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Applies a 2D max pooling over an input signal composed of several input planes.


  stride = NULL,
  padding = 0,
  dilation = 1,
  return_indices = FALSE,
  ceil_mode = FALSE



the size of the window to take a max over


the stride of the window. Default value is kernel_size


implicit zero padding to be added on both sides


a parameter that controls the stride of elements in the window


if TRUE, will return the max indices along with the outputs. Useful for nn_max_unpool2d() later.


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


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:

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

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


  • Input: \((N, C, H_{in}, W_{in})\)

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

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

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


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)