AdaptiveMaxPool3d

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

对由多个输入平面组成的输入信号应用 3D 自适应最大池化。

对于任何输入尺寸，输出尺寸为 $D_{out} \times H_{out} \times W_{out}$. 输出的通道数与输入的通道数相同。

参数

• output_size (Union[int, None, tuple[Optional[int], Optional[int], Optional[int]]) – 目标图像输出尺寸，形式为 $D_{out} \times H_{out} \times W_{out}$. 可以是元组 $(D_{out}, H_{out}, W_{out})$ 或单个 $D_{out}$ 表示一个立方体 $D_{out} \times D_{out} \times D_{out}$. $D_{out}$, $H_{out}$ and $W_{out}$ can be either an 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

形状

• 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 $(D_{out}, H_{out}, W_{out})=\text{output\_size}$.

示例

>>> # 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)

forward(input)

执行前向传播。