tfds.features.ClassLabel

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FeatureConnector for integer class labels.

Inherits From: Tensor, FeatureConnector

tfds.features.ClassLabel(
    *, num_classes=None, names=None, names_file=None
)

Args
num_classes	int, number of classes. All labels must be < num_classes.
names	list<str>, string names for the integer classes. The order in which the names are provided is kept.
names_file	str, path to a file with names for the integer classes, one per line.

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

Methods

decode_batch_example

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decode_batch_example(
    example_data
)

See base class for details.

decode_example

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decode_example(
    tfexample_data
)

Decode the feature dict to TF compatible input.

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_ragged_example

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decode_ragged_example(
    example_data
)

See base class for details.

encode_example

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encode_example(
    example_data
)

See base class for details.

from_config

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@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

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@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

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

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

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get_serialized_info()

Return the shape/dtype of features after encoding (for the adapter).

The FileAdapter then use those information to write data on disk.

This function indicates how this feature is encoded on file internally. The DatasetBuilder are written on disk as tf.train.Example proto.

Ex:

return {
    'image': tfds.features.TensorInfo(shape=(None,), dtype=tf.uint8),
    'height': tfds.features.TensorInfo(shape=(), dtype=tf.int32),
    'width': tfds.features.TensorInfo(shape=(), dtype=tf.int32),
}

FeatureConnector which are not containers should return the feature proto directly:

return tfds.features.TensorInfo(shape=(64, 64), tf.uint8)

If not defined, the retuned values are automatically deduced from the get_tensor_info function.

Returns
features	Either a dict of feature proto object, or a feature proto object

get_tensor_info

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get_tensor_info()

See base class for details.

int2str

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int2str(
    int_value
)

Conversion integer => class name string.

load_metadata

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load_metadata(
    data_dir, feature_name=None
)

See base class for details.

repr_html

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repr_html(
    ex: int
) -> str

Class labels are displayed with their name.

repr_html_batch

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repr_html_batch(
    ex: np.ndarray
) -> str

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

repr_html_ragged

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repr_html_ragged(
    ex: np.ndarray
) -> str

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

save_config

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save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

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

save_metadata

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save_metadata(
    data_dir, feature_name=None
)

See base class for details.

str2int

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str2int(
    str_value
)

Conversion class name string => integer.

to_json

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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

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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.