Main函数实现样例

通过继承或者重写初始化、warm_up以及前向推理等方法，实现了适配基类“MultimodalPARunner”后，需要在Main函数中实现基本的路径解析和输入准备，以InternVL2.5为例：

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if __name__ == '__main__':
    args = parse_arguments()
    rank = ENV.rank
    local_rank = ENV.local_rank
    world_size = ENV.world_size
    image_or_video_path = standardize_path(args.image_or_video_path)
    check_file_safety(image_or_video_path, 'r')
    file_name = safe_listdir(image_or_video_path)
    file_length = len(file_name)
    input_dict = {
        'rank': rank,
        'world_size': world_size,
        'local_rank': local_rank,
        'perf_file': PERF_FILE,
        **vars(args)
    }
    if is_image_path(image_or_video_path):
        image_path = [os.path.join(image_or_video_path, f) for f in file_name]
        video_path = None
        input_dict['image_path'] = image_path
        texts = args.input_texts_for_image
    elif is_video_path(image_or_video_path):
        video_path = [os.path.join(image_or_video_path, f) for f in file_name]
        image_path = None
        input_dict['video_path'] = video_path
        texts = args.input_texts_for_video
    else:
        logger.error("Unsupported media type, it should be a video or image, please check your input.", ErrorCode.ATB_MODELS_PARAM_OUT_OF_RANGE)
        raise KeyError("Unsupported media type, it should be a video or image, please check your input.")
    if len(texts) > file_length:
        raise ValueError(f"The number of input texts is greater than the number of files.")
    texts.extend([texts[-1]] * (file_length - len(texts)))
    input_dict['input_texts'] = texts
    pa_runner = InternvlRunner(**input_dict)
    if image_path:
        image_length = len(image_path)
        remainder = image_length % args.max_batch_size
        if remainder != 0:
            num_to_add = args.max_batch_size - remainder
            image_path.extend([image_path[-1]] * num_to_add)
            texts.extend([texts[-1]] * num_to_add)
    elif video_path:
        video_length = len(video_path)
        remainder = video_length % args.max_batch_size
        if remainder != 0:
            num_to_add = args.max_batch_size - remainder
            video_path.extend([video_path[-1]] * num_to_add)
            texts.extend([texts[-1]] * num_to_add)
    print_log(rank, logger.info, f'pa_runner: {pa_runner}')
    infer_params = {
        "mm_inputs": MultimodalInput(texts,
                                     image_path,
                                     video_path,
                                     None),
        "batch_size": args.max_batch_size,
        "max_output_length": args.max_output_length,
        "ignore_eos": args.ignore_eos,
    }
    pa_runner.warm_up()
    generate_texts, token_nums, latency = pa_runner.infer(**infer_params)
    for i, generate_text in enumerate(generate_texts):
        print_log(rank, logger.info, f'Answer[{i}]: {generate_text}')
        print_log(rank, logger.info, f'Generate[{i}] token num: {token_nums[i]}')
        print_log(rank, logger.info, f"Latency: {latency}")
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