class Trainer:
""" Train and Validation with single GPU """
def __init__(self, train_loader, val_loader, args):
self.train_loader = train_loader
self.val_loader = val_loader
self.args = args
self.model = get_model(args)
self.epochs = args.epochs
self.total_step = len(train_loader) * args.epochs
self.step = 0
self.epoch = 0
self.start_epoch = 1
self.lr = args.learning_rate
self.best_acc = 0
# Log
self.log_path = (
PROJECT_ROOT / Path(SAVE_DIR) /
Path(datetime.now().strftime("%Y%m%d%H%M%S") + "-")
).as_posix()
self.log_path = Path(self.get_dirname(self.log_path, args))
if not Path.exists(self.log_path):
Path(self.log_path).mkdir(parents=True, exist_ok=True)
self.logger = Logger("train", self.log_path, args.verbose)
self.logger.log("Checkpoint files will be saved in {}".format(self.log_path))
self.logger.add_level('STEP', 21, 'green')
self.logger.add_level('EPOCH', 22, 'cyan')
self.logger.add_level('EVAL', 23, 'yellow')
self.criterion = nn.CrossEntropyLoss()
if self.args.cuda:
self.criterion = self.criterion.cuda()
if args.half:
self.model.half()
self.criterion.half()
params = self.model.parameters()
self.optimizer = get_optimizer(args.optimizer, params, args)
def train(self):
self.eval()
for self.epoch in range(self.start_epoch, self.args.epochs+1):
self.adjust_learning_rate([int(self.args.epochs/2), int(self.args.epochs*3/4)], factor=0.1)
self.train_epoch()
self.eval()
self.logger.writer.export_scalars_to_json(
self.log_path.as_posix() + "/scalars-{}-{}-{}.json".format(
self.args.model,
self.args.seed,
self.args.activation
)
)
self.logger.writer.close()
def train_epoch(self):
self.model.train()
eval_metrics = EvaluationMetrics(['Loss', 'Acc', 'Time'])
for i, (images, labels) in enumerate(self.train_loader):
st = time.time()
self.step += 1
images = torch.autograd.Variable(images)
labels = torch.autograd.Variable(labels)
if self.args.cuda:
images = images.cuda()
labels = labels.cuda()
if self.args.half: images = images.half()
outputs, loss = self.compute_loss(images, labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
outputs = outputs.float()
loss = loss.float()
elapsed_time = time.time() - st
_, preds = torch.max(outputs, 1)
accuracy = (labels == preds.squeeze()).float().mean()
batch_size = labels.size(0)
eval_metrics.update('Loss', float(loss), batch_size)
eval_metrics.update('Acc', float(accuracy), batch_size)
eval_metrics.update('Time', elapsed_time, batch_size)
if self.step % self.args.log_step == 0:
self.logger.scalar_summary(eval_metrics.val, self.step, 'STEP')
# Histogram of parameters
for tag, p in self.model.named_parameters():
tag = tag.split(".")
tag = "Train_{}".format(tag[0]) + "/" + "/".join(tag[1:])
try:
self.logger.writer.add_histogram(tag, p.clone().cpu().data.numpy(), self.step)
self.logger.writer.add_histogram(tag+'/grad', p.grad.clone().cpu().data.numpy(), self.step)
except Exception as e:
print("Check if variable {} is not used: {}".format(tag, e))
self.logger.scalar_summary(eval_metrics.avg, self.step, 'EPOCH')
def eval(self):
self.model.eval()
eval_metrics = EvaluationMetrics(['Loss', 'Acc', 'Time'])
for i, (images, labels) in enumerate(self.val_loader):
st = time.time()
images = torch.autograd.Variable(images)
labels = torch.autograd.Variable(labels)
if self.args.cuda:
images = images.cuda()
labels = labels.cuda()
if self.args.half: images = images.half()
outputs, loss = self.compute_loss(images, labels)
outputs = outputs.float()
loss = loss.float()
elapsed_time = time.time() - st
_, preds = torch.max(outputs, 1)
accuracy = (labels == preds.squeeze()).float().mean()
batch_size = labels.size(0)
eval_metrics.update('Loss', float(loss), batch_size)
eval_metrics.update('Acc', float(accuracy), batch_size)
eval_metrics.update('Time', elapsed_time, batch_size)
# Save best model
if eval_metrics.avg['Acc'] > self.best_acc:
self.save()
self.logger.log("Saving best model: epoch={}".format(self.epoch))
self.best_acc = eval_metrics.avg['Acc']
self.maybe_delete_old_pth(log_path=self.log_path.as_posix(), max_to_keep=1)
self.logger.scalar_summary(eval_metrics.avg, self.step, 'EVAL')
def get_dirname(self, path, args):
path += "{}-".format(getattr(args, 'dataset'))
path += "{}-".format(getattr(args, 'seed'))
path += "{}".format(getattr(args, 'model'))
return path
def save(self, filename=None):
if filename is None:
filename = os.path.join(self.log_path, 'model-{}.pth'.format(self.epoch))
torch.save({
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'epoch': self.start_epoch,
'best_acc': self.best_acc,
'args': self.args
}, filename)
def load(self, filename=None):
if filename is None: filename = self.log_path
S = torch.load(filename) if self.args.cuda else torch.load(filename, map_location=lambda storage, location: storage)
self.model.load_state_dict(S['model'])
self.optimizer.load_state_dict(S['optimizer'])
self.epoch = S['epoch']
self.best_acc = S['best_acc']
self.args = S['args']
def maybe_delete_old_pth(self, log_path, max_to_keep):
"""Model filename must end with xxx-xxx-[epoch].pth
"""
# filename and time
pths = [(f, int(f[:-4].split("-")[-1])) for f in os.listdir(log_path) if f.endswith('.pth')]
if len(pths) > max_to_keep:
sorted_pths = sorted(pths, key=lambda tup: tup[1])
for i in range(len(pths) - max_to_keep):
os.remove(os.path.join(log_path, sorted_pths[i][0]))
def show_current_model(self):
print("\n".join("{}: {}".format(k, v) for k, v in sorted(vars(self.args).items())))
model_parameters = filter(lambda p: p.requires_grad, self.model.parameters())
total_params = np.sum([np.prod(p.size()) for p in model_parameters])
print('%s\n\n'%(type(self.model)))
print('%s\n\n'%(inspect.getsource(self.model.__init__)))
print('%s\n\n'%(inspect.getsource(self.model.forward)))
# Total 95
print("*"*40 + "%10s" % self.args.model + "*"*45)
print("*"*40 + "PARAM INFO" + "*"*45)
print("-"*95)
print("| %40s | %25s | %20s |" % ("Param Name", "Shape", "Number of Params"))
print("-"*95)
for name, param in self.model.named_parameters():
if param.requires_grad:
print("| %40s | %25s | %20d |" % (name, list(param.size()), np.prod(param.size())))
print("-"*95)
print("Total Params: %d" % (total_params))
print("*"*95)
def adjust_learning_rate(self, milestone, factor=0.1):
if self.epoch in milestone:
self.lr *= factor
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.lr
def compute_loss(self, images, labels):
outputs = self.model(images)
loss = self.criterion(outputs, labels)
return outputs, loss
class Defender(Trainer):
""" Perform various adversarial attacks and defense on a pretrained model
Scheme generates Tensor, not Variable
"""
def __init__(self, val_loader, args, **kwargs):
self.val_loader = val_loader
self.args = args
self.model = get_model(args)
self.step = 0
self.cuda = self.args.cuda
self.log_path = (
PROJECT_ROOT / Path("experiments") /
Path(datetime.now().strftime("%Y%m%d%H%M%S") + "-")).as_posix()
self.log_path = Path(self.get_dirname(self.log_path, args))
if not Path.exists(self.log_path):
Path(self.log_path).mkdir(parents=True, exist_ok=True)
self.logger = Logger("defense", self.log_path, args.verbose)
self.logger.log("Checkpoint files will be saved in {}".format(
self.log_path))
self.logger.add_level("ATTACK", 21, 'yellow')
self.logger.add_level("DEFENSE", 22, 'cyan')
self.logger.add_level("TEST", 23, 'white')
self.logger.add_level("DIST", 11, 'white')
self.kwargs = kwargs
if args.domain_restrict:
self.artifact = get_artifact(self.model, val_loader, args)
self.kwargs['artifact'] = self.artifact
def defend(self):
self.model.eval()
defense_scheme = getattr(defenses,
self.args.defense)(self.model, self.args,
**self.kwargs)
source = self.model
if self.args.source is not None and (self.args.ckpt_name !=
self.args.ckpt_src):
target = self.args.ckpt_name
self.args.model = self.args.source
self.args.ckpt_name = self.args.ckpt_src
source = get_model(self.args)
self.logger.log("Transfer attack from {} -> {}".format(
self.args.ckpt_src, target))
attack_scheme = getattr(attacks, self.args.attack)(source, self.args,
**self.kwargs)
eval_metrics = EvaluationMetrics(
['Test/Acc', 'Test/Top5', 'Test/Time'])
eval_def_metrics = EvaluationMetrics(
['Def-Test/Acc', 'Def-Test/Top5', 'Def-Test/Time'])
attack_metrics = EvaluationMetrics(
['Attack/Acc', 'Attack/Top5', 'Attack/Time'])
defense_metrics = EvaluationMetrics(
['Defense/Acc', 'Defense/Top5', 'Defense/Time'])
dist_metrics = EvaluationMetrics(['L0', 'L1', 'L2', 'Li'])
for i, (images, labels) in enumerate(self.val_loader):
self.step += 1
if self.cuda:
images = images.cuda()
labels = labels.cuda()
if self.args.half: images = images.half()
# Inference
st = time.time()
outputs = self.model(self.to_var(images, self.cuda, True))
outputs = outputs.float()
_, preds = torch.topk(outputs, 5)
acc = (labels == preds.data[:, 0]).float().mean()
top5 = torch.sum(
(labels.unsqueeze(1).repeat(1, 5) == preds.data).float(),
dim=1).mean()
eval_metrics.update('Test/Acc', float(acc), labels.size(0))
eval_metrics.update('Test/Top5', float(top5), labels.size(0))
eval_metrics.update('Test/Time', time.time() - st, labels.size(0))
# Attacker
st = time.time()
adv_images, adv_labels = attack_scheme.generate(images, labels)
if isinstance(adv_images, Variable):
adv_images = adv_images.data
attack_metrics.update('Attack/Time',
time.time() - st, labels.size(0))
# Lp distance
diff = torch.abs(
denormalize(adv_images, self.args.dataset) -
denormalize(images, self.args.dataset))
L0 = torch.sum((torch.sum(diff, dim=1) > 1e-3).float().view(
labels.size(0), -1),
dim=1).mean()
diff = diff.view(labels.size(0), -1)
L1 = torch.norm(diff, p=1, dim=1).mean()
L2 = torch.norm(diff, p=2, dim=1).mean()
Li = torch.max(diff, dim=1)[0].mean()
dist_metrics.update('L0', float(L0), labels.size(0))
dist_metrics.update('L1', float(L1), labels.size(0))
dist_metrics.update('L2', float(L2), labels.size(0))
dist_metrics.update('Li', float(Li), labels.size(0))
# Defender
st = time.time()
def_images, def_labels = defense_scheme.generate(
adv_images, adv_labels)
if isinstance(
def_images, Variable
): # FIXME - Variable in Variable out for all methods
def_images = def_images.data
defense_metrics.update('Defense/Time',
time.time() - st, labels.size(0))
self.calc_stats('Attack', adv_images, images, adv_labels, labels,
attack_metrics)
self.calc_stats('Defense', def_images, images, def_labels, labels,
defense_metrics)
# Defense-Inference for shift of original image
st = time.time()
def_images_org, _ = defense_scheme.generate(images, labels)
if isinstance(
def_images_org, Variable
): # FIXME - Variable in Variable out for all methods
def_images_org = def_images_org.data
outputs = self.model(self.to_var(def_images_org, self.cuda, True))
outputs = outputs.float()
_, preds = torch.topk(outputs, 5)
acc = (labels == preds.data[:, 0]).float().mean()
top5 = torch.sum(
(labels.unsqueeze(1).repeat(1, 5) == preds.data).float(),
dim=1).mean()
eval_def_metrics.update('Def-Test/Acc', float(acc), labels.size(0))
eval_def_metrics.update('Def-Test/Top5', float(top5),
labels.size(0))
eval_def_metrics.update('Def-Test/Time',
time.time() - st, labels.size(0))
if self.step % self.args.log_step == 0 or self.step == len(
self.val_loader):
self.logger.scalar_summary(eval_metrics.avg, self.step, 'TEST')
self.logger.scalar_summary(eval_def_metrics.avg, self.step,
'TEST')
self.logger.scalar_summary(attack_metrics.avg, self.step,
'ATTACK')
self.logger.scalar_summary(defense_metrics.avg, self.step,
'DEFENSE')
self.logger.scalar_summary(dist_metrics.avg, self.step, 'DIST')
defense_rate = eval_metrics.avg[
'Test/Acc'] - defense_metrics.avg['Defense/Acc']
if eval_metrics.avg['Test/Acc'] - attack_metrics.avg[
'Attack/Acc']:
defense_rate /= eval_metrics.avg[
'Test/Acc'] - attack_metrics.avg['Attack/Acc']
else:
defense_rate = 0
defense_rate = 1 - defense_rate
defense_top5 = eval_metrics.avg[
'Test/Top5'] - defense_metrics.avg['Defense/Top5']
if eval_metrics.avg['Test/Top5'] - attack_metrics.avg[
'Attack/Top5']:
defense_top5 /= eval_metrics.avg[
'Test/Top5'] - attack_metrics.avg['Attack/Top5']
else:
defense_top5 = 0
defense_top5 = 1 - defense_top5
self.logger.log(
"Defense Rate Top1: {:5.3f} | Defense Rate Top5: {:5.3f}".
format(defense_rate, defense_top5), 'DEFENSE')
if self.step % self.args.img_log_step == 0:
image_dict = {
'Original':
to_np(denormalize(images, self.args.dataset))[0],
'Attacked':
to_np(denormalize(adv_images, self.args.dataset))[0],
'Defensed':
to_np(denormalize(def_images, self.args.dataset))[0],
'Perturbation':
to_np(denormalize(images - adv_images,
self.args.dataset))[0]
}
self.logger.image_summary(image_dict, self.step)
def calc_stats(self, method, gen_images, images, gen_labels, labels,
metrics):
"""gen_images: Generated from attacker or defender
Currently just calculating acc and artifact
"""
success_rate = 0
if not isinstance(gen_images, Variable):
gen_images = self.to_var(gen_images.clone(), self.cuda, True)
gen_outputs = self.model(gen_images)
gen_outputs = gen_outputs.float()
_, gen_preds = torch.topk(F.softmax(gen_outputs, dim=1), 5)
if isinstance(gen_preds, Variable):
gen_preds = gen_preds.data
gen_acc = (labels == gen_preds[:, 0]).float().mean()
gen_top5 = torch.sum(
(labels.unsqueeze(1).repeat(1, 5) == gen_preds).float(),
dim=1).mean()
metrics.update('{}/Acc'.format(method), float(gen_acc), labels.size(0))
metrics.update('{}/Top5'.format(method), float(gen_top5),
labels.size(0))
def to_var(self, x, cuda, volatile=False):
"""For CPU inference manual cuda setting is needed
"""
if cuda:
x = x.cuda()
return torch.autograd.Variable(x, volatile=volatile)
