nnf_avg_pool2d: Avg_pool2d
In torch: Tensors and Neural Networks with 'GPU' Acceleration

nnf_avg_pool2d

R Documentation

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`