tfds.features.BBoxFeature

FeatureConnector for a normalized bounding box.

Inherits From: Tensor

tfds.features.BBoxFeature()

Input:

tfds.features.BBox tuple.

Output:

bbox: tf.Tensor of type tf.float32 and shape [4,] which contains the normalized coordinates of the bounding box [ymin, xmin, ymax, xmax]

Example:

In the DatasetInfo object:

features=features.FeatureDict({
    'bbox': features.BBox(shape=(None, 64, 64, 3)),
})

During generation:

yield {
    'input': tfds.feature.BBox(ymin=0.3, xmin=0.8, ymax=0.5, xmax=1.0),
}

Attributes:

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`

View source

decode_batch_example(
    example_data
)

See base class for details.

`decode_example`

View source

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`

View source

decode_ragged_example(
    example_data
)

See base class for details.

`encode_example`

View source

encode_example(
    bbox
)

See base class for details.

`get_serialized_info`

View source

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`

View source

get_tensor_info()

See base class for details.

`load_metadata`

View source

load_metadata(
    data_dir, feature_name
)

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

`save_metadata`

View source

save_metadata(
    data_dir, feature_name
)

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