neg = layer_values_Ap[layer_name]
triplet_loss_data_B_Bp_A[layer_name] = triplet_loss_dict(
anchor, pos, neg, triplet_loss_type, regularize_lambda,
triplet_loss_margins[layer_name]['B_Bp_A'])
model_var_B_Bp_A = x_Anat, y_Ainput, x_Bnat, layer_values_A[
'x4'], layer_values_A['pre_softmax'], layer_values_B['x4'], layer_values_B[
'pre_softmax'], is_training
model_var_attack = x_Aadv, a_Axent, y_Ainput, is_training, a_Aaccuracy
# model_var = n_Anum_correct, n_Axent, a_Anum_correct, a_Axent, x_Anat, x_Aadv, y_Ainput, is_training
model_var = n_Anum_correct, n_Axent, x_Anat, y_Ainput, is_training, n_Apredict
saver = tf.train.Saver(max_to_keep=3)
var_main_encoder = trainable_in('main_encoder')
if is_finetune:
print('finetuning')
if dataset_type == 'imagenet':
# restore_var_list = slim.get_variables_to_restore(exclude=tf.get_collection(tf.GraphKeys.MODEL_VARIABLES, scope='logits'))
var_main_encoder_var = tf.get_collection(tf.GraphKeys.MODEL_VARIABLES,
scope='main_encoder')
restore_var_list = remove_duplicate_node_from_list(
var_main_encoder, var_main_encoder_var)
else:
var_main_encoder_var = tf.get_collection(tf.GraphKeys.MODEL_VARIABLES,
scope='main_encoder')
restore_var_list = remove_duplicate_node_from_list(
var_main_encoder, var_main_encoder_var)
def one_test(dataset_type,
model_load_direction,
attack_steps,
attack_step_size,
loss_func,
rand_start=1,
use_rand=True,
model_name=None,
momentum=0.0,
save_filename=None,
black_attack=False,
vis_lossland_scape=False,
model_type='ConvNet'):
# dataset_type = 'cifar10'
# model_load_direction = 'models/model_0_renamed'
# model_load_direction = '/mnt/md0/FSRobust/cifar_models/triplet/switch_adv_only/cifar10,A_Ap_B,A1_Ap_B_1,'
# model_load_direction ='/mnt/md0/FSRobust/cifar_models/triplet/April15/switch_adv_only_hardneg_mar0.03_lam10/cifar10,A_Ap_B,A1_Ap_B_1,'
# model_load_direction = '/mnt/md0/FSRobust/cifar_models/triplet/backup/ml2_only/cifar10,A_Ap_B,A1_Ap_B_1,_0'
# dataset_type = 'mnist'
# model_load_direction = 'mnist_models/reproduce-secret'
# model_load_direction = '/mnt/md0/FSRobust/mnist_models/April2/new_schedule_multilayer/mnist,A_Ap_B,A1_Ap_B_1,' #93.21%
# model_load_direction = '/mnt/md0/FSRobust/mnist_models/April2/ml2_only_train_both/mnist,A_Ap_B,A1_Ap_B_1,' #ALP l2
precision = tf.float32
model = None
input_shape = None
if dataset_type == 'cifar10':
input_shape = [None, 32, 32, 3]
with open('config_cifar.json') as config_file:
config = json.load(config_file)
data_path = config['data_path']
from learning.model_vanilla import ModelVani
if model_type == 'Res20':
from learning.model_cifar10_resnet import CifarResNet
model = CifarResNet(precision=precision,
ratio=config['mask_ratio'])
elif model_type == 'ConvNet':
from learning.convnet_cifar import CifarConvNet
model = CifarConvNet(precision=precision,
ratio=config['mask_ratio'])
elif model_type == 'Res50':
from learning.model_cifar10_resnet import CifarResNet
model = CifarResNet(precision=precision,
ratio=config['mask_ratio'],
mode='50')
elif model_type == 'Res101':
from learning.model_cifar10_res101 import CifarResNetUpdate
model = CifarResNetUpdate(precision=precision,
ratio=config['mask_ratio'],
mode='101')
else:
model = ModelVani(precision=precision)
raw_dataset = dataloader.cifar10_input.CIFAR10Data(data_path)
elif dataset_type == 'mnist':
with open('config_mnist.json') as config_file:
config = json.load(config_file)
input_shape = [None, 28, 28]
if config['model_type'] == 'MLP':
from learning.model_mnist_mlp import ModelMNISTMLP
model = ModelMNISTMLP(precision=precision,
ratio=config['mask_ratio'])
else:
model = ModelMNIST(precision=precision)
data_path = config['data_path']
raw_dataset = dataloader.mnist_input.MNISTData(data_path,
dataset=dataset_type)
elif dataset_type == 'imagenet':
with open('config_imagenet.json') as config_file:
config = json.load(config_file)
# config["epsilon"] = config["epsilon"] / 255.0
# config["step_size"] = config["step_size"] / 255.0
input_shape = [None, 64, 64, 3]
raw_dataset = dataloader.mnist_input.MNISTData(
config['tiny_imagenet_data_dir_np'], dataset="imagenet")
if black_attack:
model_type = model_type
else:
model_type = config['model_type']
if model_type == 'Res20':
from learning.model_imagenet_res20 import ModelImagenet
model = ModelImagenet(batch_size=None,
precision=precision,
label_smoothing=0.1)
elif model_type == 'Res50':
from learning.model_imagenet_res50 import ModelImagenet
model = ModelImagenet(batch_size=None,
precision=precision,
label_smoothing=0.1)
elif dataset_type == 'imagenet_01':
with open('config_imagenet.json') as config_file:
config = json.load(config_file)
input_shape = [None, 64, 64, 3]
raw_dataset = dataloader.mnist_input.MNISTData(
config['tiny_imagenet_data_dir_np'], dataset="imagenet")
if model_name.startswith('res101'):
from learning.model_imagenet_res101 import ModelImagenet
model = ModelImagenet(0)
config["epsilon"] = config["epsilon"] / 255.0
elif model_name.startswith('res50'):
from learning.model_imagenet_res50 import ModelImagenet
model = ModelImagenet(0)
config["epsilon"] = config["epsilon"] / 255.0
x_Anat = tf.placeholder(precision, shape=input_shape)
y_Ainput = tf.placeholder(tf.int64, shape=None)
is_training = tf.placeholder(tf.bool, shape=None)
layer_values_A, n_Axent, n_Amean_xent, _, n_Anum_correct, n_Aaccuracy, _, n_mask = model._encoder(
x_Anat, y_Ainput, is_training)
xent_loss = model.y_xent
model_var_attack = x_Anat, n_Axent, y_Ainput, is_training, n_Aaccuracy
var_main_encoder = trainable_in('main_encoder')
print("mode dir", model_load_direction)
if vis_lossland_scape:
from vis_loss_landscape import visualize_landscape
visualize_landscape(model_load_direction, model_var_attack,
var_main_encoder, config, raw_dataset.eval_data,
config["epsilon"], 300, xent_loss, dataset_type)
elif black_attack:
test_black_attack(model_load_direction,
model_var_attack,
n_Anum_correct,
var_main_encoder,
attack_steps,
attack_step_size,
config,
dataset_type,
raw_dataset,
n_mask,
layer_values_A['x4'],
layer_values_A['pre_softmax'],
loss_func,
rand_start,
use_rand=use_rand,
momentum=momentum,
load_filename=save_filename)
else:
test_model(model_load_direction,
model_var_attack,
n_Anum_correct,
var_main_encoder,
attack_steps,
attack_step_size,
config,
dataset_type,
raw_dataset,
n_mask,
layer_values_A['x4'],
layer_values_A['pre_softmax'],
loss_func,
rand_start,
use_rand=use_rand,
momentum=momentum,
save_filename=save_filename)
print("mode dir", model_load_direction, 'rand start', rand_start,
'loss_func', loss_func, 'step num', attack_steps, 'step size',
attack_step_size, save_filename)