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 \(D_{out} \times H_{out} \times W_{out}\), 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)
>>> 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)