openvino/docs/ops/pooling/AdaptiveMaxPool_8.md

AdaptiveMaxPool

Versioned name: AdaptiveMaxPool-8

Category: Pooling

Short description: Applies max pooling with adaptive kernel size over the input.

Detailed description: This operation calculates the output based on the first input and output_size determined by the second input. The kernel dimensions are calculated using the following formulae for the NCDHW input case:

\f[ \begin{array}{lcl} d_{start} &=& floor(i*D_{in}/D_{out})\ d_{end} &=& ceil((i+1)D_{in}/D_{out})\ h_{start} &=& floor(jH_{in}/H_{out})\ h_{end} &=& ceil((j+1)H_{in}/H_{out})\ w_{start} &=& floor(kW_{in}/W_{out})\ w_{end} &=& ceil((k+1)*W_{in}/W_{out}) \end{array} \f]

The output is calculated following this formula:

\f[ Output(i,j,k) = max(Input[d_{start}:d_{end}, h_{start}:h_{end}, w_{start}:w_{end}]) \f]

Attributes:

• index_element_type

• Description: the type of the second output containing indices

• Range of values: "i64" or "i32"

• Type: string

• Default value: "i64"

• Required: No

Inputs:

• 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.
• 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.

Outputs:

• 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].
• 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).

Types

• T: floating-point type.
• T_SHAPE: int32 or int64.

Examples

<layer ... type="AdaptiveMaxPool" ... >
    <data output_type="i64"/>
    <input> 
        <port id="0">
            <dim>1</dim>
            <dim>3</dim>
            <dim>32</dim>
            <dim>32</dim>
        </port>
    </input>
    <input> 
        <port id="1">
            <dim>2</dim>
        </port>
    </input>
    <output>
        <port id="1">
            <dim>1</dim>
            <dim>3</dim>
            <dim>16</dim>
            <dim>16</dim>
        </port>
        <port id="2">
            <dim>1</dim>
            <dim>3</dim>
            <dim>16</dim>
            <dim>16</dim>
        </port>
    </output>
</layer>