nn_max_pool3d function

Applies a 3D max pooling over an input signal composed of several input planes.

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:

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

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

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)
}