def model_scaling(layer_setting, arch_setting):
    """Scaling operation to the layer's parameters according to the
    arch_setting."""
    # scale width
    new_layer_setting = copy.deepcopy(layer_setting)
    for layer_cfg in new_layer_setting:
        for block_cfg in layer_cfg:
            block_cfg[1] = make_divisible(block_cfg[1] * arch_setting[0], 8)

    # scale depth
    split_layer_setting = [new_layer_setting[0]]
    for layer_cfg in new_layer_setting[1:-1]:
        tmp_index = [0]
        for i in range(len(layer_cfg) - 1):
            if layer_cfg[i + 1][1] != layer_cfg[i][1]:
                tmp_index.append(i + 1)
        tmp_index.append(len(layer_cfg))
        for i in range(len(tmp_index) - 1):
            split_layer_setting.append(layer_cfg[tmp_index[i]:tmp_index[i +
                                                                        1]])
    split_layer_setting.append(new_layer_setting[-1])

    num_of_layers = [len(layer_cfg) for layer_cfg in split_layer_setting[1:-1]]
    new_layers = [
        int(math.ceil(arch_setting[1] * num)) for num in num_of_layers
    ]

    merge_layer_setting = [split_layer_setting[0]]
    for i, layer_cfg in enumerate(split_layer_setting[1:-1]):
        if new_layers[i] <= num_of_layers[i]:
            tmp_layer_cfg = layer_cfg[:new_layers[i]]
        else:
            tmp_layer_cfg = copy.deepcopy(layer_cfg) + [layer_cfg[-1]] * (
                new_layers[i] - num_of_layers[i])
        if tmp_layer_cfg[0][3] == 1 and i != 0:
            merge_layer_setting[-1] += tmp_layer_cfg.copy()
        else:
            merge_layer_setting.append(tmp_layer_cfg.copy())
    merge_layer_setting.append(split_layer_setting[-1])

    return merge_layer_setting
Scaling operation to the layer's parameters according to the
    arch_setting.
mmpretrain/models/backbones/efficientnet.py