LPPool2d

class torch.nn.LPPool2d(norm_type, kernel_size, stride=None, ceil_mode=False)

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 (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

Note

When ceil_mode is True, sliding windows may 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.

Shape:

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

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

  $$H_{out}=\lfloor \frac{H_{in}-\text{kernel\_size}[0]}{\text{stride}[0]}+1\rfloor$$
  $$W_{out}=\lfloor \frac{W_{in}-\text{kernel\_size}[1]}{\text{stride}[1]}+1\rfloor$$

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)

forward(input)

Runs the forward pass.

Return type

Tensor