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tfds.features.FeaturesDict

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

Inherits From: FeatureConnector

tfds.features.FeaturesDict(
    feature_dict
)

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),
}

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

Attributes
`dtype`	Return the dtype (or dict of dtype) of this FeatureConnector.
`shape`	Return the shape (or dict of shape) of this FeatureConnector.

Methods

`decode_batch_example`

decode_batch_example(
    tfexample_data
)

Decode multiple features batched in a single tf.Tensor.

This function is used to decode features wrapped in tfds.features.Sequence(). By default, this function apply decode_example on each individual elements using tf.map_fn. However, for optimization, features can overwrite this method to apply a custom batch decoding.

Args
`tfexample_data`	Same `tf.Tensor` inputs as `decode_example`, but with and additional first dimension for the sequence length.

Returns
`tensor_data`	Tensor or dictionary of tensor, output of the tf.data.Dataset object

`decode_example`

decode_example(
    serialized_example, decoders=None
)

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.

`decode_ragged_example`

decode_ragged_example(
    tfexample_data
)

Decode nested features from a tf.RaggedTensor.

This function is used to decode features wrapped in nested tfds.features.Sequence(). By default, this function apply decode_batch_example on the flat values of the ragged tensor. For optimization, features can overwrite this method to apply a custom batch decoding.

Args
`tfexample_data`	`tf.RaggedTensor` inputs containing the nested encoded examples.

Returns
`tensor_data`	The decoded `tf.RaggedTensor` or dictionary of tensor, output of the tf.data.Dataset object

`encode_example`

encode_example(
    example_dict
)

See base class for details.

`from_config`

@classmethod
from_config(
    root_dir: str
) -> "FeatureConnector"

Reconstructs the FeatureConnector from the config file.

Usage:

features = FeatureConnector.from_config('path/to/features.json')

Args
`root_dir`	Directory containing to the features.json file.

Returns
The reconstructed feature instance.

`from_json`

@classmethod
from_json(
    value: tfds.typing.Json
) -> "FeatureConnector"

FeatureConnector factory.

This function should be called from the tfds.features.FeatureConnector base class. Subclass should implement the from_json_content.

Example:

feature = tfds.features.FeatureConnector.from_json(
    {'type': 'Image', 'content': {'shape': [32, 32, 3], 'dtype': 'uint8'} }
)
assert isinstance(feature, tfds.features.Image)

Args
`value`	`dict(type=, content=)` containing the feature to restore. Match dict returned by `to_json`.

Returns
The reconstructed FeatureConnector.

`from_json_content`

@classmethod
from_json_content(
    value: tfds.typing.Json
) -> "FeaturesDict"

FeatureConnector factory (to overwrite).

Subclasses should overwritte this method. importing the feature connector from the config.

This function should not be called directly. FeatureConnector.from_json should be called instead.

This function See existing FeatureConnector for example of implementation.

Args
`value`	FeatureConnector information. Match the `dict` returned by `to_json_content`.

Returns
The reconstructed FeatureConnector.

`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.

`repr_html`

repr_html(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object.

`repr_html_batch`

repr_html_batch(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Sequence).

`repr_html_ragged`

repr_html_ragged(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Nested sequence).

`save_config`

save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

Args
`root_dir`	`path/to/dir` containing the `features.json`

`save_metadata`

save_metadata(
    data_dir, feature_name=None
)

See base class for details.

`to_json`

to_json() -> tfds.typing.Json

Exports the FeatureConnector to Json.

Each feature is serialized as a dict(type=..., content=...).

type: The cannonical name of the feature (module.FeatureName).
content: is specific to each feature connector and defined in to_json_content. Can contain nested sub-features (like for tfds.features.FeaturesDict and tfds.features.Sequence).

For example:

tfds.features.FeaturesDict({
    'input': tfds.features.Image(),
    'target': tfds.features.ClassLabel(num_classes=10),
})

Is serialized as:

{
    "type": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
    "content": {
        "input": {
            "type": "tensorflow_datasets.core.features.image_feature.Image",
            "content": {
                "shape": [null, null, 3],
                "dtype": "uint8",
                "encoding_format": "png"
            }
        },
        "target": {
            "type": "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
            "num_classes": 10
        }
    }
}

Returns
A `dict(type=, content=)`. Will be forwarded to `from_json` when reconstructing the feature.

`to_json_content`

to_json_content() -> tfds.typing.Json

FeatureConnector factory (to overwrite).

This function should be overwritten by the subclass to allow re-importing the feature connector from the config. See existing FeatureConnector for example of implementation.

Returns
Dict containing the FeatureConnector metadata. Will be forwarded to `from_json_content` when reconstructing the feature.

`values`

values()

`contains`

__contains__(
    k
)

`getitem`

__getitem__(
    key
)

Return the feature associated with the key.

`iter`

__iter__()

`len`

__len__()

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Last updated 2021-01-07 UTC.