Open FelixZhang7 opened 3 years ago
在网络搜索完后，得到的最高精度网络结构为genotype = Genotype(normal=[('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('dil_conv_5x5', 2), ('sep_conv_3x3', 1), ('avg_pool_3x3', 3), ('avg_pool_3x3', 1), ('sep_conv_5x5', 1), ('dil_conv_5x5', 4)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_5x5', 1), ('dil_conv_5x5', 2), ('sep_conv_3x3', 3), ('sep_conv_5x5', 4), ('sep_conv_5x5', 3)], reduce_concat=range(2, 6))，如何将这种形式变成可读可加载的网络结构呢？
在网络搜索完后，得到的最高精度网络结构为genotype = Genotype(normal=[('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('dil_conv_5x5', 2), ('sep_conv_3x3', 1), ('avg_pool_3x3', 3), ('avg_pool_3x3', 1), ('sep_conv_5x5', 1), ('dil_conv_5x5', 4)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_5x5', 1), ('dil_conv_5x5', 2), ('sep_conv_3x3', 3), ('sep_conv_5x5', 4), ('sep_conv_5x5', 3)], reduce_concat=range(2, 6))，如何将这种形式变成可读可加载的网络结构呢？