def print_train_params()

in utils.py [0:0]

• 37 lines of code
• 3 McCabe index (conditional complexity)

def print_train_params(config, setting, method, norm, save_dir):
    params = config["parameters"]

    model = params["model_config"]["model_class"]
    dataset = params["dataset"]

    model_name_map = {
        "cpreresnet20": "cPreResNet20",
        "resnet18": "ResNet18",
        "vgg19": "VGG19",
    }

    method_name_map = {
        "lcs_l": "LCS+L",
        "lcs_p": "LCS+P",
        "ns": "NS",
        "us": "US",
        "lec": "LEC",
        "target_topk": "TopK Target",
        "target_bit_width": "Bit Width Target",
    }

    dataset_name_map = {"cifar10": "CIFAR-10", "imagenet": "ImageNet"}

    setting_name_map = {
        "unstructured_sparsity": "Unstructured sparsity",
        "structured_sparsity": "Structured sparsity",
        "quantized": "Quantized",
    }

    msg = f"{setting_name_map[setting]} ({method_name_map[method]}) training:"
    msg += f" {model_name_map[model]} on {dataset_name_map[dataset]} w/ {norm}."

    if method == "target_topk":
        topk_target = params["topk"]
        msg += f" TopK target: {topk_target}."
    elif method == "target_bit_width":
        bit_width_target = params["num_bits"]
        msg += f" Bit width target: {bit_width_target}."

    print()
    print(msg)
    print(f"Saving to {save_dir}.")
    print()
    time.sleep(5)