import re from functools import partial from .image_base import ImageBaseDataset from .utils import build_judge, DEBUG_MESSAGE from ..smp import * from ..utils import track_progress_rich SYSTEM_PROMPT = """\ Please act as an impartial evaluator and assess the quality of the responses provided by two AI assistants to a given user prompt and accompanying image. You will be provided with Assistant A's and Assistant B's answers. Your task is to determine which assistant's response is superior. Start your evaluation by generating your own answer to the prompt and image. Ensure that you complete your answer before reviewing any assistant responses. When evaluating the assistants' responses, compare each one to your own answer. First, assess whether the assistants' answers are helpful and relevant. A response is considered helpful if it appropriately addresses the prompt, follows the given instructions, and is well-organized. A relevant answer closely aligns with the context or requirements of the prompt. When applicable, consider the creativity and novelty of each assistant's response and evaluate the writing quality of both responses. Then, identify and correct any errors or inaccuracies in the assistants' answers. Lastly, identify any critical information missing from the assistants' responses that should have been included to improve the answer. After providing your explanation, you must output only one of the following choices as your final verdict with a label: 1. Assistant A is significantly better: [[A>>B]] 2. Assistant A is slightly better: [[A>B]] 3. Tie, relatively the same: [[A=B]] 4. Assistant B is slightly better: [[B>A]] 5. Assistant B is significantly better: [[B>>A]] Example output: "My final verdict is tie: [[A=B]]".\ """ SYSTEM_PROMPT_GT = """\ Please act as an impartial evaluator and assess the quality of the responses provided by two AI assistants to a given user prompt and accompanying image. You will be provided with Assistant A's and Assistant B's answers. Your task is to determine which assistant's response is superior. Start your evaluation by generating your own answer to the prompt and image. Ensure that you complete your answer before reviewing any assistant responses. When evaluating the assistants' responses, compare each one to your own answer. First, assess whether the assistants' answers are helpful and relevant. A response is considered helpful if it appropriately addresses the prompt, follows the given instructions, and is well-organized. A relevant answer closely aligns with the context or requirements of the prompt. When applicable, consider the creativity and novelty of each assistant's response and evaluate the writing quality of both responses. Then, identify and correct any errors or inaccuracies in the assistants' answers. Lastly, identify any critical information missing from the assistants' responses that should have been included to improve the answer. Please refer to the provided Ground Truth answer, which constitutes the key fact relevant to the question. After providing your explanation, you must output only one of the following choices as your final verdict with a label: 1. Assistant A is significantly better: [[A>>B]] 2. Assistant A is slightly better: [[A>B]] 3. Tie, relatively the same: [[A=B]] 4. Assistant B is slightly better: [[B>A]] 5. Assistant B is significantly better: [[B>>A]] Example output: "My final verdict is tie: [[A=B]]".\ """ PROMPT_TEMPLATE = """**INPUT**: <|User Prompt|>\n{question} <|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|> <|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|> """ PROMPT_TEMPLATE_GT = """**INPUT**: <|User Prompt|>\n{question} <|Ground Truth|>\n{gt} <|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|> <|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|> """ REGEX_PATTERN = re.compile("\[\[([AB<>=]+)\]\]") # noqa: W605 def get_score(judgement, pattern=REGEX_PATTERN): matches = pattern.findall(judgement) matches = [m for m in matches if m != ""] if len(set(matches)) == 0: return None, True elif len(set(matches)) == 1: return matches[0].strip("\n"), False else: return None, True def MMAlignBench_auxeval(model, line): if 'gt' in line and str(line['gt']) != 'nan': config = dict(question=line['question'], gt=line['gt'], answer_1=line['A'], answer_2=line['B']) prompt = SYSTEM_PROMPT_GT + '\n' + PROMPT_TEMPLATE_GT.format(**config) # prompt = PROMPT_TEMPLATE.format(**config) print('gt_prompt'+prompt) else: config = dict(question=line['question'], answer_1=line['A'], answer_2=line['B']) prompt = SYSTEM_PROMPT + '\n' + PROMPT_TEMPLATE.format(**config) # prompt = PROMPT_TEMPLATE.format(**config) print('prompt'+prompt) prefix = 'data:image/jpeg;base64,' img = prefix + line['image'] messages = [ dict(type='text', value=prompt), dict(type='image', value=img) ] retry = 2 while retry: resp = model.generate(messages) score, try_again = get_score(resp) if not try_again: break retry -= 1 if score is None: return 'Unknown' return [score, resp] class MMAlignBench(ImageBaseDataset): TYPE = 'VQA' DATASET_URL = {'MMAlignBench': 'https://opencompass.openxlab.space/utils/VLMEval/MMAlignBench.tsv'} DATASET_MD5 = {'MMAlignBench': 'd00d8e61c99257cbaf76d8d5e926f01e'} score_map = { 'A>>B': -2, 'A>B': -1, 'A=B': 0, 'B>A': 1, 'B>>A': 2 } # Given one data record, return the built prompt (a multi-modal message), can override def build_prompt(self, line): if isinstance(line, int): line = self.data.iloc[line] if self.meta_only: tgt_path = toliststr(line['image_path']) else: tgt_path = self.dump_image(line) question = line['question'] msgs = [] if isinstance(tgt_path, list): msgs.extend([dict(type='image', value=p) for p in tgt_path]) else: msgs = [dict(type='image', value=tgt_path)] # WildVision adopts text first msgs = [dict(type='text', value=question)] + msgs return msgs @classmethod def gen_eval_base(self, eval_file, b64_map): data = load(eval_file) data['B'] = data.pop('prediction') data['A'] = data.pop('claude3_sonnet') data['image'] = [b64_map[x] for x in data['index']] return data # It returns a DataFrame @classmethod def evaluate(self, eval_file, **judge_kwargs): # We adopt pairwise evaluation (twice for a pair) for this dataset suffix = eval_file.split('.')[-1] model = judge_kwargs['model'] storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') score_file = eval_file.replace(f'.{suffix}', f'_{model}_score.csv') tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') nproc = judge_kwargs.pop('nproc', 4) if not osp.exists(storage): raw_data = MMAlignBench('MMAlignBench').data b64_map = {x: y for x, y in zip(raw_data['index'], raw_data['image'])} data = self.gen_eval_base(eval_file, b64_map) # judge_kwargs['system_prompt'] = SYSTEM_PROMPT judge_kwargs['temperature'] = 0 judge_kwargs['img_detail'] = 'high' judge_kwargs['timeout'] = 300 model = build_judge(max_tokens=4096, **judge_kwargs) assert model.working(), ( 'MMAlignBench evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE ) lt = len(data) lines = [data.iloc[i] for i in range(lt)] tups = [(model, line) for line in lines] indices = [line['index'] for line in lines] ans = load(tmp_file) if osp.exists(tmp_file) else {} tups = [x for x, i in zip(tups, indices) if i not in ans] indices = [i for i in indices if i not in ans] if len(indices): new_results = track_progress_rich( MMAlignBench_auxeval, tups, nproc=nproc, chunksize=nproc, keys=indices, save=tmp_file, ) ans = load(tmp_file) for k, v in zip(indices, new_results): ans[k] = {'score': v[0], 'resp': v[1]} else: for k,v in ans.items(): ans[k] = {'score': v[0], 'resp': v[1]} # breakpoint() data['score'] = [ans[x]['score'] for x in data['index']] data['judge'] = [ans[x]['resp'] for x in data['index']] data.pop('image') dump(data, storage) data = load(storage) lt = len(data) scores = defaultdict(lambda: 0) type_scores = defaultdict(lambda: defaultdict(lambda: 0)) for i in range(lt): item = data.iloc[i] if item['score'] not in self.score_map: score = 0 else: score = self.score_map[item['score']] if '_rev' in item['index']: score = -score scores[score] += 1 type = item['type'] type_scores[type][score] += 1 name_map = { 2: 'Much Better', 1: 'Better', 0: 'Tie', -1: 'Worse', -2: 'Much Worse' } scores = {name_map[k]: v for k, v in scores.items()} scores['Reward'] = ( 100 * scores.get('Much Better', 0) + 50 * scores.get('Better', 0) - 50 * scores.get('Worse', 0) - 100 * scores.get('Much Worse', 0) ) / lt scores['Win Rate'] = (scores.get('Better', 0) + scores.get('Much Better', 0)) / lt scores = {k: [v] for k, v in scores.items()} scores = pd.DataFrame(scores) for type_name, type_score_dict in type_scores.items(): type_score_dict = {name_map[k]: v for k, v in type_score_dict.items()} type_lt = sum(type_score_dict.values()) type_score_dict['Reward'] = ( ( 100 * type_score_dict.get('Much Better', 0) + 50 * type_score_dict.get('Better', 0) - 50 * type_score_dict.get('Worse', 0) - 100 * type_score_dict.get('Much Worse', 0) ) / type_lt if type_lt > 0 else 0 ) type_score_dict['Win Rate'] = ( (type_score_dict.get('Better', 0) + type_score_dict.get('Much Better', 0)) / type_lt if type_lt > 0 else 0 ) # 将该类型的得分添加到结果中 type_score_df = pd.DataFrame( { f"{type_name}_Much Better": [type_score_dict.get('Much Better', 0)], f"{type_name}_Better": [type_score_dict.get('Better', 0)], f"{type_name}_Tie": [type_score_dict.get('Tie', 0)], f"{type_name}_Worse": [type_score_dict.get('Worse', 0)], f"{type_name}_Much Worse": [type_score_dict.get('Much Worse', 0)], f"{type_name}_Reward": [type_score_dict['Reward']], f"{type_name}_Win Rate": [type_score_dict['Win Rate']], } ) scores = pd.concat([scores, type_score_df], axis=1) dump(scores, score_file) return scores