tfds.features.Sequence

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

Inherits From: FeatureConnector

tfds.features.Sequence(
    feature: feature_lib.FeatureConnectorArg,
    length: Optional[int] = None,
    *,
    doc: feature_lib.DocArg = None
)

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 Sequence does 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.

Args
feature	The features to wrap (any feature supported)
length	int, length of the sequence if static and known in advance
doc	Documentation of this feature (e.g. description).

Attributes
doc
dtype	Return the dtype (or dict of dtype) of this FeatureConnector.
feature	The inner feature.
flat_features
flat_sequence_ranks
flat_serialized_info
np_dtype
numpy_dtype
shape	Return the shape (or dict of shape) of this FeatureConnector.
tf_dtype
tf_example_spec	Returns the tf.Example proto structure.

Methods

catalog_documentation

View source

catalog_documentation() -> List[feature_lib.CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

cls_from_name

View source

@classmethod
cls_from_name(
    python_class_name: str
) -> Type['FeatureConnector']

Returns the feature class for the given Python class.

decode_batch_example

View source

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

View source

decode_example(
    serialized_example, *, decoders=None
)

Decode the feature dict to TF compatible input.

Note: If eager is not enabled, this function will be executed as a tensorflow graph (in tf.data.Dataset.map(features.decode_example)).

Args
tfexample_data	Data or dictionary of data, as read by the tf-example reader. It correspond to the tf.Tensor() (or dict of tf.Tensor()) extracted from the tf.train.Example, matching the info defined in get_serialized_info().

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

decode_example_np

View source

decode_example_np(
    serialized_example, *, decoders=None
)

Encode the feature dict into NumPy-compatible input.

Args
example_data	Value to convert to NumPy.

Returns
np_data	Data as NumPy-compatible type: either a Python primitive (bytes, int, etc) or a NumPy array.

decode_ragged_example

View source

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

deserialize_example

View source

deserialize_example(
    serialized_example: Union[tf.Tensor, bytes], *, decoders=None
) -> utils.TensorDict

Decodes the tf.train.Example data into tf.Tensor.

See serialize_example to encode the data into proto.

Args
serialized_example	The tensor-like object containing the serialized tf.train.Example proto.
decoders	Eventual decoders to apply (see documentation)

Returns
The decoded features tensors.

deserialize_example_np

View source

deserialize_example_np(
    serialized_example: Union[tf.Tensor, bytes], *, decoders=None
) -> utils.NpArrayOrScalarDict

encode_example

View source

encode_example(
    example_dict
)

Encode the feature dict into tf-example compatible input.

The input example_data can be anything that the user passed at data generation. For example:

For features:

features={
    'image': tfds.features.Image(),
    'custom_feature': tfds.features.CustomFeature(),
}

At data generation (in _generate_examples), if the user yields:

yield {
    'image': 'path/to/img.png',
    'custom_feature': [123, 'str', lambda x: x+1]
}

Then
tfds.features.Image.encode_example will get 'path/to/img.png' as input tfds.features.CustomFeature.encode_example will get `[123, 'str', lambda x: x+1] as input

Args
example_data	Value or dictionary of values to convert into tf-example compatible data.

Returns
tfexample_data	Data or dictionary of data to write as tf-example. Data can be a list or numpy array. Note that numpy arrays are flattened so it's the feature connector responsibility to reshape them in decode_example(). Note that tf.train.Example only supports int64, float32 and string so the data returned here should be integer, float or string. User type can be restored in decode_example().

from_config

View source

@classmethod
from_config(
    root_dir: str
) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

Usage:

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

Args
root_dir	Directory containing the features.json file.

Returns
The reconstructed feature instance.

from_json

View source

@classmethod
from_json(
    value: 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

View source

@classmethod
from_json_content(
    value: Union[Json, feature_pb2.Sequence]
) -> 'Sequence'

FeatureConnector factory (to overwrite).

Subclasses should overwrite this method. This method is used when importing the feature connector from the config.

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

See existing FeatureConnectors for implementation examples.

Args
value	FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by to_json_content.
doc	Documentation of this feature (e.g. description).

Returns
The reconstructed FeatureConnector.

from_proto

View source

@classmethod
from_proto(
    feature_proto: feature_pb2.Feature
) -> T

Instantiates a feature from its proto representation.

get_serialized_info

View source

get_serialized_info()

See base class for details.

get_tensor_info

View source

get_tensor_info()

See base class for details.

get_tensor_spec

View source

get_tensor_spec() -> TreeDict[tf.TensorSpec]

Returns the tf.TensorSpec of this feature (not the element spec!).

Note that the output of this method may not correspond to the element spec of the dataset. For example, currently this method does not support RaggedTensorSpec.

load_metadata

View source

load_metadata(
    *args, **kwargs
)

See base class for details.

repr_html

View source

repr_html(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object.

repr_html_batch

View source

repr_html_batch(
    ex: np.ndarray
) -> str

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

repr_html_ragged

View source

repr_html_ragged(
    ex: np.ndarray
) -> str

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

save_config

View source

save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

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

save_metadata

View source

save_metadata(
    *args, **kwargs
)

See base class for details.

serialize_example

View source

serialize_example(
    example_data
) -> bytes

Encodes nested data values into tf.train.Example bytes.

See deserialize_example to decode the proto into tf.Tensor.

Args
example_data	Example data to encode (numpy-like nested dict)

Returns
The serialized tf.train.Example.

to_json

View source

to_json() -> 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",
            "content": {
              "num_classes": 10
            }
        }
    }
}

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

to_json_content

View source

to_json_content() -> feature_pb2.Sequence

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
The FeatureConnector metadata in either a dict, or a Feature proto. This output is used in from_json_content when reconstructing the feature.

to_proto

View source

to_proto() -> feature_pb2.Feature

Exports the FeatureConnector to the Feature proto.

For features that have a specific schema defined in a proto, this function needs to be overriden. If there's no specific proto schema, then the feature will be represented using JSON.

Returns
The feature proto describing this feature.

__contains__

View source

__contains__(
    key: str
) -> bool

__getitem__

View source

__getitem__(
    key
)

Convenience method to access the underlying features.

Class Variables
ALIASES	[]