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ncl_cifar.py
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ncl_cifar.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import SGD, lr_scheduler
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from sklearn.metrics import adjusted_rand_score as ari_score
from sklearn.cluster import KMeans, DBSCAN
from utils.util import BCE, PairEnum, cluster_acc, Identity, AverageMeter, seed_torch, BCE_softlabels
from utils import ramps
from models.resnet import ResNet, BasicBlock
from data.cifarloader import CIFAR10Loader, CIFAR10LoaderMix, CIFAR100Loader, CIFAR100LoaderMix
from data.svhnloader import SVHNLoader, SVHNLoaderMix
from tqdm import tqdm
import numpy as np
import os
from models.NCL import NCLMemory
def train(model, train_loader, unlabeled_eval_loader, args):
print ('Start Neighborhood Contrastive Learning:')
optimizer = SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=args.gamma)
criterion1 = nn.CrossEntropyLoss()
criterion2 = BCE()
for epoch in range(args.epochs):
loss_record = AverageMeter()
model.train()
exp_lr_scheduler.step()
w = args.rampup_coefficient * ramps.sigmoid_rampup(epoch, args.rampup_length)
for batch_idx, ((x, x_bar), label, idx) in enumerate(tqdm(train_loader)):
x, x_bar, label = x.to(device), x_bar.to(device), label.to(device)
idx = idx.to(device)
mask_lb = label < args.num_labeled_classes
feat, feat_q, output1, output2 = model(x, 'feat_logit')
feat_bar, feat_k, output1_bar, output2_bar = model(x_bar, 'feat_logit')
prob1, prob1_bar, prob2, prob2_bar = F.softmax(output1, dim=1), F.softmax(output1_bar, dim=1), F.softmax(
output2, dim=1), F.softmax(output2_bar, dim=1)
rank_feat = (feat[~mask_lb]).detach()
if args.bce_type == 'cos':
# default: cosine similarity with threshold
feat_row, feat_col = PairEnum(F.normalize(rank_feat, dim=1))
tmp_distance_ori = torch.bmm(feat_row.view(feat_row.size(0), 1, -1), feat_col.view(feat_row.size(0), -1, 1))
tmp_distance_ori = tmp_distance_ori.squeeze()
target_ulb = torch.zeros_like(tmp_distance_ori).float() - 1
target_ulb[tmp_distance_ori > args.costhre] = 1
elif args.bce_type == 'RK':
# top-k rank statics
rank_idx = torch.argsort(rank_feat, dim=1, descending=True)
rank_idx1, rank_idx2 = PairEnum(rank_idx)
rank_idx1, rank_idx2 = rank_idx1[:, :args.topk], rank_idx2[:, :args.topk]
rank_idx1, _ = torch.sort(rank_idx1, dim=1)
rank_idx2, _ = torch.sort(rank_idx2, dim=1)
rank_diff = rank_idx1 - rank_idx2
rank_diff = torch.sum(torch.abs(rank_diff), dim=1)
target_ulb = torch.ones_like(rank_diff).float().to(device)
target_ulb[rank_diff > 0] = -1
prob1_ulb, _ = PairEnum(prob2[~mask_lb])
_, prob2_ulb = PairEnum(prob2_bar[~mask_lb])
# basic loss
loss_ce = criterion1(output1[mask_lb], label[mask_lb])
loss_bce = criterion2(prob1_ulb, prob2_ulb, target_ulb)
consistency_loss = F.mse_loss(prob1, prob1_bar) F.mse_loss(prob2, prob2_bar)
loss = loss_ce loss_bce w * consistency_loss
# NCL loss for unlabeled data
loss_ncl_ulb = ncl_ulb(feat_q[~mask_lb], feat_k[~mask_lb], label[~mask_lb], epoch, False, ncl_la.memory.clone().detach())
# NCL loss for labeled data
loss_ncl_la = ncl_la(feat_q[mask_lb], feat_k[mask_lb], label[mask_lb], epoch, True)
if epoch > 0:
loss = loss_ncl_ulb * args.w_ncl_ulb loss_ncl_la * args.w_ncl_la
else:
loss = loss_ncl_la * args.w_ncl_la
# ===================backward=====================
loss_record.update(loss.item(), x.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
args.head = 'head2'
print('Train Epoch: {} Avg Loss: {:.4f}'.format(epoch, loss_record.avg))
print('test on unlabeled classes')
test(model, unlabeled_eval_loader, args)
def test(model, test_loader, args):
model.eval()
preds=np.array([])
targets=np.array([])
for batch_idx, (x, label, _) in enumerate(tqdm(test_loader)):
x, label = x.to(device), label.to(device)
output1, output2 = model(x)
if args.head == 'head1':
output = output1
else:
output = output2
_, pred = output.max(1)
targets = np.append(targets, label.cpu().numpy())
preds = np.append(preds, pred.cpu().numpy())
acc, nmi, ari = cluster_acc(targets.astype(int), preds.astype(int)), nmi_score(targets, preds), ari_score(targets, preds)
print('Test acc {:.4f}, nmi {:.4f}, ari {:.4f}'.format(acc, nmi, ari))
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description='cluster',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--gamma', type=float, default=0.1)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=1e-4)
parser.add_argument('--epochs', default=200, type=int)
parser.add_argument('--rampup_length', default=150, type=int)
parser.add_argument('--rampup_coefficient', type=float, default=50)
parser.add_argument('--step_size', default=170, type=int)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--num_unlabeled_classes', default=5, type=int)
parser.add_argument('--num_labeled_classes', default=5, type=int)
parser.add_argument('--dataset_root', type=str, default='./data/datasets/CIFAR/')
parser.add_argument('--exp_root', type=str, default='./data/experiments/')
parser.add_argument('--warmup_model_dir', type=str, default='./data/experiments/pretrained/auto_novel/resnet_rotnet_cifar10.pth')
parser.add_argument('--topk', default=5, type=int)
parser.add_argument('--model_name', type=str, default='resnet')
parser.add_argument('--dataset_name', type=str, default='cifar10', help='options: cifar10, cifar100')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--bce_type', type=str, default='cos')
parser.add_argument('--hard_negative_start', default=1000, type=int)
parser.add_argument('--knn', default=-1, type=int)
parser.add_argument('--w_ncl_la', type=float, default=0.1)
parser.add_argument('--w_ncl_ulb', type=float, default=1.0)
parser.add_argument('--costhre', type=float, default=0.95)
parser.add_argument('--m_size', default=2000, type=int)
parser.add_argument('--m_t', type=float, default=0.05)
parser.add_argument('--w_pos', type=float, default=0.2)
parser.add_argument('--hard_iter', type=int, default=5)
parser.add_argument('--num_hard', type=int, default=400)
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
torch.backends.cudnn.benchmark = True
seed_torch(args.seed)
runner_name = os.path.basename(__file__).split(".")[0]
model_dir = os.path.join(args.exp_root, runner_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
args.model_dir = model_dir '/' '{}.pth'.format(args.model_name)
model = ResNet(BasicBlock, [2,2,2,2], args.num_labeled_classes, args.num_unlabeled_classes).to(device)
num_classes = args.num_labeled_classes args.num_unlabeled_classes
def copy_param(model, pretrain_dir):
pre_dict = torch.load(pretrain_dir)
new = list(pre_dict.items())
dict_len = len(pre_dict.items())
model_kvpair = model.state_dict()
count = 0
for count in range(dict_len):
layer_name, weights = new[count]
if 'contrastive_head' not in layer_name and 'shortcut' not in layer_name:
if 'backbone' in layer_name:
model_kvpair[layer_name[9:]] = weights
# else:
# model_kvpair[layer_name] = weights
print (layer_name[9:])
else:
continue
model.load_state_dict(model_kvpair)
return model
if args.mode == 'train':
state_dict = torch.load(args.warmup_model_dir)
model.load_state_dict(state_dict, strict=False)
for name, param in model.named_parameters():
if 'head' not in name and 'layer4' not in name:
param.requires_grad = False
if args.dataset_name == 'cifar10':
mix_train_loader = CIFAR10LoaderMix(root=args.dataset_root, batch_size=args.batch_size, split='train', aug='twice', shuffle=True, labeled_list=range(args.num_labeled_classes), unlabeled_list=range(args.num_labeled_classes, num_classes))
unlabeled_eval_loader = CIFAR10Loader(root=args.dataset_root, batch_size=args.batch_size, split='train', aug=None, shuffle=False, target_list = range(args.num_labeled_classes, num_classes))
unlabeled_eval_loader_test = CIFAR10Loader(root=args.dataset_root, batch_size=args.batch_size, split='test', aug=None, shuffle=False, target_list = range(args.num_labeled_classes, num_classes))
labeled_eval_loader = CIFAR10Loader(root=args.dataset_root, batch_size=args.batch_size, split='test', aug=None, shuffle=False, target_list = range(args.num_labeled_classes))
elif args.dataset_name == 'cifar100':
mix_train_loader = CIFAR100LoaderMix(root=args.dataset_root, batch_size=args.batch_size, split='train', aug='twice', shuffle=True, labeled_list=range(args.num_labeled_classes), unlabeled_list=range(args.num_labeled_classes, num_classes))
unlabeled_eval_loader = CIFAR100Loader(root=args.dataset_root, batch_size=args.batch_size, split='train', aug=None, shuffle=False, target_list = range(args.num_labeled_classes, num_classes))
unlabeled_eval_loader_test = CIFAR100Loader(root=args.dataset_root, batch_size=args.batch_size, split='test', aug=None, shuffle=False, target_list = range(args.num_labeled_classes, num_classes))
labeled_eval_loader = CIFAR100Loader(root=args.dataset_root, batch_size=args.batch_size, split='test', aug=None, shuffle=False, target_list = range(args.num_labeled_classes))
ncl_ulb = NCLMemory(512, args.m_size, args.m_t, args.num_unlabeled_classes, args.knn, args.w_pos, args.hard_iter, args.num_hard, args.hard_negative_start).to(device)
ncl_la = NCLMemory(512, args.m_size, args.m_t, args.num_labeled_classes, args.knn, args.w_pos, args.hard_iter, args.num_hard, args.hard_negative_start).to(device)
if args.mode == 'train':
train(model, mix_train_loader, unlabeled_eval_loader, args)
torch.save(model.state_dict(), args.model_dir)
print("model saved to {}.".format(args.model_dir))
else:
print("model loaded from {}.".format(args.model_dir))
model.load_state_dict(torch.load(args.model_dir))
print('Evaluating on Head1')
args.head = 'head1'
print('test on labeled classes (test split)')
test(model, labeled_eval_loader, args)
print('Evaluating on Head2')
args.head = 'head2'
print('test on unlabeled classes (train split)')
test(model, unlabeled_eval_loader, args)
print('test on unlabeled classes (test split)')
test(model, unlabeled_eval_loader_test, args)