def create_trainer(self, cycloss, z_cycloss, encoder, generator,
encoder_loss, standard_loss, standard_discriminator,
encoder_discriminator):
metrics = []
metrics.append(standard_loss.metrics)
d_vars = standard_discriminator.variables()
g_vars = generator.variables() + encoder.variables()
print("D_VARS", d_vars)
print("G_VARS", g_vars)
#d_loss = standard_loss.d_loss
#g_loss = standard_loss.g_loss + cycloss
loss1 = ("g_loss", standard_loss.g_loss)
loss2 = ("d_loss", standard_loss.d_loss)
loss = hc.Config({
'sample': [standard_loss.d_loss, standard_loss.g_loss],
'metrics': {
'g_loss': loss1[1],
'd_loss': loss2[1]
}
})
trainer = ConsensusTrainer(self,
self.config.trainer,
loss=loss,
g_vars=g_vars,
d_vars=d_vars)
return trainer
def create(self):
config = self.config
ops = self.ops
with tf.device(self.device):
#x_input = tf.identity(self.inputs.x, name='input')
xa_input = tf.identity(self.inputs.xa, name='xa_i')
xb_input = tf.identity(self.inputs.xb, name='xb_i')
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = self.create_component(config.generator,
input=xa_input,
name='b_generator')
za = ga.controls["z"]
zb = gb.controls["z"]
self.uniform_sample = ga.sample
xba = ga.sample
xab = gb.sample
xa_hat = ga.reuse(gb.sample)
xb_hat = gb.reuse(ga.sample)
z_shape = self.ops.shape(za)
uz_shape = z_shape
uz_shape[-1] = uz_shape[-1] // len(
config.z_distribution.projections)
ue = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
features_a = ops.concat([ga.sample, xa_input], axis=0)
features_b = ops.concat([gb.sample, xb_input], axis=0)
stacked_a = ops.concat([xa_input, ga.sample], axis=0)
stacked_b = ops.concat([xb_input, gb.sample], axis=0)
stacked_z = ops.concat([ue.sample, za, zb], axis=0)
da = self.create_component(config.discriminator,
name='a_discriminator',
input=stacked_a,
features=[features_b])
db = self.create_component(config.discriminator,
name='b_discriminator',
input=stacked_b,
features=[features_a])
dz = self.create_component(config.z_discriminator,
name='z_discriminator',
input=stacked_z)
if config.ali_z:
features_alia = ops.concat([za, ue.sample], axis=0)
features_alib = ops.concat([zb, ue.sample], axis=0)
uga = ga.reuse(tf.zeros_like(xb_input),
replace_controls={"z": ue.sample})
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": ue.sample})
stacked_alia = ops.concat([xa_input, uga], axis=0)
stacked_alib = ops.concat([xb_input, ugb], axis=0)
dalia = self.create_component(config.ali_discriminator,
name='alia_discriminator',
input=stacked_alia,
features=[features_alib])
dalib = self.create_component(config.ali_discriminator,
name='alib_discriminator',
input=stacked_alib,
features=[features_alia])
lalia = self.create_loss(config.loss, dalia, None, None, 2)
lalib = self.create_loss(config.loss, dalib, None, None, 2)
la = self.create_loss(config.loss, da, xa_input, ga.sample, 2)
lb = self.create_loss(config.loss, db, xb_input, gb.sample, 2)
lz = self.create_loss(config.loss, dz, None, None, 3)
d_vars = da.variables() + db.variables() + lz.variables()
if config.ali_z:
d_vars += dalia.variables() + dalib.variables()
g_vars = ga.variables() + gb.variables()
d_loss = la.d_loss + lb.d_loss + lz.d_loss
g_loss = la.g_loss + lb.g_loss + lz.g_loss
metrics = {
'ga_loss': la.g_loss,
'gb_loss': lb.g_loss,
'gz_loss': lz.g_loss,
'da_loss': la.d_loss,
'db_loss': lb.d_loss,
'dz_loss': lz.d_loss
}
if config.ali_z:
d_loss += lalib.d_loss + lalia.d_loss
g_loss += lalib.g_loss + lalia.g_loss
metrics['galia_loss'] = lalia.g_loss
metrics['galib_loss'] = lalib.g_loss
metrics['dalia_loss'] = lalia.d_loss
metrics['dalib_loss'] = lalib.d_loss
loss = hc.Config({'sample': [d_loss, g_loss], 'metrics': metrics})
trainer = ConsensusTrainer(self,
config.trainer,
loss=loss,
g_vars=g_vars,
d_vars=d_vars)
self.initialize_variables()
self.trainer = trainer
self.generator = ga
self.encoder = gb # this is the other gan
self.uniform_distribution = hc.Config({"sample": za}) #uniform_encoder
self.zb = zb
self.z_hat = gb.sample
self.x_input = xa_input
self.autoencoded_x = xa_hat
self.cyca = xa_hat
self.cycb = xb_hat
self.xba = xba
self.xab = xab
rgb = tf.cast((self.generator.sample + 1) * 127.5, tf.int32)
self.generator_int = tf.bitwise.bitwise_or(rgb,
0xFF000000,
name='generator_int')
def create(self):
config = self.config
ops = self.ops
with tf.device(self.device):
#x_input = tf.identity(self.inputs.x, name='input')
xa_input = tf.identity(self.inputs.xa, name='xa_i')
xb_input = tf.identity(self.inputs.xb, name='xb_i')
if config.same_g:
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = hc.Config({
"sample": ga.reuse(xa_input),
"controls": {
"z": ga.controls['z']
},
"reuse": ga.reuse
})
else:
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = self.create_component(config.generator,
input=xa_input,
name='b_generator')
za = ga.controls["z"]
zb = gb.controls["z"]
self.uniform_sample = ga.sample
xba = ga.sample
xab = gb.sample
xa_hat = ga.reuse(gb.sample)
xb_hat = gb.reuse(ga.sample)
z_shape = self.ops.shape(za)
uz_shape = z_shape
uz_shape[-1] = uz_shape[-1] // len(
config.z_distribution.projections)
ue = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue2 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue3 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue4 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
print('ue', ue.sample)
zua = ue.sample
zub = ue2.sample
uga = ga.reuse(tf.zeros_like(xb_input),
replace_controls={"z": zua})
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zub})
xa = xa_input
xb = xb_input
t0 = ops.concat([xb, xa], axis=3)
t1 = ops.concat([ugb, uga], axis=3)
t2 = ops.concat([gb.sample, ga.sample], axis=3)
f0 = ops.concat([za, zb], axis=3)
f1 = ops.concat([zub, zua], axis=3)
f2 = ops.concat([zb, za], axis=3)
features = ops.concat([f0, f1, f2], axis=0)
stack = [t0, t1, t2]
if config.mess2:
xbxa = ops.concat([xb_input, xa_input], axis=3)
gbga = ops.concat([gb.sample, ga.sample], axis=3)
fa = ops.concat([za, zb], axis=3)
fb = ops.concat([za, zb], axis=3)
features = ops.concat([fa, fb], axis=0)
stack = [xbxa, gbga]
if config.mess6:
t0 = ops.concat([xb, xa], axis=3)
t1 = ops.concat([gb.sample, uga], axis=3)
t2 = ops.concat([gb.sample, xa], axis=3)
t3 = ops.concat([xb, ga.sample], axis=3)
t4 = ops.concat([ugb, ga.sample], axis=3)
features = None
stack = [t0, t1, t2, t3, t4]
if config.mess7:
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zua})
t0 = ops.concat([xb, ga.sample], axis=3)
t1 = ops.concat([ugb, uga], axis=3)
t2 = ops.concat([gb.sample, xa], axis=3)
features = None
stack = [t0, t1, t2]
if config.mess8:
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zua})
uga2 = ga.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zub})
ugb2 = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zub})
t0 = ops.concat([xb, ga.sample, xa, gb.sample], axis=3)
t1 = ops.concat([ugb, uga, uga2, ugb2], axis=3)
features = None
stack = [t0, t1]
if config.mess10:
t0 = ops.concat([xb, xa], axis=3)
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zua})
t1 = ops.concat([ugb, uga], axis=3)
t2 = ops.concat([gb.sample, xa], axis=3)
t3 = ops.concat([xb, ga.sample], axis=3)
features = None
stack = [t0, t1, t2, t3]
if config.mess11:
t0 = ops.concat([xa, xb, ga.sample, gb.sample], axis=3)
ugbga = ga.reuse(ugb)
ugagb = gb.reuse(uga)
t1 = ops.concat([ga.sample, gb.sample, uga, ugb], axis=3)
features = None
stack = [t0, t1]
if config.mess12:
t0 = ops.concat([xb, xa], axis=3)
t2 = ops.concat([gb.sample, ga.sample], axis=3)
f0 = ops.concat([za, zb], axis=3)
f2 = ops.concat([zua, zub], axis=3)
features = ops.concat([f0, f2], axis=0)
stack = [t0, t2]
if config.mess13:
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zua})
features = None
t0 = ops.concat([xa, gb.sample], axis=3)
t1 = ops.concat([ga.sample, xb], axis=3)
t2 = ops.concat([uga, ugb], axis=3)
stack = [t0, t1, t2]
if config.mess14:
features = None
t0 = ops.concat([xa, gb.sample], axis=3)
t1 = ops.concat([ga.sample, xb], axis=3)
stack = [t0, t1]
stacked = ops.concat(stack, axis=0)
d = self.create_component(config.discriminator,
name='alia_discriminator',
input=stacked,
features=[features])
l = self.create_loss(config.loss, d, xa_input, ga.sample,
len(stack))
d_vars = d.variables()
if config.same_g:
g_vars = ga.variables()
else:
g_vars = ga.variables() + gb.variables()
d_loss = l.d_loss
g_loss = l.g_loss
metrics = {'g_loss': l.g_loss, 'd_loss': l.d_loss}
if (config.alpha):
#t0 = ops.concat([zua,zub], axis=3)
#t1 = ops.concat([za,zb], axis=3)
t0 = zua
t1 = za
t2 = zb
netzd = tf.concat(axis=0, values=[t0, t1, t2])
z_d = self.create_component(config.z_discriminator,
name='z_discriminator',
input=netzd)
print("Z_D", z_d)
lz = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
d_loss += lz.d_loss
g_loss += lz.g_loss
d_vars += z_d.variables()
metrics["a_gloss"] = lz.g_loss
metrics["a_dloss"] = lz.d_loss
if (config.mess13):
t0 = ops.concat([xb, ga.sample], axis=3)
t1 = ops.concat([gb.sample, xa], axis=3)
t2 = ops.concat([ugb, uga], axis=3)
stack = [t0, t1, t2]
features = None
stacked = tf.concat(axis=0, values=stack)
d2 = self.create_component(config.discriminator,
name='align_2',
input=stacked,
features=[features])
lz = self.create_loss(config.loss, d2, xa_input, ga.sample,
len(stack))
d_vars += d2.variables()
d_loss += lz.d_loss
g_loss += lz.g_loss
metrics["mess13_g"] = lz.g_loss
metrics["mess13_d"] = lz.d_loss
if (config.mess14):
t0 = ops.concat([xb, xa], axis=3)
t2 = ops.concat([ugb, uga], axis=3)
stack = [t0, t2]
features = None
stacked = tf.concat(axis=0, values=stack)
d3 = self.create_component(config.discriminator,
name='align_3',
input=stacked,
features=[features])
lz = self.create_loss(config.loss, d3, xa_input, ga.sample,
len(stack))
d_vars += d3.variables()
d_loss += lz.d_loss
g_loss += lz.g_loss
metrics["mess14_g"] = lz.g_loss
metrics["mess14_d"] = lz.d_loss
loss = hc.Config({'sample': [d_loss, g_loss], 'metrics': metrics})
trainer = ConsensusTrainer(self,
config.trainer,
loss=loss,
g_vars=g_vars,
d_vars=d_vars)
self.session.run(tf.global_variables_initializer())
self.trainer = trainer
self.generator = ga
self.encoder = gb # this is the other gan
self.uniform_distribution = hc.Config({"sample": zb}) #uniform_encoder
self.zb = zb
self.z_hat = gb.sample
self.x_input = xa_input
self.autoencoded_x = xa_hat
self.cyca = xa_hat
self.cycb = xb_hat
self.xba = xba
self.xab = xab
self.uga = uga
self.ugb = ugb
rgb = tf.cast((self.generator.sample + 1) * 127.5, tf.int32)
self.generator_int = tf.bitwise.bitwise_or(rgb,
0xFF000000,
name='generator_int')
def create(self):
config = self.config
ops = self.ops
with tf.device(self.device):
#x_input = tf.identity(self.inputs.x, name='input')
xa_input = tf.identity(self.inputs.xa, name='xa_i')
xb_input = tf.identity(self.inputs.xb, name='xb_i')
if config.same_g:
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = hc.Config({
"sample": ga.reuse(xa_input),
"controls": {
"z": ga.controls['z']
},
"reuse": ga.reuse
})
else:
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = self.create_component(config.generator,
input=xa_input,
name='b_generator')
za = ga.controls["z"]
zb = gb.controls["z"]
self.uniform_sample = ga.sample
xba = ga.sample
xab = gb.sample
xa_hat = ga.reuse(gb.sample)
xb_hat = gb.reuse(ga.sample)
z_shape = self.ops.shape(za)
uz_shape = z_shape
uz_shape[-1] = uz_shape[-1] // len(
config.z_distribution.projections)
ue = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue2 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue3 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue4 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
print('ue', ue.sample)
zua = ue.sample
zub = ue2.sample
ga2 = self.create_component(config.generator,
input=tf.zeros_like(xb_input),
name='ua_generator')
gb2 = self.create_component(config.generator,
input=tf.zeros_like(xb_input),
name='ub_generator')
uga = ga2.reuse(tf.zeros_like(xb_input),
replace_controls={"z": zua})
ugb = gb2.reuse(tf.zeros_like(xb_input),
replace_controls={"z": zub})
ugbprime = gb.reuse(uga)
ugbzb = gb.controls['z']
xa = xa_input
xb = xb_input
t0 = xb
t1 = gb.sample
t2 = ugbprime
f0 = za
f1 = zb
f2 = ugbzb
stack = [t0, t1, t2]
stacked = ops.concat(stack, axis=0)
features = ops.concat([f0, f1, f2], axis=0)
xa_hat = ugbprime
d = self.create_component(config.discriminator,
name='d_ab',
input=stacked,
features=[features])
l = self.create_loss(config.loss, d, xa_input, ga.sample,
len(stack))
loss1 = l
d_loss1 = l.d_loss
g_loss1 = l.g_loss
d_vars1 = d.variables()
g_vars1 = ga2.variables() + gb2.variables() + gb.variables(
) + ga.variables() #gb.variables()# + gb.variables()
d_loss = l.d_loss
g_loss = l.g_loss
metrics = {'g_loss': l.g_loss, 'd_loss': l.d_loss}
g_vars2 = ga.variables()
trainers = []
if config.cyc:
t0 = xa
t1 = ga.sample
f0 = zb
f1 = za
stack = [t0, t1]
stacked = ops.concat(stack, axis=0)
features = ops.concat([f0, f1], axis=0)
d = self.create_component(config.discriminator,
name='d2_ab',
input=stacked,
features=[features])
l = self.create_loss(config.loss, d, xa_input, ga.sample,
len(stack))
d_loss1 += l.d_loss
g_loss1 += l.g_loss
d_vars1 += d.variables()
metrics["gloss2"] = l.g_loss
metrics["dloss2"] = l.d_loss
#loss2=l
#g_loss2 = loss2.g_loss
#d_loss2 = loss2.d_loss
if (config.alpha):
t0 = zub
t1 = zb
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.z_discriminator,
name='z_discriminator',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
d_vars1 += z_d.variables()
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
if (config.ug):
t0 = xb
t1 = ugb
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.discriminator,
name='ug_discriminator',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
d_vars1 += z_d.variables()
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
if (config.alpha2):
t0 = zua
t1 = za
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.z_discriminator,
name='z_discriminator2',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
d_vars1 += z_d.variables()
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
if config.ug2:
t0 = xa
t1 = uga
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.discriminator,
name='ug_discriminator2',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
d_vars1 += z_d.variables()
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
if config.ug3:
t0 = tf.concat(values=[xa, xb], axis=3)
t1 = tf.concat(values=[uga, ugb], axis=3)
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.discriminator,
name='ug_discriminator3',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
d_vars1 += z_d.variables()
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
lossa = hc.Config({
'sample': [d_loss1, g_loss1],
'metrics': metrics
})
#lossb = hc.Config({'sample': [d_loss2, g_loss2], 'metrics': metrics})
trainers += [
ConsensusTrainer(self,
config.trainer,
loss=lossa,
g_vars=g_vars1,
d_vars=d_vars1)
]
#trainers += [ConsensusTrainer(self, config.trainer, loss = lossb, g_vars = g_vars2, d_vars = d_vars2)]
trainer = MultiTrainerTrainer(trainers)
self.session.run(tf.global_variables_initializer())
self.trainer = trainer
self.generator = ga
self.encoder = gb # this is the other gan
self.uniform_distribution = hc.Config({"sample": zb}) #uniform_encoder
self.zb = zb
self.z_hat = gb.sample
self.x_input = xa_input
self.autoencoded_x = xa_hat
self.cyca = xa_hat
self.cycb = xb_hat
self.xba = xba
self.xab = xab
self.uga = uga
self.ugb = ugb
rgb = tf.cast((self.generator.sample + 1) * 127.5, tf.int32)
self.generator_int = tf.bitwise.bitwise_or(rgb,
0xFF000000,
name='generator_int')
def create(self):
config = self.config
ops = self.ops
with tf.device(self.device):
x_input = tf.identity(self.inputs.x, name='input')
def random_like(x):
return UniformDistribution(
self,
config.z_distribution,
output_shape=self.ops.shape(x)).sample
# q(z|x)
encoder = self.create_component(config.encoder,
input=x_input,
name='z_encoder')
print("ENCODER ", encoder.sample)
self.encoder = encoder
z_shape = self.ops.shape(encoder.sample)
style = self.create_component(config.style_encoder,
input=x_input,
name='style')
self.style = style
self.styleb = style # hack for sampler
self.random_style = random_like(style.sample)
uz_shape = z_shape
uz_shape[-1] = uz_shape[-1] // len(
config.z_distribution.projections)
UniformDistribution = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
direction, slider = self.create_controls(
self.ops.shape(UniformDistribution.sample))
z = UniformDistribution.sample + slider * direction
#projected_encoder = UniformDistribution(self, config.encoder, z=encoder.sample)
feature_dim = len(ops.shape(z)) - 1
#stack_z = tf.concat([encoder.sample, z], feature_dim)
#stack_encoded = tf.concat([encoder.sample, encoder.sample], feature_dim)
stack_z = z
generator = self.create_component(config.generator,
input=stack_z,
features=[style.sample])
self.uniform_sample = generator.sample
x_hat = generator.reuse(encoder.sample)
features_zs = ops.concat([encoder.sample, z], axis=0)
stacked_xg = ops.concat([x_input, generator.sample], axis=0)
if config.u_to_z:
u_to_z = self.create_component(config.u_to_z,
name='u_to_z',
input=random_like(z))
gu = generator.reuse(u_to_z.sample)
stacked_xg = ops.concat([x_input, gu], axis=0)
features_zs = ops.concat([encoder.sample, u_to_z.sample],
axis=0)
standard_discriminator = self.create_component(
config.discriminator,
name='discriminator',
input=stacked_xg,
features=[features_zs])
standard_loss = self.create_loss(config.loss,
standard_discriminator, x_input,
generator, 2)
l = standard_loss
d_loss1 = l.d_loss
g_loss1 = l.g_loss
d_vars1 = standard_discriminator.variables()
g_vars1 = generator.variables() + encoder.variables(
) + style.variables()
metrics = {'g_loss': l.g_loss, 'd_loss': l.d_loss}
t0 = random_like(
style.sample
) #tf.concat([random_like(style1), random_like(style1)], axis=1)
t1 = style.sample #tf.concat([style1, style2], axis=1)
stack = [t0, t1]
stacked = ops.concat(stack, axis=0)
features = None
z_d = self.create_component(config.z_discriminator,
name='forcerandom_discriminator',
input=stacked)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=x_input,
generator=generator,
split=2)
metrics["forcerandom_gloss"] = loss3.g_loss
metrics["forcerandom_dloss"] = loss3.d_loss
if config.forcerandom:
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
d_vars1 += z_d.variables()
if config.u_to_z:
g_vars1 += u_to_z.variables()
lossa = hc.Config({
'sample': [d_loss1, g_loss1],
'metrics': metrics
})
trainer = ConsensusTrainer(self,
config.trainer,
loss=lossa,
g_vars=g_vars1,
d_vars=d_vars1)
self.session.run(tf.global_variables_initializer())
self.trainer = trainer
self.generator = generator
self.uniform_distribution = uniform_encoder
self.slider = slider
self.direction = direction
self.z = z
self.z_hat = encoder.sample
self.x_input = x_input
self.autoencoded_x = x_hat
rgb = tf.cast((self.generator.sample + 1) * 127.5, tf.int32)
self.generator_int = tf.bitwise.bitwise_or(rgb,
0xFF000000,
name='generator_int')
self.random_z = tf.random_uniform(ops.shape(
UniformDistribution.sample),
-1,
1,
name='random_z')
if hasattr(generator, 'mask_generator'):
self.mask_generator = generator.mask_generator
self.mask = mask
self.autoencode_mask = generator.mask_generator.sample
self.autoencode_mask_3_channel = generator.mask
def create(self):
config = self.config
ops = self.ops
with tf.device(self.device):
#x_input = tf.identity(self.inputs.x, name='input')
xa_input = tf.identity(self.inputs.xa, name='xa_i')
xb_input = tf.identity(self.inputs.xb, name='xb_i')
if config.same_g:
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = hc.Config({
"sample": ga.reuse(xa_input),
"controls": {
"z": ga.controls['z']
},
"reuse": ga.reuse
})
else:
ga = self.create_component(config.generator,
input=xb_input,
name='a_generator')
gb = self.create_component(config.generator,
input=xa_input,
name='b_generator')
za = ga.controls["z"]
zb = gb.controls["z"]
self.uniform_sample = gb.sample
xba = ga.sample
xab = gb.sample
xa_hat = ga.reuse(gb.sample)
xb_hat = gb.reuse(ga.sample)
xa = xa_input
xb = xb_input
if config.ignore_random:
t0 = xb
t1 = gb.sample
f0 = za
f1 = zb
stack = [t0, t1]
stacked = ops.concat(stack, axis=0)
features = ops.concat([f0, f1], axis=0)
self.inputs.x = xb
ugb = gb.sample
zub = zb
sourcezub = zb
else:
z_shape = self.ops.shape(za)
uz_shape = z_shape
uz_shape[-1] = uz_shape[-1] // len(
config.z_distribution.projections)
ue = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue2 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue3 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
ue4 = UniformDistribution(self,
config.z_distribution,
output_shape=uz_shape)
print('ue', ue.sample)
zua = ue.sample
zub = ue2.sample
ue2 = UniformDistribution(
self,
config.z_distribution,
output_shape=[self.ops.shape(za)[0], config.source_linear])
zub = ue2.sample
uz_to_gz = self.create_component(config.uz_to_gz,
name='uzb_to_gzb',
input=zub)
zub = uz_to_gz.sample
sourcezub = zub
ugb = gb.reuse(tf.zeros_like(xa_input),
replace_controls={"z": zub})
t0 = xb
t1 = gb.sample
t2 = ugb
f0 = za
f1 = zb
f2 = zub
stack = [t0, t1, t2]
stacked = ops.concat(stack, axis=0)
features = ops.concat([f0, f1, f2], axis=0)
d = self.create_component(config.discriminator,
name='d_ab',
input=stacked,
features=[features])
l = self.create_loss(config.loss, d, xa_input, ga.sample,
len(stack))
loss1 = l
d_loss1 = l.d_loss
g_loss1 = l.g_loss
d_vars1 = d.variables()
g_vars1 = gb.variables() + ga.variables()
if not config.ignore_random:
g_vars1 += uz_to_gz.variables(
) #gb.variables()# + gb.variables()
d_loss = l.d_loss
g_loss = l.g_loss
metrics = {'g_loss': l.g_loss, 'd_loss': l.d_loss}
if config.inline_alpha:
t0 = zub
t1 = zb
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.z_discriminator,
name='z_discriminator',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
d_vars1 += z_d.variables()
d_loss1 += loss3.d_loss
g_loss1 += loss3.g_loss
trainers = []
if config.separate_alpha:
t0 = zub
t1 = zb
netzd = tf.concat(axis=0, values=[t0, t1])
z_d = self.create_component(config.z_discriminator,
name='z_discriminator',
input=netzd)
loss3 = self.create_component(config.loss,
discriminator=z_d,
x=xa_input,
generator=ga,
split=2)
metrics["za_gloss"] = loss3.g_loss
metrics["za_dloss"] = loss3.d_loss
g_vars1 = gb.variables() + ga.variables(
) #gb.variables()# + gb.variables()
trainers += [
ConsensusTrainer(self,
config.trainer,
loss=loss3,
g_vars=uz_to_gz.variables(),
d_vars=z_d.variables())
]
lossa = hc.Config({
'sample': [d_loss1, g_loss1],
'metrics': metrics
})
#lossb = hc.Config({'sample': [d_loss2, g_loss2], 'metrics': metrics})
trainers += [
ConsensusTrainer(self,
config.trainer,
loss=lossa,
g_vars=g_vars1,
d_vars=d_vars1)
]
#trainers += [ConsensusTrainer(self, config.trainer, loss = lossb, g_vars = g_vars2, d_vars = d_vars2)]
trainer = MultiTrainerTrainer(trainers)
self.session.run(tf.global_variables_initializer())
self.trainer = trainer
self.generator = ga
self.encoder = hc.Config({"sample": ugb}) # this is the other gan
self.uniform_distribution = hc.Config({"sample":
zub}) #uniform_encoder
self.uniform_distribution_source = hc.Config({"sample": sourcezub
}) #uniform_encoder
self.zb = zb
self.z_hat = gb.sample
self.x_input = xa_input
self.autoencoded_x = xa_hat
self.cyca = xa_hat
self.cycb = xb_hat
self.xba = xba
self.xab = xab
self.uga = ugb
self.ugb = ugb
rgb = tf.cast((self.generator.sample + 1) * 127.5, tf.int32)
self.generator_int = tf.bitwise.bitwise_or(rgb,
0xFF000000,
name='generator_int')
def create(self):
config = self.config
ops = self.ops
with tf.device(self.device):
#x_input = tf.identity(self.inputs.x, name='input')
xa_input = tf.identity(self.inputs.xa, name='xa_i')
xb_input = tf.identity(self.inputs.xb, name='xb_i')
def random_like(x):
return UniformDistribution(
self,
config.z_distribution,
output_shape=self.ops.shape(x)).sample
#y=a
#x=b
zgx = self.create_component(config.encoder,
input=xa_input,
name='xa_to_x')
zgy = self.create_component(config.encoder,
input=xb_input,
name='xb_to_y')
zx = zgx.sample
zy = zgy.sample
z_noise = random_like(zx)
n_noise = random_like(zx)
if config.style:
stylex = self.create_component(config.style_discriminator,
input=xb_input,
name='xb_style')
styley = self.create_component(config.style_discriminator,
input=xa_input,
name='xa_style')
zy = tf.concat(values=[zy, z_noise], axis=3)
zx = tf.concat(values=[zx, n_noise], axis=3)
gy = self.create_component(config.generator,
features=[styley.sample],
input=zy,
name='gy_generator')
y = hc.Config({"sample": xa_input})
zx = self.create_component(config.encoder,
input=y.sample,
name='xa_to_x',
reuse=True).sample
zx = tf.concat(values=[zx, z_noise], axis=3)
gx = self.create_component(config.generator,
features=[stylex.sample],
input=zx,
name='gx_generator')
else:
gy = self.create_component(config.generator,
features=[z_noise],
input=zy,
name='gy_generator')
y = hc.Config({"sample": xa_input})
zx = self.create_component(config.encoder,
input=y.sample,
name='xa_to_x',
reuse=True).sample
gx = self.create_component(config.generator,
features=[z_noise],
input=zx,
name='gx_generator')
stylex = hc.Config({"sample": random_like(y.sample)})
self.y = y
self.gy = gy
self.gx = gx
ga = gy
gb = gx
self.uniform_sample = gb.sample
xba = ga.sample
xab = gb.sample
xa_hat = ga.reuse(zx)
xb_hat = gb.reuse(zy)
xa = xa_input
xb = xb_input
self.styleb = stylex
self.random_style = random_like(stylex.sample)
t0 = xb
t1 = gx.sample
f0 = gy.sample
f1 = y.sample
stack = [t0, t1]
stacked = ops.concat(stack, axis=0)
features = ops.concat([f0, f1], axis=0)
self.inputs.x = xa
ugb = gb.reuse(random_like(zy))
zub = zy
sourcezub = zy
d = self.create_component(config.discriminator,
name='d_ab',
input=stacked,
features=[features])
l = self.create_loss(config.loss, d, xa_input, ga.sample,
len(stack))
loss1 = l
d_loss1 = l.d_loss
g_loss1 = l.g_loss
d_vars1 = d.variables()
g_vars1 = gb.variables() + ga.variables() + zgx.variables(
) + zgy.variables()
d_loss = l.d_loss
g_loss = l.g_loss
metrics = {'g_loss': l.g_loss, 'd_loss': l.d_loss}
trainers = []
lossa = hc.Config({
'sample': [d_loss1, g_loss1],
'metrics': metrics
})
#lossb = hc.Config({'sample': [d_loss2, g_loss2], 'metrics': metrics})
trainers += [
ConsensusTrainer(self,
config.trainer,
loss=lossa,
g_vars=g_vars1,
d_vars=d_vars1)
]
#trainers += [ConsensusTrainer(self, config.trainer, loss = lossb, g_vars = g_vars2, d_vars = d_vars2)]
trainer = MultiTrainerTrainer(trainers)
self.session.run(tf.global_variables_initializer())
self.trainer = trainer
self.generator = gb
self.encoder = hc.Config({"sample": ugb}) # this is the other gan
self.uniform_distribution = hc.Config({"sample":
zub}) #uniform_encoder
self.uniform_distribution_source = hc.Config({"sample": sourcezub
}) #uniform_encoder
self.zb = zy
self.z_hat = gb.sample
self.x_input = xa_input
self.autoencoded_x = xb_hat
self.cyca = xa_hat
self.cycb = xb_hat
self.xba = xba
self.xab = xab
self.uga = y.sample
self.ugb = ugb
rgb = tf.cast((self.generator.sample + 1) * 127.5, tf.int32)
self.generator_int = tf.bitwise.bitwise_or(rgb,
0xFF000000,
name='generator_int')