def build_anogan(self):
# Generator
self.g = self.generator(self.z, self.y)
self.g_test = self.generator(self.z, self.y, reuse=True, is_train=False)
# Discriminator
d_real_fm, d_real = self.discriminator(self.x)
d_fake_fm, d_fake = self.discriminator(self.g_test, reuse=True)
t_vars = tf.trainable_variables()
d_params = [v for v in t_vars if v.name.startswith('disc')]
g_params = [v for v in t_vars if v.name.startswith('gen')]
self.d_op = tf.train.AdamOptimizer(learning_rate=self.d_lr,
beta1=self.beta1, beta2=self.beta2).minimize(loss=self.d_loss,
var_list=d_params)
if self.detect:
self.d_loss = t.l1_loss(d_fake_fm, d_real_fm) # disc loss
self.r_loss = t.l1_loss(self.x, self.g) # residual loss
self.ano_loss = (1. - self.lambda_) * self.r_loss + self.lambda_ * self.d_loss
tf.summary.scalar("loss/d_loss", self.d_loss)
tf.summary.scalar("loss/r_loss", self.r_loss)
tf.summary.scalar("loss/ano_loss", self.ano_loss)
self.ano_op = tf.train.AdamOptimizer(learning_rate=self.g_lr,
beta1=self.beta1, beta2=self.beta2).minimize(loss=self.ano_loss,
var_list=g_params)
else:
self.d_real_loss = t.sce_loss(d_real, tf.ones_like(d_real))
self.d_fake_loss = t.sce_loss(d_fake, tf.zeros_like(d_fake))
self.d_loss = self.d_real_loss + self.d_fake_loss
self.g_loss = t.sce_loss(d_fake, tf.ones_like(d_fake))
# Summary
tf.summary.scalar("loss/d_fake_loss", self.d_fake_loss)
tf.summary.scalar("loss/d_real_loss", self.d_real_loss)
tf.summary.scalar("loss/d_loss", self.d_loss)
tf.summary.scalar("loss/g_loss", self.r_loss)
self.g_op = tf.train.AdamOptimizer(learning_rate=self.d_lr,
beta1=self.beta1, beta2=self.beta2).minimize(loss=self.d_loss,
var_list=d_params)
# Merge summary
self.merged = tf.summary.merge_all()
# Model saver
self.saver = tf.train.Saver(max_to_keep=1)
if not self.detect:
self.writer = tf.summary.FileWriter('./orig-model/', self.s.graph)
else:
self.writer = tf.summary.FileWriter('./ano-model/', self.s.graph)
def build_began(self):
# Generator
self.g = self.generator(self.z)
# Discriminator
d_real = self.discriminator(self.x)
d_fake = self.discriminator(self.g, reuse=True)
# Loss
d_real_loss = t.l1_loss(self.x, d_real)
d_fake_loss = t.l1_loss(self.g, d_fake)
self.d_loss = d_real_loss - self.k * d_fake_loss
self.g_loss = d_fake_loss
# Convergence Metric
self.balance = self.gamma * d_real_loss - self.g_loss # (=d_fake_loss)
self.m_global = d_real_loss + tf.abs(self.balance)
# k_t update
self.k_update = tf.assign(
self.k,
tf.clip_by_value(self.k + self.lambda_k * self.balance, 0, 1))
# Summary
tf.summary.scalar("loss/d_loss", self.d_loss)
tf.summary.scalar("loss/d_real_loss", d_real_loss)
tf.summary.scalar("loss/d_fake_loss", d_fake_loss)
tf.summary.scalar("loss/g_loss", self.g_loss)
tf.summary.scalar("misc/balance", self.balance)
tf.summary.scalar("misc/m_global", self.m_global)
tf.summary.scalar("misc/k_t", self.k)
tf.summary.scalar("misc/d_lr", self.d_lr)
tf.summary.scalar("misc/g_lr", self.g_lr)
# Optimizer
t_vars = tf.trainable_variables()
d_params = [v for v in t_vars if v.name.startswith('d')]
g_params = [v for v in t_vars if v.name.startswith('g')]
self.d_op = tf.train.AdamOptimizer(learning_rate=self.d_lr_update,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.d_loss, var_list=d_params)
self.g_op = tf.train.AdamOptimizer(learning_rate=self.g_lr_update,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.g_loss, var_list=g_params)
# Merge summary
self.merged = tf.summary.merge_all()
# Model saver
self.saver = tf.train.Saver(max_to_keep=1)
self.writer = tf.summary.FileWriter('./model/', self.s.graph)
def build_dualgan(self):
# Generator
self.g = self.generator(self.z)
# Discriminator
d_real = self.discriminator(self.x)
d_fake = self.discriminator(self.g, reuse=True)
# Loss
d_real_loss = t.l1_loss(self.x, d_real)
d_fake_loss = t.l1_loss(self.g, d_fake)
self.d_loss = d_real_loss + d_fake_loss
self.g_loss = d_fake_loss
# Summary
tf.summary.scalar("loss/d_real_loss", d_real_loss)
tf.summary.scalar("loss/d_fake_loss", d_fake_loss)
tf.summary.scalar("loss/d_loss", self.d_loss)
tf.summary.scalar("loss/g_loss", self.g_loss)
# Optimizer
t_vars = tf.trainable_variables()
d_params = [v for v in t_vars if v.name.startswith('d')]
g_params = [v for v in t_vars if v.name.startswith('g')]
self.d_op = tf.train.RMSPropOptimizer(learning_rate=self.d_lr,
decay=self.decay).minimize(
self.d_loss,
var_list=d_params)
self.g_op = tf.train.RMSPropOptimizer(learning_rate=self.g_lr,
decay=self.decay).minimize(
self.g_loss,
var_list=g_params)
# Merge summary
self.merged = tf.summary.merge_all()
# Model saver
self.saver = tf.train.Saver(max_to_keep=1)
self.writer = tf.summary.FileWriter('./model/', self.s.graph)
def build_stargan(self):
def gp_loss(real, fake, eps=self.epsilon):
# alpha = tf.random_uniform(shape=real.get_shape(), minval=0., maxval=1., name='alpha')
# diff = fake - real # fake data - real data
# interpolates = real + alpha * diff
interpolates = eps * real + (1. - eps) * fake
d_interp = self.discriminator(interpolates, reuse=True)
gradients = tf.gradients(d_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean(tf.square(slopes - 1.))
return gradient_penalty
x_img_a = self.x_A[:, :, :, :self.channel]
x_attr_a = self.x_A[:, :, :, self.channel:]
x_img_b = self.x_B[:, :, :, :self.channel]
# x_attr_b = self.x_B[:, :, :, self.channel:]
# Generator
self.fake_B = self.generator(self.x_A)
gen_in = tf.concat([self.fake_B, x_attr_a], axis=3)
self.fake_A = self.generator(gen_in, reuse=True)
# Discriminator
d_src_real_b, d_aux_real_b = self.discriminator(x_img_b)
g_src_fake_b, g_aux_fake_b = self.discriminator(
self.fake_B, reuse=True) # used at updating G net
d_src_fake_b, d_aux_fake_b = self.discriminator(
self.fake_x_B, reuse=True) # used at updating D net
# using WGAN-GP losses
gp = gp_loss(x_img_b, self.fake_x_B)
d_src_loss = tf.reduce_mean(d_src_fake_b) - tf.reduce_mean(
d_src_real_b) + gp
d_aux_loss = t.sce_loss(d_aux_real_b, self.y_B)
self.d_loss = d_src_loss + self.lambda_cls * d_aux_loss
g_src_loss = -tf.reduce_mean(g_src_fake_b)
g_aux_fake_loss = t.sce_loss(g_aux_fake_b, self.y_B)
g_rec_loss = t.l1_loss(x_img_a, self.fake_A)
self.g_loss = g_src_loss + self.lambda_cls * g_aux_fake_loss + self.lambda_rec * g_rec_loss
# Summary
tf.summary.scalar("loss/d_src_loss", d_src_loss)
tf.summary.scalar("loss/d_aux_loss", d_aux_loss)
tf.summary.scalar("loss/d_loss", self.d_loss)
tf.summary.scalar("loss/g_src_loss", g_src_loss)
tf.summary.scalar("loss/g_aux_fake_loss", g_aux_fake_loss)
tf.summary.scalar("loss/g_rec_loss", g_rec_loss)
tf.summary.scalar("loss/g_loss", self.g_loss)
# Optimizer
t_vars = tf.trainable_variables()
d_params = [v for v in t_vars if v.name.startswith('d')]
g_params = [v for v in t_vars if v.name.startswith('g')]
self.d_op = tf.train.AdamOptimizer(
learning_rate=self.d_lr * self.lr_decay,
beta1=self.beta1).minimize(self.d_loss, var_list=d_params)
self.g_op = tf.train.AdamOptimizer(
learning_rate=self.g_lr * self.lr_decay,
beta1=self.beta1).minimize(self.g_loss, var_list=g_params)
# Merge summary
self.merged = tf.summary.merge_all()
# Model saver
self.saver = tf.train.Saver(max_to_keep=1)
self.writer = tf.summary.FileWriter('./model/', self.s.graph)