def build_wgan_global_discriminator(self, x, reuse=False, training=True):
with tf.variable_scope('discriminator_global', reuse=reuse):
cnum = 64
x = dis_conv(x, cnum, name='conv1', training=training)
x = dis_conv(x, cnum*2, name='conv2', training=training)
x = dis_conv(x, cnum*4, name='conv3', training=training)
x = dis_conv(x, cnum*4, name='conv4', training=training)
x = flatten(x, name='flatten')
return x
def build_wgan_local_discriminator(self, x, reuse=False, training=True):
with tf.variable_scope('discriminator_local', reuse=reuse):
cnum = 64
x = dis_conv(x, cnum, name='conv1', training=training)
x = dis_conv(x, cnum * 2, name='conv2', training=training)
x = dis_conv(x, cnum * 4, name='conv3', training=training)
x = dis_conv(x, cnum * 8, name='conv4',
training=training) # 32x16x16x512
return x
def build_wgan_global_discriminator_verbose(self,
x,
reuse=False,
training=True):
with tf.variable_scope('discriminator_global', reuse=reuse):
cnum = 64
x1 = dis_conv(x, cnum, name='conv1', training=training)
x2 = dis_conv(x1, cnum * 2, name='conv2', training=training)
x3 = dis_conv(x2, cnum * 4, name='conv3', training=training)
x4 = dis_conv(x3, cnum * 4, name='conv4', training=training)
x5 = flatten(x4, name='flatten')
return x1, x2, x3, x4, x5
def build_wgan_global_discriminator(self, x, reuse=False, training=True):
with tf.variable_scope('discriminator_global', reuse=reuse):
cnum = 64
x = dis_conv(x, cnum, name='conv1', training=training)
x = dis_conv(x, cnum * 2, name='conv2', training=training)
x = dis_conv(x, cnum * 4, name='conv3', training=training)
x = dis_conv(x, cnum * 4, name='conv4',
training=training) # 32x16x16x256
# remove flatten layer to make it like PatchGAN
# x = flatten(x, name='flatten')
# channel=1, kernel=5, stride=1
x = tf.layers.conv2d(x, 1, 5, 1, 'SAME', name='patch')
x = tf.sigmoid(x, name='patch_sigmoid')
return x
def build_sn_patch_gan_discriminator(self, x, reuse=False, training=True):
with tf.compat.v1.variable_scope('sn_patch_gan', reuse=reuse):
cnum = 64
x = dis_conv(x, cnum, name='conv1', training=training)
x = dis_conv(x, cnum*2, name='conv2', training=training)
x = dis_conv(x, cnum*4, name='conv3', training=training)
x = dis_conv(x, cnum*4, name='conv4', training=training)
x = dis_conv(x, cnum*4, name='conv5', training=training)
x = dis_conv(x, cnum*4, name='conv6', training=training)
x = flatten(x, name='flatten')
return x
def build_SNGAN_discriminator(self,
x,
batch_size=32,
reuse=False,
training=True):
with tf.variable_scope('discriminator', reuse=reuse):
cnum = 64
x = dis_conv(x, cnum, name='conv1', training=training)
x = dis_conv(x, 2 * cnum, name='conv2', training=training)
x = dis_conv(x, 4 * cnum, name='conv3', training=training)
x = dis_conv(x, 4 * cnum, name='conv4', training=training)
x = dis_conv(x, 4 * cnum, name='conv5', training=training)
x = dis_conv(x, 4 * cnum, name='conv6', training=training)
return x
