def testMultiModel(self):
x = np.random.random_integers(0, high=255, size=(3, 5, 4, 3))
y = np.random.random_integers(0, high=9, size=(3, 5, 1, 1))
hparams = slicenet.slicenet_params1_tiny()
p_hparams = problem_hparams.image_cifar10(hparams)
hparams.problems = [p_hparams]
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
"target_space_id": tf.constant(1, dtype=tf.int32),
}
model = multimodel.MultiModel(hparams, p_hparams)
sharded_logits, _, _ = model.model_fn(features, True)
logits = tf.concat(sharded_logits, 0)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (3, 1, 1, 1, 10))
def testMultiModel(self):
x = np.random.random_integers(0, high=255, size=(3, 5, 5, 3))
y = np.random.random_integers(0, high=9, size=(3, 5, 1, 1))
hparams = multimodel.multimodel_tiny()
hparams.add_hparam("data_dir", "")
problem = registry.problem("image_cifar10")
p_hparams = problem.get_hparams(hparams)
hparams.problems = [p_hparams]
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
"target_space_id": tf.constant(1, dtype=tf.int32),
}
model = multimodel.MultiModel(hparams, tf.estimator.ModeKeys.TRAIN,
p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (3, 1, 1, 1, 10))