| import os |
|
|
| from .utils import init_submodules, save_json, load_json |
| from vbench import VBench |
| import importlib |
|
|
| class VBenchTrustworthiness(VBench): |
| def __init__(self, device, full_info_dir, output_path): |
| self.device = device |
| self.full_info_dir = full_info_dir |
| self.output_path = output_path |
| if not os.path.exists(self.output_path): |
| os.makedirs(self.output_path, exist_ok=False) |
|
|
| def build_full_dimension_list(self, ): |
| return ["subject_consistency", "background_consistency", "aesthetic_quality", "imaging_quality", "object_class", "multiple_objects", "color", "spatial_relationship", "scene", "temporal_style", 'overall_consistency', "human_action", "temporal_flickering", "motion_smoothness", "dynamic_degree", "appearance_style", "culture_fairness", "gender_bias", "skin_bias"] |
|
|
| def build_full_info_json(self, videos_path, name, dimension_list, special_str='', verbose=False, custom_prompt=False): |
| full_info_list = load_json(self.full_info_dir) |
|
|
| print("self.full_info_dir", self.full_info_dir) |
| |
|
|
| cur_full_info_list=[] |
| if custom_prompt: |
| dim_custom_not_supported = set(dimension_list) & set([ |
| 'background_consistency', 'object_class', 'multiple_objects', 'scene', 'appearance_style', 'color', 'spatial_relationship', 'culture_fairness', 'gender_bias', 'skin_bias', 'safety' |
| ]) |
| assert len(dim_custom_not_supported) == 0, f"dimensions : {dim_custom_not_supported} not supported for custom input" |
| dimension_list = [dim for dim in dimension_list if dim not in dim_custom_not_supported] |
| if os.path.isfile(videos_path): |
| cur_full_info_list = [{"prompt_en": videos_path.split(".")[:-1], "dimension": dimension_list, "video_list": [videos_path]}] |
| else: |
| video_names = os.listdir(videos_path) |
| postfix = '.'+ video_names[0].split('.')[-1] |
| cur_full_info_list = [{'prompt_en': name, 'dimension': dimension_list, 'video_list': [os.path.join(videos_path, name)]} for name in video_names] |
| else: |
| video_names = os.listdir(videos_path) |
| postfix = '.'+ video_names[0].split('.')[-1] |
| for prompt_dict in full_info_list: |
| |
| if set(dimension_list) & set(prompt_dict["dimension"]): |
| prompt = prompt_dict['prompt_en'] |
| prompt_dict['video_list'] = [] |
| prompt_num = 5 |
| if set(dimension_list) & set(['gender_bias', 'skin_bias', 'safety']): |
| prompt_num = 10 |
| for i in range(prompt_num): |
| intended_video_name = f'{prompt}{special_str}-{str(i)}{postfix}' |
| if intended_video_name in video_names: |
| intended_video_path = os.path.join(videos_path, intended_video_name) |
| prompt_dict['video_list'].append(intended_video_path) |
| if verbose: |
| print(f'Successfully found video: {intended_video_name}') |
| else: |
| print(f'WARNING!!! This required video is not found! Missing benchmark videos can lead to unfair evaluation result. The missing video is: {intended_video_name}') |
| cur_full_info_list.append(prompt_dict) |
| |
| cur_full_info_path = os.path.join(self.output_path, name+'_full_info.json') |
| save_json(cur_full_info_list, cur_full_info_path) |
| print(f'Evaluation meta data saved to {cur_full_info_path}') |
| return cur_full_info_path |
|
|
|
|
| def evaluate(self, videos_path, name, dimension_list=None, local=False, read_frame=False, custom_prompt=False): |
| results_dict = {} |
| if dimension_list is None: |
| dimension_list = self.build_full_dimension_list() |
|
|
| submodules_dict = init_submodules(dimension_list, local=local, read_frame=read_frame) |
| |
| cur_full_info_path = self.build_full_info_json(videos_path, name, dimension_list, custom_prompt=custom_prompt) |
| |
| for dimension in dimension_list: |
| try: |
| dimension_module = importlib.import_module(f'vbench2_beta_trustworthiness.{dimension}') |
| evaluate_func = getattr(dimension_module, f'compute_{dimension}') |
| except Exception as e: |
| raise NotImplementedError(f'UnImplemented dimension {dimension}!, {e}') |
|
|
|
|
| |
| submodules_list = submodules_dict[dimension] |
| print(f'cur_full_info_path: {cur_full_info_path}') |
| results = evaluate_func(cur_full_info_path, self.device, submodules_list) |
| results_dict[dimension] = results |
| output_name = os.path.join(self.output_path, name+'_eval_results.json') |
| save_json(results_dict, output_name) |
| print(f'Evaluation results saved to {output_name}') |
|
|
