def _testResnet(self, img_size, output_size):
vocab_size = 9
batch_size = 2
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 = resnet_tiny_cpu()
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 = resnet.Resnet(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, ) + output_size + (1, vocab_size))
def _test_resnet(self, img_size, output_size):
vocab_size = 9
batch_size = 2
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 = resnet_tiny_cpu()
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 = resnet.Resnet(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, ) + output_size + (1, vocab_size))