/PyTorch

# LPPool2d

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:

$f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}$
• 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

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
• kernel_size – the size of the window
• stride – the stride of the window. Default value is kernel_size
• ceil_mode – when True, will use ceil instead of floor to compute the output shape
Shape:
• Input: $(N, C, H_{in}, W_{in})$
• Output: $(N, C, H_{out}, W_{out})$ , where

$H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor$
$W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor$

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)


© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.LPPool2d.html