torch (version 0.11.0)

nn_avg_pool3d: Applies a 3D 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 $(N,C,D,H,W)$, output $(N,C,D_{out},H_{out},W_{out})$ and kernel_size $(kD,kH,kW)$ can be precisely described as:

Usage

nn_avg_pool3d(
  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 all three 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

Shape

• Input: $(N,C,D_{in},H_{in},W_{in})$

• Output: $(N,C,D_{out},H_{out},W_{out})$, where

$$D_{out}=\lfloor \frac{D_{in}+2\times \text{padding}[0]-\text{kernel\_size}[0]}{\text{stride}[0]}+1\rfloor$$ $$H_{out}=\lfloor \frac{H_{in}+2\times \text{padding}[1]-\text{kernel\_size}[1]}{\text{stride}[1]}+1\rfloor$$ $$W_{out}=\lfloor \frac{W_{in}+2\times \text{padding}[2]-\text{kernel\_size}[2]}{\text{stride}[2]}+1\rfloor$$

Details

$$\begin{array}{ll}\text{out}(N_i,C_j,d,h,w)=& \sum _{k=0}^{kD-1}\sum _{m=0}^{kH-1}\sum _{n=0}^{kW-1}\\ & \frac{\text{input}(N_i,C_j,\text{stride}[0]\times d+k,\text{stride}[1]\times h+m,\text{stride}[2]\times w+n)}{kD\times kH\times kW}\end{array}$$

If padding is non-zero, then the input is implicitly zero-padded on all three sides for padding number of points.

The parameters kernel_size, stride can either be:

• a single int -- in which case the same value is used for the depth, height and width dimension

• a tuple of three ints -- in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension

Examples

if (torch_is_installed()) {

# pool of square window of size=3, stride=2
m <- nn_avg_pool3d(3, stride = 2)
# pool of non-square window
m <- nn_avg_pool3d(c(3, 2, 2), stride = c(2, 1, 2))
input <- torch_randn(20, 16, 50, 44, 31)
output <- m(input)
}