def sample_generator_images(hparams):
"""Sample random images from the generator"""
# Get a session
sess = tf.Session()
# Create the generator
z_batch = tf.Variable(tf.random_normal([hparams.batch_size, 100]))
x_hat_batch, restore_dict, restore_path = celebA_model_def.dcgan_gen(z_batch, hparams)
# Intialize and restore model parameters
init_op = tf.global_variables_initializer()
sess.run(init_op)
restorer = tf.train.Saver(var_list=restore_dict)
restorer.restore(sess, restore_path)
images = sess.run(x_hat_batch)
images = {i: image for (i, image) in enumerate(images)}
# Reset TensorFlow graph
sess.close()
tf.reset_default_graph()
return images
def dcgan_estimator(hparams):
sess = tf.Session()
y_batch = tf.placeholder(tf.float32,
shape=(hparams.batch_size, hparams.n_input),
name='y_batch')
z_batch = tf.Variable(tf.random_normal([hparams.batch_size, 100]),
name='z_batch')
x_hat_batch, restore_dict_gen, restore_path_gen = celebA_model_def.dcgan_gen(
z_batch, hparams)
prob, restore_dict_discrim, restore_path_discrim = celebA_model_def.dcgan_discrim(
x_hat_batch, hparams)
y_hat_batch = tf.zeros(x_hat_batch, name='y2_batch')
m_loss1_batch = tf.abs(tf.reduce_mean(y_batch - y_hat_batch), 1)
m_loss2_batch = tf.reduce_mean((y_batch - y_hat_batch)**2, 1)
zp_loss_batch = tf.reduce_sum(z_batch**2, 1)
d_loss1_batch = -tf.log(prob)
d_loss2_batch = tf.log(1 - prob)
m_loss1 = tf.reduce_mean(m_loss1_batch)
m_loss2 = tf.reduce_mean(m_loss2_batch)
zp_loss = tf.reduce_mean(zp_loss_batch)
d_loss1 = tf.reduce_mean(d_loss1_batch)
d_loss2 = tf.reduce_mean(d_loss2_batch)
total_loss_batch = hparams.mloss1_weight * m_loss1_batch \
+ hparams.mloss2_weight * m_loss2_batch \
+ hparams.zprior_weight * zp_loss_batch \
+ hparams.dloss1_weight * d_loss1_batch \
+ hparams.dloss2_weight * d_loss2_batch
var_list = [z_batch]
global_step = tf.Variable(0, trainable=False, name='global_step')
learning_rate = utils.get_learning_rate(global_step, hparams)
with tf.variable_scope(tf.get_variable_scope(), reuse=False):
opt = utils.get_optimizer(learning_rate, hparams)
update_op = opt.minimize(total_loss,
var_list=var_list,
global_step=global_step,
name='update_op')
opt_reinit_op = utils.get_opt_reinit_op(opt, var_list, global_step)
init_op = tf.global_variables_initializer()
sess.run(init_op)
restorer_gen = tf.train.Saver(var_list=restore_dict_gen)
restorer_discrim = tf.train.Saver(var_list=restore_dict_discrim)
restorer_gen.restore(sess, restore_path_gen)
restorer_discrim.restore(sess, restore_path_discrim)
def estimator(y_batch_val, z_batch_val, hparams):
"""Function that returns the estimated image"""
best_keeper = utils.BestKeeper(hparams)
assign_z_opt_op = z_batch.assign(z_batch_val)
feed_dict = {y_batch: y_batch_val}
for i in range(hparams.num_random_restarts):
sess.run(opt_reinit_op)
sess.run(assign_z_opt_op)
for j in range(hparams.max_update_iter):
_, lr_val, total_loss_val, \
m_loss1_val, \
m_loss2_val, \
zp_loss_val, \
d_loss1_val, \
d_loss2_val = sess.run([update_op, learning_rate, total_loss,
m_loss1,
m_loss2,
zp_loss,
d_loss1,
d_loss2], feed_dict=feed_dict)
logging_format = 'rr {} iter {} lr {} total_loss {} m_loss1 {} m_loss2 {} zp_loss {} d_loss1 {} d_loss2 {}'
print logging_format.format(i, j, lr_val, total_loss_val,
m_loss1_val, m_loss2_val,
zp_loss_val, d_loss1_val,
d_loss2_val)
x_hat_batch_val, z_batch_val, total_loss_batch_val = sess.run(
[x_hat_batch, z_batch, total_loss_batch], feed_dict=feed_dict)
best_keeper.report(x_hat_batch_val, z_batch_val,
total_loss_batch_val)
return best_keeper.get_best()
return estimator
def dcgan_estimator(hparams):
# pylint: disable = C0326
# Get a session
sess = tf.Session()
# Set up palceholders
A = tf.placeholder(tf.float32,
shape=(hparams.n_input, hparams.num_measurements),
name='A')
y_batch = tf.placeholder(tf.float32,
shape=(hparams.batch_size,
hparams.num_measurements),
name='y_batch')
# Create the generator
z_batch = tf.Variable(tf.random_normal([hparams.batch_size, 100]),
name='z_batch')
x_hat_batch, restore_dict_gen, restore_path_gen = celebA_model_def.dcgan_gen(
z_batch, hparams)
# Create the discriminator
prob, restore_dict_discrim, restore_path_discrim = celebA_model_def.dcgan_discrim(
x_hat_batch, hparams)
# measure the estimate
if hparams.measurement_type == 'project':
y_hat_batch = tf.identity(x_hat_batch, name='y2_batch')
else:
measurement_is_sparse = (hparams.measurement_type
in ['inpaint', 'superres'])
y_hat_batch = tf.matmul(x_hat_batch,
A,
b_is_sparse=measurement_is_sparse,
name='y2_batch')
# define all losses
m_loss1_batch = tf.reduce_mean(tf.abs(y_batch - y_hat_batch), 1)
m_loss2_batch = tf.reduce_mean((y_batch - y_hat_batch)**2, 1)
zp_loss_batch = tf.reduce_sum(z_batch**2, 1)
d_loss1_batch = -tf.log(prob)
d_loss2_batch = tf.log(1 - prob)
# define total loss
total_loss_batch = hparams.mloss1_weight * m_loss1_batch \
+ hparams.mloss2_weight * m_loss2_batch \
+ hparams.zprior_weight * zp_loss_batch \
+ hparams.dloss1_weight * d_loss1_batch \
+ hparams.dloss2_weight * d_loss2_batch
total_loss = tf.reduce_mean(total_loss_batch)
# Compute means for logging
m_loss1 = tf.reduce_mean(m_loss1_batch)
m_loss2 = tf.reduce_mean(m_loss2_batch)
zp_loss = tf.reduce_mean(zp_loss_batch)
d_loss1 = tf.reduce_mean(d_loss1_batch)
d_loss2 = tf.reduce_mean(d_loss2_batch)
# Set up gradient descent
var_list = [z_batch]
global_step = tf.Variable(0, trainable=False, name='global_step')
learning_rate = utils.get_learning_rate(global_step, hparams)
with tf.variable_scope(tf.get_variable_scope(), reuse=False):
opt = utils.get_optimizer(learning_rate, hparams)
update_op = opt.minimize(total_loss,
var_list=var_list,
global_step=global_step,
name='update_op')
opt_reinit_op = utils.get_opt_reinit_op(opt, var_list, global_step)
# Intialize and restore model parameters
init_op = tf.global_variables_initializer()
sess.run(init_op)
restorer_gen = tf.train.Saver(var_list=restore_dict_gen)
restorer_discrim = tf.train.Saver(var_list=restore_dict_discrim)
restorer_gen.restore(sess, restore_path_gen)
restorer_discrim.restore(sess, restore_path_discrim)
def estimator(A_val, y_batch_val, hparams):
"""Function that returns the estimated image"""
best_keeper = utils.BestKeeper(hparams)
if hparams.measurement_type == 'project':
feed_dict = {y_batch: y_batch_val}
else:
feed_dict = {A: A_val, y_batch: y_batch_val}
for i in range(hparams.num_random_restarts):
sess.run(opt_reinit_op)
for j in range(hparams.max_update_iter):
if hparams.gif and ((j % hparams.gif_iter) == 0):
images = sess.run(x_hat_batch, feed_dict=feed_dict)
for im_num, image in enumerate(images):
save_dir = '{0}/{1}/'.format(hparams.gif_dir, im_num)
utils.set_up_dir(save_dir)
save_path = save_dir + '{0}.png'.format(j)
image = image.reshape(hparams.image_shape)
save_image(image, save_path)
_, lr_val, total_loss_val, \
m_loss1_val, \
m_loss2_val, \
zp_loss_val, \
d_loss1_val, \
d_loss2_val = sess.run([update_op, learning_rate, total_loss,
m_loss1,
m_loss2,
zp_loss,
d_loss1,
d_loss2], feed_dict=feed_dict)
logging_format = 'rr {} iter {} lr {} total_loss {} m_loss1 {} m_loss2 {} zp_loss {} d_loss1 {} d_loss2 {}'
print logging_format.format(i, j, lr_val, total_loss_val,
m_loss1_val, m_loss2_val,
zp_loss_val, d_loss1_val,
d_loss2_val)
x_hat_batch_val, total_loss_batch_val = sess.run(
[x_hat_batch, total_loss_batch], feed_dict=feed_dict)
best_keeper.report(x_hat_batch_val, total_loss_batch_val)
return best_keeper.get_best()
return estimator