torch (version 0.0.3)

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

Shape

• Input: $(N,C,D_{in},H_{in},W_{in})$

• Output: $(N,C,D_{out},H_{out},W_{out})$, where $$D_{out}=\lfloor \frac{D_{in}+2\times \text{padding}[0]-\text{dilation}[0]\times (\text{kernel\_size}[0]-1)-1}{\text{stride}[0]}+1\rfloor$$

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

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

Details

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

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

Examples

# NOT RUN {
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)

}
# }