MaxPool2d#

classtorch.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$(N,C,H_{out},W_{out})$(N,C,Hout​,Wout​) andkernel_size$(kH,kW)$(kH,kW)can be precisely described as:

out(Ni​,Cj​,h,w)=​m=0,…,kH−1max​n=0,…,kW−1max​input(Ni​,Cj​,stride[0]×h+m,stride[1]×w+n)​

Ifpadding is non-zero, then the input is implicitly padded with negative infinity on both sidesforpadding number of points.dilation controls the spacing between the kernel points.It is harder to describe, but thislink has a nice visualization of whatdilation does.

Note

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

The parameterskernel_size,stride,padding,dilation can either be:

• a singleint – in which case the same value is used for the height and width dimension

• atuple of two ints – in which case, the firstint is used for the height dimension,and the secondint 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 iskernel_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) – ifTrue, will return the max indices along with the outputs.Useful fortorch.nn.MaxUnpool2d later

• ceil_mode (bool) – when True, will useceil instead offloor to compute the output shape

Shape:

• Input:$(N,C,H_{in},W_{in})$(N,C,Hin​,Win​) or$(C,H_{in},W_{in})$(C,Hin​,Win​)

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

  $$H_{out}=\lfloor \frac{H_{in}+2\ast \text{padding[0]}-\text{dilation[0]}\times (\text{kernel\_size[0]}-1)-1}{\text{stride[0]}}+1\rfloor$$Hout​=⌊stride[0]Hin​+2∗padding[0]−dilation[0]×(kernel_size[0]−1)−1​+1⌋
  $$W_{out}=\lfloor \frac{W_{in}+2\ast \text{padding[1]}-\text{dilation[1]}\times (\text{kernel\_size[1]}-1)-1}{\text{stride[1]}}+1\rfloor$$Wout​=⌊stride[1]Win​+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)

forward(input)[source]#

Runs the forward pass.