def __init__(self, latent_spec, **kwargs):
"""
Args:
latent_spec (list): List of latent distributions.
[(Distribution, bool)]
The boolean indicates if the distribution should be used for
regularization.
"""
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
super(RegularizedGAN, self).__init__(**kwargs)
d = {
'latent_code_influence': {
'tensor': 'get_latent_code_influence_g_input_tensor',
},
'linear_interpolation': {
'tensor': 'get_linear_interpolation_g_input_tensor',
},
}
self.sampling_functions.update(d)
def __init__(self, output_dist, latent_spec, batch_size, image_shape, network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product([x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(isinstance(x, (Gaussian, Categorical, Bernoulli)) for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, (Categorical, Bernoulli))])
image_size = image_shape[0]
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size // 4 * image_size // 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size // 4, image_size // 4, 128]).
custom_deconv2d([0, image_size // 2, image_size // 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
flatten())
else:
raise NotImplementedError
def __init__(self, output_dist, latent_spec, batch_size, image_shape, network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product([x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(isinstance(x, (Gaussian, Categorical, Bernoulli)) for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, (Categorical, Bernoulli))])
image_size = image_shape[0]
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size / 4 * image_size / 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size / 4, image_size / 4, 128]).
custom_deconv2d([0, image_size // 2, image_size // 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
flatten())
else:
raise NotImplementedError
def __init__(self,
output_dist,
latent_spec,
is_reg,
batch_size,
image_shape,
network_type,
impr=False):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.impr = impr
self.output_dist = output_dist
self.latent_spec = latent_spec
self.is_reg = is_reg
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
self.keys = ['prob', 'logits', 'features']
if self.is_reg:
self.encoder_dim = self.reg_latent_dist.dist_flat_dim
self.keys = self.keys + ['reg_dist_info']
else:
self.encoder_dim = None
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
self.set_D_net()
self.set_G_net()
def __init__(self, output_dist, latent_spec, batch_size, image_shape, network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product([x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(isinstance(x, (Gaussian, Categorical, Bernoulli)) for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, (Categorical, Bernoulli))])
class RegularizedGAN(object):
def __init__(self, output_dist, latent_spec, batch_size, image_shape,
network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
image_size = image_shape[0]
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size / 4 * image_size / 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size / 4, image_size / 4, 128]).
custom_deconv2d([0, image_size / 2, image_size / 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
flatten())
elif network_type == "celebA":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_conv2d(256, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(image_size / 16 * image_size / 16 * 448).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size / 16, image_size / 16, 448]).
# I am *pretty sure* each of these dimensions grows by 2x
# because the stride==2.
custom_deconv2d([0, image_size / 8, image_size / 8, 256], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0, image_size / 4, image_size / 4, 128], k_h=4, k_w=4).
apply(tf.nn.relu).
custom_deconv2d([0, image_size / 2, image_size / 2, 64], k_h=4, k_w=4).
apply(tf.nn.relu).
custom_deconv2d([0, image_size / 1, image_size / 1, 3], k_h=4, k_w=4).
apply(tf.nn.tanh).
flatten())
elif network_type == "face":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = \
(shared_template.
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size / 4 * image_size / 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size / 4, image_size / 4, 128]).
custom_deconv2d([0, image_size / 2, image_size / 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
apply(tf.nn.sigmoid).
flatten())
else:
raise NotImplementedError
def discriminate(self, x_var):
d_out = self.discriminator_template.construct(input=x_var)
d = tf.nn.sigmoid(d_out[:, 0])
reg_dist_flat = self.encoder_template.construct(input=x_var)
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
return d, self.reg_latent_dist.sample(
reg_dist_info), reg_dist_info, reg_dist_flat
def generate(self, z_var):
x_dist_flat = self.generator_template.construct(input=z_var)
x_dist_info = self.output_dist.activate_dist(x_dist_flat)
return self.output_dist.sample(x_dist_info), x_dist_info
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(
nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
class RegularizedGAN(object):
def __init__(self, output_dist, latent_spec, batch_size, image_shape, network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product([x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(isinstance(x, (Gaussian, Categorical, Bernoulli)) for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, (Categorical, Bernoulli))])
def custom_batch_norm(self, input_layer, epsilon=1e-5):
shape = input_layer.shape
shp = shape[-1]
mean, variance = tf.nn.moments(input_layer, [0])
return tf.nn.batch_normalization( input_layer, mean, variance, None, None, epsilon)
def create_discriminator(self, inp):
image_size = self.image_shape[0]
if self.network_type == "mnist":
with tf.variable_scope("d_net"):
with tf.variable_scope("conv1") as scope:
inp_ = tf.reshape(inp,[-1]+list(self.image_shape))
kernel = tf.get_variable('weight',[4,4,self.image_shape[-1],64], \
initializer = tf.truncated_normal_initializer(5e-2))
conv = tf.nn.conv2d(inp_,kernel,[1,1,1,1], padding='SAME')
bias = tf.get_variable('bias',[64], initializer=tf.constant_initializer(0.1))
pre_act = tf.nn.bias_add(conv,bias)
# applying batch_norm
pre_act = self.custom_batch_norm(pre_act)
# leaky ReLu with alpha = 0.01
conv1 = tf.maximum(0.01*pre_act, pre_act, name=scope.name)
pool1 = tf.nn.max_pool(conv1, ksize=[1,3,3,1], strides=[1,2,2,1], padding='SAME', name='pool1')
with tf.variable_scope("conv2") as scope:
kernel = tf.get_variable('weight',[4,4,64,128], \
initializer = tf.truncated_normal_initializer(5e-2))
conv = tf.nn.conv2d(pool1,kernel,[1,1,1,1], padding='SAME')
bias = tf.get_variable('bias',[128], initializer=tf.constant_initializer(0.1))
pre_act = tf.nn.bias_add(conv,bias)
# applying batch_norm
pre_act = self.custom_batch_norm(pre_act)
# leaky ReLu with alpha = 0.01
conv2 = tf.maximum(0.01*pre_act, pre_act, name=scope.name)
pool2 = tf.nn.max_pool(conv1, ksize=[1,3,3,1], strides=[1,2,2,1], padding='SAME', name='pool2')
with tf.variable_scope("fc3") as scope:
inp_ = tf.reshape(pool2, [self.batch_size, -1])
dim = inp_.get_shape()[1].value
weights = tf.get_variable('weight',[dim,1024],\
initializer=tf.truncated_normal_initializer(4e-3))
bias = tf.get_variable('bias',[1024],initializer=tf.constant_initializer(0.1))
pre_act = tf.matmul(inp_,weights) + bias
# applying batch_norm
pre_act = self.custom_batch_norm(pre_act)
# leaky ReLu with alpha = 0.01
fc3 = tf.maximum(0.01*pre_act, pre_act, name=scope.name)
with tf.variable_scope("fc4") as scope:
weights = tf.get_variable('weight',[1024,128],\
initializer=tf.truncated_normal_initializer(4e-3))
bias = tf.get_variable('bias',[128],initializer=tf.constant_initializer(0.1))
pre_act = tf.nn.bias_add(tf.matmul(fc3,weights),bias,name=scope.name)
# applying batch_norm
pre_act = self.custom_batch_norm(pre_act)
# leaky ReLu with alpha = 0.01
fc4 = tf.maximum(0.01*pre_act, pre_act, name=scope.name)
with tf.variable_scope("d_temp") as scope:
weights = tf.get_variable('weight',[1024,1],\
initializer=tf.truncated_normal_initializer(4e-3))
bias = tf.get_variable('bias',[1],initializer=tf.constant_initializer(0.1))
d_temp = tf.nn.bias_add(tf.matmul(fc3,weights),bias,name=scope.name)
self.discriminator_template = d_temp
with tf.variable_scope("enc_temp") as scope:
weights = tf.get_variable('weight',[128,self.reg_latent_dist.dist_flat_dim],\
initializer=tf.truncated_normal_initializer(4e-3))
bias = tf.get_variable('bias',[self.reg_latent_dist.dist_flat_dim],\
initializer=tf.constant_initializer(0.1))
enc_temp = tf.nn.bias_add(tf.matmul(fc4,weights),bias,name=scope.name)
self.encoder_template = enc_temp
else:
raise NotImplementedError
def create_generator(self, inp):
image_size = self.image_shape[0]
if self.network_type == "mnist":
with tf.variable_scope("g_net"):
with tf.variable_scope("fc1") as scope:
weight_shape = [inp.shape[-1].value,1024]
weights = tf.get_variable('weight', weight_shape,\
initializer=tf.truncated_normal_initializer(0.1))
bias = tf.get_variable('bias',[1024],initializer=tf.constant_initializer(0.1))
pre_act = tf.nn.bias_add(tf.matmul(inp,weights),bias)
# applying batch_norm
pre_act = self.custom_batch_norm(pre_act)
# applying activation
fc1 = tf.nn.relu(pre_act, name=scope.name)
with tf.variable_scope("fc2") as scope:
weight_shape = [1024, image_size/4*image_size/4*128]
weights = tf.get_variable('weight',weight_shape,initializer=tf.truncated_normal_initializer(0.1))
bias = tf.get_variable('bias',[weight_shape[-1]],initializer=tf.constant_initializer(0.1))
pre_act = tf.nn.bias_add(tf.matmul(fc1,weights),bias)
# applying batch_norm
pre_act = self.custom_batch_norm(pre_act)
# applying activation
fc2 = tf.nn.relu(pre_act, name=scope.name)
fc2_r = tf.reshape(fc2,[-1, image_size/4, image_size/4, 128])
with tf.variable_scope("deconv3") as scope:
output_shape = [inp.shape[0].value, image_size/2, image_size/2, 64]
kernel = tf.get_variable('weight', [4, 4, 64, 128],\
initializer=tf.random_normal_initializer(stddev=0.02))
deconv = tf.nn.conv2d_transpose(fc2_r, kernel,\
output_shape=output_shape,strides=[1, 2, 2, 1])
bias = tf.get_variable('bias', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, bias), output_shape)
# applying batch_norm
pre_act = self.custom_batch_norm(deconv)
# applying activation
deconv3 = tf.nn.relu(pre_act, name=scope.name)
with tf.variable_scope("deconv4") as scope:
output_shape = [inp.shape[0].value, image_size, image_size, 1]
kernel = tf.get_variable('weight', [4, 4, 1, 64],\
initializer=tf.random_normal_initializer(stddev=0.02))
deconv = tf.nn.conv2d_transpose(deconv3, kernel,\
output_shape=output_shape,strides=[1, 2, 2, 1])
bias = tf.get_variable('bias', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, bias), output_shape)
# applying batch_norm
pre_act = self.custom_batch_norm(deconv)
# applying activation
deconv4 = tf.nn.relu(pre_act, name=scope.name)
self.generator_template = deconv4
else:
raise NotImplementedError
def discriminate(self, x_var):
self.create_discriminator(inp=x_var)
d_out = self.discriminator_template
d = tf.nn.sigmoid(d_out[:, 0])
reg_dist_flat = self.encoder_template
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
return d, self.reg_latent_dist.sample(reg_dist_info), reg_dist_info, reg_dist_flat
def generate(self, z_var):
self.create_generator(inp=z_var)
x_dist_flat = self.generator_template
x_dist_info = self.output_dist.activate_dist(x_dist_flat)
return self.output_dist.sample(x_dist_info), x_dist_info
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec, self.latent_dist.split_var(z_var)):
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec, self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
class RegularizedGAN(object):
def __init__(self, output_dist, latent_spec, is_reg, batch_size,
image_shape, network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.is_reg = is_reg
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
self.keys = ['prob', 'logits', 'features']
if self.is_reg:
self.encoder_dim = self.reg_latent_dist.dist_flat_dim
self.keys = self.keys + ['reg_dist_info']
else:
self.encoder_dim = None
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
self.set_D_net()
self.set_G_net()
def set_D_net(self):
with tf.variable_scope("d_net"):
if self.network_type == "mnist":
self.D_model = D.InfoGAN_MNIST_net(
image_shape=self.image_shape,
is_reg=self.is_reg,
encoder_dim=self.encoder_dim)
shared_template = self.D_model.shared_template
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = self.D_model.encoder_template
else:
if self.network_type == 'dcgan':
self.D_model = D.dcgan_net(image_shape=self.image_shape,
is_reg=self.is_reg,
encoder_dim=self.encoder_dim)
elif self.network_type == 'deeper_dcgan':
self.D_model = D.deeper_dcgan_net(
image_shape=self.image_shape,
is_reg=self.is_reg,
encoder_dim=self.encoder_dim)
else:
raise NotImplementedError
self.shared_template = self.D_model.shared_template
self.discriminator_template = self.shared_template.custom_fully_connected(
1)
self.encoder_template = self.D_model.encoder_template
def set_G_net(self):
with tf.variable_scope("g_net"):
if self.network_type == 'mnist':
self.gen_model = G.InfoGAN_mnist_net()
self.generator_template = self.gen_model.infoGAN_mnist_net(
self.image_shape)
else:
if self.network_type == 'dcgan':
self.gen_model = G.dcgan_net()
elif self.network_type == 'deeper_dcgan':
self.gen_model = G.deeper_dcgan_net()
else:
raise NotImplementedError
self.generator_template = self.gen_model.gen_net(
self.image_shape)
def discriminate(self, x_var):
d_features = self.shared_template.construct(input=x_var)
d_logits = self.discriminator_template.construct(input=x_var)[:, 0]
d_prob = tf.nn.sigmoid(d_logits)
d_dict = dict.fromkeys(self.keys)
d_dict['features'] = d_features
d_dict['logits'] = d_logits
d_dict['prob'] = d_prob
if self.is_reg:
reg_dist_flat = self.encoder_template.construct(input=x_var)
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
d_dict['reg_dist_info'] = reg_dist_info
return d_dict
def generate(self, z_var):
x_dist_flat = self.generator_template.construct(input=z_var)
if self.network_type == "mnist":
x_dist_info = self.output_dist.activate_dist(x_dist_flat)
return self.output_dist.sample(x_dist_info), x_dist_info
else:
return x_dist_flat, None
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(
nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
class RegularizedGAN(object):
def __init__(self, output_dist, latent_spec, batch_size, image_shape, network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product([x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(isinstance(x, (Gaussian, Categorical, Bernoulli)) for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, (Categorical, Bernoulli))])
image_size = image_shape[0]
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size // 4 * image_size // 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size // 4, image_size // 4, 128]).
custom_deconv2d([0, image_size // 2, image_size // 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
flatten())
else:
raise NotImplementedError
def discriminate(self, x_var):
d_out = self.discriminator_template.construct(input=x_var)
d = tf.nn.sigmoid(d_out[:, 0])
reg_dist_flat = self.encoder_template.construct(input=x_var)
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
return d, self.reg_latent_dist.sample(reg_dist_info), reg_dist_info, reg_dist_flat
def generate(self, z_var):
x_dist_flat = self.generator_template.construct(input=z_var)
x_dist_info = self.output_dist.activate_dist(x_dist_flat)
return self.output_dist.sample(x_dist_info), x_dist_info
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec, self.latent_dist.split_var(z_var)):
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec, self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
class RegularizedGAN(object):
def __init__(self, output_dist, latent_spec, batch_size, image_shape,
network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
image_size = image_shape[0]
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
#SOMEWHAT CONSISTENT. MIGHT CHANGE
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size / 4 * image_size / 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size / 4, image_size / 4, 128]).
custom_deconv2d([0, image_size / 2, image_size / 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
flatten())
#HEART!!!
elif network_type == 'heart':
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_fully_connected(1024).
fc_batch_norm().
apply(leaky_rectify))
self.discriminator_template = shared_template.custom_fully_connected(
1)
#THIS ENCODER DOESNT SEEM CONISTENT WITH FACES. THAT'S OKAY. WILL
#TRY ANYWAY.
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
self.generator_template = \
(pt.template("input").
custom_fully_connected(1024).
fc_batch_norm().
apply(tf.nn.relu).
custom_fully_connected(image_size / 4 * image_size / 4 * 128).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, image_size / 4, image_size / 4, 128]).
custom_deconv2d([0, image_size / 2, image_size / 2, 64], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
#THIS CONV APPEARS TO BE EXTRA. WILL KEEP ANYWAY
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
flatten())
else:
raise NotImplementedError
def discriminate(self, x_var):
d_out = self.discriminator_template.construct(input=x_var)
d = tf.nn.sigmoid(d_out[:, 0])
reg_dist_flat = self.encoder_template.construct(input=x_var)
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
return d, self.reg_latent_dist.sample(
reg_dist_info), reg_dist_info, reg_dist_flat
def generate(self, z_var):
x_dist_flat = self.generator_template.construct(input=z_var)
x_dist_info = self.output_dist.activate_dist(x_dist_flat)
return self.output_dist.sample(x_dist_info), x_dist_info
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(
nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
def __init__(self, output_dist, latent_spec, batch_size, image_shape,
network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
pstr('output_dist', self.output_dist)
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
pstr('latent_dist', self.latent_dist)
pstr('x in latent_spec', [x for x, _ in self.latent_spec])
pstr('xreg in latent_spec', [xreg for _, xreg in self.latent_spec])
#for x in enumerate(self.latent_spec):
# print '------------------------'
# for y in enumerate(x):
# pstrall('x----reg',y)
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
#for x in self.reg_latent_dist.dists:
# pstr('x in reg_latent_dist.dists',x)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
pstr('image_shape', image_shape)
pstr('image_shape[0]', image_shape[0])
image_size = image_shape[0]
#self.image_shape = (178, 218, 1)
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
#conv_batch_norm().
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_conv2d(256, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
#custom_fully_connected(1024).
#fc_batch_norm().
#apply(leaky_rectify).
custom_conv2d(512, k_h=4, k_w=4))
#conv_batch_norm().
#apply(leaky_rectify2))
#linear
#apply(tf.nn.sigmoid))
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
s = self.image_shape[0]
s2, s4, s8, s16, s32 = int(s / 2), int(s / 4), int(s / 8), int(
s / 16), int(s / 32)
self.generator_template = \
(pt.template("input").
custom_fully_connected(s16 * s16 * 512).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, s16, s16, 512]).
#custom_fully_connected(s32 * s32 * 1024).
#fc_batch_norm().
#apply(tf.nn.relu).
#reshape([-1, s32, s32, 1024]).
#custom_deconv2d([0, s16, s16, 512], k_h=4, k_w=4).
#conv_batch_norm().
#apply(tf.nn.relu).
custom_deconv2d([0, s8, s8, 256], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0, s4, s4, 128], k_h=4, k_w=4).
#conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0, s2, s2, 64], k_h=4, k_w=4).
#conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
apply(tf.nn.tanh))
else:
raise NotImplementedError
class RegularizedGAN(object):
def __init__(self, output_dist, latent_spec, batch_size, image_shape,
network_type):
"""
:type output_dist: Distribution
:type latent_spec: list[(Distribution, bool)]
:type batch_size: int
:type network_type: string
"""
self.output_dist = output_dist
pstr('output_dist', self.output_dist)
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
pstr('latent_dist', self.latent_dist)
pstr('x in latent_spec', [x for x, _ in self.latent_spec])
pstr('xreg in latent_spec', [xreg for _, xreg in self.latent_spec])
#for x in enumerate(self.latent_spec):
# print '------------------------'
# for y in enumerate(x):
# pstrall('x----reg',y)
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
self.batch_size = batch_size
self.network_type = network_type
self.image_shape = image_shape
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
#for x in self.reg_latent_dist.dists:
# pstr('x in reg_latent_dist.dists',x)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
pstr('image_shape', image_shape)
pstr('image_shape[0]', image_shape[0])
image_size = image_shape[0]
#self.image_shape = (178, 218, 1)
if network_type == "mnist":
with tf.variable_scope("d_net"):
shared_template = \
(pt.template("input").
reshape([-1] + list(image_shape)).
custom_conv2d(64, k_h=4, k_w=4).
#conv_batch_norm().
apply(leaky_rectify).
custom_conv2d(128, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
custom_conv2d(256, k_h=4, k_w=4).
conv_batch_norm().
apply(leaky_rectify).
#custom_fully_connected(1024).
#fc_batch_norm().
#apply(leaky_rectify).
custom_conv2d(512, k_h=4, k_w=4))
#conv_batch_norm().
#apply(leaky_rectify2))
#linear
#apply(tf.nn.sigmoid))
self.discriminator_template = shared_template.custom_fully_connected(
1)
self.encoder_template = \
(shared_template.
custom_fully_connected(128).
fc_batch_norm().
apply(leaky_rectify).
custom_fully_connected(self.reg_latent_dist.dist_flat_dim))
with tf.variable_scope("g_net"):
s = self.image_shape[0]
s2, s4, s8, s16, s32 = int(s / 2), int(s / 4), int(s / 8), int(
s / 16), int(s / 32)
self.generator_template = \
(pt.template("input").
custom_fully_connected(s16 * s16 * 512).
fc_batch_norm().
apply(tf.nn.relu).
reshape([-1, s16, s16, 512]).
#custom_fully_connected(s32 * s32 * 1024).
#fc_batch_norm().
#apply(tf.nn.relu).
#reshape([-1, s32, s32, 1024]).
#custom_deconv2d([0, s16, s16, 512], k_h=4, k_w=4).
#conv_batch_norm().
#apply(tf.nn.relu).
custom_deconv2d([0, s8, s8, 256], k_h=4, k_w=4).
conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0, s4, s4, 128], k_h=4, k_w=4).
#conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0, s2, s2, 64], k_h=4, k_w=4).
#conv_batch_norm().
apply(tf.nn.relu).
custom_deconv2d([0] + list(image_shape), k_h=4, k_w=4).
apply(tf.nn.tanh))
else:
raise NotImplementedError
def discriminate(self, x_var):
d_out = self.discriminator_template.construct(input=x_var)
d = tf.nn.sigmoid(d_out[:, 0])
#d = tf.nn.sigmoid(d_out)
reg_dist_flat = self.encoder_template.construct(input=x_var)
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
return d, self.reg_latent_dist.sample(
reg_dist_info), reg_dist_info, reg_dist_flat, d_out
def generate(self, z_var):
x_dist_flat = self.generator_template.construct(input=z_var)
x_dist_info = self.output_dist.activate_dist(x_dist_flat)
return self.output_dist.sample(x_dist_info), x_dist_info
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
pstr('reg_z split_var z_i', z_i)
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(
nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
class RegularizedGAN(GANModel):
def __init__(self, latent_spec, **kwargs):
"""
Args:
latent_spec (list): List of latent distributions.
[(Distribution, bool)]
The boolean indicates if the distribution should be used for
regularization.
"""
self.latent_spec = latent_spec
self.latent_dist = Product([x for x, _ in latent_spec])
self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])
self.nonreg_latent_dist = Product(
[x for x, reg in latent_spec if not reg])
assert all(
isinstance(x, (Gaussian, Categorical, Bernoulli))
for x in self.reg_latent_dist.dists)
self.reg_cont_latent_dist = Product(
[x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])
self.reg_disc_latent_dist = Product([
x for x in self.reg_latent_dist.dists
if isinstance(x, (Categorical, Bernoulli))
])
super(RegularizedGAN, self).__init__(**kwargs)
d = {
'latent_code_influence': {
'tensor': 'get_latent_code_influence_g_input_tensor',
},
'linear_interpolation': {
'tensor': 'get_linear_interpolation_g_input_tensor',
},
}
self.sampling_functions.update(d)
def __getstate__(self):
pickling_dict = super(RegularizedGAN, self).__getstate__()
del pickling_dict['encoder_template']
return pickling_dict
def build_g_input(self):
return self.latent_dist.sample_prior(self.batch_size)
def get_g_feed_dict(self):
return None
def get_reg_dist_info(self, x_var):
reg_dist_flat = self.encoder_template.construct(input=x_var)
reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)
return reg_dist_info
def disc_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(z_i)
return self.reg_disc_latent_dist.join_vars(ret)
def cont_reg_z(self, reg_z_var):
ret = []
for dist_i, z_i in zip(self.reg_latent_dist.dists,
self.reg_latent_dist.split_var(reg_z_var)):
if isinstance(dist_i, Gaussian):
ret.append(z_i)
return self.reg_cont_latent_dist.join_vars(ret)
def disc_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, (Categorical, Bernoulli)):
ret.append(dist_info_i)
return self.reg_disc_latent_dist.join_dist_infos(ret)
def cont_reg_dist_info(self, reg_dist_info):
ret = []
for dist_i, dist_info_i in zip(
self.reg_latent_dist.dists,
self.reg_latent_dist.split_dist_info(reg_dist_info)):
if isinstance(dist_i, Gaussian):
ret.append(dist_info_i)
return self.reg_cont_latent_dist.join_dist_infos(ret)
def reg_z(self, z_var=None):
""" Return the variables with distribution bool == True (concatenated). """
ret = []
if z_var is None:
z_var = self.g_input
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if reg_i:
ret.append(z_i)
return self.reg_latent_dist.join_vars(ret)
def nonreg_z(self, z_var):
ret = []
for (_, reg_i), z_i in zip(self.latent_spec,
self.latent_dist.split_var(z_var)):
if not reg_i:
ret.append(z_i)
return self.nonreg_latent_dist.join_vars(ret)
def reg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if reg_i:
ret.append(dist_info_i)
return self.reg_latent_dist.join_dist_infos(ret)
def nonreg_dist_info(self, dist_info):
ret = []
for (_, reg_i), dist_info_i in zip(
self.latent_spec, self.latent_dist.split_dist_info(dist_info)):
if not reg_i:
ret.append(dist_info_i)
return self.nonreg_latent_dist.join_dist_infos(ret)
def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):
reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)
reg_idx = 0
nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_z_vars[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_z_vars[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_vars(ret)
def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):
reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)
reg_idx = 0
nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(
nonreg_dist_info)
nonreg_idx = 0
ret = []
for idx, (dist_i, reg_i) in enumerate(self.latent_spec):
if reg_i:
ret.append(reg_dist_infos[reg_idx])
reg_idx += 1
else:
ret.append(nonreg_dist_infos[nonreg_idx])
nonreg_idx += 1
return self.latent_dist.join_dist_infos(ret)
###########################################################################
# SAMPLING
###########################################################################
def get_train_g_input_tensor(self):
if len(self.reg_latent_dist.dists) > 0:
sampling_type = 'latent_code_influence'
else:
sampling_type = 'random'
return make_list(self.get_g_input_tensor(sampling_type))
def get_g_input_value(self, sampling_type, **kwargs):
# TODO: self.get_g_input_tensor should return a Tensor and
# this method should .eval() its result with a tf.Session()
if sampling_type == 'random':
return self.get_g_input(sampling_type, 'value', **kwargs)
return self.get_g_input_tensor(sampling_type, **kwargs)
def get_linear_interpolation_g_input_tensor(self,
n_samples=10,
n_variations=10):
"""
Returns:
(ndarray, str):
"""
# TODO: return a tensor instead of ndarray
with tf.Session():
n = n_samples * n_variations
all_z_start = self.latent_dist.sample_prior(n_samples).eval()
all_z_end = self.latent_dist.sample_prior(n_samples).eval()
coefficients = np.linspace(start=0, stop=1, num=n_variations)
z_var = []
for z_start, z_end in zip(all_z_start, all_z_end):
for coeff in coefficients:
z_var.append(coeff * z_start + (1 - coeff) * z_end)
other = self.latent_dist.sample_prior(self.batch_size - n).eval()
z_var = np.concatenate([z_var, other], axis=0)
z_var = np.asarray(z_var, dtype=np.float32)
return z_var, 'linear_interpolations'
def get_latent_code_influence_g_input_tensor(self,
n_samples=10,
n_variations=10,
min_continuous=-2.,
max_continuous=2.):
"""
Args:
n_samples (int): The number of different samples (n_columns).
n_variations (int): The number of variations for each latent code
(n_rows).
min_continuous (float): The minimum value for a continuous latent
code
max_continuous (float): The maximum value for a continuous latent
code
Returns:
(ndarray, str):
"""
# TODO: return a tensor instead of ndarray
if len(self.reg_latent_dist.dists) == 0:
raise ValueError('The model must have at least one regularization '
'latent distribution.')
with tf.Session():
# (n, d) with 10 * 10 samples + other samples
n = n_samples * n_variations
fixed_noncat = self.nonreg_latent_dist.sample_prior(n_samples)
fixed_noncat = np.tile(fixed_noncat.eval(), [n_variations, 1])
other = self.nonreg_latent_dist.sample_prior(self.batch_size - n)
other = other.eval()
fixed_noncat = np.concatenate([fixed_noncat, other], axis=0)
fixed_cat = self.reg_latent_dist.sample_prior(n_samples).eval()
fixed_cat = np.tile(fixed_cat, [n_variations, 1])
other = self.reg_latent_dist.sample_prior(self.batch_size - n)
other = other.eval()
fixed_cat = np.concatenate([fixed_cat, other], axis=0)
offset = 0
z_vars_and_names = []
for dist_idx, dist in enumerate(self.reg_latent_dist.dists):
if isinstance(dist, Gaussian):
assert dist.dim == 1, "Only dim=1 is currently supported"
vary_cat = dist.varying_values(min_continuous, max_continuous,
n_variations)
vary_cat = np.repeat(vary_cat, n_samples)
other = np.zeros(self.batch_size - n)
vary_cat = np.concatenate((vary_cat, other)).reshape((-1, 1))
vary_cat = np.asarray(vary_cat, dtype=np.float32)
cur_cat = np.copy(fixed_cat)
cur_cat[:, offset:offset + 1] = vary_cat
offset += 1
elif isinstance(dist, Categorical):
lookup = np.eye(dist.dim, dtype=np.float32)
cat_ids = []
for idx in xrange(n_variations):
cat_ids.extend([idx] * n_samples)
cat_ids.extend([0] * (self.batch_size - n))
cur_cat = np.copy(fixed_cat)
cur_cat[:, offset:offset + dist.dim] = lookup[cat_ids]
offset += dist.dim
elif isinstance(dist, Bernoulli):
assert dist.dim == 1, "Only dim=1 is currently supported"
lookup = np.eye(dist.dim, dtype=np.float32)
cat_ids = []
for idx in xrange(n_variations):
cat_ids.extend([int(idx / 5)] * n_samples)
cat_ids.extend([0] * (self.batch_size - n))
cur_cat = np.copy(fixed_cat)
cur_cat[:, offset:offset + dist.dim] = np.expand_dims(
np.array(cat_ids), axis=-1)
# import ipdb; ipdb.set_trace()
offset += dist.dim
else:
raise NotImplementedError
# (n, d)
# The 10 first rows have different z and c and are tiled 10 times
# except for the varying c that is the same by blocks of 10 rows
# and linearly varies between blocks
z_var = np.concatenate([fixed_noncat, cur_cat], axis=1)
# Images where each column had a different fixed z and c
# The varying c varies along each column
# (a different value for each row)
name = 'image_{}_{}'.format(dist_idx, dist.__class__.__name__)
z_vars_and_names.append((z_var, name))
return z_vars_and_names
