Datasets:
jblitzar
/
github-python-corpus

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layer_name,weights=new[count]
model_kvpair[key]=weights
count+=1
model.load_state_dict(model_kvpair, strict=False)
return model
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description='cluster',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--warmup_lr', type=float, default=0.1)
parser.add_argument('--lr', type=float, default=0.05)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=1e-5)
parser.add_argument('--warmup_epochs', default=10, type=int)
parser.add_argument('--epochs', default=60, type=int)
parser.add_argument('--rampup_length', default=5, type=int)
parser.add_argument('--rampup_coefficient', type=float, default=100.0)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--update_interval', default=5, type=int)
parser.add_argument('--n_clusters', default=30, 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('--pretrain_dir', type=str, default='./data/experiments/pretrained/resnet18_imagenet_classif_882_ICLR18.pth')
parser.add_argument('--dataset_root', type=str, default='./data/datasets/ImageNet/')
parser.add_argument('--exp_root', type=str, default='./data/experiments/')
parser.add_argument('--model_name', type=str, default='resnet18')
parser.add_argument('--save_txt_name', type=str, default='result.txt')
parser.add_argument('--subset', type=str, default='A')
parser.add_argument('--DTC', type=str, default='TEP')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
seed_torch(args.seed)
runner_name = os.path.basename(__file__).split(".")[0]
model_dir= args.exp_root +'{}/{}'.format(runner_name, args.DTC)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
args.model_dir = model_dir+'/'+args.model_name+'.pth'
args.save_txt_path= args.exp_root + '{}/{}'.format(runner_name, args.save_txt_name)
loader_30_train = ImageNetLoader30(batch_size=args.batch_size, path=args.dataset_root, subset=args.subset, aug=None, shuffle=True)
loader_30_train_twice = ImageNetLoader30(batch_size=args.batch_size, path=args.dataset_root, subset=args.subset, aug='twice', shuffle=True)
loader_30_eval = ImageNetLoader30(batch_size=args.batch_size, path=args.dataset_root, subset=args.subset, aug=None)
model= resnet18(num_classes=882)
model=copy_param(model, args.pretrain_dir)
model.last = Identity()
acc_init, nmi_init, ari_init, init_centers, init_probs = init_prob_kmeans(model, loader_30_eval, args)
args.p_targets = target_distribution(init_probs)
model= resnet18(num_classes=args.n_clusters)
model=copy_param(model, args.pretrain_dir, loc=-2)
print('load pretrained state_dict from {}'.format(args.pretrain_dir))
model.center= Parameter(torch.Tensor(args.n_clusters, args.n_clusters))
model=model.to(device)
model.center.data = torch.tensor(init_centers).float().to(device)
warmup_train(model, loader_30_train, loader_30_eval, args)
if args.DTC == 'Baseline':
Baseline_train(model, loader_30_train, loader_30_eval, args)
elif args.DTC == 'PI':
PI_train(model, loader_30_train_twice, loader_30_eval, args)
elif args.DTC == 'TE':
TE_train(model, loader_30_train, loader_30_eval, args)
elif args.DTC == 'TEP':
TEP_train(model, loader_30_train, loader_30_eval, args)
acc, nmi, ari, _ = test(model, loader_30_eval, args)
print('Subset {} init ACC {:.4f}, NMI {:.4f}, ARI {:.4f}'.format(args.subset, acc_init, nmi_init, ari_init))
print('Subset {} final ACC {:.4f}, NMI {:.4f}, ARI {:.4f}'.format(args.subset, acc, nmi, ari))
if args.save_txt:
with open(args.save_txt_path, 'a') as f:
f.write("{:.4f}, {:.4f}, {:.4f}\n".format(acc, nmi, ari))
# <FILESEP>
import sys
from functools import partialmethod
from loguru import logger
STDOUT_LEVELS = ["GENERATION", "PROMPT"]
INIT_LEVELS = ["INIT", "INIT_OK", "INIT_WARN", "INIT_ERR"]
MESSAGE_LEVELS = ["MESSAGE"]
# By default we're at error level or higher
verbosity = 20
quiet = 0
def set_logger_verbosity(count):
global verbosity
# The count comes reversed. So count = 0 means minimum verbosity
# While count 5 means maximum verbosity
# So the more count we have, the lowe we drop the versbosity maximum
verbosity = 20 - (count * 10)
def quiesce_logger(count):
global quiet
# The bigger the count, the more silent we want our logger