SpatialConvolutionMap.py 文件源码

def random(nin, nout, nto):
            nker = nto * nout
            tbl = torch.Tensor(nker, 2)
            fi = torch.randperm(nin)
            frcntr = 0
            nfi = math.floor(nin / nto)  # number of distinct nto chunks
            totbl = tbl.select(1, 1)
            frtbl = tbl.select(1, 0)
            fitbl = fi.narrow(0, 0, (nfi * nto))  # part of fi that covers distinct chunks
            ufrtbl = frtbl.unfold(0, nto, nto)
            utotbl = totbl.unfold(0, nto, nto)
            ufitbl = fitbl.unfold(0, nto, nto)

            # start fill_ing frtbl
            for i in range(nout):  # fro each unit in target map
                ufrtbl.select(0, i).copy_(ufitbl.select(0, frcntr))
                frcntr += 1
                if frcntr - 1 == nfi:  # reset fi
                    fi.copy_(torch.randperm(nin))
                    frcntr = 1

            for tocntr in range(utotbl.size(0)):
                utotbl.select(0, tocntr).fill_(tocntr)

            return tbl