Shortcuts

MaxPool2d

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

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) , output (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) and kernel_size (kH,kW)(kH, kW) can be precisely described as:

out(Ni,Cj,h,w)=maxm=0,,kH1maxn=0,,kW1input(Ni,Cj,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}

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.

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 – 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 both 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.MaxUnpool2d later

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

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

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

    Hout=Hin+2padding[0]dilation[0]×(kernel_size[0]1)1stride[0]+1H_{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
    Wout=Win+2padding[1]dilation[1]×(kernel_size[1]1)1stride[1]+1W_{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

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)

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources