def main(_, run_eval_loop=True):
# Fetch and generate images to run through Inception.
with tf.name_scope('inputs'):
real_data, num_classes = _get_real_data(
FLAGS.num_images_generated, FLAGS.dataset_dir)
generated_data = _get_generated_data(
FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)
# Compute Frechet Inception Distance.
if FLAGS.eval_frechet_inception_distance:
fid = util.get_frechet_inception_distance(
real_data, generated_data, FLAGS.num_images_generated,
FLAGS.num_inception_images)
tf.summary.scalar('frechet_inception_distance', fid)
# Compute normal Inception scores.
if FLAGS.eval_real_images:
inc_score = util.get_inception_scores(
real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
else:
inc_score = util.get_inception_scores(
generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
tf.summary.scalar('inception_score', inc_score)
# If conditional, display an image grid of difference classes.
if FLAGS.conditional_eval and not FLAGS.eval_real_images:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)
# Create ops that write images to disk.
image_write_ops = None
if FLAGS.conditional_eval and FLAGS.write_to_disk:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
else:
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_imgs = tfgan.eval.image_reshaper(
generated_data[:100], num_cols=FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
tf.image.encode_png(uint8_images[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(_, run_eval_loop=True):
# Fetch and generate images to run through Inception.
with tf.name_scope('inputs'):
real_data, num_classes = _get_real_data(
FLAGS.num_images_generated, FLAGS.dataset_dir)
generated_data = _get_generated_data(
FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)
# Compute Frechet Inception Distance.
if FLAGS.eval_frechet_inception_distance:
fid = util.get_frechet_inception_distance(
real_data, generated_data, FLAGS.num_images_generated,
FLAGS.num_inception_images)
tf.summary.scalar('frechet_inception_distance', fid)
# Compute normal Inception scores.
if FLAGS.eval_real_images:
inc_score = util.get_inception_scores(
real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
else:
inc_score = util.get_inception_scores(
generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
tf.summary.scalar('inception_score', inc_score)
# If conditional, display an image grid of difference classes.
if FLAGS.conditional_eval and not FLAGS.eval_real_images:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)
# Create ops that write images to disk.
image_write_ops = None
if FLAGS.conditional_eval and FLAGS.write_to_disk:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
else:
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_imgs = tfgan.eval.image_reshaper(
generated_data[:100], num_cols=FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
tf.image.encode_png(uint8_images[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(_, run_eval_loop=True):
with tf.name_scope('inputs'):
noise, one_hot_labels = _get_generator_inputs(
FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)
# Generate images.
with tf.variable_scope('Generator'): # Same scope as in train job.
images = networks.conditional_generator((noise, one_hot_labels))
# Visualize images.
reshaped_img = tfgan.eval.image_reshaper(
images, num_cols=FLAGS.num_images_per_class)
tf.summary.image('generated_images', reshaped_img, max_outputs=1)
# Calculate evaluation metrics.
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Cross_entropy',
util.mnist_cross_entropy(
images, one_hot_labels, FLAGS.classifier_filename))
# Write images to disk.
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
tf.image.encode_png(data_provider.float_image_to_uint8(reshaped_img[0])))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
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(_, run_eval_loop=True):
with tf.name_scope('inputs'):
noise, one_hot_labels = _get_generator_inputs(
FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)
# Generate images.
with tf.variable_scope('Generator'): # Same scope as in train job.
images = networks.conditional_generator((noise, one_hot_labels))
# Visualize images.
reshaped_img = tfgan.eval.image_reshaper(
images, num_cols=FLAGS.num_images_per_class)
tf.summary.image('generated_images', reshaped_img, max_outputs=1)
# Calculate evaluation metrics.
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Cross_entropy',
util.mnist_cross_entropy(
images, one_hot_labels, FLAGS.classifier_filename))
# Write images to disk.
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
tf.image.encode_png(data_provider.float_image_to_uint8(reshaped_img[0])))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
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(_, run_eval_loop=True):
# Fetch real images.
with tf.name_scope('inputs'):
real_images, _, _ = data_provider.provide_data(
'train', FLAGS.num_images_generated, FLAGS.dataset_dir)
image_write_ops = None
if FLAGS.eval_real_images:
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(real_images, FLAGS.classifier_filename))
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('Generator'):
images = networks.unconditional_generator(
tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]))
sess = tf.Session()
saver = tf.train.Saver()
if not restore_from_checkpoint(sess, saver):
raise NotImplementedError
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(images, options=options, run_metadata=run_metadata)
cg = CompGraph('gan_mnist', run_metadata, tf.get_default_graph())
cg_tensor_dict = cg.get_tensors()
cg_sorted_keys = sorted(cg_tensor_dict.keys())
cg_sorted_items = []
for cg_key in cg_sorted_keys:
cg_sorted_items.append(tf.shape(cg_tensor_dict[cg_key]))
cg_sorted_shape = sess.run(cg_sorted_items)
cg.op_analysis(dict(zip(cg_sorted_keys, cg_sorted_shape)),
'gan_mnist.pickle')
exit(0)
#tf.summary.scalar('MNIST_Frechet_distance',
# util.mnist_frechet_distance(
# real_images, images, FLAGS.classifier_filename))
#tf.summary.scalar('MNIST_Classifier_score',
# util.mnist_score(images, FLAGS.classifier_filename))
if FLAGS.num_images_generated >= 100:
reshaped_images = tfgan.eval.image_reshaper(
images[:100, ...], num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
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(_, 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(_, run_eval_loop=True):
# Fetch real images.
# with tf.name_scope('inputs'):
# real_images, _, _ = data_provider.provide_data(
# 'train', FLAGS.num_images_generated, FLAGS.dataset_dir)
image_write_ops = None
if FLAGS.eval_real_images:
pass
'''
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(real_images, FLAGS.classifier_filename))
'''
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('Generator'):
images = networks.unconditional_generator(
tf.random_normal(
[FLAGS.num_images_generated, FLAGS.noise_dims]))
'''
tf.summary.scalar('MNIST_Frechet_distance',
util.mnist_frechet_distance(
real_images, images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
'''
if FLAGS.num_images_generated >= 100:
reshaped_images = tfgan.eval.image_reshaper(images[:100, ...],
num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'unconditional_gan.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
print("**********{}".format([
tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)
]))
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
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(_, run_eval_loop=True):
# Fetch real images.
with tf.name_scope('inputs'):
real_images, _, _ = data_provider.provide_data(
'train', FLAGS.num_images_generated, FLAGS.dataset_dir)
image_write_ops = None
if FLAGS.eval_real_images:
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(real_images, FLAGS.classifier_filename))
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.variable_scope('Generator'):
images = networks.unconditional_generator(
tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
is_training=False)
tf.summary.scalar('MNIST_Frechet_distance',
util.mnist_frechet_distance(
real_images, images, FLAGS.classifier_filename))
tf.summary.scalar('MNIST_Classifier_score',
util.mnist_score(images, FLAGS.classifier_filename))
if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
reshaped_images = tfgan.eval.image_reshaper(
images[:100, ...], num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
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 _get_write_image_ops(eval_dir, filename, images):
"""Create Ops that write images to disk."""
return tf.write_file(
'%s/%s' % (eval_dir, filename),
tf.image.encode_png(data_provider.float_image_to_uint8(images)))
def main(_, run_eval_loop=True):
if FLAGS.conditional_eval:
FLAGS.checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, 'conditional')
else:
FLAGS.checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, 'unconditional')
print('Checkpoint dir: {}'.format(FLAGS.checkpoint_dir))
# Fetch and generate images to run through Inception.
with tf.name_scope('inputs'):
real_data, num_classes = _get_real_data(
FLAGS.num_images_generated, FLAGS.dataset_dir)
generated_data = _get_generated_data(
FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)
model_name = 'gan_cifar_'
if FLAGS.conditional_eval:
model_name = model_name + 'cond'
else:
model_name = model_name + 'uncond'
sess = tf.Session()
saver = tf.train.Saver()
if not restore_from_checkpoint(sess, saver):
raise NotImplementedError
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(generated_data, options=options, run_metadata=run_metadata)
cg = CompGraph(model_name, run_metadata, tf.get_default_graph())
cg_tensor_dict = cg.get_tensors()
cg_sorted_keys = sorted(cg_tensor_dict.keys())
cg_sorted_items = []
for cg_key in cg_sorted_keys:
cg_sorted_items.append(tf.shape(cg_tensor_dict[cg_key]))
cg_sorted_shape = sess.run(cg_sorted_items)
cg.op_analysis(dict(zip(cg_sorted_keys, cg_sorted_shape)),
'{}.pickle'.format(model_name))
exit(0)
# Compute Frechet Inception Distance.
#if FLAGS.eval_frechet_inception_distance:
# fid = util.get_frechet_inception_distance(
# real_data, generated_data, FLAGS.num_images_generated,
# FLAGS.num_inception_images)
# tf.summary.scalar('frechet_inception_distance', fid)
# Compute normal Inception scores.
#if FLAGS.eval_real_images:
# inc_score = util.get_inception_scores(
# real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
#else:
# inc_score = util.get_inception_scores(
# generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
#tf.summary.scalar('inception_score', inc_score)
# If conditional, display an image grid of difference classes.
#if FLAGS.conditional_eval and not FLAGS.eval_real_images:
# reshaped_imgs = util.get_image_grid(
# generated_data, FLAGS.num_images_generated, num_classes,
# FLAGS.num_images_per_class)
# tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)
# Create ops that write images to disk.
image_write_ops = None
if FLAGS.conditional_eval:
reshaped_imgs = util.get_image_grid(
generated_data, FLAGS.num_images_generated, num_classes,
FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
else:
if FLAGS.num_images_generated >= 100:
reshaped_imgs = tfgan.eval.image_reshaper(
generated_data[:100], num_cols=FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
tf.image.encode_png(uint8_images[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 cnn_model(features, labels, mode):
n_classes = 2
trainable = True
learning_rate = 1e-5
images = features['images']
filenames = features['filenames']
# setup batch normalization
if mode == tf.estimator.ModeKeys.TRAIN:
norm_params = {'is_training': True}
else:
norm_params = {'is_training': False, 'updates_collections': None}
# create the network
with tf.contrib.framework.arg_scope(
[layers.conv2d, layers.fully_connected],
activation_fn=_leaky_relu,
normalizer_fn=layers.batch_norm,
normalizer_params=norm_params):
# activation_fn=tf.nn.leaky_relu, normalizer_fn=None):
if FLAGS.hyper_mode == "regular":
print("set up the network in regular mode")
base_size = 1024
conv1 = layers.conv2d(images,
int(base_size / 32), [4, 4],
stride=2,
trainable=trainable)
conv2 = layers.conv2d(conv1,
int(base_size / 16), [4, 4],
stride=2,
trainable=trainable)
elif FLAGS.hyper_mode == "tiny":
print("set up the network in tiny mode")
# bypass first two layer.
base_size = int(1024 / 4)
conv2 = images
conv3 = layers.conv2d(conv2,
int(base_size / 8), [4, 4],
stride=2,
trainable=trainable)
conv4 = layers.conv2d(conv3,
int(base_size / 4), [4, 4],
stride=2,
trainable=trainable)
conv5 = layers.conv2d(conv4,
int(base_size / 2), [4, 4],
stride=2,
trainable=trainable)
flat = layers.flatten(conv5)
fc1 = layers.fully_connected(flat, base_size)
fc1_dropout = layers.dropout(
fc1, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
logits = layers.fully_connected(fc1_dropout,
n_classes,
activation_fn=None)
if FLAGS.hyper_mode == "regular":
visual_sample = conv1
elif FLAGS.hyper_mode == "tiny":
visual_sample = conv3
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions={
'visual_sample': visual_sample,
'class': predicted_classes,
'prob': tf.nn.softmax(logits),
'images': data_provider.float_image_to_uint8(images),
'filenames': filenames
})
groundtruth_classes = tf.argmax(labels, 1)
loss = tf.losses.softmax_cross_entropy(onehot_labels=labels,
logits=logits)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode,
loss=loss,
train_op=train_op)
precision = tf.metrics.precision(labels=groundtruth_classes,
predictions=predicted_classes)
recall = tf.metrics.recall(labels=groundtruth_classes,
predictions=predicted_classes)
eval_metric_ops = {
'eval/accuracy':
tf.metrics.accuracy(labels=groundtruth_classes,
predictions=predicted_classes),
'eval/precision':
precision,
'eval/recall':
recall
# 'f1_score': f1_score
}
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops=eval_metric_ops)
def main(_, run_eval_loop=True):
# Fetch and generate images to run through Inception.
with tf.name_scope('inputs'):
real_data, num_classes = _get_real_data(
# FLAGS.bs, FLAGS.dataset_dir)
FLAGS.bs,
FLAGS.dl)
generated_data = _get_generated_data(FLAGS.bs, FLAGS.conditional_eval,
num_classes)
# Compute Frechet Inception Distance.
if FLAGS.eval_frechet_inception_distance:
fid = util.get_frechet_inception_distance(real_data, generated_data,
FLAGS.bs,
FLAGS.num_inception_images)
tf.summary.scalar('frechet_inception_distance', fid)
# Compute normal Inception scores.
if FLAGS.eval_real_images:
inc_score = util.get_inception_scores(real_data, FLAGS.bs,
FLAGS.num_inception_images)
else:
inc_score = util.get_inception_scores(generated_data, FLAGS.bs,
FLAGS.num_inception_images)
tf.summary.scalar('inception_score', inc_score)
# If conditional, display an image grid of difference classes.
if FLAGS.conditional_eval and not FLAGS.eval_real_images:
reshaped_imgs = util.get_image_grid(generated_data, FLAGS.bs,
num_classes,
FLAGS.num_images_per_class)
tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)
# Create ops that write images to disk.
image_write_ops = None
if FLAGS.conditional_eval and FLAGS.write_to_disk:
reshaped_imgs = util.get_image_grid(generated_data, FLAGS.bs,
num_classes,
FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'conditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
else:
if FLAGS.bs >= 100 and FLAGS.write_to_disk:
reshaped_imgs = tfgan.eval.image_reshaper(
generated_data[:100], num_cols=FLAGS.num_images_per_class)
uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
image_write_ops = tf.write_file(
'%s/%s' % (FLAGS.eval_dir, 'unconditional_cifar10.png'),
tf.image.encode_png(uint8_images[0]))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
checkpoint_path = tf_saver.latest_checkpoint(FLAGS.ckpt)
if (checkpoint_path is None):
print("error, no checkpoint path")
# set mkl env
mkl_setup()
sess_config = create_config_proto()
session_creator = monitored_session.ChiefSessionCreator(
scaffold=None,
checkpoint_filename_with_path=checkpoint_path,
master=FLAGS.master,
config=sess_config)
with monitored_session.MonitoredSession(session_creator=session_creator,
hooks=None) as session:
eval_ops = image_write_ops
for warmup_i in range(FLAGS.nw):
session.run(eval_ops, feed_dict=None)
start = time.time()
for batch_i in range(FLAGS.nb):
session.run(eval_ops, feed_dict=None)
end = time.time()
inference_time = end - start
print("Batch size:", FLAGS.bs, "\nBatches number:", FLAGS.nb)
print("Time spent per BATCH: %.4f ms" % (inference_time * 1000 /
(FLAGS.nb)))
print("Total samples/sec: %.4f samples/s" %
(FLAGS.nb * FLAGS.bs / inference_time))
def _get_write_image_ops(eval_dir, filename, images):
"""Create Ops that write images to disk."""
return tf.write_file(
'%s/%s'% (eval_dir, filename),
tf.image.encode_png(data_provider.float_image_to_uint8(images)))
