MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=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_sizecan be precisely described as:
paddingis non-zero, then the input is implicitly zero-padded on both sides for
paddingnumber of points.
dilationcontrols the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what
When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.
dilationcan 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 int is used for the height dimension, and the second int for the width dimension
kernel_size – the size of the window to take a max over
stride – the stride of the window. Default value is
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 for
ceil_mode – when True, will use ceil instead of floor to 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)