This operation implements bilateral slicing on the input according to the guide map.
For more information of bilateral slicing, please refer to Deep Bilateral Learning for Real-Time Image Enhancement <https://groups.csail.mit.edu/graphics/hdrnet/data/hdrnet.pdf>_
Args:
x(Variable): The input tensor, which is a 4-D tensor with shape
[N, C, H, W], N is the batch size, C is the channel
number, H and W is the feature height and width.
The data type is float32 and float64.
guide(Variable): Input grid tensor of shape [N, H, W]. The
data type is float32 and float64.
grid(Variable): Input grid tensor of shape [N, C, D, H, W]. The
data type is float32 and float64.
has_offset(bool): Whether to slice with affine offset.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
Variable: Output of shape [N, C, H, W]. The data type is same as input tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.data(name='x', shape=[None, 3, 101, 60], dtype='float32')