import waitGPU
waitGPU.wait(utilization=20, available_memory=10000, interval=10)
import problems as pblm
from trainer import *
import setproctitle
if __name__ == "__main__":
args = pblm.argparser(prefix='mnist',
method='task_spec_robust',
opt='adam',
starting_epsilon=0.05,
epsilon=0.2,
thres=0.035)
kwargs = pblm.args2kwargs(args)
setproctitle.setproctitle('python')
print("threshold for classification error: {:.1%}".format(args.thres))
print('Matrix type: {0}\t\t'
'Category: {1}\t\t'
'Epoch number: {2}\t\t'
'Targeted epsilon: {3}\t\t'
'Starting epsilon: {4}\t\t'
'Sechduled length: {5}'.format(args.type, args.category, args.epochs,
args.epsilon, args.starting_epsilon,
args.schedule_length),
end='\n')
if args.l1_proj is not None:
print('Projection vectors: {0}\t\t'
'Train estimation: {1}\t\t'
'Test estimation: {2}'.format(args.l1_proj, args.l1_train,
model = pblm.mnist_model_wide(args.model_factor).cuda()
elif m == 'deep':
#print("Using deep model with model_factor={}".format(args.model_factor))
#_, test_loader = pblm.mnist_loaders(64//(2**args.model_factor))
#model = pblm.mnist_model_deep(args.model_factor).cuda()
print('using customised deep model')
model = pblm.mnist_model_deep_custom().cuda()
elif m == '500':
model = pblm.mnist_500().cuda()
else:
model = pblm.mnist_model().cuda()
return model
if __name__ == "__main__":
args = pblm.argparser(opt='adam', verbose=200, starting_epsilon=0.01)
print("saving file to {}".format(args.prefix))
setproctitle.setproctitle(args.prefix)
train_log = open(args.prefix + "_train.log", "w")
test_log = open(args.prefix + "_test.log", "w")
train_loader, _ = pblm.mnist_loaders(args.batch_size)
_, test_loader = pblm.mnist_loaders(args.test_batch_size)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
for X, y in train_loader:
break
kwargs = pblm.args2kwargs(args, X=Variable(X.cuda()))
best_err = 1
from cifar import select_model
import problems as pblm
from trainer import *
import setproctitle
import random
if __name__ == "__main__":
args = pblm.argparser(prefix='cifar',
method='task_spec_robust',
epsilon=0.03486,
l1_proj=50,
l1_train='median',
starting_epsilon=0.001,
opt='sgd',
lr=0.05,
thres=0.35)
kwargs = pblm.args2kwargs(args)
setproctitle.setproctitle('python')
print("threshold for classification error: {:.1%}".format(args.thres))
print('Matrix type: {0}\t\t'
'Category: {1}\t\t'
'Epoch number: {2}\t\t'
'Targeted epsilon: {3}\t\t'
'Starting epsilon: {4}\t\t'
'Sechduled length: {5}'.format(args.type, args.category, args.epochs,
args.epsilon, args.starting_epsilon,
args.schedule_length),
end='\n')
if args.l1_proj is not None:
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N,
factor=args.resnet_factor).cuda()
else:
model = pblm.cifar_model().cuda()
return model
if __name__ == "__main__":
args = pblm.argparser(epsilon=0.0347,
starting_epsilon=0.001,
batch_size=50,
opt='sgd',
lr=0.05)
print("saving file to {}".format(args.prefix))
setproctitle.setproctitle(args.prefix)
train_log = open(args.prefix + "_train.log", "w")
test_log = open(args.prefix + "_test.log", "w")
train_loader, test_loader = pblm.cifar_loaders(args.batch_size)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
random.seed(0)
numpy.random.seed(0)
import waitGPU
waitGPU.wait(utilization=20, available_memory=10000, interval=10)
import problems as pblm
from trainer import *
import setproctitle
if __name__ == "__main__":
args = pblm.argparser(prefix='mnist',
opt='adam',
starting_epsilon=0.05,
epsilon=0.2,
thres=0.04)
kwargs = pblm.args2kwargs(args)
setproctitle.setproctitle('python')
print("saving file to {}".format(args.proctitle))
if args.method == 'overall_robust':
print("threshold for classification error: {:.1%}".format(args.thres))
elif args.method != 'baseline':
raise ValueError("Unknown training method.")
saved_filepath = ('../saved_log/' + args.proctitle)
model_filepath = os.path.dirname('../models/' + args.proctitle)
if not os.path.exists(saved_filepath):
os.makedirs(saved_filepath)
if not os.path.exists(model_filepath):
os.makedirs(model_filepath)
model_path = ('../models/' + args.proctitle + '.pth')
train_log = open(saved_filepath + '/train_log.txt', "w")
elif m == 'large':
model = pblm.cifar_model_large().cuda()
# elif m == 'resNet':
# model = pblm.cifar_model_resnet().cuda()
else:
raise ValueError('model argument not recognized for imagenet')
return model
if __name__ == "__main__":
args = pblm.argparser(prefix='imagenet',
gan_type='biggan',
starting_epsilon=0.01,
opt='sgd',
lr=0.05,
batch_size_test=8,
proj=50,
norm_train='l2_normal',
norm_test='l2',
epsilon=0.1412,
seed=0)
setproctitle.setproctitle('python')
kwargs = pblm.args2kwargs(args)
print("saving file to {}".format(args.proctitle))
saved_filepath = ('./saved_log/' + args.proctitle)
model_filepath = os.path.dirname('./models/' + args.proctitle)
if not os.path.exists(saved_filepath):
os.makedirs(saved_filepath)
if not os.path.exists(model_filepath):
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
else:
model = pblm.cifar_model().cuda()
return model
if __name__ == "__main__":
args = pblm.argparser(prefix='cifar',
epsilon=0.03486,
starting_epsilon=0.001,
l1_proj=50,
l1_train='median',
opt='sgd',
lr=0.05,
ratio=0)
setproctitle.setproctitle('python')
kwargs = pblm.args2kwargs(args)
print("saving file to {}".format(args.proctitle))
if args.method == 'overall_robust':
print("threshold for classification error: {:.1%}".format(args.thres))
elif args.method != 'baseline':
raise ValueError("Unknown training method.")
saved_filepath = ('../saved_log/' + args.proctitle)
model_filepath = os.path.dirname('../models/' + args.proctitle)
if not os.path.exists(saved_filepath):
print("Using deep model with model_factor={}".format(
args.model_factor))
_, test_loader = pblm.mnist_loaders(64 // (2**args.model_factor))
model = pblm.mnist_model_deep(args.model_factor).cuda()
elif m == '500':
model = pblm.mnist_500().cuda()
else:
print('``` Use default MNIST model.')
model = pblm.mnist_model().cuda()
return model
if __name__ == "__main__":
args = pblm.argparser(opt='adam',
verbose=200,
starting_epsilon=0.01,
batch_size=30,
epsilon=0.3)
print("saving file to {}".format(args.prefix))
setproctitle.setproctitle(args.prefix)
train_log = open(args.prefix + "_train.log", "w")
test_log = open(args.prefix + "_test.log", "w")
train_loader, _ = pblm.mnist_loaders(args.batch_size)
_, test_loader = pblm.mnist_loaders(args.test_batch_size)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
for X, y in train_loader:
break
import waitGPU
waitGPU.wait(utilization=40, available_memory=8000, interval=20)
import problems as pblm
from trainer import *
import setproctitle
if __name__ == "__main__":
args = pblm.argparser(prefix='mnist',
gan_type='ACGAN',
opt='adam',
batch_size_test=10,
proj=50,
norm_train='l2_normal',
norm_test='l2',
epsilon=1.58,
seed=0)
kwargs = pblm.args2kwargs(args)
setproctitle.setproctitle('python')
print("saving file to {}".format(args.proctitle))
saved_filepath = ('./saved_log/' + args.proctitle)
model_filepath = os.path.dirname('./models/' + args.proctitle)
if not os.path.exists(saved_filepath):
os.makedirs(saved_filepath)
if not os.path.exists(model_filepath):
os.makedirs(model_filepath)
model_path = ('./models/' + args.proctitle)
train_res = open(saved_filepath + '/train_res.txt', "w")
