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.. _op_ai_onnx_LpPool-2:

LpPool - version 2
==================

This page documents version **2** of operator **LpPool**. See :doc:`LpPool` for the latest version (since version 22).

- **Domain**: ``ai.onnx``
- **Since version**: 2

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.

**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 (1)
-------------------------------------

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

* old version: 1
* new version: 2
* breaking: no

**Documentation:**

* line similarity: 0.83 (+1/-1 lines)

.. code-block:: diff

    --- LpPool v1
    +++ LpPool v2
    @@ -1,5 +1,5 @@
     
    - LpPool consumes an input tensor X and applies Lp pooling across the
    + 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