function expand(net)

    convCount = 0

    poolCount = 0

    for i=1,net:size() do

        if torch.typename(net:get(i)) == 'nn.SpatialConvolution' then

            convCount = convCount + 1

            nInputPlane = net:get(i).nInputPlane

            nOutputPlane = net:get(i).nOutputPlane

            kW = net:get(i).kW

            kH = net:get(i).kH

            dilationW = 2^(convCount-1)

            dilationH = 2^(convCount-1)

            net:insert(nn.SpatialDilatedConvolution(nInputPlane,nOutputPlane,kW,kH,1,1,0,0,dilationW,dilationH), i+1)

            net:get(i+1).weight = net:get(i).weight

            net:get(i+1).bias = net:get(i).bias

            net:remove(i)

        elseif torch.typename(net:get(i)) == 'nn.SpatialMaxPooling' then

            poolCount = poolCount + 1

            kW = net:get(i).kW

            kH = net:get(i).kH

            dilationW = 2^(poolCount-1)

            dilationH = 2^(poolCount-1)

            net:insert(nn.SpatialDilatedMaxPooling(kW,kH,1,1,0,0,dilationW,dilationH), i+1)

            net:get(i+1).weight = net:get(i).weight

            net:get(i+1).bias = net:get(i).bias

            net:remove(i)

        elseif torch.typename(net:get(i)) == 'nn.View' then

            net:insert(nn.Identity(),i+1)

            net:remove(i)

        elseif torch.typename(net:get(i)) == 'nn.Linear' then

            convCount = convCount + 1

            j = i - 1

            while true do

                if torch.typename(net:get(j)) == 'nn.SpatialDilatedConvolution' then

                    break

                end

                j = j - 1

            end

            local nInputPlane = net:get(j).nOutputPlane

            local outputSize = net:get(i).weight:size(1)

            local inputSize = net:get(i).weight:size(2)

            local nOutputPlane = outputSize

            kW = torch.sqrt(inputSize/nInputPlane)

            kH = kW

            dilationW = 2^(convCount-1)

            dilationH = 2^(convCount-1)

            net:insert(nn.SpatialDilatedConvolution(nInputPlane,nOutputPlane,kW,kH,1,1,0,0,dilationW,dilationH), i+1)

            net:get(i+1).weight = net:get(i).weight:resize(nOutputPlane,nInputPlane,kH,kW)

            net:get(i+1).bias = net:get(i).bias

            net:remove(i)

        elseif torch.typename(net:get(i)) == 'nn.LogSoftMax' then

            net:insert(nn.SpatialLogSoftMax(), i+1)

            net:remove(i)

        end

    end

    for i=net:size(),1,-1 do

        if torch.typename(net:get(i)) == 'nn.Identity' then

            net:remove(i)

        end

    end

    return net

end