86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
## MaxPool <a name="MaxPool"></a> {#openvino_docs_ops_pooling_MaxPool_1}
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**Versioned name**: *MaxPool-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)
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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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* **Default value**: None
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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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* **Default value**: None
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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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* **Default value**: None
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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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* **Default value**: None
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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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**Mathematical Formulation**
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\f[
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output_{j} = max(x_{0}, ..., x_{i})
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\f]
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**Example**
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```xml
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<layer ... type="MaxPool" ... >
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<data auto_pad="same_upper" kernel="3,3" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
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<input> ... </input>
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<output> ... </output>
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</layer>
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``` |