def test_split_fn(model, L, refield=32, min_size=256, sf=1, modulo=1):
    """
    Args:
        model: trained model
        L: input Low-quality image
        refield: effective receptive filed of the network, 32 is enough
        min_size: min_sizeXmin_size image, e.g., 256X256 image
        sf: scale factor for super-resolution, otherwise 1
        modulo: 1 if split

    Returns:
        E: estimated result
    """
    h, w = L.size()[-2:]
    if h*w <= min_size**2:
        L = torch.nn.ReplicationPad2d((0, int(np.ceil(w/modulo)*modulo-w), 0, int(np.ceil(h/modulo)*modulo-h)))(L)
        E = model(L)
        E = E[..., :h*sf, :w*sf]
    else:
        top = slice(0, (h//2//refield+1)*refield)
        bottom = slice(h - (h//2//refield+1)*refield, h)
        left = slice(0, (w//2//refield+1)*refield)
        right = slice(w - (w//2//refield+1)*refield, w)
        Ls = [L[..., top, left], L[..., top, right], L[..., bottom, left], L[..., bottom, right]]

        if h * w <= 4*(min_size**2):
            Es = [model(Ls[i]) for i in range(4)]
        else:
            Es = [test_split_fn(model, Ls[i], refield=refield, min_size=min_size, sf=sf, modulo=modulo) for i in range(4)]

        b, c = Es[0].size()[:2]
        E = torch.zeros(b, c, sf * h, sf * w).type_as(L)

        E[..., :h//2*sf, :w//2*sf] = Es[0][..., :h//2*sf, :w//2*sf]
        E[..., :h//2*sf, w//2*sf:w*sf] = Es[1][..., :h//2*sf, (-w + w//2)*sf:]
        E[..., h//2*sf:h*sf, :w//2*sf] = Es[2][..., (-h + h//2)*sf:, :w//2*sf]
        E[..., h//2*sf:h*sf, w//2*sf:w*sf] = Es[3][..., (-h + h//2)*sf:, (-w + w//2)*sf:]
    return E

    Args:
        model: trained model
        L: input Low-quality image
        refield: effective receptive filed of the network, 32 is enough
        min_size: min_sizeXmin_size image, e.g., 256X256 image
        sf: scale factor for super-resolution, otherwise 1
        modulo: 1 if split

    Returns:
        E: estimated result
    
utils/utils_model.py