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train.py
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train.py
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import torch
from network import Network
from metric import valid
from torch.utils.data import Dataset
import numpy as np
import argparse
import random
from loss import Loss
from dataloader import load_data
import os
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.optimize import linear_sum_assignment
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# MNIST-USPS
# BDGP
# CCV
# Fashion
# Caltech-2V
# Caltech-3V
# Caltech-4V
# Caltech-5V
Dataname = 'MNIST-USPS'
parser = argparse.ArgumentParser(description='train')
parser.add_argument('--dataset', default=Dataname)
parser.add_argument('--batch_size', default=256, type=int)
parser.add_argument("--temperature_f", default=0.5)
parser.add_argument("--temperature_l", default=1.0)
parser.add_argument("--learning_rate", default=0.0003)
parser.add_argument("--weight_decay", default=0.)
parser.add_argument("--workers", default=8)
parser.add_argument("--mse_epochs", default=200)
parser.add_argument("--con_epochs", default=50)
parser.add_argument("--tune_epochs", default=50)
parser.add_argument("--feature_dim", default=512)
parser.add_argument("--high_feature_dim", default=128)
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The code has been optimized.
# The seed was fixed for the performance reproduction, which was higher than the values shown in the paper.
if args.dataset == "MNIST-USPS":
args.con_epochs = 50
seed = 10
if args.dataset == "BDGP":
args.con_epochs = 10
seed = 10
if args.dataset == "CCV":
args.con_epochs = 50
seed = 3
if args.dataset == "Fashion":
args.con_epochs = 100
seed = 10
if args.dataset == "Caltech-2V":
args.con_epochs = 50
seed = 10
if args.dataset == "Caltech-3V":
args.con_epochs = 50
seed = 10
if args.dataset == "Caltech-4V":
args.con_epochs = 50
seed = 10
if args.dataset == "Caltech-5V":
args.con_epochs = 50
seed = 5
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# np.random.seed(seed)
# random.seed(seed)
torch.backends.cudnn.deterministic = True
# setup_seed(seed)
dataset, dims, view, data_size, class_num = load_data(args.dataset)
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
)
def pretrain(epoch):
tot_loss = 0.
criterion = torch.nn.MSELoss()
for batch_idx, (xs, _, _) in enumerate(data_loader):
for v in range(view):
xs[v] = xs[v].to(device)
optimizer.zero_grad()
_, _, xrs, _ = model(xs)
loss_list = []
for v in range(view):
loss_list.append(criterion(xs[v], xrs[v]))
loss = sum(loss_list)
loss.backward()
optimizer.step()
tot_loss += loss.item()
print('Epoch {}'.format(epoch), 'Loss:{:.6f}'.format(tot_loss / len(data_loader)))
def contrastive_train(epoch):
tot_loss = 0.
mes = torch.nn.MSELoss()
for batch_idx, (xs, _, _) in enumerate(data_loader):
for v in range(view):
xs[v] = xs[v].to(device)
optimizer.zero_grad()
hs, qs, xrs, zs = model(xs)
loss_list = []
for v in range(view):
for w in range(v+1, view):
loss_list.append(criterion.forward_feature(hs[v], hs[w]))
loss_list.append(criterion.forward_label(qs[v], qs[w]))
loss_list.append(mes(xs[v], xrs[v]))
loss = sum(loss_list)
loss.backward()
optimizer.step()
tot_loss += loss.item()
print('Epoch {}'.format(epoch), 'Loss:{:.6f}'.format(tot_loss/len(data_loader)))
def make_pseudo_label(model, device):
loader = torch.utils.data.DataLoader(
dataset,
batch_size=data_size,
shuffle=False,
)
model.eval()
scaler = MinMaxScaler()
for step, (xs, y, _) in enumerate(loader):
for v in range(view):
xs[v] = xs[v].to(device)
with torch.no_grad():
hs, _, _, _ = model.forward(xs)
for v in range(view):
hs[v] = hs[v].cpu().detach().numpy()
hs[v] = scaler.fit_transform(hs[v])
kmeans = KMeans(n_clusters=class_num, n_init=100)
new_pseudo_label = []
for v in range(view):
Pseudo_label = kmeans.fit_predict(hs[v])
Pseudo_label = Pseudo_label.reshape(data_size, 1)
Pseudo_label = torch.from_numpy(Pseudo_label)
new_pseudo_label.append(Pseudo_label)
return new_pseudo_label
def match(y_true, y_pred):
y_true = y_true.astype(np.int64)
y_pred = y_pred.astype(np.int64)
assert y_pred.size == y_true.size
D = max(y_pred.max(), y_true.max()) + 1
w = np.zeros((D, D), dtype=np.int64)
for i in range(y_pred.size):
w[y_pred[i], y_true[i]] += 1
row_ind, col_ind = linear_sum_assignment(w.max() - w)
new_y = np.zeros(y_true.shape[0])
for i in range(y_pred.size):
for j in row_ind:
if y_true[i] == col_ind[j]:
new_y[i] = row_ind[j]
new_y = torch.from_numpy(new_y).long().to(device)
new_y = new_y.view(new_y.size()[0])
return new_y
def fine_tuning(epoch, new_pseudo_label):
loader = torch.utils.data.DataLoader(
dataset,
batch_size=data_size,
shuffle=False,
)
tot_loss = 0.
cross_entropy = torch.nn.CrossEntropyLoss()
for batch_idx, (xs, _, idx) in enumerate(loader):
for v in range(view):
xs[v] = xs[v].to(device)
optimizer.zero_grad()
_, qs, _, _ = model(xs)
loss_list = []
for v in range(view):
p = new_pseudo_label[v].numpy().T[0]
with torch.no_grad():
q = qs[v].detach().cpu()
q = torch.argmax(q, dim=1).numpy()
p_hat = match(p, q)
loss_list.append(cross_entropy(qs[v], p_hat))
loss = sum(loss_list)
loss.backward()
optimizer.step()
tot_loss += loss.item()
print('Epoch {}'.format(epoch), 'Loss:{:.6f}'.format(tot_loss / len(data_loader)))
accs = []
nmis = []
purs = []
if not os.path.exists('./models'):
os.makedirs('./models')
T = 1
for i in range(T):
print("ROUND:{}".format(i+1))
setup_seed(seed)
model = Network(view, dims, args.feature_dim, args.high_feature_dim, class_num, device)
print(model)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
criterion = Loss(args.batch_size, class_num, args.temperature_f, args.temperature_l, device).to(device)
epoch = 1
while epoch <= args.mse_epochs:
pretrain(epoch)
epoch += 1
while epoch <= args.mse_epochs + args.con_epochs:
contrastive_train(epoch)
if epoch == args.mse_epochs + args.con_epochs:
acc, nmi, pur = valid(model, device, dataset, view, data_size, class_num, eval_h=False)
epoch += 1
new_pseudo_label = make_pseudo_label(model, device)
while epoch <= args.mse_epochs + args.con_epochs + args.tune_epochs:
fine_tuning(epoch, new_pseudo_label)
if epoch == args.mse_epochs + args.con_epochs + args.tune_epochs:
acc, nmi, pur = valid(model, device, dataset, view, data_size, class_num, eval_h=False)
state = model.state_dict()
torch.save(state, './models/' + args.dataset + '.pth')
print('Saving..')
accs.append(acc)
nmis.append(nmi)
purs.append(pur)
epoch += 1