def train_detector(gpu, arch, adv_data_arch, img_classifier_model_path,
dataset, args):
print("using GPU {}".format(gpu))
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
img_classifier_network = build_network(dataset, arch,
img_classifier_model_path)
layer_number = 3 if dataset in [
"ImageNet", "CIFAR-10", "CIFAR-100", "SVHN"
] else 2
if args.use_cv_transform:
image_transform = ImageTransformCV2(dataset, [1, 2])
detector_model_path = '{}/train_pytorch_model/ROTATE_DET/cv2_rotate_model/IMG_ROTATE_DET@{}_{}@model_{}@data_{}@epoch_{}@lr_{}@batch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.protocol, arch, adv_data_arch,
args.epochs, args.lr, args.batch_size)
os.makedirs(os.path.dirname(detector_model_path), exist_ok=True)
else:
image_transform = ImageTransformTorch(dataset, [5, 15])
detector_model_path = '{}/train_pytorch_model/ROTATE_DET/IMG_ROTATE_DET@{}_{}@model_{}@data_{}@epoch_{}@lr_{}@batch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.protocol, arch, adv_data_arch,
args.epochs, args.lr, args.batch_size)
os.makedirs(os.path.dirname(detector_model_path), exist_ok=True)
detector = Detector(dataset, img_classifier_network, CLASS_NUM[dataset],
image_transform, layer_number)
detector.cuda()
optimizer = torch.optim.SGD(detector.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
cudnn.benchmark = True
if args.protocol == SPLIT_DATA_PROTOCOL.TRAIN_ALL_TEST_ALL:
args.balance = True
else:
args.balance = False
if dataset == "ImageNet":
train_dataset = AdversaryRandomAccessNpyDataset(
IMAGE_DATA_ROOT[dataset] +
"/adversarial_images/{}".format(adv_data_arch), True,
args.protocol, config.META_ATTACKER_PART_I,
config.META_ATTACKER_PART_II, args.balance, dataset)
else:
train_dataset = AdversaryDataset(
IMAGE_DATA_ROOT[dataset] +
"/adversarial_images/{}".format(adv_data_arch),
True,
args.protocol,
config.META_ATTACKER_PART_I,
config.META_ATTACKER_PART_II,
balance=args.balance)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
train_epochs(detector_model_path, train_loader, detector, optimizer, arch,
args, args.gpu)
def evaluate_speed(args):
# 0-shot的时候请传递args.num_updates = 0
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpus[0][0])
# IMG_ROTATE_DET@CIFAR-10_TRAIN_II_TEST_I@conv3@epoch_20@lr_0.001@batch_100@no_fix_cnn_params.pth.tar
extract_pattern_detail = re.compile(
".*?IMG_ROTATE_DET@(.*?)_(.*?)@(.*?)@epoch_(\d+)@lr_(.*?)@batch_(\d+)@(.*?)\.pth.tar")
result = defaultdict(dict)
# IMG_ROTATE_DET@CIFAR-10_TRAIN_I_TEST_II@conv3@epoch_20@lr_0.001@batch_100@no_fix_cnn_params.pth.tar
for model_path in glob.glob("{}/train_pytorch_model/ROTATE_DET/cv2_rotate_model/IMG_ROTATE_DET*".format(PY_ROOT)):
ma = extract_pattern_detail.match(model_path)
dataset = ma.group(1)
if dataset!="CIFAR-10":
continue
split_protocol = SPLIT_DATA_PROTOCOL[ma.group(2)]
if split_protocol != args.protocol:
continue
arch = ma.group(3)
epoch = int(ma.group(4))
lr = float(ma.group(5))
batch_size = int(ma.group(6))
print("evaluate_accuracy model :{}".format(os.path.basename(model_path)))
tot_num_tasks = 20000
num_classes = 2
num_query = 15
old_num_update = args.num_updates
all_shots = [0, 1,5]
num_updates = args.num_updates
for shot in all_shots:
report_shot = shot
if shot == 0:
num_updates = 0
shot = 1
else:
num_updates = args.num_updates
meta_task_dataset = MetaTaskDataset(tot_num_tasks, num_classes, shot, num_query,
dataset, is_train=False, load_mode=LOAD_TASK_MODE.LOAD,
protocol=split_protocol, no_random_way=True, adv_arch="conv3") #FIXME adv arch还没做其他architecture的代码
data_loader = DataLoader(meta_task_dataset, batch_size=100, shuffle=False, pin_memory=True)
img_classifier_network = Conv3(IN_CHANNELS[dataset], IMAGE_SIZE[dataset],
CLASS_NUM[dataset])
image_transform = ImageTransformCV2(dataset, [1, 2])
layer_number = 3 if dataset in ["CIFAR-10", "CIFAR-100", "SVHN"] else 2
model = Detector(dataset, img_classifier_network, CLASS_NUM[dataset],image_transform, layer_number,num_classes=2)
checkpoint = torch.load(model_path, map_location=lambda storage, location: storage)
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
print("=> loaded checkpoint '{}' (epoch {})"
.format(model_path, checkpoint['epoch']))
evaluate_result = speed_test(model, data_loader, lr, num_updates, update_BN=False)
result[dataset][report_shot] = evaluate_result
break
with open("{}/train_pytorch_model/ROTATE_DET/cv2_rotate_model/speed_test.json".format(PY_ROOT), "w") as file_obj:
file_obj.write(json.dumps(result))
file_obj.flush()
def evaluate_cross_arch(args):
# 0-shot的时候请传递args.num_updates = 0
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpus[0][0])
# IMG_ROTATE_DET@CIFAR-10_TRAIN_ALL_TEST_ALL@model_conv3@data_conv3@epoch_10@lr_0.0001@batch_100@no_fix_cnn_params.pth.tar
extract_pattern_detail = re.compile(
".*?IMG_ROTATE_DET@(.*?)_(.*?)@model_(.*?)@data_(.*?)@epoch_(\d+)@lr_(.*?)@batch_(\d+)\.pth.tar")
result = defaultdict(dict)
update_BN = False
for model_path in glob.glob("{}/train_pytorch_model/ROTATE_DET/cv2_rotate_model/IMG_ROTATE_DET*".format(PY_ROOT)):
ma = extract_pattern_detail.match(model_path)
dataset = ma.group(1)
split_protocol = SPLIT_DATA_PROTOCOL[ma.group(2)]
if split_protocol != args.protocol:
continue
arch = ma.group(3)
adv_arch = ma.group(4)
if adv_arch != args.cross_arch_source:
continue
epoch = int(ma.group(5))
lr = float(ma.group(6))
batch_size = int(ma.group(7))
print("evaluate_accuracy model :{}".format(os.path.basename(model_path)))
tot_num_tasks = 20000
num_classes = 2
num_query = 15
old_num_update = args.num_updates
for shot in [0,1,5]:
if shot == 0:
shot = 1
num_update = 0
else:
num_update = old_num_update
meta_task_dataset = MetaTaskDataset(tot_num_tasks, num_classes, shot, num_query,
dataset, is_train=False, load_mode=args.load_mode,
protocol=split_protocol, no_random_way=True, adv_arch=args.cross_arch_target, fetch_attack_name=False)
data_loader = DataLoader(meta_task_dataset, batch_size=100, shuffle=False, pin_memory=True)
img_classifier_network = Conv3(IN_CHANNELS[dataset], IMAGE_SIZE[dataset],
CLASS_NUM[dataset])
image_transform = ImageTransformCV2(dataset, [1, 2])
layer_number = 3 if dataset in ["CIFAR-10", "CIFAR-100","SVHN"] else 2
model = Detector(dataset, img_classifier_network, CLASS_NUM[dataset],image_transform, layer_number, num_classes=2)
checkpoint = torch.load(model_path, map_location=lambda storage, location: storage)
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
print("=> loaded checkpoint '{}' (epoch {})"
.format(model_path, checkpoint['epoch']))
evaluate_result = finetune_eval_task_accuracy(model, data_loader, lr, num_update, update_BN=update_BN) # FIXME update_BN=False会很高
if num_update == 0:
shot = 0
result["{}@{}-->{}".format(dataset, args.cross_arch_source, args.cross_arch_target)][shot] = evaluate_result
with open("{}/train_pytorch_model/ROTATE_DET/cv2_rotate_model/cross_arch_{}--{}_using_{}_result_updateBN_{}.json".format(PY_ROOT, args.cross_arch_source,
args.cross_arch_target, args.protocol, update_BN), "w") as file_obj:
file_obj.write(json.dumps(result))
file_obj.flush()
def build_rotate_detector(dataset, arch, adv_arch, protocol):
img_classifier_network = Conv3(IN_CHANNELS[dataset], IMAGE_SIZE[dataset],
CLASS_NUM[dataset])
model_path = "{}/train_pytorch_model/white_box_model/IMG_ROTATE_DET@{}_{}@model_{}@data_{}@epoch_10@lr_0.0001@batch_100@no_fix_cnn_params.pth.tar".format(
PY_ROOT, dataset, protocol, arch, adv_arch)
checkpoint = torch.load(model_path,
map_location=lambda storage, location: storage)
print("load {} to detector".format(model_path))
image_transform = ImageTransformTorch(dataset, [5, 15])
layer_number = 3 if dataset in ["CIFAR-10", "CIFAR-100", "SVHN"] else 2
detector = Detector(dataset, img_classifier_network, CLASS_NUM[dataset],
image_transform, layer_number)
detector.load_state_dict(checkpoint['state_dict'], strict=True)
detector.cuda()
return detector
def evaluate_whitebox_attack(args):
# 0-shot的时候请传递args.num_updates = 0
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpus[0][0])
attacks = ["FGSM", "CW_L2"]
# IMG_ROTATE_DET@CIFAR-10_TRAIN_II_TEST_I@conv3@epoch_20@lr_0.001@batch_100@no_fix_cnn_params.pth.tar
extract_pattern_detail = re.compile(
".*?IMG_ROTATE_DET@(.*?)_(.*?)@(.*?)@epoch_(\d+)@lr_(.*?)@batch_(\d+)@(.*?)\.pth.tar")
result = defaultdict(dict)
# IMG_ROTATE_DET@CIFAR-10_TRAIN_I_TEST_II@conv3@epoch_20@lr_0.001@batch_100@no_fix_cnn_params.pth.tar
model_path = "{}/train_pytorch_model/white_box_model/IMG_ROTATE_DET@{}_{}@model_{}@data_{}@epoch_10@lr_0.0001@batch_100@no_fix_cnn_params.pth.tar".format(
PY_ROOT, args.dataset, args.protocol, "conv3", args.adv_arch)
assert os.path.exists(model_path), "{} not exists".format(model_path)
ma = extract_pattern_detail.match(model_path)
dataset = ma.group(1)
# if dataset != "MNIST":
# continue
split_protocol = SPLIT_DATA_PROTOCOL[ma.group(2)]
arch = ma.group(3)
epoch = int(ma.group(4))
lr = float(ma.group(5))
batch_size = int(ma.group(6))
print("evaluate_accuracy model :{}".format(os.path.basename(model_path)))
tot_num_tasks = 20000
num_classes = 2
num_query = 15
old_num_update = args.num_updates
all_shots = [1,5]
detector = "RotateDet"
checkpoint = torch.load(model_path, map_location=lambda storage, location: storage)
for attack_name in attacks:
for shot in all_shots:
root_folder = IMAGE_DATA_ROOT[dataset] + "/adversarial_images/white_box@data_{}@det_{}/{}/".format(args.adv_arch,
detector,
attack_name)
if shot == 0:
shot = 1
num_update = 0
else:
num_update = old_num_update
meta_task_dataset = WhiteBoxMetaTaskDataset(tot_num_tasks, num_classes, shot, num_query,
dataset, args.load_mode, detector, attack_name, root_folder)
data_loader = DataLoader(meta_task_dataset, batch_size=100, shuffle=False, pin_memory=True)
img_classifier_network = Conv3(IN_CHANNELS[dataset], IMAGE_SIZE[dataset],
CLASS_NUM[dataset])
image_transform = ImageTransformTorch(dataset, [5, 15])
layer_number = 3 if dataset in ["CIFAR-10", "CIFAR-100", "SVHN"] else 2
model = Detector(dataset, img_classifier_network, CLASS_NUM[dataset],image_transform, layer_number,num_classes=2)
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
print("=> loaded checkpoint '{}' (epoch {})"
.format(model_path, checkpoint['epoch']))
evaluate_result = finetune_eval_task_rotate(model, data_loader, lr, num_update, update_BN=args.eval_update_BN)
if num_update == 0:
shot = 0
result["{}_{}_{}_{}".format(dataset, attack_name, detector, args.adv_arch)][shot] = evaluate_result
if args.eval_update_BN:
update_BN_str="UpdateBN"
else:
update_BN_str = "NoUpdateBN"
with open("{}/train_pytorch_model/white_box_model/white_box_RotateDet_{}_{}_using_{}__result.json".format(PY_ROOT,args.dataset, update_BN_str, args.protocol), "w") as file_obj:
file_obj.write(json.dumps(result))
file_obj.flush()