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.. _op_ai_onnx_LpPool-18:

LpPool - version 18
===================

This page documents version **18** of operator **LpPool**. See :doc:`LpPool` for the latest version (since version 22).

- **Domain**: ``ai.onnx``
- **Since version**: 18

LpPool consumes an input tensor X and applies Lp pooling across
the tensor according to kernel sizes, stride sizes, and pad lengths.
Lp pooling consisting of computing the Lp norm on all values of a subset
of the input tensor according to the kernel size and downsampling the
data into the output tensor Y for further processing. The output spatial shape will be following:

.. code-block::

    output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)

or

.. code-block::

    output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)

if ceil_mode is enabled ``pad_shape[i]`` is the sum of pads along axis ``i``.

``auto_pad`` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:

.. code-block::

    VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
    SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])

And pad shape will be following if ``SAME_UPPER`` or ``SAME_LOWER``:

.. code-block::

    pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]


**Inputs**

- **X** (*T*): Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.

**Outputs**

- **Y** (*T*): Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

**Type Constraints**

- **T**: Constrain input and output types to float tensors.
  Allowed types: tensor(double), tensor(float), tensor(float16).

Differences with previous version (11)
--------------------------------------

**SchemaDiff**: ``LpPool`` (domain ``'ai.onnx'``)

* old version: 11
* new version: 18
* breaking: no

**Documentation:**

* line similarity: 0.33 (+19/-1 lines)

.. code-block:: diff

    --- LpPool v11
    +++ LpPool v18
    @@ -3,4 +3,22 @@
      the tensor according to kernel sizes, stride sizes, and pad lengths.
      Lp pooling consisting of computing the Lp norm on all values of a subset
      of the input tensor according to the kernel size and downsampling the
    - data into the output tensor Y for further processing.
    + data into the output tensor Y for further processing. The output spatial shape will be following:
    + ```
    + output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
    + ```
    + or
    + ```
    + output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
    + ```
    + if ceil_mode is enabled `pad_shape[i]` is the sum of pads along axis `i`.
    +
    + `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
    + ```
    + VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
    + SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
    + ```
    + And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
    + ```
    + pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
    + ```