File size: 6,579 Bytes
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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 # cuda or cpu
self.full_info_dir = full_info_dir # full json file that VBench originally provides
self.output_path = output_path # output directory to save VBench results
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=[] # to save the prompt and video path info for the current dimensions
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 the prompt belongs to any dimension we want to evaluate
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): # video index for the same prompt
intended_video_name = f'{prompt[:180]}{special_str}-{str(i)}{postfix}'
if intended_video_name in video_names: # if the video exists
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}') # TODO: to delete
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}')
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