class torch.nn.MaxPool3d(kernel_size: Union[T, Tuple[T, ...]], stride: Optional[Union[T, Tuple[T, ...]]] = None, padding: Union[T, Tuple[T, ...]] = 0, dilation: Union[T, Tuple[T, ...]] = 1, return_indices: bool = False, ceil_mode: bool = False)[source]

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

In the simplest case, the output value of the layer with input size (N,C,D,H,W)(N, C, D, H, W) , output (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) and kernel_size (kD,kH,kW)(kD, kH, kW) can be precisely described as:

out(Ni,Cj,d,h,w)=maxk=0,,kD1maxm=0,,kH1maxn=0,,kW1input(Ni,Cj,stride[0]×d+k,stride[1]×h+m,stride[2]×w+n)\begin{aligned} \text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ & \text{input}(N_i, C_j, \text{stride[0]} \times d + k, \text{stride[1]} \times h + m, \text{stride[2]} \times w + n) \end{aligned}

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what dilation does.

The parameters kernel_size, stride, padding, dilation can either be:

  • a single int – in which case the same value is used for the depth, height and width dimension

  • a tuple of three ints – in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension

  • kernel_size – the size of the window to take a max over

  • stride – the stride of the window. Default value is kernel_size

  • padding – implicit zero padding to be added on all three sides

  • dilation – a parameter that controls the stride of elements in the window

  • return_indices – if True, will return the max indices along with the outputs. Useful for torch.nn.MaxUnpool3d later

  • ceil_mode – when True, will use ceil instead of floor to compute the output shape

  • Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in})

  • Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) , where

    Dout=Din+2×padding[0]dilation[0]×(kernel_size[0]1)1stride[0]+1D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
    Hout=Hin+2×padding[1]dilation[1]×(kernel_size[1]1)1stride[1]+1H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
    Wout=Win+2×padding[2]dilation[2]×(kernel_size[2]1)1stride[2]+1W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor


>>> # pool of square window of size=3, stride=2
>>> m = nn.MaxPool3d(3, stride=2)
>>> # pool of non-square window
>>> m = nn.MaxPool3d((3, 2, 2), stride=(2, 1, 2))
>>> input = torch.randn(20, 16, 50,44, 31)
>>> output = m(input)


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