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)

forward(input)

Runs the forward pass.