tfg.datasets.features.Pose

FeatureConnector for 3d pose representations.

tfg.datasets.features.Pose()

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

dict: A dictionary containing the rotation and translation of the object (see output format below).

Output
A dictionary containing: 'R': A `float32` tensor with shape `[3, 3]` denoting the 3D rotation matrix. 't': A `float32` tensor with shape `[3,]` denoting the translation vector.

Output

A dictionary containing:

'R': A float32 tensor with shape [3, 3] denoting the 3D rotation matrix.
't': A float32 tensor with shape [3,] denoting the translation vector.

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.
`doc`	Documentation of this feature (e.g. description).

Raises
`ValueError`	If one of the given features is not recognized

Attributes
`doc`
`dtype`	Return the dtype (or dict of dtype) of this FeatureConnector.
`numpy_dtype`
`shape`	Return the shape (or dict of shape) of this FeatureConnector.
`tf_example_spec`	Returns the `tf.Example` proto structure.

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.

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

`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.

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.

`encode_example`

View source

encode_example(
    example_dict
)

Convert the given pose 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')

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
) -> 'Pose'

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()

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.

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

`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.

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

Returns
The serialized `tf.train.Example`.

`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.

`values`

values()

`contains`

__contains__(
    k
)

`getitem`

__getitem__(
    key
)

Return the feature associated with the key.

`iter`

__iter__()

`len`

__len__()

Class Variables
ALIASES	`[]`

tfg.datasets.features.Pose

Output

Args

Raises

Attributes

Methods

catalog_documentation

cls_from_name

decode_batch_example

decode_example

decode_ragged_example

deserialize_example

encode_example

from_config

Usage:

from_json

Example:

from_json_content

from_proto

get_serialized_info

get_tensor_info

get_tensor_spec

items

keys

load_metadata

repr_html

repr_html_batch

repr_html_ragged

save_config

save_metadata

serialize_example

to_json

For example:

Is serialized as:

to_json_content

to_proto

values

__contains__

__getitem__

__iter__

__len__

Class Variables

`catalog_documentation`

`cls_from_name`

`decode_batch_example`

`decode_example`

`decode_ragged_example`

`deserialize_example`

`encode_example`

`from_config`

`from_json`

`from_json_content`

`from_proto`

`get_serialized_info`

`get_tensor_info`

`get_tensor_spec`

`items`

`keys`

`load_metadata`

`repr_html`

`repr_html_batch`

`repr_html_ragged`

`save_config`

`save_metadata`

`serialize_example`

`to_json`

`to_json_content`

`to_proto`

`values`

`contains`

`getitem`

`iter`

`len`