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tfds.features.Sequence

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Class Sequence

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

Sequence correspond to sequence of tfds.features.FeatureConnector. 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.

Example:

At construction time:

tfds.features.Sequence(tfds.features.Image(), length=NB_FRAME)

or:

tfds.features.Sequence({
    '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__

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

Construct a sequence dict.

Args:

  • feature: 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.

feature

The inner feature.

shape

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

Methods

__getitem__

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__getitem__(key)

Convenience method to access the underlying features.

decode_example

decode_example(
    *args,
    **kwargs
)

Decode the serialize examples.

Args:

  • serialized_example: Nested dict of tf.Tensor
  • decoders: Nested dict of Decoder objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.

Returns:

  • example: Nested dict containing the decoded nested examples.

encode_example

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encode_example(example_dict)

get_serialized_info

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get_serialized_info()

See base class for details.

get_tensor_info

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get_tensor_info()

See base class for details.

load_metadata

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load_metadata(
    *args,
    **kwargs
)

See base class for details.

save_metadata

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save_metadata(
    *args,
    **kwargs
)

See base class for details.