MaxPool2D module
Applies a 2D max pooling over an input signal composed of several input planes.
nn_max_pool2d( kernel_size, stride = NULL, padding = 0, dilation = 1, return_indices = FALSE, ceil_mode = FALSE )
kernel_size |
the size of the window to take a max over |
stride |
the stride of the window. Default value is |
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 |
ceil_mode |
when |
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
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
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) }
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