def quantized_args_dict()

in get_training_params.py [0:0]

• 22 lines of code
• 12 McCabe index (conditional complexity)

def quantized_args_dict(args):
    q_dict = gen_args_dict(args)

    if args.bit_width is not None and args.method == "target_bit_width":
        q_dict["num_bits"] = args.bit_width

    if args.eval_bit_widths is not None and args.method != "lcs_l":
        q_dict["eval_param_grid"] = args.eval_bit_widths

    if args.bit_width_limits is not None and args.method in ("lcs_p", "lcs_l"):
        min_bits, max_bits = [int(x) for x in args.bit_width_limits]
        q_dict["min_bits"] = min_bits
        q_dict["max_bits"] = max_bits
        if args.method == "lcs_l":
            range_len = max_bits - min_bits + 1
            alpha_grid = [
                np.floor(x * 1000) / 1000
                for x in np.linspace(0, 1, range_len + 1)
            ][1:]
            q_dict["alpha_grid"] = alpha_grid
        elif args.method == "lcs_p":
            if args.eval_bit_widths is None:
                eval_param_grid = np.arange(min_bits, max_bits + 1)
                q_dict["eval_param_grid"] = eval_param_grid

    return q_dict