class torch.nn.AdaptiveMaxPool3d(output_size, return_indices=False)[source]

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

The output is of size Dout×Hout×WoutD_{out} \times H_{out} \times W_{out}, for any input size. The number of output features is equal to the number of input planes.

  • output_size – the target output size of the image of the form Dout×Hout×WoutD_{out} \times H_{out} \times W_{out}. Can be a tuple (Dout,Hout,Wout)(D_{out}, H_{out}, W_{out}) or a single DoutD_{out} for a cube Dout×Dout×DoutD_{out} \times D_{out} \times D_{out}. DoutD_{out}, HoutH_{out} and WoutW_{out} can be either a int, or None which means the size will be the same as that of the input.

  • return_indices – if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool3d. Default: False

  • 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,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) or (C,Dout,Hout,Wout)(C, D_{out}, H_{out}, W_{out}), where (Dout,Hout,Wout)=output_size(D_{out}, H_{out}, W_{out})=\text{output\_size}.


>>> # target output size of 5x7x9
>>> m = nn.AdaptiveMaxPool3d((5,7,9))
>>> input = torch.randn(1, 64, 8, 9, 10)
>>> output = m(input)
>>> # target output size of 7x7x7 (cube)
>>> m = nn.AdaptiveMaxPool3d(7)
>>> input = torch.randn(1, 64, 10, 9, 8)
>>> output = m(input)
>>> # target output size of 7x9x8
>>> m = nn.AdaptiveMaxPool3d((7, None, None))
>>> input = torch.randn(1, 64, 10, 9, 8)
>>> output = m(input)


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