def test_glow_inference(self):
hparams = glow.glow_hparams()
hparams.depth = 15
hparams.n_levels = 2
hparams.data_dir = ''
curr_dir = tempfile.mkdtemp()
# Training pipeline
with tf.Graph().as_default():
cifar_problem = problems.problem(
'image_cifar10_plain_random_shift')
hparams.problem = cifar_problem
model = glow.Glow(hparams, tf_estimator.ModeKeys.TRAIN)
train_dataset = cifar_problem.dataset(MODES.TRAIN)
one_shot = train_dataset.make_one_shot_iterator()
x_batch, y_batch = self.batch(one_shot)
features = {'inputs': x_batch, 'targets': y_batch}
model_path = os.path.join(curr_dir, 'model')
model(features)
with tf.Session() as session:
saver = tf.train.Saver()
session.run(tf.global_variables_initializer())
init_op = tf.get_collection('glow_init_op')
session.run(init_op)
z = session.run([model.z])
mean_z = np.mean(z)
is_undefined = np.isnan(mean_z) or np.isinf(mean_z)
self.assertTrue(not is_undefined)
saver.save(session, model_path)
# Inference pipeline
with tf.Graph().as_default():
cifar_problem = problems.problem(
'image_cifar10_plain_random_shift')
hparams.problem = cifar_problem
model = glow.Glow(hparams, tf_estimator.ModeKeys.PREDICT)
test_dataset = cifar_problem.dataset(MODES.EVAL)
one_shot = test_dataset.make_one_shot_iterator()
x_batch, y_batch = self.batch(one_shot)
features = {'inputs': x_batch, 'targets': y_batch}
model_path = os.path.join(curr_dir, 'model')
predictions = model.infer(features)
with tf.Session() as session:
saver = tf.train.Saver()
saver.restore(session, model_path)
predictions_np = session.run(predictions)
self.assertTrue(np.all(predictions_np <= 255))
self.assertTrue(np.all(predictions_np >= 0))
def test_glow(self):
with tf.Graph().as_default():
hparams = glow.glow_hparams()
hparams.depth = 15
hparams.n_levels = 2
hparams.init_batch_size = 256
hparams.batch_size = 1
cifar_problem = problems.problem(
'image_cifar10_plain_random_shift')
hparams.problem = cifar_problem
model = glow.Glow(hparams, tf.estimator.ModeKeys.TRAIN)
train_dataset = cifar_problem.dataset(MODES.TRAIN)
one_shot = train_dataset.make_one_shot_iterator()
x_batch, y_batch = self.batch(one_shot)
features = {'inputs': x_batch, 'targets': y_batch}
_, obj_dict = model.body(features)
objective = obj_dict['training']
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Run initialization.
init_op = tf.get_collection('glow_init_op')
sess.run(init_op)
# Run forward pass.
obj_np = sess.run(objective)
mean_obj = np.mean(obj_np)
# Check that one forward-propagation does not NaN, i.e
# initialization etc works as expected.
self.assertTrue(mean_obj > 0 and mean_obj < 10.0)
def test_glow(self):
with tf.Graph().as_default():
hparams = glow.glow_hparams()
model = glow.Glow(hparams, tf.estimator.ModeKeys.TRAIN)
cifar_problem = problems.problem(
'image_cifar10_plain_random_shift')
train_dataset = cifar_problem.dataset(MODES.TRAIN)
one_shot = train_dataset.make_one_shot_iterator()
x_batch, y_batch = self.batch(one_shot)
features = {'inputs': x_batch, 'targets': y_batch}
_, obj_dict = model.body(features)
objective = obj_dict['training']
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
obj_np = sess.run(objective)
mean_obj = np.mean(obj_np)
# Check that one forward-propagation does not NaN, i.e
# initialization etc works as expected.
is_undefined = np.isnan(mean_obj) or np.isinf(mean_obj)
self.assertTrue(not is_undefined)
