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 test_get_image_grid(self):
util.get_image_grid(tf.zeros([6, 28, 28, 1]),
batch_size=6,
num_classes=3,
num_images_per_class=1)
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 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))
