- 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_sizecan be precisely described as:
paddingis non-zero, then the input is implicitly zero-padded on both sides for
paddingnumber of points.
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.
paddingcan 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 (Union[int, Tuple[int, int]]) – the size of the window
stride (Union[int, Tuple[int, int]]) – the stride of the window. Default value is
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.
Input: or .
Output: or , where
>>> # 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)