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LPPool2d
class torch.nn.LPPool2d(norm_type, kernel_size, stride=None, ceil_mode=False)
[source]-
Applies a 2D power-average pooling over an input signal composed of several input planes.
On each window, the function computed is:
- At p = , 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 firstint
is used for the height dimension, and the secondint
for the width dimension
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.- Parameters
- Shape:
-
- Input:
Output: , where
Examples:
>>> # power-2 pool of square window of size=3, stride=2 >>> m = nn.LPPool2d(2, 3, stride=2) >>> # pool of non-square window of power 1.2 >>> m = nn.LPPool2d(1.2, (3, 2), stride=(2, 1)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input)
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https://pytorch.org/docs/2.1/generated/torch.nn.LPPool2d.html