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MaxPool3d
class torch.nn.MaxPool3d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)
[source]-
Applies a 3D max pooling over an input signal composed of several input planes.
In the simplest case, the output value of the layer with input size , output and
kernel_size
can be precisely described as:If
padding
is non-zero, then the input is implicitly padded with negative infinity on both sides forpadding
number of points.dilation
controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of whatdilation
does.Note
When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.
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 firstint
is used for the depth dimension, the secondint
for the height dimension and the thirdint
for the width dimension
- Parameters
-
- kernel_size (Union[int, Tuple[int, int, int]]) – the size of the window to take a max over
- stride (Union[int, Tuple[int, int, int]]) – the stride of the window. Default value is
kernel_size
- padding (Union[int, Tuple[int, int, int]]) – Implicit negative infinity padding to be added on all three sides
- dilation (Union[int, Tuple[int, int, int]]) – a parameter that controls the stride of elements in the window
- return_indices (bool) – if
True
, will return the max indices along with the outputs. Useful fortorch.nn.MaxUnpool3d
later - ceil_mode (bool) – when True, will use
ceil
instead offloor
to compute the output shape
- Shape:
-
- Input: or .
Output: or , where
Examples:
>>> # pool of square window of size=3, stride=2 >>> m = nn.MaxPool3d(3, stride=2) >>> # pool of non-square window >>> m = nn.MaxPool3d((3, 2, 2), stride=(2, 1, 2)) >>> input = torch.randn(20, 16, 50, 44, 31) >>> output = m(input)
- a single
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https://pytorch.org/docs/2.1/generated/torch.nn.MaxPool3d.html