# nn_max_pool2d: MaxPool2D module In torch: Tensors and Neural Networks with 'GPU' Acceleration

 nn_max_pool2d R Documentation

## MaxPool2D module

### Description

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

### Usage

nn_max_pool2d(
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 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) ={} & \max_{m=0, …, kH-1} \max_{n=0, …, 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

### Shape

• Input: (N, C, H_{in}, W_{in})

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

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


torch documentation built on Jan. 24, 2023, 1:05 a.m.