def main():
args = parser.parse_args()
pp = pprint.PrettyPrinter()
pp.pprint(vars(args))
# Data
filename_queue = get_filename_queue(
split_file=os.path.join(args.data_dir, 'splits', args.dataset,
args.split + '.lst'),
data_dir=os.path.join(args.data_dir, args.dataset))
# test data
test_filename_queue = get_filename_queue(
split_file=os.path.join(args.data_dir, 'splits', args.dataset,
'test.lst'),
data_dir=os.path.join(args.data_dir, args.dataset))
image, label = get_input_cifar10(filename_queue)
output_size = 32
c_dim = 3
test_image, test_label = get_input_cifar10(test_filename_queue)
image_batch = create_batch([image, label],
batch_size=args.batch_size,
num_preprocess_threads=16,
min_queue_examples=10000)
test_image_batch = tf.train.shuffle_batch([test_image, test_label],
batch_size=args.batch_size,
num_threads=16,
capacity=10000 +
3 * args.batch_size,
min_after_dequeue=10000)
config = vars(args)
discriminator = models.get_discriminator(args.d_architecture,
scope='discriminator',
output_size=output_size,
c_dim=args.c_dim,
f_dim=args.df_dim,
is_training=True)
gen_adv_examples(discriminator, args.model_file, image_batch,
test_image_batch, config)
def main():
args = parser.parse_args()
pp = pprint.PrettyPrinter()
pp.pprint(vars(args))
# Test data
test_filename_queue = get_filename_queue(
split_file=os.path.join(args.data_dir, 'splits', args.dataset,
'test.lst'),
data_dir=os.path.join(args.data_dir, args.dataset))
test_image, test_label = get_input_cifar10(test_filename_queue)
test_image_batch = tf.train.shuffle_batch([test_image, test_label],
batch_size=args.batch_size,
num_threads=16,
capacity=10000 +
3 * args.batch_size,
min_after_dequeue=10000)
config = vars(args)
discriminator1 = models.get_discriminator(args.d_architecture1,
scope='discriminator1',
output_size=args.output_size,
c_dim=args.c_dim,
f_dim=args.df_dim,
is_training=True)
discriminator2 = models.get_discriminator(args.d_architecture2,
scope='discriminator2',
output_size=args.output_size,
c_dim=args.c_dim,
f_dim=args.df_dim,
is_training=True)
black_box_attacks(discriminator1, discriminator2, args.model_file1,
args.model_file2, test_image_batch, config)