metric_utils.py [402:449]:
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def deprecated_score_from_method_for_dataset(my_scorer,
                                  method,
                                  dataset,
                                  input,
                                  output,
                                  score_type='clip', 
                                  ):  # psnr, lpips
    # print("\n\n\n")
    # print(f"______{method}___{dataset}___{score_type}_________")
    scores = {}
    final_res = 0
    examples = meta_examples[dataset]
    for i in range(len(examples)):
        
        # compare entire folder for clip
        if score_type == 'clip':
            novel_view = osp(pred_path, examples[i], 'colors')
        # compare first image for other metrics
        else:
            if method == '3d_fuse': method = '3d_fuse_0'
            novel_view = list(
                glob.glob(
                    osp(pred_path, examples[i], 'colors',
                        'step_0000*')))[0]

        score_i = my_scorer.score_gt(
            [], [novel_view])
        scores[examples[i]] = score_i
        final_res += score_i
    # print(scores, " Avg : ", final_res / len(examples))
    # print("``````````````````````")
    return scores

# results organization 2
def score_from_method_for_dataset(my_scorer,
                                  input_path,
                                  pred_path,
                                  score_type='clip', 
                                  rgb_name='lambertian', 
                                  result_folder='results/images', 
                                  first_str='*0000*'
                                  ):  # psnr, lpips
    scores = {}
    final_res = 0
    examples = os.listdir(input_path)
    for i in range(len(examples)):
        # ref path
        ref_path = osp(input_path, examples[i], 'rgba.png') 
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threestudio/scripts/metric_utils.py [326:373]:
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def deprecated_score_from_method_for_dataset(my_scorer,
                                  method,
                                  dataset,
                                  input,
                                  output,
                                  score_type='clip', 
                                  ):  # psnr, lpips
    # print("\n\n\n")
    # print(f"______{method}___{dataset}___{score_type}_________")
    scores = {}
    final_res = 0
    examples = meta_examples[dataset]
    for i in range(len(examples)):
        
        # compare entire folder for clip
        if score_type == 'clip':
            novel_view = osp(pred_path, examples[i], 'colors')
        # compare first image for other metrics
        else:
            if method == '3d_fuse': method = '3d_fuse_0'
            novel_view = list(
                glob.glob(
                    osp(pred_path, examples[i], 'colors',
                        'step_0000*')))[0]

        score_i = my_scorer.score_gt(
            [], [novel_view])
        scores[examples[i]] = score_i
        final_res += score_i
    # print(scores, " Avg : ", final_res / len(examples))
    # print("``````````````````````")
    return scores

# results organization 2
def score_from_method_for_dataset(my_scorer,
                                  input_path,
                                  pred_path,
                                  score_type='clip', 
                                  rgb_name='lambertian', 
                                  result_folder='results/images', 
                                  first_str='*0000*'
                                  ):  # psnr, lpips
    scores = {}
    final_res = 0
    examples = os.listdir(input_path)
    for i in range(len(examples)):
        # ref path
        ref_path = osp(input_path, examples[i], 'rgba.png') 
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