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.

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.

ValueError If one of the given features is not recognized

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

Methods

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

encode_example

View source

encode_example(
    example_dict
)

Convert the given pose into a dict convertible to tf example.

from_json_content

View source

@classmethod
from_json_content(
    value
) -> "Pose"

get_serialized_info

get_serialized_info()

See base class for details.

get_tensor_info

get_tensor_info()

See base class for details.

items

items()

keys

keys()

load_metadata

load_metadata(
    data_dir, feature_name=None
)

See base class for details.

save_metadata

save_metadata(
    data_dir, feature_name=None
)

See base class for details.

to_json_content

View source

to_json_content()

values

values()

__contains__

__contains__(
    k
)

__getitem__

__getitem__(
    key
)

Return the feature associated with the key.

__iter__

__iter__()

__len__

__len__()