def test_video_audio_input(self):
params = exp_cfg.kinetics600(is_training=True)
params.feature_shape = (2, 224, 224, 3)
params.min_image_size = 224
params.output_audio = True
params.audio_feature = AUDIO_KEY
params.audio_feature_shape = (15, 256)
decoder = video_input.Decoder()
decoder.add_feature(params.audio_feature,
tf.io.VarLenFeature(dtype=tf.float32))
parser = video_input.Parser(params).parse_fn(params.is_training)
seq_example, label = fake_seq_example()
input_tensor = tf.constant(seq_example.SerializeToString())
decoded_tensors = decoder.decode(input_tensor)
output_tensor = parser(decoded_tensors)
features, label = output_tensor
image = features['image']
audio = features['audio']
self.assertAllEqual(image.shape, (2, 224, 224, 3))
self.assertAllEqual(label.shape, (600, ))
self.assertEqual(audio.shape, (15, 256))
def test_decoder(self):
decoder = video_input.Decoder()
seq_example, label = fake_seq_example()
serialized_example = seq_example.SerializeToString()
decoded_tensors = decoder.decode(tf.convert_to_tensor(serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertCountEqual([video_input.IMAGE_KEY, video_input.LABEL_KEY],
results.keys())
self.assertEqual(label, results[video_input.LABEL_KEY])
def _get_decoder_fn(self, params):
if params.tfds_name:
decoder = video_input.VideoTfdsDecoder(
image_key=params.image_field_key, label_key=params.label_field_key)
else:
decoder = video_input.Decoder(
image_key=params.image_field_key, label_key=params.label_field_key)
if self.task_config.train_data.output_audio:
assert self.task_config.train_data.audio_feature, 'audio feature is empty'
decoder.add_feature(self.task_config.train_data.audio_feature,
tf.io.VarLenFeature(dtype=tf.float32))
return decoder.decode
def test_decode_audio(self):
decoder = video_input.Decoder()
decoder.add_feature(AUDIO_KEY, tf.io.VarLenFeature(dtype=tf.float32))
seq_example, label = fake_seq_example()
serialized_example = seq_example.SerializeToString()
decoded_tensors = decoder.decode(tf.convert_to_tensor(serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertCountEqual(
[video_input.IMAGE_KEY, video_input.LABEL_KEY, AUDIO_KEY],
results.keys())
self.assertEqual(label, results[video_input.LABEL_KEY])
self.assertEqual(results[AUDIO_KEY].shape, (10, 256))
def test_video_input(self):
params = exp_cfg.kinetics600(is_training=True)
params.feature_shape = (2, 224, 224, 3)
params.min_image_size = 224
decoder = video_input.Decoder()
parser = video_input.Parser(params).parse_fn(params.is_training)
seq_example, label = fake_seq_example()
input_tensor = tf.constant(seq_example.SerializeToString())
decoded_tensors = decoder.decode(input_tensor)
output_tensor = parser(decoded_tensors)
image_features, label = output_tensor
image = image_features['image']
self.assertAllEqual(image.shape, (2, 224, 224, 3))
self.assertAllEqual(label.shape, (600, ))
def test_video_input_image_shape_label_type(self):
params = exp_cfg.kinetics600(is_training=True)
params.feature_shape = (2, 168, 224, 1)
params.min_image_size = 168
params.label_dtype = 'float32'
params.one_hot = False
decoder = video_input.Decoder()
parser = video_input.Parser(params).parse_fn(params.is_training)
seq_example, label = fake_seq_example()
input_tensor = tf.constant(seq_example.SerializeToString())
decoded_tensors = decoder.decode(input_tensor)
output_tensor = parser(decoded_tensors)
image_features, label = output_tensor
image = image_features['image']
self.assertAllEqual(image.shape, (2, 168, 224, 1))
self.assertAllEqual(label.shape, (1,))
self.assertDTypeEqual(label, tf.float32)
def test_video_input_augmentation_returns_shape(self):
params = exp_cfg.kinetics600(is_training=True)
params.feature_shape = (2, 224, 224, 3)
params.min_image_size = 224
params.temporal_stride = 2
params.aug_type = common.Augmentation(type='autoaug',
autoaug=common.AutoAugment())
decoder = video_input.Decoder()
parser = video_input.Parser(params).parse_fn(params.is_training)
seq_example, label = fake_seq_example()
input_tensor = tf.constant(seq_example.SerializeToString())
decoded_tensors = decoder.decode(input_tensor)
output_tensor = parser(decoded_tensors)
image_features, label = output_tensor
image = image_features['image']
self.assertAllEqual(image.shape, (2, 224, 224, 3))
self.assertAllEqual(label.shape, (600, ))
