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LPAFT_AFF.py
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LPAFT_AFF.py
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import torch.nn as nn
import torch
import argparse
from autoattack import AutoAttack
import numpy as np
import os
import time
import copy
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.datasets as datasets
from torch.utils.data.sampler import SubsetRandomSampler
# from models.resnet_multi_bn import resnet
from models.resnet_multi_bn import resnet18
from utils import *
import torchvision.transforms as transforms
import torch.nn.functional as F
from data.dataset import CIFAR10IndexPseudoLabelEnsemble, CIFAR100IndexPseudoLabelEnsemble
from optimizer.lars import LARS
from models.resnet import resnet18 as resnet18_single
parser = argparse.ArgumentParser(description='DynACL++ (LPAFT-AFF)')
parser.add_argument('--experiment', type=str,
help='location for saving trained models,\
we recommend to specify it as a subdirectory of the pretraining export path',
required=True)
parser.add_argument('--data', type=str, default='data/CIFAR10',
help='location of the data')
parser.add_argument('--dataset', type=str, default='cifar10',
help='which dataset to be used, (cifar10 or cifar100)')
parser.add_argument('--batch_size', type=int, default=512, help='batch size')
parser.add_argument('--batch_size_AT', type=int, default=128, help='batch size')
parser.add_argument('--epochs', default=25, type=int,
help='number of total epochs to run')
parser.add_argument('--epochs_head', default=10, type=int,
help='number of epochs to train head')
parser.add_argument('--print_freq', default=50,
type=int, help='print frequency')
parser.add_argument('--checkpoint', default='', type=str,
help='saving pretrained model')
parser.add_argument('--optimizer', default='sgd',
type=str, help='optimizer type')
parser.add_argument('--lr', default=0.1, type=float, help='optimizer lr')
parser.add_argument('--lr_head', default=0.01, type=float, help='optimizer lr')
parser.add_argument('--twoLayerProj', action='store_true',
help='if specified, use two layers linear head for simclr proj head')
parser.add_argument('--pgd_iter', default=5, type=int,
help='how many iterations employed to attack the model')
parser.add_argument('--gpu_id', type=str, default='0')
parser.add_argument('--val_frequency', type=int, default=5)
parser.add_argument('--label_path', type=str, default='', help='path of label')
parser.add_argument('--bnNameCnt', type=int, default=1,
help='0 for normal route, 1 for adv route')
parser.add_argument('--eval-only', action='store_true',)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
n_gpu = torch.cuda.device_count()
device = 'cuda'
pseudo_label = torch.load(args.label_path, map_location="cpu").numpy().tolist()
def main():
global args
assert args.dataset in ['cifar100', 'cifar10']
save_dir = os.path.join('checkpoints', args.experiment)
if os.path.exists(save_dir) is not True:
os.system("mkdir -p {}".format(save_dir))
log = logger(path=save_dir)
log.info(str(args))
num_classes = 10 if args.dataset != 'cifar100' else 100
bn_names = ['normal', 'pgd']
model = resnet18(pretrained=False, bn_names=bn_names)
model.fc = nn.Linear(512, num_classes)
model.cuda()
cudnn.benchmark = True
tfs_val = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
tfs_test = transforms.Compose([
transforms.ToTensor(),
])
# dataset process
if args.dataset == 'cifar10':
train_datasets = CIFAR10IndexPseudoLabelEnsemble(root=args.data,
transform=tfs_val,
pseudoLabel=pseudo_label,
download=True)
val_train_datasets = datasets.CIFAR10(
root=args.data, train=True, transform=tfs_val, download=True)
test_datasets = datasets.CIFAR10(
root=args.data, train=False, transform=tfs_test, download=True)
num_classes = 10
elif args.dataset == 'cifar100':
train_datasets = CIFAR100IndexPseudoLabelEnsemble(root=args.data,
transform=tfs_val,
pseudoLabel=pseudo_label,
download=True)
val_train_datasets = datasets.CIFAR100(
root=args.data, train=True, transform=tfs_val, download=True)
test_datasets = datasets.CIFAR100(
root=args.data, train=False, transform=tfs_test, download=True)
num_classes = 100
else:
print("unknow dataset")
assert False
train_loader = torch.utils.data.DataLoader(
train_datasets,
num_workers=4,
batch_size=args.batch_size,
shuffle=True, drop_last=True)
val_train_loader = torch.utils.data.DataLoader(
val_train_datasets,
num_workers=4,
batch_size=args.batch_size,
shuffle=True)
val_train_loader_AT = torch.utils.data.DataLoader(
val_train_datasets,
num_workers=4,
batch_size=args.batch_size_AT,
shuffle=True)
test_loader = torch.utils.data.DataLoader(
test_datasets,
num_workers=4,
batch_size=args.batch_size)
if args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
elif args.optimizer == 'lars':
optimizer = LARS(model.parameters(), lr=args.lr, weight_decay=1e-6)
elif args.optimizer == 'sgd':
optimizer = torch.optim.SGD(
model.parameters(), lr=args.lr, weight_decay=1e-4, momentum=0.9)
optimizer_head = torch.optim.SGD(
model.fc.parameters(), lr=args.lr_head, weight_decay=1e-4, momentum=0.9)
else:
print("no defined optimizer")
assert False
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[10,20], gamma=0.1)
scheduler_head = torch.optim.lr_scheduler.MultiStepLR(
optimizer_head, milestones=[], gamma=0.1)
start_epoch = 1
assert args.checkpoint != ''
state_dict = torch.load(args.checkpoint, map_location="cpu")
if 'state_dict' in state_dict:
state_dict = state_dict['state_dict']
state_dict['fc.weight'] = torch.zeros(num_classes, 512).cuda()
state_dict['fc.bias'] = torch.zeros(num_classes).cuda()
model.load_state_dict(state_dict, strict=False)
if args.eval_only:
validate(val_train_loader, test_loader,
model, log, num_classes=num_classes)
return
for name, param in model.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
assert len(parameters) == 2 # fc.weight, fc.bias
# linear probing
log.info("Starts linear probing")
for epoch in range(start_epoch, args.epochs_head + 1):
log.info("current lr is {}".format(
optimizer_head.state_dict()['param_groups'][0]['lr']))
train_head(train_loader, model, optimizer_head, scheduler_head, epoch, log)
# Pseudo finetuning
log.info('Starts pseudo finetuning')
for name, param in model.named_parameters():
param.requires_grad = True
for epoch in range(start_epoch, args.epochs + 1):
log.info("current lr is {}".format(
optimizer.state_dict()['param_groups'][0]['lr']))
train(train_loader, model, optimizer, scheduler, epoch, log)
if(epoch % 5 == 0):
save_checkpoint({
'epoch': epoch,
'state_dict': model.state_dict(),
'optim': optimizer.state_dict(),
}, filename=os.path.join(save_dir, 'model.pt'))
if epoch % args.val_frequency == 0 and epoch > 0:
acc, tacc, rtacc = validate(val_train_loader, test_loader,
model, log, num_classes=num_classes)
# evaluate acc
save_checkpoint({
'epoch': epoch,
'state_dict': model.state_dict(),
'optim': optimizer.state_dict(),
'acc': acc,
'tacc': tacc,
'rtacc': rtacc,
}, filename=os.path.join(save_dir, 'model_{}.pt'.format(epoch)))
log.info('Pseudo-label finetune ends. \nTest: (SLF evaluation)')
validate(val_train_loader, test_loader, model, log, num_classes=num_classes)
# AFF evalution
log.info('Starts AFF evaluation')
# zero init FC
model.fc.weight = torch.nn.Parameter(torch.zeros(model.fc.weight.shape))
model.fc.bias = torch.nn.Parameter(torch.zeros(model.fc.bias.shape))
model.fc.cuda()
optimizer_AFF = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=2e-4)
scheduler_AFF = torch.optim.lr_scheduler.MultiStepLR(optimizer_AFF, milestones=[15,20], gamma=0.1)
for epoch in range(1, args.epochs + 1):
# adjust learning rate for SGD
log.info("current lr is {}".format(
optimizer_AFF.state_dict()['param_groups'][0]['lr']))
# linear classification
train_AFF(args, model, device, val_train_loader_AT, optimizer_AFF, epoch, log)
scheduler_AFF.step()
# save checkpoint
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
'optim': optimizer.state_dict(),
}, os.path.join(save_dir, 'model_finetune.pt'))
model_save = resnet18_single(num_classes=num_classes) # original resnet (without multi BatchNorm)
state_dict = torch.load(os.path.join(save_dir, 'model_finetune.pt'))['state_dict']
state_dict = cvt_state_dict(state_dict,args)
model_save.load_state_dict(state_dict)
model_save.eval().cuda()
_, test_tacc = eval_test(model_save, device, test_loader, log, advFlag=None)
test_atacc = eval_adv_test(model_save, device, test_loader, epsilon=8/255, alpha=2/255,
criterion=F.cross_entropy, log=log, attack_iter=20)
log.info("On the final model (AFF evaluation), test tacc is {}, test atacc is {}".format(
test_tacc, test_atacc))
log_path = 'checkpoints/' + args.experiment + '/robustness_result.txt'
runAA(model_save, log_path)
torch.save({
'state_dict': model_save.state_dict(),
}, os.path.join(save_dir, 'model_full_finetune_singleBN.pt'))
def train(train_loader, model, optimizer, scheduler, epoch, log):
losses = AverageMeter()
losses.reset()
data_time_meter = AverageMeter()
train_time_meter = AverageMeter()
end = time.time()
for i, (inputs, _, targets, _) in enumerate(train_loader):
data_time = time.time() - end
data_time_meter.update(data_time)
inputs = inputs.cuda()
loss = trades_loss_dual(model=model,
x_natural=inputs,
y=targets.long().cuda(),
optimizer=optimizer,
perturb_steps=10,
natural_mode='pgd')
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(float(loss.detach().cpu()), inputs.shape[0])
train_time = time.time() - end
end = time.time()
train_time_meter.update(train_time)
# torch.cuda.empty_cache()
if i % args.print_freq == 0:
log.info('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'data_time: {data_time.val:.2f}\t'
'iter_train_time: {train_time.avg:.2f}\t'.format(
epoch, i, len(train_loader), loss=losses,
data_time=data_time_meter, train_time=train_time_meter))
scheduler.step()
return losses.avg
def train_head(train_loader, model, optimizer, scheduler, epoch, log):
model.eval()
losses = AverageMeter()
losses.reset()
data_time_meter = AverageMeter()
train_time_meter = AverageMeter()
end = time.time()
criterion = nn.CrossEntropyLoss()
for i, (inputs, _, targets, _) in enumerate(train_loader):
data_time = time.time() - end
data_time_meter.update(data_time)
inputs = inputs.cuda()
outputs = model.eval()(inputs, 'pgd')
loss = criterion(outputs, targets.long().cuda())
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(float(loss.detach().cpu()), inputs.shape[0])
train_time = time.time() - end
end = time.time()
train_time_meter.update(train_time)
# torch.cuda.empty_cache()
if i % args.print_freq == 0:
log.info('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'data_time: {data_time.val:.2f}\t'
'iter_train_time: {train_time.avg:.2f}\t'.format(
epoch, i, len(train_loader), loss=losses,
data_time=data_time_meter, train_time=train_time_meter))
scheduler.step()
return losses.avg
def validate(val_loader, test_loader, model, log, num_classes=10):
"""
Run evaluation
"""
model.eval()
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
train_time_meter = AverageMeter()
losses = AverageMeter()
losses.reset()
end = time.time()
# train a fc on the representation
for param in model.parameters():
param.requires_grad = False
previous_fc = model.fc
ch = model.fc.in_features
model.fc = nn.Linear(ch, num_classes)
model.cuda()
epochs_max = 25
lr = 0.01
parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
assert(len(parameters) == 2)
optimizer = torch.optim.SGD(
parameters, lr=lr, weight_decay=2e-4, momentum=0.9, nesterov=True)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[10,20], gamma=0.1)
for epoch in range(epochs_max):
log.info("current lr is {}".format(
optimizer.state_dict()['param_groups'][0]['lr']))
for i, (sample) in enumerate(val_loader):
x, y = sample[0].cuda(), sample[1].cuda()
p = model.eval()(x, 'pgd')
loss = criterion(p, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(float(loss.detach().cpu()))
train_time = time.time() - end
end = time.time()
train_time_meter.update(train_time)
scheduler.step()
log.info('Test epoch: ({0})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'train_time: {train_time.avg:.2f}\t'.format(
epoch, loss=losses, train_time=train_time_meter))
acc = []
round = 0
for loader in [val_loader, test_loader, test_loader]:
round += 1
losses = AverageMeter()
losses.reset()
top1 = AverageMeter()
for i, (inputs, targets) in enumerate(loader):
inputs = inputs.cuda()
targets = targets.cuda()
if round == 3:
inputs = pgd_attack(model, inputs, targets, device,
eps=8.0/255, alpha=2.0/255, iters=20, advFlag='pgd').data
# compute output
with torch.no_grad():
outputs = model.eval()(inputs, 'pgd')
loss = criterion(outputs, targets)
outputs = outputs.float()
loss = loss.float()
# measure accuracy and record loss
prec1 = accuracy(outputs.data, targets)[0]
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
if i % args.print_freq == 0:
log.info('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accuracy {top1.val:.3f} ({top1.avg:.3f})'.format(
i, len(loader), loss=losses, top1=top1))
acc.append(top1.avg)
# recover every thing
model.fc = previous_fc
model.cuda()
for param in model.parameters():
param.requires_grad = True
log.info('train_accuracy {acc:.3f}'
.format(acc=acc[0]))
log.info('test_accuracy {tacc:.3f}'
.format(tacc=acc[1]))
log.info('test_robust_accuracy {rtacc:.3f}'
.format(rtacc=acc[2]))
return acc
def save_checkpoint(state, filename='weight.pt'):
"""
Save the training model
"""
torch.save(state, filename)
def runAA(model, log_path):
model.eval()
global args
tfs_test = transforms.Compose([
transforms.ToTensor(),
])
if args.dataset == 'cifar10':
test_datasets = datasets.CIFAR10(
root=args.data, train=False, transform=tfs_test, download=True)
else:
test_datasets = datasets.CIFAR100(
root=args.data, train=False, transform=tfs_test, download=True)
test_loader = torch.utils.data.DataLoader(
test_datasets, batch_size=10000, pin_memory=True, num_workers=4)
adversary = AutoAttack(model, norm='Linf', eps=8/255, version='standard', log_path=log_path)
for images, labels in test_loader:
images = images.cuda()
labels = labels.cuda()
adversary.run_standard_evaluation(images, labels, bs=100)
def train_AFF(args, model, device, train_loader, optimizer, epoch, log):
model.train()
dataTimeAve = AverageMeter()
totalTimeAve = AverageMeter()
end = time.time()
# criterion = nn.CrossEntropyLoss().cuda()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
dataTime = time.time() - end
dataTimeAve.update(dataTime)
optimizer.zero_grad()
loss = trades_loss_dual(model=model,
x_natural=data,
y=target,
optimizer=optimizer,
natural_mode='normal')
loss.backward()
optimizer.step()
totalTime = time.time() - end
totalTimeAve.update(totalTime)
end = time.time()
# print progress
if batch_idx % 10 == 0:
log.info('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tData time: {:.3f}\tTotal time: {:.3f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item(), dataTimeAve.avg, totalTimeAve.avg))
def cvt_state_dict(state_dict, args):
# deal with adv bn
state_dict_new = copy.deepcopy(state_dict)
if args.bnNameCnt >= 0:
for name, item in state_dict.items():
if 'bn' in name:
assert 'bn_list' in name
state_dict_new[name.replace(
'.bn_list.{}'.format(args.bnNameCnt), '')] = item
name_to_del = []
for name, item in state_dict_new.items():
if 'bn' in name and 'adv' in name:
name_to_del.append(name)
if 'bn_list' in name:
name_to_del.append(name)
if 'fc' in name:
name_to_del.append(name)
for name in np.unique(name_to_del):
del state_dict_new[name]
# deal with down sample layer
keys = list(state_dict_new.keys())[:]
name_to_del = []
for name in keys:
if 'downsample.conv' in name:
state_dict_new[name.replace(
'downsample.conv', 'downsample.0')] = state_dict_new[name]
name_to_del.append(name)
if 'downsample.bn' in name:
state_dict_new[name.replace(
'downsample.bn', 'downsample.1')] = state_dict_new[name]
name_to_del.append(name)
for name in np.unique(name_to_del):
del state_dict_new[name]
state_dict_new['fc.weight'] = state_dict['fc.weight']
state_dict_new['fc.bias'] = state_dict['fc.bias']
return state_dict_new
if __name__ == '__main__':
main()