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main_unlearn.py
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147 lines (128 loc) · 6.37 KB
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import utils
import torch
import time
import os
import argparse
import unlearn
import numpy as np
import xlwt
from metrics import CR, MIACR
import wandb
from torch.utils.data import DataLoader, ConcatDataset, dataset
from models import resnet, vit
import random
def get_save_dir():
if args.unlearn_type == 'random':
save_dir = os.path.join(args.model_name +'_' + args.data_name,
args.unlearn_name +'_nips_forget' + str(int(100-args.retain_ratio*100)) +'_epoch' + str(args.num_epochs))
elif args.unlearn_type == 'class':
save_dir = os.path.join(args.model_name + '_' + args.data_name,
args.unlearn_name + '_nips_forget_class_epoch' + str(args.num_epochs))
return save_dir
if __name__ == '__main__':
flag = time.time()
parser = argparse.ArgumentParser()
parser.add_argument(
'--unlearn_name',
type=str)
parser.add_argument(
'--unlearn_type',
type=str,
default='random')
parser.add_argument('--model_name', type=str, default='resnet18')
parser.add_argument('--data_name', type=str, default='cifar10')
parser.add_argument('--data_dir', type=str, default='../data')
parser.add_argument('--model_dir', type=str, default='../resnet18-cifar10/fine_model_baseline/final_model.pth')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--num_epochs', type=int, default=20)
parser.add_argument('--num_classes', type=int, default=10)
parser.add_argument('--retain_ratio', type=float, default=0.9)
parser.add_argument('--learning_rate', type=float, default=0.1)
parser.add_argument('--milestones', type=str, default=None)
parser.add_argument('--seed', type=int, default=10)
parser.add_argument("--msteps", type=int, default=5, help="scrub")
parser.add_argument("--kd_T", type=float, default=2, help="scrub")
parser.add_argument("--beta", type=float, default=1, help="scrub")
parser.add_argument("--gamma", type=float, default=1.0, help="scrub")
parser.add_argument('--mask_path', type=str, default=None, help="salun")
parser.add_argument('--worst_case', type=bool, default=False, help="worst_case")
# evaluation
parser.add_argument('--cal_sizes', type=str, default='1000')
parser.add_argument('--alphas', type=str, default='0.05,0.1,0.15,0.2')
args = parser.parse_args()
utils.setup_seed(args.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
save_dir = get_save_dir()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
train_retain_dl, train_forget_dl, test_retain_dl, test_forget_dl, retain_cal_loader, forget_cal_loader, forget_class = utils.generate_dataset(args.unlearn_type,
args.num_classes,
args.batch_size,
args.data_dir,
args.data_name,
args.model_name,
args.retain_ratio,
worst_case=args.worst_case)
full_train_dl = DataLoader(
ConcatDataset((train_retain_dl.dataset, train_forget_dl.dataset)),
batch_size=args.batch_size,
)
if args.model_name == 'resnet18':
net = resnet.ResNet18(num_classes=args.num_classes)
unlearning_teacher = resnet.ResNet18(num_classes=args.num_classes).to(device) if args.unlearn_name == 'teacher' else None
elif args.model_name == 'vit':
if args.unlearn_name == 'retrain':
net = vit.ViT(num_classes=args.num_classes, pretrained=True)
else:
net = vit.ViT(num_classes=args.num_classes, pretrained=False)
unlearning_teacher = vit.ViT(num_classes=args.num_classes, pretrained=True).to(device) if args.unlearn_name == 'teacher' else None
if torch.cuda.is_available() and torch.cuda.device_count()>1:
print("Using {} GPUs.".format(torch.cuda.device_count()))
net = torch.nn.DataParallel(net)
if args.unlearn_name != 'retrain':
net.load_state_dict(torch.load(args.model_dir))
net = net.to(device)
# wandb
proj_name = '{}_{}_{}_epoch{}_seed'.format(args.model_name, args.data_name, args.unlearn_name, args.num_epochs)
watermark = "seed{}_lr{}".format(args.seed, args.learning_rate)
wandb.init(project=proj_name, name=watermark)
wandb.config.update(args)
wandb.watch(net)
mask = None
if args.mask_path is not None and args.unlearn_name=='salun':
mask = torch.load(args.mask_path)
kwargs = {
"model": net,
"train_retain_dl": train_retain_dl,
"train_forget_dl": train_forget_dl,
"test_retain_dl": test_retain_dl,
"test_forget_dl": test_forget_dl,
"dampening_constant": 1, # Lambda for ssd
"selection_weighting": 10, # Alpha for ssd
"num_classes": args.num_classes,
"dataset_name": 'cifar10',
"device": device,
"model_name": args.model_name,
"num_epochs": args.num_epochs,
"learning_rate": args.learning_rate,
"milestones": np.array(args.milestones.split(',')).astype(int) if args.milestones is not None else None,
"batch_size": args.batch_size,
"full_train_dl": full_train_dl,
"unlearning_teacher": unlearning_teacher,
"unlearn_type": args.unlearn_type,
"forget_class": forget_class,
"args": args,
"mask": mask,
"save_dir": save_dir
}
start = time.time()
if args.unlearn_name == 'sfron':
train_acc, forget_acc, test_acc, net = getattr(unlearn, args.unlearn_name)(
**kwargs
)
else:
train_acc, forget_acc, test_acc = getattr(unlearn, args.unlearn_name)(
**kwargs
)
wandb.save("wandb_{}_{}_{}_{}_{}.h5".format(args.model_name, args.data_name, args.unlearn_name, args.num_epochs, args.learning_rate))
torch.save(net.state_dict(), os.path.join(save_dir, 'seed_'+str(args.seed)+'.pth'))