tfds.features.SequenceDict

Class SequenceDict

Composite FeatureConnector for a dict where each value is a list.

Inherits From: FeaturesDict

Defined in core/features/sequence_feature.py.

SequenceDict correspond to sequence of tfds.features.FeatureDict. At generation time, a list for each of the sequence element is given. The output of tf.data.Dataset will batch all the elements of the sequence together.

If the length of the sequence is static and known in advance, it should be specified in the constructor using the length param.

Note that SequenceDict do not support features which are of type tf.io.FixedLenSequenceFeature and do not support empty sequences.

Example: At construction time:

tfds.SequenceDict({
    'frame': tfds.features.Image(shape=(64, 64, 3))
    'action': tfds.features.ClassLabel(['up', 'down', 'left', 'right'])
}, length=NB_FRAME)

During data generation:

yield {
    'frame': np.ones(shape=(NB_FRAME, 64, 64, 3)),
    'action': ['left', 'left', 'up', ...],
}

Tensor returned by .as_dataset():

{
    'frame': tf.Tensor(shape=(NB_FRAME, 64, 64, 3), dtype=tf.uint8),
    'action': tf.Tensor(shape=(NB_FRAME,), dtype=tf.int64),
}

At generation time, you can specify a list of features dict, a dict of list values or a stacked numpy array. The lists will automatically be distributed into their corresponding FeatureConnector.

__init__

__init__(
    feature_dict,
    length=None,
    **kwargs
)

Construct a sequence dict.

Args:

• feature_dict: dict, the features to wrap
• length: int, length of the sequence if static and known in advance
• **kwargs: dict, constructor kwargs of tfds.features.FeaturesDict

Properties

dtype

Return the dtype (or dict of dtype) of this FeatureConnector.

serialized_keys

List of the flattened feature keys after serialization.

shape

Return the shape (or dict of shape) of this FeatureConnector.

Methods

__getitem__

__getitem__(key)

Return the feature associated with the key.

__iter__

__iter__()

__len__

__len__()

decode_example

decode_example(tfexample_dict)

encode_example

encode_example(example_dict)

get_serialized_info

get_serialized_info()

See base class for details.

get_tensor_info

get_tensor_info()

See base class for details.

items

items()

keys

keys()

load_metadata

load_metadata(
    data_dir,
    feature_name=None
)

See base class for details.

save_metadata

save_metadata(
    data_dir,
    feature_name=None
)

See base class for details.

values

values()