tfg.datasets.features.VoxelGrid

FeatureConnector for voxel grids.

tfg.datasets.features.VoxelGrid(
    shape
)

During _generate_examples, the feature connector accepts as input any of:

  • dict: dictionary containing the path to a {.mat} file and the key under which the voxel grid is accessible inside the file. Structure of the dictionary: { 'path': 'path/to/file.mat', 'key': 'foo' }

  • np.ndarray: float32 numpy array of shape [X,Y,Z] representing the voxel grid.

A float32 Tensor with shape [X,Y,Z] containing the voxel occupancies.

shape Tensor shape
dtype Tensor dtype
encoding Internal encoding. See tfds.features.Encoding for available values.
doc Documentation of this feature (e.g. description).
serialized_dtype Tensor dtype. Used to validate that serialized examples have this dtype. If None then defaults to dtype
serialized_shape Tensor shape. Used to validate that serialized examples have this shape. If None then defaults to shape

doc

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

numpy_dtype

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

Methods

catalog_documentation

catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

cls_from_name

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

Returns the feature class for the given Python class.

decode_batch_example

decode_batch_example(
    example_data
)

See base class for details.

decode_example

decode_example(
    tfexample_data
)

See base class for details.

decode_example_np

decode_example_np(
    example_data
)

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

decode_ragged_example(
    example_data
)

See base class for details.

encode_example

View source

encode_example(
    example_data
)

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/dir')

Args
root_dir Directory containing the features.json file.

Returns
The reconstructed feature instance.

from_json

@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
) -> 'VoxelGrid'

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

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

Instantiates a feature from its proto representation.

get_serialized_info

get_serialized_info()

See base class for details.

get_tensor_info

get_tensor_info() -> feature_lib.TensorInfo

See base class for details.

get_tensor_spec

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

load_metadata(
    data_dir: epath.PathLike, feature_name: Optional[str]
)

Restore the feature metadata from disk.

If a dataset is re-loaded and generated files exists on disk, this function will restore the feature metadata from the saved file.

Args
data_dir path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name the name of the feature (from the FeaturesDict key)

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: epath.PathLike, feature_name: Optional[str]
) -> None

Save the feature metadata on disk.

This function is called after the data has been generated (by _download_and_prepare) to save the feature connector info with the generated dataset.

Some dataset/features dynamically compute info during _download_and_prepare. For instance:

  • Labels are loaded from the downloaded data
  • Vocabulary is created from the downloaded data
  • ImageLabelFolder compute the image dtypes/shape from the manual_dir

After the info have been added to the feature, this function allow to save those additional info to be restored the next time the data is loaded.

By default, this function do not save anything, but sub-classes can overwrite the function.

Args
data_dir path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name the name of the feature (from the FeaturesDict key)

to_json

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()

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

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.

ALIASES ['tensorflow_datasets.core.features.feature.Tensor']