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train.py
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train.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from utils import AverageMeter, accuracy_top1
from attacks.natural import natural_attack
from attacks.adv import adv_attack, batch_adv_attack
def standard_loss(args, model, x, y):
logits = model(x)
loss = nn.CrossEntropyLoss()(logits, y)
return loss, logits
def adv_loss(args, model, x, y):
model.eval()
x_adv = batch_adv_attack(args, model, x, y)
model.train()
logits_adv = model(x_adv)
loss = nn.CrossEntropyLoss()(logits_adv, y)
return loss, logits_adv
LOSS_FUNC = {
'': standard_loss,
'ST': standard_loss,
'AT': adv_loss,
}
def train(args, model, optimizer, loader, writer, epoch):
model.train()
loss_logger = AverageMeter()
acc_logger = AverageMeter()
iterator = tqdm(enumerate(loader), total=len(loader), ncols=95)
for i, (inp, target) in iterator:
inp = inp.cuda()
target = target.cuda()
loss, logits = LOSS_FUNC[args.train_loss](args, model, inp, target)
acc = accuracy_top1(logits, target)
loss_logger.update(loss.item(), inp.size(0))
acc_logger.update(acc, inp.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
desc = 'Train Epoch: {} | Loss {:.4f} | Accuracy {:.4f} ||'.format(epoch, loss_logger.avg, acc_logger.avg)
iterator.set_description(desc)
if writer is not None:
descs = ['loss', 'accuracy']
vals = [loss_logger, acc_logger]
for d, v in zip(descs, vals):
writer.add_scalar('train_{}'.format(d), v.avg, epoch)
return loss_logger.avg, acc_logger.avg
def train_model(args, model, optimizer, train_loader, test_loader, writer, schedule, resume=0):
for epoch in range(resume+1, args.epochs+1):
train(args, model, optimizer, train_loader, writer, epoch)
last_epoch = (epoch == (args.epochs - 1))
should_log = (epoch % args.log_gap == 0)
if should_log or last_epoch:
# nat_clean_train_loss, nat_clean_train_acc = natural_attack(
# args, model, train_loader, writer, epoch, 'clean_train')
nat_clean_test_loss, nat_clean_test_acc = natural_attack(
args, model, test_loader, writer, epoch, 'clean_test')
robust_target = (args.train_loss in ['AT', 'TRADES', 'MART'])
# if robust_target:
# adv_clean_train_loss, adv_clean_train_acc, _ = adv_attack(
# args, model, train_loader, writer, epoch, 'clean_train')
# adv_clean_test_loss, adv_clean_test_acc, _ = adv_attack(
# args, model, test_loader, writer, epoch, 'clean_test')
# else:
# adv_clean_train_loss, adv_clean_train_acc, adv_clean_test_loss, adv_clean_test_acc = -1, -1, -1, -1
checkpoint = {
'model': model.state_dict(),
'epoch': epoch,
'train_acc': -1,
'train_loss': -1,
# 'nat_clean_train_acc': nat_clean_train_acc,
'nat_clean_test_acc': nat_clean_test_acc,
# 'adv_clean_train_acc': adv_clean_train_acc,
# 'adv_clean_test_acc': adv_clean_test_acc,
}
torch.save(checkpoint, args.model_path)
schedule.step()
return model
def poison_train_model(args, model, optimizer, poison_train_loader,
clean_test_loader, schedule, writer):
for epoch in range(1, args.epochs+1):
train_loss, train_acc = train(args, model, optimizer, poison_train_loader, writer, epoch)
last_epoch = (epoch == (args.epochs - 1))
should_log = (epoch % args.log_gap == 0)
if should_log or last_epoch:
# nat_clean_train_loss, nat_clean_train_acc = natural_attack(
# args, model, clean_train_loader, writer, epoch, 'clean_train')
nat_clean_test_loss, nat_clean_test_acc = natural_attack(
args, model, clean_test_loader, writer, epoch, 'clean_test')
# nat_tar_test_loss, nat_tar_test_acc = tar_attack(
# args, model, clean_test_loader, writer, epoch, 'clean_tar')
# nat_poison_train_loss, nat_poison_train_acc = natural_attack(
# args, model, poison_train_loader, writer, epoch, 'poison_train')
# robust_target = (args.train_loss in ['AT', 'TRADES'])
# if robust_target:
# adv_clean_train_loss, adv_clean_train_acc, _ = adv_attack(
# args, model, clean_train_loader, writer, epoch, 'clean_train')
# adv_clean_test_loss, adv_clean_test_acc, _ = adv_attack(
# args, model, clean_test_loader, writer, epoch, 'clean_test')
# adv_poison_train_loss, adv_poison_train_acc, _ = adv_attack(
# args, model, poison_train_loader, writer, epoch, 'poison_train')
# else:
# adv_clean_test_acc = -1
# adv_poison_train_acc = -1
checkpoint = {
'model': model.state_dict(),
'epoch': epoch,
'train_acc': train_acc,
'train_loss': train_loss,
'nat_clean_test_acc': nat_clean_test_acc,
# 'nat_tar_test_acc': nat_tar_test_acc,
# 'nat_poison_train_acc': nat_poison_train_acc,
# 'adv_clean_train_acc': adv_clean_train_acc,
# 'adv_clean_test_acc': adv_clean_test_acc,
# 'adv_poison_train_acc': adv_poison_train_acc,
}
torch.save(checkpoint, args.model_path)
schedule.step()
return model
def eval_model(args, model, loader):
model.eval()
args.eps = args.eps
keys, values = [], []
keys.append('Model')
values.append(args.tensorboard_path)
# Natural
acc, name = natural_attack(args, model, loader)
keys.append(name)
values.append(acc)
# Save results
import csv
csv_fn = '{}.csv'.format(args.tensorboard_path)
with open(csv_fn, 'w') as f:
write = csv.writer(f)
write.writerow(keys)
write.writerow(values)
print('=> csv file is saved at [{}]'.format(csv_fn))