RandomTranslation layer

keras.layers.RandomTranslation(
    height_factor,
    width_factor,
    fill_mode="reflect",
    interpolation="bilinear",
    seed=None,
    fill_value=0.0,
    data_format=None,
    **kwargs
)

This layer will apply random translations to each image during training,
filling empty space according to `fill_mode`.

Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
of integer or floating point dtype. By default, the layer will output
floats.

**Note:** This layer is safe to use inside a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) or `grain` pipeline
(independently of which backend you're using).

# Input shape
    3D (unbatched) or 4D (batched) tensor with shape:
    `(..., height, width, channels)`, in `"channels_last"` format,
    or `(..., channels, height, width)`, in `"channels_first"` format.

# Output shape
    3D (unbatched) or 4D (batched) tensor with shape:
    `(..., target_height, target_width, channels)`,
    or `(..., channels, target_height, target_width)`,
    in `"channels_first"` format.

# Arguments
    height_factor: a float represented as fraction of value, or a tuple of
        size 2 representing lower and upper bound for shifting vertically. A
        negative value means shifting image up, while a positive value means
        shifting image down. When represented as a single positive float,
        this value is used for both the upper and lower bound. For instance,
        `height_factor=(-0.2, 0.3)` results in an output shifted by a random
        amount in the range `[-20%, +30%]`. `height_factor=0.2` results in
        an output height shifted by a random amount in the range
        `[-20%, +20%]`.
    width_factor: a float represented as fraction of value, or a tuple of
        size 2 representing lower and upper bound for shifting horizontally.
        A negative value means shifting image left, while a positive value
        means shifting image right. When represented as a single positive
        float, this value is used for both the upper and lower bound. For
        instance, `width_factor=(-0.2, 0.3)` results in an output shifted
        left by 20%, and shifted right by 30%. `width_factor=0.2` results
        in an output height shifted left or right by 20%.
    fill_mode: Points outside the boundaries of the input are filled
        according to the given mode. Available methods are `"constant"`,
        `"nearest"`, `"wrap"` and `"reflect"`. Defaults to `"constant"`.
        - `"reflect"`: `(d c b a | a b c d | d c b a)`
            The input is extended by reflecting about the edge of the last
            pixel.
        - `"constant"`: `(k k k k | a b c d | k k k k)`
            The input is extended by filling all values beyond
            the edge with the same constant value k specified by
            `fill_value`.
        - `"wrap"`: `(a b c d | a b c d | a b c d)`
            The input is extended by wrapping around to the opposite edge.
        - `"nearest"`: `(a a a a | a b c d | d d d d)`
            The input is extended by the nearest pixel.
        Note that when using torch backend, `"reflect"` is redirected to
        `"mirror"` `(c d c b | a b c d | c b a b)` because torch does not
        support `"reflect"`.
        Note that torch backend does not support `"wrap"`.
    interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
    seed: Integer. Used to create a random seed.
    fill_value: a float represents the value to be filled outside the
        boundaries when `fill_mode="constant"`.
    data_format: string, either `"channels_last"` or `"channels_first"`.
        The ordering of the dimensions in the inputs. `"channels_last"`
        corresponds to inputs with shape `(batch, height, width, channels)`
        while `"channels_first"` corresponds to inputs with shape
        `(batch, channels, height, width)`. It defaults to the
        `image_data_format` value found in your Keras config file at
        `~/.keras/keras.json`. If you never set it, then it will be
        `"channels_last"`.
    **kwargs: Base layer keyword arguments, such as `name` and `dtype`.

# Example

layer = keras.layers.RandomTranslation(bounding_box_format="xyxy")
images = np.random.randint(0, 255, (4, 224, 224, 3), dtype="uint8")

bounding_boxes = {
    "boxes": np.array([
        [[10, 20, 100, 150], [50, 60, 200, 250]],
        [[15, 25, 110, 160], [55, 65, 210, 260]],
        [[20, 30, 120, 170], [60, 70, 220, 270]],
        [[25, 35, 130, 180], [65, 75, 230, 280]],
    ], dtype="float32"),
    "labels": np.array([[0, 1], [1, 2], [2, 3], [0, 3]], dtype="int32")
}

labels = keras.ops.one_hot(
    np.array([0, 1, 2, 3]),
    num_classes=4
)

segmentation_masks = np.random.randint(0, 3, (4, 224, 224, 1), dtype="uint8")

output = layer(
    {
        "images": images,
        "bounding_boxes": bounding_boxes,
        "labels": labels,
        "segmentation_masks": segmentation_masks
    },
    training=True
)