def _get_dummy_input(self, input_type, module=None):
"""Get dummy input for the given input type."""
if input_type == 'image_tensor':
images = np.random.randint(
low=0, high=255, size=(1, 8, 64, 64, 3), dtype=np.uint8)
# images = np.zeros((1, 8, 64, 64, 3), dtype=np.uint8)
return images, images
elif input_type == 'tf_example':
example = tfexample_utils.make_video_test_example(
image_shape=(64, 64, 3),
audio_shape=(20, 128),
label=random.randint(0, 100)).SerializeToString()
images = tf.nest.map_structure(
tf.stop_gradient,
tf.map_fn(
module._decode_tf_example,
elems=tf.constant([example]),
fn_output_signature={
video_classification.video_input.IMAGE_KEY: tf.string,
}))
images = images[video_classification.video_input.IMAGE_KEY]
return [example], images
else:
raise ValueError(f'{input_type}')
def setUp(self):
super(VideoClassificationTaskTest, self).setUp()
data_dir = os.path.join(self.get_temp_dir(), 'data')
tf.io.gfile.makedirs(data_dir)
self._data_path = os.path.join(data_dir, 'data.tfrecord')
# pylint: disable=g-complex-comprehension
examples = [
tfexample_utils.make_video_test_example(image_shape=(36, 36, 3),
audio_shape=(20, 128),
label=random.randint(
0, 100))
for _ in range(2)
]
# pylint: enable=g-complex-comprehension
tfexample_utils.dump_to_tfrecord(self._data_path, tf_examples=examples)
