nn_avg_pool2d function

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:

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

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

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)
}