nn_lp_pool2d: Applies a 2D power-average pooling over an input signal...

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Applies a 2D power-average pooling over an input signal composed of several input planes.

Description

On each window, the function computed is:

Usage

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

Arguments

norm_type	if inf than one gets max pooling if 0 you get sum pooling ( proportional to the avg pooling)
kernel_size	the size of the window
stride	the stride of the window. Default value is kernel_size
ceil_mode	when TRUE, will use ceil instead of floor to compute the output shape

Details

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

Shape

• 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

Note

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.

Examples

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)
}

torch documentation built on May 29, 2024, 9:54 a.m.