torch / nn / torch.nn
MaxPool2d¶
-
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)[source]¶ 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:If
padding
is non-zero, then the input is implicitly zero-padded on both sides forpadding
number of points.dilation
controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of whatdilation
does.The parameters
kernel_size
,stride
,padding
,dilation
can either be:a single
int
– in which case the same value is used for the height and width dimensiona
tuple
of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension
- Parameters
kernel_size – the size of the window to take a max over
stride – the stride of the window. Default value is
kernel_size
padding – implicit zero padding to be added on both sides
dilation – a parameter that controls the stride of elements in the window
return_indices – if
True
, will return the max indices along with the outputs. Useful fortorch.nn.MaxUnpool2d
laterceil_mode – when True, will use ceil instead of floor to compute the output shape
- Shape:
Input:
Output: , where
Examples:
>>> # 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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