norm_factor = np.sum(gradient, axis=(1, 2))  # [C]归一化
    for i in range(len(norm_factor)):
        norm_factor[i] = 1. / norm_factor[i] if norm_factor[i] > 0. else 0.  # 避免除零
    alpha = indicate * norm_factor[:, np.newaxis, np.newaxis]  # [C,H,W]