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:
The output is calculated with the following formula:
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
orint64
.
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>