AdaptiveAvgPool3d — PyTorch 2.7 documentation (original) (raw)

class torch.nn.AdaptiveAvgPool3d(output_size)[source][source]¶

Applies a 3D adaptive average pooling over an input signal composed of several input planes.

The output is of size D x 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] , Optional_[_int] ] ]) – the target output size of the form D x H x W. Can be a tuple (D, H, W) or a single number D for a cube D x D x D. D, 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,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) or (C,Din,Hin,Win)(C, D_{in}, H_{in}, W_{in}).
• Output: (N,C,S0,S1,S2)(N, C, S_{0}, S_{1}, S_{2}) or (C,S0,S1,S2)(C, S_{0}, S_{1}, S_{2}), where S=output_sizeS=\text{output\_size}.

Examples

target output size of 5x7x9

m = nn.AdaptiveAvgPool3d((5, 7, 9)) input = torch.randn(1, 64, 8, 9, 10) output = m(input)

target output size of 7x7x7 (cube)

m = nn.AdaptiveAvgPool3d(7) input = torch.randn(1, 64, 10, 9, 8) output = m(input)

target output size of 7x9x8

m = nn.AdaptiveAvgPool3d((7, None, None)) input = torch.randn(1, 64, 10, 9, 8) output = m(input)