wanet.py

'''
WaNet - Imperceptible Warping-based Backdoor Attack
This file is modified based on the following source:

link : https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release
The original license is placed at the end of this file.

@inproceedings{
    nguyen2021wanet,
    title={WaNet - Imperceptible Warping-based Backdoor Attack},
    author={Tuan Anh Nguyen and Anh Tuan Tran},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=eEn8KTtJOx}
}

The update include:
1. data preprocess and dataset setting
2. model setting
3. args and config
4. during training the backdoor attack generalization to lower poison ratio (generalize_to_lower_pratio)
5. save process

basic sturcture for main:
1. config args, save_path, fix random seed
2. set the clean train data and clean test data
3. set the device, model, criterion, optimizer, training schedule.
4. set the backdoor warping
5. training with backdoor modification simultaneously
6. save attack result



The original license is placed at the end of this file.

Note that since this attack rely on batch-wise modification of the input data,
when this method encounters lower poison ratio, the original implementation
will fail (poison ratio < 1 / batch size), we add a function named generalize_to_lower_pratio
to generalize the attack to lower the poison ratio. The basic idea is to calculate the theoretical
the number of poison samples each batch should have, then randomly select batches to do poisoning.
This change may result in instability and a higher variance in final
results' metrics, but it is a necessary change to make the attack workable in a low poison ratio.
Please be careful when you use this attack in a low poison ratio, and interpret the results with
caution.
'''

import logging
import os
import sys

sys.path = ["./"] + sys.path

import argparse
import numpy as np
import torch.nn.functional as F
import torch.utils.data as data
import random
import time
import torch
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage

to_pil = ToPILImage()
from torch.utils.data import DataLoader

from utils.aggregate_block.dataset_and_transform_generate import get_dataset_normalization, get_dataset_denormalization
from utils.aggregate_block.model_trainer_generate import generate_cls_model
from utils.trainer_cls import Metric_Aggregator
from utils.save_load_attack import save_attack_result
from utils.aggregate_block.train_settings_generate import argparser_opt_scheduler
from attack.badnet import add_common_attack_args, BadNet
from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform
from utils.trainer_cls import all_acc, given_dataloader_test, general_plot_for_epoch


def generalize_to_lower_pratio(pratio, bs):
    if pratio * bs >= 1:
        # the normal case that each batch can have at least one poison sample
        return pratio * bs
    else:
        # then randomly return number of poison sample
        if np.random.uniform(0,
                             1) < pratio * bs:  # eg. pratio = 1/1280, then 1/10 of batch(bs=128) should contains one sample
            return 1
        else:
            return 0


class ProbTransform(torch.nn.Module):
    def __init__(self, f, p=1):
        super(ProbTransform, self).__init__()
        self.f = f
        self.p = p

    def forward(self, x):
        if random.random() < self.p:
            return self.f(x)
        else:
            return x


class PostTensorTransform(torch.nn.Module):
    def __init__(self, args):
        super(PostTensorTransform, self).__init__()
        self.random_crop = ProbTransform(
            transforms.RandomCrop((args.input_height, args.input_width), padding=args.random_crop), p=0.8
        )
        self.random_rotation = ProbTransform(transforms.RandomRotation(args.random_rotation), p=0.5)
        if args.dataset == "cifar10":
            self.random_horizontal_flip = transforms.RandomHorizontalFlip(p=0.5)

    def forward(self, x):
        for module in self.children():
            x = module(x)
        return x


class Denormalize:
    def __init__(self, args, expected_values, variance):
        self.n_channels = args.input_channel
        self.expected_values = expected_values
        self.variance = variance
        assert self.n_channels == len(self.expected_values)

    def __call__(self, x):
        x_clone = x.clone()
        for channel in range(self.n_channels):
            x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel]
        return x_clone


class Denormalizer:
    def __init__(self, args):
        self.denormalizer = self._get_denormalizer(args)

    def _get_denormalizer(self, args):
        denormalizer = Denormalize(args, get_dataset_normalization(args.dataset).mean,
                                   get_dataset_normalization(args.dataset).std)
        return denormalizer

    def __call__(self, x):
        if self.denormalizer:
            x = self.denormalizer(x)
        return x


class Wanet(BadNet):

    def __init__(self):
        super(Wanet, self).__init__()

    def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
        parser = add_common_attack_args(parser)
        parser.add_argument('--bd_yaml_path', type=str, default='./config/attack/wanet/default.yaml',
                            help='path for yaml file provide additional default attributes')
        parser.add_argument("--cross_ratio", type=float, )  # default=2)  # rho_a = pratio, rho_n = pratio * cross_ratio
        parser.add_argument("--random_rotation", type=int, )  # default=10)
        parser.add_argument("--random_crop", type=int, )  # default=5)
        parser.add_argument("--s", type=float, )  # default=0.5)
        parser.add_argument("--k", type=int, )  # default=4)
        parser.add_argument(
            "--grid_rescale", type=float, )  # default=1
        return parser

    def stage1_non_training_data_prepare(self):
        logging.info("stage1 start")

        assert "args" in self.__dict__
        args = self.args

        train_dataset_without_transform, \
        train_img_transform, \
        train_label_transform, \
        test_dataset_without_transform, \
        test_img_transform, \
        test_label_transform, \
        clean_train_dataset_with_transform, \
        clean_train_dataset_targets, \
        clean_test_dataset_with_transform, \
        clean_test_dataset_targets \
            = self.benign_prepare()

        logging.info("Be careful, here must replace the regular train tranform with test transform.")
        # you can find in the original code that get_transform function has pretensor_transform=False always.
        clean_train_dataset_with_transform.wrap_img_transform = test_img_transform

        clean_train_dataloader = DataLoader(clean_train_dataset_with_transform, pin_memory=args.pin_memory,
                                            batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True)

        clean_test_dataloader = DataLoader(clean_test_dataset_with_transform, pin_memory=args.pin_memory,
                                           batch_size=args.batch_size,
                                           num_workers=args.num_workers, shuffle=True)
        self.stage1_results = clean_train_dataset_with_transform, \
                              clean_train_dataloader, \
                              clean_test_dataset_with_transform, \
                              clean_test_dataloader

    def stage2_training(self):
        logging.info(f"stage2 start")
        assert 'args' in self.__dict__
        args = self.args
        agg = Metric_Aggregator()

        clean_train_dataset_with_transform, \
        clean_train_dataloader, \
        clean_test_dataset_with_transform, \
        clean_test_dataloader = self.stage1_results

        self.device = torch.device(
            (
                f"cuda:{[int(i) for i in args.device[5:].split(',')][0]}" if "," in args.device else args.device

            ) if torch.cuda.is_available() else "cpu"
        )

        netC = generate_cls_model(
            model_name=args.model,
            num_classes=args.num_classes,
            image_size=args.img_size[0],
        ).to(self.device, non_blocking=args.non_blocking)

        if "," in args.device:
            netC = torch.nn.DataParallel(
                netC,
                device_ids=[int(i) for i in args.device[5:].split(",")]  # eg. "cuda:2,3,7" -> [2,3,7]
            )

        optimizerC, schedulerC = argparser_opt_scheduler(netC, args=args)

        logging.info("Train from scratch!!!")
        best_clean_acc = 0.0
        best_bd_acc = 0.0
        best_cross_acc = 0.0
        epoch_current = 0

        # set the backdoor warping
        ins = torch.rand(1, 2, args.k, args.k) * 2 - 1  # generate (1,2,4,4) shape [-1,1] gaussian
        ins = ins / torch.mean(
            torch.abs(ins))  # scale up, increase var, so that mean of positive part and negative be +1 and -1
        noise_grid = (
            F.upsample(ins, size=args.input_height, mode="bicubic",
                       align_corners=True)  # here upsample and make the dimension match
                .permute(0, 2, 3, 1)
                .to(self.device, non_blocking=args.non_blocking)
        )
        array1d = torch.linspace(-1, 1, steps=args.input_height)
        x, y = torch.meshgrid(array1d,
                              array1d)  # form two mesh grid correspoding to x, y of each position in height * width matrix
        identity_grid = torch.stack((y, x), 2)[None, ...].to(
            self.device,
            non_blocking=args.non_blocking)  # stack x,y like two layer, then add one more dimension at first place. (have torch.Size([1, 32, 32, 2]))

        # filter out transformation that not reversible
        transforms_reversible = transforms.Compose(
            list(
                filter(
                    lambda x: isinstance(x, (transforms.Normalize, transforms.Resize, transforms.ToTensor)),
                    (clean_test_dataset_with_transform.wrap_img_transform.transforms)
                )
            )
        )
        # get denormalizer
        for trans_t in (clean_test_dataset_with_transform.wrap_img_transform.transforms):
            if isinstance(trans_t, transforms.Normalize):
                denormalizer = get_dataset_denormalization(trans_t)
                logging.info(f"{denormalizer}")

        reversible_test_dataset = (clean_test_dataset_with_transform)
        reversible_test_dataset.wrap_img_transform = transforms_reversible

        reversible_test_dataloader = torch.utils.data.DataLoader(reversible_test_dataset, batch_size=args.batch_size,
                                                                 pin_memory=args.pin_memory,
                                                                 num_workers=args.num_workers, shuffle=False)
        self.bd_test_dataset = prepro_cls_DatasetBD_v2(
            clean_test_dataset_with_transform.wrapped_dataset, save_folder_path=f"{args.save_path}/bd_test_dataset"
        )
        self.cross_test_dataset = prepro_cls_DatasetBD_v2(
            clean_test_dataset_with_transform.wrapped_dataset, save_folder_path=f"{args.save_path}/cross_test_dataset"
        )
        for batch_idx, (inputs, targets) in enumerate(reversible_test_dataloader):
            with torch.no_grad():
                inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device,
                                                                                                     non_blocking=args.non_blocking)
                bs = inputs.shape[0]

                # Evaluate Backdoor
                grid_temps = (identity_grid + args.s * noise_grid / args.input_height) * args.grid_rescale
                grid_temps = torch.clamp(grid_temps, -1, 1)

                ins = torch.rand(bs, args.input_height, args.input_height, 2).to(self.device,
                                                                                 non_blocking=args.non_blocking) * 2 - 1
                grid_temps2 = grid_temps.repeat(bs, 1, 1, 1) + ins / args.input_height
                grid_temps2 = torch.clamp(grid_temps2, -1, 1)

                inputs_bd = denormalizer(F.grid_sample(inputs, grid_temps.repeat(bs, 1, 1, 1), align_corners=True))

                if args.attack_label_trans == "all2one":
                    position_changed = (
                            args.attack_target != targets)  # since if label does not change, then cannot tell if the poison is effective or not.
                    targets_bd = (torch.ones_like(targets) * args.attack_target)[position_changed]
                    inputs_bd = inputs_bd[position_changed]
                if args.attack_label_trans == "all2all":
                    position_changed = torch.ones_like(targets) # here assume all2all is the bd label = (true label + 1) % num_classes
                    targets_bd = torch.remainder(targets + 1, args.num_classes)
                    inputs_bd = inputs_bd

                targets = targets.detach().clone().cpu()
                y_poison_batch = targets_bd.detach().clone().cpu().tolist()
                for idx_in_batch, t_img in enumerate(inputs_bd.detach().clone().cpu()):
                    self.bd_test_dataset.set_one_bd_sample(
                        selected_index=int(
                            batch_idx * int(args.batch_size) + torch.where(position_changed.detach().clone().cpu())[0][
                                idx_in_batch]),
                        # manually calculate the original index, since we do not shuffle the dataloader
                        img=(t_img),
                        bd_label=int(y_poison_batch[idx_in_batch]),
                        label=int(targets[torch.where(position_changed.detach().clone().cpu())[0][idx_in_batch]]),
                    )

                # Evaluate cross
                if args.cross_ratio:
                    inputs_cross = denormalizer(F.grid_sample(inputs, grid_temps2, align_corners=True))
                    for idx_in_batch, t_img in enumerate(inputs_cross.detach().clone().cpu()):
                        self.cross_test_dataset.set_one_bd_sample(
                            selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch),
                            # manually calculate the original index, since we do not shuffle the dataloader
                            img=(t_img),
                            bd_label=int(targets[idx_in_batch]),
                            label=int(targets[idx_in_batch]),
                        )

        bd_test_dataset_with_transform = dataset_wrapper_with_transform(
            self.bd_test_dataset,
            clean_test_dataset_with_transform.wrap_img_transform,
        )
        self.bd_test_dataset.subset(
            np.where(self.bd_test_dataset.poison_indicator == 1)[0].tolist()
        )
        bd_test_dataloader = DataLoader(bd_test_dataset_with_transform,
                                        pin_memory=args.pin_memory,
                                        batch_size=args.batch_size,
                                        num_workers=args.num_workers,
                                        shuffle=False)
        if args.cross_ratio:
            cross_test_dataset_with_transform = dataset_wrapper_with_transform(
                self.cross_test_dataset,
                clean_test_dataset_with_transform.wrap_img_transform,
            )
            cross_test_dataloader = DataLoader(cross_test_dataset_with_transform,
                                               pin_memory=args.pin_memory,
                                               batch_size=args.batch_size,
                                               num_workers=args.num_workers,
                                               shuffle=False)
        else:
            cross_test_dataloader = None

        test_dataloaders = (clean_test_dataloader, bd_test_dataloader, cross_test_dataloader)

        train_loss_list = []
        train_mix_acc_list = []
        train_clean_acc_list = []
        train_asr_list = []
        train_ra_list = []
        train_cross_acc_only_list = []

        clean_test_loss_list = []
        bd_test_loss_list = []
        cross_test_loss_list = []
        ra_test_loss_list = []
        test_acc_list = []
        test_asr_list = []
        test_ra_list = []
        test_cross_acc_list = []

        for epoch in range(epoch_current, args.epochs):
            logging.info("Epoch {}:".format(epoch + 1))

            train_epoch_loss_avg_over_batch, \
            train_mix_acc, \
            train_clean_acc, \
            train_asr, \
            train_ra, \
            train_cross_acc = self.train_step(netC, optimizerC, schedulerC, clean_train_dataloader, noise_grid,
                                              identity_grid, epoch, args)

            clean_test_loss_avg_over_batch, \
            bd_test_loss_avg_over_batch, \
            cross_test_loss_avg_over_batch, \
            ra_test_loss_avg_over_batch, \
            test_acc, \
            test_asr, \
            test_ra, \
            test_cross_acc \
                = self.eval_step(
                netC,
                clean_test_dataset_with_transform,
                clean_test_dataloader,
                bd_test_dataloader,
                cross_test_dataloader,
                args,
            )

            agg({
                "epoch": epoch,

                "train_epoch_loss_avg_over_batch": train_epoch_loss_avg_over_batch,
                "train_acc": train_mix_acc,
                "train_acc_clean_only": train_clean_acc,
                "train_asr_bd_only": train_asr,
                "train_ra_bd_only": train_ra,
                "train_cross_acc_only": train_cross_acc,

                "clean_test_loss_avg_over_batch": clean_test_loss_avg_over_batch,
                "bd_test_loss_avg_over_batch": bd_test_loss_avg_over_batch,
                "cross_test_loss_avg_over_batch": cross_test_loss_avg_over_batch,
                "ra_test_loss_avg_over_batch": ra_test_loss_avg_over_batch,
                "test_acc": test_acc,
                "test_asr": test_asr,
                "test_ra": test_ra,
                "test_cross_acc": test_cross_acc,
            })

            train_loss_list.append(train_epoch_loss_avg_over_batch)
            train_mix_acc_list.append(train_mix_acc)
            train_clean_acc_list.append(train_clean_acc)
            train_asr_list.append(train_asr)
            train_ra_list.append(train_ra)
            train_cross_acc_only_list.append(train_cross_acc)

            clean_test_loss_list.append(clean_test_loss_avg_over_batch)
            bd_test_loss_list.append(bd_test_loss_avg_over_batch)
            cross_test_loss_list.append(cross_test_loss_avg_over_batch)
            ra_test_loss_list.append(ra_test_loss_avg_over_batch)
            test_acc_list.append(test_acc)
            test_asr_list.append(test_asr)
            test_ra_list.append(test_ra)
            test_cross_acc_list.append(test_cross_acc)

            general_plot_for_epoch(
                {
                    "Train Acc": train_mix_acc_list,
                    "Train Acc (clean sample only)": train_clean_acc_list,
                    "Train ASR": train_asr_list,
                    "Train RA": train_ra_list,
                    "Train Cross Acc": train_cross_acc_only_list,
                    "Test C-Acc": test_acc_list,
                    "Test ASR": test_asr_list,
                    "Test RA": test_ra_list,
                    "Test Cross Acc": test_cross_acc_list,
                },
                save_path=f"{args.save_path}/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 Cross Loss": cross_test_loss_list,
                    "Test RA Loss": ra_test_loss_list,
                },
                save_path=f"{args.save_path}/loss_metric_plots.png",
                ylabel="percentage",
            )

            agg.to_dataframe().to_csv(f"{args.save_path}/attack_df.csv")

            if args.frequency_save != 0 and epoch % args.frequency_save == args.frequency_save - 1:
                state_dict = {
                    "netC": netC.state_dict(),
                    "schedulerC": schedulerC.state_dict(),
                    "optimizerC": optimizerC.state_dict(),
                    "epoch_current": epoch,
                    "identity_grid": identity_grid,
                    "noise_grid": noise_grid,
                }
                torch.save(state_dict, args.save_path + "/state_dict.pt")

        agg.summary().to_csv(f"{args.save_path}/attack_df_summary.csv")

        ### save the poison train data for wanet

        # set the container for the poison train data
        bd_train_dataset = prepro_cls_DatasetBD_v2(
            clean_train_dataset_with_transform.wrapped_dataset,
            save_folder_path=f"{args.save_path}/bd_train_dataset"
        )
        clean_train_dataloader_without_shuffle = torch.utils.data.DataLoader(clean_train_dataset_with_transform,
                                                                             batch_size=args.batch_size,
                                                                             pin_memory=args.pin_memory,
                                                                             num_workers=args.num_workers,
                                                                             shuffle=False)
        # iterate through the clean train data
        netC.eval()
        rate_bd = args.pratio
        with torch.no_grad():
            for batch_idx, (inputs, targets) in enumerate(clean_train_dataloader_without_shuffle):
                inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device,
                                                                                                     non_blocking=args.non_blocking)
                bs = inputs.shape[0]

                # Create backdoor data
                num_bd = int(generalize_to_lower_pratio(rate_bd, bs))  # int(bs * rate_bd)
                num_cross = int(num_bd * args.cross_ratio)
                grid_temps = (identity_grid + args.s * noise_grid / args.input_height) * args.grid_rescale
                grid_temps = torch.clamp(grid_temps, -1, 1)

                ins = torch.rand(num_cross, args.input_height, args.input_height, 2).to(self.device,
                                                                                        non_blocking=args.non_blocking) * 2 - 1
                grid_temps2 = grid_temps.repeat(num_cross, 1, 1, 1) + ins / args.input_height
                grid_temps2 = torch.clamp(grid_temps2, -1, 1)

                inputs_bd = F.grid_sample(inputs[:num_bd], grid_temps.repeat(num_bd, 1, 1, 1), align_corners=True)
                if args.attack_label_trans == "all2one":
                    targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target
                if args.attack_label_trans == "all2all":
                    targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes)

                inputs_cross = F.grid_sample(inputs[num_bd: (num_bd + num_cross)], grid_temps2, align_corners=True)

                input_changed = torch.cat([inputs_bd, inputs_cross, ], dim=0)

                input_changed = denormalizer(  # since we normalized once, we need to denormalize it back.
                    input_changed
                ).detach().clone().cpu()
                target_changed = torch.cat([targets_bd, targets[num_bd: (num_bd + num_cross)], ],
                                           dim=0).detach().clone().cpu()

                # save to the container
                for idx_in_batch, t_img in enumerate(
                        input_changed
                ):
                    # here we know it starts from 0 and they are consecutive
                    bd_train_dataset.set_one_bd_sample(
                        selected_index=int(batch_idx * int(args.batch_size) + idx_in_batch),
                        img=(t_img),
                        bd_label=int(target_changed[idx_in_batch]),
                        label=int(targets[idx_in_batch]),
                    )

        save_attack_result(
            model_name=args.model,
            num_classes=args.num_classes,
            model=netC.cpu().state_dict(),
            data_path=args.dataset_path,
            img_size=args.img_size,
            clean_data=args.dataset,
            bd_train=bd_train_dataset,
            bd_test=self.bd_test_dataset,
            save_path=args.save_path,
        )

    def train_step(self, netC, optimizerC, schedulerC, train_dataloader, noise_grid, identity_grid, epoch, args):
        logging.info(" Train:")
        netC.train()
        rate_bd = args.pratio

        criterion_CE = torch.nn.CrossEntropyLoss()

        transforms = PostTensorTransform(args).to(self.device, non_blocking=args.non_blocking)
        total_time = 0

        batch_loss_list = []
        batch_predict_list = []
        batch_label_list = []
        batch_poison_indicator_list = []
        batch_original_targets_list = []

        for batch_idx, (inputs, targets) in enumerate(train_dataloader):
            optimizerC.zero_grad()

            inputs, targets = inputs.to(self.device, non_blocking=args.non_blocking), targets.to(self.device,
                                                                                                 non_blocking=args.non_blocking)
            bs = inputs.shape[0]

            # Create backdoor data
            num_bd = int(generalize_to_lower_pratio(rate_bd, bs))  # int(bs * rate_bd)
            num_cross = int(num_bd * args.cross_ratio)
            grid_temps = (identity_grid + args.s * noise_grid / args.input_height) * args.grid_rescale
            grid_temps = torch.clamp(grid_temps, -1, 1)

            ins = torch.rand(num_cross, args.input_height, args.input_height, 2).to(self.device,
                                                                                    non_blocking=args.non_blocking) * 2 - 1
            grid_temps2 = grid_temps.repeat(num_cross, 1, 1, 1) + ins / args.input_height
            grid_temps2 = torch.clamp(grid_temps2, -1, 1)

            inputs_bd = F.grid_sample(inputs[:num_bd], grid_temps.repeat(num_bd, 1, 1, 1), align_corners=True)
            if args.attack_label_trans == "all2one":
                targets_bd = torch.ones_like(targets[:num_bd]) * args.attack_target
            if args.attack_label_trans == "all2all":
                targets_bd = torch.remainder(targets[:num_bd] + 1, args.num_classes)

            inputs_cross = F.grid_sample(inputs[num_bd: (num_bd + num_cross)], grid_temps2, align_corners=True)

            total_inputs = torch.cat([inputs_bd, inputs_cross, inputs[(num_bd + num_cross):]], dim=0)
            total_inputs = transforms(total_inputs)
            total_targets = torch.cat([targets_bd, targets[num_bd:]], dim=0)
            start = time.time()
            total_preds = netC(total_inputs)
            total_time += time.time() - start

            loss_ce = criterion_CE(total_preds, total_targets)

            loss = loss_ce
            loss.backward()

            optimizerC.step()

            batch_loss_list.append(loss.item())
            batch_predict_list.append(torch.max(total_preds, -1)[1].detach().clone().cpu())
            batch_label_list.append(total_targets.detach().clone().cpu())

            poison_indicator = torch.zeros(bs)
            poison_indicator[:num_bd] = 1  # all others are cross/clean samples cannot conut up to train acc
            poison_indicator[num_bd:num_cross + num_bd] = 2  # indicate for the cross terms

            batch_poison_indicator_list.append(poison_indicator)
            batch_original_targets_list.append(targets.detach().clone().cpu())

        if isinstance(schedulerC, torch.optim.lr_scheduler.ReduceLROnPlateau):
            schedulerC.step(loss.item())
        else:
            schedulerC.step()

        train_epoch_loss_avg_over_batch, \
        train_epoch_predict_list, \
        train_epoch_label_list, \
        train_epoch_poison_indicator_list, \
        train_epoch_original_targets_list = sum(batch_loss_list) / len(batch_loss_list), \
                                            torch.cat(batch_predict_list), \
                                            torch.cat(batch_label_list), \
                                            torch.cat(batch_poison_indicator_list), \
                                            torch.cat(batch_original_targets_list)

        train_mix_acc = all_acc(train_epoch_predict_list, train_epoch_label_list)

        train_bd_idx = torch.where(train_epoch_poison_indicator_list == 1)[0]
        train_cross_idx = torch.where(train_epoch_poison_indicator_list == 2)[0]
        train_clean_idx = torch.where(train_epoch_poison_indicator_list == 0)[0]
        train_clean_acc = all_acc(
            train_epoch_predict_list[train_clean_idx],
            train_epoch_label_list[train_clean_idx],
        )
        train_asr = all_acc(
            train_epoch_predict_list[train_bd_idx],
            train_epoch_label_list[train_bd_idx],
        )
        train_cross_acc = all_acc(
            train_epoch_predict_list[train_cross_idx],
            train_epoch_label_list[train_cross_idx],
        )
        train_ra = all_acc(
            train_epoch_predict_list[train_bd_idx],
            train_epoch_original_targets_list[train_bd_idx],
        )

        return train_epoch_loss_avg_over_batch, \
               train_mix_acc, \
               train_clean_acc, \
               train_asr, \
               train_ra, \
               train_cross_acc

    def eval_step(
            self,
            netC,
            clean_test_dataset_with_transform,
            clean_test_dataloader,
            bd_test_dataloader,
            cross_test_dataloader,
            args,
    ):
        clean_metrics, clean_epoch_predict_list, clean_epoch_label_list = given_dataloader_test(
            netC,
            clean_test_dataloader,
            criterion=torch.nn.CrossEntropyLoss(),
            non_blocking=args.non_blocking,
            device=self.device,
            verbose=0,
        )
        clean_test_loss_avg_over_batch = clean_metrics['test_loss_avg_over_batch']
        test_acc = clean_metrics['test_acc']
        bd_metrics, bd_epoch_predict_list, bd_epoch_label_list = given_dataloader_test(
            netC,
            bd_test_dataloader,
            criterion=torch.nn.CrossEntropyLoss(),
            non_blocking=args.non_blocking,
            device=self.device,
            verbose=0,
        )
        bd_test_loss_avg_over_batch = bd_metrics['test_loss_avg_over_batch']
        test_asr = bd_metrics['test_acc']

        self.bd_test_dataset.getitem_all_switch = True  # change to return the original label instead
        ra_test_dataset_with_transform = dataset_wrapper_with_transform(
            self.bd_test_dataset,
            clean_test_dataset_with_transform.wrap_img_transform,
        )
        ra_test_dataloader = DataLoader(ra_test_dataset_with_transform,
                                        pin_memory=args.pin_memory,
                                        batch_size=args.batch_size,
                                        num_workers=args.num_workers,
                                        shuffle=False)
        ra_metrics, ra_epoch_predict_list, ra_epoch_label_list = given_dataloader_test(
            netC,
            ra_test_dataloader,
            criterion=torch.nn.CrossEntropyLoss(),
            non_blocking=args.non_blocking,
            device=self.device,
            verbose=0,
        )
        ra_test_loss_avg_over_batch = ra_metrics['test_loss_avg_over_batch']
        test_ra = ra_metrics['test_acc']
        self.bd_test_dataset.getitem_all_switch = False  # switch back

        cross_metrics, cross_epoch_predict_list, cross_epoch_label_list = given_dataloader_test(
            netC,
            cross_test_dataloader,
            criterion=torch.nn.CrossEntropyLoss(),
            non_blocking=args.non_blocking,
            device=self.device,
            verbose=0,
        )
        cross_test_loss_avg_over_batch = cross_metrics['test_loss_avg_over_batch']
        test_cross_acc = cross_metrics['test_acc']

        return clean_test_loss_avg_over_batch, \
               bd_test_loss_avg_over_batch, \
               cross_test_loss_avg_over_batch, \
               ra_test_loss_avg_over_batch, \
               test_acc, \
               test_asr, \
               test_ra, \
               test_cross_acc


if __name__ == '__main__':
    attack = Wanet()
    parser = argparse.ArgumentParser(description=sys.argv[0])
    parser = attack.set_args(parser)
    parser = attack.set_bd_args(parser)
    args = parser.parse_args()
    attack.add_bd_yaml_to_args(args)
    attack.add_yaml_to_args(args)
    args = attack.process_args(args)
    attack.prepare(args)
    attack.stage1_non_training_data_prepare()
    attack.stage2_training()

'''
                    GNU GENERAL PUBLIC LICENSE
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actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.

  If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.

  A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License.  You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.

  Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.

  12. No Surrender of Others' Freedom.

  If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License.  If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all.  For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
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
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work.  The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.

  14. Revised Versions of this License.

  The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time.  Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.

  Each version is given a distinguishing version number.  If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation.  If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.

  If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.

  Later license versions may give you additional or different
permissions.  However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.

  15. Disclaimer of Warranty.

  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

  16. Limitation of Liability.

  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.

  17. Interpretation of Sections 15 and 16.

  If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.

                     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>.
'''