def main(_, run_eval_loop=True):
with tf.name_scope('inputs'):
images = data_provider.provide_data('validation',
FLAGS.batch_size,
dataset_dir=FLAGS.dataset_dir,
patch_size=FLAGS.patch_size)
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('generator'):
reconstructions, _, prebinary = networks.compression_model(
images,
num_bits=FLAGS.bits_per_patch,
depth=FLAGS.model_depth,
is_training=False)
summaries.add_reconstruction_summaries(images, reconstructions, prebinary)
# Visualize losses.
pixel_loss_per_example = tf.reduce_mean(tf.abs(images - reconstructions),
axis=[1, 2, 3])
pixel_loss = tf.reduce_mean(pixel_loss_per_example)
tf.summary.histogram('pixel_l1_loss_hist', pixel_loss_per_example)
tf.summary.scalar('pixel_l1_loss', pixel_loss)
# Create ops to write images to disk.
uint8_images = data_provider.float_image_to_uint8(images)
uint8_reconstructions = data_provider.float_image_to_uint8(reconstructions)
uint8_reshaped = summaries.stack_images(uint8_images,
uint8_reconstructions)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'compression.png'),
tf.image.encode_png(uint8_reshaped[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
master=FLAGS.master,
hooks=[
tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)
],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def main(_):
if not tf.gfile.Exists(FLAGS.train_log_dir):
tf.gfile.MakeDirs(FLAGS.train_log_dir)
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
# Put input pipeline on CPU to reserve GPU for training.
with tf.name_scope('inputs'), tf.device('/cpu:0'):
images = data_provider.provide_data('train',
FLAGS.batch_size,
dataset_dir=FLAGS.dataset_dir,
patch_size=FLAGS.patch_size)
# Manually define a GANModel tuple. This is useful when we have custom
# code to track variables. Note that we could replace all of this with a
# call to `tfgan.gan_model`, but we don't in order to demonstrate some of
# TFGAN's flexibility.
with tf.variable_scope('generator') as gen_scope:
reconstructions, _, prebinary = networks.compression_model(
images, num_bits=FLAGS.bits_per_patch, depth=FLAGS.model_depth)
gan_model = _get_gan_model(generator_inputs=images,
generated_data=reconstructions,
real_data=images,
generator_scope=gen_scope)
summaries.add_reconstruction_summaries(images, reconstructions,
prebinary)
tfgan.eval.add_gan_model_summaries(gan_model)
# Define the GANLoss tuple using standard library functions.
with tf.name_scope('loss'):
gan_loss = tfgan.gan_loss(
gan_model,
generator_loss_fn=tfgan.losses.least_squares_generator_loss,
discriminator_loss_fn=tfgan.losses.
least_squares_discriminator_loss,
add_summaries=FLAGS.weight_factor > 0)
# Define the standard pixel loss.
l1_pixel_loss = tf.norm(gan_model.real_data -
gan_model.generated_data,
ord=1)
# Modify the loss tuple to include the pixel loss. Add summaries as well.
gan_loss = tfgan.losses.combine_adversarial_loss(
gan_loss,
gan_model,
l1_pixel_loss,
weight_factor=FLAGS.weight_factor)
# Get the GANTrain ops using the custom optimizers and optional
# discriminator weight clipping.
with tf.name_scope('train_ops'):
gen_lr, dis_lr = _lr(FLAGS.generator_lr, FLAGS.discriminator_lr)
gen_opt, dis_opt = _optimizer(gen_lr, dis_lr)
train_ops = tfgan.gan_train_ops(
gan_model,
gan_loss,
generator_optimizer=gen_opt,
discriminator_optimizer=dis_opt,
summarize_gradients=True,
colocate_gradients_with_ops=True,
aggregation_method=tf.AggregationMethod.
EXPERIMENTAL_ACCUMULATE_N)
tf.summary.scalar('generator_lr', gen_lr)
tf.summary.scalar('discriminator_lr', dis_lr)
# Determine the number of generator vs discriminator steps.
train_steps = tfgan.GANTrainSteps(
generator_train_steps=1,
discriminator_train_steps=int(FLAGS.weight_factor > 0))
# Run the alternating training loop. Skip it if no steps should be taken
# (used for graph construction tests).
status_message = tf.string_join([
'Starting train step: ',
tf.as_string(tf.train.get_or_create_global_step())
],
name='status_message')
if FLAGS.max_number_of_steps == 0: return
tfgan.gan_train(
train_ops,
FLAGS.train_log_dir,
tfgan.get_sequential_train_hooks(train_steps),
hooks=[
tf.train.StopAtStepHook(num_steps=FLAGS.max_number_of_steps),
tf.train.LoggingTensorHook([status_message], every_n_iter=10)
],
master=FLAGS.master,
is_chief=FLAGS.task == 0)
def main(_):
if not tf.gfile.Exists(FLAGS.train_log_dir):
tf.gfile.MakeDirs(FLAGS.train_log_dir)
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
# Put input pipeline on CPU to reserve GPU for training.
with tf.name_scope('inputs'), tf.device('/cpu:0'):
images = data_provider.provide_data(
'train', FLAGS.batch_size, dataset_dir=FLAGS.dataset_dir,
patch_size=FLAGS.patch_size)
# Manually define a GANModel tuple. This is useful when we have custom
# code to track variables. Note that we could replace all of this with a
# call to `tfgan.gan_model`, but we don't in order to demonstrate some of
# TFGAN's flexibility.
with tf.variable_scope('generator') as gen_scope:
reconstructions, _, prebinary = networks.compression_model(
images,
num_bits=FLAGS.bits_per_patch,
depth=FLAGS.model_depth)
gan_model = _get_gan_model(
generator_inputs=images,
generated_data=reconstructions,
real_data=images,
generator_scope=gen_scope)
summaries.add_reconstruction_summaries(images, reconstructions, prebinary)
tfgan.eval.add_gan_model_summaries(gan_model)
# Define the GANLoss tuple using standard library functions.
with tf.name_scope('loss'):
gan_loss = tfgan.gan_loss(
gan_model,
generator_loss_fn=tfgan.losses.least_squares_generator_loss,
discriminator_loss_fn=tfgan.losses.least_squares_discriminator_loss,
add_summaries=FLAGS.weight_factor > 0)
# Define the standard pixel loss.
l1_pixel_loss = tf.norm(gan_model.real_data - gan_model.generated_data,
ord=1)
# Modify the loss tuple to include the pixel loss. Add summaries as well.
gan_loss = tfgan.losses.combine_adversarial_loss(
gan_loss, gan_model, l1_pixel_loss, weight_factor=FLAGS.weight_factor)
# Get the GANTrain ops using the custom optimizers and optional
# discriminator weight clipping.
with tf.name_scope('train_ops'):
gen_lr, dis_lr = _lr(FLAGS.generator_lr, FLAGS.discriminator_lr)
gen_opt, dis_opt = _optimizer(gen_lr, dis_lr)
train_ops = tfgan.gan_train_ops(
gan_model,
gan_loss,
generator_optimizer=gen_opt,
discriminator_optimizer=dis_opt,
summarize_gradients=True,
colocate_gradients_with_ops=True,
aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
tf.summary.scalar('generator_lr', gen_lr)
tf.summary.scalar('discriminator_lr', dis_lr)
# Determine the number of generator vs discriminator steps.
train_steps = tfgan.GANTrainSteps(
generator_train_steps=1,
discriminator_train_steps=int(FLAGS.weight_factor > 0))
# Run the alternating training loop. Skip it if no steps should be taken
# (used for graph construction tests).
status_message = tf.string_join(
['Starting train step: ',
tf.as_string(tf.train.get_or_create_global_step())],
name='status_message')
if FLAGS.max_number_of_steps == 0: return
tfgan.gan_train(
train_ops,
FLAGS.train_log_dir,
tfgan.get_sequential_train_hooks(train_steps),
hooks=[tf.train.StopAtStepHook(num_steps=FLAGS.max_number_of_steps),
tf.train.LoggingTensorHook([status_message], every_n_iter=10)],
master=FLAGS.master,
is_chief=FLAGS.task == 0)