def testFirstSampleIsReturnedDuringInference(self):
with self.session() as sess:
tf.keras.backend.set_learning_phase(0)
chosen_latent, chosen_keypoints = dynamics._choose_sample(
self.sampled_latent, self.sampled_keypoints, self.sample_losses)
chosen_latent, chosen_keypoints = sess.run(
[chosen_latent, chosen_keypoints])
# Check that the 0th sample is chosen for each example in the batch:
np.testing.assert_array_equal(chosen_latent, 0.0 * chosen_latent)
np.testing.assert_array_equal(chosen_keypoints, 0.0 * chosen_keypoints)
def testBestSampleIsReturnedDuringTraining(self):
with self.session() as sess:
tf.keras.backend.set_learning_phase(1)
chosen_latent, chosen_keypoints = dynamics._choose_sample(
self.sampled_latent, self.sampled_keypoints, self.sample_losses)
chosen_latent, chosen_keypoints = sess.run(
[chosen_latent, chosen_keypoints])
# Check output shapes:
self.assertEqual(chosen_latent.shape,
(self.batch_size, self.latent_code_size))
self.assertEqual(chosen_keypoints.shape,
(self.batch_size, self.latent_code_size * 2))
# Check that the correct sample is chosen for each example in the batch:
self.assertEqual(list(chosen_latent[:, 0]), self.best_samples)
self.assertEqual(list(chosen_keypoints[:, 0]), self.best_samples)
