def main(config):
# For fast training
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.extractor_save_dir):
os.makedirs(config.extractor_save_dir)
# Data loader
train_loader = get_loader(config.dataset,
config.data_dir,
batch_size=config.batch_size,
mode='train',
shuffle=True,
num_workers=config.num_workers)
val_loader = get_loader(config.dataset,
config.data_dir,
batch_size=config.batch_size,
mode='val',
shuffle=False,
num_workers=config.num_workers)
# Training model
Solver = solver_selector[config.mode]
model = get_model(config.dataset)
solver = Solver(train_loader, val_loader, config)
solver.train(model, config.attack_method)
def evaluate(config):
# Data loader
val_loader = get_loader(config.dataset,
config.data_dir,
batch_size=np.inf,
mode='val',
shuffle=False,
num_workers=config.num_workers)
test_loader = get_loader(config.dataset,
config.data_dir,
batch_size=np.inf,
mode='test',
shuffle=False,
num_workers=config.num_workers)
# Evaluating model
model = get_model(config.dataset, model_save_dir=config.model_save_dir)
evaluator = Evaluator(model, config.dataset)
val_acc = {}
print('Val set:')
val_acc['cln'] = evaluator.evaluate(val_loader)
print('Clean:', val_acc['cln'])
val_acc['fgsm'] = evaluator.evaluate_fgsm(val_loader)
print('FGSM:', val_acc['fgsm'])
val_acc['pgd'] = evaluator.evaluate_pgd(val_loader, num_iter=50)
print('PGD:', val_acc['pgd'])
test_acc = {}
print('Test set:')
test_acc['cln'] = evaluator.evaluate(test_loader)
print('Clean:', test_acc['cln'])
test_acc['fgsm'] = evaluator.evaluate_fgsm(test_loader)
print('FGSM:', test_acc['fgsm'])
test_acc['pgd'] = evaluator.evaluate_pgd(test_loader, num_iter=50)
print('PGD:', test_acc['pgd'])
test_acc['cw'] = evaluator.evaluate_cw(test_loader)
print('CW:', test_acc['cw'])
test_acc['loss_sensitivity'] = evaluator.evaluate_robust(test_loader)
print('loss_sensitivity:', test_acc['loss_sensitivity'])
for i in [
5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150
]:
acc_pgd = evaluator.evaluate_pgd(test_loader, num_iter=i)
print('PGD_{}: {}'.format(i, acc_pgd))
return val_acc, test_acc
def main(config):
# For fast training
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.model_save_dir):
os.makedirs(config.model_save_dir)
if not os.path.exists(config.extractor_save_dir):
os.makedirs(config.extractor_save_dir)
# Feature extractor
extractor = FeatureExtractor(config)
extractor.run()
# Graph constructor
graph = GraphConstructor(config)
# Data loader
train_loader = get_loader(config.dataset,
config.data_dir,
batch_size=config.batch_size,
mode='train',
shuffle=True,
num_workers=config.num_workers,
graph=graph,
anchor_size=config.anchor_size)
val_loader = get_loader(config.dataset,
config.data_dir,
batch_size=config.batch_size,
mode='val',
shuffle=False,
num_workers=config.num_workers)
# Training model
Solver = Solver_ATMR
model = get_model(config.dataset)
solver = Solver(train_loader, val_loader, graph, config)
solver.train(model, config.attack_method)
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
os.makedirs(out_dir + '/examples')
ckpt = net_to_ckpt[args.net]
img_nums = [[int(x.split(' ')[0].split('_')[2][:-5]), (int(x.split(' ')[1]))] for x in open('./ilsvrc_metadata/val.txt').read().split('\n') if x]
img_nums = np.array(img_nums)
img, lab = tf.placeholder(shape=[None, 224, 224, 3], dtype=tf.float32), tf.placeholder(shape=[None], dtype=tf.int32)
sess = tf.InteractiveSession()
logits, xent = loader.get_model(sess, img, lab, ckpt, 224)
g, = tf.gradients(xent, [img])
def load_img(k):
im_, _ = loader.load_img(img_nums[k,0])
la_ = img_nums[k,1]
return im_, la_
def get_top5(x):
if len(x.shape) == 3:
x = np.expand_dims(x, 0)
np_logits = logits.eval(feed_dict={
img:x,
})