191 lines
6.2 KiB
Markdown
191 lines
6.2 KiB
Markdown
## AvgPool<a name="AvgPool"></a> {#openvino_docs_ops_pooling_AvgPool_1}
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**Versioned name**: *AvgPool-1*
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**Category**: *Pooling*
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**Short description**: [Reference](http://caffe.berkeleyvision.org/tutorial/layers/pooling.html)
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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. Output shape is calculated as follows:
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`H_out = (H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1`
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`W_out = (H + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1`
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`D_out = (H + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1`
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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.
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* **Range of values**: integer values starting from 0
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* **Type**: int[]
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* **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.
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* **Range of values**: integer values starting from 0
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* **Type**: int[]
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* **Required**: *yes*
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* **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.
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* **Range of values**: integer values starting from 0
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* **Type**: int[]
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* **Required**: *yes*
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* **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.
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* **Range of values**: integer values starting from 1
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* **Type**: int[]
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* **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.
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* **Range of values**: true or false
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* **Type**: boolean
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* **Required**: *yes*
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* *rounding_type*
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* **Description**: *rounding_type* is a type of rounding to be applied.
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* **Range of values**:
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* *ceil*
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* *floor*
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* **Type**: string
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* **Default value**: *floor*
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* **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`.
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* *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).
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* *valid* - do not use padding.
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* **Type**: string
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* **Default value**: *explicit*
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* **Required**: *no*
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* **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified.
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**Inputs**:
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* **1**: 3D, 4D or 5D input tensor. **Required.**
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**Outputs**:
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* **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]`.
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**Mathematical Formulation**
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\f[
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output_{j} = \frac{\sum_{i = 0}^{n}x_{i}}{n}
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\f]
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**Examples**
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```xml
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<layer ... type="AvgPool" ... >
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<data auto_pad="same_upper" exclude-pad="true" kernel="2,2" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<output>
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<port id="1">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</output>
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</layer>
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<layer ... type="AvgPool" ... >
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<data auto_pad="same_upper" exclude-pad="false" kernel="5,5" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<output>
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<port id="1">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</output>
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</layer>
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<layer ... type="AvgPool" ... >
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<data auto_pad="explicit" exclude-pad="true" kernel="5,5" pads_begin="1,1" pads_end="1,1" strides="3,3"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<output>
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<port id="1">
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<dim>1</dim>
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<dim>3</dim>
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<dim>10</dim>
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<dim>10</dim>
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</port>
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</output>
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</layer>
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<layer ... type="AvgPool" ... >
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<data auto_pad="explicit" exclude-pad="false" kernel="5,5" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<output>
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<port id="1">
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<dim>1</dim>
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<dim>3</dim>
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<dim>15</dim>
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<dim>15</dim>
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</port>
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</output>
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</layer>
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<layer ... type="AvgPool" ... >
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<data auto_pad="valid" exclude-pad="true" kernel="5,5" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<output>
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<port id="1">
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<dim>1</dim>
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<dim>3</dim>
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<dim>14</dim>
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<dim>14</dim>
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</port>
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</output>
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</layer>
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```
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