class torch.nn.MaxPool2d(kernel_size: Union[T, Tuple[T, ...]], stride: Optional[Union[T, Tuple[T, ...]]] = None, padding: Union[T, Tuple[T, ...]] = 0, dilation: Union[T, Tuple[T, ...]] = 1, return_indices: bool = False, ceil_mode: bool = False)
Applies a 2D max pooling over an input signal composed of several input planes.
In the simplest case, the output value of the layer with input size , output and
kernel_size can be precisely described as:
padding is non-zero, then the input is implicitly zero-padded on both sides for
padding number of points.
dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what
dilation can either be:
int– in which case the same value is used for the height and width dimension
tupleof two ints – in which case, the first
intis used for the height dimension, and the second
intfor the width dimension
True, will return the max indices along with the outputs. Useful for
floorto compute the output shape
Output: , where
>>> # pool of square window of size=3, stride=2 >>> m = nn.MaxPool2d(3, stride=2) >>> # pool of non-square window >>> m = nn.MaxPool2d((3, 2), stride=(2, 1)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input)
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