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,
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

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

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

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

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

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

## 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)
}