AvgPool2d#
- class torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)[source]#
Applies a 2D 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 both sides forpadding
number of points.Note
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.
Note
pad should be at most half of effective kernel size.
The parameters
kernel_size
,stride
,padding
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 (Union[int, tuple[int, int]]) – the size of the window
stride (Union[int, tuple[int, int]]) – the stride of the window. Default value is
kernel_size
padding (Union[int, tuple[int, int]]) – implicit zero padding to be added on both sides
ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape
count_include_pad (bool) – when True, will include the zero-padding in the averaging calculation
divisor_override (Optional[int]) – if specified, it will be used as divisor, otherwise size of the pooling region will be used.
- Shape:
Input: or .
Output: or , where
Per the note above, if
ceil_mode
is True and , we skip the last window as it would start in the bottom padded region, resulting in being reduced by one.The same applies for .
Examples:
>>> # pool of square window of size=3, stride=2 >>> m = nn.AvgPool2d(3, stride=2) >>> # pool of non-square window >>> m = nn.AvgPool2d((3, 2), stride=(2, 1)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input)