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On each window, the function computed is:


nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)



if inf than one gets max pooling if 0 you get sum pooling ( proportional to the avg pooling)


the size of the window


the stride of the window. Default value is kernel_size


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


$$ f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} $$

  • At p = \(\infty\), one gets Max Pooling

  • At p = 1, one gets Sum Pooling (which is proportional to average pooling)

The parameters kernel_size, stride 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


If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case.


  • Input: \((N, C, H_{in}, W_{in})\)

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

$$ H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor $$ $$ W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor $$


if (torch_is_installed()) {

# power-2 pool of square window of size=3, stride=2
m <- nn_lp_pool2d(2, 3, stride = 2)
# pool of non-square window of power 1.2
m <- nn_lp_pool2d(1.2, c(3, 2), stride = c(2, 1))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)