def _test_xception(self, img_size):
vocab_size = 9
batch_size = 3
x = np.random.random_integers(0,
high=255,
size=(batch_size, img_size, img_size, 3))
y = np.random.random_integers(1,
high=vocab_size - 1,
size=(batch_size, 1, 1, 1))
hparams = xception.xception_tiny()
p_hparams = problem_hparams.test_problem_hparams(
vocab_size, vocab_size, hparams)
p_hparams.input_modality["inputs"] = modalities.ImageModality(hparams)
p_hparams.target_modality = modalities.ClassLabelModality(
hparams, vocab_size)
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = xception.Xception(hparams, tf.estimator.ModeKeys.TRAIN,
p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (batch_size, 1, 1, 1, vocab_size))
def _testXception(self, img_size, output_size):
vocab_size = 9
batch_size = 3
x = np.random.random_integers(0,
high=255,
size=(batch_size, img_size, img_size, 3))
y = np.random.random_integers(1,
high=vocab_size - 1,
size=(batch_size, 1, 1, 1))
hparams = xception.xception_tiny()
p_hparams = problem_hparams.test_problem_hparams(
vocab_size, vocab_size)
p_hparams.input_modality["inputs"] = (registry.Modalities.IMAGE, None)
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = xception.Xception(hparams, tf.estimator.ModeKeys.TRAIN,
p_hparams)
sharded_logits, _ = model.model_fn(features)
logits = tf.concat(sharded_logits, 0)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, output_size + (1, vocab_size))
def testXception(self):
vocab_size = 9
x = np.random.random_integers(1, high=vocab_size - 1, size=(3, 5, 1, 1))
y = np.random.random_integers(1, high=vocab_size - 1, size=(3, 1, 1, 1))
hparams = xception.xception_tiny()
p_hparams = problem_hparams.test_problem_hparams(vocab_size, vocab_size)
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = xception.Xception(
hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
sharded_logits, _ = model.model_fn(features)
logits = tf.concat(sharded_logits, 0)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (3, 5, 1, 1, vocab_size))
def _test_xception(self, img_size):
vocab_size = 9
batch_size = 3
x = np.random.randint(256, size=(batch_size, img_size, img_size, 3))
y = np.random.randint(1, high=vocab_size, size=(batch_size, 1, 1, 1))
hparams = xception.xception_tiny()
p_hparams = problem_hparams.test_problem_hparams(
vocab_size, vocab_size, hparams)
p_hparams.modality["inputs"] = modalities.ModalityType.IMAGE
p_hparams.modality["targets"] = modalities.ModalityType.CLASS_LABEL
with self.test_session() as session:
features = {
"inputs": tf.constant(x, dtype=tf.int32),
"targets": tf.constant(y, dtype=tf.int32),
}
model = xception.Xception(hparams, tf_estimator.ModeKeys.TRAIN,
p_hparams)
logits, _ = model(features)
session.run(tf.global_variables_initializer())
res = session.run(logits)
self.assertEqual(res.shape, (batch_size, 1, 1, 1, vocab_size))
