def __init__(self, model_VarList, model, epsilon, num_steps, step_size,
random_start, dataset_type, config):
self.x_Aadv, self.x_Aadv_attack, self.y_Ainput, self.is_training = model_VarList
self.random_start = random_start
self.epsilon = epsilon
if dataset_type == 'cifar10' or dataset_type == 'cifar100' or dataset_type == 'imagenet':
self.upper = 255
else:
self.upper = 1
x_max = tf.clip_by_value(self.x_Aadv + epsilon, 0, self.upper)
x_min = tf.clip_by_value(self.x_Aadv - epsilon, 0, self.upper)
print("the attack upper", self.upper, "the epsilon", epsilon,
"step size", step_size)
new_adv_x = self.x_Aadv_attack
for loop in range(num_steps):
_, a_Axent_temp, _, _, _, _, _, _ = \
model._encoder(new_adv_x, self.y_Ainput, self.is_training,
mask_effective_attack=config['mask_effective_attack'])
fsm_grad = tf.sign(tf.gradients(a_Axent_temp, new_adv_x)[0])
new_adv_x = new_adv_x + fsm_grad * step_size
new_adv_x = tf.clip_by_value(new_adv_x, x_min, x_max)
new_adv_x = tf.stop_gradient(new_adv_x)
self.final_new_adv_attack = new_adv_x
layer_values_Ap_emb, _, _, _, _, _, _, _ = \
model._encoder(self.final_new_adv_attack, self.y_Ainput, self.is_training,
mask_effective_attack=config['mask_effective_attack'])
self.Ap_emb = reshape_cal_len(layer_values_Ap_emb['x4'])[0]
self.invariance_generator = InvarianceGenerator()
def forward_(self, fea_input, recos_target, is_training):
with tf.variable_scope('decoder', reuse=tf.AUTO_REUSE):
self.fea_input = fea_input
self.is_training = is_training
print('self.fea_input', self.fea_input)
x, dims = reshape_cal_len(self.fea_input)
W_conv1 = self._weight_variable([dims, 1024], scope='w1')
b_conv1 = self._bias_variable([1024], scope='b1')
h_conv1 = tf.nn.xw_plus_b(x, W_conv1, b_conv1)
h_conv1 = self._batch_norm('bn11', h_conv1)
h_conv1 = tf.nn.relu(h_conv1)
W_conv11 = self._weight_variable([1024, 1024], scope='conv_w11')
b_conv11 = self._bias_variable([1024], scope='b11')
h_conv1 = tf.nn.xw_plus_b(h_conv1, W_conv11, b_conv11)
h_conv1 = self._batch_norm('bn12', h_conv1)
h_conv1 = tf.nn.relu(h_conv1)
W_fc2 = self._weight_variable([1024, 3*32*32], scope='fcw2')
b_fc2 = self._bias_variable([3*32*32], scope='fcb2')
x_last = tf.matmul(h_conv1, W_fc2) + b_fc2
output = tf.reshape(x_last, [-1, 32, 32, 3])
print('recos_target shape', recos_target.shape)
recos_target_norm = recos_target/255-0.5
loss = tf.reduce_mean((output - recos_target_norm) ** 2)
return loss, output, recos_target_norm
'layer_values_A1': layer_values_A1,
'n_Amean_xent_d': n_Amean_xent_d,
'n_Aaccuracy_d': n_Aaccuracy_d
})
### Calculate triplet loss
triplet_loss_data_A_Ap_B = dict()
mse_loss_A_Ap = dict()
mse_loss_A_B = dict()
triplet_loss_data_A1_Ap_B_list = [dict() for _ in range(A1_Ap_B_num)]
# mse_loss_data_A1_Ap_B_list = [dict() for _ in range(A1_Ap_B_num)]
triplet_loss_data_B_Bp_A = dict()
#construct embed
f_x4_nat = reshape_cal_len(layer_values_A['x4'])[0]
for layer_name in triplet_loss_layers:
if Use_A_Ap_B:
if switch_an_neg:
anchor = layer_values_A[
layer_name] # TODO: already switched anchor and pos
pos = layer_values_Ap[layer_name]
else:
pos = layer_values_A[
layer_name] #TODO: already switched anchor and pos
anchor = layer_values_Ap[layer_name]
if Use_B_Bp_A:
neg = layer_values_Bp[layer_name]
else:
neg = layer_values_B[layer_name]
