nn_max_pool3d {torch} | R Documentation |
Applies a 3D max pooling over an input signal composed of several input
planes.
Description
In the simplest case, the output value of the layer with input size (N,C,D,H,W)
,
output (N,C,Dout,Hout,Wout)
and kernel_size
(kD,kH,kW)
can be precisely described as:
Usage
nn_max_pool3d(
kernel_size,
stride = NULL,
padding = 0,
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 all three 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 torch_nn.MaxUnpool3d later
|
ceil_mode |
when TRUE, will use ceil instead of floor to compute the output shape
|
Details
\mboxout(Ni,Cj,d,h,w)=maxk=0,…,kD−1maxm=0,…,kH−1maxn=0,…,kW−1\mboxinput(Ni,Cj,\mboxstride[0]×d+k,\mboxstride[1]×h+m,\mboxstride[2]×w+n)
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 depth, height and width dimension
a tuple
of three ints – in which case, the first int
is used for the depth dimension,
the second int
for the height dimension and the third int
for the width dimension
Shape
Input: (N,C,Din,Hin,Win)
Output: (N,C,Dout,Hout,Wout)
, where
Dout=⌊\mboxstride[0]Din+2×\mboxpadding[0]−\mboxdilation[0]×(\mboxkernel_size[0]−1)−1+1⌋
Hout=⌊\mboxstride[1]Hin+2×\mboxpadding[1]−\mboxdilation[1]×(\mboxkernel_size[1]−1)−1+1⌋
Wout=⌊\mboxstride[2]Win+2×\mboxpadding[2]−\mboxdilation[2]×(\mboxkernel_size[2]−1)−1+1⌋
Examples
if (torch_is_installed()) {
# pool of square window of size=3, stride=2
m <- nn_max_pool3d(3, stride = 2)
# pool of non-square window
m <- nn_max_pool3d(c(3, 2, 2), stride = c(2, 1, 2))
input <- torch_randn(20, 16, 50, 44, 31)
output <- m(input)
}
[Package
torch version 0.13.0
Index]