def adv_attack(args, path):
if args.hyper:
paths = glob(path + '/*.pt')
path = [x for x in paths if 'hypermnist_mean50_0.9866.pt' in x][0]
path = './hypermnist_0_0.984871875.pt'
#path = './hypermnist_disc_0.97311875.pt'
#path = './hypermnist_disc_0.97581875.pt'
path = './hypermnist_disc_0.8857125.pt'
hypernet = utils.load_hypernet(path)
run_adv_hyper(args, hypernet)
else:
paths = ['mnist_model_small2_0.pt',
'mnist_model_small2_1.pt',
'mnist_model_small2_2.pt',
'mnist_model_small2_3.pt',
'mnist_model_small2_4.pt',
'mnist_model_small2_5.pt',
'mnist_model_small2_6.pt',
'mnist_model_small2_7.pt',
'mnist_model_small2_8.pt',
'mnist_model_small2_9.pt'
]
models = []
for path in paths:
model = get_network(args)
model.load_state_dict(torch.load(path))
models.append(model.eval())
run_adv_model(args, models)
def adv_attack(args, path):
if args.hyper:
paths = glob(path + '/*.pt')
path = [x for x in paths if 'hypermnist_0_0.984390625.pt' in x][0]
hypernet = utils.load_hypernet(path)
run_adv_hyper(args, hypernet)
else:
paths = ['mnist_model_small2_0.pt',
'mnist_model_small2_1.pt',
'mnist_model_small2_2.pt',
'mnist_model_small2_3.pt',
'mnist_model_small2_4.pt',]
models = []
for path in paths:
model = get_network(args)
model.load_state_dict(torch.load(path))
models.append(model.eval())
run_adv_model(args, models)
def run_anomaly(args, path):
if args.hyper:
paths = glob(path + '/*.pt')
path = [x for x in paths if 'hypermnist_0_0.984390625.pt' in x][0]
hypernet = utils.load_hypernet(path)
if args.dataset == 'omniglot':
run_anomaly_omni(args, hypernet)
elif args.dataset == 'notmnist':
run_anomaly_notmnist(args, hypernet)
elif args.dataset == 'mnist':
run_anomaly_mnist(args, hypernet)
else:
raise NotImplementedError
else:
model = get_network(args)
path = 'mnist_clf.pt'
model.load_state_dict(torch.load(path))
run_adv_model(args, model)
def run_measure(args, path):
paths = glob(path + '/*.pt')
path = './hypermnist_0_0.984871875.pt'
#path = './hypermnist_disc_0.97311875.pt'
#path = './hypermnist_disc_0.97581875.pt'
path = './hypermnist_disc_0.8857125.pt'
hypernet = utils.load_hypernet(path)
mpaths = [
'mnist_model_small2_0.pt', 'mnist_model_small2_1.pt',
'mnist_model_small2_2.pt', 'mnist_model_small2_3.pt',
'mnist_model_small2_4.pt', 'mnist_model_small2_5.pt',
'mnist_model_small2_6.pt', 'mnist_model_small2_7.pt',
'mnist_model_small2_8.pt', 'mnist_model_small2_9.pt'
]
models = []
for path in mpaths:
model = get_network(args)
models.append(torch.load(path))
measure_models(args, hypernet, models)
def load_models(args, path):
model = get_network(args)
paths = glob(path + '*.pt')
print(path)
paths = [path for path in paths if 'mnist' in path]
natpaths = natsort.natsorted(paths)
accs = []
losses = []
natpaths = [x for x in natpaths if 'hypermnist_mi_0.987465625' in x]
for i, path in enumerate(natpaths):
print("loading model {}".format(path))
if args.hyper:
hn = utils.load_hypernet(path)
for i in range(10):
samples = utils.sample_hypernet(hn)
print('sampled a batches of {} networks'.format(len(
samples[0])))
for i, sample in enumerate(
zip(samples[0], samples[1], samples[2])):
model = utils.weights_to_clf(sample, model,
args.stat['layer_names'])
acc, loss = test(args, model)
print(i, ': Test Acc: {}, Loss: {}'.format(acc, loss))
accs.append(acc)
losses.append(loss)
#acc, loss = train(args, model)
#print ('Test1 Acc: {}, Loss: {}'.format(acc, loss))
#extract_weights_all(args, model, i)
print(accs, losses)
else:
ckpt = torch.load(path)
state = ckpt['state_dict']
try:
model.load_state_dict()
except RuntimeError:
model_dict = model.state_dict()
filtered = {k: v for k, v in state.items() if k in model_dict}
model_dict.update(filtered)
model.load_state_dict(filtered)
def run_acc(args, path):
path = './exp_models/hypermnist_acc_2_0.988115.pt'
hypernet = utils.load_hypernet(path)
model = get_network(args)
measure_acc(args, hypernet, model)
from foolbox.criteria import Misclassification
foolbox_logger = logging.getLogger('foolbox')
logging.disable(logging.CRITICAL)
import warnings
warnings.filterwarnings("ignore")
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
import torch
import matplotlib.pyplot as plt
import adversarial_test as adv
import adversarial_test as adv
args = adv.load_args()
arch = adv.get_network(args)
get_ipython().run_line_magic('clear', '')
import utils
hypernet = utils.load_hypernet('hypermnist_0_0.984390625.pt')
import netdef
args.stat = netdef.nets()[args.net]
model_base, fmodel_base = adv.sample_fmodel(args, hypernet, arch)
criterion = Misclassification()
fgs = foolbox.attacks.BIM(fmodel_base, criterion)
import datagen
_, test = datagen.load_mnist(args)
batch = []
y = []
for (data, target) in test:
batch.append(data)
y.append(target)
if len(batch) == 32:
break
