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# AdaptiveMaxPool3d

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

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

  The output is of size D o u t×H o u t×W o u t D_{out} \times H_{out} \times W_{out}D o u t​×H o u t​×W o u t​, 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 image of the form D o u t×H o u t×W o u t D_{out} \times H_{out} \times W_{out}D o u t​×H o u t​×W o u t​. Can be a tuple (D o u t,H o u t,W o u t)(D_{out}, H_{out}, W_{out})(D o u t​,H o u t​,W o u t​) or a single D o u t D_{out}D o u t​ for a cube D o u t×D o u t×D o u t D_{out} \times D_{out} \times D_{out}D o u t​×D o u t​×D o u t​. D o u t D_{out}D o u t​, H o u t H_{out}H o u t​ and W o u t W_{out}W o u t​ can be either a `int`, or `None` which means the size will be the same as that of the input.

    - return_indices (bool) – if `True`, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool3d. Default: `False`

  Shape:

    - Input: (N,C,D i n,H i n,W i n)(N, C, D_{in}, H_{in}, W_{in})(N,C,D in​,H in​,W in​) or (C,D i n,H i n,W i n)(C, D_{in}, H_{in}, W_{in})(C,D in​,H in​,W in​).

    - Output: (N,C,D o u t,H o u t,W o u t)(N, C, D_{out}, H_{out}, W_{out})(N,C,D o u t​,H o u t​,W o u t​) or (C,D o u t,H o u t,W o u t)(C, D_{out}, H_{out}, W_{out})(C,D o u t​,H o u t​,W o u t​), where (D o u t,H o u t,W o u t)=output_size(D_{out}, H_{out}, W_{out})=\text{output\_size}(D o u t​,H o u t​,W o u t​)=output_size.

  Examples

      >>> # 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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