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prune.py
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prune.py
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import argparse
import copy
import json
import numpy as np
import os
import pickle
import random
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import ConcatDataset, DataLoader, Subset
from base_model import BaseModel
from datasets import get_dataset
from pruner import get_pruner
from utils import seed_worker
from pyvacy import optim, analysis
parser = argparse.ArgumentParser()
parser.add_argument('device', default=0, type=int, help="GPU id to use")
parser.add_argument('config_path', default=0, type=str, help="config file path")
parser.add_argument('--dataset_name', default='mnist', type=str)
parser.add_argument('--model_name', default='mnist', type=str)
parser.add_argument('--num_cls', default=10, type=int)
parser.add_argument('--input_dim', default=1, type=int)
parser.add_argument('--image_size', default=28, type=int)
parser.add_argument('--hidden_size', default=128, type=int)
parser.add_argument('--seed', default=7, type=int)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--epochs', default=50, type=int)
parser.add_argument('--early_stop', default=5, type=int, help="patience for early stopping")
parser.add_argument('--lr', default=0.001, type=float)
parser.add_argument('--weight_decay', default=5e-4, type=float)
parser.add_argument('--optimizer', default="adam", type=str)
parser.add_argument('--prune_epochs', default=50, type=int)
parser.add_argument('--pruner_name', default='l1unstructure', type=str)
parser.add_argument('--prune_sparsity', default=0.7, type=float)
parser.add_argument('--defend', default="", type=str, help="'' if no defense, else ppb")
parser.add_argument('--adaptive', action='store_true')
parser.add_argument('--shadow_num', default=5, type=int)
parser.add_argument('--defend_arg', default=4, type=float)
def main(args):
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
device = f"cuda:{args.device}"
cudnn.benchmark = True
prune_lr = args.lr
if args.defend == "":
prune_prefix = f"{args.pruner_name}_{args.prune_sparsity}"
else:
prune_prefix = f"{args.pruner_name}_{args.prune_sparsity}_{args.defend}_{args.defend_arg}"
save_folder = f"results/{args.dataset_name}_{args.model_name}"
print(f"Save Folder: {save_folder}")
trainset = get_dataset(args.dataset_name, train=True)
testset = get_dataset(args.dataset_name, train=False)
if testset is None:
total_dataset = trainset
else:
total_dataset = ConcatDataset([trainset, testset])
total_size = len(total_dataset)
data_path = f"{save_folder}/data_index.pkl"
# load data split for the pretrained victim and shadow model
with open(data_path, 'rb') as f:
victim_train_list, victim_dev_list, victim_test_list, attack_split_list \
= pickle.load(f)
# train and prune the victim model
victim_train_dataset = Subset(total_dataset, victim_train_list)
victim_dev_dataset = Subset(total_dataset, victim_dev_list)
victim_test_dataset = Subset(total_dataset, victim_test_list)
print(f"Total Data Size: {total_size}, "
f"Victim Train Size: {len(victim_train_list)}, "
f"Victim Dev Size: {len(victim_dev_list)}, "
f"Victim Test Size: {len(victim_test_list)}")
victim_train_loader = DataLoader(victim_train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4,
pin_memory=True, worker_init_fn=seed_worker)
victim_dev_loader = DataLoader(victim_dev_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4,
pin_memory=True, worker_init_fn=seed_worker)
victim_test_loader = DataLoader(victim_test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4,
pin_memory=True, worker_init_fn=seed_worker)
victim_model_save_folder = save_folder + "/victim_model"
# load pretrained model
victim_model_path = f"{victim_model_save_folder}/best.pth"
victim_model = BaseModel(args.model_name, num_cls=args.num_cls, input_dim=args.input_dim, device=device)
victim_model.load(victim_model_path)
test_acc, test_loss = victim_model.test(victim_test_loader, "Pretrained Victim")
victim_acc = test_acc
print("Prune Victim Model")
pruned_model_save_folder = f"{save_folder}/{prune_prefix}_model"
victim_model_path = f"{victim_model_save_folder}/best.pth"
victim_model.load(victim_model_path)
org_state = copy.deepcopy(victim_model.model.state_dict())
if not os.path.exists(pruned_model_save_folder):
os.makedirs(pruned_model_save_folder)
# prune victim model
if args.defend == "adv":
attack_model_type = "mia_fc"
else:
attack_model_type = ""
victim_pruned_model = BaseModel(
args.model_name, num_cls=args.num_cls, input_dim=args.input_dim, lr=prune_lr,
weight_decay=args.weight_decay, save_folder=pruned_model_save_folder, device=device,
optimizer=args.optimizer, attack_model_type=attack_model_type)
victim_pruned_model.model.load_state_dict(org_state)
pruner = get_pruner(args.pruner_name, victim_pruned_model.model, sparsity=args.prune_sparsity)
victim_pruned_model.model = pruner.compress()
best_acc = 0
count = 0
for epoch in range(args.prune_epochs):
pruner.update_epoch(epoch)
if args.defend == "":
train_acc, train_loss = victim_pruned_model.train(victim_train_loader, f"Epoch {epoch} Prune Train")
elif args.defend == "ppb":
train_acc, train_loss = victim_pruned_model.train_defend_ppb(
victim_train_loader, log_pref=f"Epoch {epoch} Victim Prune Train With PPB", defend_arg=args.defend_arg)
elif args.defend == "adv":
train_acc, train_loss = victim_pruned_model.train_defend_adv(
victim_train_loader, victim_dev_loader, log_pref=f"Epoch {epoch} Victim Prune Train With ADV",
privacy_theta=args.defend_arg)
dev_acc, dev_loss = victim_pruned_model.test(victim_dev_loader, f"Epoch {epoch} Prune Dev")
test_acc, test_loss = victim_pruned_model.test(victim_test_loader, f"Epoch {epoch} Prune Test")
if dev_acc > best_acc:
best_acc = dev_acc
pruner.export_model(model_path=f"{pruned_model_save_folder}/best.pth",
mask_path=f"{pruned_model_save_folder}/best_mask.pth")
count = 0
elif args.early_stop > 0:
count += 1
if count > args.early_stop:
print(f"Early Stop at Epoch {epoch}")
break
victim_prune_acc = test_acc
# prune shadow models
shadow_acc_list = []
shadow_prune_acc_list = []
for shadow_ind in range(args.shadow_num):
attack_train_list, attack_dev_list, attack_test_list = attack_split_list[shadow_ind]
attack_train_dataset = Subset(total_dataset, attack_train_list)
attack_dev_dataset = Subset(total_dataset, attack_dev_list)
attack_test_dataset = Subset(total_dataset, attack_test_list)
attack_train_loader = DataLoader(attack_train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4,
pin_memory=True, worker_init_fn=seed_worker)
attack_dev_loader = DataLoader(attack_dev_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4,
pin_memory=True, worker_init_fn=seed_worker)
attack_test_loader = DataLoader(attack_test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4,
pin_memory=True, worker_init_fn=seed_worker)
# load pretrained shadow model
shadow_model_path = f"{save_folder}/shadow_model_{shadow_ind}/best.pth"
shadow_model = BaseModel(args.model_name, num_cls=args.num_cls, input_dim=args.input_dim, device=device)
shadow_model.load(shadow_model_path)
test_acc, _ = shadow_model.test(attack_test_loader, f"Pretrain Shadow")
shadow_acc = test_acc
org_state = copy.deepcopy(shadow_model.model.state_dict())
pruned_shadow_model_save_folder = \
f"{save_folder}/shadow_{prune_prefix}_model_{shadow_ind}"
if not os.path.exists(pruned_shadow_model_save_folder):
os.makedirs(pruned_shadow_model_save_folder)
# prune shadow models
shadow_pruned_model = BaseModel(
args.model_name, num_cls=args.num_cls, input_dim=args.input_dim, lr=prune_lr,
weight_decay=args.weight_decay, save_folder=pruned_shadow_model_save_folder, device=device,
optimizer=args.optimizer, attack_model_type=attack_model_type)
shadow_pruned_model.model.load_state_dict(org_state)
pruner = get_pruner(args.pruner_name, shadow_pruned_model.model, sparsity=args.prune_sparsity,)
shadow_pruned_model.model = pruner.compress()
best_acc = 0
count = 0
for epoch in range(args.prune_epochs):
pruner.update_epoch(epoch)
if args.defend == "":
train_acc, train_loss = shadow_pruned_model.train(
attack_train_loader, f"Epoch {epoch} Shadow Prune Train")
elif args.defend == "ppb":
train_acc, train_loss = shadow_pruned_model.train_defend_ppb(
attack_train_loader, f"Epoch {epoch} Shadow Prune Train With PPB", defend_arg=args.defend_arg)
elif args.defend == "adv":
train_acc, train_loss = shadow_pruned_model.train_defend_adv(
attack_train_loader, attack_dev_loader, log_pref=f"Epoch {epoch} Victim Prune Train With ADV",
privacy_theta=args.defend_arg)
dev_acc, dev_loss = shadow_pruned_model.test(attack_dev_loader, f"Epoch {epoch} Shadow Prune Dev")
test_acc, test_loss = shadow_pruned_model.test(attack_test_loader, f"Epoch {epoch} Shadow Prune Test")
if dev_acc > best_acc:
best_acc = dev_acc
pruner.export_model(model_path=f"{pruned_shadow_model_save_folder}/best.pth",
mask_path=f"{pruned_shadow_model_save_folder}/best_mask.pth")
count = 0
elif args.early_stop > 0:
count += 1
if count > args.early_stop:
print(f"Early Stop at Epoch {epoch}")
break
shadow_prune_acc = test_acc
shadow_acc_list.append(shadow_acc), shadow_prune_acc_list.append(shadow_prune_acc)
return victim_acc, victim_prune_acc, np.mean(shadow_acc_list), np.mean(shadow_prune_acc_list)
if __name__ == '__main__':
args = parser.parse_args()
with open(args.config_path) as f:
t_args = argparse.Namespace()
t_args.__dict__.update(json.load(f))
args = parser.parse_args(namespace=t_args)
args.prune_epochs = int(args.epochs) // 2
print(args)
main(args)