def generator(self, x, reuse=None):
with tf.variable_scope('generator', reuse=reuse):
def residual_block(x, f, name=""):
with tf.variable_scope(name, reuse=reuse):
skip_connection = tf.identity(x,
name='gen-skip_connection-1')
x = t.conv2d(x, f, 3, 1, name='gen-conv2d-1')
x = t.instance_norm(x, reuse=reuse, name='gen-inst_norm-1')
x = tf.nn.relu(x)
x = t.conv2d(x, f, 3, 1, name='gen-conv2d-2')
x = tf.nn.relu(x)
return skip_connection + x
shortcut = tf.identity(x, name='shortcut-init')
x = t.conv2d(x, self.gf_dim * 1, 7, 1, name='gen-conv2d-1')
x = t.instance_norm(x,
affine=False,
reuse=reuse,
name='gen-inst_norm-1')
x = tf.nn.relu(x)
for i in range(1, 3):
x = t.conv2d(x,
self.gf_dim * (2**i),
3,
2,
name='gen-conv2d-%d' % (i + 1))
x = t.instance_norm(x,
affine=False,
reuse=reuse,
name='gen-inst_norm-%d' % (i + 1))
x = tf.nn.relu(x)
# 9 Residual Blocks
for i in range(9):
x = residual_block(x,
self.gf_dim * 4,
name='gen-residual_block-%d' % (i + 1))
for i in range(1, 3):
x = t.deconv2d(x,
self.gf_dim * (2**i),
3,
2,
name='gen-deconv2d-%d' % i)
x = t.instance_norm(x,
affine=False,
reuse=reuse,
name='gen-inst_norm-%d' % (i + 3))
x = tf.nn.relu(x)
x = t.conv2d(x, self.gf_dim * 1, 7, 1, name='gen-conv2d-4')
x = tf.nn.tanh(x)
return shortcut + x
def residual_block(x, f, name="0"):
with tf.variable_scope("residual_block-" + name):
scope_name = "residual_block-" + name
x = t.conv2d(x, f=f, k=3, s=1)
x = t.instance_norm(x, affine=True, name=scope_name + '_0')
x = tf.nn.relu(x)
x = t.conv2d(x, f=f, k=3, s=1)
x = t.instance_norm(x, affine=True, name=scope_name + '_1')
return x
def u(x, f, name=''):
x = t.deconv2d(x,
f=f,
k=3,
s=2,
name='gen-u-deconv2d-%s' % name)
x = t.instance_norm(x, name='gen-u-ins_norm-%s' % name)
x = tf.nn.relu(x)
return x
def conv_in_relu(x, f, k, s, de=False, name=""):
if not de:
x = t.conv2d(x, f=f, k=k, s=s)
else:
x = t.deconv2d(x, f=f, k=k, s=s)
x = t.instance_norm(x, name=name)
x = tf.nn.relu(x)
return x
def residual_block(x, f, name=""):
with tf.variable_scope(name, reuse=reuse):
skip_connection = tf.identity(x,
name='gen-skip_connection-1')
x = t.conv2d(x, f, 3, 1, name='gen-conv2d-1')
x = t.instance_norm(x, reuse=reuse, name='gen-inst_norm-1')
x = tf.nn.relu(x)
x = t.conv2d(x, f, 3, 1, name='gen-conv2d-2')
x = tf.nn.relu(x)
return skip_connection + x
def discriminator(self, x, reuse=None):
with tf.variable_scope('discriminator', reuse=reuse):
x = t.conv2d(x, self.df_dim * 1, 4, 2, name='disc-conv2d-1')
x = tf.nn.leaky_relu(x)
for i in range(1, 3):
x = t.conv2d(x,
self.df_dim * (2**i),
4,
2,
name='disc-conv2d-%d' % (i + 1))
x = t.instance_norm(x,
reuse=reuse,
name='disc-inst_norm-%d' % i)
x = tf.nn.leaky_relu(x)
x = t.conv2d(x, self.df_dim * 8, 4, 1, name='disc-conv2d-4')
x = t.instance_norm(x, reuse=reuse, name='disc-inst_norm-3')
x = tf.nn.leaky_relu(x)
x = t.conv2d(x, 1, 4, 1, name='disc-conv2d-5')
return x
def R(x, f, name=''):
x = t.conv2d(x, f=f, k=3, s=1, name='gen-R-conv2d-%s-0' % name)
x = t.conv2d(x, f=f, k=3, s=1, name='gen-R-conv2d-%s-1' % name)
x = t.instance_norm(x, name='gen-R-ins_norm-%s' % name)
x = tf.nn.relu(x)
return x
def residual_block(x, f, name=''):
x = t.conv2d(x, f=f, k=4, s=2, name='disc-conv2d-%s' % name)
x = t.instance_norm(x, name='disc-ins_norm-%s' % name)
x = tf.nn.leaky_relu(x, alpha=0.2)
return x