Applies a 2D average pooling over an input signal composed of several input planes.

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,
  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 both 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

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

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)
}