tfds.features.FeaturesDict

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Class FeaturesDict

Composite FeatureConnector; each feature in dict has its own connector.

The encode/decode method of the spec feature will recursively encode/decode every sub-connector given on the constructor. Other features can inherit from this class and call super() in order to get nested container.

Example:

For DatasetInfo:

features = tfds.features.FeaturesDict({
    'input': tfds.features.Image(),
    'output': tf.int32,
})

At generation time:

for image, label in generate_examples:
  yield {
      'input': image,
      'output': label
  }

At tf.data.Dataset() time:

for example in tfds.load(...):
  tf_input = example['input']
  tf_output = example['output']

For nested features, the FeaturesDict will internally flatten the keys for the features and the conversion to tf.train.Example. Indeed, the tf.train.Example proto do not support nested feature, while tf.data.Dataset does. But internal transformation should be invisible to the user.

Example:

tfds.features.FeaturesDict({
    'input': tf.int32,
    'target': {
        'height': tf.int32,
        'width': tf.int32,
    },
})

Will internally store the data as:

{
    'input': tf.io.FixedLenFeature(shape=(), dtype=tf.int32),
    'target/height': tf.io.FixedLenFeature(shape=(), dtype=tf.int32),
    'target/width': tf.io.FixedLenFeature(shape=(), dtype=tf.int32),
}

__init__

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__init__(feature_dict)

Initialize the features.

Args:

feature_dict (dict): Dictionary containing the feature connectors of a example. The keys should correspond to the data dict as returned by tf.data.Dataset(). Types (tf.int32,...) and dicts will automatically be converted into FeatureConnector.

Raises:

  • ValueError: If one of the given features is not recognized

Properties

dtype

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

shape

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

Methods

__contains__

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__contains__(k)

__getitem__

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__getitem__(key)

Return the feature associated with the key.

__iter__

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__iter__()

__len__

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__len__()

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)

See base class for details.

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.

items

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items()

keys

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keys()

load_metadata

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load_metadata(
    data_dir,
    feature_name=None
)

See base class for details.

save_metadata

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save_metadata(
    data_dir,
    feature_name=None
)

See base class for details.

values

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values()