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import argparse
import copy
from pathlib import Path
from omegaconf import OmegaConf
from datetime import datetime
from utils import *
from trainer import *
import method
import log_utils
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
if __name__ == '__main__':
parser = argparse.ArgumentParser("Machine Unlearning")
############################################# 方法/数据集/模型 #############################################
# TODO: 添加其他方法/数据集/模型
parser.add_argument('--method', type=str, default="boundary_shrink",
choices=['random_label', "finetune", "gradient_ascent",
'boundary_shrink', 'boundary_expand',
"salun", "l2ul_adv", "l2ul_imp", "bad_teacher",
"fisher", "wood_fisher",
"delete", # my method
"pass", "ablation"
], help='unlearning method')
parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['cifar10', "cifar100", "tiny_imagenet", "vggface"], help='dataset name')
# parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['cifar10', "cifar100", "tiny_imagenet"], help='dataset name')
parser.add_argument('--model_name', type=str, default='resnet18', choices=['resnet18', "vgg16", "vit-s-16", "swin-t"], help='model name')
parser.add_argument('--exps_dir', type=str, default="~/boundary_unlearn/classification/exps", help='experiments directory')
##########################################################################################################
############################################# train from scratch设置 #####################################
parser.add_argument('--train_from_scratch', action='store_true', help='Train model from scratch')
parser.add_argument('--retrain_from_scratch', action='store_true', help='Retrain model from scratch')
parser.add_argument('--debug', action='store_true') # debug 模式跳过一些命令执行
parser.add_argument('--optim_name', type=str, default='sgd', choices=['sgd', 'adam'], help='optimizer name')
##########################################################################################################
############################################# lr、unlr 设置,取决于model和dataset ########################
parser.add_argument('--batch_size', type=int, default=None, help='batch size') # INFO: 是train from scratch 和 unlearn 的batch size
parser.add_argument('--pretrain_epoch', type=int, default=None, help='train from scratch epoch')
parser.add_argument('--pretrain_lr', type=float, default=None , help='learning rate')
parser.add_argument('--unlearn_epoch', type=int, default=None, help='unlearning epoch')
parser.add_argument('--unlearn_rate', type=float, default=None) # 是真正的遗忘学习率
parser.add_argument('--finetune_epoch', type=int, default=None)
parser.add_argument('--finetune_lr', type=float, default=None)
##########################################################################################################
############################################# 遗忘任务设置 #################################################
# TODO: 添加-1表示all forget class的功能,或者添加其他的遗忘函数,不要写死在一个里面。可以指定比例
parser.add_argument('--forget_class', type=int, default=1, help='forget class') # INFO:调整为1
# FIXME: 对cifar10和cifar100不能使用4,后面可以给cifar10添加一个permute_map
# parser.add_argument('--forget_class', type=int, default=1, help='forget class')
# NOTE:如果只用于cifar10表示类别索引,用于cifar100和tiny_imagenet表示遗忘的类别数目
##########################################################################################################
parser.add_argument("--freeze_linear", action="store_true") # 实验方法,是否冻结线性层,默认不冻结
############################################# 默认参数,不需要任何调整 #######################################
parser.add_argument('--extra_exp', type=str, help='optional extra experiment for boundary shrink',
choices=['curv', 'weight_assign', None])
parser.add_argument("--fixed_noise_label", type=str2bool, default=True) # 用于random label遗忘算法,是否固定随机标签
parser.add_argument("--approx_different", type=str2bool, default=True) # 用于random,默认近似不同False
parser.add_argument("--retain_data", type=str2bool, default=False)
parser.add_argument("--salun_mask", type=str2bool, default=True) # 只是用了调试去除mask,salun和random label是不是相同的
##########################################################################################################
parser.add_argument("--alpha", type=float, default=0.2) # 用于fisher/ wood fwood fisher算法,遗忘的的系数
parser.add_argument("--threshold_ratio", type=float, default=0.5)
parser.add_argument("--adv_lambda", type=float, default=0.1) # 默认0.1
parser.add_argument("--reg_lambda", type=float, default=0)
parser.add_argument("--adv_eps", type=float, default=0.4)
##########################################################################################################
############################################# 蒸馏方法设置 #################################################
parser.add_argument('--soft_label', type=str, default="inf")
##########################################################################################################
parser.add_argument("--ablation_a", default=None, type=float)
parser.add_argument("--ablation_t", default=None, type=float)
parser.add_argument("--wo_dataaug", action="store_true") # 默认为false,使用dataaug
parser.add_argument("--description", type=str, default="", help="Description for this run")
parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument("--seed", type=int, default=2022)
parser.add_argument('--load_original_model_path', type=str, default=None)
parser.add_argument('--load_retrain_model_path', type=str, default=None)
# NOTE: 临时参数
args = parser.parse_args()
config = OmegaConf.load(f'config/{args.dataset_name}_{args.model_name}.yaml') # 除了args用法,可以通过字典用法访问
keys = ["pretrain_epoch", "pretrain_lr", "batch_size", "unlearn_epoch", "unlearn_rate"]
if args.dataset_name == "vggface":
keys += ["finetune_epoch", "finetune_lr"]
for key in keys:
if getattr(args, key) is None:
setattr(args, key, config[key])
if any([getattr(args, key) is None for key in keys]):
raise ValueError(f"some key are not set")
print(args)
model_name = args.model_name
# 在cifar10和其他数据集上,forget_class有相同的含义。 不同版本的代码已经修正
forget_class = args.forget_class
num_workers = args.num_workers
description = f"{args.dataset_name}_{model_name}_forget{forget_class}" # 只用于unlearn方法,pretrain不需要
if args.freeze_linear:
description = "freeze_linear_" + description
# TODO: 建议把method拿出作为一个文件夹,后面也不需要过多修改
method_description = f"{args.method}"
vice_description = f"{args.description}" if args.description else "" # 是args.description,不是description
now = datetime.now()
formatted_time = now.strftime("%m%d-%H:%M:%S")
vice_description += f"_{formatted_time}"
seed_torch(args.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
assert device.type == 'cuda', 'only support cuda'
# device 是一个torch.device对象,不能使用device == "cuda"进行判断
path = Path(args.exps_dir).expanduser() # 保存日志、结果、模型、配置的根目录
create_dir(path)
transform_train, transform_test = get_transforms(args.dataset_name, args.model_name, wo_dataaug=args.wo_dataaug) # INFO:可以选择是否需要数据增广
if args.dataset_name == "vggface":
config_path = 'config/vggface_sample.yaml'
dir_path = "/mnt/Datasets/vggface2"
try:
sample_config = OmegaConf.load(config_path)
except FileNotFoundError:
samples = create_dataset(dir_path)
conf = OmegaConf.create(samples)
OmegaConf.save(conf, config_path)
sample_config = OmegaConf.load(config_path)
pretrain_train_dataset = vggface_dataset(sample_config, transform_train, mode='pretrain', train=True)
pretrain_test_dataset = vggface_dataset(sample_config, transform_test, mode='pretrain', train=False)
pretrain_train_loader, pretrain_test_loader = get_dataloader(pretrain_train_dataset, pretrain_test_dataset, args.batch_size, num_workers)
trainset, testset = get_dataset(args.dataset_name, transform_train, transform_test)
train_loader, test_loader = get_dataloader(trainset, testset, args.batch_size, num_workers)
num_classes = max(train_loader.dataset.targets) + 1
assert forget_class < num_classes, 'forget class must less than num_classes' # 无论是类别索引或类别数量,都应该小于总类别数
# if args.dataset_name == "cifar10" or args.dataset_name == "vggface":
# forget_class_index = [forget_class]
# else:
# # forget_class_index = random.sample(range(0, num_classes), forget_class)
# permutation_map = getattr(config, "permutation_map")
# forget_class_index = permutation_map[:forget_class]
permutation_map = getattr(config, "permutation_map")
forget_class_index = permutation_map[:forget_class]
note_print(f"forget class index: {forget_class_index}")
num_forget = float("inf") # 全部用于遗忘
train_forget_loader, train_remain_loader, test_forget_loader, test_remain_loader, repair_class_loader, \
train_forget_index, train_remain_index, test_forget_index, test_remain_index \
= get_unlearn_loader(trainset, testset, forget_class_index, args.batch_size, num_forget, num_workers)
if args.train_from_scratch or args.retrain_from_scratch:
ckpt_path = path/ "test_pretrained_model" # 防止不小心跑的覆盖了原来的模型
create_dir(ckpt_path)
else:
ckpt_path = path / "pretrained_model"
ori_model, retrain_model = None, None
if args.train_from_scratch: # original 和 retrain 不使用description, 其他的unlearn使用
print('=' * 100)
print(' ' * 25 + 'train original model from scratch')
print('=' * 100)
if args.dataset_name == "vggface":
ori_model = train_save_model(pretrain_train_loader, pretrain_test_loader, model_name, args.optim_name, args.pretrain_lr,
args.pretrain_epoch, ckpt_path, f"{args.dataset_name}_{model_name}_pretrain_model_{args.description}_{formatted_time}")
ori_model.fc = torch.nn.Linear(ori_model.fc.in_features, 10)
ori_model = ori_model.to("cuda")
ori_model = finetune_save_model(train_loader, test_loader, ori_model, args.optim_name, args.finetune_lr,
args.finetune_epoch, ckpt_path, f"{args.dataset_name}_{model_name}_original_model_{args.description}_{formatted_time}")
else:
ori_model = train_save_model(train_loader, test_loader, model_name, args.optim_name, args.pretrain_lr,
args.pretrain_epoch, ckpt_path, f"{args.dataset_name}_{model_name}_original_model_{args.description}_{formatted_time}")
print('\noriginal model acc:\n', test_each_classes(ori_model, test_loader, num_classes))
if args.retrain_from_scratch:
print('=' * 100)
print(' ' * 25 + 'retrain model from scratch')
print('=' * 100)
if args.dataset_name == "vggface":
retrain_model = train_save_model(pretrain_train_loader, pretrain_test_loader, model_name, args.optim_name, args.pretrain_lr,
args.pretrain_epoch, ckpt_path, f"{args.dataset_name}_{model_name}_pretrain_model_{args.description}_{formatted_time}")
retrain_model.fc = torch.nn.Linear(retrain_model.fc.in_features, 10)
retrain_model = retrain_model.to("cuda")
retrain_model = finetune_save_model(train_remain_loader, test_remain_loader, retrain_model, args.optim_name, args.finetune_lr,
args.finetune_epoch, ckpt_path, f"{args.dataset_name}_{model_name}_retrain_forget{forget_class}_model_{args.description}_{formatted_time}")
else:
retrain_model = train_save_model(train_remain_loader, test_remain_loader, model_name, args.optim_name, args.pretrain_lr,
args.pretrain_epoch, ckpt_path, f"{args.dataset_name}_{model_name}_retrain_forget{forget_class}_model_{args.description}_{formatted_time}")
print('\nretrain model acc:\n', test_each_classes(retrain_model, test_loader, num_classes))
if args.train_from_scratch or args.retrain_from_scratch:
note_print('train/retrain from scratch done,结束运行')
exit(1)
print('=' * 100)
print(' ' * 25 + 'load original model and retrain model')
print('=' * 100)
# 加载测试original model
if args.load_original_model_path:
original_model_path = Path(args.load_original_model_path)
else:
original_model_path = ckpt_path / f'{args.dataset_name}_{model_name}_original_model.pth'
note_print(f"load original model from {original_model_path}")
ori_model = load_model(original_model_path, model_name, num_classes)
if not args.debug:
# _, acc = test(ori_model, train_loader)
# note_print(f"original model的性能是")
# print(f"train acc:{acc:.2%}")
# _, acc = test(ori_model, test_loader)
# print(f"test acc:{acc:.2%}")
_, acc = test(ori_model, train_forget_loader)
print(f"forget train acc:{acc:.2%}")
# FIXME:
# print(f"remain train has been blocked")
_, acc = test(ori_model, train_remain_loader)
print(f"remain train acc:{acc:.2%}")
_, acc = test(ori_model, test_forget_loader)
print(f"forget test acc:{acc:.2%}")
_, acc = test(ori_model, test_remain_loader)
print(f"remain test acc:{acc:.2%}")
# print('\noriginal model acc:\n', test_each_classes(ori_model, test_loader, num_classes))
# 加载测试retrain model
if args.load_retrain_model_path:
retrain_model_path = Path(args.load_retrain_model_path)
else:
retrain_model_path = ckpt_path / f'{args.dataset_name}_{model_name}_retrain_forget{forget_class}_model.pth'
note_print(f"load retrain model from {retrain_model_path}")
# NOTE: 代码中可以不使用retrain_model
retrain_model = load_model(retrain_model_path, model_name, num_classes)
if not args.debug:
_, acc = test(retrain_model, train_forget_loader)
note_print(f"\nretrain model的性能是")
print(f"forget train acc:{acc:.2%}")
# FIXME:
# print(f"remain train has been blocked")
_, acc = test(retrain_model, train_remain_loader)
print(f"remain train acc:{acc:.2%}")
_, acc = test(retrain_model, test_forget_loader)
print(f"forget test acc:{acc:.2%}")
_, acc = test(retrain_model, test_remain_loader)
print(f"remain test acc:{acc:.2%}")
# print('\nretrain model acc:\n', test_each_classes(retrain_model, test_loader, num_classes))
create_dir(path / description)
create_dir(path / description / method_description)
create_dir(path / description / method_description / vice_description)
args_dict = vars(args)
config = OmegaConf.create(args_dict)
OmegaConf.save(config, path / description / method_description / vice_description / "config.yaml")
logger, console_handler = log_utils.setup_logger(path / description / method_description / vice_description, logger_name="train_log")
log_utils.enable_console_logging(logger, console_handler, True)
unlearn_model = None
loader_dict = {"train_forget": train_forget_loader, "train_remain": train_remain_loader,
"test_forget": test_forget_loader, "test_remain": test_remain_loader,
"test": test_loader,
}
print('*' * 100)
if args.method:
note_print(' ' * 25 + f'begin {args.method.replace("_", " ")} unlearning')
print('*' * 100)
if args.freeze_linear:
for name, param in ori_model.named_parameters():
if "fc" in name:
print(f"freeze {name}")
param.requires_grad_(False)
disable_bn = False
if forget_class == 1 and args.dataset_name == "tiny_imagenet": # 在iamgenet上进行单类遗忘
disable_bn = True
note_print("disable bn for tiny imagenet for single class forget")
experiment_path = path / description / method_description / vice_description
if args.method == "random_label":
unlearn_model = method.random_label(ori_model, train_forget_loader, num_classes,
args.unlearn_epoch, args.unlearn_rate,
fixed_noise_label = args.fixed_noise_label,
logger = logger, console_handler = console_handler,
loader_dict=loader_dict, experiment_path = experiment_path,
approx_different = args.approx_different, disable_bn = disable_bn)
elif args.method == "finetune":
# FIXME:暂时不在tiny imagenet中使用finetune方法
unlearn_model = method.finetune(ori_model, train_remain_loader,
unlearn_epoch= args.unlearn_epoch, unlearn_rate= args.unlearn_rate,
logger = logger, console_handler = console_handler,
loader_dict=loader_dict, experiment_path = experiment_path) # 存在一些salun实现的finetune专属参数,但是没有使用
elif args.method == "gradient_ascent":
unlearn_model = method.gradient_ascent(ori_model, train_forget_loader,
unlearn_epoch=args.unlearn_epoch, unlearn_rate=args.unlearn_rate,
logger=logger, console_handler=console_handler,
loader_dict=loader_dict, experiment_path= experiment_path, disable_bn = disable_bn)
elif args.method == 'boundary_shrink':
unlearn_model = method.boundary_shrink( ori_model, train_forget_loader, args.unlearn_epoch, args.unlearn_rate,
logger = logger, console_handler = console_handler,
loader_dict = loader_dict, experiment_path = experiment_path, disable_bn = disable_bn,
extra_exp=args.extra_exp,
)
elif args.method == 'boundary_expand': # NOTE: 使用resnet18之外的模型可能存在问题,必须具有fc
unlearn_model = method.boundary_expand( ori_model, train_forget_loader, args.unlearn_epoch, args.unlearn_rate, num_classes,
logger = logger, console_handler = console_handler,
loader_dict = loader_dict, experiment_path = experiment_path, disable_bn = disable_bn,
freeze_linear = args.freeze_linear # 赋值
)
elif args.method == "salun":
unlearn_model = method.salun(ori_model, train_forget_loader, num_classes,
unlearn_epoch=args.unlearn_epoch, unlearn_rate=args.unlearn_rate,
fixed_noise_label=args.fixed_noise_label,
logger=logger, console_handler=console_handler,
loader_dict=loader_dict, experiment_path= experiment_path,
threshold_ratio=args.threshold_ratio,
approx_different=args.approx_different,
retain_data=args.retain_data, disable_bn = disable_bn,
mask=args.salun_mask)
elif args.method == "bad_teacher":
good_teacher_model = copy.deepcopy(ori_model).to("cuda")
bad_teacher_model = get_model(model_name, num_classes).to("cuda")
filtered_remain_index = random.sample(train_remain_index, int(0.3*len(train_remain_index))) if args.retain_data else []
class UnLearningData(Dataset):
def __init__(self, dataset, forget_index, remain_index):
super().__init__()
self.dataset = dataset
self.index = forget_index + remain_index
self.len = len(forget_index) + len(remain_index)
self.forget_index_len = len(forget_index)
def __len__(self):
return self.len
def __getitem__(self, index):
mapped_index = self.index[index]
x = self.dataset[mapped_index][0]
y = 1 if index < self.forget_index_len else 0
return x, y
unlearn_dataset = UnLearningData(trainset, train_forget_index, filtered_remain_index)
unlearn_loader = torch.utils.data.DataLoader(unlearn_dataset, batch_size=args.batch_size, shuffle=True, num_workers=num_workers)
unlearn_model = method.bad_teacher(ori_model, bad_teacher_model, good_teacher_model, unlearn_loader,
args.unlearn_epoch, args.unlearn_rate,
logger = logger, console_handler = console_handler,
loader_dict=loader_dict, experiment_path = experiment_path, disable_bn = disable_bn)
elif args.method == "l2ul_adv":
unlearn_model = method.l2ul_adv(ori_model, train_forget_loader, num_classes,
args.unlearn_epoch, args.unlearn_rate,
logger = logger, console_handler = console_handler,
loader_dict=loader_dict, experiment_path = experiment_path, disable_bn = disable_bn,
adv_eps = args.adv_eps,
adv_lambda=args.adv_lambda)
elif args.method == "l2ul_imp":
unlearn_model = method.l2ul_adv(ori_model, train_forget_loader, num_classes,
args.unlearn_epoch, args.unlearn_rate,
logger = logger, console_handler = console_handler,
loader_dict=loader_dict, experiment_path = experiment_path, disable_bn = disable_bn,
adv_eps = args.adv_eps, adv_lambda=args.adv_lambda,
reg_lambda=args.reg_lambda) # 使用相同的函数,只是添加了一个参数
elif args.method == "fisher":
unlearn_model = method.fisher(ori_model, train_forget_loader, train_remain_loader,
alpha=args.alpha, num_classes=num_classes,
logger=logger, console_handler=console_handler,
loader_dict=loader_dict, experiment_path = experiment_path,
freeze_linear = args.freeze_linear)
elif args.method == "wood_fisher":
train_remain_sampler = SubsetRandomSampler(train_remain_index) # 45000
train_remain_loader_sole = torch.utils.data.DataLoader(dataset=trainset, batch_size=1, # bs设置为1用于专门的wfisher计算
sampler=train_remain_sampler,
num_workers=num_workers)
unlearn_model = method.wood_fisher(ori_model, train_forget_loader, train_remain_loader, train_remain_loader_sole,
alpha=args.alpha,
retain_data=args.retain_data,
logger=logger, console_handler=console_handler,
loader_dict=loader_dict, experiment_path= experiment_path)
elif args.method == 'delete':
unlearn_model = method.delete(ori_model, train_forget_loader,
args.unlearn_epoch, args.unlearn_rate,
logger=logger, console_handler=console_handler,
loader_dict=loader_dict, experiment_path= experiment_path, disable_bn = disable_bn,
############## 我的额外自定义参数开始
soft_label=args.soft_label
)
elif args.method == 'ablation':
unlearn_model = method.my_method_ablation(ori_model, train_forget_loader,
args.unlearn_epoch, args.unlearn_rate,
logger=logger, console_handler=console_handler,
loader_dict=loader_dict, experiment_path= experiment_path,
############## 我的额外自定义参数开始
soft_label=args.soft_label,
alpha = args.ablation_a,
temperature = args.ablation_t
)
elif args.method == 'pass':
pass
else:
raise ValueError('method not found') # 未找到方法
if unlearn_model:
# torch.save(unlearn_model.state_dict(), path / description / vice_description / f"ckpt.pth")
torch.save(unlearn_model, path / description / method_description / vice_description / f"ckpt.pth")
# 快速开发直接保存模型,不保存参数。但是在不同的开发环境下运行可能导致错误。
# 因为lora的原因,暂时直接保存模型,不保存参数
# NOTE:默认在unlearn method已经打印过了
# note_print(f"\nunlearn model的性能是")
# now = time.time()
# _, test_acc = test(unlearn_model, test_loader)
# _, forget_acc = test(unlearn_model, test_forget_loader)
# _, remain_acc = test(unlearn_model, test_remain_loader)
# _, train_forget_acc = test(unlearn_model, train_forget_loader)
# _, train_remain_acc = test(unlearn_model, train_remain_loader)
# logger.info('test acc:{:.2%}, train forget acc:{:.2%}, train remain acc:{:.2%}, test forget acc:{:.2%}, test remain acc:{:.2%}\n taken time {}'
# .format(test_acc, train_forget_acc, train_remain_acc, forget_acc, remain_acc, time.time()-now)) # 好像是不需要,注意一下
# print('\nretrain model acc:\n', test_each_classes(unlearn_model, test_loader, num_classes))
import evaluation
test_remain_len = len(test_remain_index)
# 从train_remain_index中随机选取test_len个
import random
random.shuffle(train_remain_index)
train_remain_index = train_remain_index[:test_remain_len]
logger.info(f"train remain size: {len(train_remain_index)}")
train_remain_sampler = SubsetRandomSampler(train_remain_index) # 重新采一个train remain,大小和test remain相同
train_remain_loader = DataLoader(train_remain_loader.dataset, batch_size=args.batch_size, sampler=train_remain_sampler)
if args.train_from_scratch or args.method == "pass":
mia_result = evaluation.SVC_MIA(
shadow_train=train_remain_loader,
shadow_test=test_remain_loader,
target_train=train_forget_loader,
target_test=None,
model=ori_model,
)
print(f"original model\n {mia_result}")
if args.retrain_from_scratch or args.method == "pass":
mia_result = evaluation.SVC_MIA(
shadow_train=train_remain_loader,
shadow_test=test_remain_loader,
target_train=train_forget_loader,
target_test=None,
model=retrain_model,
)
print(f"retrain model\n {mia_result}")
if unlearn_model:
logger.info("start mia evaluation")
mia_result = evaluation.SVC_MIA(
shadow_train=train_remain_loader,
shadow_test=test_remain_loader,
target_train=train_forget_loader,
target_test=None,
model=unlearn_model,
)
logger.info(f"unlearn model\n {mia_result}")
logger.info("运行结束")
exit()