def testImagenetMultiResolutionPreprocessExample(self, resize_method):
example = {"inputs": tf.random_uniform([64, 64, 3], minval=-1.)}
mode = tf.estimator.ModeKeys.TRAIN
hparams = HParams(resolutions=[8, 16, 32])
if resize_method is not None:
hparams.resize_method = resize_method
problem = imagenet.ImageImagenetMultiResolutionGen()
preprocessed_example = problem.preprocess_example(
example, mode, hparams)
self.assertLen(preprocessed_example, 1)
self.assertEqual(preprocessed_example["inputs"].shape, (42, 32, 3))
def testCelebaMultiResolutionPreprocessExample(self, resize_method):
example = {"inputs": tf.random_uniform([218, 178, 3], minval=-1.)}
mode = tf.estimator.ModeKeys.TRAIN
hparams = HParams(resolutions=[8, 16, 32])
if resize_method is not None:
hparams.resize_method = resize_method
problem = celeba.ImageCelebaMultiResolution()
preprocessed_example = problem.preprocess_example(
example, mode, hparams)
self.assertLen(preprocessed_example, 2)
self.assertEqual(preprocessed_example["inputs"].shape, (138, 138, 3))
self.assertEqual(preprocessed_example["targets"].shape, (42, 32, 3))
