class torch.nn.AvgPool3d(kernel_size: Union[T, Tuple[T, T, T]], stride: Optional[Union[T, Tuple[T, T, T]]] = None, padding: Union[T, Tuple[T, T, T]] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override=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 , output and
kernel_size can be precisely described as:
padding is non-zero, then the input is implicitly zero-padded on all three sides for
padding number of points.
stride can either be:
int– in which case the same value is used for the depth, height and width dimension
tupleof three ints – in which case, the first
intis used for the depth dimension, the second
intfor the height dimension and the third
intfor the width dimension
floorto compute the output shape
kernel_sizewill be used
Output: , where
>>> # pool of square window of size=3, stride=2 >>> m = nn.AvgPool3d(3, stride=2) >>> # pool of non-square window >>> m = nn.AvgPool3d((3, 2, 2), stride=(2, 1, 2)) >>> input = torch.randn(20, 16, 50,44, 31) >>> output = m(input)
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