nn_avg_pool3d: Applies a 3D average pooling over an input signal composed of...

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

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

Details

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

Shape

• Input: (N, C, D_{in}, H_{in}, W_{in})

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

D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor

H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor

W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor

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)
}

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