# nn_avg_pool3d: Applies a 3D average pooling over an input signal composed of... In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

In the simplest case, the output value of the layer with input size (N, C, D, H, W), output (N, C, D_{out}, H_{out}, W_{out}) and kernel_size (kD, kH, kW) can be precisely described as:

\begin{array}{ll} \mbox{out}(N_i, C_j, d, h, w) = & ∑_{k=0}^{kD-1} ∑_{m=0}^{kH-1} ∑_{n=0}^{kW-1} \\ & \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW} \end{array}

## Usage

 1 2 3 4 5 6 7 8 nn_avg_pool3d( kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL ) 

## Arguments

 kernel_size the size of the window stride the stride of the window. Default value is kernel_size padding implicit zero padding to be added on all three sides ceil_mode when TRUE, will use ceil instead of floor to compute the output shape count_include_pad when TRUE, will include the zero-padding in the averaging calculation divisor_override if specified, it will be used as divisor, otherwise kernel_size will be used

## Details

If padding is non-zero, then the input is implicitly zero-padded on all three sides for padding number of points.

The parameters kernel_size, stride can either be:

• a single int – in which case the same value is used for the depth, height and width dimension

• a tuple of three ints – in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension

## Shape

• Input: (N, C, D_{in}, H_{in}, W_{in})

• Output: (N, C, D_{out}, H_{out}, W_{out}), where

D_{out} = ≤ft\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor

H_{out} = ≤ft\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor

W_{out} = ≤ft\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor

## Examples

  1 2 3 4 5 6 7 8 9 10 if (torch_is_installed()) { # pool of square window of size=3, stride=2 m = nn_avg_pool3d(3, stride=2) # pool of non-square window m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2)) input = torch_randn(20, 16, 50,44, 31) output = m(input) } 

torch documentation built on Oct. 7, 2021, 9:22 a.m.