nn_avg_pool2d: Applies a 2D average pooling over an input signal composed of... In torch: Tensors and Neural Networks with 'GPU' Acceleration

 nn_avg_pool2d R Documentation

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

Description

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 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} ∑_{m=0}^{kH-1} ∑_{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} = ≤ft\lfloor\frac{H_{in} + 2 \times \mbox{padding}[0] - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor

W_{out} = ≤ft\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)
}


torch documentation built on Jan. 24, 2023, 1:05 a.m.