torch: nn_max_pool3d – R documentation

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nn_max_pool3d

Applies a 3D max 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_max_pool3d(
  kernel_size,
  stride = NULL,
  padding = 0,
  dilation = 1,
  return_indices = FALSE,
  ceil_mode = FALSE
)

Arguments

`kernel_size`	the size of the window to take a max over
`stride`	the stride of the window. Default value is `kernel_size`
`padding`	implicit zero padding to be added on all three sides
`dilation`	a parameter that controls the stride of elements in the window
`return_indices`	if `TRUE`, will return the max indices along with the outputs. Useful for `torch_nn.MaxUnpool3d` later
`ceil_mode`	when TRUE, will use `ceil` instead of `floor` to compute the output shape

Details

\begin{array}{ll} \mbox{out}(N_i, C_j, d, h, w) = & \max_{k=0, …, kD-1} \max_{m=0, …, kH-1} \max_{n=0, …, kW-1} \\ & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n) \end{array}

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link_ has a nice visualization of what dilation does. The parameters kernel_size, stride, padding, dilation 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} = ≤ft\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor

H_{out} = ≤ft\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor

W_{out} = ≤ft\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor

Examples

if (torch_is_installed()) {
# pool of square window of size=3, stride=2
m <- nn_max_pool3d(3, stride=2)
# pool of non-square window
m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
input <- torch_randn(20, 16, 50,44, 31)
output <- m(input)

}

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release