AdaptiveAvgPool2d — PyTorch 2.7 documentation (original) (raw)

class torch.nn.AdaptiveAvgPool2d(output_size)[source][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,Hin,Win)(N, C, H_{in}, W_{in}) or (C,Hin,Win)(C, H_{in}, W_{in}).
• Output: (N,C,S0,S1)(N, C, S_{0}, S_{1}) or (C,S0,S1)(C, S_{0}, S_{1}), whereS=output_sizeS=\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)