AvgPool3d

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AvgPool3d#

class brainstate.nn.AvgPool3d(kernel_size, stride=1, padding='VALID', channel_axis=-1, name=None, in_size=None)#

Applies a 3D average pooling over an input signal composed of several input planes.

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

\[\begin{split}\begin{aligned} \text{out}(N_i, d, h, w, C_j) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\ & \frac{\text{input}(N_i, \text{stride}[0] \times d + k, \text{stride}[1] \times h + m, \text{stride}[2] \times w + n, C_j)} {kD \times kH \times kW} \end{aligned}\end{split}\]

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

Shape:

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

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

    \[D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor\]
    \[H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor\]
    \[W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor\]
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 = AvgPool3d(3, stride=2)
>>> # pool of non-square window
>>> m = AvgPool3d((3, 2, 2), stride=(2, 1, 2))
>>> input = brainstate.random.randn(20, 50, 44, 31, 16)
>>> output = m(input)
>>> output.shape
(20, 24, 43, 15, 16)
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