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

## Usage

nn_avg_pool2d(
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

$$out(N_i, C_j, h, w) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)$$

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

The parameters kernel_size, stride, padding can either be:

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

• a tuple of two ints -- in which case, the first int is used for the height dimension, and the second int for the width dimension

## Shape

• Input: $$(N, C, H_{in}, W_{in})$$

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

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

## Examples

if (torch_is_installed()) {

# pool of square window of size=3, stride=2
m <- nn_avg_pool2d(3, stride = 2)
# pool of non-square window
m <- nn_avg_pool2d(c(3, 2), stride = c(2, 1))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}