.. _op_ai_onnx_AveragePool-10:

AveragePool - version 10
========================

This page documents version **10** of operator **AveragePool**. See :doc:`AveragePool` for the latest version (since version 22).

- **Domain**: ``ai.onnx``
- **Since version**: 10

AveragePool consumes an input tensor X and applies average pooling across
the tensor according to kernel sizes, stride sizes, and pad lengths.
average pooling consisting of computing the average 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] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)

or

.. code-block::

    output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)

if ceil_mode is enabled

.. code-block::

    * pad_shape[i] is 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] - kernel_spatial_shape[i] + 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] + kernel_spatial_shape[i] - input_spatial_shape[i]

The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).

**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. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].

**Outputs**

- **Y** (*T*): Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used

**Type Constraints**

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

Differences with previous version (7)
-------------------------------------

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

* old version: 7
* new version: 10
* breaking: no

**Documentation:**

* line similarity: 0.87 (+7/-0 lines)

.. code-block:: diff

    --- AveragePool v7
    +++ AveragePool v10
    @@ -6,7 +6,14 @@
      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] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
    + ```
    + or
    + ```
    + output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
    + ```
    + if ceil_mode is enabled
     
    + ```
      * pad_shape[i] is sum of pads along axis i
      ```