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train_trapdoor.py
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train_trapdoor.py
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import sys
sys.path.append(".")
sys.path.append("lib")
import argparse
import os
import logging
from datetime import datetime
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
import random
import numpy as np
from models.resnet import *
from models.mnist2layer import *
from models.densenet import densenet169
from lib.robustbench.utils import load_model
from models.trapdoor import CoreModel, craft_trapdoors, DatasetWrapper
from misc.load_dataset import LoadDataset
import misc.utils as utils
def train(model, dataloader, optim, epoch, wrapper):
criterion = nn.CrossEntropyLoss()
model.train()
train_iter, total_loss, total_cor, total_num = 0, 0, 0, 0
for img, classId in dataloader:
img, classId = wrapper(img, classId)
img = img.cuda()
classId = classId.cuda()
out = model(img)
loss = criterion(out, classId)
optim.zero_grad()
loss.backward()
optim.step()
# display
total_num += img.shape[0]
total_cor += (out.argmax(dim=-1) == classId).sum().item()
total_loss += loss.item() * img.shape[0]
if train_iter % 10 == 0:
train_lr = optim.param_groups[0]['lr']
logging.info("E:{}, lr:{:.2e}, Acc:{:.4f}, L:{:.6f}".format(
epoch, train_lr, total_cor / total_num, total_loss / total_num))
train_iter += 1
def test(model, dataloader, wrapper, prefix=""):
criterion = nn.CrossEntropyLoss()
model.eval()
total_cor, total_num, total_loss = 0, 0, 0
for img, classId in dataloader:
img, classId = wrapper(img, classId)
img = img.cuda()
classId = classId.cuda()
out = model(img)
loss = criterion(out, classId)
total_cor += (out.argmax(dim=-1) == classId).sum().item()
total_num += img.shape[0]
total_loss += loss.item() + img.shape[0]
acc = total_cor / total_num
logging.info("{}Test Acc: {:.4f}, L:{:.6f}".format(
prefix, acc, total_loss / total_num))
return acc, total_loss
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train Trapdoor AE detector")
parser.add_argument("--dataset", default="cifar10", type=str)
parser.add_argument("--model", default="", type=str)
parser.add_argument("--lr", default=0.001, type=float)
parser.add_argument("--results_dir", default="./results", type=str)
parser.add_argument("--data_path", default="./dataset", type=str)
parser.add_argument('--inject_ratio', type=float,
help='injection ratio', default=0.5)
parser.add_argument('--mask_ratio', type=float, help='mask ratio',
default=None)
parser.add_argument('--num_cluster', type=int, help='', default=7)
parser.add_argument("--batch_size", default=256, type=int)
parser.add_argument('--seed', type=int, help='', default=0)
args = parser.parse_args()
random.seed(args.seed)
torch.random.manual_seed(args.seed)
if args.dataset == "MNIST":
classifier = Mnist2LayerNet()
cls_path = "pretrain/MNIST_Net.pth"
key = "model"
cls_norm = [(0.), (1.)]
# trapdoor params
mask_ratio = 0.1 if args.mask_ratio is None else args.mask_ratio
pattern_size = 3
epochs = 200
elif args.dataset == "cifar10":
if args.model == "":
classifier = densenet169()
cls_path = "pretrain/densenet169.pt"
key = None
cls_norm = [(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)]
else:
classifier = load_model(model_name=args.model, dataset=args.dataset,
threat_model='Linf')
cls_norm = [(0., 0., 0.), (1., 1., 1.)]
# trapdoor params
mask_ratio = 0.1 if args.mask_ratio is None else args.mask_ratio
pattern_size = 3
epochs = 200
elif args.dataset == "gtsrb":
classifier = ResNet18(num_classes=43)
cls_path = "pretrain/gtsrb_ResNet18_E87_97.85.pth"
key = "model"
cls_norm = [(0.3337, 0.3064, 0.3171), (0.2672, 0.2564, 0.2629)]
# trapdoor params
mask_ratio = 0.1 if args.mask_ratio is None else args.mask_ratio
pattern_size = 3
epochs = 200
else:
raise NotImplementedError()
# set results dir
dir_name = 'Trapdoor-{}-{:.2f}-{:.2f}-'.format(
args.dataset, args.inject_ratio, mask_ratio)
args.results_dir = os.path.join(
args.results_dir, dir_name +
datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
)
# log
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
utils.make_logger(args.dataset, args.results_dir)
logging.info(args)
# define model
model = CoreModel(args.dataset, classifier, cls_norm)
logging.info("{}".format(model))
# load clean trained
model.load_classifier(cls_path, key)
target_ls = list(range(model.num_classes))
pattern_dict = craft_trapdoors(
target_ls, model.img_shape, args.num_cluster,
pattern_size=pattern_size, mask_ratio=mask_ratio)
norm = False
train_data = LoadDataset(
args.dataset, args.data_path, train=True, download=False,
resize_size=(28, 28), hdf5_path=None, random_flip=False, norm=norm)
test_data = LoadDataset(
args.dataset, args.data_path, train=False, download=False,
resize_size=(28, 28), hdf5_path=None, random_flip=False, norm=norm)
train_loader = DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, num_workers=4,
pin_memory=True, drop_last=True)
test_loader = DataLoader(
test_data, batch_size=args.batch_size, shuffle=False, num_workers=4,
pin_memory=True)
# DATA wrappers
data_wrapper = DatasetWrapper(target_ls, pattern_dict, args.inject_ratio)
test_wrapper = DatasetWrapper(target_ls, pattern_dict, 1)
def clean_wrapper(x, y):
return x, y
# train utils
optim = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = ReduceLROnPlateau(
optim, 'min', factor=np.sqrt(0.1),
patience=5, min_lr=0.5e-6)
model = model.cuda()
# train with trapdoor injection.
# when training, keep the model with expected normal acc and best trap acc
best_bd_acc, best_nm_acc = 0, 0
for epoch in range(epochs):
train(model, train_loader, optim, epoch, data_wrapper)
normal_acc, normal_loss = test(model, test_loader, clean_wrapper)
bd_acc, bd_loss = test(model, test_loader, test_wrapper, "Trapdoor ")
params = {
"state_dict": model.state_dict(),
"optim": optim.state_dict(),
"normal_acc": normal_acc,
"backdoor_acc": bd_acc,
'target_ls': target_ls,
'pattern_dict': pattern_dict
}
scheduler.step(normal_loss)
if normal_acc > model.expect_acc:
best_bd_acc = utils.save_best(
best_bd_acc, args.dataset, bd_acc, params, epoch, "TrapdoorB",
args.results_dir)
if best_nm_acc < normal_acc:
best_nm_acc = normal_acc
best_nm_acc = utils.save_best(
best_nm_acc, args.dataset, normal_acc, params, epoch,
"TrapdoorN", args.results_dir)
torch.save(
params, os.path.join(
args.results_dir, "{}_{}.pth".format("Trapdoor", args.dataset)))
del params