[Specification] MaxPool-14 and AvgPool-14 - new ceiling mode CEIL_TORCH
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| .. {#openvino_docs_ops_pooling_AvgPool_14} | ||||||
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| AvgPool | ||||||
| ======= | ||||||
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| .. meta:: | ||||||
| :description: Learn about AvgPool-14 - a pooling operation, which can | ||||||
| be performed on a 3D, 4D or 5D input tensor. | ||||||
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| **Versioned name**: *AvgPool-14* | ||||||
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| **Category**: *Pooling* | ||||||
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| **Short description**: Performs the average pooling operation on input. | ||||||
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| **Detailed description**: `Reference <http://cs231n.github.io/convolutional-networks/#pool>`__. Average Pool is a pooling operation that performs down-sampling by dividing the input into pooling regions of size specified by kernel attribute and computing the average values of each region. | ||||||
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| **Attributes**: *Pooling* attributes are specified in the ``data`` node, which is a child of the layer node. | ||||||
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| * *strides* | ||||||
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| * **Description**: *strides* is a distance (in pixels) to slide the window on the feature map over the (z, y, x) axes for 3D poolings and (y, x) axes for 2D poolings. For example, *strides* equal "4,2,1" means sliding the window 4 pixel at a time over depth dimension, 2 over height dimension and 1 over width dimension. | ||||||
| * **Range of values**: integer values starting from 0 | ||||||
| * **Type**: int[] | ||||||
| * **Required**: *yes* | ||||||
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| * *pads_begin* | ||||||
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| * **Description**: *pads_begin* is a number of pixels to add to the beginning along each axis. For example, *pads_begin* equal "1,2" means adding 1 pixel to the top of the input and 2 to the left of the input. | ||||||
| * **Range of values**: integer values starting from 0 | ||||||
| * **Type**: int[] | ||||||
| * **Required**: *yes* | ||||||
| * **Note**: the attribute is ignored when *auto_pad* attribute is specified. | ||||||
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| * *pads_end* | ||||||
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| * **Description**: *pads_end* is a number of pixels to add to the ending along each axis. For example, *pads_end* equal "1,2" means adding 1 pixel to the bottom of the input and 2 to the right of the input. | ||||||
| * **Range of values**: integer values starting from 0 | ||||||
| * **Type**: int[] | ||||||
| * **Required**: *yes* | ||||||
| * **Note**: the attribute is ignored when *auto_pad* attribute is specified. | ||||||
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| * *kernel* | ||||||
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| * **Description**: *kernel* is a size of each filter. For example, *kernel* equal (2, 3) means that each filter has height equal to 2 and width equal to 3. | ||||||
| * **Range of values**: integer values starting from 1 | ||||||
| * **Type**: int[] | ||||||
| * **Required**: *yes* | ||||||
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| * *exclude-pad* | ||||||
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| * **Description**: *exclude-pad* is a type of pooling strategy for values in the padding area. For example, if *exclude-pad* is "true", then zero-values that came from padding are not included in averaging calculation. | ||||||
| * **Range of values**: true or false | ||||||
| * **Type**: boolean | ||||||
| * **Required**: *yes* | ||||||
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| * *rounding_type* | ||||||
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| * **Description**: *rounding_type* is a type of rounding to be applied. *ceil_torch* does not allow the last pooling to start in the padding area. | ||||||
| * **Range of values**: | ||||||
| * *floor* | ||||||
| * *ceil* | ||||||
| * *ceil_torch* | ||||||
| * **Type**: string | ||||||
| * **Default value**: *floor* | ||||||
| * **Required**: *no* | ||||||
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| * *auto_pad* | ||||||
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| * **Description**: *auto_pad* how the padding is calculated. Possible values: | ||||||
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| * *explicit*: use explicit padding values from `pads_begin` and `pads_end`. | ||||||
| * *same_upper (same_lower)* the input is padded to match the output size. In case of odd padding value an extra padding is added at the end (at the beginning). | ||||||
| * *valid* - do not use padding. | ||||||
| * **Type**: string | ||||||
| * **Default value**: *explicit* | ||||||
| * **Required**: *no* | ||||||
| * **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified. | ||||||
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| **Input**: | ||||||
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| * **1**: 3D, 4D or 5D input tensor. Input shape can be either ``[N, C, H]``, ``[N, C, H, W]`` or ``[N, C, H, W, D]``. **Required.** | ||||||
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| **Output**: | ||||||
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| * **1**: The output shape is ``[N, C, H_out]``, ``[N, C, H_out, W_out]`` or ``[N, C, H_out, W_out, D_out]``. Output shape calculation rules and examples can be found in :doc:`Pooling Operators shape inference rules <openvino_docs_pooling_shape_rules>`. | ||||||
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| **Types** | ||||||
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| * *T*: floating point or integer type. | ||||||
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| * *T_IND*: ``int64`` or ``int32``. | ||||||
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| **Examples** | ||||||
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| .. code-block:: xml | ||||||
| :force: | ||||||
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| <layer ... type="AvgPool" ... > | ||||||
| <data auto_pad="same_upper" exclude-pad="true" kernel="2,2" pads_begin="0,0" pads_end="1,1" strides="2,2"/> | ||||||
| <input> | ||||||
| <port id="0"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </input> | ||||||
| <output> | ||||||
| <port id="1"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </output> | ||||||
| </layer> | ||||||
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| <layer ... type="AvgPool" ... > | ||||||
| <data auto_pad="same_upper" exclude-pad="false" kernel="5,5" pads_begin="0,0" pads_end="1,1" strides="2,2"/> | ||||||
| <input> | ||||||
| <port id="0"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </input> | ||||||
| <output> | ||||||
| <port id="1"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </output> | ||||||
| </layer> | ||||||
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| <layer ... type="AvgPool" ... > | ||||||
| <data auto_pad="explicit" exclude-pad="true" kernel="5,5" pads_begin="1,1" pads_end="1,1" strides="3,3"/> | ||||||
| <input> | ||||||
| <port id="0"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </input> | ||||||
| <output> | ||||||
| <port id="1"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>10</dim> | ||||||
| <dim>10</dim> | ||||||
| </port> | ||||||
| </output> | ||||||
| </layer> | ||||||
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| <layer ... type="AvgPool" ... > | ||||||
| <data auto_pad="explicit" exclude-pad="false" kernel="5,5" pads_begin="1,1" pads_end="1,1" strides="2,2"/> | ||||||
| <input> | ||||||
| <port id="0"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </input> | ||||||
| <output> | ||||||
| <port id="1"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>15</dim> | ||||||
| <dim>15</dim> | ||||||
| </port> | ||||||
| </output> | ||||||
| </layer> | ||||||
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| <layer ... type="AvgPool" ... > | ||||||
| <data auto_pad="valid" exclude-pad="true" kernel="5,5" pads_begin="1,1" pads_end="1,1" strides="2,2"/> | ||||||
| <input> | ||||||
| <port id="0"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>32</dim> | ||||||
| <dim>32</dim> | ||||||
| </port> | ||||||
| </input> | ||||||
| <output> | ||||||
| <port id="1"> | ||||||
| <dim>1</dim> | ||||||
| <dim>3</dim> | ||||||
| <dim>14</dim> | ||||||
| <dim>14</dim> | ||||||
| </port> | ||||||
| </output> | ||||||
| </layer> | ||||||
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How does it slide when it's zero?