AdaptiveMaxPool3d

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

對由數個輸入平面組成的輸入訊號，應用 3D 自適應最大池化。

對於任何輸入大小，輸出大小為 $D_{out} \times H_{out} \times W_{out}$。輸出特徵的數量等於輸入平面的數量。

參數

• 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) – 若 True，將返回輸出以及索引。有助於傳遞給 nn.MaxUnpool3d。預設值：False

形狀

• 輸入：$(N, C, D_{in}, H_{in}, W_{in})$ 或 $(C, D_{in}, H_{in}, W_{in})$。

• 輸出： $(N, C, D_{out}, H_{out}, W_{out})$ 或 $(C, D_{out}, H_{out}, W_{out})$，其中 $(D_{out}, H_{out}, W_{out})=\text{output\_size}$。

範例

>>> # 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)