# ------- variables
X = tf.placeholder(tf.float32, [BATCH_SIZE, HEIGHT, WIDTH, 3])
Y = tf.placeholder(tf.float32, [BATCH_SIZE, HEIGHT, WIDTH, 3])
MASK = tf.placeholder(tf.float32, [BATCH_SIZE, HEIGHT, WIDTH, 3])
IT = tf.placeholder(tf.float32)
# ------- structure
input = tf.concat([X, MASK], 3)
vec_en = mm.encoder(input, reuse=False)
vec_con = mm.contextual_block(vec_en, vec_en, MASK)
I_co = mm.decoder(vec_en, reuse=False)
I_ge = mm.decoder(vec_con, reuse=True)
image_result = I_ge * (1 - MASK) + Y * MASK
D_real_red = mm.discriminator_red(Y, reuse=False)
D_fake_red = mm.discriminator_red(image_result, reuse=True)
# ------- Loss
Loss_D_red = tf.reduce_mean(tf.nn.relu(1 + D_fake_red)) + tf.reduce_mean(
tf.nn.relu(1 - D_real_red))
Loss_D = Loss_D_red
def build_model(self):
# ------- variables
tf.compat.v1.disable_eager_execution()
self.X = tf.compat.v1.placeholder(
tf.float32,
[self.batch_size, self.input_height, self.input_width, 3])
self.Y = tf.compat.v1.placeholder(
tf.float32,
[self.batch_size, self.input_height, self.input_width, 3])
self.MASK = tf.compat.v1.placeholder(
tf.float32,
[self.batch_size, self.input_height, self.input_width, 3])
IT = tf.compat.v1.placeholder(tf.float32)
# ------- structure
input = tf.concat([self.X, self.MASK], 3)
vec_en = mm.encoder(input, reuse=False, name='G_en')
vec_con = mm.contextual_block(vec_en,
vec_en,
self.MASK,
3,
50.0,
'CB1',
stride=1)
I_co = mm.decoder(vec_en,
self.input_height,
self.input_height,
reuse=False,
name='G_de')
I_ge = mm.decoder(vec_con,
self.input_height,
self.input_height,
reuse=True,
name='G_de')
self.image_result = I_ge * (1 - self.MASK) + self.Y * self.MASK
D_real_red = mm.discriminator_red(self.Y, reuse=False, name='disc_red')
D_fake_red = mm.discriminator_red(self.image_result,
reuse=True,
name='disc_red')
# ------- Loss
Loss_D_red = tf.reduce_mean(
input_tensor=tf.nn.relu(1 + D_fake_red)) + tf.reduce_mean(
input_tensor=tf.nn.relu(1 - D_real_red))
Loss_D = Loss_D_red
Loss_gan_red = -tf.reduce_mean(input_tensor=D_fake_red)
Loss_gan = Loss_gan_red
Loss_s_re = tf.reduce_mean(input_tensor=tf.abs(I_ge - self.Y))
Loss_hat = tf.reduce_mean(input_tensor=tf.abs(I_co - self.Y))
A = tf.image.rgb_to_yuv((self.image_result + 1) / 2.0)
A_Y = tf.cast(A[:, :, :, 0:1] * 255.0, dtype=tf.int32)
B = tf.image.rgb_to_yuv((self.Y + 1) / 2.0)
B_Y = tf.cast(B[:, :, :, 0:1] * 255.0, dtype=tf.int32)
ssim = tf.reduce_mean(input_tensor=tf.image.ssim(A_Y, B_Y, 255.0))
alpha = IT / 1000000
Loss_G = 0.1 * Loss_gan + 10 * Loss_s_re + 5 * (1 - alpha) * Loss_hat
# --------------------- variable & optimizer
var_D = [
v for v in tf.compat.v1.global_variables()
if v.name.startswith('disc_red')
]
var_G = [
v for v in tf.compat.v1.global_variables()
if v.name.startswith('G_en') or v.name.startswith('G_de')
or v.name.startswith('CB1')
]
update_ops = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimize_D = tf.compat.v1.train.AdamOptimizer(learning_rate=0.0004,
beta1=0.5,
beta2=0.9).minimize(
Loss_D,
var_list=var_D)
optimize_G = tf.compat.v1.train.AdamOptimizer(learning_rate=0.0001,
beta1=0.5,
beta2=0.9).minimize(
Loss_G,
var_list=var_G)
config = tf.compat.v1.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.4
# config.gpu_options.allow_growth = False
self.sess = tf.compat.v1.Session(config=config)
init = tf.compat.v1.global_variables_initializer()
self.sess.run(init)
saver = tf.compat.v1.train.Saver()
if self.is_mosaic:
Restore = tf.compat.v1.train.import_meta_graph(
self.mosaic_model_name)
Restore.restore(
self.sess,
tf.train.latest_checkpoint(self.mosaic_checkpoint_name))
else:
Restore = tf.compat.v1.train.import_meta_graph(self.bar_model_name)
Restore.restore(
self.sess,
tf.train.latest_checkpoint(self.bar_checkpoint_name))
# ------- variables
X = tf.placeholder(tf.float32, [batch_size, Height, Width, 3])
Y = tf.placeholder(tf.float32, [batch_size, Height, Width, 3])
MASK = tf.placeholder(tf.float32, [batch_size, Height, Width, 3])
IT = tf.placeholder(tf.float32)
# ------- structure
input = tf.concat([X, MASK], 3)
vec_en = mm.encoder(input, reuse=False, name='G_en')
vec_con = mm.contextual_block(vec_en, vec_en, MASK, 3, 50.0, 'CB1', stride=1)
I_co = mm.decoder(vec_en, Height, reuse=False, name='G_de')
I_ge = mm.decoder(vec_con, Height, reuse=True, name='G_de')
image_result = I_ge * (1 - MASK) + Y * MASK
D_real_red = mm.discriminator_red(Y, reuse=False, name='disc_red')
D_fake_red = mm.discriminator_red(image_result, reuse=True, name='disc_red')
# ------- Loss
Loss_D_red = tf.reduce_mean(tf.nn.relu(1 + D_fake_red)) + tf.reduce_mean(
tf.nn.relu(1 - D_real_red))
Loss_D = Loss_D_red