def test_read_video_level_input(self, include_video_id):
params = yt8m_configs.yt8m(is_training=False)
params.global_batch_size = 4
params.segment_labels = False
params.input_path = self.data_path
params.include_video_id = include_video_id
reader = self.create_input_reader(params)
dataset = reader.read()
iterator = iter(dataset)
example = next(iterator)
for k, v in example.items():
logging.info('DEBUG read example %r %r %r', k, v.shape, type(v))
if include_video_id:
self.assertCountEqual(
['video_matrix', 'labels', 'num_frames', 'video_ids'],
example.keys())
else:
self.assertCountEqual(['video_matrix', 'labels', 'num_frames'],
example.keys())
batch_size = params.global_batch_size
self.assertEqual(
example['video_matrix'].shape.as_list(),
[batch_size, params.max_frames,
sum(params.feature_sizes)])
self.assertEqual(example['labels'].shape.as_list(),
[batch_size, params.num_classes])
self.assertEqual(example['num_frames'].shape.as_list(),
[batch_size, 1])
if include_video_id:
self.assertEqual(example['video_ids'].shape.as_list(),
[batch_size, 1])
def test_read_video_level_float_input(self, include_video_id):
data_dir = os.path.join(self.get_temp_dir(), 'data2')
tf.io.gfile.makedirs(data_dir)
data_path = os.path.join(data_dir, 'data2.tfrecord')
examples = [
utils.MakeExampleWithFloatFeatures(self.num_segment)
for _ in range(8)
]
tfexample_utils.dump_to_tfrecord(data_path, tf_examples=examples)
params = yt8m_configs.yt8m(is_training=False)
params.global_batch_size = 4
params.segment_labels = False
params.input_path = data_path
params.num_frames = 2
params.max_frames = 2
params.feature_names = ('VIDEO_EMBEDDING/context_feature/floats',
'FEATURE/feature/floats')
params.feature_sources = ('context', 'feature')
params.feature_dtypes = ('float32', 'float32')
params.feature_sizes = (256, 2048)
params.feature_from_bytes = (False, False)
params.include_video_id = include_video_id
reader = self.create_input_reader(params)
dataset = reader.read()
iterator = iter(dataset)
example = next(iterator)
for k, v in example.items():
logging.info('DEBUG read example %r %r %r', k, v.shape, type(v))
logging.info('DEBUG read example %r', example['video_matrix'][0, 0, :])
if include_video_id:
self.assertCountEqual(
['video_matrix', 'labels', 'num_frames', 'video_ids'],
example.keys())
else:
self.assertCountEqual(['video_matrix', 'labels', 'num_frames'],
example.keys())
# Check tensor values.
expected_context = examples[0].context.feature[
'VIDEO_EMBEDDING/context_feature/floats'].float_list.value
expected_feature = examples[0].feature_lists.feature_list[
'FEATURE/feature/floats'].feature[0].float_list.value
expected_labels = examples[0].context.feature[
params.label_field].int64_list.value
self.assertAllEqual(
expected_feature,
example['video_matrix'][0, 0, params.feature_sizes[0]:])
self.assertAllEqual(
expected_context,
example['video_matrix'][0, 0, :params.feature_sizes[0]])
self.assertAllEqual(
np.nonzero(example['labels'][0, :].numpy())[0], expected_labels)
# Check tensor shape.
batch_size = params.global_batch_size
self.assertEqual(
example['video_matrix'].shape.as_list(),
[batch_size, params.max_frames,
sum(params.feature_sizes)])
self.assertEqual(example['labels'].shape.as_list(),
[batch_size, params.num_classes])
self.assertEqual(example['num_frames'].shape.as_list(),
[batch_size, 1])
if include_video_id:
self.assertEqual(example['video_ids'].shape.as_list(),
[batch_size, 1])