88 lines
2.6 KiB
Markdown
88 lines
2.6 KiB
Markdown
## AdaptiveMaxPool<a name="AdaptiveMaxPool"></a> {#openvino_docs_ops_pooling_AdaptiveMaxPool_8}
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**Versioned name**: *AdaptiveMaxPool-8*
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**Category**: *Pooling*
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**Short description**: Applies max pooling with adaptive kernel size over the input.
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**Detailed description**: This operation calculates the output based on the first input and `output_size` determined by the second input.
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The kernel dimensions are calculated using the following formulae for the `NCDHW` input case:
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\f[
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\begin{array}{lcl}
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d_{start} &=& floor(i*D_{in}/D_{out})\\
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d_{end} &=& ceil((i+1)*D_{in}/D_{out})\\
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h_{start} &=& floor(j*H_{in}/H_{out})\\
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h_{end} &=& ceil((j+1)*H_{in}/H_{out})\\
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w_{start} &=& floor(k*W_{in}/W_{out})\\
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w_{end} &=& ceil((k+1)*W_{in}/W_{out})
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\end{array}
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\f]
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The output is calculated following this formula:
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\f[
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Output(i,j,k) = max(Input[d_{start}:d_{end}, h_{start}:h_{end}, w_{start}:w_{end}])
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\f]
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**Attributes**:
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* *index_element_type*
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* **Description**: the type of the second output containing indices
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* **Range of values**: "i64" or "i32"
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* **Type**: string
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* **Default value**: "i64"
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* **Required**: *No*
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**Inputs**:
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* **1**: 3D, 4D, or 5D input tensor of shape `[N, C, H]`, `[N, C, H, W]` or `[N, C, D, H, W]` and type *T*. Required.
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* **2**: 1D tensor describing output shape for spatial dimensions. Can be `[H_out]` for 3D input, `[H_out, W_out]` for 4D input, `[D_out, H_out, W_out]` for 5D input and of type *T_SHAPE*. Required.
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**Outputs**:
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* **1**: Output of type *T* and shape `[N, C, H_out]`, `[N, C, H_out, W_out]` or `[N, C, D_out, H_out, W_out]`.
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* **2**: Output of type specified by *index_element_type* and same shape as the first output containing indices of elements in the first output. The values of indices are computed as if input was flatten 1-D tensor, so the values are in the range `[0, N * C * H * W * D)`.
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**Types**
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* *T*: floating-point type.
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* *T_SHAPE*: `int32` or `int64`.
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**Examples**
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```xml
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<layer ... type="AdaptiveMaxPool" ... >
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<data output_type="i64"/>
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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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<input>
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<port id="1">
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<dim>2</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>16</dim>
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<dim>16</dim>
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</port>
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<port id="2">
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<dim>1</dim>
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<dim>3</dim>
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<dim>16</dim>
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<dim>16</dim>
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</port>
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</output>
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</layer>
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```
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