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backdoor_main.py
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import os
import time
import argparse
import logging
import numpy as np
import torch
import torch.nn as nn
import pandas as pd
from collections import OrderedDict
import models
from models.model_for_cifar.model_select import select_model
from datasets.poison_tool_cifar import get_backdoor_loader, get_test_loader
if torch.cuda.is_available():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
device = torch.device('cuda')
else:
device = torch.device('cpu')
seed = 98
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(seed)
np.random.seed(seed)
def train_step(args, model, criterion, optimizer, data_loader):
model.train()
total_correct = 0
total_loss = 0.0
for i, (images, labels) in enumerate(data_loader):
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
output = model(images)
loss = criterion(output, labels)
pred = output.data.max(1)[1]
total_correct += pred.eq(labels.view_as(pred)).sum()
total_loss += loss.item()
nn.utils.clip_grad_norm_(model.parameters(), max_norm=20, norm_type=2)
loss.backward()
optimizer.step()
loss = total_loss / len(data_loader)
acc = float(total_correct) / len(data_loader.dataset)
return loss, acc
def test(model, criterion, data_loader):
model.eval()
total_correct = 0
total_loss = 0.0
with torch.no_grad():
for i, (images, labels) in enumerate(data_loader):
images, labels = images.to(device), labels.to(device)
output = model(images)
total_loss += criterion(output, labels).item()
pred = output.data.max(1)[1]
total_correct += pred.eq(labels.data.view_as(pred)).sum()
loss = total_loss / len(data_loader)
acc = float(total_correct) / len(data_loader.dataset)
return loss, acc
def save_checkpoint(state, epoch, is_best, args):
if is_best:
filepath = os.path.join(args.save_root, args.model_name + '_' + args.trigger_type + '_' + args.dataset + '_' + f'target_label{args.target_label}' + '_' + f'poison_rate{args.poison_rate}' + '_' + f'epoch{epoch}.tar')
torch.save(state, filepath)
def main(args):
logger = logging.getLogger(__name__)
logging.basicConfig(
format='[%(asctime)s] - %(message)s',
datefmt='%Y/%m/%d %H:%M:%S',
level=logging.DEBUG,
handlers=[
logging.FileHandler(os.path.join(args.log_root, 'output.log')),
logging.StreamHandler()
])
logger.info(args)
logger.info('----------- Backdoored Data Initialization --------------')
_, backdoor_data_loader = get_backdoor_loader(args)
clean_test_loader, bad_test_loader = get_test_loader(args)
logger.info('----------- Backdoor Model Initialization --------------')
net = select_model(args, dataset=args.dataset,
model_name=args.model_name,
pretrained=False,
pretrained_models_path=None
)
print(net)
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.schedule, gamma=0.1)
logger.info('----------- Backdoor Model Training--------------')
logger.info('Epoch \t lr \t Time \t TrainLoss \t TrainACC \t PoisonLoss \t PoisonACC \t CleanLoss \t CleanACC')
for epoch in range(0, args.epochs + 1):
start = time.time()
lr = optimizer.param_groups[0]['lr']
train_loss, train_acc = train_step(args=args, model=net, criterion=criterion, optimizer=optimizer,
data_loader=backdoor_data_loader)
cl_test_loss, cl_test_acc = test(model=net, criterion=criterion, data_loader=clean_test_loader)
po_test_loss, po_test_acc = test(model=net, criterion=criterion, data_loader=bad_test_loader)
scheduler.step()
end = time.time()
logger.info(
'%d \t %.3f \t %.1f \t %.4f \t %.4f \t %.4f \t %.4f \t %.4f \t %.4f',
epoch, lr, end - start, train_loss, train_acc, po_test_loss, po_test_acc,
cl_test_loss, cl_test_acc)
if epoch % args.save_every == 0 and epoch != 0 and epoch >= 50:
# save checkpoint at interval epoch
is_best = True
save_checkpoint({
'epoch': epoch,
'state_dict': net.state_dict(),
'clean_acc': cl_test_acc,
'bad_acc': po_test_acc,
'optimizer': optimizer.state_dict(),
}, epoch, is_best, args)
logger.info('[INFO] Save model weight epoch {}'.format(epoch))
def get_arguments():
parser = argparse.ArgumentParser()
# various path
parser.add_argument('--cuda', type=int, default=1, help='cuda available')
parser.add_argument('--save_every', type=int, default=5, help='save checkpoints every few epochs')
parser.add_argument('--log_root', type=str, default='logs/', help='logs are saved here')
parser.add_argument('--log_name', type=str, default=None, help='logs name')
parser.add_argument('--save', type=int, default=1, help='whether save the weight')
parser.add_argument('--save_root', type=str, default='weights/', help='where to save the weight')
parser.add_argument('--model_name', type=str, default='ResNet18',
choices=['ResNet18', 'vit_small_patch16_224'])
parser.add_argument('--schedule', type=int, nargs='+', default=[40, 80],
help='Decrease learning rate at these epochs.')
parser.add_argument('--dataset', type=str, default='CIFAR10', help='name of image dataset')
parser.add_argument('--batch_size', type=int, default=128, help='The size of batch')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='weight decay')
parser.add_argument('--num_classes', type=int, default=10, help='number of classes')
parser.add_argument('--lr', type=int, default=0.1, help='the number of epochs for unlearning')
parser.add_argument('--epochs', type=int, default=60, help='the number of epochs for training')
# VITs CIFAR10
parser.add_argument('--img_size', type=int, default=32)
parser.add_argument('--patch', type=int, default=4)
parser.add_argument('--crop_size', type=int, default=32)
# backdoor attacks
parser.add_argument('--target_label', type=int, default=0, help='class of target label')
parser.add_argument('--trigger_type', type=str, default='gridTrigger', help='type of backdoor trigger')
parser.add_argument('--target_type', type=str, default='all2one', help='type of backdoor label')
parser.add_argument('--trig_w', type=int, default=3, help='width of trigger pattern')
parser.add_argument('--trig_h', type=int, default=3, help='height of trigger pattern')
parser.add_argument('--poison_rate', type=float, default=0.1, help='ratio of backdoor poisoned data')
return parser
if __name__ == '__main__':
print(torch.cuda.device_count())
print(torch.cuda.current_device())
args = get_arguments().parse_args()
if args.dataset == 'CIFAR10':
model_names = ['ResNet18']
# trigger_pools_cifar = ['onePixelTrigger', 'gridTrigger', 'wanetTrigger', 'trojanTrigger', 'blendTrigger',
# 'signalTrigger', 'CLTrigger', 'smoothTrigger', 'dynamicTrigger', 'nashTrigger']
trigger_pools_cifar = ['onePixelTrigger']
poison_rates = [0.0]
# args.epochs = 1
for model_name in model_names:
args.model_name = model_name
# args.trigger_types = trigger_pools_cifar
# print(args.model_name)
if args.model_name == 'vit_small_patch16_224' or args.model_name == 'vit_base_patch16_224':
args.lr = 0.001
args.epochs = 11
args.img_size = 224
args.schedule = [10, 20]
for trigger_type in trigger_pools_cifar:
args.trigger_type = trigger_type
# print('attack_type:', args.attack_type)
for poison_rate in poison_rates:
args.poison_rate = poison_rate
main(args)