def load_models(dataset):
archs = []
model_path_list = []
if dataset == "CIFAR-10" or dataset == "CIFAR-100":
for arch in ["resnet-110","WRN-28-10","WRN-34-10","resnext-8x64d","resnext-16x64d"]:
test_model_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/{}/checkpoint.pth.tar".format(
PY_ROOT, dataset, arch)
if os.path.exists(test_model_path):
archs.append(arch)
model_path_list.append(test_model_path)
else:
log.info(test_model_path + " does not exist!")
elif dataset == "TinyImageNet":
# for arch in ["vgg11_bn","resnet18","vgg16_bn","resnext64_4","densenet121"]:
for arch in MODELS_TEST_STANDARD[dataset]:
test_model_path = "{}/train_pytorch_model/real_image_model/{}@{}@*.pth.tar".format(
PY_ROOT, dataset, arch)
test_model_path = list(glob.glob(test_model_path))[0]
if os.path.exists(test_model_path):
archs.append(arch)
model_path_list.append(test_model_path)
else:
log.info(test_model_path + "does not exist!")
else:
for arch in ["inceptionv3","inceptionv4", "inceptionresnetv2","resnet101", "resnet152"]:
test_model_list_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/checkpoints/{}*.pth".format(
PY_ROOT, dataset, arch)
test_model_path = list(glob.glob(test_model_list_path))
if len(test_model_path) == 0: # this arch does not exists in args.dataset
continue
archs.append(arch)
model_path_list.append(test_model_path[0])
models = []
print("begin construct model")
if dataset == "TinyImageNet":
for idx, arch in enumerate(archs):
model = MetaLearnerModelBuilder.construct_tiny_imagenet_model(arch, dataset)
model_path = model_path_list[idx]
model.load_state_dict(torch.load(model_path, map_location=lambda storage, location: storage)["state_dict"])
model.cuda()
model.eval()
models.append(model)
else:
for arch in archs:
model = StandardModel(dataset, arch, no_grad=True)
model.cuda()
model.eval()
models.append(model)
print("end construct model")
return models
def main(args, arch):
model = StandardModel(args["dataset"], arch, False)
model.cuda()
model.eval()
# attack related settings
if args["attack_method"] == "zoo" or args[
"attack_method"] == "autozoom_bilin":
if args["img_resize"] is None:
args["img_resize"] = model.input_size[-1]
log.info(
"Argument img_resize is not set and not using autoencoder, set to image original size:{}"
.format(args["img_resize"]))
codec = None
if args["attack_method"] == "zoo_ae" or args[
"attack_method"] == "autozoom_ae":
codec = Codec(model.input_size[-1],
IN_CHANNELS[args["dataset"]],
args["compress_mode"],
args["resize"],
use_tanh=args["use_tanh"])
codec.load_codec(args["codec_path"])
codec.cuda()
decoder = codec.decoder
args["img_resize"] = decoder.input_shape[1]
log.info(
"Loading autoencoder: {}, set the attack image size to:{}".format(
args["codec_path"], args["img_resize"]))
# setup attack
if args["attack_method"] == "zoo":
blackbox_attack = ZOO(model, args["dataset"], args)
elif args["attack_method"] == "zoo_ae":
blackbox_attack = ZOO_AE(model, args["dataset"], args, decoder)
elif args["attack_method"] == "autozoom_bilin":
blackbox_attack = AutoZOOM_BiLIN(model, args["dataset"], args)
elif args["attack_method"] == "autozoom_ae":
blackbox_attack = AutoZOOM_AE(model, args["dataset"], args, decoder)
target_str = "untargeted" if args[
"attack_type"] != "targeted" else "targeted_{}".format(
args["target_type"])
save_result_path = args[
"exp_dir"] + "/data_{}@arch_{}@attack_{}@{}_result.json".format(
args["dataset"], arch, args["attack_method"], target_str)
attack_framework = AutoZoomAttackFramework(args)
attack_framework.attack_dataset_images(args, blackbox_attack, arch, model,
codec, save_result_path)
model.cpu()
def test_attack(threshold, arch, dataset, test_loader):
target_model = StandardModel(dataset, arch, no_grad=False)
if torch.cuda.is_available():
target_model = target_model.cuda()
target_model.eval()
attack = LinfPGDAttack(target_model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=threshold, nb_iter=30,
eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)
all_count = 0
success_count = 0
all_adv_images = []
all_true_labels = []
for idx, (img, true_label) in enumerate(test_loader):
img = img.cuda()
true_label = true_label.cuda().long()
adv_image = attack.perturb(img, true_label) # (3, 224, 224), float
if adv_image is None:
continue
adv_label = target_model.forward(adv_image).max(1)[1].detach().cpu().numpy().astype(np.int32)
# adv_image = np.transpose(adv_image, (0, 2, 3, 1)) # N,C,H,W -> (N, H, W, 3), float
all_count += len(img)
true_label_np = true_label.detach().cpu().numpy().astype(np.int32)
success_count+= len(np.where(true_label_np != adv_label)[0])
all_adv_images.append(adv_image.cpu().detach().numpy())
all_true_labels.append(true_label_np)
attack_success_rate = success_count / float(all_count)
log.info("Before train. Attack success rate is {:.3f}".format(attack_success_rate))
return target_model, np.concatenate(all_adv_images,0), np.concatenate(all_true_labels, 0) # N,224,224,3
def main(args, arch):
model = StandardModel(args.dataset, arch, args.solver != "fake_zero")
model.cuda()
model.eval()
if args.init_size is None:
args.init_size = model.input_size[-1]
log.info(
"Argument init_size is not set and not using autoencoder, set to image original size:{}"
.format(args.init_size))
target_str = "untargeted" if not args.targeted else "targeted_{}".format(
args.target_type)
save_result_path = args.exp_dir + "/data_{}@arch_{}@solver_{}@{}_result.json".format(
args.dataset, arch, args.solver, target_str)
if os.path.exists(save_result_path):
model.cpu()
return
attack_framework = ZooAttackFramework(args)
attack_framework.attack_dataset_images(args, arch, model, save_result_path)
model.cpu()
def main():
model = StandardModel(args.dataset,
args.arch,
no_grad=False,
load_pretrained=False)
model.cuda()
model.train()
device = torch.device("cuda")
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model_path = '{}/train_pytorch_model/adversarial_train/TRADES/{}@{}@epoch_{}@batch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.arch, args.epochs, args.batch_size)
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print("After trained, the model will save to {}".format(model_path))
for epoch in range(1, args.epochs + 1):
# adjust learning rate for SGD
adjust_learning_rate(optimizer, epoch)
# adversarial training
train(args, model, device, train_loader, optimizer, epoch)
# evaluation on natural examples
print(
'================================================================')
eval_train(model, device, train_loader)
eval_test(model, device, test_loader)
print(
'================================================================')
# save checkpoint
if epoch % args.save_freq == 0:
state = {
'state_dict': model.state_dict(),
'epoch': epoch,
'optimizer': optimizer.state_dict()
}
torch.save(state, os.path.join(model_dir, model_path))
def generate(datasetname, batch_size):
save_dir_path = "{}/data_adv_defense/guided_denoiser".format(PY_ROOT)
os.makedirs(save_dir_path, exist_ok=True)
set_log_file(save_dir_path + "/generate_{}.log".format(datasetname))
data_loader = DataLoaderMaker.get_img_label_data_loader(datasetname, batch_size, is_train=True)
attackers = []
for model_name in MODELS_TRAIN_STANDARD[datasetname] + MODELS_TEST_STANDARD[datasetname]:
model = StandardModel(datasetname, model_name, no_grad=False)
model = model.cuda().eval()
linf_PGD_attack =LinfPGDAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.031372, nb_iter=30,
eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)
l2_PGD_attack = L2PGDAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"),eps=4.6,
nb_iter=30,clip_min=0.0, clip_max=1.0, targeted=False)
FGSM_attack = FGSM(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"))
momentum_attack = MomentumIterativeAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.031372, nb_iter=30,
eps_iter=0.01, clip_min=0.0, clip_max=1.0, targeted=False)
attackers.append(linf_PGD_attack)
attackers.append(l2_PGD_attack)
attackers.append(FGSM_attack)
attackers.append(momentum_attack)
log.info("Create model {} done!".format(model_name))
generate_and_save_adv_examples(datasetname, data_loader, attackers, save_dir_path)
PY_ROOT, args.dataset, arch)
test_model_list_path = list(glob.glob(test_model_list_path))
if len(test_model_list_path
) == 0: # this arch does not exists in args.dataset
continue
archs.append(arch)
else:
assert args.arch is not None
archs = [args.arch]
args.arch = ", ".join(archs)
log.info('Command line is: {}'.format(' '.join(sys.argv)))
log.info("Log file is written in {}".format(log_file_path))
log.info('Called with args:')
print_args(args)
surrogate_model = StandardModel(args.dataset, args.surrogate_arch, False)
surrogate_model.cuda()
surrogate_model.eval()
attacker = SwitchNeg(args.dataset, args.batch_size, args.targeted,
args.target_type, args.epsilon, args.norm, 0.0, 1.0,
args.max_queries)
for arch in archs:
if args.attack_defense:
save_result_path = args.exp_dir + "/{}_{}_result.json".format(
arch, args.defense_model)
else:
save_result_path = args.exp_dir + "/{}_result.json".format(arch)
if os.path.exists(save_result_path):
continue
log.info("Begin attack {} on {}, result will be saved to {}".format(
arch, args.dataset, save_result_path))
f.write(socket.gethostname() + ":" + args.results_folder + "\n")
def onehot(ind, num_classes):
vector = np.zeros([num_classes])
vector[ind] = 1
return vector.astype(np.float32)
logger.info("build dataloader")
train_loader = DataLoaderMaker.get_img_label_data_loader(
args.dataset, args.batch_size, True)
val_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset,
args.batch_size, False)
model = StandardModel(args.dataset, args.arch, no_grad=False)
model.cuda()
model.train()
def anneal_lr(epoch):
if epoch < 100:
return 1.
elif epoch < 150:
return 0.1
else:
return 0.01
pgd_kwargs = {
"eps": 16. / 255.,
"eps_iter": 4. / 255.,
def attack_all_images(self, args, arch, tmp_dump_path, result_dump_path):
# subset_pos用于回调函数汇报汇总统计结果
model = StandardModel(args.dataset, arch, no_grad=True)
model.cuda()
model.eval()
# 带有缩减功能的，攻击成功的图片自动删除掉
for data_idx, data_tuple in enumerate(self.dataset_loader):
if os.path.exists(tmp_dump_path):
with open(tmp_dump_path, "r") as file_obj:
json_content = json.load(file_obj)
resume_batch_idx = int(json_content["batch_idx"]) # resume
for key in [
'query_all', 'correct_all', 'not_done_all',
'success_all', 'success_query_all'
]:
if key in json_content:
setattr(
self, key,
torch.from_numpy(np.asarray(
json_content[key])).float())
if data_idx < resume_batch_idx: # resume
continue
if args.dataset == "ImageNet":
if model.input_size[-1] >= 299:
images, true_labels = data_tuple[1], data_tuple[2]
else:
images, true_labels = data_tuple[0], data_tuple[2]
else:
images, true_labels = data_tuple[0], data_tuple[1]
if images.size(-1) != model.input_size[-1]:
images = F.interpolate(images,
size=model.input_size[-1],
mode='bilinear',
align_corners=True)
# skip_batch_index_list = np.nonzero(np.asarray(chunk_skip_indexes[data_idx]))[0].tolist()
selected = torch.arange(
data_idx * args.batch_size,
min((data_idx + 1) * args.batch_size,
self.total_images)) # 选择这个batch的所有图片的index
img_idx_to_batch_idx = ImageIdxToOrigBatchIdx(args.batch_size)
images, true_labels = images.cuda(), true_labels.cuda()
first_finetune = True
finetune_queue = FinetuneQueue(args.batch_size, args.meta_seq_len,
img_idx_to_batch_idx)
prior_size = model.input_size[
-1] if not args.tiling else args.tile_size
assert args.tiling == (args.dataset == "ImageNet")
if args.tiling:
upsampler = Upsample(size=(model.input_size[-2],
model.input_size[-1]))
else:
upsampler = lambda x: x
with torch.no_grad():
logit = model(images)
pred = logit.argmax(dim=1)
query = torch.zeros(images.size(0)).cuda()
correct = pred.eq(true_labels).float() # shape = (batch_size,)
not_done = correct.clone() # shape = (batch_size,)
if args.targeted:
if args.target_type == 'random':
target_labels = torch.randint(
low=0,
high=CLASS_NUM[args.dataset],
size=true_labels.size()).long().cuda()
invalid_target_index = target_labels.eq(true_labels)
while invalid_target_index.sum().item() > 0:
target_labels[invalid_target_index] = torch.randint(
low=0,
high=logit.shape[1],
size=target_labels[invalid_target_index].shape
).long().cuda()
invalid_target_index = target_labels.eq(true_labels)
elif args.target_type == 'least_likely':
target_labels = logit.argmin(dim=1)
elif args.target_type == "increment":
target_labels = torch.fmod(true_labels + 1,
CLASS_NUM[args.dataset])
else:
raise NotImplementedError('Unknown target_type: {}'.format(
args.target_type))
else:
target_labels = None
prior = torch.zeros(images.size(0), IN_CHANNELS[args.dataset],
prior_size, prior_size).cuda()
prior_step = self.gd_prior_step if args.norm == 'l2' else self.eg_prior_step
image_step = self.l2_image_step if args.norm == 'l2' else self.linf_step
proj_step = self.l2_proj_step if args.norm == 'l2' else self.linf_proj_step # 调用proj_maker返回的是一个函数
criterion = self.cw_loss if args.data_loss == "cw" else self.xent_loss
adv_images = images.clone()
for step_index in range(1, args.max_queries + 1):
# Create noise for exporation, estimate the gradient, and take a PGD step
dim = prior.nelement() / images.size(
0) # nelement() --> total number of elements
exp_noise = args.exploration * torch.randn_like(prior) / (
dim**0.5
) # parameterizes the exploration to be done around the prior
exp_noise = exp_noise.cuda()
q1 = upsampler(
prior + exp_noise
) # 这就是Finite Difference算法， prior相当于论文里的v，这个prior也会更新，把梯度累积上去
q2 = upsampler(
prior -
exp_noise) # prior 相当于累积的更新量，用这个更新量，再去修改image，就会变得非常准
# Loss points for finite difference estimator
q1_images = adv_images + args.fd_eta * q1 / self.norm(q1)
q2_images = adv_images + args.fd_eta * q2 / self.norm(q2)
predict_by_target_model = False
if (step_index <= args.warm_up_steps or
(step_index - args.warm_up_steps) % args.meta_predict_steps
== 0):
log.info("predict from target model")
predict_by_target_model = True
with torch.no_grad():
q1_logits = model(q1_images)
q2_logits = model(q2_images)
q1_logits = q1_logits / torch.norm(
q1_logits, p=2, dim=-1,
keepdim=True) # 加入normalize
q2_logits = q2_logits / torch.norm(
q2_logits, p=2, dim=-1, keepdim=True)
finetune_queue.append(q1_images.detach(),
q2_images.detach(),
q1_logits.detach(),
q2_logits.detach())
if step_index >= args.warm_up_steps:
q1_images_seq, q2_images_seq, q1_logits_seq, q2_logits_seq = finetune_queue.stack_history_track(
)
finetune_times = args.finetune_times if first_finetune else random.randint(
3, 5) # FIXME
log.info("begin finetune for {} times".format(
finetune_times))
self.meta_finetuner.finetune(
q1_images_seq, q2_images_seq, q1_logits_seq,
q2_logits_seq, finetune_times, first_finetune,
img_idx_to_batch_idx)
first_finetune = False
else:
with torch.no_grad():
q1_logits, q2_logits = self.meta_finetuner.predict(
q1_images, q2_images, img_idx_to_batch_idx)
q1_logits = q1_logits / torch.norm(
q1_logits, p=2, dim=-1, keepdim=True)
q2_logits = q2_logits / torch.norm(
q2_logits, p=2, dim=-1, keepdim=True)
l1 = criterion(q1_logits, true_labels, target_labels)
l2 = criterion(q2_logits, true_labels, target_labels)
# Finite differences estimate of directional derivative
est_deriv = (l1 - l2) / (args.fd_eta * args.exploration
) # 方向导数 , l1和l2是loss
# 2-query gradient estimate
est_grad = est_deriv.view(-1, 1, 1,
1) * exp_noise # B, C, H, W,
# Update the prior with the estimated gradient
prior = prior_step(
prior, est_grad,
args.online_lr) # 注意，修正的是prior,这就是bandit算法的精髓
grad = upsampler(prior) # prior相当于梯度
## Update the image:
adv_images = image_step(
adv_images,
grad * correct.view(-1, 1, 1, 1), # 注意correct也是删减过的
args.image_lr) # prior放大后相当于累积的更新量，可以用来更新
adv_images = proj_step(images, args.epsilon, adv_images)
adv_images = torch.clamp(adv_images, 0, 1)
with torch.no_grad():
adv_logit = model(adv_images) #
adv_pred = adv_logit.argmax(dim=1)
adv_prob = F.softmax(adv_logit, dim=1)
adv_loss = criterion(adv_logit, true_labels, target_labels)
## Continue query count
if predict_by_target_model:
query = query + 2 * not_done
if args.targeted:
not_done = not_done * (
1 - adv_pred.eq(target_labels).float()
).float() # not_done初始化为 correct, shape = (batch_size,)
else:
not_done = not_done * adv_pred.eq(
true_labels).float() # 只要是跟原始label相等的，就还需要query，还没有成功
success = (1 - not_done) * correct
success_query = success * query
not_done_loss = adv_loss * not_done
not_done_prob = adv_prob[torch.arange(adv_images.size(0)),
true_labels] * not_done
log.info('Attacking image {} - {} / {}, step {}'.format(
data_idx * args.batch_size,
(data_idx + 1) * args.batch_size, self.total_images,
step_index))
log.info(' not_done: {:.4f}'.format(
len(
np.where(not_done.detach().cpu().numpy().astype(
np.int32) == 1)[0]) / float(args.batch_size)))
log.info(' fd_scalar: {:.9f}'.format(
(l1 - l2).mean().item()))
if success.sum().item() > 0:
log.info(' mean_query: {:.4f}'.format(
success_query[success.byte()].mean().item()))
log.info(' median_query: {:.4f}'.format(
success_query[success.byte()].median().item()))
if not_done.sum().item() > 0:
log.info(' not_done_loss: {:.4f}'.format(
not_done_loss[not_done.byte()].mean().item()))
log.info(' not_done_prob: {:.4f}'.format(
not_done_prob[not_done.byte()].mean().item()))
not_done_np = not_done.detach().cpu().numpy().astype(np.int32)
done_img_idx_list = np.where(not_done_np == 0)[0].tolist()
delete_all = False
if done_img_idx_list:
for skip_index in done_img_idx_list: # 两次循环，第一次循环先汇报出去，第二次循环删除
batch_idx = img_idx_to_batch_idx[skip_index]
pos = selected[batch_idx].item()
# 先汇报被删减的值self.query_all
for key in [
'query', 'correct', 'not_done', 'success',
'success_query', 'not_done_loss',
'not_done_prob'
]:
value_all = getattr(self, key + "_all")
value = eval(key)[skip_index].item()
value_all[pos] = value
images, adv_images, prior, query, true_labels, target_labels, correct, not_done = \
self.delete_tensor_by_index_list(done_img_idx_list, images, adv_images, prior, query,
true_labels, target_labels, correct, not_done)
img_idx_to_batch_idx.del_by_index_list(done_img_idx_list)
delete_all = images is None
if delete_all:
break
# report to all stats the rest unsuccess
for key in [
'query', 'correct', 'not_done', 'success', 'success_query',
'not_done_loss', 'not_done_prob'
]:
for img_idx, batch_idx in img_idx_to_batch_idx.proj_dict.items(
):
pos = selected[batch_idx].item()
value_all = getattr(self, key + "_all")
value = eval(key)[img_idx].item()
value_all[
pos] = value # 由于value_all是全部图片都放在一个数组里，当前batch选择出来
img_idx_to_batch_idx.proj_dict.clear()
tmp_info_dict = {
"batch_idx": data_idx + 1,
"batch_size": args.batch_size
}
for key in [
'query_all', 'correct_all', 'not_done_all', 'success_all',
'success_query_all'
]:
value_all = getattr(self, key).detach().cpu().numpy().tolist()
tmp_info_dict[key] = value_all
with open(tmp_dump_path, "w") as result_file_obj:
json.dump(tmp_info_dict, result_file_obj, sort_keys=True)
log.info('Saving results to {}'.format(result_dump_path))
meta_info_dict = {
"avg_correct":
self.correct_all.mean().item(),
"avg_not_done":
self.not_done_all[self.correct_all.byte()].mean().item(),
"mean_query":
self.success_query_all[self.success_all.byte()].mean().item(),
"median_query":
self.success_query_all[self.success_all.byte()].median().item(),
"max_query":
self.success_query_all[self.success_all.byte()].max().item(),
"correct_all":
self.correct_all.detach().cpu().numpy().astype(np.int32).tolist(),
"not_done_all":
self.not_done_all.detach().cpu().numpy().astype(np.int32).tolist(),
"query_all":
self.query_all.detach().cpu().numpy().astype(np.int32).tolist(),
"not_done_loss":
self.not_done_loss_all[self.not_done_all.byte()].mean().item(),
"not_done_prob":
self.not_done_prob_all[self.not_done_all.byte()].mean().item(),
"args":
vars(args)
}
with open(result_dump_path, "w") as result_file_obj:
json.dump(meta_info_dict, result_file_obj, sort_keys=True)
log.info("done, write stats info to {}".format(result_dump_path))
self.query_all.fill_(0)
self.correct_all.fill_(0)
self.not_done_all.fill_(0)
self.success_all.fill_(0)
self.success_query_all.fill_(0)
self.not_done_loss_all.fill_(0)
self.not_done_prob_all.fill_(0)
model.cpu()
transform=train_preprocessor)
elif dataset == "TinyImageNet":
train_dataset = TinyImageNet(IMAGE_DATA_ROOT[dataset],
train_preprocessor,
train=True)
batch_size = args.batch_size
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
print('==> Building model..')
arch_list = MODELS_TRAIN_STANDARD[args.dataset]
model_dict = {}
for arch in arch_list:
if StandardModel.check_arch(arch, args.dataset):
print("begin use arch {}".format(arch))
model = StandardModel(args.dataset, arch, no_grad=True)
model_dict[arch] = model.eval()
print("use arch {} done".format(arch))
print("==> Save gradient..")
for arch, model in model_dict.items():
dump_path = "{}/benign_images_logits_pair/{}/{}_images.npy".format(
PY_ROOT, args.dataset, arch)
os.makedirs(os.path.dirname(dump_path), exist_ok=True)
model = model.cuda()
save_image_logits_pairs(model, train_loader, dump_path, args.batch_size,
args.max_items)
model.cpu()
def __init__(self, dataset, batch_size, meta_arch, meta_train_type, distill_loss, data_loss, norm, targeted, use_softmax, mode="meta"):
if mode == "meta":
target_str = "targeted_attack_random" if targeted else "untargeted_attack"
# 2Q_DISTILLATION@CIFAR-100@TRAIN_I_TEST_II@model_resnet34@loss_pair_mse@dataloss_cw_l2_untargeted_attack@epoch_4@meta_batch_size_30@num_support_50@num_updates_12@lr_0.001@inner_lr_0.01.pth.tar
self.meta_model_path = "{root}/train_pytorch_model/meta_simulator/{meta_train_type}@{dataset}@{split}@model_{meta_arch}@loss_{loss}@dataloss_{data_loss}_{norm}_{target_str}*inner_lr_0.01.pth.tar".format(
root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset, split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II,
meta_arch=meta_arch, loss=distill_loss, data_loss=data_loss, norm=norm, target_str=target_str)
log.info("start using {}".format(self.meta_model_path))
self.meta_model_path = glob.glob(self.meta_model_path)
pattern = re.compile(".*model_(.*?)@.*inner_lr_(.*?)\.pth.*")
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
log.info("load meta model {}".format(self.meta_model_path))
ma = pattern.match(os.path.basename(self.meta_model_path))
arch = ma.group(1)
self.inner_lr = float(ma.group(2))
meta_backbone = self.construct_model(arch, dataset)
self.meta_network = MetaNetwork(meta_backbone)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)
self.meta_network.load_state_dict(self.pretrained_weights["state_dict"])
log.info("Load model in epoch {}.".format(self.pretrained_weights["epoch"]))
self.pretrained_weights = self.pretrained_weights["state_dict"]
elif mode == "vanilla":
target_str = "targeted" if targeted else "untargeted"
arch = meta_arch
# 2Q_DISTILLATION@CIFAR-100@TRAIN_I_TEST_II@model_resnet34@loss_pair_mse@dataloss_cw_l2_untargeted_attack@epoch_4@meta_batch_size_30@num_support_50@num_updates_12@lr_0.001@inner_lr_0.01.pth.tar
self.meta_model_path = "{root}/train_pytorch_model/vanilla_simulator/{dataset}@{norm}_norm_{target_str}@{meta_arch}*.tar".format(
root=PY_ROOT, dataset=dataset,
meta_arch=meta_arch,norm=norm, target_str=target_str)
log.info("start using {}".format(self.meta_model_path))
self.meta_model_path = glob.glob(self.meta_model_path)
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
log.info("load meta model {}".format(self.meta_model_path))
self.inner_lr = 0.01
self.meta_network = self.construct_model(meta_arch, dataset)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)
log.info("Load model in epoch {}.".format(self.pretrained_weights["epoch"]))
self.pretrained_weights = self.pretrained_weights["state_dict"]
elif mode == "deep_benign_images":
arch = "resnet34"
self.inner_lr = 0.01
self.meta_network = self.construct_model(arch, dataset)
self.meta_model_path = "{root}/train_pytorch_model/real_image_model/{dataset}@{arch}@epoch_200@lr_0.1@batch_200.pth.tar".format(
root=PY_ROOT, dataset=dataset, arch=arch)
assert os.path.exists(self.meta_model_path), "{} does not exists!".format(self.meta_model_path)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)[
"state_dict"]
elif mode == "random_init":
arch = "resnet34"
self.inner_lr = 0.01
self.meta_network = self.construct_model(arch, dataset)
self.pretrained_weights = self.meta_network.state_dict()
elif mode == 'ensemble_avg':
self.inner_lr = 0.01
self.archs = ["densenet-bc-100-12","resnet-110","vgg19_bn"]
self.meta_network = [] # meta_network和pretrained_weights都改成list
self.pretrained_weights = []
for arch in self.archs:
model = StandardModel(dataset, arch, no_grad=False, load_pretrained=True)
model.eval()
model.cuda()
self.meta_network.append(model)
self.pretrained_weights.append(model.state_dict())
elif mode == "benign_images":
self.inner_lr = 0.01
self.meta_model_path = "{root}/train_pytorch_model/meta_simulator_on_benign_images/{dataset}@{split}*@inner_lr_0.01.pth.tar".format(
root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset,
split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II)
self.meta_model_path = glob.glob(self.meta_model_path)
pattern = re.compile(".*model_(.*?)@.*")
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
ma = pattern.match(os.path.basename(self.meta_model_path))
log.info("Loading meta model from {}".format(self.meta_model_path))
arch = ma.group(1)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)["state_dict"]
meta_backbone = self.construct_model(arch, dataset)
self.meta_network = MetaNetwork(meta_backbone)
self.meta_network.load_state_dict(self.pretrained_weights)
self.meta_network.eval()
self.meta_network.cuda()
elif mode == "reptile_on_benign_images":
self.inner_lr = 0.01
self.meta_model_path = "{root}/train_pytorch_model/meta_simulator_reptile_on_benign_images/{dataset}@{split}*@inner_lr_0.01.pth.tar".format(
root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset,
split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II)
self.meta_model_path = glob.glob(self.meta_model_path)
pattern = re.compile(".*model_(.*?)@.*")
assert len(self.meta_model_path) > 0
self.meta_model_path = self.meta_model_path[0]
log.info("Loading meta model from {}".format(self.meta_model_path))
ma = pattern.match(os.path.basename(self.meta_model_path))
arch = ma.group(1)
self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)["state_dict"]
meta_backbone = self.construct_model(arch, dataset)
self.meta_network = MetaNetwork(meta_backbone)
self.meta_network.load_state_dict(self.pretrained_weights)
self.meta_network.eval()
self.meta_network.cuda()
self.arch = arch
self.dataset = dataset
self.need_pair_distance = (distill_loss.lower()=="pair_mse")
# self.need_pair_distance = False
self.softmax = nn.Softmax(dim=1)
self.mse_loss = nn.MSELoss(reduction="mean")
self.pair_wise_distance = nn.PairwiseDistance(p=2)
self.use_softmax = use_softmax
if mode != "ensemble_avg":
self.meta_network.load_state_dict(self.pretrained_weights)
self.meta_network.eval()
self.meta_network.cuda()
self.batch_size = batch_size
if mode == 'ensemble_avg':
self.batch_weights = defaultdict(dict)
for idx in range(len(self.pretrained_weights)):
for i in range(batch_size):
self.batch_weights[idx][i] = self.pretrained_weights[idx]
else:
self.batch_weights = dict()
for i in range(batch_size):
self.batch_weights[i] = self.pretrained_weights
def __init__(self, tot_num_tasks, dataset, inner_batch_size, protocol):
"""
Args:
num_samples_per_class: num samples to generate "per class" in one batch
batch_size: size of meta batch size (e.g. number of functions)
"""
self.img_size = IMAGE_SIZE[dataset]
self.dataset = dataset
if protocol == SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II:
self.model_names = MODELS_TRAIN_STANDARD[self.dataset]
elif protocol == SPLIT_DATA_PROTOCOL.TRAIN_II_TEST_I:
self.model_names = MODELS_TEST_STANDARD[self.dataset]
elif protocol == SPLIT_DATA_PROTOCOL.TRAIN_ALL_TEST_ALL:
self.model_names = MODELS_TRAIN_STANDARD[
self.dataset] + MODELS_TEST_STANDARD[self.dataset]
self.model_dict = {}
for arch in self.model_names:
if StandardModel.check_arch(arch, dataset):
model = StandardModel(dataset, arch, no_grad=False).eval()
if dataset != "ImageNet":
model = model.cuda()
self.model_dict[arch] = model
is_train = True
preprocessor = DataLoaderMaker.get_preprocessor(
IMAGE_SIZE[dataset], is_train)
if dataset == "CIFAR-10":
train_dataset = CIFAR10(IMAGE_DATA_ROOT[dataset],
train=is_train,
transform=preprocessor)
elif dataset == "CIFAR-100":
train_dataset = CIFAR100(IMAGE_DATA_ROOT[dataset],
train=is_train,
transform=preprocessor)
elif dataset == "MNIST":
train_dataset = MNIST(IMAGE_DATA_ROOT[dataset],
train=is_train,
transform=preprocessor)
elif dataset == "FashionMNIST":
train_dataset = FashionMNIST(IMAGE_DATA_ROOT[dataset],
train=is_train,
transform=preprocessor)
elif dataset == "TinyImageNet":
train_dataset = TinyImageNet(IMAGE_DATA_ROOT[dataset],
preprocessor,
train=is_train)
elif dataset == "ImageNet":
preprocessor = DataLoaderMaker.get_preprocessor(
IMAGE_SIZE[dataset], is_train, center_crop=True)
sub_folder = "/train" if is_train else "/validation" # Note that ImageNet uses pretrainedmodels.utils.TransformImage to apply transformation
train_dataset = ImageFolder(IMAGE_DATA_ROOT[dataset] + sub_folder,
transform=preprocessor)
self.train_dataset = train_dataset
self.total_num_images = len(train_dataset)
self.all_tasks = dict()
all_images_indexes = np.arange(self.total_num_images).tolist()
for i in range(tot_num_tasks):
self.all_tasks[i] = {
"image": random.sample(all_images_indexes, inner_batch_size),
"arch": random.choice(list(self.model_dict.keys()))
}
if __name__ == "__main__":
args = get_parse_args()
if args.norm == "l2":
args.epsilon = 4.6
elif args.norm == "linf":
args.epsilon = 0.031372
if args.dataset == "ImageNet":
args.epsilon = 0.05
data_loader = get_img_label_data_loader(args.dataset, args.batch_size,
True)
test_data_loader = get_img_label_data_loader(args.dataset, args.batch_size,
False)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
target_model = StandardModel(args.dataset, args.model, no_grad=True)
target_model = target_model.cuda()
current_state = State(
(args.batch_size, IN_CHANNELS[args.dataset],
IMAGE_SIZE[args.dataset][0], IMAGE_SIZE[args.dataset][1]), args.norm,
args.epsilon)
fcn = MyFCN((IN_CHANNELS[args.dataset], IMAGE_SIZE[args.dataset][0],
IMAGE_SIZE[args.dataset][1]), args.n_actions)
fcn.apply(fcn.init_weights)
optimizer = Adam(fcn.parameters(), lr=args.learning_rate)
agent = PixelWiseA3C(fcn, optimizer, args.episode_len, args.gamma)
agent.model.cuda()
agent.shared_model.cuda()
i = 0
episode = 0
save_model_path = "{}/train_pytorch_model/sign_player/{}_untargeted_{}_attack_on_{}.pth.tar".format(
PY_ROOT, args.dataset, args.norm, args.model)
def main():
args = get_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
logger.info(args)
model_path = '{}/train_pytorch_model/adversarial_train/fast_adv_train/{}@{}@epoch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.arch, args.epochs)
out_dir = os.path.dirname(model_path)
os.makedirs(out_dir, exist_ok=True)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
start_start_time = time.time()
train_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, True)
test_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, False)
epsilon = (args.epsilon / 255.) / std
pgd_alpha = (args.pgd_alpha / 255.) / std
model = StandardModel(args.dataset, args.arch, no_grad=False)
model.apply(initialize_weights)
model.cuda()
model.train()
opt = torch.optim.SGD(model.parameters(), lr=args.lr_max, momentum=0.9, weight_decay=5e-4)
model, opt = amp.initialize(model, opt, opt_level="O2", loss_scale=1.0, master_weights=False)
criterion = nn.CrossEntropyLoss()
if args.attack == 'free':
delta = torch.zeros(args.batch_size, 3, 32, 32).cuda()
delta.requires_grad = True
elif args.attack == 'fgsm' and args.fgsm_init == 'previous':
delta = torch.zeros(args.batch_size, 3, 32, 32).cuda()
delta.requires_grad = True
if args.attack == 'free':
assert args.epochs % args.attack_iters == 0
epochs = int(math.ceil(args.epochs / args.attack_iters))
else:
epochs = args.epochs
if args.lr_schedule == 'cyclic':
lr_schedule = lambda t: np.interp([t], [0, args.epochs * 2 // 5, args.epochs], [0, args.lr_max, 0])[0]
elif args.lr_schedule == 'piecewise':
def lr_schedule(t):
if t / args.epochs < 0.5:
return args.lr_max
elif t / args.epochs < 0.75:
return args.lr_max / 10.
else:
return args.lr_max / 100.
prev_robust_acc = 0.
logger.info('Epoch \t Time \t LR \t \t Train Loss \t Train Acc')
for epoch in range(epochs):
start_time = time.time()
train_loss = 0
train_acc = 0
train_n = 0
for i, (X, y) in enumerate(train_loader):
X = X.cuda().float()
y = y.cuda().long()
if i == 0:
first_batch = X, y
lr = lr_schedule(epoch + (i + 1) / len(train_loader))
opt.param_groups[0].update(lr=lr)
if args.attack == 'pgd':
delta = attack_pgd(model, X, y, epsilon, pgd_alpha, args.attack_iters, args.restarts, opt)
elif args.attack == 'fgsm':
if args.fgsm_init == 'zero':
delta = torch.zeros_like(X, requires_grad=True)
delta.requires_grad = True
elif args.fgsm_init == 'random':
delta = torch.zeros_like(X).cuda()
delta[:, 0, :, :].uniform_(-epsilon[0][0][0].item(), epsilon[0][0][0].item())
delta[:, 1, :, :].uniform_(-epsilon[1][0][0].item(), epsilon[1][0][0].item())
delta[:, 2, :, :].uniform_(-epsilon[2][0][0].item(), epsilon[2][0][0].item())
delta.requires_grad = True
elif args.fgsm_init == 'previous':
delta.requires_grad = True
output = model(X + delta[:X.size(0)])
loss = F.cross_entropy(output, y)
with amp.scale_loss(loss, opt) as scaled_loss:
scaled_loss.backward()
grad = delta.grad.detach()
delta.data = clamp(delta + args.fgsm_alpha * epsilon * torch.sign(grad), -epsilon, epsilon)
delta = delta.detach()
elif args.attack == 'free':
delta.requires_grad = True
for j in range(args.attack_iters):
epoch_iters = epoch * args.attack_iters + (i * args.attack_iters + j + 1) / len(train_loader)
lr = lr_schedule(epoch_iters)
opt.param_groups[0].update(lr=lr)
output = model(clamp(X + delta[:X.size(0)], lower_limit, upper_limit))
loss = F.cross_entropy(output, y)
opt.zero_grad()
with amp.scale_loss(loss, opt) as scaled_loss:
scaled_loss.backward()
grad = delta.grad.detach()
delta.data = clamp(delta + epsilon * torch.sign(grad), -epsilon, epsilon)
nn.utils.clip_grad_norm_(model.parameters(), 0.5)
opt.step()
delta.grad.zero_()
elif args.attack == 'none':
delta = torch.zeros_like(X)
output = model(clamp(X + delta[:X.size(0)], lower_limit, upper_limit))
loss = criterion(output, y)
if args.attack != 'free':
opt.zero_grad()
with amp.scale_loss(loss, opt) as scaled_loss:
scaled_loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.5)
opt.step()
train_loss += loss.item() * y.size(0)
train_acc += (output.max(1)[1] == y).sum().item()
train_n += y.size(0)
if args.overfit_check:
# Check current PGD robustness of model using random minibatch
X, y = first_batch['input'], first_batch['target']
pgd_delta = attack_pgd(model, X, y, epsilon, pgd_alpha, args.attack_iters, args.restarts, opt)
with torch.no_grad():
output = model(clamp(X + pgd_delta[:X.size(0)], lower_limit, upper_limit))
robust_acc = (output.max(1)[1] == y).sum().item() / y.size(0)
if robust_acc - prev_robust_acc < -0.5:
break
prev_robust_acc = robust_acc
best_state_dict = copy.deepcopy(model.state_dict())
train_time = time.time()
logger.info('%d \t %.1f \t %.4f \t %.4f \t %.4f',
epoch, train_time - start_time, lr, train_loss/train_n, train_acc/train_n)
torch.save(best_state_dict, model_path)
logger.info('Total time: %.4f', train_time - start_start_time)
