class torch.nn.LPPool2d(norm_type: float, kernel_size: Union[T, Tuple[T, ...]], stride: Optional[Union[T, Tuple[T, ...]]] = None, ceil_mode: bool = False)
[source]
Applies a 2D power-average pooling over an input signal composed of several input planes.
On each window, the function computed is:
The parameters kernel_size
, stride
can either be:
int
– in which case the same value is used for the height and width dimensiontuple
of two ints – in which case, the first int
is used for the height dimension, and the second int
for the width dimensionNote
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.
kernel_size
ceil
instead of floor
to compute the output shapeOutput: $(N, C, H_{out}, W_{out})$ , 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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Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.LPPool2d.html