| import os |
|
|
| from .utils import get_prompt_from_filename, init_submodules, save_json, load_json |
| import importlib |
| from itertools import chain |
| from pathlib import Path |
|
|
|
|
| class VBench2(object): |
| def __init__(self, device, full_info_dir, output_path): |
| self.device = device |
| self.full_info_dir = full_info_dir |
| self.output_path = output_path |
| os.makedirs(self.output_path, exist_ok=True) |
|
|
| def build_full_dimension_list(self, ): |
| return ["Human_Anatomy", "Human_Identity", "Human_Clothes", "Diversity", "Composition", "Dynamic_Spatial_Relationship", |
| "Dynamic_Attribute", "Motion_Order_Understanding", "Human_Interaction", "Complex_Landscape", 'Complex_Plot', "Camera_Motion", |
| "Motion_Rationality", "Instance_Preservation", "Mechanics", "Thermotics", "Material", "Multi-View_Consistency"] |
|
|
| def check_dimension_requires_extra_info(self, dimension_list): |
| dim_custom_not_supported = set(dimension_list) & set([ |
| 'Composition', 'Dynamic_Attribute', 'Dynamic_Spatial_Relationship', 'Instance_Preservation', 'Complex_Plot', 'Complex_Landscape', |
| 'Motion_Rationality', 'Motion_Order_Understanding', 'Mechanics', 'Thermotics', 'Material', "Camera_Motion", "Human_Interaction" |
| ]) |
|
|
| assert len(dim_custom_not_supported) == 0, f"dimensions : {dim_custom_not_supported} not supported for custom input" |
|
|
| def build_full_info_json(self, videos_path, name, dimension_list, prompt_list=[], special_str='', verbose=False, mode='vbench_standard', **kwargs): |
| cur_full_info_list=[] |
| if mode=='custom_input': |
| self.check_dimension_requires_extra_info(dimension_list) |
| video_names = os.listdir(videos_path) |
| assert len(video_names)>0, f"ERROR : The video files is empty" |
| cur_full_info_list = [] |
| prompt_check_list = [] |
| for filename in video_names: |
| postfix = Path(os.path.join(videos_path, filename)).suffix |
| if postfix.lower() not in ['.mp4']: |
| continue |
| if dimension_list[0]=='Diversity': |
| prompt_en = get_prompt_from_filename(filename) |
| if prompt_en in prompt_check_list: |
| continue |
| prompt_check_list.append(prompt_en) |
| item = { |
| "prompt_en": prompt_en, |
| "dimension": dimension_list, |
| "video_list": [] |
| } |
| for ite in range(20): |
| item['video_list'].append(os.path.join(videos_path, f'{prompt_en}{special_str}-{str(ite)}{postfix}')) |
| cur_full_info_list.append(item) |
| else: |
| cur_full_info_list.append({ |
| "prompt_en": get_prompt_from_filename(filename), |
| "dimension": dimension_list, |
| "video_list": [os.path.join(videos_path, filename)] |
| }) |
|
|
| else: |
| full_info_list = load_json(self.full_info_dir) |
| video_names = os.listdir(videos_path) |
| |
| postfix = Path(video_names[0]).suffix |
| for prompt_dict in full_info_list: |
| |
| if set(dimension_list) & set(prompt_dict["dimension"]): |
| prompt = prompt_dict['prompt_en'] |
| prompt_dict['video_list'] = [] |
| if prompt_dict["dimension"][0]=='Diversity': |
| num=20 |
| else: |
| num=3 |
| for i in range(num): |
| intended_video_name = f'{prompt[:180]}{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}') |
| raise |
| 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, prompt_list=[], dimension_list=None, local=False, read_frame=False, mode='vbench_standard', **kwargs): |
| 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, prompt_list, mode=mode, **kwargs) |
| |
| for dimension in dimension_list: |
| try: |
| if dimension=="Multi-View_Consistency": |
| dimension_change = "Multi_View_Consistency" |
| else: |
| dimension_change = dimension |
| dimension_module = importlib.import_module(f'vbench2.{dimension_change.lower()}') |
| evaluate_func = getattr(dimension_module, f'compute_{dimension_change.lower()}') |
| 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, **kwargs) |
| 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}') |
|
|
