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FeatureConnector for triangle meshes.

tfg.datasets.features.TriangleMesh()

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

• str: path to a {obj,stl,ply,glb} triangle mesh.
• trimesh.Trimesh: A triangle mesh object.
• trimesh.Scene: A scene object containing multiple TriangleMesh objects.
• dict: A dictionary containing the vertices and faces of the mesh (see output format below).

Methods

catalog_documentation

catalog_documentation() -> List[feature_lib.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(
    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.

decode_example

decode_example(
    serialized_example, *, decoders=None
)

Decode the feature dict to TF compatible input.

decode_example_np

decode_example_np(
    serialized_example, *, decoders=None
)

Encode the feature dict into NumPy-compatible input.

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.

deserialize_example

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.

deserialize_example_np

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

encode_example

View source

encode_example(
    path_or_trianglemesh
)

Convert the given triangle mesh into a dict convertible to tf example.

from_config

@classmethod
from_config(
    root_dir: str
) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

Usage:

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

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)

from_json_content

View source

@classmethod
from_json_content(
    value
) -> 'TriangleMesh'

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.

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()

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.

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.

save_metadata

save_metadata(
    data_dir, feature_name=None
)

See base class for details.

serialize_example

serialize_example(
    example_data
) -> bytes

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

See deserialize_example to decode the proto into tf.Tensor.

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
            }
        }
    }
}

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.

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.

values

values()

__contains__

__contains__(
    k
)

__getitem__

__getitem__(
    key
)

Return the feature associated with the key.

__iter__

__iter__()

__len__

__len__()