[Specification] MaxPool-14 and AvgPool-14 - new ceiling mode CEIL_TORCH

docs/articles_en/documentation/openvino-ir-format/operation-sets/available-opsets/opset14.rst

@@ -30,7 +30,7 @@ Table of Contents
 * :doc:`Assign <openvino_docs_ops_infrastructure_Assign_3>`
 * :doc:`Atan <openvino_docs_ops_arithmetic_Atan_1>`
 * :doc:`Atanh <openvino_docs_ops_arithmetic_Atanh_3>`
-* :doc:`AvgPool <openvino_docs_ops_pooling_AvgPool_1>`
+* :doc:`AvgPool <openvino_docs_ops_pooling_AvgPool_14>`
 * :doc:`BatchNormInference <openvino_docs_ops_normalization_BatchNormInference_5>`
 * :doc:`BatchToSpace <openvino_docs_ops_movement_BatchToSpace_2>`
 * :doc:`BinaryConvolution <openvino_docs_ops_convolution_BinaryConvolution_1>`
@@ -120,7 +120,7 @@ Table of Contents
 * :doc:`LSTMSequence <openvino_docs_ops_sequence_LSTMSequence_1>`
 * :doc:`MatMul <openvino_docs_ops_matrix_MatMul_1>`
 * :doc:`MatrixNMS <openvino_docs_ops_sort_MatrixNms_8>`
-* :doc:`MaxPool <openvino_docs_ops_pooling_MaxPool_8>`
+* :doc:`MaxPool <openvino_docs_ops_pooling_MaxPool_14>`
 * :doc:`Maximum <openvino_docs_ops_arithmetic_Maximum_1>`
 * :doc:`Minimum <openvino_docs_ops_arithmetic_Minimum_1>`
 * :doc:`Mish <openvino_docs_ops_activation_Mish_4>`

docs/articles_en/documentation/openvino-ir-format/operation-sets/operations-specifications.rst

@@ -23,6 +23,7 @@ Operation Specifications
    Atan-1 <openvino_docs_ops_arithmetic_Atan_1>
    Atanh-3 <openvino_docs_ops_arithmetic_Atanh_3>
    AvgPool-1 <openvino_docs_ops_pooling_AvgPool_1>
+   AvgPool-14 <openvino_docs_ops_pooling_AvgPool_14>
    BatchNormInference-1 <openvino_docs_ops_normalization_BatchNormInference_1>
    BatchNormInference-5 <openvino_docs_ops_normalization_BatchNormInference_5>
    BatchToSpace-2 <openvino_docs_ops_movement_BatchToSpace_2>
@@ -127,6 +128,7 @@ Operation Specifications
    MatrixNms-8 <openvino_docs_ops_sort_MatrixNms_8>
    MaxPool-1 <openvino_docs_ops_pooling_MaxPool_1>
    MaxPool-8 <openvino_docs_ops_pooling_MaxPool_8>
+   MaxPool-14 <openvino_docs_ops_pooling_MaxPool_14>
    Maximum-1 <openvino_docs_ops_arithmetic_Maximum_1>
    Minimum-1 <openvino_docs_ops_arithmetic_Minimum_1>
    Mish-4 <openvino_docs_ops_activation_Mish_4>

docs/articles_en/documentation/openvino-ir-format/operation-sets/operations-specifications/pooling/avg-pool-14.rst

@@ -0,0 +1,206 @@
+.. {#openvino_docs_ops_pooling_AvgPool_14}
+
+AvgPool
+=======
+
+
+.. meta::
+  :description: Learn about AvgPool-1 - a pooling operation, which can 
+                be performed on a 3D, 4D or 5D input tensor.
+
+**Versioned name**: *AvgPool-1*
+
+**Category**: *Pooling*
+
+**Short description**: `Reference <http://caffe.berkeleyvision.org/tutorial/layers/pooling.html>`__
+
+**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. Output shape is calculated as follows:
+
+``H_out = (H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1``
+
+``W_out = (H + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1``
+
+``D_out = (H + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1``
+
+**Attributes**: *Pooling* attributes are specified in the ``data`` node, which is a child of the layer node.
+
+* *strides*
+
+  * **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*
+
+* *pads_begin*
+
+  * **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.
+
+* *pads_end*
+
+  * **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.
+
+* *kernel*
+
+  * **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*
+
+* *exclude-pad*
+
+  * **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*
+
+* *rounding_type*
+
+  * **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**:
+    * *ceil*
+    * *ceil_torch*
+    * *floor*
+  * **Type**: string
+  * **Default value**: *floor*
+  * **Required**: *no*
+
+* *auto_pad*
+
+  * **Description**: *auto_pad* how the padding is calculated. Possible values:
+
+    * *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.
+
+**Inputs**:
+
+* **1**: 3D, 4D or 5D input tensor. **Required.**
+
+**Outputs**:
+
+* **1**: Input shape can be either ``[N,C,H]``, ``[N,C,H,W]`` or ``[N,C,H,W,D]``. Then the corresponding output shape is ``[N,C,H_out]``, ``[N,C,H_out,W_out]`` or ``[N,C,H_out,W_out,D_out]``.
+
+**Mathematical Formulation**
+
+.. math::
+
+   output_{j} = \frac{\sum_{i = 0}^{n}x_{i}}{n}
+
+**Examples**
+
+.. code-block:: xml
+   :force:
+
+   <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>
+
+   <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>
+
+   <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>
+
+   <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>
+
+   <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>
+
+