def __init__(self,input_batch, masking_batch, grad_scaling, scope='vgg_net', reuse=None):
with tf.variable_scope(scope, reuse=reuse):
# Adversarial layer
W_adv = tf.get_variable('adv_weights', [1] + input_shape,initializer=tf.contrib.layers.xavier_initializer())
# channel mean is not subtracted in input_batch so it's the original masked image
W_adv1 = tf.where(masking_batch,tf.zeros(tf.shape(W_adv),dtype=tf.float32),W_adv)
W_adv2 = tf.where(tf.greater(input_batch+grad_scaling*W_adv1,255.0), (1./grad_scaling)*(255.0 - input_batch), W_adv1)
W_adv3 = tf.where(tf.less(input_batch+grad_scaling*W_adv2,0.0), (-1./grad_scaling)*input_batch , W_adv2)
# we subtract the channel mean before passing it to vgg16
adv_img = input_batch + grad_scaling*W_adv3 - channel_mean
self.W_adv3 = W_adv3
self.adv_input = adv_img
# layer 1
conv1_1 = conv_relu('conv1_1', adv_img,
kernel_size=3, stride=1, output_dim=64)
conv1_2 = conv_relu('conv1_2', conv1_1,
kernel_size=3, stride=1, output_dim=64)
pool1 = pool('pool1', conv1_2, kernel_size=2, stride=2)
# layer 2
conv2_1 = conv_relu('conv2_1', pool1,
kernel_size=3, stride=1, output_dim=128)
conv2_2 = conv_relu('conv2_2', conv2_1,
kernel_size=3, stride=1, output_dim=128)
pool2 = pool('pool2', conv2_2, kernel_size=2, stride=2)
# layer 3
conv3_1 = conv_relu('conv3_1', pool2,
kernel_size=3, stride=1, output_dim=256)
conv3_2 = conv_relu('conv3_2', conv3_1,
kernel_size=3, stride=1, output_dim=256)
conv3_3 = conv_relu('conv3_3', conv3_2,
kernel_size=3, stride=1, output_dim=256)
pool3 = pool('pool3', conv3_3, kernel_size=2, stride=2)
# layer 4
conv4_1 = conv_relu('conv4_1', pool3,
kernel_size=3, stride=1, output_dim=512)
conv4_2 = conv_relu('conv4_2', conv4_1,
kernel_size=3, stride=1, output_dim=512)
conv4_3 = conv_relu('conv4_3', conv4_2,
kernel_size=3, stride=1, output_dim=512)
pool4 = pool('pool4', conv4_3, kernel_size=2, stride=2)
# layer 5
conv5_1 = conv_relu('conv5_1', pool4,
kernel_size=3, stride=1, output_dim=512)
conv5_2 = conv_relu('conv5_2', conv5_1,
kernel_size=3, stride=1, output_dim=512)
conv5_3 = conv_relu('conv5_3', conv5_2,
kernel_size=3, stride=1, output_dim=512)
pool5 = pool('pool5', conv5_3, kernel_size=2, stride=2)
self.image_feat_grid = pool5
def vgg_pool5(input_batch, name):
with tf.variable_scope(name):
# layer 1
conv1_1 = conv_relu('conv1_1', input_batch,
kernel_size=3, stride=1, output_dim=64)
conv1_2 = conv_relu('conv1_2', conv1_1,
kernel_size=3, stride=1, output_dim=64)
pool1 = pool('pool1', conv1_2, kernel_size=2, stride=2)
# layer 2
conv2_1 = conv_relu('conv2_1', pool1,
kernel_size=3, stride=1, output_dim=128)
conv2_2 = conv_relu('conv2_2', conv2_1,
kernel_size=3, stride=1, output_dim=128)
pool2 = pool('pool2', conv2_2, kernel_size=2, stride=2)
# layer 3
conv3_1 = conv_relu('conv3_1', pool2,
kernel_size=3, stride=1, output_dim=256)
conv3_2 = conv_relu('conv3_2', conv3_1,
kernel_size=3, stride=1, output_dim=256)
conv3_3 = conv_relu('conv3_3', conv3_2,
kernel_size=3, stride=1, output_dim=256)
pool3 = pool('pool3', conv3_3, kernel_size=2, stride=2)
# layer 4
conv4_1 = conv_relu('conv4_1', pool3,
kernel_size=3, stride=1, output_dim=512)
conv4_2 = conv_relu('conv4_2', conv4_1,
kernel_size=3, stride=1, output_dim=512)
conv4_3 = conv_relu('conv4_3', conv4_2,
kernel_size=3, stride=1, output_dim=512)
pool4 = pool('pool4', conv4_3, kernel_size=2, stride=2)
# layer 5
conv5_1 = conv_relu('conv5_1', pool4,
kernel_size=3, stride=1, output_dim=512)
conv5_2 = conv_relu('conv5_2', conv5_1,
kernel_size=3, stride=1, output_dim=512)
conv5_3 = conv_relu('conv5_3', conv5_2,
kernel_size=3, stride=1, output_dim=512)
pool5 = pool('pool5', conv5_3, kernel_size=2, stride=2)
return pool5
def vgg_conv5(input_batch, name, reuse=None):
with tf.variable_scope(name, reuse=reuse):
# layer 1
conv1_1 = conv_relu('conv1_1', input_batch,
kernel_size=3, stride=1, output_dim=64)
conv1_2 = conv_relu('conv1_2', conv1_1,
kernel_size=3, stride=1, output_dim=64)
pool1 = pool('pool1', conv1_2, kernel_size=2, stride=2)
# layer 2
conv2_1 = conv_relu('conv2_1', pool1,
kernel_size=3, stride=1, output_dim=128)
conv2_2 = conv_relu('conv2_2', conv2_1,
kernel_size=3, stride=1, output_dim=128)
pool2 = pool('pool2', conv2_2, kernel_size=2, stride=2)
# layer 3
conv3_1 = conv_relu('conv3_1', pool2,
kernel_size=3, stride=1, output_dim=256)
conv3_2 = conv_relu('conv3_2', conv3_1,
kernel_size=3, stride=1, output_dim=256)
conv3_3 = conv_relu('conv3_3', conv3_2,
kernel_size=3, stride=1, output_dim=256)
pool3 = pool('pool3', conv3_3, kernel_size=2, stride=2)
# layer 4
conv4_1 = conv_relu('conv4_1', pool3,
kernel_size=3, stride=1, output_dim=512)
conv4_2 = conv_relu('conv4_2', conv4_1,
kernel_size=3, stride=1, output_dim=512)
conv4_3 = conv_relu('conv4_3', conv4_2,
kernel_size=3, stride=1, output_dim=512)
pool4 = pool('pool4', conv4_3, kernel_size=2, stride=2)
# layer 5
conv5_1 = conv_relu('conv5_1', pool4,
kernel_size=3, stride=1, output_dim=512)
conv5_2 = conv_relu('conv5_2', conv5_1,
kernel_size=3, stride=1, output_dim=512)
conv5_3 = conv_relu('conv5_3', conv5_2,
kernel_size=3, stride=1, output_dim=512)
return conv5_3