def residule_block(x, dim, ks=3, s=1, in_name='res'):
p = int((ks - 1) / 2)
y = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=in_name + '_c1'), name=in_name + '_bn1')
y = tf.pad(tf.nn.relu(y), [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=in_name + '_c2'), name=in_name + '_bn2')
return y + x
def generator_resnet(image, options, reuse=False, name="generator"):
with tf.variable_scope(name):
# image is 256 x 256 x input_c_dim
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
def residule_block(x, dim, ks=3, s=1, in_name='res'):
p = int((ks - 1) / 2)
y = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=in_name + '_c1'), name=in_name + '_bn1')
y = tf.pad(tf.nn.relu(y), [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=in_name + '_c2'), name=in_name + '_bn2')
return y + x
# Justin Johnson's model from https://github.com/jcjohnson/fast-neural-style/
# The network with 9 blocks consists of: c7s1-32, d64, d128, R128, R128, R128,
# R128, R128, R128, R128, R128, R128, u64, u32, c7s1-3
c0 = tf.pad(image, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
c1 = tf.nn.relu(instance_norm(conv2d(c0, options.gf_dim, 7, 1, padding='VALID', name='g_e1_c'), 'g_e1_bn'))
c2 = tf.nn.relu(instance_norm(conv2d(c1, options.gf_dim*2, 3, 2, name='g_e2_c'), 'g_e2_bn'))
c3 = tf.nn.relu(instance_norm(conv2d(c2, options.gf_dim*4, 3, 2, name='g_e3_c'), 'g_e3_bn'))
# define G network with 9 resnet blocks
r1 = residule_block(c3, options.gf_dim * 4, in_name='g_r1')
r2 = residule_block(r1, options.gf_dim * 4, in_name='g_r2')
r3 = residule_block(r2, options.gf_dim * 4, in_name='g_r3')
r4 = residule_block(r3, options.gf_dim * 4, in_name='g_r4')
r5 = residule_block(r4, options.gf_dim * 4, in_name='g_r5')
r6 = residule_block(r5, options.gf_dim * 4, in_name='g_r6')
r7 = residule_block(r6, options.gf_dim * 4, in_name='g_r7')
r8 = residule_block(r7, options.gf_dim * 4, in_name='g_r8')
r9 = residule_block(r8, options.gf_dim * 4, in_name='g_r9')
d1 = deconv2d(r9, options.gf_dim*2, 3, 2, name='g_d1_dc')
d1 = tf.nn.relu(instance_norm(d1, 'g_d1_bn'))
d2 = deconv2d(d1, options.gf_dim, 3, 2, name='g_d2_dc')
d2 = tf.nn.relu(instance_norm(d2, 'g_d2_bn'))
d2 = tf.pad(d2, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
pred = tf.nn.tanh(conv2d(d2, options.output_c_dim, 7, 1, padding='VALID', name='g_pred_c'))
return pred
def discriminator(image, options, reuse=False, name="discriminator"):
with tf.variable_scope(name):
# image is 256 x 256 x input_c_dim
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
h0 = lrelu(conv2d(image, options.df_dim, name='d_h0_conv'))
# h0 is (128 x 128 x self.df_dim)
h1 = lrelu(instance_norm(conv2d(h0, options.df_dim*2, name='d_h1_conv'), name='d_bn1'))
# h1 is (64 x 64 x self.df_dim*2)
h2 = lrelu(instance_norm(conv2d(h1, options.df_dim*4, name='d_h2_conv'), name='d_bn2'))
# h2 is (32x 32 x self.df_dim*4)
h3 = lrelu(instance_norm(conv2d(h2, options.df_dim*8, s=1, name='d_h3_conv'), name='d_bn3'))
# h3 is (32 x 32 x self.df_dim*8)
h4 = conv2d(h3, 1, s=1, name='d_h3_pred')
# h4 is (32 x 32 x 1)
return h4
def generator_unet(image, options, reuse=False, name="generator"):
dropout_rate = 0.5 if options.is_training else 1.0
with tf.variable_scope(name):
# image is 256 x 256 x input_c_dim
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
# image is (256 x 256 x input_c_dim)
e1 = instance_norm(conv2d(image, options.gf_dim, name='g_e1_conv'))
# e1 is (128 x 128 x self.gf_dim)
e2 = instance_norm(conv2d(lrelu(e1), options.gf_dim*2, name='g_e2_conv'), 'g_bn_e2')
# e2 is (64 x 64 x self.gf_dim*2)
e3 = instance_norm(conv2d(lrelu(e2), options.gf_dim*4, name='g_e3_conv'), 'g_bn_e3')
# e3 is (32 x 32 x self.gf_dim*4)
e4 = instance_norm(conv2d(lrelu(e3), options.gf_dim*8, name='g_e4_conv'), 'g_bn_e4')
# e4 is (16 x 16 x self.gf_dim*8)
e5 = instance_norm(conv2d(lrelu(e4), options.gf_dim*8, name='g_e5_conv'), 'g_bn_e5')
# e5 is (8 x 8 x self.gf_dim*8)
e6 = instance_norm(conv2d(lrelu(e5), options.gf_dim*8, name='g_e6_conv'), 'g_bn_e6')
# e6 is (4 x 4 x self.gf_dim*8)
e7 = instance_norm(conv2d(lrelu(e6), options.gf_dim*8, name='g_e7_conv'), 'g_bn_e7')
# e7 is (2 x 2 x self.gf_dim*8)
e8 = instance_norm(conv2d(lrelu(e7), options.gf_dim*8, name='g_e8_conv'), 'g_bn_e8')
# e8 is (1 x 1 x self.gf_dim*8)
d1 = deconv2d(tf.nn.relu(e8), options.gf_dim*8, name='g_d1')
d1 = tf.nn.dropout(d1, dropout_rate)
d1 = tf.concat([instance_norm(d1, 'g_bn_d1'), e7], 3)
# d1 is (2 x 2 x self.gf_dim*8*2)
d2 = deconv2d(tf.nn.relu(d1), options.gf_dim*8, name='g_d2')
d2 = tf.nn.dropout(d2, dropout_rate)
d2 = tf.concat([instance_norm(d2, 'g_bn_d2'), e6], 3)
# d2 is (4 x 4 x self.gf_dim*8*2)
d3 = deconv2d(tf.nn.relu(d2), options.gf_dim*8, name='g_d3')
d3 = tf.nn.dropout(d3, dropout_rate)
d3 = tf.concat([instance_norm(d3, 'g_bn_d3'), e5], 3)
# d3 is (8 x 8 x self.gf_dim*8*2)
d4 = deconv2d(tf.nn.relu(d3), options.gf_dim*8, name='g_d4')
d4 = tf.concat([instance_norm(d4, 'g_bn_d4'), e4], 3)
# d4 is (16 x 16 x self.gf_dim*8*2)
d5 = deconv2d(tf.nn.relu(d4), options.gf_dim*4, name='g_d5')
d5 = tf.concat([instance_norm(d5, 'g_bn_d5'), e3], 3)
# d5 is (32 x 32 x self.gf_dim*4*2)
d6 = deconv2d(tf.nn.relu(d5), options.gf_dim*2, name='g_d6')
d6 = tf.concat([instance_norm(d6, 'g_bn_d6'), e2], 3)
# d6 is (64 x 64 x self.gf_dim*2*2)
d7 = deconv2d(tf.nn.relu(d6), options.gf_dim, name='g_d7')
d7 = tf.concat([instance_norm(d7, 'g_bn_d7'), e1], 3)
# d7 is (128 x 128 x self.gf_dim*1*2)
d8 = deconv2d(tf.nn.relu(d7), options.output_c_dim, name='g_d8')
# d8 is (256 x 256 x output_c_dim)
return tf.nn.tanh(d8)