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:

## Usage

nn_avg_pool3d(
kernel_size,
stride = NULL,
ceil_mode = FALSE,
divisor_override = NULL
)

## Arguments

kernel_size

the size of the window

stride

the stride of the window. Default value is kernel_size

ceil_mode

when TRUE, will use ceil instead of floor to compute the output shape

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

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

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

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

## Examples

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)
}