torch / nn / torch.nn
AvgPool3d¶
-
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)[source]¶ 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:If
padding
is non-zero, then the input is implicitly zero-padded on all three sides forpadding
number of points.The parameters
kernel_size
,stride
can either be:a single
int
– in which case the same value is used for the depth, height and width dimensiona
tuple
of three ints – in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension
- Parameters
kernel_size – the size of the window
stride – the stride of the window. Default value is
kernel_size
padding – implicit zero padding to be added on all three sides
ceil_mode – when True, will use ceil instead of floor to compute the output shape
count_include_pad – when True, will include the zero-padding in the averaging calculation
divisor_override – if specified, it will be used as divisor, otherwise
kernel_size
will be used
- Shape:
Input:
Output: , where
Examples:
>>> # 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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