dbd.py

'''
Backdoor Defense Via Decoupling The Training Process
This file is modified based on the following source:
link : https://github.com/SCLBD/DBD

@inproceedings{huang2021backdoor,
  title={Backdoor Defense via Decoupling the Training Process},
  author={Huang, Kunzhe and Li, Yiming and Wu, Baoyuan and Qin, Zhan and Ren, Kui},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

The defense method is called dbd.
The license is bellow the code

The update include:
    1. data preprocess and dataset setting
    2. model setting
    3. args and config
    4. save process
    5. new standard: robust accuracy
    6. add some new backdone such as mobilenet efficientnet and densenet, reconstruct the backbone of vgg and preactresnet
    7. Different data augmentation (transform) methods are used
    8. rewrite the dateset
basic sturcture for defense method:
    1. basic setting: args
    2. attack result(model, train data, test data)
    3. dbd defense:
        a. self-supervised learning generates feature extractor
        b. learning model using extracted features
        c. the samples with poor confidence were excluded, and semi-supervised learning was used to continue the learning model
    4. test the result and get ASR, ACC, RA 
'''


import logging
import time
import argparse
import shutil
import sys
import os



sys.path.append('../')
sys.path.append(os.getcwd())
from utils.defense_utils.dbd.data.prefetch import PrefetchLoader
from defense.base import defense
from utils.log_assist import get_git_info
import numpy as np
import torch
import yaml
from utils.trainer_cls import Metric_Aggregator, general_plot_for_epoch
from pprint import pformat
from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform, get_transform_prefetch

from utils.defense_utils.dbd.data.utils import (
    get_loader,
    get_semi_idx,
)
from utils.defense_utils.dbd.data.dataset import PoisonLabelDataset, SelfPoisonDataset, MixMatchDataset
from utils.aggregate_block.fix_random import fix_random
from utils.save_load_attack import load_attack_result, save_defense_result
# from utils_db.box import get_information
from utils.defense_utils.dbd.model.model import SelfModel, LinearModel
from utils.defense_utils.dbd.model.utils import (
    get_network_dbd,
    load_state,
    get_criterion,
    get_optimizer,
    get_scheduler,
)
from utils.bd_dataset_v2 import xy_iter, slice_iter
from utils.defense_utils.dbd.utils_db.setup import (
    load_config,
)
from utils.defense_utils.dbd.utils_db.trainer.log import result2csv
from utils.defense_utils.dbd.utils_db.trainer.simclr import simclr_train
from utils.defense_utils.dbd.utils_db.trainer.semi import mixmatch_train
from utils.defense_utils.dbd.utils_db.trainer.simclr import linear_test, poison_linear_record, poison_linear_train
from utils.aggregate_block.dataset_and_transform_generate import get_transform_self

def get_information(args,result,config_ori):
    config = config_ori
    aug_transform = get_transform_self(args.dataset, *([args.input_height,args.input_width]) , train = True, prefetch =args.prefetch)
    
    x = slice_iter(result["bd_train"], axis=0)
    y = slice_iter(result["bd_train"], axis=1)

    self_poison_train_data = SelfPoisonDataset(x,y, aug_transform,args)
    
    self_poison_train_loader_ori = torch.utils.data.DataLoader(self_poison_train_data, batch_size=args.batch_size_self, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True)
    if args.prefetch:
        # x,y: PIL.Image.Image -> SelfPoisonDataset: Tensor with trans, no normalization [0,255]-> PrefetchLoader: Tensor with trans [0,1], with normalization
        self_poison_train_loader = PrefetchLoader(self_poison_train_loader_ori, self_poison_train_data.mean, self_poison_train_data.std)
    else:
        # x,y: PIL.Image.Image, [0,255] -> SelfPoisonDataset: Tensor with trans [0,1] with normalization
        self_poison_train_loader = self_poison_train_loader_ori
        
    backbone = get_network_dbd(args)
    self_model = SelfModel(backbone)
    self_model = self_model.to(args.device)
    criterion = get_criterion(config["criterion"])
    criterion = criterion.to(args.device)
    optimizer = get_optimizer(self_model, config["optimizer"])
    scheduler = get_scheduler(optimizer, config["lr_scheduler"])
    resumed_epoch = load_state(
        self_model, args.resume, args.checkpoint_load, 0, optimizer, scheduler,
    )
    box = {
      'self_poison_train_loader': self_poison_train_loader,
      'self_model': self_model,
      'criterion': criterion,
      'optimizer': optimizer,
      'scheduler': scheduler,
      'resumed_epoch': resumed_epoch
    }
    return box

class dbd(defense):
    r"""Backdoor Defense Via Decoupling The Training Process
    
    basic structure: 
    
    1. config args, save_path, fix random seed
    2. load the backdoor attack data and backdoor test data
    3. dbd defense:
        a. self-supervised learning generates feature extractor
        b. learning model using extracted features
        c. the samples with poor confidence were excluded, and semi-supervised learning was used to continue the learning model
    4. test the result and get ASR, ACC, RC with regard to the chosen threshold and interval
       
    .. code-block:: python
    
        parser = argparse.ArgumentParser(description=sys.argv[0])
        dbd.add_arguments(parser)
        args = parser.parse_args()
        dbd_method = dbd(args)
        if "result_file" not in args.__dict__:
            args.result_file = 'one_epochs_debug_badnet_attack'
        elif args.result_file is None:
            args.result_file = 'one_epochs_debug_badnet_attack'
        result = dbd_method.defense(args.result_file)
    
    .. Note::
        @article{huang2022backdoor,
        title={Backdoor defense via decoupling the training process},
        author={Huang, Kunzhe and Li, Yiming and Wu, Baoyuan and Qin, Zhan and Ren, Kui},
        journal={arXiv preprint arXiv:2202.03423},
        year={2022}
        }

    Args:
        baisc args: in the base class
        epoch_self (int): the epoch of warmup during the self-supervised learning
        batch_size_self (int): the batch size of self-supervised learning
        temperature (float): the temperature in the loss function of self-supervised learning
        epsilon (float): the threshold of the presuppossed ratio of the backdoor data to separate the poisoned data in the semi-supervised learning (please be careful to choose the threshold)
        epoch_warmup (int): the epoch of warmup during the semi-supervised learning
        config_pretrain (str): the path of the config file of the self-supervised learning
        config_semi (str): the path of the config file of the semi-supervised learning
        
    """ 
    
    def __init__(self,args):
        with open(args.yaml_path, 'r') as f:
            defaults = yaml.safe_load(f)

        defaults.update({k:v for k,v in args.__dict__.items() if v is not None})

        args.__dict__ = defaults

        args.terminal_info = sys.argv

        args.num_classes = get_num_classes(args.dataset)
        args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset)
        args.img_size = (args.input_height, args.input_width, args.input_channel)
        args.dataset_path = f"{args.dataset_path}/{args.dataset}"

        self.args = args

        if 'result_file' in args.__dict__ :
            if args.result_file is not None:
                self.set_result(args.result_file)

    def add_arguments(parser):
        parser.add_argument('--device', type=str, help='cuda, cpu')
        parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory")
        parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?")
        parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch')
        parser.add_argument('--amp', type=lambda x: str(x) in ['True','true','1'])

        parser.add_argument('--checkpoint_load', type=str, help='the location of load model')
        parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved')
        parser.add_argument('--log', type=str, help='the location of log')
        parser.add_argument("--dataset_path", type=str, help='the location of data')
        parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny') 
        parser.add_argument('--result_file', type=str, help='the location of result')
    
        parser.add_argument('--epochs', type=int)
        parser.add_argument('--batch_size', type=int)
        parser.add_argument("--num_workers", type=float)
        parser.add_argument('--lr', type=float)
        parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr')
        parser.add_argument('--steplr_stepsize', type=int)
        parser.add_argument('--steplr_gamma', type=float)
        parser.add_argument('--steplr_milestones', type=list)
        parser.add_argument('--model', type=str, help='resnet18')
        
        parser.add_argument('--client_optimizer', type=int)
        parser.add_argument('--sgd_momentum', type=float)
        parser.add_argument('--wd', type=float, help='weight decay of sgd')
        parser.add_argument('--frequency_save', type=int,
                        help=' frequency_save, 0 is never')

        parser.add_argument('--random_seed', type=int, help='random seed')
        parser.add_argument('--yaml_path', type=str, default="./config/defense/dbd/config.yaml", help='the path of yaml')

        #set the parameter for the dbd defense
        parser.add_argument('--epoch_warmup',type=int )
        parser.add_argument('--batch_size_self',type=int )
        parser.add_argument('--temperature',type=int )
        parser.add_argument('--epsilon',type=int )
        parser.add_argument('--epoch_self',type=int )

        parser.add_argument('--config_pretrain',type=str )
        parser.add_argument('--config_semi',type=str )

        parser.add_argument('--num_workers_semi',type=int )
    
    def set_result(self, result_file):
        attack_file = 'record/' + result_file
        save_path = 'record/' + result_file + f'/defense/{self.__class__.__name__}/'
        if not (os.path.exists(save_path)):
            os.makedirs(save_path)
        # assert(os.path.exists(save_path))    
        self.args.save_path = save_path
        if self.args.checkpoint_save is None:
            self.args.checkpoint_save = save_path + 'checkpoint/'
            if not (os.path.exists(self.args.checkpoint_save)):
                os.makedirs(self.args.checkpoint_save) 
        if self.args.log is None:
            self.args.log = save_path + 'log/'
            if not (os.path.exists(self.args.log)):
                os.makedirs(self.args.log)  
        self.result = load_attack_result(attack_file + '/attack_result.pt')

    def set_logger(self):
        args = self.args
        logFormatter = logging.Formatter(
            fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s',
            datefmt='%Y-%m-%d:%H:%M:%S',
        )
        logger = logging.getLogger()

        fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log')
        fileHandler.setFormatter(logFormatter)
        logger.addHandler(fileHandler)

        consoleHandler = logging.StreamHandler()
        consoleHandler.setFormatter(logFormatter)
        logger.addHandler(consoleHandler)

        logger.setLevel(logging.INFO)
        logging.info(pformat(args.__dict__))

        try:
            logging.info(pformat(get_git_info()))
        except:
            logging.info('Getting git info fails.')

    def set_devices(self):
        # self.device = torch.device(
        #     (
        #         f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device
        #         # since DataParallel only allow .to("cuda")
        #     ) if torch.cuda.is_available() else "cpu"
        # )
        self.device = self.args.device

    def mitigation(self):
        args = self.args
        result = self.result
        self.set_devices()
        fix_random(self.args.random_seed)
        logger = logging.getLogger()
        logging.info("===Setup running===")

        agg = Metric_Aggregator()
        # remove the transforms except ToTensor
        # bd_train_trans = result["bd_train"].wrap_img_transform
        # bd_test_trans = result["bd_test"].wrap_img_transform
        # clean_test_trans = result["clean_test"].wrap_img_transform

        # result["bd_train"].wrap_img_transform = torchvision.transforms.ToTensor()
        # result["bd_test"].wrap_img_transform = torchvision.transforms.ToTensor()
        # result["clean_test"].wrap_img_transform = torchvision.transforms.ToTensor()

        # Turn off all transforms, so that the dataset return PIL.Image.Image object
        self.result["bd_train"].wrap_img_transform = None
        self.result["bd_test"].wrap_img_transform = None
        self.result["clean_test"].wrap_img_transform = None


        if args.checkpoint_load == None:
            args.resume = 'False' 
        else :
            args.resume = args.checkpoint_load
        
        if 'config_pretrain' not in self.args or self.args.config_pretrain is None:
            if args.dataset == 'cifar10':
                config_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),'../utils/defense_utils/dbd/config_z/pretrain' , 'squareTrigger' , args.dataset , 'example.yaml')
            else:
                config_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),'../utils/defense_utils/dbd/config_z/pretrain/' , 'squareTrigger/imagenet/example.yaml')
        else:
            config_file = self.args.config_pretrain
        config_ori = load_config(config_file)
        try:
            gpu = int(os.environ['CUDA_VISIBLE_DEVICES'])
        except:
            print('CUDA_VISIBLE_DEVICES is not set. Set GPU=1 now.')
            gpu = 0
    
        logging.info("===Prepare data===")
        # args.model = 'resnet'
        information = get_information(args,result,config_ori)

        self_poison_train_loader = information['self_poison_train_loader']
        self_model = information['self_model']
        criterion = information['criterion']
        optimizer = information['optimizer']
        scheduler = information['scheduler']
        resumed_epoch = information['resumed_epoch']

        # a.self-supervised learning generates feature extractor
        agg = Metric_Aggregator()
        self_loss_list = []
        for epoch in range(args.epoch_self - resumed_epoch):
        
            self_train_result = simclr_train(
                self_model, self_poison_train_loader, criterion, optimizer, logger, False
            )

            if scheduler is not None:
                scheduler.step()
                logger.info(
                    "Adjust learning rate to {}".format(optimizer.param_groups[0]["lr"])
                )

        
            result_self = {"self_train": self_train_result}

            saved_dict = {
                "epoch": epoch + resumed_epoch + 1,
                "result": result_self,
                "model_state_dict": self_model.state_dict(),
                "optimizer_state_dict": optimizer.state_dict(),
            }
            if scheduler is not None:
                saved_dict["scheduler_state_dict"] = scheduler.state_dict()

            ckpt_path = os.path.join(args.checkpoint_save, "self_latest_model.pt")
            torch.save(saved_dict, ckpt_path)
            logger.info("Save the latest model to {}".format(ckpt_path))

            agg({
                "epoch": epoch,
                "loss_self": self_train_result["loss"],
            })
            self_loss_list.append(self_train_result["loss"])
            agg.to_dataframe().to_csv(os.path.join(f"{args.save_path}","self_df.csv"))

            general_plot_for_epoch(
                {
                    "self loss": self_loss_list,
                },
                save_path=os.path.join(f"{args.save_path}","self_loss.png"),
                ylabel="loss",
            )
        agg.summary().to_csv(os.path.join(f"{args.save_path}","self_df_summary.csv"))
        if 'config_semi' not in self.args or self.args.config_semi is None:
            if args.dataset == 'cifar10':
                config_file_semi = os.path.join(os.path.abspath(os.path.dirname(__file__)),'../utils/defense_utils/dbd/config_z/semi' , 'badnets' , args.dataset , 'example.yaml')
            else:
                config_file_semi = os.path.join(os.path.abspath(os.path.dirname(__file__)),'../utils/defense_utils/dbd/config_z/semi' , 'badnets/imagenet/example.yaml')
        else:
            config_file_semi = self.args.config_semi
    
    
        finetune_config= load_config(config_file_semi)
        pretrain_config= load_config(
            config_file
        )
        pretrain_ckpt_path = ckpt_path
        # merge the pretrain and finetune config
        pretrain_config.update(finetune_config)

        pretrain_config['warmup']['criterion']['sce']['num_classes'] = args.num_classes
        pretrain_config['warmup']['num_epochs'] = args.epoch_warmup

        args.batch_size = 128
        logging.info("\n===Prepare data===")
        
        # If prefetch is True, Normalize will not be added to the transform. Normalize will be called by PrefecthLoader.
        # If prefetch is False, Normalize will be added to the transform.
        train_transform = get_transform_prefetch(args.dataset, *([args.input_height,args.input_width]) , train = True,prefetch=args.prefetch)
        
        x = slice_iter(result["bd_train"], axis=0)
        y = slice_iter(result["bd_train"], axis=1)
        
        # train transform will not be called in xy_iter since it only be used to pass x,y to PoisonLabelDataset. 
        # TODO: change xy_iter to a dict to avoid confusion
        dataset_ori = xy_iter(
            x,y,train_transform
        )
        
        # train transform will be called in PoisonLabelDataset
        dataset = PoisonLabelDataset(dataset_ori, train_transform, np.zeros(len(dataset_ori)), True,args)
        poison_train_loader_ori = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=True,pin_memory=True)
        poison_eval_loader_ori = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True)
        
        if args.prefetch:
            # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans, no normalization [0,255]-> PrefetchLoader: Tensor with trans [0,1], with normalization
            poison_train_loader = PrefetchLoader(poison_train_loader_ori, dataset.mean, dataset.std) 
            poison_eval_loader = PrefetchLoader(poison_eval_loader_ori, dataset.mean, dataset.std)
        else:
            # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization
            poison_train_loader = poison_train_loader_ori 
            poison_eval_loader = poison_eval_loader_ori  

        test_transform = get_transform(args.dataset, *([args.input_height,args.input_width]) , train = False)
        x = slice_iter(result["bd_test"], axis=0)
        y = slice_iter(result["bd_test"], axis=1)
        ori_y = slice_iter(result["bd_test"], axis=4)
        dataset_ori_bd = xy_iter(
            x,y,train_transform
        )
        # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization
        dataset_te_bd = PoisonLabelDataset(dataset_ori_bd, test_transform, np.zeros(len(dataset_ori_bd)), False,args)
        poison_test_loader = torch.utils.data.DataLoader(dataset_te_bd, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True)

        dataset_ori_bd_cl = xy_iter(
            x,ori_y,train_transform
        )
        # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization
        dataset_te_bd_cl = PoisonLabelDataset(dataset_ori_bd_cl, test_transform, np.zeros(len(dataset_ori_bd)), False,args)
        poison_clean_test_loader = torch.utils.data.DataLoader(dataset_te_bd_cl, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True)

        

        x = slice_iter(result["clean_test"], axis=0)
        y = slice_iter(result["clean_test"], axis=1)

        dataset_ori_cl = xy_iter(
            x,y,train_transform
        )
        # x,y: PIL.Image.Image -> PoisonLabelDataset: Tensor with trans [0,1], with normalization
        dataset_te_cl = PoisonLabelDataset(dataset_ori_cl, test_transform, np.zeros(len(dataset_ori_cl)), False,args)
        clean_test_loader = torch.utils.data.DataLoader(dataset_te_cl, batch_size=args.batch_size, num_workers=args.num_workers,drop_last=False, shuffle=False,pin_memory=True)

        backbone = get_network_dbd(args)

        self_model = SelfModel(backbone)
        self_model = self_model.to(args.device)
        # # Load backbone from the pretrained model.
        loc = os.path.join(args.checkpoint_save, "self_latest_model.pt")
        load_state(
            self_model, pretrain_config["pretrain_checkpoint"], loc, args.device, logger
        )
        linear_model = LinearModel(backbone, backbone.feature_dim, args.num_classes)
        linear_model.linear.to(args.device)
        warmup_criterion = get_criterion(pretrain_config["warmup"]["criterion"])
        logger.info("Create criterion: {} for warmup".format(warmup_criterion))
        warmup_criterion = warmup_criterion.to(args.device)
        semi_criterion = get_criterion(pretrain_config["semi"]["criterion"])
        semi_criterion = semi_criterion.to(args.device)
        logger.info("Create criterion: {} for semi-training".format(semi_criterion))
        optimizer = get_optimizer(linear_model, pretrain_config["optimizer"])
        logger.info("Create optimizer: {}".format(optimizer))
        scheduler = get_scheduler(optimizer, pretrain_config["lr_scheduler"])
        logger.info("Create learning rete scheduler: {}".format(pretrain_config["lr_scheduler"]))
        if args.checkpoint_load == '' or args.checkpoint_load is None:
            resume = 'False'
        resumed_epoch, best_acc, best_epoch = load_state(
            linear_model,
            resume,
            args.checkpoint_load,
            gpu,
            logger,
            optimizer,
            scheduler,
            is_best=True,
        )

        # b. learning model using extracted features
        agg = Metric_Aggregator()
        num_epochs = args.epoch_warmup + args.epochs
        train_loss_list = []
        train_mix_acc_list = []

        clean_test_loss_list = []
        bd_test_loss_list = []
        ra_test_loss_list = []
        test_acc_list = []
        test_asr_list = []
        test_ra_list = []
        for epoch in range(num_epochs - resumed_epoch):
            
            logger.info("===Epoch: {}/{}===".format(epoch + resumed_epoch + 1, num_epochs))
            if (epoch + resumed_epoch + 1) <= args.epoch_warmup:
                logger.info("Poisoned linear warmup...")
                poison_train_result = poison_linear_train(
                    linear_model, poison_train_loader, warmup_criterion, optimizer, logger,
                )
                flag = 0
            else:
                record_list = poison_linear_record(
                    linear_model, poison_eval_loader, warmup_criterion
                )
                logger.info("Mining clean data from poisoned dataset...")
                # c. the samples with poor confidence were excluded, and semi-supervised learning was used to continue the learning model
                semi_idx = get_semi_idx(record_list, args.epsilon, logger)
                xdata = MixMatchDataset(dataset, semi_idx, labeled=True,args=args)
                udata = MixMatchDataset(dataset, semi_idx, labeled=False,args=args)
                pretrain_config["semi"]["loader"]['num_workers'] = args.num_workers_semi
                # If prefetch, prefetchloader is used to load data. Else, dataloader is used.
                # PIL->tensor with trans and normalization
                if args.model == 'vit_b_16':
                    pretrain_config["semi"]["loader"]['batch_size'] = 32
                    logger.info('We adjusted the batch size of the dataloader for vit model')
                xloader = get_loader(
                    xdata, pretrain_config["semi"]["loader"], shuffle=True, drop_last=True
                )
                uloader = get_loader(
                    udata, pretrain_config["semi"]["loader"], shuffle=True, drop_last=True
                )
                logger.info("MixMatch training...")
                poison_train_result = mixmatch_train(
                    args,
                    linear_model,
                    xloader,
                    uloader,
                    semi_criterion,
                    optimizer,
                    epoch,
                    logger,
                    **pretrain_config["semi"]["mixmatch"]
                )
                flag = 1
            logger.info("Test model on clean data...")
            clean_test_result = linear_test(
                linear_model, clean_test_loader, warmup_criterion, logger
            )
            logger.info("Test model on poison data...")
            poison_test_result = linear_test(
                linear_model, poison_test_loader, warmup_criterion, logger
            )
            logger.info("Test model on poison data with clean label...")
            poison_clean_test_result = linear_test(
                linear_model, poison_clean_test_loader, warmup_criterion, logger
            )
            if scheduler is not None:
                scheduler.step()
                logger.info(
                    "Adjust learning rate to {}".format(optimizer.param_groups[0]["lr"])
                )

            is_best = False
            if clean_test_result["acc"] > best_acc:
                is_best = True
                best_acc = clean_test_result["acc"]
                best_epoch = epoch + resumed_epoch + 1
            logger.info("Best test accuaracy {} in epoch {}".format(best_acc, best_epoch))

            saved_dict = {
                "epoch": epoch + resumed_epoch + 1,
                "result": result,
                "model_state_dict": linear_model.state_dict(),
                "optimizer_state_dict": optimizer.state_dict(),
                "best_acc": best_acc,
                "best_epoch": best_epoch,
            }
            if scheduler is not None:
                saved_dict["scheduler_state_dict"] = scheduler.state_dict()

            if is_best:
                ckpt_path = os.path.join(args.checkpoint_save, "best_model.pt")
                torch.save(saved_dict, ckpt_path)
                logger.info("Save the best model to {}".format(ckpt_path))
            ckpt_path = os.path.join(args.checkpoint_save, "semi_latest_model.pt")
            torch.save(saved_dict, ckpt_path)
            logger.info("Save the latest model to {}".format(ckpt_path))

            try:
                agg({
                    "epoch": epoch,
                    "is_pretrain": flag,

                    "train_epoch_loss_avg_over_batch": poison_train_result["loss"],
                    "train_acc": poison_train_result["acc"],
                    "xloss": 0,
                    "uloss": 0,
        
                    "clean_test_loss_avg_over_batch": clean_test_result['loss'],
                    "bd_test_loss_avg_over_batch": poison_test_result['loss'],
                    "ra_test_loss_avg_over_batch": poison_clean_test_result['loss'],
                    "test_acc": clean_test_result['acc'],
                    "test_asr": poison_test_result['acc'],
                    "test_ra": poison_clean_test_result['acc'],
                })
                train_loss_list.append(poison_train_result["loss"])
                train_mix_acc_list.append(poison_train_result["acc"])
            except:
                agg({
                    "epoch": epoch,
                    "is_pretrain": flag,

                    "train_epoch_loss_avg_over_batch": poison_train_result["loss"],
                    "train_acc": 0,
                    "xloss": poison_train_result["xloss"],
                    "uloss": poison_train_result["uloss"],
        
                    "clean_test_loss_avg_over_batch": clean_test_result['loss'],
                    "bd_test_loss_avg_over_batch": poison_test_result['loss'],
                    "ra_test_loss_avg_over_batch": poison_clean_test_result['loss'],
                    "test_acc": clean_test_result['acc'],
                    "test_asr": poison_test_result['acc'],
                    "test_ra": poison_clean_test_result['acc'],
                })
                train_loss_list.append(poison_train_result["loss"])
                train_mix_acc_list.append(0)

            clean_test_loss_list.append(clean_test_result['loss'])
            bd_test_loss_list.append(poison_test_result['loss'])
            ra_test_loss_list.append(poison_clean_test_result['loss'])

            test_acc_list.append(clean_test_result['acc'])
            test_asr_list.append(poison_test_result['acc'])
            test_ra_list.append(poison_clean_test_result['acc'])

            general_plot_for_epoch(
                {
                    "Train Acc": train_mix_acc_list,
                    "Test C-Acc": test_acc_list,
                    "Test ASR": test_asr_list,
                    "Test RA": test_ra_list,
                },
                save_path=os.path.join(f"{args.save_path}","train_acc_like_metric_plots.png"),
                ylabel="percentage",
            )

            general_plot_for_epoch(
                {
                    "Train Loss": train_loss_list,
                    "Test Clean Loss": clean_test_loss_list,
                    "Test Backdoor Loss": bd_test_loss_list,
                    "Test RA Loss": ra_test_loss_list,
                },
                save_path=os.path.join(f"{args.save_path}","train_loss_metric_plots.png"),
                ylabel="percentage",
            )

            agg.to_dataframe().to_csv(os.path.join(f"{args.save_path}","train_df.csv"))

        agg.summary().to_csv(os.path.join(f"{args.save_path}","train_df_summary.csv"))
        agg.summary().to_csv(os.path.join(f"{args.save_path}",f"{self.__class__.__name__}_df_summary.csv"))
        
        save_defense_result(
            model_name=self.args.model,
            num_classes=self.args.num_classes,
            model=linear_model.cpu().state_dict(),
            save_path=self.args.save_path,
        )

        result = {}
        result['model'] = linear_model
        return result

    
    def defense(self,result_file):
        self.set_result(result_file)
        self.set_logger()
        result = self.mitigation()
        return result




if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=sys.argv[0])
    dbd.add_arguments(parser)
    args = parser.parse_args()
    dbd_method = dbd(args)
    if "result_file" not in args.__dict__:
        args.result_file = 'one_epochs_debug_badnet_attack'
    elif args.result_file is None:
        args.result_file = 'one_epochs_debug_badnet_attack'
    result = dbd_method.defense(args.result_file)
    

    
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#   Nothing in this License shall be construed as excluding or limiting
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#   12. No Surrender of Others' Freedom.

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# License would be to refrain entirely from conveying the Program.

#   13. Use with the GNU Affero General Public License.

#   Notwithstanding any other provision of this License, you have
# permission to link or combine any covered work with a work licensed
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# option of following the terms and conditions either of that numbered
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# by the Free Software Foundation.

#   If the Program specifies that a proxy can decide which future
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#                      END OF TERMS AND CONDITIONS

#             How to Apply These Terms to Your New Programs

#   If you develop a new program, and you want it to be of the greatest
# possible use to the public, the best way to achieve this is to make it
# free software which everyone can redistribute and change under these terms.

#   To do so, attach the following notices to the program.  It is safest
# to attach them to the start of each source file to most effectively
# state the exclusion of warranty; and each file should have at least
# the "copyright" line and a pointer to where the full notice is found.

#     <one line to give the program's name and a brief idea of what it does.>
#     Copyright (C) <year>  <name of author>

#     This program is free software: you can redistribute it and/or modify
#     it under the terms of the GNU General Public License as published by
#     the Free Software Foundation, either version 3 of the License, or
#     (at your option) any later version.

#     This program is distributed in the hope that it will be useful,
#     but WITHOUT ANY WARRANTY; without even the implied warranty of
#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#     GNU General Public License for more details.

#     You should have received a copy of the GNU General Public License
#     along with this program.  If not, see <https://www.gnu.org/licenses/>.

# Also add information on how to contact you by electronic and paper mail.

#   If the program does terminal interaction, make it output a short
# notice like this when it starts in an interactive mode:

#     <program>  Copyright (C) <year>  <name of author>
#     This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
#     This is free software, and you are welcome to redistribute it
#     under certain conditions; type `show c' for details.

# The hypothetical commands `show w' and `show c' should show the appropriate
# parts of the General Public License.  Of course, your program's commands
# might be different; for a GUI interface, you would use an "about box".

#   You should also get your employer (if you work as a programmer) or school,
# if any, to sign a "copyright disclaimer" for the program, if necessary.
# For more information on this, and how to apply and follow the GNU GPL, see
# <https://www.gnu.org/licenses/>.

#   The GNU General Public License does not permit incorporating your program
# into proprietary programs.  If your program is a subroutine library, you
# may consider it more useful to permit linking proprietary applications with
# the library.  If this is what you want to do, use the GNU Lesser General
# Public License instead of this License.  But first, please read
# <https://www.gnu.org/licenses/why-not-lgpl.html>.