attr = getattr(args, kwarg)
if attr is not None:
config_kwargs[kwarg] = attr
logger = tf.get_logger()
logger.setLevel(tf.logging.INFO)
if args.logger:
config_kwargs['logger'] = logger
print('Loading tensorflow session...')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
print('Loading model...')
model = load_model_from_path(args.model).load(session)
print('Loading dataset...')
if args.dataset == 'cifar10':
from realsafe.dataset import cifar10
dataset = cifar10.load_dataset_for_classifier(model,
offset=args.offset,
load_target=True)
else:
from realsafe.dataset import imagenet
dataset = imagenet.load_dataset_for_classifier(model,
offset=args.offset,
load_target=True)
dataset = dataset.take(args.count)
print('Loading attack...')
import numpy as np
import os
from keras.datasets.cifar10 import load_data
from realsafe import BIM, CrossEntropyLoss
from realsafe.model.loader import load_model_from_path
batch_size = 100
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../example/cifar10/resnet56.py')
rs_model = load_model_from_path(model_path)
model = rs_model.load(session)
_, (xs_test, ys_test) = load_data()
xs_test = (xs_test / 255.0) * (model.x_max - model.x_min) + model.x_min
ys_test = ys_test.reshape(len(ys_test))
xs_ph = tf.placeholder(model.x_dtype, shape=(batch_size, *model.x_shape))
lgs, lbs = model.logits_and_labels(xs_ph)
def iteration_callback(xs, xs_adv):
delta = tf.abs(xs_adv - xs)
return tf.reduce_max(tf.reshape(delta, (xs.shape[0], -1)), axis=1)
import numpy as np
import os
from realsafe.dataset import cifar10, dataset_to_iterator
from realsafe.model.loader import load_model_from_path
from realsafe.model.ensemble import EnsembleModel, EnsembleRandomnessModel
batch_size = 100
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../example/cifar10/resnet56.py')
model = load_model_from_path(model_path).load(session)
e_model = EnsembleModel([model, model], [0.5, 0.5])
er_model = EnsembleRandomnessModel(model, 10, session)
ds = cifar10.load_dataset_for_classifier(model).batch(batch_size).take(1)
_, xs, ys = next(dataset_to_iterator(ds, session))
xs_ph = tf.placeholder(model.x_dtype, shape=(batch_size, *model.x_shape))
labels = model.labels(xs_ph)
e_labels = e_model.labels(xs_ph)
er_labels = er_model.labels(xs_ph)
labels_np = session.run(labels, feed_dict={xs_ph: xs})
e_labels_np = session.run(e_labels, feed_dict={xs_ph: xs})
er_labels_np = session.run(er_labels, feed_dict={xs_ph: xs})
