AdaptiveAvgPool#

Versioned name: AdaptiveAvgPool-8

Category: Pooling

Short description: Applies average 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:

\[\begin{split}\begin{array}{lcl} d_{start} &=& \lfloor i \cdot \frac{D_{in}}{D_{out}}\rfloor\\ d_{end} &=& \lceil(i+1) \cdot \frac{D_{in}}{D_{out}}\rceil\\ h_{start} &=& \lfloor j \cdot \frac{H_{in}}{H_{out}}\rfloor\\ h_{end} &=& \lceil(j+1) \cdot \frac{H_{in}}{H_{out}}\rceil\\ w_{start} &=& \lfloor k \cdot \frac{W_{in}}{W_{out}}\rfloor\\ w_{end} &=& \lceil(k+1) \cdot \frac{W_{in}}{W_{out}}\rceil \end{array}\end{split}\]

The output is calculated with the following formula:

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

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].

Types

  • T: floating-point type.

  • T_SHAPE: int32 or int64.

Examples

<layer ... type="AdaptiveAvgPool" ... >
    <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="2">
            <dim>1</dim>
            <dim>3</dim>
            <dim>16</dim>
            <dim>16</dim>
        </port>
    </output>
</layer>