mlx.nn.MaxPool3d

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mlx.nn.MaxPool3d#

class MaxPool3d(kernel_size: int | Tuple[int, int, int], stride: int | Tuple[int, int, int] | None = None, padding: int | Tuple[int, int, int] | None = 0)#

Applies 3-dimensional max pooling.

Spatially downsamples the input by taking the maximum of a sliding window of size kernel_size and sliding stride stride.

The parameters kernel_size, stride, and padding can either be:

  • a single int – in which case the same value is used for the depth, height, and width axis.

  • a tuple of three int s – in which case, the first int is used for the depth axis, the second int for the height axis, and the third int for the width axis.

Parameters:

  • kernel_size (int or tuple(int, int, int)) – The size of the pooling window.

  • stride (int or tuple(int, int, int), optional) – The stride of the pooling window. Default: kernel_size.

  • padding (int or tuple(int, int, int), optional) – How much negative infinity padding to apply to the input. The padding is applied on both sides of the depth, height and width axis. Default: 0.

Examples

>>> import mlx.core as mx
>>> import mlx.nn.layers as nn
>>> x = mx.random.normal(shape=(8, 16, 32, 32, 4))
>>> pool = nn.MaxPool3d(kernel_size=2, stride=2)
>>> pool(x)

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