def METHOD_NAME(root_eval, root_res, dataset, device="cuda:0", num_images_per_prompt=4):
    with open(os.path.join(root_eval, dataset, dataset + '.txt'), 'r') as fr:
        text_list = fr.readlines()
        text_list = [_.strip() for _ in text_list]
    clip_scores = []
    scores = []
    for seed in range(num_images_per_prompt):
        if 'stablediffusion' in root_res:
            format = '.png'
        else:
            format = '.jpg'
        image_list = [os.path.join(root_res, dataset, 'images_' + str(seed),
                                   str(idx) + '_' +  str(seed) + format) for idx in range(len(text_list))]
        image_ids = [str(idx) + '_' +  str(seed) + format for idx in range(len(text_list))]
        score = cal_clipscore(image_ids=image_ids, image_paths=image_list, text_list=text_list, device=device)
        clip_score = np.mean([s['CLIPScore'] for s in score.values()])
        clip_scores.append(clip_score)
        scores.append(score)
    print("clip_score:", np.mean(clip_scores), clip_scores)
    return np.mean(clip_scores), scores