def main2(args):
best_prec1 = 0.0
torch.backends.cudnn.deterministic = not args.cudaNoise
torch.manual_seed(time.time())
if args.init != "None":
args.name = "lrnet_%s" % args.init
if args.tensorboard:
configure(f"runs/{args.name}")
dstype = nondigits(args.dataset)
if dstype == "cifar":
means = [125.3, 123.0, 113.9]
stds = [63.0, 62.1, 66.7]
elif dstype == "imgnet":
means = [123.3, 118.1, 108.0]
stds = [54.1, 52.6, 53.2]
normalize = transforms.Normalize(
mean=[x / 255.0 for x in means],
std=[x / 255.0 for x in stds],
)
writer = SummaryWriter(log_dir="runs/%s" % args.name, comment=str(args))
args.classes = onlydigits(args.dataset)
if args.augment:
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: F.pad(x.unsqueeze(0), (4, 4, 4, 4),
mode="reflect").squeeze()),
transforms.ToPILImage(),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
else:
transform_train = transforms.Compose(
[transforms.ToTensor(), normalize])
if args.cutout:
transform_train.transforms.append(
Cutout(n_holes=args.n_holes, length=args.length))
transform_test = transforms.Compose([transforms.ToTensor(), normalize])
kwargs = {"num_workers": 1, "pin_memory": True}
assert dstype in ["cifar", "cinic", "imgnet"]
if dstype == "cifar":
train_loader = torch.utils.data.DataLoader(
datasets.__dict__[args.dataset.upper()]("../data",
train=True,
download=True,
transform=transform_train),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
val_loader = torch.utils.data.DataLoader(
datasets.__dict__[args.dataset.upper()]("../data",
train=False,
transform=transform_test),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
elif dstype == "cinic":
cinic_directory = "%s/cinic10" % args.dir
cinic_mean = [0.47889522, 0.47227842, 0.43047404]
cinic_std = [0.24205776, 0.23828046, 0.25874835]
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(cinic_directory + '/train',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=cinic_mean,
std=cinic_std)
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
print("Using CINIC10 dataset")
val_loader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(cinic_directory + '/valid',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=cinic_mean,
std=cinic_std)
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
elif dstype == "imgnet":
print("Using converted imagenet")
train_loader = torch.utils.data.DataLoader(
IMGNET("%s" % args.dir,
train=True,
transform=transform_train,
target_transform=None,
classes=args.classes),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
val_loader = torch.utils.data.DataLoader(
IMGNET("%s" % args.dir,
train=False,
transform=transform_test,
target_transform=None,
classes=args.classes),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
else:
print("Error matching dataset %s" % dstype)
##print("main bn:")
##print(args.batchnorm)
##print("main fixup:")
##print(args.fixup)
if args.prune:
pruner_state = getPruneMask(args)
if pruner_state is None:
print("Failed to prune network, aborting")
return None
if args.arch.lower() == "constnet":
model = WideResNet(
args.layers,
args.classes,
args.widen_factor,
droprate=args.droprate,
use_bn=args.batchnorm,
use_fixup=args.fixup,
varnet=args.varnet,
noise=args.noise,
lrelu=args.lrelu,
sigmaW=args.sigmaW,
init=args.init,
dropl1=args.dropl1,
)
elif args.arch.lower() == "leakynet":
model = LRNet(
args.layers,
args.classes,
args.widen_factor,
droprate=args.droprate,
use_bn=args.batchnorm,
use_fixup=args.fixup,
varnet=args.varnet,
noise=args.noise,
lrelu=args.lrelu,
sigmaW=args.sigmaW,
init=args.init,
)
else:
print("arch %s is not supported" % args.arch)
return None
##draw(args,model) complex installation
param_num = sum([p.data.nelement() for p in model.parameters()])
print(f"Number of model parameters: {param_num}")
if torch.cuda.device_count() > 1:
start = int(args.device[0])
end = int(args.device[2]) + 1
torch.cuda.set_device(start)
dev_list = []
for i in range(start, end):
dev_list.append("cuda:%d" % i)
model = torch.nn.DataParallel(model, device_ids=dev_list)
model = model.cuda()
if args.freeze > 0:
cnt = 0
for name, param in model.named_parameters():
if intersection(['scale'], name.split('.')):
cnt = cnt + 1
if cnt == args.freeze:
break
if cnt >= args.freeze_start:
## if intersection(['conv','conv1'],name.split('.')):
## print("Freezing Block: %s" % name.split('.')[1:3] )
if not intersection(['conv_res', 'fc'], name.split('.')):
param.requires_grad = False
print("Freezing Block: %s" % name)
elif args.freeze < 0:
cnt = 0
for name, param in model.named_parameters():
if intersection(['scale'], name.split('.')):
cnt = cnt + 1
if cnt > args.layers - 3 + args.freeze - 1:
## if intersection(['conv','conv1'],name.split('.')):
## print("Freezing Block: %s" % name )
if not intersection(['conv_res', 'fc'], name.split('.')):
param.requires_grad = False
print("Freezing Block: %s" % name)
if args.res_freeze > 0:
cnt = 0
for name, param in model.named_parameters():
if intersection(['conv_res'], name.split('.')):
cnt = cnt + 1
if cnt > args.res_freeze_start:
param.requires_grad = False
print("Freezing Block: %s" % name)
if cnt >= args.res_freeze:
break
elif args.res_freeze < 0:
cnt = 0
for name, param in model.named_parameters():
if intersection(['conv_res'], name.split('.')):
cnt = cnt + 1
if cnt > 3 + args.res_freeze:
param.requires_grad = False
print("Freezing Block: %s" % name)
if args.prune:
if args.prune_epoch >= 100:
weightsFile = "runs/%s-net/checkpoint.pth.tar" % args.prune
else:
weightsFile = "runs/%s-net/model_epoch_%d.pth.tar" % (
args.prune, args.prune_epoch)
if os.path.isfile(weightsFile):
print(f"=> loading checkpoint {weightsFile}")
checkpoint = torch.load(weightsFile)
model.load_state_dict(checkpoint["state_dict"])
print(
f"=> loaded checkpoint '{weightsFile}' (epoch {checkpoint['epoch']})"
)
else:
if args.prune_epoch == 0:
print(f"=> No source data, Restarting network from scratch")
else:
print(f"=> no checkpoint found at {weightsFile}, aborting...")
return None
else:
if args.resume:
tarfile = "runs/%s-net/checkpoint.pth.tar" % args.resume
if os.path.isfile(tarfile):
print(f"=> loading checkpoint {args.resume}")
checkpoint = torch.load(tarfile)
args.start_epoch = checkpoint["epoch"]
best_prec1 = checkpoint["best_prec1"]
model.load_state_dict(checkpoint["state_dict"])
print(
f"=> loaded checkpoint '{tarfile}' (epoch {checkpoint['epoch']})"
)
else:
print(f"=> no checkpoint found at {tarfile}, aborting...")
return None
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss().cuda()
if args.optimizer.lower() == 'sgd':
optimizer = torch.optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
nesterov=args.nesterov,
weight_decay=args.weight_decay,
)
elif args.optimizer.lower() == 'radam':
optimizer = RAdam(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay)
if args.prune and pruner_state is not None:
cutoff_retrain = prunhild.cutoff.LocalRatioCutoff(args.cutoff)
params_retrain = get_params_for_pruning(args, model)
pruner_retrain = prunhild.pruner.CutoffPruner(params_retrain,
cutoff_retrain)
pruner_retrain.load_state_dict(pruner_state)
pruner_retrain.prune(update_state=False)
pruned_weights_count = count_pruned_weights(params_retrain,
args.cutoff)
params_left = param_num - pruned_weights_count
print("Pruned %d weights, New model size: %d/%d (%d%%)" %
(pruned_weights_count, params_left, param_num,
int(100 * params_left / param_num)))
else:
pruner_retrain = None
if args.eval:
best_prec1 = validate(args, val_loader, model, criterion, 0, None)
else:
if args.varnet:
save_checkpoint(
args,
{
"epoch": 0,
"state_dict": model.state_dict(),
"best_prec1": 0.0,
},
True,
)
best_prec1 = 0.0
turns_above_50 = 0
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(args, optimizer, epoch + 1)
train(args, train_loader, model, criterion, optimizer, epoch,
pruner_retrain, writer)
prec1 = validate(args, val_loader, model, criterion, epoch, writer)
correlation.measure_correlation(model, epoch, writer=writer)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
if args.savenet:
save_checkpoint(
args,
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"best_prec1": best_prec1,
},
is_best,
)
if args.symmetry_break:
if prec1 > 50.0:
turns_above_50 += 1
if turns_above_50 > 3:
return epoch
writer.close()
print("Best accuracy: ", best_prec1)
return best_prec1
def main(args):
save_folder = '%s_%s' % (args.dataset, args.affix)
log_folder = os.path.join(args.log_root, save_folder)
model_folder = os.path.join(args.model_root, save_folder)
makedirs(log_folder)
makedirs(model_folder)
setattr(args, 'log_folder', log_folder)
setattr(args, 'model_folder', model_folder)
logger = create_logger(log_folder, args.todo, 'info')
print_args(args, logger)
model = WideResNet(depth=34, num_classes=10, widen_factor=10, dropRate=0.0)
attack = FastGradientSignUntargeted(model,
args.epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=args.perturbation_type)
if torch.cuda.is_available():
model.cuda()
trainer = Trainer(args, logger, attack)
if args.todo == 'train':
transform_train = tv.transforms.Compose([
tv.transforms.ToTensor(),
tv.transforms.Lambda(lambda x: F.pad(x.unsqueeze(0),
(4,4,4,4), mode='constant', value=0).squeeze()),
tv.transforms.ToPILImage(),
tv.transforms.RandomCrop(32),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
])
tr_dataset = tv.datasets.CIFAR10(args.data_root,
train=True,
transform=transform_train,
download=True)
tr_loader = DataLoader(tr_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4)
# evaluation during training
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4)
trainer.train(model, tr_loader, te_loader, args.adv_train)
elif args.todo == 'test':
pass
else:
raise NotImplementedError
def getPruneMask(args):
baseTar = "runs/%s-net/checkpoint.pth.tar" % args.prune
if os.path.isfile(baseTar):
classes = onlydigits(args.prune_classes)
if classes == 0:
classes = args.classes
fullModel = WideResNet(
args.layers,
classes,
args.widen_factor,
droprate=args.droprate,
use_bn=args.batchnorm,
use_fixup=args.fixup,
varnet=args.varnet,
noise=args.noise,
lrelu=args.lrelu,
sigmaW=args.sigmaW,
)
if torch.cuda.device_count() > 1:
start = int(args.device[0])
end = int(args.device[2]) + 1
torch.cuda.set_device(start)
dev_list = []
for i in range(start, end):
dev_list.append("cuda:%d" % i)
fullModel = torch.nn.DataParallel(fullModel, device_ids=dev_list)
fullModel = fullModel.cuda()
print(f"=> loading checkpoint {baseTar}")
checkpoint = torch.load(baseTar)
fullModel.load_state_dict(checkpoint["state_dict"])
# --------------------------- #
# --- Pruning Setup Start --- #
cutoff = prunhild.cutoff.LocalRatioCutoff(args.cutoff)
# don't prune the final bias weights
params = get_params_for_pruning(args, fullModel)
print(params)
pruner = prunhild.pruner.CutoffPruner(params,
cutoff,
prune_online=True)
pruner.prune()
print(
f"=> loaded checkpoint '{baseTar}' (epoch {checkpoint['epoch']})")
if torch.cuda.device_count() > 1:
start = int(args.device[0])
end = int(args.device[2]) + 1
for i in range(start, end):
torch.cuda.set_device(i)
torch.cuda.empty_cache()
mask = pruner.state_dict()
if args.randomize_mask:
mask = randomize_mask(mask, args.cutoff)
return mask
else:
print(f"=> no checkpoint found at {baseTar}")
return None
data, label = tensor2cuda(data), tensor2cuda(label)
break
adv_list = []
pred_list = []
with torch.no_grad():
model = WideResNet(depth=34, num_classes=10, widen_factor=10, dropRate=0.0)
load_model(model, args.load_checkpoint)
if torch.cuda.is_available():
model.cuda()
attack = FastGradientSignUntargeted(model,
max_epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=perturbation_type)
adv_data = attack.perturb(data, label, 'mean', False)
output = model(adv_data, _eval=True)
pred = torch.max(output, dim=1)[1]
adv_list.append(adv_data.cpu().numpy().squeeze() * 255.0) # (N, 28, 28)
pred_list.append(pred.cpu().numpy())
def main(args):
save_folder = '%s_%s' % (args.dataset, args.affix)
log_folder = os.path.join(args.log_root, save_folder)
model_folder = os.path.join(args.model_root, save_folder)
makedirs(log_folder)
makedirs(model_folder)
setattr(args, 'log_folder', log_folder)
setattr(args, 'model_folder', model_folder)
logger = create_logger(log_folder, args.todo, 'info')
print_args(args, logger)
# Using a WideResNet model
model = WideResNet(depth=34, num_classes=10, widen_factor=1, dropRate=0.0)
flop, param = get_model_infos(model, (1, 3, 32, 32))
logger.info('Model Info: FLOP = {:.2f} M, Params = {:.2f} MB'.format(
flop, param))
# Configuring the train attack mode
if args.adv_train_mode == 'FGSM':
train_attack = FastGradientSignUntargeted(model,
args.epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=args.perturbation_type,
logger=logger)
elif args.adv_train_mode == 'CW':
mean = [0]
std = [1]
inputs_box = (min((0 - m) / s for m, s in zip(mean, std)),
max((1 - m) / s for m, s in zip(mean, std)))
train_attack = carlini_wagner_L2.L2Adversary(targeted=False,
confidence=0.0,
search_steps=10,
optimizer_lr=5e-4,
logger=logger)
# Configuring the test attack mode
if args.adv_test_mode == 'FGSM':
test_attack = FastGradientSignUntargeted(model,
args.epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=args.perturbation_type,
logger=logger)
elif args.adv_test_mode == 'CW':
mean = [0]
std = [1]
inputs_box = (min((0 - m) / s for m, s in zip(mean, std)),
max((1 - m) / s for m, s in zip(mean, std)))
test_attack = carlini_wagner_L2.L2Adversary(targeted=False,
confidence=0.0,
search_steps=10,
optimizer_lr=5e-4,
logger=logger)
if torch.cuda.is_available():
model.cuda()
trainer = Trainer(args, logger, train_attack, test_attack)
if args.todo == 'train':
transform_train = tv.transforms.Compose([
tv.transforms.ToTensor(),
tv.transforms.Lambda(lambda x: F.pad(
x.unsqueeze(0),
(4, 4, 4, 4), mode='constant', value=0).squeeze()),
tv.transforms.ToPILImage(),
tv.transforms.RandomCrop(32),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
])
tr_dataset = tv.datasets.CIFAR10(args.data_root,
train=True,
transform=transform_train,
download=True)
tr_loader = DataLoader(tr_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# evaluation during training
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
trainer.train(model, tr_loader, te_loader, args.adv_train)
elif args.todo == 'test':
pass
else:
raise NotImplementedError
def main():
global args, best_prec1
args = parser.parse_args()
if args.tensorboard:
configure(f"runs/{args.name}")
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]],
)
if args.augment:
transform_train = transforms.Compose(
[
transforms.ToTensor(),
transforms.Lambda(
lambda x: F.pad(
x.unsqueeze(0), (4, 4, 4, 4), mode="reflect"
).squeeze()
),
transforms.ToPILImage(),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]
)
else:
transform_train = transforms.Compose([transforms.ToTensor(), normalize])
if args.cutout:
transform_train.transforms.append(
Cutout(n_holes=args.n_holes, length=args.length)
)
transform_test = transforms.Compose([transforms.ToTensor(), normalize])
kwargs = {"num_workers": 1, "pin_memory": True}
assert args.dataset == "cifar10" or args.dataset == "cifar100"
train_loader = torch.utils.data.DataLoader(
datasets.__dict__[args.dataset.upper()](
"../data", train=True, download=True, transform=transform_train
),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
val_loader = torch.utils.data.DataLoader(
datasets.__dict__[args.dataset.upper()](
"../data", train=False, transform=transform_test
),
batch_size=args.batch_size,
shuffle=True,
**kwargs,
)
model = WideResNet(
args.layers,
args.dataset == "cifar10" and 10 or 100,
args.widen_factor,
droprate=args.droprate,
use_bn=args.batchnorm,
use_fixup=args.fixup,
)
param_num = sum([p.data.nelement() for p in model.parameters()])
print(f"Number of model parameters: {param_num}")
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model = model.cuda()
if args.resume:
if os.path.isfile(args.resume):
print(f"=> loading checkpoint {args.resume}")
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
best_prec1 = checkpoint["best_prec1"]
model.load_state_dict(checkpoint["state_dict"])
print(f"=> loaded checkpoint '{args.resume}' (epoch {checkpoint['epoch']})")
else:
print(f"=> no checkpoint found at {args.resume}")
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
nesterov=args.nesterov,
weight_decay=args.weight_decay,
)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch + 1)
train(train_loader, model, criterion, optimizer, epoch)
prec1 = validate(val_loader, model, criterion, epoch)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"best_prec1": best_prec1,
},
is_best,
)
print("Best accuracy: ", best_prec1)
test_dataset = TEST_DATASETS[args.dataset]
dataset_config = DATASET_CONFIGS[args.dataset]
# instantiate the model instance.
wrn = WideResNet(
args.dataset,
dataset_config['size'],
dataset_config['channels'],
dataset_config['classes'],
total_block_number=args.total_block_number,
widen_factor=args.widen_factor,
)
# prepare cuda if needed.
if cuda:
wrn.cuda()
# run the given command.
if args.test:
utils.load_checkpoint(wrn, args.model_dir, best=True)
utils.validate(
wrn, test_dataset, test_size=args.test_size,
cuda=cuda, verbose=True
)
else:
train(
wrn, train_dataset, test_dataset=test_dataset,
model_dir=args.model_dir,
lr=args.lr,
batch_size=args.batch_size,
test_size=args.test_size,