def test_grad_through_normalize():
tensor = torch.rand((2, 1, 28, 28))
tensor.requires_grad_()
mean = torch.tensor((0., ))
std = torch.tensor((1., ))
normalize = NormalizeByChannelMeanStd(mean, std)
loss = (normalize(tensor)**2).sum()
loss.backward()
assert torch_allclose(2 * tensor, tensor.grad)
class Transform(object):
classifier_training = transforms.Compose([
transforms.Normalize((-1., -1., -1.), (2., 2., 2.)),
transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD)
])
classifier_testing = transforms.Compose([
transforms.Normalize((-1., -1., -1.), (2., 2., 2.)),
transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD)
])
default = transforms.ToTensor()
gan_deprocess_layer = []
classifier_preprocess_layer = NormalizeByChannelMeanStd(MEAN, STD).cuda()
class Transform(object):
gan_training = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
classifier_training = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD)
])
classifier_testing = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD)
])
# transformations used during testing / generating attacks
# we assume loaded images to be in [0, 1]
# since g(z) is in [-1, 1], the output should be deprocessed into [0, 1]
default = transforms.ToTensor()
gan_deprocess_layer = NormalizeByChannelMeanStd([-1., -1., -1.],
[2., 2., 2.]).cuda()
classifier_preprocess_layer = NormalizeByChannelMeanStd(
CIFAR10_MEAN, CIFAR10_STD).cuda()
def __init__(self, block, num_blocks, num_classes=10):
super(PreActResNet, self).__init__()
self.in_planes = 64
self.nomalize = NormalizeByChannelMeanStd(
mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])
self.conv1 = nn.Conv2d(3,
64,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
self.linear_main = nn.Linear(512 * block.expansion, num_classes)
def get_models(args,
train=True,
as_ensemble=False,
model_file=None,
leaky_relu=False,
dataset=None):
mean = torch.tensor([0.4914, 0.4822, 0.4465], dtype=torch.float32).cuda()
if (dataset == "STL-10"):
std = torch.tensor([0.2023, 0.1994, 0.2010],
dtype=torch.float32).cuda()
#std = torch.tensor([0.2471, 0.2435, 0.2616], dtype=torch.float32).cuda()
elif (dataset == "CIFAR-10"):
std = torch.tensor([0.2023, 0.1994, 0.2010],
dtype=torch.float32).cuda()
normalizer = NormalizeByChannelMeanStd(mean=mean, std=std)
if model_file:
state_dict = torch.load(model_file)
if train:
print('Loading pre-trained models...')
if args.arch.lower() == 'resnet':
model = ResNet18() #depth=args.depth, leaky_relu=leaky_relu)
else:
raise ValueError('[{:s}] architecture is not supported yet...')
# we include input normalization as a part of the model
model = ModelWrapper(model, normalizer)
if model_file:
model.load_state_dict(state_dict)
if train:
model.train()
else:
model.eval()
model = model.cuda()
return model
def get_models(args, train=True, as_ensemble=False, model_file=None, leaky_relu=False):
models = []
mean = torch.tensor([0.4914, 0.4822, 0.4465], dtype=torch.float32).cuda()
std = torch.tensor([0.2023, 0.1994, 0.2010], dtype=torch.float32).cuda()
normalizer = NormalizeByChannelMeanStd(mean=mean, std=std)
if model_file:
state_dict = torch.load(model_file)
if train:
print('Loading pre-trained models...')
iter_m = state_dict.keys() if model_file else range(args.model_num)
for i in iter_m:
if args.arch.lower() == 'resnet':
model = ResNet(depth=args.depth, leaky_relu=leaky_relu)
else:
raise ValueError('[{:s}] architecture is not supported yet...')
# we include input normalization as a part of the model
model = ModelWrapper(model, normalizer)
if model_file:
model.load_state_dict(state_dict[i])
if train:
model.train()
else:
model.eval()
model = model.cuda()
models.append(model)
if as_ensemble:
assert not train, 'Must be in eval mode when getting models to form an ensemble'
ensemble = Ensemble(models)
ensemble.eval()
return ensemble
else:
return models
def main():
args = get_args()
if not os.path.exists(args.fname):
os.makedirs(args.fname)
logger = logging.getLogger(__name__)
logging.basicConfig(
format='[%(asctime)s] - %(message)s',
datefmt='%Y/%m/%d %H:%M:%S',
level=logging.DEBUG,
handlers=[
logging.FileHandler(
os.path.join(args.fname,
'eval.log' if args.eval else 'output.log')),
logging.StreamHandler()
])
logger.info(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
transforms = [Crop(32, 32), FlipLR()]
# transforms = [Crop(32, 32)]
if args.cutout:
transforms.append(Cutout(args.cutout_len, args.cutout_len))
if args.val:
try:
dataset = torch.load("cifar10_validation_split.pth")
except:
print(
"Couldn't find a dataset with a validation split, did you run "
"generate_validation.py?")
return
val_set = list(
zip(transpose(dataset['val']['data'] / 255.),
dataset['val']['labels']))
val_batches = Batches(val_set,
args.batch_size,
shuffle=False,
num_workers=2)
else:
dataset = cifar10(args.data_dir)
train_set = list(
zip(transpose(pad(dataset['train']['data'], 4) / 255.),
dataset['train']['labels']))
train_set_x = Transform(train_set, transforms)
train_batches = Batches(train_set_x,
args.batch_size,
shuffle=True,
set_random_choices=True,
num_workers=2)
test_set = list(
zip(transpose(dataset['test']['data'] / 255.),
dataset['test']['labels']))
test_batches = Batches(test_set,
args.batch_size,
shuffle=False,
num_workers=2)
trn_epsilon = (args.trn_epsilon / 255.)
trn_pgd_alpha = (args.trn_pgd_alpha / 255.)
tst_epsilon = (args.tst_epsilon / 255.)
tst_pgd_alpha = (args.tst_pgd_alpha / 255.)
if args.model == 'PreActResNet18':
model = PreActResNet18()
elif args.model == 'WideResNet':
model = WideResNet(34,
10,
widen_factor=args.width_factor,
dropRate=0.0)
elif args.model == 'DenseNet121':
model = DenseNet121()
elif args.model == 'ResNet18':
model = ResNet18()
else:
raise ValueError("Unknown model")
### temp testing ###
model = model.cuda()
# model = nn.DataParallel(model).cuda()
model.train()
##################################
# load pretrained model if needed
if args.trn_adv_models != 'None':
if args.trn_adv_arch == 'PreActResNet18':
trn_adv_model = PreActResNet18()
elif args.trn_adv_arch == 'WideResNet':
trn_adv_model = WideResNet(34,
10,
widen_factor=args.width_factor,
dropRate=0.0)
elif args.trn_adv_arch == 'DenseNet121':
trn_adv_model = DenseNet121()
elif args.trn_adv_arch == 'ResNet18':
trn_adv_model = ResNet18()
trn_adv_model = nn.DataParallel(trn_adv_model).cuda()
trn_adv_model.load_state_dict(
torch.load(
os.path.join('./adv_models', args.trn_adv_models,
'model_best.pth'))['state_dict'])
logger.info(f'loaded adv_model: {args.trn_adv_models}')
else:
trn_adv_model = None
if args.tst_adv_models != 'None':
if args.tst_adv_arch == 'PreActResNet18':
tst_adv_model = PreActResNet18()
elif args.tst_adv_arch == 'WideResNet':
tst_adv_model = WideResNet(34,
10,
widen_factor=args.width_factor,
dropRate=0.0)
elif args.tst_adv_arch == 'DenseNet121':
tst_adv_model = DenseNet121()
elif args.tst_adv_arch == 'ResNet18':
tst_adv_model = ResNet18()
### temp testing ###
tst_adv_model = tst_adv_model.cuda()
tst_adv_model.load_state_dict(
torch.load(
os.path.join('./adv_models', args.tst_adv_models,
'model_best.pth')))
# tst_adv_model = nn.DataParallel(tst_adv_model).cuda()
# tst_adv_model.load_state_dict(torch.load(os.path.join('./adv_models',args.tst_adv_models, 'model_best.pth'))['state_dict'])
logger.info(f'loaded adv_model: {args.tst_adv_models}')
else:
tst_adv_model = None
##################################
if args.l2:
decay, no_decay = [], []
for name, param in model.named_parameters():
if 'bn' not in name and 'bias' not in name:
decay.append(param)
else:
no_decay.append(param)
params = [{
'params': decay,
'weight_decay': args.l2
}, {
'params': no_decay,
'weight_decay': 0
}]
else:
params = model.parameters()
opt = torch.optim.SGD(params,
lr=args.lr_max,
momentum=0.9,
weight_decay=5e-4)
criterion = nn.CrossEntropyLoss()
if args.trn_attack == 'free':
delta = torch.zeros(args.batch_size, 3, 32, 32).cuda()
delta.requires_grad = True
elif args.trn_attack == 'fgsm' and args.trn_fgsm_init == 'previous':
delta = torch.zeros(args.batch_size, 3, 32, 32).cuda()
delta.requires_grad = True
if args.trn_attack == 'free':
epochs = int(math.ceil(args.epochs / args.trn_attack_iters))
else:
epochs = args.epochs
if args.lr_schedule == 'superconverge':
lr_schedule = lambda t: np.interp([t], [
0, args.epochs * 2 // 5, args.epochs
], [0, args.lr_max, 0])[0]
elif args.lr_schedule == 'piecewise':
def lr_schedule(t):
if t / args.epochs < 0.5:
return args.lr_max
elif t / args.epochs < 0.75:
return args.lr_max / 10.
else:
return args.lr_max / 100.
elif args.lr_schedule == 'linear':
lr_schedule = lambda t: np.interp([t], [
0, args.epochs // 3, args.epochs * 2 // 3, args.epochs
], [args.lr_max, args.lr_max, args.lr_max / 10, args.lr_max / 100])[0]
elif args.lr_schedule == 'onedrop':
def lr_schedule(t):
if t < args.lr_drop_epoch:
return args.lr_max
else:
return args.lr_one_drop
elif args.lr_schedule == 'multipledecay':
def lr_schedule(t):
return args.lr_max - (t //
(args.epochs // 10)) * (args.lr_max / 10)
elif args.lr_schedule == 'cosine':
def lr_schedule(t):
return args.lr_max * 0.5 * (1 + np.cos(t / args.epochs * np.pi))
best_test_robust_acc = 0
best_val_robust_acc = 0
if args.resume:
### temp testing ###
model.load_state_dict(
torch.load(os.path.join(args.fname, 'model_best.pth')))
start_epoch = args.resume
# model.load_state_dict(torch.load(os.path.join(args.fname, f'model_{start_epoch-1}.pth')))
# opt.load_state_dict(torch.load(os.path.join(args.fname, f'opt_{start_epoch-1}.pth')))
# logger.info(f'Resuming at epoch {start_epoch}')
# best_test_robust_acc = torch.load(os.path.join(args.fname, f'model_best.pth'))['test_robust_acc']
if args.val:
best_val_robust_acc = torch.load(
os.path.join(args.fname, f'model_val.pth'))['val_robust_acc']
else:
start_epoch = 0
if args.eval:
if not args.resume:
logger.info(
"No model loaded to evaluate, specify with --resume FNAME")
return
logger.info("[Evaluation mode]")
logger.info(
'Epoch \t Train Time \t Test Time \t LR \t \t Train Loss \t Train Acc \t Train Robust Loss \t Train Robust Acc \t Test Loss \t Test Acc \t Test Robust Loss \t Test Robust Acc'
)
for epoch in range(start_epoch, epochs):
model.train()
start_time = time.time()
train_loss = 0
train_acc = 0
train_robust_loss = 0
train_robust_acc = 0
train_n = 0
for i, batch in enumerate(train_batches):
if args.eval:
break
X, y = batch['input'], batch['target']
if args.mixup:
X, y_a, y_b, lam = mixup_data(X, y, args.mixup_alpha)
X, y_a, y_b = map(Variable, (X, y_a, y_b))
lr = lr_schedule(epoch + (i + 1) / len(train_batches))
opt.param_groups[0].update(lr=lr)
if args.trn_attack == 'pgd':
# Random initialization
if args.mixup:
delta = attack_pgd(model,
X,
y,
trn_epsilon,
trn_pgd_alpha,
args.trn_attack_iters,
args.trn_restarts,
args.trn_norm,
mixup=True,
y_a=y_a,
y_b=y_b,
lam=lam,
adv_models=trn_adv_model)
else:
delta = attack_pgd(model,
X,
y,
trn_epsilon,
trn_pgd_alpha,
args.trn_attack_iters,
args.trn_restarts,
args.trn_norm,
adv_models=trn_adv_model)
delta = delta.detach()
elif args.trn_attack == 'fgsm':
delta = attack_pgd(model,
X,
y,
trn_epsilon,
args.trn_fgsm_alpha * trn_epsilon,
1,
1,
args.trn_norm,
adv_models=trn_adv_model,
rand_init=args.trn_fgsm_init)
delta = delta.detach()
# Standard training
elif args.trn_attack == 'none':
delta = torch.zeros_like(X)
# The Momentum Iterative Attack
elif args.trn_attack == 'tmim':
if trn_adv_model is None:
adversary = MomentumIterativeAttack(
model,
nb_iter=args.trn_attack_iters,
eps=trn_epsilon,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps_iter=trn_pgd_alpha,
clip_min=0,
clip_max=1,
targeted=False)
else:
trn_adv_model = nn.Sequential(
NormalizeByChannelMeanStd(CIFAR10_MEAN, CIFAR10_STD),
trn_adv_model)
adversary = MomentumIterativeAttack(
trn_adv_model,
nb_iter=args.trn_attack_iters,
eps=trn_epsilon,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps_iter=trn_pgd_alpha,
clip_min=0,
clip_max=1,
targeted=False)
data_adv = adversary.perturb(X, y)
delta = data_adv - X
delta = delta.detach()
robust_output = model(
normalize(
torch.clamp(X + delta[:X.size(0)],
min=lower_limit,
max=upper_limit)))
if args.mixup:
robust_loss = mixup_criterion(criterion, robust_output, y_a,
y_b, lam)
else:
robust_loss = criterion(robust_output, y)
if args.l1:
for name, param in model.named_parameters():
if 'bn' not in name and 'bias' not in name:
robust_loss += args.l1 * param.abs().sum()
opt.zero_grad()
robust_loss.backward()
opt.step()
output = model(normalize(X))
if args.mixup:
loss = mixup_criterion(criterion, output, y_a, y_b, lam)
else:
loss = criterion(output, y)
train_robust_loss += robust_loss.item() * y.size(0)
train_robust_acc += (robust_output.max(1)[1] == y).sum().item()
train_loss += loss.item() * y.size(0)
train_acc += (output.max(1)[1] == y).sum().item()
train_n += y.size(0)
train_time = time.time()
model.eval()
test_loss = 0
test_acc = 0
test_robust_loss = 0
test_robust_acc = 0
test_n = 0
for i, batch in enumerate(test_batches):
X, y = batch['input'], batch['target']
# Random initialization
if args.tst_attack == 'none':
delta = torch.zeros_like(X)
elif args.tst_attack == 'pgd':
delta = attack_pgd(model,
X,
y,
tst_epsilon,
tst_pgd_alpha,
args.tst_attack_iters,
args.tst_restarts,
args.tst_norm,
adv_models=tst_adv_model,
rand_init=args.tst_fgsm_init)
elif args.tst_attack == 'fgsm':
delta = attack_pgd(model,
X,
y,
tst_epsilon,
tst_epsilon,
1,
1,
args.tst_norm,
rand_init=args.tst_fgsm_init,
adv_models=tst_adv_model)
# The Momentum Iterative Attack
elif args.tst_attack == 'tmim':
if tst_adv_model is None:
adversary = MomentumIterativeAttack(
model,
nb_iter=args.tst_attack_iters,
eps=tst_epsilon,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps_iter=tst_pgd_alpha,
clip_min=0,
clip_max=1,
targeted=False)
else:
tmp_model = nn.Sequential(
NormalizeByChannelMeanStd(cifar10_mean, cifar10_std),
tst_adv_model).to(device)
adversary = MomentumIterativeAttack(
tmp_model,
nb_iter=args.tst_attack_iters,
eps=tst_epsilon,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps_iter=tst_pgd_alpha,
clip_min=0,
clip_max=1,
targeted=False)
data_adv = adversary.perturb(X, y)
delta = data_adv - X
# elif args.tst_attack == 'pgd':
# if tst_adv_model is None:
# tmp_model = nn.Sequential(NormalizeByChannelMeanStd(cifar10_mean, cifar10_std), model).to(device)
# adversary = PGDAttack(tmp_model, nb_iter=args.tst_attack_iters,
# eps = tst_epsilon,
# loss_fn=nn.CrossEntropyLoss(reduction="sum"),
# eps_iter=tst_pgd_alpha, clip_min = 0, clip_max = 1, targeted=False)
# else:
# tmp_model = nn.Sequential(NormalizeByChannelMeanStd(cifar10_mean, cifar10_std), tst_adv_model).to(device)
# adversary = PGDAttack(tmp_model, nb_iter=args.tst_attack_iters,
# eps = tst_epsilon,
# loss_fn=nn.CrossEntropyLoss(reduction="sum"),
# eps_iter=tst_pgd_alpha, clip_min = 0, clip_max = 1, targeted=False)
# data_adv = adversary.perturb(X, y)
# delta = data_adv - X
delta = delta.detach()
robust_output = model(
normalize(
torch.clamp(X + delta[:X.size(0)],
min=lower_limit,
max=upper_limit)))
robust_loss = criterion(robust_output, y)
output = model(normalize(X))
loss = criterion(output, y)
test_robust_loss += robust_loss.item() * y.size(0)
test_robust_acc += (robust_output.max(1)[1] == y).sum().item()
test_loss += loss.item() * y.size(0)
test_acc += (output.max(1)[1] == y).sum().item()
test_n += y.size(0)
test_time = time.time()
if args.val:
val_loss = 0
val_acc = 0
val_robust_loss = 0
val_robust_acc = 0
val_n = 0
for i, batch in enumerate(val_batches):
X, y = batch['input'], batch['target']
# Random initialization
if args.tst_attack == 'none':
delta = torch.zeros_like(X)
elif args.tst_attack == 'pgd':
delta = attack_pgd(model,
X,
y,
tst_epsilon,
tst_pgd_alpha,
args.tst_attack_iters,
args.tst_restarts,
args.tst_norm,
early_stop=args.eval)
elif args.tst_attack == 'fgsm':
delta = attack_pgd(model,
X,
y,
tst_epsilon,
tst_epsilon,
1,
1,
args.tst_norm,
early_stop=args.eval,
rand_init=args.tst_fgsm_init)
delta = delta.detach()
robust_output = model(
normalize(
torch.clamp(X + delta[:X.size(0)],
min=lower_limit,
max=upper_limit)))
robust_loss = criterion(robust_output, y)
output = model(normalize(X))
loss = criterion(output, y)
val_robust_loss += robust_loss.item() * y.size(0)
val_robust_acc += (robust_output.max(1)[1] == y).sum().item()
val_loss += loss.item() * y.size(0)
val_acc += (output.max(1)[1] == y).sum().item()
val_n += y.size(0)
if not args.eval:
logger.info(
'%d \t %.1f \t \t %.1f \t \t %.4f \t %.4f \t %.4f \t %.4f \t \t %.4f \t \t %.4f \t %.4f \t %.4f \t \t %.4f',
epoch, train_time - start_time, test_time - train_time, lr,
train_loss / train_n, train_acc / train_n,
train_robust_loss / train_n, train_robust_acc / train_n,
test_loss / test_n, test_acc / test_n,
test_robust_loss / test_n, test_robust_acc / test_n)
if args.val:
logger.info('validation %.4f \t %.4f \t %.4f \t %.4f',
val_loss / val_n, val_acc / val_n,
val_robust_loss / val_n, val_robust_acc / val_n)
if val_robust_acc / val_n > best_val_robust_acc:
torch.save(
{
'state_dict': model.state_dict(),
'test_robust_acc': test_robust_acc / test_n,
'test_robust_loss': test_robust_loss / test_n,
'test_loss': test_loss / test_n,
'test_acc': test_acc / test_n,
'val_robust_acc': val_robust_acc / val_n,
'val_robust_loss': val_robust_loss / val_n,
'val_loss': val_loss / val_n,
'val_acc': val_acc / val_n,
}, os.path.join(args.fname, f'model_val.pth'))
best_val_robust_acc = val_robust_acc / val_n
# save checkpoint
if (epoch + 1) % args.chkpt_iters == 0 or epoch + 1 == epochs:
torch.save(model.state_dict(),
os.path.join(args.fname, f'model_{epoch}.pth'))
torch.save(opt.state_dict(),
os.path.join(args.fname, f'opt_{epoch}.pth'))
# save best
if test_robust_acc / test_n > best_test_robust_acc:
torch.save(
{
'state_dict': model.state_dict(),
'test_robust_acc': test_robust_acc / test_n,
'test_robust_loss': test_robust_loss / test_n,
'test_loss': test_loss / test_n,
'test_acc': test_acc / test_n,
}, os.path.join(args.fname, f'model_best.pth'))
best_test_robust_acc = test_robust_acc / test_n
else:
logger.info(
'%d \t %.1f \t \t %.1f \t \t %.4f \t %.4f \t %.4f \t %.4f \t \t %.4f \t \t %.4f \t %.4f \t %.4f \t \t %.4f',
epoch, train_time - start_time, test_time - train_time, -1, -1,
-1, -1, -1, test_loss / test_n, test_acc / test_n,
test_robust_loss / test_n, test_robust_acc / test_n)
return
def main():
global args, best_prec1
args = parser.parse_args()
print(args)
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
torch.cuda.set_device(int(args.gpu))
setup_seed(args.seed)
model = jigsaw_model(args.class_number)
normalize = NormalizeByChannelMeanStd(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
model = nn.Sequential(normalize, model)
model.cuda()
cudnn.benchmark = True
train_trans = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor()
])
val_trans = transforms.Compose([transforms.ToTensor()])
#dataset process
train_dataset = datasets.CIFAR10(args.data,
train=True,
transform=train_trans,
download=True)
test_dataset = datasets.CIFAR10(args.data,
train=False,
transform=val_trans,
download=True)
valid_size = 0.1
indices = list(range(len(train_dataset)))
split = int(np.floor(valid_size * len(train_dataset)))
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = torch.utils.data.Subset(train_dataset, train_idx)
valid_sampler = torch.utils.data.Subset(train_dataset, valid_idx)
train_loader = torch.utils.data.DataLoader(train_sampler,
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(valid_sampler,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda step: cosine_annealing(
step,
args.epochs * len(train_loader),
1, # since lr_lambda computes multiplicative factor
1e-6 / args.lr))
print('std training')
train_acc = []
ta = []
#gernerate order of jigsaw
permutation = np.array(
[np.random.permutation(16) for i in range(args.class_number - 1)])
np.save('permutation.npy', permutation)
if os.path.exists(args.save_dir) is not True:
os.mkdir(args.save_dir)
for epoch in range(args.epochs):
print(optimizer.state_dict()['param_groups'][0]['lr'])
acc, loss = train(train_loader, model, criterion, optimizer, epoch,
scheduler, permutation)
# evaluate on validation set
tacc, tloss = validate(val_loader, model, criterion, permutation)
train_acc.append(acc)
ta.append(tacc)
# remember best prec@1 and save checkpoint
is_best = tacc > best_prec1
best_prec1 = max(tacc, best_prec1)
if is_best:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=os.path.join(args.save_dir, 'best_model.pt'))
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=os.path.join(args.save_dir, 'model.pt'))
plt.plot(train_acc, label='train_acc')
plt.plot(ta, label='TA')
plt.legend()
plt.savefig(os.path.join(args.save_dir, 'net_train.png'))
plt.close()
model_path = os.path.join(args.save_dir, 'best_model.pt')
model.load_state_dict(torch.load(model_path)['state_dict'])
print('testing result of ta best model')
tacc, tloss = validate(test_loader, model, criterion, permutation)
def main():
global args, best_sa
args = parser.parse_args()
print(args)
torch.cuda.set_device(int(args.gpu))
os.makedirs(args.save_dir, exist_ok=True)
if args.seed:
setup_seed(args.seed)
if args.task == 'rotation':
print('train for rotation classification')
class_number = 4
else:
print('train for supervised classification')
if args.dataset == 'cifar10':
class_number = 10
elif args.dataset == 'fmnist':
class_number = 10
elif args.dataset == 'cifar100':
class_number = 100
else:
print('error dataset')
assert 0
# prepare dataset
if args.dataset == 'cifar10':
print('training on cifar10 dataset')
model = resnet18(num_classes=class_number)
model.normalize = NormalizeByChannelMeanStd(
mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616])
train_loader, val_loader, test_loader = cifar10_dataloaders(batch_size= args.batch_size, data_dir =args.data)
elif args.dataset == 'cifar_10_10':
print('training on cifar10 subset')
model = resnet18(num_classes=class_number)
model.normalize = NormalizeByChannelMeanStd(
mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616])
train_loader, val_loader, test_loader = cifar10_subset_dataloaders(batch_size= args.batch_size, data_dir =args.data)
elif args.dataset == 'cifar100':
model = resnet18(num_classes=class_number)
model.normalize = NormalizeByChannelMeanStd(
mean=[0.5071, 0.4865, 0.4409], std=[0.2673, 0.2564, 0.2762])
train_loader, val_loader, test_loader = cifar100_dataloaders(batch_size= args.batch_size, data_dir =args.data)
elif args.dataset == 'fmnist':
model = resnet18(num_classes=class_number)
model.normalize = NormalizeByChannelMeanStd(
mean=[0.2860], std=[0.3530])
model.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, bias=False)
train_loader, val_loader, test_loader = fashionmnist_dataloaders(batch_size= args.batch_size, data_dir =args.data)
else:
print('dataset not support')
model.cuda()
criterion = nn.CrossEntropyLoss()
decreasing_lr = list(map(int, args.decreasing_lr.split(',')))
if args.prune_type == 'lt':
print( 'report lottery tickets setting')
initalization = deepcopy(model.state_dict())
else:
initalization = None
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=decreasing_lr, gamma=0.1)
if args.resume:
print('resume from checkpoint')
checkpoint = torch.load(args.resume, map_location = torch.device('cuda:'+str(args.gpu)))
best_sa = checkpoint['best_sa']
start_epoch = checkpoint['epoch']
all_result = checkpoint['result']
start_state = checkpoint['state']
if start_state>0:
current_mask = extract_mask(checkpoint['state_dict'])
prune_model_custom(model, current_mask)
check_sparsity(model)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
initalization = checkpoint['init_weight']
print('loading state:', start_state)
print('loading from epoch: ',start_epoch, 'best_sa=', best_sa)
else:
all_result = {}
all_result['train'] = []
all_result['test_ta'] = []
all_result['ta'] = []
start_epoch = 0
start_state = 0
print('######################################## Start Standard Training Iterative Pruning ########################################')
print(model.normalize)
for state in range(start_state, args.pruning_times):
print('******************************************')
print('pruning state', state)
print('******************************************')
for epoch in range(start_epoch, args.epochs):
print(optimizer.state_dict()['param_groups'][0]['lr'])
check_sparsity(model)
acc = train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
tacc = validate(val_loader, model, criterion)
# evaluate on test set
test_tacc = validate(test_loader, model, criterion)
scheduler.step()
all_result['train'].append(acc)
all_result['ta'].append(tacc)
all_result['test_ta'].append(test_tacc)
# remember best prec@1 and save checkpoint
is_best_sa = tacc > best_sa
best_sa = max(tacc, best_sa)
save_checkpoint({
'state': state,
'result': all_result,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_sa': best_sa,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'init_weight': initalization
}, is_SA_best=is_best_sa, pruning=state, save_path=args.save_dir)
plt.plot(all_result['train'], label='train_acc')
plt.plot(all_result['ta'], label='val_acc')
plt.plot(all_result['test_ta'], label='test_acc')
plt.legend()
plt.savefig(os.path.join(args.save_dir, str(state)+'net_train.png'))
plt.close()
#report result
check_sparsity(model, True)
print('report best SA={}'.format(best_sa))
all_result = {}
all_result['train'] = []
all_result['test_ta'] = []
all_result['ta'] = []
best_sa = 0
start_epoch = 0
if args.prune_type == 'pt':
print('report loading pretrained weight')
initalization = torch.load(os.path.join(args.save_dir, '0model_SA_best.pth.tar'), map_location = torch.device('cuda:'+str(args.gpu)))['state_dict']
#pruning_model(model, args.rate)
#current_mask = extract_mask(model.state_dict())
#remove_prune(model)
#rewind weight to init
pruning_model(model, args.rate)
check_sparsity(model)
current_mask = torch.load(os.path.join(args.mask_path, '{}checkpoint.pth.tar'.format(state+1)))['state_dict']
remove_prune(model)
#rewind weight to init
model.load_state_dict(initalization)
prune_model_custom(model, current_mask)
check_sparsity(model)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=decreasing_lr, gamma=0.1)
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
os.makedirs('logs', exist_ok=True)
# Model
MEAN = torch.Tensor([0.4914, 0.4822, 0.4465])
STD = torch.Tensor([0.2023, 0.1994, 0.2010])
norm = NormalizeByChannelMeanStd(mean=MEAN, std=STD)
print('==> Building model..')
net = torch.nn.DataParallel(nn.Sequential(norm, ResNet18_feat()).cuda())
cudnn.benchmark = True
checkpoint = torch.load('./checkpoint/batch_adv0_grad0_lambda_1.0.t7')
net.load_state_dict(checkpoint['net'])
#freeze last layer
# net.module[-1].linear.weight.requires_grad = False
# net.module[-1].linear.bias.requires_grad = False
if args.resume or args.test:
# Load checkpoint.
print('==> Resuming from checkpoint..')
def main():
global args, best_sa
args = parser.parse_args()
print(args)
torch.cuda.set_device(int(args.gpu))
os.makedirs(args.save_dir, exist_ok=True)
if args.seed:
setup_seed(args.seed)
_, val_loader, _ = cifar10_dataloaders()
class_number = 10
model = resnet18(num_classes=class_number)
# prepare dataset
img_results = torch.load("results.pth", map_location="cpu")
model.normalize = NormalizeByChannelMeanStd(mean=[0.4914, 0.4822, 0.4465],
std=[0.2470, 0.2435, 0.2616])
model.cuda()
criterion = nn.CrossEntropyLoss()
initalization = deepcopy(model.state_dict())
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
all_result = {}
all_result['train'] = []
all_result['test_ta'] = []
all_result['ta'] = []
start_epoch = 0
start_state = 0
print(
'######################################## Start Standard Training Iterative Pruning ########################################'
)
print(model.normalize)
for state in range(start_state, args.pruning_times):
print('******************************************')
print('pruning state', state)
print('******************************************')
for epoch in range(start_epoch, args.epochs):
print(optimizer.state_dict()['param_groups'][0]['lr'])
check_sparsity(model)
acc = train(img_results, model, criterion, optimizer, epoch)
# evaluate on validation set
tacc = validate(val_loader, model, criterion)
# evaluate on test set
all_result['train'].append(acc)
all_result['ta'].append(tacc)
# remember best prec@1 and save checkpoint
is_best_sa = tacc > best_sa
best_sa = max(tacc, best_sa)
save_checkpoint(
{
'state': state,
'result': all_result,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_sa': best_sa,
'optimizer': optimizer.state_dict(),
'init_weight': initalization
},
is_SA_best=is_best_sa,
pruning=state,
save_path=args.save_dir)
plt.plot(all_result['train'], label='train_acc')
plt.plot(all_result['ta'], label='val_acc')
plt.plot(all_result['test_ta'], label='test_acc')
plt.legend()
plt.savefig(
os.path.join(args.save_dir,
str(state) + 'net_train.png'))
plt.close()
#report result
check_sparsity(model, True)
print('report best SA={}'.format(best_sa))
all_result = {}
all_result['train'] = []
all_result['test_ta'] = []
all_result['ta'] = []
best_sa = 0
start_epoch = 0
if args.prune_type == 'pt':
print('report loading pretrained weight')
initalization = torch.load(
os.path.join(args.save_dir, '0model_SA_best.pth.tar'),
map_location=torch.device('cuda:' +
str(args.gpu)))['state_dict']
pruning_model(model, args.rate)
check_sparsity(model)
current_mask = extract_mask(model.state_dict())
remove_prune(model)
#rewind weight to init
model.load_state_dict(initalization)
#pruning using custom mask
prune_model_custom(model, current_mask)
check_sparsity(model)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
def init_advertorch(self, model, device, attack_params, dataset_params):
mean = dataset_params['mean']
std = dataset_params['std']
num_classes = dataset_params['num_classes']
self.normalize = NormalizeByChannelMeanStd(mean=mean, std=std)
basic_model = model
if (attack_params['bpda'] == True):
preprocess = attack_params['preprocess']
preprocess_bpda_wrapper = BPDAWrapper(
preprocess, forwardsub=preprocess.back_approx)
attack_model = nn.Sequential(self.normalize,
preprocess_bpda_wrapper,
basic_model).to(device)
else:
attack_model = nn.Sequential(self.normalize,
basic_model).to(device)
attack_name = attack_params['attack'].lower()
if (attack_name == 'pgd'):
iterations = attack_params['iterations']
stepsize = attack_params['stepsize']
epsilon = attack_params['epsilon']
attack = advertorch.attacks.LinfPGDAttack
random = attack_params['random']
# Return attack dictionary
return {
'attack': attack,
'iterations': iterations,
'epsilon': epsilon,
'stepsize': stepsize,
'model': attack_model,
'random': random
}
elif (attack_name == 'cw'):
iterations = attack_params['iterations']
epsilon = attack_params['epsilon']
attack = advertorch.attacks.CarliniWagnerL2Attack
# Return attack dictionary
return {
'attack': attack,
'iterations': iterations,
'epsilon': epsilon,
'model': attack_model,
'num_classes': num_classes
}
elif (attack_name == 'fgsm'):
epsilon = attack_params['epsilon']
attack = advertorch.attacks.FGSM
# Return attack dictionary
return {
'attack': attack,
'iterations': 1,
'epsilon': epsilon,
'model': attack_model,
'num_classes': num_classes
}
else:
# Right way to handle exception in python see https://stackoverflow.com/questions/2052390/manually-raising-throwing-an-exception-in-python
# Explains all the traps of using exception, does a good job!! I mean the link :)
raise ValueError("Unsupported attack")
def main():
global args, best_prec1, best_ata
args = parser.parse_args()
print(args)
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
torch.cuda.set_device(int(args.gpu))
setup_seed(args.seed)
model = ResNet50()
normalize = NormalizeByChannelMeanStd(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
model = nn.Sequential(normalize, model)
model.cuda()
cudnn.benchmark = True
if args.pretrained_model:
model_dict_pretrain = torch.load(
args.pretrained_model,
map_location=torch.device('cuda:' + str(args.gpu)))
model.load_state_dict(model_dict_pretrain, strict=False)
print('model loaded:', args.pretrained_model)
#dataset process
train_trans = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor()
])
val_trans = transforms.Compose([transforms.ToTensor()])
#dataset process
train_dataset = datasets.CIFAR10(args.data,
train=True,
transform=train_trans,
download=True)
test_dataset = datasets.CIFAR10(args.data,
train=False,
transform=val_trans,
download=True)
valid_size = 0.1
indices = list(range(len(train_dataset)))
split = int(np.floor(valid_size * len(train_dataset)))
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = torch.utils.data.Subset(train_dataset, train_idx)
valid_sampler = torch.utils.data.Subset(train_dataset, valid_idx)
train_loader = torch.utils.data.DataLoader(train_sampler,
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(valid_sampler,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
decreasing_lr = list(map(int, args.decreasing_lr.split(',')))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=decreasing_lr,
gamma=0.1)
print('adv training')
train_acc = []
ta = []
ata = []
if os.path.exists(args.save_dir) is not True:
os.mkdir(args.save_dir)
for epoch in range(args.epochs):
print(optimizer.state_dict()['param_groups'][0]['lr'])
acc, loss = train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
tacc, tloss = validate(val_loader, model, criterion)
atacc, atloss = validate_adv(val_loader, model, criterion)
scheduler.step()
train_acc.append(acc)
ta.append(tacc)
ata.append(atacc)
# remember best prec@1 and save checkpoint
is_best = tacc > best_prec1
best_prec1 = max(tacc, best_prec1)
ata_is_best = atacc > best_ata
best_ata = max(atacc, best_ata)
if is_best:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=os.path.join(args.save_dir, 'best_model.pt'))
if ata_is_best:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=os.path.join(args.save_dir, 'ata_best_model.pt'))
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=os.path.join(args.save_dir, 'model.pt'))
plt.plot(train_acc, label='train_acc')
plt.plot(ta, label='TA')
plt.plot(ata, label='ATA')
plt.legend()
plt.savefig(os.path.join(args.save_dir, 'net_train.png'))
plt.close()
best_model_path = os.path.join(args.save_dir, 'ata_best_model.pt')
print('start testing ATA best model')
model.load_state_dict(torch.load(best_model_path)['state_dict'])
tacc, tloss = validate(test_loader, model, criterion)
atacc, atloss = validate_adv(test_loader, model, criterion)
best_model_path = os.path.join(args.save_dir, 'best_model.pt')
print('start testing TA best model')
model.load_state_dict(torch.load(best_model_path)['state_dict'])
tacc, tloss = validate(test_loader, model, criterion)
atacc, atloss = validate_adv(test_loader, model, criterion)
def main():
global args, best_prec1, ata_best_prec1
args = parser.parse_args()
print(args)
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
torch.cuda.set_device(int(args.gpu))
setup_seed(args.seed)
# Data Preprocess
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
data_transforms = {
'train': transforms.Compose([
transforms.ToPILImage(),
transforms.Pad(2),
transforms.RandomCrop(32),
# transforms.RandomHorizontalFlip(),
# transforms.RandomRotation(5),
transforms.ToTensor(),
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'val': transforms.Compose([
transforms.ToPILImage(),
#transforms.Pad(2),
#transforms.RandomCrop(32),
transforms.ToTensor(),
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
}
if args.dataset == 'cifar':
train_dataset = CifarDataset(args.data, True, data_transforms['train'], args.percent)
test_dataset = CifarDataset(args.data, False, data_transforms['val'], 1)
elif args.dataset == 'imagenet':
train_dataset = ImageNetDataset(args.data, True, data_transforms['train'], args.percent)
test_dataset = ImageNetDataset(args.data, False, data_transforms['val'], 1)
elif args.dataset == 'imagenet224':
data_transforms = {
'train': transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'val': transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
}
train_dataset = datasets.ImageNet(args.data, 'train', True, data_transforms['train'])
test_dataset = datasets.ImageNet(args.data, 'train', True, data_transforms['val'])
valid_size = 0.1
indices = list(range(len(train_dataset)))
split = int(np.floor(valid_size*len(train_dataset)))
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = torch.utils.data.Subset(train_dataset, train_idx)
valid_sampler = torch.utils.data.Subset(train_dataset, valid_idx)
train_loader = torch.utils.data.DataLoader(
train_sampler,
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
valid_sampler,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
# define model
n_split = 4
selfie_model = get_selfie_model(n_split)
selfie_model = selfie_model.cuda()
P=get_P_model()
normalize = NormalizeByChannelMeanStd(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
P = nn.Sequential(normalize, P)
P = P.cuda()
#define optimizer and scheduler
params_list = [{'params': selfie_model.parameters(), 'lr': args.lr,
'weight_decay': args.weight_decay},]
params_list.append({'params': P.parameters(), 'lr': args.lr, 'weight_decay': args.weight_decay})
optimizer = torch.optim.SGD(params_list, lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay, nesterov = True)
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda step: cosine_annealing(
step,
args.epochs * len(train_loader),
1, # since lr_lambda computes multiplicative factor
1e-7 / args.lr))
print("Training model.")
step = 0
if os.path.exists(args.modeldir) is not True:
os.mkdir(args.modeldir)
stats_ = stats(args.modeldir, args.start_epoch)
if args.epochs > 0:
#order of patches
all_seq=[np.random.permutation(16) for ind in range(400)]
pickle.dump(all_seq, open(os.path.join(args.modeldir, 'img_test_seq.pkl'),'wb'))
# all_seq=pickle.load(open(os.path.join(args.modeldir, 'img_test_seq.pkl'),'rb'))
print("Begin selfie training...")
for epoch in range(args.start_epoch, args.epochs):
print("The learning rate is {}".format(optimizer.param_groups[0]['lr']))
trainObj, top1 = train_selfie_adv(train_loader, selfie_model, P, criterion, optimizer, epoch, scheduler)
valObj, prec1 = val_selfie(val_loader, selfie_model, P, criterion, all_seq)
adv_valObj, adv_prec1 = val_pgd_selfie(val_loader, selfie_model, P, criterion, all_seq)
stats_._update(trainObj, top1, valObj, prec1,adv_valObj, adv_prec1)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
ata_is_best = adv_prec1 > ata_best_prec1
ata_best_prec1 = max(adv_prec1, ata_best_prec1)
if is_best:
torch.save(
{
'epoch': epoch,
'P_state': P.state_dict(),
'selfie_state': selfie_model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, os.path.join(args.modeldir, 'adv_selfie_TA_model_best.pth.tar'))
if ata_is_best:
torch.save(
{
'epoch': epoch,
'P_state': P.state_dict(),
'selfie_state': selfie_model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, os.path.join(args.modeldir, 'adv_selfie_ATA_model_best.pth.tar'))
torch.save(
{
'epoch': epoch,
'P_state': P.state_dict(),
'selfie_state': selfie_model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, os.path.join(args.modeldir, 'adv_selfie_checkpoint.pth.tar'))
plot_curve(stats_, args.modeldir, True)
data = stats_
sio.savemat(os.path.join(args.modeldir,'stats.mat'), {'data':data})
print("testing ATA best selfie model from checkpoint...")
model_path = os.path.join(args.modeldir, 'adv_selfie_ATA_model_best.pth.tar')
model_loaded = torch.load(model_path)
P.load_state_dict(model_loaded['P_state'])
selfie_model.load_state_dict(model_loaded['selfie_state'])
print("Best ATA selfie model loaded! ")
valObj, prec1 = val_selfie(test_loader, selfie_model, P, criterion, all_seq)
adv_valObj, adv_prec1 = val_pgd_selfie(test_loader, selfie_model, P, criterion, all_seq)
print("testing TA best selfie model from checkpoint...")
model_path = os.path.join(args.modeldir, 'adv_selfie_TA_model_best.pth.tar')
model_loaded = torch.load(model_path)
P.load_state_dict(model_loaded['P_state'])
selfie_model.load_state_dict(model_loaded['selfie_state'])
print("Best TA selfie model loaded! ")
valObj, prec1 = val_selfie(test_loader, selfie_model, P, criterion, all_seq)
adv_valObj, adv_prec1 = val_pgd_selfie(test_loader, selfie_model, P, criterion, all_seq)
def __init__(
self,
clip_values,
step=200,
means=None,
stds=None,
gan_ckpt='styleGAN.pt',
encoder_ckpt='encoder.pt',
optimize_noise=True,
use_noise_regularize=False,
use_lpips=False,
apply_fit=False,
apply_predict=True,
mse=500,
lr_rampup=0.05,
lr_rampdown=0.05,
noise=0.05,
noise_ramp=0.75,
noise_regularize=1e5,
lr=0.1
):
super(InvGAN, self).__init__()
#print("invgan")
#pdb.set_trace()
self._apply_fit = apply_fit
self._apply_predict = apply_predict
# setup normalization parameters
if means is None:
means = (0.0, 0.0, 0.0) # identity operation
if len(means) != 3:
raise ValueError("means must have 3 values, one per channel")
self.means = means
if stds is None:
stds = (1.0, 1.0, 1.0) # identity operation
if len(stds) != 3:
raise ValueError("stds must have 3 values, one per channel")
self.stds = stds
self.clip_values = clip_values
# setup optimization parameters
self.optimize_noise = optimize_noise
self.use_noise_regularize = use_noise_regularize
self.use_lpips = use_lpips
self.step = step
self.mse = mse
self.lr = lr
self.lr_rampup = lr_rampup
self.lr_rampdown = lr_rampdown
self.noise = noise
self.noise_ramp = noise_ramp
self.noise_regularize = noise_regularize
# setup generator
self.generator = Generator(256, 512, 8)
#self.generator.load_state_dict(torch.load(gan_ckpt)['g_ema'])
self.generator.load_state_dict(torch.load(maybe_download_weights_from_s3(gan_ckpt))['g_ema'])
self.generator.eval()
self.generator.cuda()
self.deprocess_layer = NormalizeByChannelMeanStd([-1., -1., -1.], [2., 2., 2.]).cuda()
# setup encoder
self.encoder = Encoder()
#self.encoder.load_state_dict(torch.load(encoder_ckpt)['netE'])
self.encoder.load_state_dict(torch.load(maybe_download_weights_from_s3(encoder_ckpt))['netE'])
self.encoder.eval()
self.encoder.cuda()
# setup loss
if use_lpips:
self.lpips = PerceptualLoss().cuda()
# estimate latent code statistics
n_mean_latent = 10000
with torch.no_grad():
noise_sample = torch.randn(n_mean_latent, 512, device='cuda')
latent_out = self.generator.style(noise_sample)
latent_mean = latent_out.mean(0)
self.latent_std = ((latent_out - latent_mean).pow(2).sum() / n_mean_latent) ** 0.5
def __init__(self,
block,
layers,
num_classes=1000,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(
replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.normalize = NormalizeByChannelMeanStd(
mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616])
self.conv1 = nn.Conv2d(3,
self.inplanes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.Identity()
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
