def mynima(args):
    model_pth = './tmp3/emd_loss_epoch_49_train_0.05391903784470127_0.12613263790013726.pth'
    model = InferenceModel(path_to_model=model_pth)
    mode = detect_mode(args)
    (method, pth) = detect_pth(args)
    if (method == 'pic'):
        name = os.path.basename(pth)
        image = {name: pth}
    elif (method == 'folder'):
        image = handle_folder(pth)
    score = start(mode, model, image)
    write_json(args, score)
    return
def mynima(args):
    model_pth = './tmp3/emd_loss_epoch_49_train_0.05391903784470127_0.12613263790013726.pth'
    model = InferenceModel(path_to_model=model_pth)
    mode = detect_mode(args)
    (method, pth) = detect_pth(args)
    if (method == 'pic'):
        name = os.path.basename(pth)
        image = {name: pth}
    elif (method == 'folder'):
        image = handle_folder(pth)
    score = start(mode, model, image)
    write_json(args, score)
    return