def encode_decode(self, x_var, x_exemplar, reuse=False):
with tf.variable_scope("encode_decode") as scope:
if reuse == True:
scope.reuse_variables()
x_var = tf.concat([x_var, x_exemplar], axis=3)
conv1 = tf.nn.relu(
instance_norm(conv2d(x_var, output_dim=64, k_w=7, k_h=7, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
conv2 = tf.nn.relu(
instance_norm(conv2d(conv1, output_dim=128, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c2'), scope='e_in2'))
conv3 = tf.nn.relu(
instance_norm(conv2d(conv2, output_dim=256, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c3'), scope='e_in3'))
r1 = Residual(conv3, residual_name='re_1')
r2 = Residual(r1, residual_name='re_2')
r3 = Residual(r2, residual_name='re_3')
r4 = Residual(r3, residual_name='re_4')
r5 = Residual(r4, residual_name='re_5')
r6 = Residual(r5, residual_name='re_6')
g_deconv1 = tf.nn.relu(instance_norm(de_conv(r6, output_shape=[self.batch_size,
self.output_size/2, self.output_size/2, 128], name='gen_deconv1'), scope="gen_in"))
# for 1
g_deconv_1_1 = tf.nn.relu(instance_norm(de_conv(g_deconv1,
output_shape=[self.batch_size, self.output_size, self.output_size, 32], name='g_deconv_1_1'), scope='gen_in_1_1'))
g_deconv_1_1_x = tf.concat([g_deconv_1_1, x_var], axis=3)
x_tilde1 = conv2d(g_deconv_1_1_x, output_dim=self.channel, k_w=7, k_h=7, d_h=1, d_w=1, name='gen_conv_1_2')
return tf.nn.tanh(x_tilde1)
def encode_decode(self, x, reuse=False):
with tf.variable_scope("encode_decode") as scope:
if reuse == True:
scope.reuse_variables()
conv1 = tf.nn.relu(
instance_norm(conv2d(x,
output_dim=64,
k_w=7,
k_h=7,
d_w=1,
d_h=1,
name='e_c1'),
scope='e_in1'))
conv2 = tf.nn.relu(
instance_norm(conv2d(conv1,
output_dim=128,
k_w=4,
k_h=4,
d_w=2,
d_h=2,
name='e_c2'),
scope='e_in2'))
conv3 = tf.nn.relu(
instance_norm(conv2d(conv2,
output_dim=256,
k_w=4,
k_h=4,
d_w=2,
d_h=2,
name='e_c3'),
scope='e_in3'))
r1 = Residual(conv3, residual_name='re_1')
r2 = Residual(r1, residual_name='re_2')
r3 = Residual(r2, residual_name='re_3')
r4 = Residual(r3, residual_name='re_4')
r5 = Residual(r4, residual_name='re_5')
r6 = Residual(r5, residual_name='re_6')
g_deconv1 = tf.nn.relu(
instance_norm(de_conv(r6,
output_shape=[
self.batch_size,
self.output_size / 2,
self.output_size / 2, 128
],
name='gen_deconv1'),
scope="gen_in"))
# for 1
g_deconv_1_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='g_deconv_1_1'),
scope='gen_in_1_1'))
g_deconv_1_1_x = tf.concat([g_deconv_1_1, x], axis=3)
x_tilde1 = conv2d(g_deconv_1_1_x,
output_dim=self.channel,
k_w=7,
k_h=7,
d_h=1,
d_w=1,
name='gen_conv_1_2')
# for 2
g_deconv_2_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='g_deconv_2_1'),
scope='gen_in_2_1'))
g_deconv_2_1_x = tf.concat([g_deconv_2_1, x], axis=3)
x_tilde2 = conv2d(g_deconv_2_1_x,
output_dim=self.channel,
k_w=7,
k_h=7,
d_h=1,
d_w=1,
name='gen_conv_2_2')
# for 3
g_deconv_3_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='gen_deconv3_1'),
scope='gen_in_3_1'))
g_deconv_3_1_x = tf.concat([g_deconv_3_1, x], axis=3)
g_deconv_3_2 = conv2d(g_deconv_3_1_x,
output_dim=32,
k_w=3,
k_h=3,
d_h=1,
d_w=1,
name='gen_conv_3_2')
x_tilde3 = conv2d(g_deconv_3_2,
output_dim=3,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name='gen_conv_3_3')
# for 4
g_deconv_4_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='gen_deconv4_1'),
scope='gen_in_4_1'))
g_deconv_4_1_x = tf.concat([g_deconv_4_1, x], axis=3)
g_deconv_4_2 = conv2d(g_deconv_4_1_x,
output_dim=32,
k_w=3,
k_h=3,
d_h=1,
d_w=1,
name='gen_conv_4_2')
x_tilde4 = conv2d(g_deconv_4_2,
output_dim=3,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name='gen_conv_4_3')
# for 5
g_deconv_5_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='gen_deconv5_1'),
scope='gen_in_5_1'))
g_deconv_5_1_x = tf.concat([g_deconv_5_1, x], axis=3)
g_deconv_5_2 = conv2d(g_deconv_5_1_x,
output_dim=32,
k_w=3,
k_h=3,
d_h=1,
d_w=1,
name='gen_conv_5_2')
x_tilde5 = conv2d(g_deconv_5_2,
output_dim=3,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name='gen_conv_5_3')
# for 6
g_deconv_6_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='gen_deconv6_1'),
scope='gen_in_6_1'))
g_deconv_6_1_x = tf.concat([g_deconv_6_1, x], axis=3)
g_deconv_6_2 = conv2d(g_deconv_6_1_x,
output_dim=32,
k_w=3,
k_h=3,
d_h=1,
d_w=1,
name='gen_conv_6_2')
x_tilde6 = conv2d(g_deconv_6_2,
output_dim=3,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name='gen_conv_6_3')
# for 7
g_deconv_7_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='g_deconv_7_1'),
scope='gen_in_7_1'))
g_deconv_7_1_x = tf.concat([g_deconv_7_1, x], axis=3)
x_tilde7 = conv2d(g_deconv_7_1_x,
output_dim=self.channel,
k_w=7,
k_h=7,
d_h=1,
d_w=1,
name='gen_conv_7_2')
# for 8
g_deconv_8_1 = tf.nn.relu(
instance_norm(de_conv(g_deconv1,
output_shape=[
self.batch_size, self.output_size,
self.output_size, 64
],
name='g_deconv_8_1'),
scope='gen_in_8_1'))
g_deconv_8_1_x = tf.concat([g_deconv_8_1, x], axis=3)
x_tilde8 = conv2d(g_deconv_8_1_x,
output_dim=self.channel,
k_w=7,
k_h=7,
d_h=1,
d_w=1,
name='gen_conv_8_2')
return tf.nn.tanh(x_tilde1), tf.nn.tanh(x_tilde2), tf.nn.tanh(x_tilde3), \
tf.nn.tanh(x_tilde4), tf.nn.tanh(x_tilde5), tf.nn.tanh(x_tilde6), tf.nn.tanh(x_tilde7), tf.nn.tanh(x_tilde8)