MaxPool2D module

Usage

nn_max_pool2d(
  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 both 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 nn_max_unpool2d() later.

ceil_mode

when TRUE, will use ceil instead of floor to compute the output shape

Details

In the simplest case, the output value of the layer with input size $(N,C,H,W)$, output $(N,C,H_{out},W_{out})$ and kernel_size $(kH,kW)$ can be precisely described as:

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

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 height and width dimension

• a tuple of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension

Shape

• Input: $(N,C,H_{in},W_{in})$

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

$$H_{out}=\lfloor \frac{H_{in}+2\ast \text{padding[0]}-\text{dilation[0]}\times (\text{kernel\_size[0]}-1)-1}{\text{stride[0]}}+1\rfloor$$

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

Examples

if (torch_is_installed()) {
# pool of square window of size=3, stride=2
m <- nn_max_pool2d(3, stride = 2)
# pool of non-square window
m <- nn_max_pool2d(c(3, 2), stride = c(2, 1))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}