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# MaxPool2d

class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

  Applies a 2D 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,H,W)(N, C, H, W)(N,C,H,W), output (N,C,H o u t,W o u t)(N, C, H_{out}, W_{out})(N,C,H o u t​,W o u t​) and `kernel_size` (k H,k W)(kH, kW)(k H,kW) can be precisely described as:

  o u t(N i,C j,h,w)=max⁡m=0,…,k H−1 max⁡n=0,…,k W−1 input(N i,C j,stride[0]×h+m,stride[1]×w+n)\begin{aligned} out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ & \text{input}(N_i, C_j, \text{stride[0]} \times h + m, \text{stride[1]} \times w + n) \end{aligned} o u t(N i​,C j​,h,w)=​m=0,…,k H−1 max​n=0,…,kW−1 max​input(N i​,C j​,stride[0]×h+m,stride[1]×w+n)​

  If `padding` is non-zero, then the input is implicitly padded with negative infinity 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.

  Note

  When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.

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

  > - a single `int` – in which case the same value is used for the height and width dimension

  > - a `tuple` of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension

  Parameters:

    - kernel_size (Union[int, Tuple[int, int]]) – the size of the window to take a max over

    - stride (Union[int, Tuple[int, int]]) – the stride of the window. Default value is `kernel_size`

    - padding (Union[int, Tuple[int, int]]) – Implicit negative infinity padding to be added on both sides

    - dilation (Union[int, Tuple[int, int]]) – a parameter that controls the stride of elements in the window

    - return_indices (bool) – if `True`, will return the max indices along with the outputs. Useful for `torch.nn.MaxUnpool2d` later

    - ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape

  Shape:

    - Input: (N,C,H i n,W i n)(N, C, H_{in}, W_{in})(N,C,H in​,W in​) or (C,H i n,W i n)(C, H_{in}, W_{in})(C,H in​,W in​)

    - Output: (N,C,H o u t,W o u t)(N, C, H_{out}, W_{out})(N,C,H o u t​,W o u t​) or (C,H o u t,W o u t)(C, H_{out}, W_{out})(C,H o u t​,W o u t​), where

      H o u t=⌊H i n+2∗padding[0]−dilation[0]×(kernel_size[0]−1)−1 stride[0]+1⌋H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]} \times (\text{kernel\_size[0]} - 1) - 1}{\text{stride[0]}} + 1\right\rfloor H o u t​=⌊stride[0]H in​+2∗padding[0]−dilation[0]×(kernel_size[0]−1)−1​+1⌋
      W o u t=⌊W i n+2∗padding[1]−dilation[1]×(kernel_size[1]−1)−1 stride[1]+1⌋W_{out} = \left\lfloor\frac{W_{in} + 2 * \text{padding[1]} - \text{dilation[1]} \times (\text{kernel\_size[1]} - 1) - 1}{\text{stride[1]}} + 1\right\rfloor W o u t​=⌊stride[1]W in​+2∗padding[1]−dilation[1]×(kernel_size[1]−1)−1​+1⌋

  Examples:

      >>> # pool of square window of size=3, stride=2
      >>> m = nn.MaxPool2d(3, stride=2)
      >>> # pool of non-square window
      >>> m = nn.MaxPool2d((3, 2), stride=(2, 1))
      >>> input = torch.randn(20, 16, 50, 32)
      >>> output = m(input)

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