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test_linearmode.py
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test_linearmode.py
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import os
import sys
import json
import time
import random
import argparse
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms
import torchvision.models as models
from torch.utils.data import DataLoader
from advertorch.utils import NormalizeByChannelMeanStd
from pruner import *
from dataset.poisoned_cifar10 import PoisonedCIFAR10
from dataset.poisoned_cifar100 import PoisonedCIFAR100
from dataset.poisoned_rimagenet import RestrictedImageNet
from dataset.clean_label_cifar10 import CleanLabelPoisonedCIFAR10
from models.resnets import resnet20s
# ResNet18
from models.model_zoo import *
from models.densenet import *
from models.vgg import *
from models.adv_resnet import resnet20s as robust_res20s
from utils_linear_mode import *
# Settings
parser = argparse.ArgumentParser(description='PyTorch pyhessian analysis')
##################################### Backdoor #################################################
parser.add_argument("--poison_ratio", type=float, default=0.01)
parser.add_argument("--patch_size", type=int, default=5, help="Size of the patch")
parser.add_argument("--random_loc", dest="random_loc", action="store_true", help="Is the location of the trigger randomly selected or not?")
parser.add_argument("--upper_right", dest="upper_right", action="store_true")
parser.add_argument("--bottom_left", dest="bottom_left", action="store_true")
parser.add_argument("--target", default=0, type=int, help="The target class")
parser.add_argument("--black_trigger", action="store_true")
parser.add_argument("--clean_label_attack", action="store_true")
parser.add_argument('--robust_model', type=str, default=None, help='checkpoint file')
parser.add_argument('--save_file', default=None, type=str)
parser.add_argument("--init", action="store_true")
parser.add_argument("--compare_retrain", action="store_true")
parser.add_argument('--max', type=int, default=49, help='checkpoint_number')
##################################### Dataset #################################################
parser.add_argument('--data', type=str, default='../data', help='location of the data corpus')
parser.add_argument('--dataset', type=str, default='cifar10', help='dataset')
parser.add_argument('--input_size', type=int, default=32, help='size of input images')
parser.add_argument('--rate', default=0.2, type=float, help='pruning rate')
parser.add_argument('--lr', default=1e-3, type=float, help='pruning rate')
##################################### General setting ############################################
parser.add_argument('--arch', type=str, default='resnet18', help='network architecture')
parser.add_argument('--seed', default=None, type=int, help='random seed')
parser.add_argument('--gpu', type=int, default=0, help='gpu device id')
parser.add_argument('--workers', type=int, default=2, help='number of workers in dataloader')
parser.add_argument('--batch_size', type=int, default=128, help='batch size')
parser.add_argument('--batch_num', type=int, default=None, help='batch number')
parser.add_argument('--pretrained_dir', type=str, default=None, help='pretrained weight')
parser.add_argument('--finetune_iter', type=int, default=0, help='batch number')
def main():
global args
args = parser.parse_args()
for arg in vars(args):
print(arg, getattr(args, arg))
torch.cuda.set_device(int(args.gpu))
if args.seed:
setup_seed(args.seed)
# prepare dataset
if args.dataset == 'cifar10':
print('Dataset = CIFAR10')
classes = 10
if args.clean_label_attack:
print('Clean Label Attack')
robust_model = robust_res20s(num_classes = classes)
robust_weight = torch.load(args.robust_model, map_location='cpu')
if 'state_dict' in robust_weight.keys():
robust_weight = robust_weight['state_dict']
robust_model.load_state_dict(robust_weight)
train_set = CleanLabelPoisonedCIFAR10(args.data, poison_ratio=args.poison_ratio, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger, robust_model=robust_model)
else:
train_set = PoisonedCIFAR10(args.data, train=True, poison_ratio=args.poison_ratio, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger)
clean_testset = PoisonedCIFAR10(args.data, train=False, poison_ratio=0, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger)
poison_testset = PoisonedCIFAR10(args.data, train=False, poison_ratio=1, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger)
train_dl = DataLoader(train_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
clean_test_dl = DataLoader(clean_testset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
poison_test_dl = DataLoader(poison_testset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
elif args.dataset == 'cifar100':
print('Dataset = CIFAR100')
classes = 100
train_set = PoisonedCIFAR100(args.data, train=True, poison_ratio=args.poison_ratio, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger)
clean_testset = PoisonedCIFAR100(args.data, train=False, poison_ratio=0, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger)
poison_testset = PoisonedCIFAR100(args.data, train=False, poison_ratio=1, patch_size=args.patch_size,
random_loc=args.random_loc, upper_right=args.upper_right, bottom_left=args.bottom_left,
target=args.target, black_trigger=args.black_trigger)
train_dl = DataLoader(train_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
clean_test_dl = DataLoader(clean_testset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
poison_test_dl = DataLoader(poison_testset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
elif args.dataset == 'rimagenet':
print('Dataset = Restricted ImageNet')
classes = 9
dataset = RestrictedImageNet(args.data)
train_dl, _, _ = dataset.make_loaders(workers=args.workers, shuffle_train=False, shuffle_val=False, batch_size=args.batch_size, poison_ratio=args.poison_ratio, target=args.target, patch_size=args.patch_size, black_trigger=args.black_trigger)
_, clean_test_dl = dataset.make_loaders(only_val=True, shuffle_train=False, shuffle_val=False, workers=args.workers, batch_size=args.batch_size, poison_ratio=0, target=args.target, patch_size=args.patch_size, black_trigger=args.black_trigger)
_, poison_test_dl = dataset.make_loaders(only_val=True, shuffle_train=False, shuffle_val=False, workers=args.workers, batch_size=args.batch_size, poison_ratio=1, target=args.target, patch_size=args.patch_size, black_trigger=args.black_trigger)
else:
raise ValueError('Unknow Datasets')
criterion = nn.CrossEntropyLoss()
overall_result = {}
for model_idx in range(args.max):
# prepare model
if args.dataset == 'rimagenet':
if args.arch == 'resnet18':
model = models.resnet18(num_classes=classes)
else:
raise ValueError('Unknow architecture')
else:
if args.arch == 'resnet18':
model = ResNet18(num_classes=classes)
elif args.arch == 'resnet20':
model = resnet20s(num_classes=classes)
elif args.arch == 'densenet100':
model = densenet_100_12(num_classes=classes)
elif args.arch == 'vgg16':
model = vgg16_bn(num_classes=classes)
else:
raise ValueError('Unknow architecture')
model.cuda()
checkpoint = torch.load(os.path.join(args.pretrained_dir, '{}checkpoint.pth.tar'.format(model_idx)), map_location='cuda:{}'.format(args.gpu))
rewind_checkpoint = checkpoint['init_weight']
if 'state_dict' in checkpoint.keys():
checkpoint = checkpoint['state_dict']
current_mask_pruned = extract_mask(checkpoint)
if len(current_mask_pruned):
prune_model_custom(model, current_mask_pruned)
model.load_state_dict(checkpoint)
model.eval()
SA = validate(clean_test_dl, model, criterion)
ASR = validate(poison_test_dl, model, criterion)
remain_weight = check_sparsity(model)
pruning_model(model, 0.2)
checkpoint_pruned = copy.deepcopy(model.state_dict())
# Test before pruning
model.eval()
SA_pruned = validate(clean_test_dl, model, criterion)
ASR_pruned = validate(poison_test_dl, model, criterion)
remain_weight_pruned = check_sparsity(model)
if args.compare_retrain:
checkpoint_retrained = torch.load(os.path.join(args.pretrained_dir, '{}checkpoint.pth.tar'.format(model_idx+1)), map_location='cuda:{}'.format(args.gpu))
if 'state_dict' in checkpoint_retrained.keys():
checkpoint_retrained = checkpoint_retrained['state_dict']
model.load_state_dict(checkpoint_retrained)
model.eval()
SA_retrained = validate(clean_test_dl, model, criterion)
ASR_retrained = validate(poison_test_dl, model, criterion)
remain_weight_retrained = check_sparsity(model)
# Linear mode connectivity
LMC_acc, LMC_loss = linear_mode_connectivity(model, checkpoint_pruned, checkpoint_retrained, train_dl, batch_number=args.batch_num, bins=10)
print('** {} checkpoint'.format(model_idx))
print('** Pruned model ===> Remain: {:.4f}% \t SA = {:.4f} \t ASR = {:.4f}'.format(remain_weight_pruned, SA_pruned, ASR_pruned))
print('** Retrained model ===> Remain: {:.4f}% \t SA = {:.4f} \t ASR = {:.4f}'.format(remain_weight_retrained, SA_retrained, ASR_retrained))
print('** Linear Mode Connectivity ===> Accuracy: {:.4f} \t Loss = {:.4f}'.format(LMC_acc, LMC_loss))
overall_result[model_idx] = {
'remain': remain_weight,
'SA': SA,
'ASR': ASR,
'remain_pruned': remain_weight_pruned,
'SA_pruned': SA_pruned,
'ASR_pruned': ASR_pruned,
'remain_retrained': remain_weight_retrained,
'SA_retrained': SA_retrained,
'ASR_retrained': ASR_retrained,
'LMC_acc': LMC_acc,
'LMC_loss': LMC_loss
}
else:
print('Compare with fintuning')
if args.init:
print('retraining')
pruned_mask = extract_mask(checkpoint_pruned)
remove_prune(model)
model.load_state_dict(rewind_checkpoint)
prune_model_custom(model, pruned_mask)
optimizer = torch.optim.SGD(model.parameters(), 1e-2, momentum=0.9, weight_decay=5e-4)
else:
# Finetuning
optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=0.9, weight_decay=5e-4)
model.train()
for i, (image, target) in enumerate(train_dl):
image = image.type(torch.FloatTensor).cuda()
target = target.cuda()
output_clean = model(image)
loss = criterion(output_clean, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i >= args.finetune_iter:
print('finetune for {} Epochs'.format(i))
break
model.eval()
SA_finetune = validate(clean_test_dl, model, criterion)
ASR_finetune = validate(poison_test_dl, model, criterion)
remain_weight_finetune = check_sparsity(model)
# Linear mode connectivity
LMC_acc, LMC_loss = linear_mode_connectivity(model, checkpoint_pruned, model.state_dict(), train_dl, batch_number=args.batch_num, bins=10)
print('** {} checkpoint'.format(model_idx))
print('** Pruned model ===> Remain: {:.4f}% \t SA = {:.4f} \t ASR = {:.4f}'.format(remain_weight_pruned, SA_pruned, ASR_pruned))
print('** Finetuned model ===> Remain: {:.4f}% \t SA = {:.4f} \t ASR = {:.4f}'.format(remain_weight_finetune, SA_finetune, ASR_finetune))
print('** Linear Mode Connectivity ===> Accuracy: {:.4f} \t Loss = {:.4f}'.format(LMC_acc, LMC_loss))
overall_result[model_idx] = {
'remain': remain_weight,
'SA': SA,
'ASR': ASR,
'remain_pruned': remain_weight_pruned,
'SA_pruned': SA_pruned,
'ASR_pruned': ASR_pruned,
'remain_finetune': remain_weight_finetune,
'SA_finetune': SA_finetune,
'ASR_finetune': ASR_finetune,
'LMC_acc': LMC_acc,
'LMC_loss': LMC_loss
}
torch.save(overall_result, args.save_file)
def validate(val_loader, model, criterion):
"""
Run evaluation
"""
losses = AverageMeter()
top1 = AverageMeter()
# switch to evaluate mode
model.eval()
for i, (image, target) in enumerate(val_loader):
image = image.type(torch.FloatTensor)
image = image.cuda()
target = target.cuda()
# compute output
with torch.no_grad():
output = model(image)
loss = criterion(output, target)
output = output.float()
loss = loss.float()
# measure accuracy and record loss
prec1 = accuracy(output.data, target)[0]
losses.update(loss.item(), image.size(0))
top1.update(prec1.item(), image.size(0))
if i % 50 == 0:
print('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accuracy {top1.val:.3f} ({top1.avg:.3f})'.format(
i, len(val_loader), loss=losses, top1=top1))
print('valid_accuracy {top1.avg:.3f}'
.format(top1=top1))
return top1.avg
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def setup_seed(seed):
print('setup random seed = {}'.format(seed))
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
if __name__ == '__main__':
main()