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:

f(X)=xXxppf(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
  • At p = \infty, 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 first int is used for the height dimension, and the second int for the width dimension


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 (Union[int, Tuple[int, int]]) – the size of the window

  • stride (Union[int, Tuple[int, int]]) – the stride of the window. Default value is kernel_size

  • ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape

  • Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) or (C,Hin,Win)(C, H_{in}, W_{in}).

  • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) or (C,Hout,Wout)(C, H_{out}, W_{out}), where

    Hout=Hinkernel_size[0]stride[0]+1H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
    Wout=Winkernel_size[1]stride[1]+1W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor


>>> # 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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