss = F.mse_loss(prob, prob_bar)
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loss = sharp_loss + w * consistency_loss
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loss_record.update(loss.item(), x.size(0))
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
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_, _, _, probs= test(model, eva_loader, args, epoch)
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if epoch%args.update_interval == 0:
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print('updating target ...')
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args.p_targets = target_distribution(probs)
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torch.save(model.state_dict(), args.model_dir)
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print("model saved to {}.".format(args.model_dir))
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def TE_train(model, train_loader, eva_loader, args):
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optimizer = SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
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w = 0
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alpha = 0.6
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ntrain = len(train_loader.dataset)
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Z = torch.zeros(ntrain, args.n_clusters).float().to(device) # intermediate values
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