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z_ema = torch.zeros(ntrain, args.n_clusters).float().to(device) # temporal outputs
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z_epoch = torch.zeros(ntrain, args.n_clusters).float().to(device) # current outputs
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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, label, idx) in enumerate(tqdm(train_loader)):
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x = x.to(device)
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feat = model(x)
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prob = feat2prob(feat, model.center)
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z_epoch[idx, :] = prob
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prob_bar = Variable(z_ema[idx, :], requires_grad=False)
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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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Z = alpha * Z + (1. - alpha) * z_epoch
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z_ema = Z * (1. / (1. - alpha ** (epoch + 1)))
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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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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 TEP_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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z_ema = torch.zeros(ntrain, args.n_clusters).float().to(device) # temporal outputs
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z_epoch = torch.zeros(ntrain, args.n_clusters).float().to(device) # current outputs
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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, label, idx) in enumerate(tqdm(train_loader)):
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x = x.to(device)
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feat = model(x)
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prob = feat2prob(feat, 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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z_epoch = probs.float().to(device)
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Z = alpha * Z + (1. - alpha) * z_epoch
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z_ema = Z * (1. / (1. - alpha ** (epoch + 1)))
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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(z_ema).float().to(device)
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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 test(model, test_loader, args, epoch=0):
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model.eval()
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acc_record = AverageMeter()
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preds=np.array([])
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targets=np.array([])
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feats = np.zeros((len(test_loader.dataset), args.n_clusters))
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probs = np.zeros((len(test_loader.dataset), args.n_clusters))
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for batch_idx, (x, label, idx) in enumerate(tqdm(test_loader)):
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x, label = x.to(device), label.to(device)
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output = model(x)
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prob = feat2prob(output, model.center)
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_, pred = prob.max(1)
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targets=np.append(targets, label.cpu().numpy())
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preds=np.append(preds, pred.cpu().numpy())
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idx = idx.data.cpu().numpy()
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feats[idx, :] = output.cpu().detach().numpy()
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probs[idx, :]= prob.cpu().detach().numpy()
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acc, nmi, ari = cluster_acc(targets.astype(int), preds.astype(int)), nmi_score(targets, preds), ari_score(targets, preds)
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print('Test acc {:.4f}, nmi {:.4f}, ari {:.4f}'.format(acc, nmi, ari))
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return acc, nmi, ari, torch.from_numpy(probs)
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def copy_param(model, pretrain_dir, loc=None):
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pre_dict = torch.load(pretrain_dir)
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new=list(pre_dict.items())
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model_kvpair=model.state_dict()
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if loc is not None:
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count=0
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for key, value in model_kvpair.items()[:loc]:
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layer_name,weights=new[count]
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model_kvpair[key]=weights
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count+=1
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else:
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count=0
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for key, value in model_kvpair.items():
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