AvgPool2d

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AvgPool2d#

class brainstate.nn.AvgPool2d(kernel_size, stride=1, padding='VALID', channel_axis=-1, name=None, in_size=None)#

Applies a 2D average pooling over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size \((N, H, W, C)\), output \((N, H_{out}, W_{out}, C)\) and kernel_size \((kH, kW)\) can be precisely described as:

\[out(N_i, h, w, C_j) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} input(N_i, stride[0] \times h + m, stride[1] \times w + n, C_j)\]

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

Shape:

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

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

    \[H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor\]
    \[W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor\]
Parameters:

  • kernel_size (int | Sequence[int] | integer | Sequence[integer]) – An integer, or a sequence of integers defining the window to reduce over.

  • stride (int | Sequence[int]) – An integer, or a sequence of integers, representing the inter-window stride. Default: 1

  • padding (str | int | Tuple[int, ...] | Sequence[Tuple[int, int]]) – Either the string ‘SAME’, the string ‘VALID’, or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension. Default: ‘VALID’

  • channel_axis (int | None) – Axis of the spatial channels for which pooling is skipped. If None, there is no channel axis. Default: -1

  • name (str | None) – The object name.

  • in_size (int | Sequence[int] | integer | Sequence[integer] | None) – The shape of the input tensor.

Examples

>>> import brainstate
>>> # pool of square window of size=3, stride=2
>>> m = AvgPool2d(3, stride=2)
>>> # pool of non-square window
>>> m = AvgPool2d((3, 2), stride=(2, 1))
>>> input = brainstate.random.randn(20, 50, 32, 16)
>>> output = m(input)
>>> output.shape
(20, 24, 31, 16)
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