How to adapt the output size of a given feature map in the Deep Learning Toolbox by using the "pool" operation?

I understand that there are currently "averagePooling2dLayer, maxPooling2dLayer, globalAveragePooling2dLayer, globalMaxPooling2dLayer" and so on for the "pool" operation. globalMaxPooling2dLayer", "maxpool" and other direct call functions, but nothing like pytorch's "adaptive_max_pool2d". function that can directly specify the size of the output feature map for a pool operation?

The following simple example is pytorch code, how can matlab achieve the same purpose?

input = torch.rand(8,3,224,224) # input tensor, NCHW
outSize = (20,20) # specify output tensor size, H_out*W_out
output = F.adaptive_max_pool2d(input,outSize) # adaptive output
print(output.shape) # result-->  torch.Size([8, 3, 20, 20])

Answer 1

[KaSyow Riyuu]

https://stackoverflow.com/questions/53841509/how-does-adaptive-pooling-in-pytorch-work

Stride = ( InputSize / OutputSize )

KernelSize = InputSize - ( OutputSize - 1) * Stride

Padding = 0

you can do adaptivepool like this

function Output = AdaptiveMaxPool(Input,OuputSize)

InputSize = size(Input)

Stride = floor( InputSize ./ OutputSize ) ;

KernelSize = InputSize - ( OutputSize -1) .* Stride ;

Output = maxpool(Input , KernelSize, "Stride", Stide) ;

end