import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.optim import Adam
from torch.autograd import Variable
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from sklearn.metrics import adjusted_rand_score as ari_score
from sklearn.decomposition import PCA
from data.omniglotloader import omniglot_alphabet_func, omniglot_evaluation_alphabets_mapping
from utils.util import cluster_acc, Identity, AverageMeter, seed_torch, str2bool
from utils import ramps
from models.vgg import VGG
from modules.module import feat2prob, target_distribution
from tqdm import tqdm
import warnings
import os
warnings.filterwarnings("ignore", category=UserWarning)
def init_prob_kmeans(model, eval_loader, args):
torch.manual_seed(1)
model = model.to(device)
# cluster parameter initiate
model.eval()
targets = np.zeros(len(eval_loader.dataset))
feats = np.zeros((len(eval_loader.dataset), 1024))
for _, (x, _, label, idx) in enumerate(eval_loader):
x = x.to(device)
_, feat = model(x)
feat = feat.view(x.size(0), -1)
idx = idx.data.cpu().numpy()
feats[idx, :] = feat.data.cpu().numpy()
targets[idx] = label.data.cpu().numpy()
# evaluate clustering performance
pca = PCA(n_components=args.n_clusters)
feats = pca.fit_transform(feats)
kmeans = KMeans(n_clusters=args.n_clusters, n_init=20)
y_pred = kmeans.fit_predict(feats)
acc, nmi, ari = cluster_acc(targets, y_pred), nmi_score(targets, y_pred), ari_score(targets, y_pred)
print('Init acc {:.4f}, nmi {:.4f}, ari {:.4f}'.format(acc, nmi, ari))
probs = feat2prob(torch.from_numpy(feats), torch.from_numpy(kmeans.cluster_centers_))
return kmeans.cluster_centers_, probs
def warmup_train(model, alphabetStr, train_loader, eval_loader, args):
optimizer = Adam(model.parameters(), lr=args.warmup_lr)
for epoch in range(args.warmup_epochs):
loss_record = AverageMeter()
model.train()
for batch_idx, (x, g_x, _, idx) in enumerate(train_loader):
_, feat = model(x.to(device))
prob = feat2prob(feat, model.center)
loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.update(loss.item(), x.size(0))
print('Warmup Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
test(model, eval_loader, args)
def Baseline_train(model, alphabetStr, train_loader, eval_loader, args):
optimizer = Adam(model.parameters(), lr=args.lr)
for epoch in range(args.epochs):
loss_record = AverageMeter()
model.train()
for batch_idx, (x, g_x, _, idx) in enumerate(train_loader):
_, feat = model(x.to(device))
prob = feat2prob(feat, model.center)
loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.update(loss.item(), x.size(0))
print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
_, _, _, probs = test(model, eval_loader, args)
if epoch % args.update_interval==0:
args.p_targets= target_distribution(probs)
torch.save(model.state_dict(), args.model_dir)
print("model saved to {}.".format(args.model_dir))
def PI_train(model, alphabetStr, train_loader, eval_loader, args):
optimizer = Adam(model.parameters(), lr=args.lr)
for epoch in range(args.epochs):
loss_record = AverageMeter()
model.train()
w = args.rampup_coefficient * ramps.sigmoid_rampup(epoch, args.rampup_length)
for batch_idx, (x, g_x, _, idx) in enumerate(train_loader):
_, feat = model(x.to(device))
_, feat_g = model(g_x.to(device))
prob = feat2prob(feat, model.center)
prob_g = feat2prob(feat_g, model.center)
loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
mse_loss = F.mse_loss(prob, prob_g)
loss=loss + w*mse_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.update(loss.item(), x.size(0))
print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
_, _, _, probs = test(model, eval_loader, args)
if epoch % args.update_interval==0:
args.p_targets= target_distribution(probs)
torch.save(model.state_dict(), args.model_dir)
print("model saved to {}.".format(args.model_dir))
def TE_train(model, alphabetStr, train_loader, eval_loader, args):
optimizer = Adam(model.parameters(), lr=args.lr)
w = 0
alpha = 0.6
ntrain = len(train_loader.dataset)
Z = torch.zeros(ntrain, args.n_clusters).float().to(device) # intermediate values
z_ema = torch.zeros(ntrain, args.n_clusters).float().to(device) # temporal outputs
z_epoch = torch.zeros(ntrain, args.n_clusters).float().to(device) # current outputs
for epoch in range(args.epochs):
loss_record = AverageMeter()
model.train()
w = args.rampup_coefficient * ramps.sigmoid_rampup(epoch, args.rampup_length)
for batch_idx, (x, _, _, idx) in enumerate(train_loader):
_, feat = model(x.to(device))
prob = feat2prob(feat, model.center)
z_epoch[idx, :] = prob
prob_bar = Variable(z_ema[idx, :], requires_grad=False)
loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
mse_loss = F.mse_loss(prob, prob_bar)
loss=loss+w*mse_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.update(loss.item(), x.size(0))
Z = alpha * Z + (1. - alpha) * z_epoch
z_ema = Z * (1. / (1. - alpha ** (epoch + 1)))
print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
_, _, _, probs = test(model, eval_loader, args)
if epoch % args.update_interval==0:
args.p_targets = target_distribution(probs)
torch.save(model.state_dict(), args.model_dir)
print("model saved to {}.".format(args.model_dir))
def TEP_train(model, alphabetStr, train_loader, eval_loader, args):
optimizer = Adam(model.parameters(), lr=args.lr)
w = 0
alpha = 0.6
ntrain = len(train_loader.dataset)
Z = torch.zeros(ntrain, args.n_clusters).float().to(device) # intermediate values
z_ema = torch.zeros(ntrain, args.n_clusters).float().to(device) # temporal outputs
z_epoch = torch.zeros(ntrain, args.n_clusters).float().to(device) # current outputs
for epoch in range(args.epochs):
loss_record = AverageMeter()
model.train()
for batch_idx, (x, g_x, _, idx) in enumerate(train_loader):
_, feat = model(x.to(device))
prob = feat2prob(feat, model.center)
loss = F.kl_div(prob.log(), args.p_targets[idx].float().to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.update(loss.item(), x.size(0))
print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
_, _, _, probs = test(model, eval_loader, args)
z_epoch = probs.float().to(device)
Z = alpha * Z + (1. - alpha) * z_epoch
z_bars = Z * (1. / (1. - alpha ** (epoch + 1)))
if epoch % args.update_interval==0:
args.p_targets = target_distribution(z_bars).float().to(device)
torch.save(model.state_dict(), args.model_dir)
print("model saved to {}.".format(args.model_dir))
def test(model, eval_loader, args):
model.eval()
targets = np.zeros(len(eval_loader.dataset))
y_pred = np.zeros(len(eval_loader.dataset))
probs= np.zeros((len(eval_loader.dataset), args.n_clusters))
for _, (x, _, label, idx) in enumerate(eval_loader):
x = x.to(device)
_, feat = model(x)
prob = feat2prob(feat, model.center)
# prob = F.softmax(logit, dim=1)
idx = idx.data.cpu().numpy()
y_pred[idx] = prob.data.cpu().detach().numpy().argmax(1)
targets[idx] = label.data.cpu().numpy()
probs[idx, :] = prob.cpu().detach().numpy()
# evaluate clustering performance
y_pred = y_pred.astype(np.int64)
acc, nmi, ari = cluster_acc(targets, y_pred), nmi_score(targets, y_pred), ari_score(targets, y_pred)
print('Test acc {:.4f}, nmi {:.4f}, ari {:.4f}'.format(acc, nmi, ari))
probs = torch.from_numpy(probs)
return acc, nmi, ari, probs
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description='cluster',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--warmup_lr', type=float, default=0.001)
parser.add_argument('--warmup_epochs', default=10, type=int)
parser.add_argument('--epochs', default=90, type=int)
parser.add_argument('--batch_size', default=100, type=int)
parser.add_argument('--update_interval', default=1, type=int)
parser.add_argument('--seed', default=1, type=int)
parser.add_argument('--save_txt', default=False, type=str2bool, help='save txt or not', metavar='BOOL')
parser.add_argument('--rampup_length', default=5, type=int)
parser.add_argument('--rampup_coefficient', default=100.0, type=float)
parser.add_argument('--n_clusters', default=10, type=int)
parser.add_argument('--num_workers', default=2, type=int)
parser.add_argument('--pretrain_dir', type=str, default='./data/experiments/pretrained/vgg6_omniglot_proto.pth')
parser.add_argument('--dataset_root', type=str, default='./data/datasets')
parser.add_argument('--exp_root', type=str, default='./data/experiments/')
parser.add_argument('--subfolder_name', type=str, default='run')
parser.add_argument('--save_txt_name', type=str, default='result.txt')
parser.add_argument('--DTC', type=str, default='PI')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
print("use cuda: {}".format(args.cuda))
device = torch.device("cuda" if args.cuda else "cpu")
seed_torch(args.seed)
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 _, alphabetStr in omniglot_evaluation_alphabets_mapping.items():
runner_name = os.path.basename(__file__).split(".")[0]
model_dir= args.exp_root + '{}/{}/{}'.format(runner_name, args.DTC, 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.DTC, args.save_txt_name)
train_Dloader, eval_Dloader = omniglot_alphabet_func(alphabet=alphabetStr, background=False, root=args.dataset_root)(batch_size=args.batch_size, num_workers=args.num_workers)
args.n_clusters = train_Dloader.num_classes
model = VGG(n_layer='4+2', out_dim=args.n_clusters, in_channels=1).to(device)
model.load_state_dict(torch.load(args.pretrain_dir), strict=False)
model.center= Parameter(torch.Tensor(args.n_clusters, args.n_clusters))
init_centers, init_probs = init_prob_kmeans(init_feat_extractor, eval_Dloader, args)
args.p_targets = target_distribution(init_probs)
model.center.data = torch.tensor(init_centers).float().to(device)
warmup_train(model, alphabetStr, train_Dloader, eval_Dloader, args)
if args.DTC == 'Baseline':
Baseline_train(model, alphabetStr, train_Dloader, eval_Dloader, args)
elif args.DTC == 'PI':
PI_train(model, alphabetStr, train_Dloader, eval_Dloader, args)
elif args.DTC == 'TE':
TE_train(model, alphabetStr, train_Dloader, eval_Dloader, args)
elif args.DTC == 'TEP':
TEP_train(model, alphabetStr, train_Dloader, eval_Dloader, args)
acc[alphabetStr], nmi[alphabetStr], ari[alphabetStr], _ = test(model, eval_Dloader, args)
print('ACC for all alphabets:',acc)
print('NMI for all alphabets:',nmi)
print('ARI for all alphabets:',ari)
avg_acc, avg_nmi, avg_ari = sum(acc.values())/float(len(acc)), sum(nmi.values())/float(len(nmi)), sum(ari.values())/float(len(ari))
print('avg ACC {:.4f}, NMI {:.4f} ARI {:.4f}'.format(avg_acc, avg_nmi, avg_ari))
if args.save_txt:
with open(args.save_txt_path, 'a') as f:
f.write("{:.4f}, {:.4f}, {:.4f}\n".format(avg_acc, avg_nmi, avg_ari))
