nnf_avg_pool2d: Avg_pool2d

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Avg_pool2d

Description

Applies 2D average-pooling operation in kH * kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.

Usage

nnf_avg_pool2d(
  input,
  kernel_size,
  stride = NULL,
  padding = 0,
  ceil_mode = FALSE,
  count_include_pad = TRUE,
  divisor_override = NULL
)

Arguments

input	input tensor (minibatch, in_channels , iH , iW)
kernel_size	size of the pooling region. Can be a single number or a tuple ⁠(kH, kW)⁠
stride	stride of the pooling operation. Can be a single number or a tuple ⁠(sH, sW)⁠. Default: kernel_size
padding	implicit zero paddings on both sides of the input. Can be a single number or a tuple ⁠(padH, padW)⁠. Default: 0
ceil_mode	when True, will use ceil instead of floor in the formula to compute the output shape. Default: FALSE
count_include_pad	when True, will include the zero-padding in the averaging calculation. Default: TRUE
divisor_override	if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: NULL

torch documentation built on June 7, 2023, 6:19 p.m.