AdaptiveMaxPool3d#

classtorch.nn.AdaptiveMaxPool3d(output_size,return_indices=False)[source]#

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

The output is of size$D_{out}\times H_{out}\times W_{out}$Dout​×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],Optional[int]]]) – the target output size of the image of the form$D_{out}\times H_{out}\times W_{out}$Dout​×Hout​×Wout​.Can be a tuple$(D_{out},H_{out},W_{out})$(Dout​,Hout​,Wout​) or a single$D_{out}$Dout​ for a cube$D_{out}\times D_{out}\times D_{out}$Dout​×Dout​×Dout​.$D_{out}$Dout​,$H_{out}$Hout​ and$W_{out}$Wout​ can be either aint, orNone which means the size will be the same as that of the input.

• return_indices (bool) – ifTrue, will return the indices along with the outputs.Useful to pass to nn.MaxUnpool3d. Default:False

Shape:

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

• Output:$(N,C,D_{out},H_{out},W_{out})$(N,C,Dout​,Hout​,Wout​) or$(C,D_{out},H_{out},W_{out})$(C,Dout​,Hout​,Wout​),where$(D_{out},H_{out},W_{out})=\text{output\_size}$(Dout​,Hout​,Wout​)=output_size.

Examples

>>># target output size of 5x7x9>>>m=nn.AdaptiveMaxPool3d((5,7,9))>>>input=torch.randn(1,64,8,9,10)>>>output=m(input)>>># target output size of 7x7x7 (cube)>>>m=nn.AdaptiveMaxPool3d(7)>>>input=torch.randn(1,64,10,9,8)>>>output=m(input)>>># target output size of 7x9x8>>>m=nn.AdaptiveMaxPool3d((7,None,None))>>>input=torch.randn(1,64,10,9,8)>>>output=m(input)

forward(input)[source]#

Runs the forward pass.