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main.py
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main.py
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import argparse
import re
import os
import time
import numpy as np
import math
from collections import OrderedDict
import torch
from utils import random_seed, create_result_dir, Logger, TableLogger, AverageMeter
from attack import AttackPGD
from adamw import AdamW
from model.model import Model, set_eps, get_eps
from model.norm_dist import set_p_norm, get_p_norm
parser = argparse.ArgumentParser(description='Adversarial Robustness')
parser.add_argument('--dataset', default='MNIST', type=str)
parser.add_argument('--model', default='MLPFeature(depth=4,width=4)', type=str) #mlp,conv
parser.add_argument('--predictor-hidden-size', default=512, type=int) # 0 means not to use linear predictor
parser.add_argument('--loss', default='cross_entropy', type=str) #cross_entropy, hinge
parser.add_argument('--p-start', default=8.0, type=float)
parser.add_argument('--p-end', default=1000.0, type=float)
parser.add_argument('--kappa', default=1.0, type=float)
parser.add_argument('--epochs', default='0,50,50,350,400', type=str) # epoch1-epoch3: inc eps; epoch2-epoch4: inc p
parser.add_argument('--eps-train', default=None, type=float)
parser.add_argument('--eps-test', default=None, type=float)
parser.add_argument('-b', '--batch-size', default=256, type=int)
parser.add_argument('--lr', default=0.01, type=float)
parser.add_argument('--beta1', default=0.9, type=float)
parser.add_argument('--beta2', default=0.99, type=float)
parser.add_argument('--epsilon', default=1e-10, type=float)
parser.add_argument('--wd', default=0.0, type=float)
parser.add_argument('--start-epoch', default=0, type=int)
parser.add_argument('--checkpoint', default=None, type=str)
parser.add_argument('--gpu', default=-1, type=int, help='GPU id to use')
parser.add_argument('--dist-url', default='tcp://localhost:23456')
parser.add_argument('--world-size', default=1)
parser.add_argument('--rank', default=0)
parser.add_argument('-p', '--print-freq', default=50, type=int, metavar='N', help='print frequency')
parser.add_argument('--result-dir', default='result/', type=str)
parser.add_argument('--filter-name', default='', type=str)
parser.add_argument('--seed', default=2020, type=int)
parser.add_argument('--visualize', action='store_true')
def cal_acc(outputs, targets):
predicted = torch.max(outputs.data, 1)[1]
return (predicted == targets).float().mean()
def parallel_reduce(*argv):
tensor = torch.FloatTensor(argv).cuda()
torch.distributed.all_reduce(tensor)
ret = tensor.cpu() / torch.distributed.get_world_size()
return ret.tolist()
def train(net, loss_fun, epoch, trainloader, optimizer, schedule, logger, train_logger, gpu, parallel, print_freq):
if logger is not None:
logger.print('Epoch %d training start' % (epoch))
net.train()
batch_time, data_time, losses, accs = [AverageMeter() for _ in range(4)]
start = time.time()
train_loader_len = len(trainloader)
for batch_idx, (inputs, targets) in enumerate(trainloader):
schedule(epoch, batch_idx)
data_time.update(time.time() - start)
inputs = inputs.cuda(gpu, non_blocking=True)
targets = targets.cuda(gpu, non_blocking=True)
outputs, worse_outputs = net(inputs, targets=targets)
loss = loss_fun(outputs, worse_outputs, targets)
with torch.no_grad():
losses.update(loss.data.item(), targets.size(0))
accs.update(cal_acc(outputs.data, targets).mean().item(), targets.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - start)
if (batch_idx + 1) % print_freq == 0 and logger is not None:
logger.print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'lr {lr:.4f}\tp {p:.2f}\teps {eps:.4f}\tkappa{kappa:.4f}\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc {acc.val:.4f} ({acc.avg:.4f})\t'.format(
epoch, batch_idx + 1, train_loader_len, batch_time=batch_time,
lr=optimizer.param_groups[0]['lr'],
p=get_p_norm(net), eps=get_eps(net), kappa=loss_fun.kappa,
loss=losses, acc=accs))
start = time.time()
loss, acc = losses.avg, accs.avg
if parallel:
loss, acc = parallel_reduce(losses.avg, accs.avg)
if train_logger is not None:
train_logger.log({'epoch': epoch, 'loss': loss, 'acc': acc})
if logger is not None:
logger.print('Epoch {0}: train loss {loss:.4f} acc {acc:.4f}'
' lr {lr:.4f} p {p:.2f} eps {eps:.4f} kappa {kappa:.4f}'.format(
epoch, loss=loss, acc=acc, lr=optimizer.param_groups[0]['lr'],
p=get_p_norm(net), eps=get_eps(net), kappa=loss_fun.kappa))
return loss, acc
@torch.no_grad()
def test(net, loss_fun, epoch, testloader, logger, test_logger, gpu, parallel, print_freq):
net.eval()
batch_time, data_time, losses, accs = [AverageMeter() for _ in range(4)]
start = time.time()
test_loader_len = len(testloader)
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs = inputs.cuda(gpu, non_blocking=True)
targets = targets.cuda(gpu, non_blocking=True)
outputs = net(inputs)
loss = loss_fun(outputs, targets)
losses.update(loss.mean().item(), targets.size(0))
accs.update(cal_acc(outputs, targets).item(), targets.size(0))
batch_time.update(time.time() - start)
start = time.time()
if (batch_idx + 1) % print_freq == 0 and logger is not None:
logger.print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc {acc.val:.4f} ({acc.avg:.4f})\t'.format(
batch_idx + 1, test_loader_len, batch_time=batch_time, loss=losses, acc=accs))
loss, acc = losses.avg, accs.avg
if parallel:
loss, acc = parallel_reduce(losses.avg, accs.avg)
if test_logger is not None:
test_logger.log({'epoch': epoch, 'loss': loss, 'acc': acc})
if logger is not None:
logger.print('Epoch %d: '%epoch + 'test loss ' + f'{loss:.4f}' + ' acc ' + f'{acc:.4f}')
return loss, acc
def gen_adv_examples(model, attacker, test_loader, gpu, parallel, logger, fast=False):
model.eval()
correct = 0
tot_num = 0
size = len(test_loader)
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs = inputs.cuda(gpu, non_blocking=True)
targets = targets.cuda(gpu, non_blocking=True)
result = torch.ones(targets.size(0), dtype=torch.bool, device=targets.device)
for i in range(1):
perturb = attacker.find(inputs, targets)
with torch.no_grad():
outputs = model(perturb)
predicted = torch.max(outputs.data, 1)[1]
result &= (predicted == targets)
correct += result.float().sum().item()
tot_num += inputs.size(0)
if fast and batch_idx * 10 >= size: break
acc = correct / tot_num * 100
if parallel:
acc, = parallel_reduce(acc)
if logger is not None:
logger.print('adversarial attack acc ' + f'{acc:.4f}')
return acc
@torch.no_grad()
def certified_test(net, eps, up, down, epoch, testloader, logger, gpu, parallel):
save_p = get_p_norm(net)
save_eps = get_eps(net)
set_eps(net, eps)
set_p_norm(net, float('inf'))
net.eval()
outputs = []
labels = []
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs = inputs.cuda(gpu, non_blocking=True)
lower = torch.max(inputs - eps, down)
upper = torch.min(inputs + eps, up)
targets = targets.cuda(gpu, non_blocking=True)
# outputs.append(net(inputs, targets=targets)[1])
outputs.append(net(inputs, lower=lower, upper=upper, targets=targets)[1])
labels.append(targets)
outputs = torch.cat(outputs, dim=0)
labels = torch.cat(labels, dim=0)
res = (outputs.max(dim=1)[1] == labels).float().mean().item()
if parallel:
res, = parallel_reduce(res)
if logger is not None:
logger.print('Epoch %d: '%epoch + ' certified acc ' + f'{res:.4f}')
set_p_norm(net, save_p)
set_eps(net, save_eps)
return res
def parse_function_call(s):
s = re.split(r'[()]', s)
if len(s) == 1:
return s[0], {}
name, params, _ = s
params = re.split(r',\s*', params)
params = dict([p.split('=') for p in params])
for key, value in params.items():
try:
params[key] = int(params[key])
except ValueError:
try:
params[key] = float(params[key])
except ValueError:
pass
return name, params
def create_schedule(args, batch_per_epoch, model, loss, optimizer):
epoch0, epoch1, epoch2, epoch3, tot_epoch = args.epochs
speed = math.log(args.p_end / args.p_start)
def num_batches(epoch, minibatch=0):
return epoch * batch_per_epoch + minibatch
def schedule(epoch, minibatch):
ratio = max(num_batches(epoch - epoch1, minibatch) / num_batches(tot_epoch - epoch1), 0)
lr_now = 0.5 * args.lr * (1 + math.cos((ratio * math.pi)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr_now
ratio = min(max(num_batches(epoch - epoch1, minibatch) / num_batches(epoch3 - epoch1), 0), 1)
if ratio >= 1:
p_norm = float('inf')
else:
p_norm = args.p_start * math.exp(speed * ratio)
set_p_norm(model, p_norm)
if epoch2 > 0:
ratio = min(max(num_batches(epoch - epoch0, minibatch) / num_batches(epoch2), 0), 1)
else:
ratio = 1.0
set_eps(model, args.eps_train * ratio)
loss.kappa = args.kappa
return schedule
import torch.nn.functional as F
def cross_entropy():
return F.cross_entropy
# The hinge loss function is a combination of max_hinge_loss and average_hinge_loss.
def hinge(mix=0.75):
def loss_fun(outputs, targets):
return mix * outputs.max(dim=1)[0].clamp(min=0).mean() + (1 - mix) * outputs.clamp(min=0).mean()
return loss_fun
class Loss():
def __init__(self, loss, kappa):
self.loss = loss
self.kappa = kappa
def __call__(self, *args):
margin_output = args[0] - torch.gather(args[0], dim=1, index=args[-1].view(-1, 1))
if len(args) == 2:
return self.loss(margin_output, args[-1])
# args[1] which corresponds to worse_outputs, is already a margin vector.
return self.kappa * self.loss(args[1], args[-1]) + (1 - self.kappa) * self.loss(margin_output, args[-1])
def main_worker(gpu, parallel, args, result_dir):
if parallel:
args.rank = args.rank + gpu
torch.distributed.init_process_group(backend='nccl', init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
torch.backends.cudnn.benchmark = True
random_seed(args.seed + args.rank) # make data aug different for different processes
torch.cuda.set_device(gpu)
assert args.batch_size % args.world_size == 0
from dataset import load_data, get_statistics, default_eps, input_dim
train_loader, test_loader = load_data(args.dataset, 'data/', args.batch_size // args.world_size, parallel,
augmentation=True)
mean, std = get_statistics(args.dataset)
num_classes = len(train_loader.dataset.classes)
from model.bound_module import Predictor, BoundFinalIdentity
from model.mlp import MLPFeature, MLP
from model.conv import ConvFeature, Conv
model_name, params = parse_function_call(args.model)
if args.predictor_hidden_size > 0:
model = locals()[model_name](input_dim=input_dim[args.dataset], **params)
predictor = Predictor(model.out_features, args.predictor_hidden_size, num_classes)
else:
model = locals()[model_name](input_dim=input_dim[args.dataset], num_classes=num_classes, **params)
predictor = BoundFinalIdentity()
model = Model(model, predictor, eps=0)
model = model.cuda(gpu)
if parallel:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu])
loss_name, params = parse_function_call(args.loss)
loss = Loss(globals()[loss_name](**params), args.kappa)
output_flag = not parallel or gpu == 0
if output_flag:
logger = Logger(os.path.join(result_dir, 'log.txt'))
for arg in vars(args):
logger.print(arg, '=', getattr(args, arg))
logger.print(train_loader.dataset.transform)
logger.print(model)
logger.print('number of params: ', sum([p.numel() for p in model.parameters()]))
logger.print('Using loss', loss)
train_logger = TableLogger(os.path.join(result_dir, 'train.log'), ['epoch', 'loss', 'acc'])
test_logger = TableLogger(os.path.join(result_dir, 'test.log'), ['epoch', 'loss', 'acc'])
else:
logger = train_logger = test_logger = None
optimizer = AdamW(model, lr=args.lr, weight_decay=args.wd, betas=(args.beta1,args.beta2), eps=args.epsilon)
if args.checkpoint:
assert os.path.isfile(args.checkpoint)
if parallel:
torch.distributed.barrier()
checkpoint = torch.load(args.checkpoint, map_location=lambda storage, loc: storage.cuda(gpu))
state_dict = checkpoint['state_dict']
if next(iter(state_dict))[0:7] == 'module.' and not parallel:
new_state_dict = OrderedDict([(k[7:], v) for k, v in state_dict.items()])
state_dict = new_state_dict
elif next(iter(state_dict))[0:7] != 'module.' and parallel:
new_state_dict = OrderedDict([('module.' + k, v) for k, v in state_dict.items()])
state_dict = new_state_dict
model.load_state_dict(state_dict)
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded '{}'".format(args.checkpoint))
if parallel:
torch.distributed.barrier()
if args.eps_test is None:
args.eps_test = default_eps[args.dataset]
if args.eps_train is None:
args.eps_train = args.eps_test
args.eps_train /= std
args.eps_test /= std
up = torch.FloatTensor((1 - mean) / std).view(-1, 1, 1).cuda(gpu)
down = torch.FloatTensor((0 - mean) / std).view(-1, 1, 1).cuda(gpu)
attacker = AttackPGD(model, args.eps_test, step_size=args.eps_test / 4, num_steps=20, up=up, down=down)
args.epochs = [int(epoch) for epoch in args.epochs.split(',')]
schedule = create_schedule(args, len(train_loader), model, loss, optimizer)
if args.visualize and output_flag:
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(result_dir)
else: writer = None
for epoch in range(args.start_epoch, args.epochs[-1]):
if parallel:
train_loader.sampler.set_epoch(epoch)
train_loss, train_acc = train(model, loss, epoch, train_loader, optimizer, schedule,
logger, train_logger, gpu, parallel, args.print_freq)
test_loss, test_acc = test(model, loss, epoch, test_loader, logger, test_logger, gpu, parallel, args.print_freq)
if writer is not None:
writer.add_scalar('curve/p', get_p_norm(model), epoch)
writer.add_scalar('curve/train loss', train_loss, epoch)
writer.add_scalar('curve/test loss', test_loss, epoch)
writer.add_scalar('curve/train acc', train_acc, epoch)
writer.add_scalar('curve/test acc', test_acc, epoch)
if epoch % 50 == 49:
if logger is not None:
logger.print('Generate adversarial examples on training dataset and test dataset (fast, inaccurate)')
robust_train_acc = gen_adv_examples(model,attacker, train_loader, gpu, parallel, logger, fast=True)
robust_test_acc = gen_adv_examples(model, attacker, test_loader, gpu, parallel, logger, fast=True)
if writer is not None:
writer.add_scalar('curve/robust train acc', robust_train_acc, epoch)
writer.add_scalar('curve/robust test acc', robust_test_acc, epoch)
if epoch % 5 == 4:
certified_acc = certified_test(model, args.eps_test, up, down, epoch, test_loader, logger, gpu, parallel)
if writer is not None:
writer.add_scalar('curve/certified acc', certified_acc, epoch)
if epoch > args.epochs[-1] - 3:
if logger is not None:
logger.print("Generate adversarial examples on test dataset")
gen_adv_examples(model, attacker, test_loader, gpu, parallel, logger)
certified_test(model, args.eps_test, up, down, epoch, test_loader, logger, gpu, parallel)
schedule(args.epochs[-1], 0)
if output_flag:
logger.print("Calculate certified accuracy on training dataset and test dataset")
certified_test(model, args.eps_test, up, down, args.epochs[-1], train_loader, logger, gpu, parallel)
certified_test(model, args.eps_test, up, down, args.epochs[-1], test_loader, logger, gpu, parallel)
if output_flag:
torch.save({
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict(),
}, os.path.join(result_dir, 'model.pth'))
if writer is not None:
writer.close()
def main(father_handle, **extra_argv):
args = parser.parse_args()
for key,val in extra_argv.items():
setattr(args, key, val)
result_dir = create_result_dir(args)
if father_handle is not None:
father_handle.put(result_dir)
if args.gpu != -1:
main_worker(args.gpu, False, args, result_dir)
else:
n_procs = torch.cuda.device_count()
args.world_size *= n_procs
args.rank *= n_procs
torch.multiprocessing.spawn(main_worker, nprocs=n_procs, args=(True, args, result_dir))
if __name__ == '__main__':
main(None)