def train_model():
# Setup session
sess = tu.setup_training_session()
##########
# Innputs
##########
# Setup async input queue of real images
X_real = du.read_celebA()
# Noise
batch_size = tf.shape(X_real)[0]
z_noise_for_D = tf.random_uniform((batch_size, FLAGS.z_dim,), minval=-1, maxval=1, name="z_input_D")
z_noise_for_G = tf.random_uniform((batch_size, FLAGS.z_dim,), minval=-1, maxval=1, name="z_input_G")
# k factor
k_factor = tf.Variable(initial_value=0., trainable=False, name='anneal_factor')
# learning rate
lr = tf.Variable(initial_value=FLAGS.learning_rate, trainable=False, name='learning_rate')
########################
# Instantiate models
########################
G = models.Generator(nb_filters=FLAGS.nb_filters_G)
D = models.Discriminator(h_dim=FLAGS.h_dim, nb_filters=FLAGS.nb_filters_D)
##########
# Outputs
##########
X_rec_real = D(X_real, output_name="X_rec_real")
X_fake_for_D = G(z_noise_for_D, output_name="X_fake_for_D")
X_rec_fake_for_D = D(X_fake_for_D, reuse=True, output_name="X_rec_fake_for_D")
X_fake_for_G = G(z_noise_for_G, reuse=True, output_name="X_fake_for_G")
X_rec_fake_for_G = D(X_fake_for_G, reuse=True, output_name="X_rec_fake_for_G")
# output images for plots
real_toplot = du.unnormalize_image(X_real, name="real_toplot")
generated_toplot = du.unnormalize_image(X_fake_for_G, name="generated_toplot")
real_rec_toplot = du.unnormalize_image(X_rec_real, name="rec_toplot")
generated_rec_toplot = du.unnormalize_image(X_rec_fake_for_G, name="generated_rec_toplot")
###########################
# Instantiate optimizers
###########################
opt = tf.train.AdamOptimizer(learning_rate=lr, name='opt')
###########################
# losses
###########################
loss_real = losses.mae(X_real, X_rec_real)
loss_fake_for_D = losses.mae(X_fake_for_D, X_rec_fake_for_D)
loss_fake_for_G = losses.mae(X_fake_for_G, X_rec_fake_for_G)
L_D = loss_real - k_factor * loss_fake_for_D
L_G = loss_fake_for_G
Convergence = loss_real + tf.abs(FLAGS.gamma * loss_real - loss_fake_for_G)
###########################
# Compute updates ops
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = opt.compute_gradients(L_G, var_list=G_vars)
G_update = opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = opt.compute_gradients(L_D, var_list=D_vars)
D_update = opt.apply_gradients(D_gradvar, name='D_loss_minimize')
update_k_factor = tf.assign(k_factor, k_factor + FLAGS.lambdak * (FLAGS.gamma * loss_real - loss_fake_for_G))
update_lr = tf.assign(lr, tf.maximum(1E-6, lr / 2))
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries for G
tf.summary.scalar("G loss", L_G)
# Add scalar symmaries for D
tf.summary.scalar("D loss", L_D)
# Add scalar symmaries for D
tf.summary.scalar("k_factor", k_factor)
tf.summary.scalar("Convergence", Convergence)
tf.summary.scalar("learning rate", lr)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord, threads = du.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [z_noise_for_D, z_noise_for_G]
list_data += [X_real, X_rec_real, X_fake_for_G, X_rec_fake_for_G, X_fake_for_D, X_rec_fake_for_D]
list_data += [generated_toplot, real_toplot]
output = sess.run(list_data)
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
# Anneal learning rate
if (e + 1) % 200 == 0:
sess.run([update_lr])
t = tqdm(range(FLAGS.nb_batch_per_epoch), desc="Epoch %i" % e, mininterval=0.5)
for batch_counter in t:
output = sess.run([G_update, D_update, update_k_factor])
if batch_counter % (FLAGS.nb_batch_per_epoch // (int(0.5 * FLAGS.nb_batch_per_epoch))) == 0:
output = sess.run([summary_op])
writer.add_summary(output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %s:' % e)
# Plot some generated images
Xf, Xr, Xrrec, Xfrec = sess.run([generated_toplot, real_toplot, real_rec_toplot, generated_rec_toplot])
vu.save_image(Xf, Xr, title="current_batch", e=e)
vu.save_image(Xrrec, Xfrec, title="reconstruction", e=e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
# Show data statistics
output = sess.run(list_data)
tu.check_data(output, list_data)
# Stop threads
coord.request_stop()
coord.join(threads)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_session()
# Placeholder for data and Mnist iterator
mnist = input_data.read_data_sets(FLAGS.raw_dir, one_hot=True)
# images = tf.constant(mnist.train.images)
if FLAGS.data_format == "NHWC":
X_real = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, 28, 28, 1])
else:
X_real = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, 1, 28, 28])
with tf.device('/cpu:0'):
imgs = mnist.train.images.astype(np.float32)
npts = imgs.shape[0]
if FLAGS.data_format == "NHWC":
imgs = imgs.reshape((npts, 28, 28, 1))
else:
imgs = imgs.reshape((npts, 1, 28, 28))
imgs = (imgs - 0.5) / 0.5
# input_images = tf.constant(imgs)
# image = tf.train.slice_input_producer([input_images], num_epochs=FLAGS.nb_epoch)
# X_real = tf.train.batch(image, batch_size=FLAGS.batch_size, num_threads=8)
#######################
# Instantiate generator
#######################
list_filters = [256, 1]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters, list_kernel_size, list_strides, list_padding, output_shape,
batch_size=FLAGS.batch_size, dset="mnist", data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters, list_kernel_size, list_strides, list_padding,
FLAGS.batch_size, data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate, name='G_opt', beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate, name='D_opt', beta1=0.5)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1)
noise_input = tf.random_uniform((FLAGS.batch_size, FLAGS.noise_dim,), minval=-1, maxval=1)
X_fake = G(noise_input)
# output images
X_G_output = du.unnormalize_image(X_fake)
X_real_output = du.unnormalize_image(X_real)
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = objectives.binary_cross_entropy_with_logits(D_fake, tf.ones_like(D_fake))
D_loss_real = objectives.binary_cross_entropy_with_logits(D_real, tf.ones_like(D_real))
D_loss_fake = objectives.binary_cross_entropy_with_logits(D_fake, tf.zeros_like(D_fake))
D_loss = D_loss_real + D_loss_fake
# ######################################################################
# # Some parameters need to be updated (e.g. BN moving average/variance)
# ######################################################################
# from tensorflow.python.ops import control_flow_ops
# update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(update_ops):
# barrier = tf.no_op(name='update_barrier')
# D_loss = control_flow_ops.with_dependencies([barrier], D_loss)
# G_loss = control_flow_ops.with_dependencies([barrier], G_loss)
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss, var_list=G_vars)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss, var_list=D_vars)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
##########################
# Group training ops
##########################
train_ops = [G_update, D_update]
loss_ops = [G_loss, D_loss, D_loss_real, D_loss_fake]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss real", D_loss_real)
tf.summary.scalar("D loss fake", D_loss_fake)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord = tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
for e in tqdm(range(FLAGS.nb_epoch), desc="\nTraining progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch), desc="Epoch %i" % e, mininterval=0.5)
for batch_counter in t:
# X_batch, _ = mnist.train.next_batch(FLAGS.batch_size)
# if FLAGS.data_format == "NHWC":
# X_batch = np.reshape(X_batch, [-1, 28, 28, 1])
# else:
# X_batch = np.reshape(X_batch, [-1, 1, 28, 28])
# X_batch = (X_batch - 0.5) / 0.5
X_batch = du.sample_batch(imgs, FLAGS.batch_size)
output = sess.run(train_ops + loss_ops + [summary_op], feed_dict={X_real: X_batch})
if batch_counter % (FLAGS.nb_batch_per_epoch // 20) == 0:
writer.add_summary(output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
lossG, lossDreal, lossDfake = [output[2], output[4], output[5]]
t.set_description('Epoch %i: - G loss: %.2f D loss real: %.2f Dloss fake: %.2f' %
(e, lossG, lossDreal, lossDfake))
# variables = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)]
# bmean = [v for v in variables if v.name == "generator/conv2D_1_1/BatchNorm/moving_mean:0"]
# print sess.run(bmean)
# raw_input()
# Plot some generated images
# output = sess.run([X_G_output, X_real_output])
output = sess.run([X_G_output, X_real_output], feed_dict={X_real: X_batch})
vu.save_image(output, FLAGS.data_format, e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_training_session()
##########
# Innputs
##########
# Setup async input queue of real images
X_real = du.read_celebA()
# Noise
batch_size = tf.shape(X_real)[0]
z_noise_for_D = tf.random_uniform((
batch_size,
FLAGS.z_dim,
),
minval=-1,
maxval=1,
name="z_input_D")
z_noise_for_G = tf.random_uniform((
batch_size,
FLAGS.z_dim,
),
minval=-1,
maxval=1,
name="z_input_G")
# k factor
k_factor = tf.Variable(initial_value=0.,
trainable=False,
name='anneal_factor')
# learning rate
lr = tf.Variable(initial_value=FLAGS.learning_rate,
trainable=False,
name='learning_rate')
########################
# Instantiate models
########################
G = models.Generator(nb_filters=FLAGS.nb_filters_G)
D = models.Discriminator(h_dim=FLAGS.h_dim, nb_filters=FLAGS.nb_filters_D)
##########
# Outputs
##########
X_rec_real = D(X_real, output_name="X_rec_real")
X_fake_for_D = G(z_noise_for_D, output_name="X_fake_for_D")
X_rec_fake_for_D = D(X_fake_for_D,
reuse=True,
output_name="X_rec_fake_for_D")
X_fake_for_G = G(z_noise_for_G, reuse=True, output_name="X_fake_for_G")
X_rec_fake_for_G = D(X_fake_for_G,
reuse=True,
output_name="X_rec_fake_for_G")
# output images for plots
real_toplot = du.unnormalize_image(X_real, name="real_toplot")
generated_toplot = du.unnormalize_image(X_fake_for_G,
name="generated_toplot")
real_rec_toplot = du.unnormalize_image(X_rec_real, name="rec_toplot")
generated_rec_toplot = du.unnormalize_image(X_rec_fake_for_G,
name="generated_rec_toplot")
###########################
# Instantiate optimizers
###########################
opt = tf.train.AdamOptimizer(learning_rate=lr, name='opt')
###########################
# losses
###########################
loss_real = losses.mae(X_real, X_rec_real)
loss_fake_for_D = losses.mae(X_fake_for_D, X_rec_fake_for_D)
loss_fake_for_G = losses.mae(X_fake_for_G, X_rec_fake_for_G)
L_D = loss_real - k_factor * loss_fake_for_D
L_G = loss_fake_for_G
Convergence = loss_real + tf.abs(FLAGS.gamma * loss_real - loss_fake_for_G)
###########################
# Compute updates ops
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = opt.compute_gradients(L_G, var_list=G_vars)
G_update = opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = opt.compute_gradients(L_D, var_list=D_vars)
D_update = opt.apply_gradients(D_gradvar, name='D_loss_minimize')
update_k_factor = tf.assign(
k_factor,
k_factor + FLAGS.lambdak * (FLAGS.gamma * loss_real - loss_fake_for_G))
update_lr = tf.assign(lr, lr / 2)
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries for G
tf.summary.scalar("G loss", L_G)
# Add scalar symmaries for D
tf.summary.scalar("D loss", L_D)
# Add scalar symmaries for D
tf.summary.scalar("k_factor", k_factor)
tf.summary.scalar("Convergence", Convergence)
tf.summary.scalar("learning rate", lr)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord, threads = du.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [z_noise_for_D, z_noise_for_G]
list_data += [
X_real, X_rec_real, X_fake_for_G, X_rec_fake_for_G, X_fake_for_D,
X_rec_fake_for_D
]
list_data += [generated_toplot, real_toplot]
output = sess.run(list_data)
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
# Anneal learning rate every 5 epoch
if (e + 1) % 5 == 0:
sess.run([update_lr])
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
output = sess.run([G_update, D_update, update_k_factor])
if batch_counter % (FLAGS.nb_batch_per_epoch //
(int(0.5 * FLAGS.nb_batch_per_epoch))) == 0:
output = sess.run([summary_op])
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %s:' % e)
# Plot some generated images
Xf, Xr, Xrrec, Xfrec = sess.run([
generated_toplot, real_toplot, real_rec_toplot,
generated_rec_toplot
])
vu.save_image(Xf, Xr, title="current_batch", e=e)
vu.save_image(Xrrec, Xfrec, title="reconstruction", e=e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
# Show data statistics
output = sess.run(list_data)
tu.check_data(output, list_data)
# Stop threads
coord.request_stop()
coord.join(threads)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_training_session()
# Setup async input queue of real images
X_real16, X_real32, X_real64 = du.read_celebA()
#######################
# Instantiate generators
#######################
G16 = models.G16()
G32 = models.G32()
G64 = models.G64()
###########################
# Instantiate discriminators
###########################
D16 = models.D16()
D32 = models.D32()
D64 = models.D64()
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate,
name='G_opt',
beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate,
name='D_opt',
beta1=0.5)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1)
noise_input = tf.random_uniform((
FLAGS.batch_size,
FLAGS.noise_dim,
),
minval=-1,
maxval=1)
X_fake16 = G16(noise_input)
D16_real = D16(X_real16, mode="D")
X_feat16, D16_fake = D16(X_fake16, reuse=True, mode="G")
X_fake32 = G32(X_fake16, X_feat16)
D32_real = D32(X_real32, mode="D")
X_feat32, D32_fake = D32(X_fake32, reuse=True, mode="G")
X_fake64 = G64(X_fake32, X_feat32)
D64_real = D64(X_real64)
D64_fake = D64(X_fake64, reuse=True)
# output images
X_fake16_output = du.unnormalize_image(X_fake16)
X_real16_output = du.unnormalize_image(X_real16)
X_fake32_output = du.unnormalize_image(X_fake32)
X_real32_output = du.unnormalize_image(X_real32)
X_fake64_output = du.unnormalize_image(X_fake64)
X_real64_output = du.unnormalize_image(X_real64)
###########################
# Instantiate losses
###########################
G16_loss = objectives.binary_cross_entropy_with_logits(
D16_fake, tf.ones_like(D16_fake))
G32_loss = objectives.binary_cross_entropy_with_logits(
D32_fake, tf.ones_like(D32_fake))
G64_loss = objectives.binary_cross_entropy_with_logits(
D64_fake, tf.ones_like(D64_fake))
G_loss = G16_loss + G32_loss + G64_loss
# Fake losses
D16_loss_fake = objectives.binary_cross_entropy_with_logits(
D16_fake, tf.zeros_like(D16_fake))
D32_loss_fake = objectives.binary_cross_entropy_with_logits(
D32_fake, tf.zeros_like(D32_fake))
D64_loss_fake = objectives.binary_cross_entropy_with_logits(
D64_fake, tf.zeros_like(D64_fake))
# Real losses
D16_loss_real = objectives.binary_cross_entropy_with_logits(
D16_real, tf.ones_like(D16_real))
D32_loss_real = objectives.binary_cross_entropy_with_logits(
D32_real, tf.ones_like(D32_real))
D64_loss_real = objectives.binary_cross_entropy_with_logits(
D64_real, tf.ones_like(D64_real))
D_loss = D16_loss_real + D32_loss_real + D64_loss_real
D_loss += D16_loss_fake + D32_loss_fake + D64_loss_fake
###########################
# Compute gradient updates
###########################
dict_G16_vars = G16.get_trainable_variables()
G16_vars = [dict_G16_vars[k] for k in dict_G16_vars.keys()]
dict_G32_vars = G32.get_trainable_variables()
G32_vars = [dict_G32_vars[k] for k in dict_G32_vars.keys()]
dict_G64_vars = G64.get_trainable_variables()
G64_vars = [dict_G64_vars[k] for k in dict_G64_vars.keys()]
G_vars = G16_vars + G32_vars + G64_vars
dict_D16_vars = D16.get_trainable_variables()
D16_vars = [dict_D16_vars[k] for k in dict_D16_vars.keys()]
dict_D32_vars = D32.get_trainable_variables()
D32_vars = [dict_D32_vars[k] for k in dict_D32_vars.keys()]
dict_D64_vars = D64.get_trainable_variables()
D64_vars = [dict_D64_vars[k] for k in dict_D64_vars.keys()]
D_vars = D16_vars + D32_vars + D64_vars
G_gradvar = G_opt.compute_gradients(G_loss,
var_list=G_vars,
colocate_gradients_with_ops=True)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss,
var_list=D_vars,
colocate_gradients_with_ops=True)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G16 loss", G16_loss)
tf.summary.scalar("G32 loss", G32_loss)
tf.summary.scalar("G64 loss", G64_loss)
# Real losses
tf.summary.scalar("D16 loss real", D16_loss_real)
tf.summary.scalar("D32 loss real", D32_loss_real)
tf.summary.scalar("D64 loss real", D64_loss_real)
# Fake losses
tf.summary.scalar("D16 loss fake", D16_loss_fake)
tf.summary.scalar("D32 loss fake", D32_loss_fake)
tf.summary.scalar("D64 loss fake", D64_loss_fake)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
du.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [noise_input]
list_data += [X_fake16, X_fake32, X_fake64]
list_data += [X_fake16_output, X_fake32_output, X_fake64_output]
output = sess.run(list_data)
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
# Update D
output = sess.run([D_update])
# Update G
output = sess.run([G_update])
if batch_counter % (FLAGS.nb_batch_per_epoch // 20) == 0:
output = sess.run([summary_op])
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %i:' % e)
# Plot some generated images
output = sess.run([
X_fake16_output,
X_real16_output,
X_fake32_output,
X_real32_output,
X_fake64_output,
X_real64_output,
])
vu.save_image(output[:2], e=e, title="size_16")
vu.save_image(output[2:4], e=e, title="size_32")
vu.save_image(output[4:6], e=e, title="size_64")
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
# Show data statistics
output = sess.run(list_data)
tu.check_data(output, list_data)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_session()
# Setup async input queue of real images
#X_input = du.input_data(sess)
#X_real, X_fake_in = X_input[0], X_input[1]
X_real, X_fake_in = du.input_data(sess)
X_real_name = X_real[1]
X_real = X_real[0]
X_fake_name = X_fake_in[1]
X_fake_in = X_fake_in[0]
#X, Y = create_datasets(no_dir, yes_dir)
#X_fake_in = tf.placeholder(tf.float32, (batch_size, 96, 96, 3))
#X_real = tf.placeholder(tf.float32, (batch_size, 96, 96, 3))
#######################
# Instantiate generator
#######################
list_filters = [256, 128, 64, 3]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters,
list_kernel_size,
list_strides,
list_padding,
output_shape,
batch_size=FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64, 128, 256]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters,
list_kernel_size,
list_strides,
list_padding,
FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=1E-4,
name='G_opt',
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1E-4,
name='D_opt',
beta1=0.5,
beta2=0.9)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1)
noise_input = tf.random_uniform((
FLAGS.batch_size,
FLAGS.noise_dim,
),
minval=-1,
maxval=1)
X_fake = G(X_fake_in)
#X_fake = G(noise_input)
# output images
#X_G_input = du.unnormalize_image(X_fake_in)
X_G_output = du.unnormalize_image(X_fake)
X_real_output = du.unnormalize_image(X_real)
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = -tf.reduce_mean(D_fake) + (tf.reduce_mean(abs(X_fake - X_real)))
D_loss = tf.reduce_mean(D_fake) - tf.reduce_mean(D_real)
epsilon = tf.random_uniform(shape=[FLAGS.batch_size, 1, 1, 1],
minval=0.,
maxval=1.)
X_hat = X_real + epsilon * (X_fake - X_real)
D_X_hat = D(X_hat, reuse=True)
grad_D_X_hat = tf.gradients(D_X_hat, [X_hat])[0]
if FLAGS.data_format == "NCHW":
red_idx = [1]
else:
red_idx = [-1]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(grad_D_X_hat), reduction_indices=red_idx))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
D_loss += 10 * gradient_penalty
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss,
var_list=G_vars,
colocate_gradients_with_ops=True)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss,
var_list=D_vars,
colocate_gradients_with_ops=True)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
##########################
# Group training ops
##########################
loss_ops = [G_loss, D_loss]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss", D_loss)
tf.summary.scalar("gradient_penalty", gradient_penalty)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
txtfile = open("/home/ubuntu/makeup_removal/WGAN-GP/testfile.txt", "w")
g_loss_list = []
d_loss_list = []
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
num = 0
for batch_counter in t:
#with_makeup_batch, without_makeup_batch = nextBatch(X, Y, num, batch_size)
num += 1
g_loss_total = 0
d_loss_total = 0
for di in range(5):
sess.run([D_update])
# #output = sess.run([G_update] + loss_ops + [summary_op])
#sess.run([D_update])
output = sess.run([G_update] + loss_ops + [summary_op])
g_loss, d_loss = sess.run([G_loss, D_loss])
g_loss_total += g_loss
d_loss_total += d_loss
#print (g_loss, d_loss)
if batch_counter % (FLAGS.nb_batch_per_epoch // 20) == 0:
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %i' % e)
g_loss_list.append(g_loss_total)
d_loss_list.append(d_loss_total)
# Plot some generated images
#output = sess.run([X_G_output, X_G_input, X_real_output])
output = sess.run(
[X_G_output, X_real_output, X_fake_name, X_real_name])
vu.save_image(output[:2], FLAGS.data_format, e)
name = output[2:]
print(name)
with open('/home/ubuntu/makeup_removal/WGAN-GP/testfile.txt',
'a') as f:
f.write(str(e))
f.write('\n')
for array in name:
for n in array:
f.write(str(n))
f.write('\n')
f.write('\n\n')
#txtfile.close()
#print (name)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
print('Finished training!')
plt.plot(range(FLAGS.nb_epoch), g_loss_list, label='g_loss')
plt.plot(range(FLAGS.nb_epoch), d_loss_list, label='d_loss')
plt.legend()
plt.title('G:1E-4, D:1E-4')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.savefig('/home/ubuntu/makeup_removal/WGAN-GP/G_4_D_4.png')
def train_model():
# Setup session
sess = tu.setup_session()
# Placeholder for data and Mnist iterator
mnist = input_data.read_data_sets(FLAGS.raw_dir, one_hot=True)
# images = tf.constant(mnist.train.images)
if FLAGS.data_format == "NHWC":
X_real = tf.placeholder(tf.float32,
shape=[FLAGS.batch_size, 28, 28, 1])
else:
X_real = tf.placeholder(tf.float32,
shape=[FLAGS.batch_size, 1, 28, 28])
with tf.device('/cpu:0'):
imgs = mnist.train.images.astype(np.float32)
npts = imgs.shape[0]
if FLAGS.data_format == "NHWC":
imgs = imgs.reshape((npts, 28, 28, 1))
else:
imgs = imgs.reshape((npts, 1, 28, 28))
imgs = (imgs - 0.5) / 0.5
# input_images = tf.constant(imgs)
# image = tf.train.slice_input_producer([input_images], num_epochs=FLAGS.nb_epoch)
# X_real = tf.train.batch(image, batch_size=FLAGS.batch_size, num_threads=8)
#######################
# Instantiate generator
#######################
list_filters = [256, 1]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters,
list_kernel_size,
list_strides,
list_padding,
output_shape,
batch_size=FLAGS.batch_size,
dset="mnist",
data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters,
list_kernel_size,
list_strides,
list_padding,
FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate,
name='G_opt',
beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate,
name='D_opt',
beta1=0.5)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1)
noise_input = tf.random_uniform((
FLAGS.batch_size,
FLAGS.noise_dim,
),
minval=-1,
maxval=1)
X_fake = G(noise_input)
# output images
X_G_output = du.unnormalize_image(X_fake)
X_real_output = du.unnormalize_image(X_real)
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = objectives.binary_cross_entropy_with_logits(
D_fake, tf.ones_like(D_fake))
D_loss_real = objectives.binary_cross_entropy_with_logits(
D_real, tf.ones_like(D_real))
D_loss_fake = objectives.binary_cross_entropy_with_logits(
D_fake, tf.zeros_like(D_fake))
D_loss = D_loss_real + D_loss_fake
# ######################################################################
# # Some parameters need to be updated (e.g. BN moving average/variance)
# ######################################################################
# from tensorflow.python.ops import control_flow_ops
# update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(update_ops):
# barrier = tf.no_op(name='update_barrier')
# D_loss = control_flow_ops.with_dependencies([barrier], D_loss)
# G_loss = control_flow_ops.with_dependencies([barrier], G_loss)
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss, var_list=G_vars)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss, var_list=D_vars)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
##########################
# Group training ops
##########################
train_ops = [G_update, D_update]
loss_ops = [G_loss, D_loss, D_loss_real, D_loss_fake]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss real", D_loss_real)
tf.summary.scalar("D loss fake", D_loss_fake)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord = tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
for e in tqdm(range(FLAGS.nb_epoch), desc="\nTraining progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
# X_batch, _ = mnist.train.next_batch(FLAGS.batch_size)
# if FLAGS.data_format == "NHWC":
# X_batch = np.reshape(X_batch, [-1, 28, 28, 1])
# else:
# X_batch = np.reshape(X_batch, [-1, 1, 28, 28])
# X_batch = (X_batch - 0.5) / 0.5
X_batch = du.sample_batch(imgs, FLAGS.batch_size)
output = sess.run(train_ops + loss_ops + [summary_op],
feed_dict={X_real: X_batch})
if batch_counter % (FLAGS.nb_batch_per_epoch // 20) == 0:
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
lossG, lossDreal, lossDfake = [output[2], output[4], output[5]]
t.set_description(
'Epoch %i: - G loss: %.2f D loss real: %.2f Dloss fake: %.2f' %
(e, lossG, lossDreal, lossDfake))
# variables = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)]
# bmean = [v for v in variables if v.name == "generator/conv2D_1_1/BatchNorm/moving_mean:0"]
# print sess.run(bmean)
# raw_input()
# Plot some generated images
# output = sess.run([X_G_output, X_real_output])
output = sess.run([X_G_output, X_real_output],
feed_dict={X_real: X_batch})
vu.save_image(output, FLAGS.data_format, e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_session()
# Setup async input queue of real images
X_real = du.input_data(sess)
#######################
# Instantiate generator
#######################
list_filters = [256, 128, 64, 3]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters,
list_kernel_size,
list_strides,
list_padding,
output_shape,
batch_size=FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64, 128, 256]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters,
list_kernel_size,
list_strides,
list_padding,
FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate,
name='G_opt',
beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate,
name='D_opt',
beta1=0.5)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1, name="noise_input")
noise_input = tf.random_uniform((
FLAGS.batch_size,
FLAGS.noise_dim,
),
minval=-1,
maxval=1,
name="noise_input")
X_fake = G(noise_input)
# output images
X_G_output = du.unnormalize_image(X_fake, name="X_G_output")
X_real_output = du.unnormalize_image(X_real, name="X_real_output")
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = objectives.binary_cross_entropy_with_logits(
D_fake, tf.ones_like(D_fake))
D_loss_real = objectives.binary_cross_entropy_with_logits(
D_real, tf.ones_like(D_real))
D_loss_fake = objectives.binary_cross_entropy_with_logits(
D_fake, tf.zeros_like(D_fake))
# G_loss = objectives.wasserstein(D_fake, -tf.ones_like(D_fake))
# D_loss_real = objectives.wasserstein(D_real, -tf.ones_like(D_real))
# D_loss_fake = objectives.wasserstein(D_fake, tf.ones_like(D_fake))
D_loss = D_loss_real + D_loss_fake
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss, var_list=G_vars)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss, var_list=D_vars)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
# clip_op = [var.assign(tf.clip_by_value(var, -0.01, 0.01)) for var in D_vars]
G_update = G_opt.minimize(G_loss, var_list=G_vars, name='G_loss_minimize')
D_update = D_opt.minimize(D_loss, var_list=D_vars, name='D_loss_minimize')
##########################
# Group training ops
##########################
train_ops = [G_update, D_update]
loss_ops = [G_loss, D_loss, D_loss_real, D_loss_fake]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss real", D_loss_real)
tf.summary.scalar("D loss fake", D_loss_fake)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord = tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [noise_input, X_real, X_fake, X_G_output, X_real_output]
output = sess.run([noise_input, X_real, X_fake, X_G_output, X_real_output])
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
list_G_loss = []
list_D_loss_real = []
list_D_loss_fake = []
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
o_D = sess.run([D_update, D_loss_real, D_loss_fake])
sess.run([G_update, G_loss])
o_G = sess.run([G_update, G_loss])
output = sess.run([summary_op])
list_G_loss.append(o_G[-1])
list_D_loss_real.append(o_D[-2])
list_D_loss_fake.append(o_D[-1])
# output = sess.run(train_ops + loss_ops + [summary_op])
# list_G_loss.append(output[2])
# list_D_loss_real.append(output[4])
# list_D_loss_fake.append(output[5])
if batch_counter % (FLAGS.nb_batch_per_epoch //
(int(0.5 * FLAGS.nb_batch_per_epoch))) == 0:
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description(
'Epoch %i: - G loss: %.3f D loss real: %.3f Dloss fake: %.3f' %
(e, np.mean(list_G_loss), np.mean(list_D_loss_real),
np.mean(list_D_loss_fake)))
# Plot some generated images
output = sess.run([X_G_output, X_real_output])
vu.save_image(output, FLAGS.data_format, e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
if e == 0:
print(len(list_data))
output = sess.run(
[noise_input, X_real, X_fake, X_G_output, X_real_output])
tu.check_data(output, list_data)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_training_session()
##########
# Innputs
##########
# Setup async input queue of real images
X_real = du.read_celebA()
# Noise
batch_size = tf.shape(X_real)[0]
z_noise = tf.random_uniform((batch_size, FLAGS.z_dim),
minval=-1,
maxval=1,
name="z_input")
epsilon = tf.random_uniform((batch_size, 1, 1, 1),
minval=0,
maxval=1,
name="epsilon")
# learning rate
lr_D = tf.Variable(initial_value=FLAGS.learning_rate,
trainable=False,
name='learning_rate')
lr_G = tf.Variable(initial_value=FLAGS.learning_rate,
trainable=False,
name='learning_rate')
########################
# Instantiate models
########################
G = models.Generator()
D = models.Discriminator()
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=lr_D, name='G_opt', beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=lr_G, name='D_opt', beta1=0.5)
##########
# Outputs
##########
X_fake = G(z_noise)
X_hat = epsilon * X_real + (1 - epsilon) * X_fake
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
D_X_hat = D(X_hat, reuse=True)
grad_D_X_hat = tf.gradients(D_X_hat, X_hat)[0]
# output images
generated_toplot = du.unnormalize_image(X_fake, name="generated_toplot")
real_toplot = du.unnormalize_image(X_real, name="real_toplot")
###########################
# losses
###########################
G_loss = losses.wasserstein(D_fake, -tf.ones_like(D_fake))
D_loss_grad = FLAGS.lbd * tf.square((tf.nn.l2_loss(grad_D_X_hat) - 1))
D_loss_real = losses.wasserstein(D_real, -tf.ones_like(D_real))
D_loss_fake = losses.wasserstein(D_fake, tf.ones_like(D_fake))
D_loss = D_loss_grad + D_loss_real + D_loss_fake
###########################
# Compute updates ops
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss, var_list=G_vars)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss, var_list=D_vars)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
# D_gradvar_fake = D_opt.compute_gradients(D_loss_fake, var_list=D_vars)
# D_update_fake = D_opt.apply_gradients(D_gradvar_fake, name='D_loss_minimize_fake')
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# tu.add_gradient_summary(D_gradvar_fake)
# Add scalar symmaries for G
tf.summary.scalar("G loss", G_loss)
# Add scalar symmaries for D
tf.summary.scalar("D loss real", D_loss_real)
tf.summary.scalar("D loss fake", D_loss_fake)
tf.summary.scalar("D loss grad", D_loss_grad)
# Add scalar symmaries for D
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord, threads = du.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [z_noise]
list_data += [X_real, X_fake]
list_data += [generated_toplot, real_toplot]
output = sess.run(list_data)
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
# Update discriminator
for i_D in range(FLAGS.ncritic):
sess.run([D_update])
# r = np.random.randint(0, 2)
# if r == 0:
# sess.run([D_update_real])
# else:
# sess.run([D_update_fake])
# Update generator
sess.run([G_update])
if batch_counter % (FLAGS.nb_batch_per_epoch //
(int(0.5 * FLAGS.nb_batch_per_epoch))) == 0:
output = sess.run([summary_op])
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %s:' % e)
# Plot some generated images
Xf, Xr = sess.run([generated_toplot, real_toplot])
vu.save_image(Xf, Xr, title="current_batch", e=e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
# Show data statistics
output = sess.run(list_data)
tu.check_data(output, list_data)
# Stop threads
coord.request_stop()
coord.join(threads)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_session()
# Setup async input queue of real images
X_real = du.input_data(sess)
#######################
# Instantiate generator
#######################
list_filters = [256, 128, 64, 3]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters,
list_kernel_size,
list_strides,
list_padding,
output_shape,
batch_size=FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64, 128, 256]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters,
list_kernel_size,
list_strides,
list_padding,
FLAGS.batch_size,
data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=1E-4,
name='G_opt',
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1E-4,
name='D_opt',
beta1=0.5,
beta2=0.9)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1)
noise_input = tf.random_uniform((
FLAGS.batch_size,
FLAGS.noise_dim,
),
minval=-1,
maxval=1)
X_fake = G(noise_input)
# output images
X_G_output = du.unnormalize_image(X_fake)
X_real_output = du.unnormalize_image(X_real)
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = -tf.reduce_mean(D_fake)
D_loss = tf.reduce_mean(D_fake) - tf.reduce_mean(D_real)
epsilon = tf.random_uniform(shape=[FLAGS.batch_size, 1, 1, 1],
minval=0.,
maxval=1.)
X_hat = X_real + epsilon * (X_fake - X_real)
D_X_hat = D(X_hat, reuse=True)
grad_D_X_hat = tf.gradients(D_X_hat, [X_hat])[0]
if FLAGS.data_format == "NCHW":
red_idx = [1]
else:
red_idx = [-1]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(grad_D_X_hat), reduction_indices=red_idx))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
D_loss += 10 * gradient_penalty
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss,
var_list=G_vars,
colocate_gradients_with_ops=True)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss,
var_list=D_vars,
colocate_gradients_with_ops=True)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
##########################
# Group training ops
##########################
loss_ops = [G_loss, D_loss]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss", D_loss)
tf.summary.scalar("gradient_penalty", gradient_penalty)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
for di in range(5):
sess.run([D_update])
output = sess.run([G_update] + loss_ops + [summary_op])
if batch_counter % (FLAGS.nb_batch_per_epoch // 20) == 0:
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %i' % e)
# Plot some generated images
output = sess.run([X_G_output, X_real_output])
vu.save_image(output, FLAGS.data_format, e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_session()
# Setup async input queue of real images
X_real = du.input_data(sess)
#######################
# Instantiate generator
#######################
list_filters = [256, 128, 64, 3]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters, list_kernel_size, list_strides, list_padding, output_shape,
batch_size=FLAGS.batch_size, data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64, 128, 256]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters, list_kernel_size, list_strides, list_padding,
FLAGS.batch_size, data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate, name='G_opt', beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate, name='D_opt', beta1=0.5)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1, name="noise_input")
noise_input = tf.random_uniform((FLAGS.batch_size, FLAGS.noise_dim,), minval=-1, maxval=1, name="noise_input")
X_fake = G(noise_input)
# output images
X_G_output = du.unnormalize_image(X_fake, name="X_G_output")
X_real_output = du.unnormalize_image(X_real, name="X_real_output")
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = objectives.binary_cross_entropy_with_logits(D_fake, tf.ones_like(D_fake))
D_loss_real = objectives.binary_cross_entropy_with_logits(D_real, tf.ones_like(D_real))
D_loss_fake = objectives.binary_cross_entropy_with_logits(D_fake, tf.zeros_like(D_fake))
# G_loss = objectives.wasserstein(D_fake, -tf.ones_like(D_fake))
# D_loss_real = objectives.wasserstein(D_real, -tf.ones_like(D_real))
# D_loss_fake = objectives.wasserstein(D_fake, tf.ones_like(D_fake))
D_loss = D_loss_real + D_loss_fake
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss, var_list=G_vars)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss, var_list=D_vars)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
# clip_op = [var.assign(tf.clip_by_value(var, -0.01, 0.01)) for var in D_vars]
G_update = G_opt.minimize(G_loss, var_list=G_vars, name='G_loss_minimize')
D_update = D_opt.minimize(D_loss, var_list=D_vars, name='D_loss_minimize')
##########################
# Group training ops
##########################
train_ops = [G_update, D_update]
loss_ops = [G_loss, D_loss, D_loss_real, D_loss_fake]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss real", D_loss_real)
tf.summary.scalar("D loss fake", D_loss_fake)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord = tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [noise_input, X_real, X_fake, X_G_output, X_real_output]
output = sess.run([noise_input, X_real, X_fake, X_G_output, X_real_output])
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
list_G_loss = []
list_D_loss_real = []
list_D_loss_fake = []
t = tqdm(range(FLAGS.nb_batch_per_epoch), desc="Epoch %i" % e, mininterval=0.5)
for batch_counter in t:
o_D = sess.run([D_update, D_loss_real, D_loss_fake])
sess.run([G_update, G_loss])
o_G = sess.run([G_update, G_loss])
output = sess.run([summary_op])
list_G_loss.append(o_G[-1])
list_D_loss_real.append(o_D[-2])
list_D_loss_fake.append(o_D[-1])
# output = sess.run(train_ops + loss_ops + [summary_op])
# list_G_loss.append(output[2])
# list_D_loss_real.append(output[4])
# list_D_loss_fake.append(output[5])
if batch_counter % (FLAGS.nb_batch_per_epoch // (int(0.5 * FLAGS.nb_batch_per_epoch))) == 0:
writer.add_summary(output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %i: - G loss: %.3f D loss real: %.3f Dloss fake: %.3f' %
(e, np.mean(list_G_loss), np.mean(list_D_loss_real), np.mean(list_D_loss_fake)))
# Plot some generated images
output = sess.run([X_G_output, X_real_output])
vu.save_image(output, FLAGS.data_format, e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
if e == 0:
print len(list_data)
output = sess.run([noise_input, X_real, X_fake, X_G_output, X_real_output])
tu.check_data(output, list_data)
print('Finished training!')
def train_model():
# Setup session
sess = tu.setup_session()
# Setup async input queue of real images
X_real = du.input_data(sess)
#######################
# Instantiate generator
#######################
list_filters = [256, 128, 64, 3]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
output_shape = X_real.get_shape().as_list()[1:]
G = models.Generator(list_filters, list_kernel_size, list_strides, list_padding, output_shape,
batch_size=FLAGS.batch_size, data_format=FLAGS.data_format)
###########################
# Instantiate discriminator
###########################
list_filters = [32, 64, 128, 256]
list_strides = [2] * len(list_filters)
list_kernel_size = [3] * len(list_filters)
list_padding = ["SAME"] * len(list_filters)
D = models.Discriminator(list_filters, list_kernel_size, list_strides, list_padding,
FLAGS.batch_size, data_format=FLAGS.data_format)
###########################
# Instantiate optimizers
###########################
G_opt = tf.train.AdamOptimizer(learning_rate=1E-4, name='G_opt', beta1=0.5, beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1E-4, name='D_opt', beta1=0.5, beta2=0.9)
###########################
# Instantiate model outputs
###########################
# noise_input = tf.random_normal((FLAGS.batch_size, FLAGS.noise_dim,), stddev=0.1)
noise_input = tf.random_uniform((FLAGS.batch_size, FLAGS.noise_dim,), minval=-1, maxval=1)
X_fake = G(noise_input)
# output images
X_G_output = du.unnormalize_image(X_fake)
X_real_output = du.unnormalize_image(X_real)
D_real = D(X_real)
D_fake = D(X_fake, reuse=True)
###########################
# Instantiate losses
###########################
G_loss = -tf.reduce_mean(D_fake)
D_loss = tf.reduce_mean(D_fake) - tf.reduce_mean(D_real)
epsilon = tf.random_uniform(
shape=[FLAGS.batch_size, 1, 1, 1],
minval=0.,
maxval=1.
)
X_hat = X_real + epsilon * (X_fake - X_real)
D_X_hat = D(X_hat, reuse=True)
grad_D_X_hat = tf.gradients(D_X_hat, [X_hat])[0]
if FLAGS.data_format == "NCHW":
red_idx = [1]
else:
red_idx = [-1]
slopes = tf.sqrt(tf.reduce_sum(tf.square(grad_D_X_hat), reduction_indices=red_idx))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
D_loss += 10 * gradient_penalty
###########################
# Compute gradient updates
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = G_opt.compute_gradients(G_loss, var_list=G_vars, colocate_gradients_with_ops=True)
G_update = G_opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = D_opt.compute_gradients(D_loss, var_list=D_vars, colocate_gradients_with_ops=True)
D_update = D_opt.apply_gradients(D_gradvar, name='D_loss_minimize')
##########################
# Group training ops
##########################
loss_ops = [G_loss, D_loss]
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries
tf.summary.scalar("G loss", G_loss)
tf.summary.scalar("D loss", D_loss)
tf.summary.scalar("gradient_penalty", gradient_penalty)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
tu.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
t = tqdm(range(FLAGS.nb_batch_per_epoch), desc="Epoch %i" % e, mininterval=0.5)
for batch_counter in t:
for di in range(5):
sess.run([D_update])
output = sess.run([G_update] + loss_ops + [summary_op])
if batch_counter % (FLAGS.nb_batch_per_epoch // 20) == 0:
writer.add_summary(output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %i' % e)
# Plot some generated images
output = sess.run([X_G_output, X_real_output])
vu.save_image(output, FLAGS.data_format, e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
print('Finished training!')