pytorch / 2 / generated / torch.nn.adaptiveavgpool2d.html

AdaptiveAvgPool2d

class torch.nn.AdaptiveAvgPool2d(output_size) [source]

Applies a 2D adaptive average pooling over an input signal composed of several input planes.

The output is of size H x W, for any input size. The number of output features is equal to the number of input planes.

Parameters

output_size (Union[int, None, Tuple[Optional[int], Optional[int]]]) – the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a square image H x H. H and W can be either a int, or None which means the size will be the same as that of the input.

Shape:
  • Input: ( N , C , H i n , W i n ) (N, C, H_{in}, W_{in}) or ( C , H i n , W i n ) (C, H_{in}, W_{in}) .
  • Output: ( N , C , S 0 , S 1 ) (N, C, S_{0}, S_{1}) or ( C , S 0 , S 1 ) (C, S_{0}, S_{1}) , where S = output_size S=\text{output\_size} .

Examples

>>> # target output size of 5x7
>>> m = nn.AdaptiveAvgPool2d((5, 7))
>>> input = torch.randn(1, 64, 8, 9)
>>> output = m(input)
>>> # target output size of 7x7 (square)
>>> m = nn.AdaptiveAvgPool2d(7)
>>> input = torch.randn(1, 64, 10, 9)
>>> output = m(input)
>>> # target output size of 10x7
>>> m = nn.AdaptiveAvgPool2d((None, 7))
>>> input = torch.randn(1, 64, 10, 9)
>>> output = m(input)

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