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

The output is of size $D_{out} \times H_{out} \times W_{out}$, 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 $D_{out} \times H_{out} \times W_{out}$. Can be a tuple $(D_{out}, H_{out}, W_{out})$ or a single $D_{out}$ for a cube $D_{out} \times D_{out} \times D_{out}$. $D_{out}$, $H_{out}$ and $W_{out}$ 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: $(N, C, D_{in}, H_{in}, W_{in})$ or $(C, D_{in}, H_{in}, W_{in})$.

• Output: $(N, C, D_{out}, H_{out}, W_{out})$ or $(C, D_{out}, H_{out}, W_{out})$, where $(D_{out}, H_{out}, W_{out})=\text{output\_size}$.

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)
>>> 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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