AdaptiveMaxPool2d#

classtorch.nn.modules.pooling.AdaptiveMaxPool2d(output_size,return_indices=False)[source]#

Applies a 2D adaptive max pooling over an input signal composed of several input planes.

The output is of size$H_{out}\times W_{out}$Hout​×Wout​, for any input size.The number of output features is equal to the number of input planes.

Parameters

• output_size (Union[int,None,tuple[Optional[int],Optional[int]]]) – the target output size of the image of the form$H_{out}\times W_{out}$Hout​×Wout​.Can be a tuple$(H_{out},W_{out})$(Hout​,Wout​) or a single$H_{out}$Hout​ for asquare image$H_{out}\times H_{out}$Hout​×Hout​.$H_{out}$Hout​ and$W_{out}$Wout​can be either aint, orNone which means the size will be the same as thatof the input.

• return_indices (bool) – ifTrue, will return the indices along with the outputs.Useful to pass to nn.MaxUnpool2d. Default:False

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$(H_{out},W_{out})=\text{output\_size}$(Hout​,Wout​)=output_size.

Examples

>>># target output size of 5x7>>>m=nn.AdaptiveMaxPool2d((5,7))>>>input=torch.randn(1,64,8,9)>>>output=m(input)>>># target output size of 7x7 (square)>>>m=nn.AdaptiveMaxPool2d(7)>>>input=torch.randn(1,64,10,9)>>>output=m(input)>>># target output size of 10x7>>>m=nn.AdaptiveMaxPool2d((None,7))>>>input=torch.randn(1,64,10,9)>>>output=m(input)

forward(input)[source]#

Runs the forward pass.