import warnings import json from .image_base import ImageBaseDataset from .utils import build_judge, DEBUG_MESSAGE from ..smp import * import pandas as pd MMMB_URLS = { 'MMMB_ar': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_ar.tsv', 'MMMB_cn': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_cn.tsv', 'MMMB_en': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_en.tsv', 'MMMB_pt': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_pt.tsv', 'MMMB_ru': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_ru.tsv', 'MMMB_tr': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_tr.tsv', } MTL_MMBench_URLS = { 'MMBench_dev_ar': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_ar.tsv', 'MMBench_dev_cn': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_cn.tsv', 'MMBench_dev_en': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_en.tsv', 'MMBench_dev_pt': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_pt.tsv', 'MMBench_dev_tr': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_tr.tsv', 'MMBench_dev_ru': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_ru.tsv', } MMMB_MD5 = { 'MMMB_ar': 'f3a18b6385f1d9701840aa42de27aead', 'MMMB_cn': '13ed82fa89730037292fcaa27f08f430', 'MMMB_en': '1cd781a71ec5a2983c090b84105d6a01', 'MMMB_pt': '548ea2b3bb2da991790386f0015d30d1', 'MMMB_ru': 'ce1cc8a0533425ab0d86b326ebfc2984', 'MMMB_tr': '0733739d43090327975294292bc5cd67' } MTL_MMBench_MD5 = { 'MMBench_dev_ar': '4271b4a0d0200e1a86380a878e0d64a4', 'MMBench_dev_cn': '2ed5135326fed02c8e51ea50dda8222f', 'MMBench_dev_en': 'd9ab776fc018b3d45785e9a5c23431c2', 'MMBench_dev_pt': '4ddfbcd27ef12444b908c03831cd0295', 'MMBench_dev_tr': '4fab39d501389d3d6cc90264bb708f11', 'MMBench_dev_ru': '5ba1171ff2e68f80637bf78349e402a5' } class ImageMCQDataset(ImageBaseDataset): TYPE = 'MCQ' DATASET_URL = { # MMBench v1.0 'MMBench_DEV_EN': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_DEV_EN.tsv', 'MMBench_TEST_EN': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_TEST_EN.tsv', 'MMBench_DEV_CN': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_DEV_CN.tsv', 'MMBench_TEST_CN': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_TEST_CN.tsv', 'MMBench': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench.tsv', # Internal 'MMBench_CN': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_CN.tsv', # Internal # MMBench v1.1 'MMBench_DEV_EN_V11': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_DEV_EN_V11.tsv', 'MMBench_TEST_EN_V11': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_TEST_EN_V11.tsv', 'MMBench_DEV_CN_V11': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_DEV_CN_V11.tsv', 'MMBench_TEST_CN_V11': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_TEST_CN_V11.tsv', 'MMBench_V11': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_V11.tsv', # Internal 'MMBench_CN_V11': 'https://opencompass.openxlab.space/utils/benchmarks/MMBench/MMBench_CN_V11.tsv', # Internal # SEEDBench Series 'SEEDBench_IMG': 'https://opencompass.openxlab.space/utils/benchmarks/SEEDBench/SEEDBench_IMG.tsv', 'SEEDBench2': 'https://huggingface.co/datasets/VLMEval/SEEDBench2/resolve/main/SEEDBench2.tsv', 'SEEDBench2_Plus': 'https://opencompass.openxlab.space/utils/benchmarks/SEEDBench/SEEDBench2_Plus.tsv', # ScienceQA Series 'ScienceQA_VAL': 'https://opencompass.openxlab.space/utils/benchmarks/ScienceQA/ScienceQA_VAL.tsv', 'ScienceQA_TEST': 'https://opencompass.openxlab.space/utils/benchmarks/ScienceQA/ScienceQA_TEST.tsv', # MMT-Bench 'MMT-Bench_ALL_MI': 'https://opencompass.openxlab.space/utils/benchmarks/MMT-Bench/MMT-Bench_ALL_MI.tsv', 'MMT-Bench_ALL': 'https://opencompass.openxlab.space/utils/benchmarks/MMT-Bench/MMT-Bench_ALL.tsv', 'MMT-Bench_VAL_MI': 'https://opencompass.openxlab.space/utils/benchmarks/MMT-Bench/MMT-Bench_VAL_MI.tsv', 'MMT-Bench_VAL': 'https://opencompass.openxlab.space/utils/benchmarks/MMT-Bench/MMT-Bench_VAL.tsv', # AesBench 'AesBench_VAL': 'https://huggingface.co/datasets/VLMEval/AesBench/resolve/main/AesBench_VAL.tsv', 'AesBench_TEST': 'https://huggingface.co/datasets/VLMEval/AesBench/resolve/main/AesBench_TEST.tsv', # Q-Bench1 'Q-Bench1_VAL': 'https://huggingface.co/datasets/zhangzicheng/qbench_tsv/resolve/main/Q-Bench1_VAL.tsv', 'Q-Bench1_TEST': 'https://huggingface.co/datasets/zhangzicheng/qbench_tsv/resolve/main/Q-Bench1_TEST.tsv', # A-Bench 'A-Bench_VAL': 'https://huggingface.co/datasets/zhangzicheng/abench_tsv/resolve/main/A-bench_VAL.tsv', 'A-Bench_TEST': 'https://huggingface.co/datasets/zhangzicheng/abench_tsv/resolve/main/A-bench_TEST.tsv', # R-Bench 'R-Bench-Dis': 'https://huggingface.co/datasets/lcysyzxdxc/R-Bench/blob/main/R-bench-dis.tsv', 'R-Bench-Ref': 'https://huggingface.co/datasets/lcysyzxdxc/R-Bench/blob/main/R-bench-ref.tsv', # Other Benchmarks 'CCBench': 'https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv', 'AI2D_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST.tsv', 'AI2D_TEST_NO_MASK': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST_NO_MASK.tsv', 'MMStar': 'https://opencompass.openxlab.space/utils/VLMEval/MMStar.tsv', 'RealWorldQA': 'https://opencompass.openxlab.space/utils/VLMEval/RealWorldQA.tsv', 'MLLMGuard_DS': 'https://opencompass.openxlab.space/utils/VLMEval/MLLMGuard_DS.tsv', 'BLINK': 'https://opencompass.openxlab.space/utils/VLMEval/BLINK.tsv', 'TaskMeAnything_v1_imageqa_random': ( 'https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-imageqa-random/' 'resolve/main/TaskMeAnything-v1-imageqa-random.tsv' ), 'A-OKVQA': 'https://huggingface.co/datasets/Allen8/A-OKVQA/resolve/main/a-okvqa.tsv', 'WorldMedQA-V': 'https://opencompass.openxlab.space/utils/VLMEval/WorldMedQA-V.tsv', 'VisOnlyQA-VLMEvalKit': ( 'https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real/' 'resolve/main/visonlyqa_vlmevalkit.tsv' ), '3DSRBench': ( 'https://huggingface.co/datasets/ccvl/3DSRBench/' 'resolve/main/3dsrbench_v1_vlmevalkit_circular.tsv' ), } DATASET_MD5 = { # MMBench v1.0 'MMBench_DEV_EN': 'b6caf1133a01c6bb705cf753bb527ed8', 'MMBench_TEST_EN': '6939fadb0ce626fefc0bdc9c64efc528', 'MMBench_DEV_CN': '08b8fc3324a5ed74155350f57be69fbd', 'MMBench_TEST_CN': '7e1239baf0ee4c8b513e19705a0f317e', 'MMBench': '4115aea3383f3dd0083be6a633e0f820', # Internal Only 'MMBench_CN': '2e053ffc90ea598b1feae13c36dc13ee', # Internal Only # MMBench v1.1 'MMBench_DEV_EN_V11': '30c05be8f2f347a50be25aa067248184', 'MMBench_TEST_EN_V11': '26f0f15381a21720255091d3e0316ce6', 'MMBench_DEV_CN_V11': '593f9b5f6bea453d870a798b34ae4f37', 'MMBench_TEST_CN_V11': '74bbe4556dac745613c7cbe5ad787050', 'MMBench_V11': 'b9276414f57af1308dcc4d0cd9b42e7c', # Internal Only 'MMBench_CN_V11': '95f6980dd1b4de38e3cbffe0305a3f25', # Internal Only # SEEDBench 'SEEDBench_IMG': '68017231464752261a2526d6ca3a10c0', 'SEEDBench2': '4ec15cf864c4f16274112284f531813e', 'SEEDBench2_Plus': 'e32d3216dc4f452b0fe497a52015d1fd', # ScienceQA 'ScienceQA_VAL': '96320d05e142e585e7204e72affd29f3', 'ScienceQA_TEST': 'e42e9e00f9c59a80d8a5db35bc32b71f', # MMT-Bench 'MMT-Bench_ALL_MI': '5272157097e19cdd7cb41e412ab3b7c7', 'MMT-Bench_ALL': 'b273a2f4c596fe4f2605de0494cd632f', 'MMT-Bench_VAL_MI': 'c7d7b998eb5cd9aa36c7d4f721472462', 'MMT-Bench_VAL': '8dd4b730f53dbf9c3aed90ca31c928e0', # AesBench 'AesBench_VAL': '3edb0c319e9187aa0b97fe7a11700a8c', 'AesBench_TEST': '58b1f7ba2cc32e1d68896d6ee716bbf8', # Q-Bench1 'Q-Bench1_VAL': '837bdb6cd2da571713543462815187b7', 'Q-Bench1_TEST': '15e759bfd58c9d5f30b23a317d347153', # A-Bench 'A-Bench_VAL': '218563ec50d34bb336c814143a5bb9c1', 'A-Bench_TEST': '567013fb033a20cf23f51d8e865bd16c', # R-Bench 'R-Bench-Dis': 'd6e961dbfc43350688af2560226830b4', 'R-Bench-Ref': '270c1cb555acb523f3fdb178ed57021d', # Other Benchmarks 'CCBench': 'f5dde47f24dc5a6fb6e595b409b466ac', 'AI2D_TEST': '0f593e0d1c7df9a3d69bf1f947e71975', 'AI2D_TEST_NO_MASK': 'fd8f463634d4fe9fbd23b876e8eea5be', 'MMStar': 'e1ecd2140806c1b1bbf54b43372efb9e', 'RealWorldQA': '4de008f55dc4fd008ca9e15321dc44b7', 'MLLMGuard_DS': '975fc0dd7119386e198c37d71e274b3f', 'BLINK': '3b6649b6a662184ea046908e5506260e', 'TaskMeAnything_v1_imageqa_random': '023fef69e2ca21827afb77c5ec3bc889', 'WorldMedQA-V': '441e63875e30c87f5750528b57b41285', "VisOnlyQA-VLMEvalKit": 'cf460a31d2acb8d3a7cecd0e69298bfa', '3DSRBench': '13a99f33164dc1b9faf0e8b8b01fd6f2', } DATASET_URL.update(MMMB_URLS) DATASET_URL.update(MTL_MMBench_URLS) DATASET_MD5.update(MMMB_MD5) DATASET_MD5.update(MTL_MMBench_MD5) 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'] options = { cand: line[cand] for cand in string.ascii_uppercase if cand in line and not pd.isna(line[cand]) } options_prompt = 'Options:\n' for key, item in options.items(): options_prompt += f'{key}. {item}\n' hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None prompt = '' if hint is not None: prompt += f'Hint: {hint}\n' prompt += f'Question: {question}\n' if len(options): prompt += options_prompt prompt += 'Please select the correct answer from the options above. \n' 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)] msgs.append(dict(type='text', value=prompt)) return msgs def evaluate(self, eval_file, **judge_kwargs): from .utils.multiple_choice import report_acc, report_acc_MMT, mcq_circular_eval, mcq_vanilla_eval # assert dataset is not None dataset_map = { 'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11', 'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11' } dataset = self.dataset_name if dataset in dataset_map: dataset = dataset_map[dataset] nproc = judge_kwargs.pop('nproc', 4) circular = False if listinstr(['mmbench', 'ccbench'], dataset.lower()): data = load(eval_file) data['index'] = [int(x) for x in data['index']] dump(data, eval_file) circular = True suffix = eval_file.split('.')[-1] model = judge_kwargs.get('model', 'exact_matching') assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} name_str = name_str_map[model] if model in name_str_map else model if model == 'exact_matching': model = None elif gpt_key_set(): model = build_judge(**judge_kwargs) if not model.working(): warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') warnings.warn(DEBUG_MESSAGE) model = None else: warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') model = None result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') data = load(eval_file) data = data.sort_values(by='index') data['prediction'] = [str(x) for x in data['prediction']] # If not choice label, then use lower case for k in data.keys(): data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) meta = self.data meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} data_map = {x: y for x, y in zip(data['index'], data['question'])} for k in data_map: assert k in meta_q_map, ( f'eval_file should be the same as or a subset of dataset {self.dataset_name}' ) if circular: data = mcq_circular_eval(model, data, meta, nproc, result_file, self.dataset_name) else: data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) # load split dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) # May have different report acc functions for different datasets if 'MMT' in dataset: acc = report_acc_MMT(data) else: acc = report_acc(data) score_file = eval_file.replace(f'.{suffix}', '_acc.csv') dump(acc, score_file) if dataset == 'AesBench_VAL': warnings.warn('Note that AesBench VAL is just a toy version of AesBench TEST. For full results, \ please evaluate on AesBench TEST. The AesBench TEST dataset is more than 20 times \ larger than the VAL dataset and the leaderboard results are based on AesBench TEST.') if dataset == 'VisOnlyQA-VLMEvalKit': warnings.warn('Note that the results on VisOnlyQA-VLMEvalKit are different from the results on \ the original VisOnlyQA. VisOnlyQA-VLMEvalKit does not include the \ chemistry__shape_multi split and uses a different evaluation prompt. Please \ explicitly specify the version of the dataset when you report results.') return acc class OpenMMMedical(ImageMCQDataset): @classmethod def supported_datasets(cls): return ['OpenMMMedical'] def load_data(self, dataset='OpenMMMedical'): image_folder = "/your/path/to/OpenMM_Medical" def generate_tsv(pth): import csv from pathlib import Path tsv_file_path = os.path.join(LMUDataRoot(), f'{dataset}.tsv') if os.path.exists(tsv_file_path): print(f'{tsv_file_path} already exists.') return path = Path(pth) json_files = [str(f) for f in path.rglob('*.json')] fieldnames = ["index", "dataset", "question_id", "question_type", "question", "A", "B", "C", "D", "E", "answer", "image_path"] index = 0 with open(tsv_file_path, 'w', encoding='utf-8', newline='') as tsv_file: writer = csv.DictWriter(tsv_file, fieldnames=fieldnames, delimiter='\t') writer.writeheader() for json_file in json_files: data_name = json_file.split('/')[-1].split('.')[0] with open(json_file, 'r', encoding='utf-8') as f: data = json.load(f) for row in data: line = {} line['index'] = index line['dataset'] = row['dataset'] line['question_id'] = row['question_id'] line['question_type'] = row['question_type'] line['question'] = row['question'] choices_letter = ["A", "B", "C", "D", "E"] for i in range(len(choices_letter)): if f"option_{choices_letter[i]}" in row: line[choices_letter[i]] = row[f"option_{choices_letter[i]}"] if row[f"option_{choices_letter[i]}"] == row['gt_answer']: line['answer'] = choices_letter[i] else: break line['image_path'] = os.path.join(image_folder, row['image_path']) index += 1 writer.writerow(line) print(f'TSV file saved to {tsv_file_path}') generate_tsv(image_folder) update_flag = True data_path = os.path.join(LMUDataRoot(), f'{dataset}.tsv') if file_size(data_path, 'GB') > 1: local_path = data_path.replace('.tsv', '_local.tsv') if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None) or update_flag: from vlmeval.tools import LOCALIZE LOCALIZE(data_path, local_path) data_path = local_path return load(data_path) # 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'] options = { cand: line[cand] for cand in string.ascii_uppercase if cand in line and not pd.isna(line[cand]) } options_prompt = 'Options:\n' for key, item in options.items(): options_prompt += f'{key}. {item}\n' hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None prompt = '' if hint is not None: prompt += f'Hint: {hint}\n' prompt += f'Question: {question}\n' prompt += options_prompt prompt += "Answer with the option's letter from the given choices directly.\n" # prompt += "Please select the correct answer from the options above. \n" msgs = [] if tgt_path: 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)] msgs.append(dict(type='text', value=prompt)) return msgs def report_acc_by_groups(self, df, group_column): res = defaultdict(list) # Check for the 'split' column if 'split' in df: splits = list(set(df['split'])) res['split'] = splits else: df['split'] = ['none'] * len(df) res['split'] = ['none'] res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']] if group_column not in df: raise ValueError(f"Column '{group_column}' not found in dataframe.") # noqa: E713 abilities = list(set(df[group_column])) abilities = ['None' if isinstance(ab, float) and pd.isna(ab) else ab for ab in abilities] abilities.sort() for ab in abilities: ab_name = ab sub_df = df[df[group_column] == ab] res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']] return pd.DataFrame(res) def evaluate(self, eval_file, **judge_kwargs): from .utils.multiple_choice import report_acc, mcq_vanilla_eval nproc = judge_kwargs.pop('nproc', 4) suffix = eval_file.split('.')[-1] model = judge_kwargs.get('model', 'exact_matching') assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125', 'gpt-4o'] name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4', 'gpt-4o': 'gpt4o'} name_str = name_str_map[model] if model in name_str_map else model if model == 'exact_matching': model = None elif gpt_key_set(): model = build_judge(**judge_kwargs) if not model.working(): warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') warnings.warn(DEBUG_MESSAGE) model = None else: warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') model = None result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') data = load(eval_file) data = data.sort_values(by='index') data['prediction'] = [str(x) for x in data['prediction']] # If not choice label, then use lower case for k in data.keys(): data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) meta = self.data meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} data_map = {x: y for x, y in zip(data['index'], data['question'])} for k in data_map: assert k in meta_q_map, ( f'eval_file should be the same as or a subset of dataset {self.dataset_name}' ) data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) # load split dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) acc = report_acc(data) for group_col in ['dataset']: acc_grouped = self.report_acc_by_groups(data, group_col) score_file_grouped = eval_file.replace(f'.{suffix}', f'_{group_col}_acc.csv') dump(acc_grouped, score_file_grouped) return acc class MMMUDataset(ImageMCQDataset): DATASET_URL = { 'MMMU_DEV_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_DEV_VAL.tsv', 'MMMU_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_TEST.tsv', } DATASET_MD5 = { 'MMMU_DEV_VAL': '585e8ad75e73f75dcad265dfd0417d64', 'MMMU_TEST': 'c19875d11a2d348d07e5eb4bdf33166d', } @staticmethod def split_MMMU(msgs): text, images = None, [] for s in msgs: if s['type'] == 'image': images.append(s['value']) elif s['type'] == 'text': assert text is None text = s['value'] text_segs = text.split('' image_idx = int(seg[0]) - 1 segs.append(dict(type='image', value=images[image_idx])) segs.append(dict(type='text', value=seg[2:])) return segs def build_prompt(self, line): msgs = super().build_prompt(line) msgs = self.split_MMMU(msgs) return msgs class MUIRDataset(ImageMCQDataset): DATASET_URL = { 'MUIRBench': 'http://opencompass.openxxlab.com/utils/VLMEval/MUIRBench.tsv' } DATASET_MD5 = { 'MUIRBench': '2e5e6fd7699761b08a7cb3ab8c0c2ec8' } @staticmethod def split_MUIR(msgs): text, images = None, [] # Separate images and text from msgs for s in msgs: if s['type'] == 'image': images.append(s['value']) elif s['type'] == 'text': assert text is None # Ensure only one text entry is expected text = s['value'] # Split text by tags text_segs = text.split('') # Initialize the segments list segs = [] # Iterate through the text segments and images for i, seg in enumerate(text_segs): # Append the image if this is not the first segment and there are still images left if i > 0 and i - 1 < len(images): segs.append(dict(type='image', value=images[i - 1])) # Append the text segment (if it's non-empty) if len(seg) > 0: segs.append(dict(type='text', value=seg)) return segs 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'] options = { cand: line[cand] for cand in string.ascii_uppercase if cand in line and not pd.isna(line[cand]) } # options_prompt = '' options_prompt = '\n'.join([f'{key}. {item}' for key, item in options.items()]) # for key, item in options.items(): # options_prompt += f'{key}. {item}\n' prompt = '' prompt += f'{question}\n' if len(options): prompt += options_prompt prompt += "\nAnswer with the option's letter from the given choices directly." 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)] msgs.append(dict(type='text', value=prompt)) msgs = self.split_MUIR(msgs) return msgs class GMAIMMBenchDataset(ImageMCQDataset): DATASET_URL = { 'GMAI-MMBench_VAL': 'https://huggingface.co/datasets/VLMEval/GMAI-MMBench/resolve/main/GMAI-MMBench_VAL.tsv', 'GMAI_mm_bench_TEST_part_1': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_1.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_2': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_2.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_3': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_3.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_4': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_4.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_5': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_5.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_6': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_6.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_7': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_7.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_8': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_8.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_9': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_9.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_10': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_10.tsv', # noqa: E501 'GMAI_mm_bench_TEST_part_11': 'https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench/resolve/main/GMAI_mm_bench_TEST_part_11.tsv', # noqa: E501 } DATASET_MD5 = { 'GMAI-MMBench_VAL': '254bd581627866f1c499d3d6b4422324', 'GMAI_mm_bench_TEST_part_1': '900d735231230a63f4ed45665c078ef4', 'GMAI_mm_bench_TEST_part_2': '1b27ab621386945d7e4a765ad2d22b0e', 'GMAI_mm_bench_TEST_part_3': '44bdc2b6267dd505d529b8cad06f0fb2', 'GMAI_mm_bench_TEST_part_4': '5a04a04fcac9f1466709f242fdb80acb', 'GMAI_mm_bench_TEST_part_5': 'c70baf8909eda9af0ddeab275c721336', 'GMAI_mm_bench_TEST_part_6': '825abc39596b644dead9350d0cfa3b96', 'GMAI_mm_bench_TEST_part_7': 'defb8aed2fb77365a76b6b9abd6a2701', 'GMAI_mm_bench_TEST_part_8': 'ff490d60b85f2bb0abb67a435b298c65', 'GMAI_mm_bench_TEST_part_9': 'ff67c86f40da93b09139ac1d1ba5dc6b', 'GMAI_mm_bench_TEST_part_10': '3dae94627b9ac0fe00180d4780fbf6dc', 'GMAI_mm_bench_TEST_part_11': 'd08dc813f0eb6bbab63cae2a9d113c4b', } @classmethod def supported_datasets(cls): return ['GMAI-MMBench_VAL', 'GMAI-MMBench_TEST'] def load_data(self, dataset): if dataset == 'GMAI-MMBench_VAL': data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv') if file_size(data_path, 'GB') > 1: local_path = data_path.replace('.tsv', '_local.tsv') if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL'): from ..tools import LOCALIZE LOCALIZE(data_path, local_path) data_path = local_path return load(data_path) elif dataset == 'GMAI-MMBench_TEST': dfs = [] for part_num in range(1, 12): part_name = f'GMAI_mm_bench_TEST_part_{part_num}' url = self.DATASET_URL[part_name] file_md5 = self.DATASET_MD5.get(part_name) tsv_path = osp.join(LMUDataRoot(), f'{part_name}.tsv') if not osp.exists(tsv_path) or (file_md5 and md5(tsv_path) != file_md5): download_file(url, filename=tsv_path) local_path = tsv_path.replace('.tsv', '_local.tsv') if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL'): from ..tools import LOCALIZE LOCALIZE(tsv_path, local_path) tsv_path = local_path # 加载数据 df = load(tsv_path) dfs.append(df) # 合并所有数据 data = pd.concat(dfs, ignore_index=True) return data else: raise ValueError(f"未知的数据集：{dataset}") def report_acc_by_groups(self, df, group_column): res = defaultdict(list) # Check for the 'split' column if 'split' in df: splits = list(set(df['split'])) res['split'] = splits else: df['split'] = ['none'] * len(df) res['split'] = ['none'] res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']] if group_column not in df: raise ValueError(f"Column '{group_column}' not found in dataframe.") # noqa: E713 abilities = list(set(df[group_column])) abilities = ['None' if isinstance(ab, float) and pd.isna(ab) else ab for ab in abilities] abilities.sort() for ab in abilities: ab_name = ab sub_df = df[df[group_column] == ab] res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']] return pd.DataFrame(res) def evaluate(self, eval_file, **judge_kwargs): from .utils.multiple_choice import report_acc, mcq_vanilla_eval nproc = judge_kwargs.pop('nproc', 4) suffix = eval_file.split('.')[-1] model = judge_kwargs.get('model', 'exact_matching') assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} name_str = name_str_map[model] if model in name_str_map else model if model == 'exact_matching': model = None elif gpt_key_set(): model = build_judge(**judge_kwargs) if not model.working(): warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') warnings.warn(DEBUG_MESSAGE) model = None else: warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') model = None result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') data = load(eval_file) data = data.sort_values(by='index') data['prediction'] = [str(x) for x in data['prediction']] # If not choice label, then use lower case for k in data.keys(): data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) meta = self.data meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} data_map = {x: y for x, y in zip(data['index'], data['question'])} for k in data_map: assert k in meta_q_map, ( f'eval_file should be the same as or a subset of dataset {self.dataset_name}' ) data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) # load split dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) acc = report_acc(data) for group_col in ['clinical vqa task', 'department', 'perceptual granularity']: acc_grouped = self.report_acc_by_groups(data, group_col) score_file_grouped = eval_file.replace(f'.{suffix}', f'_{group_col}_acc.csv') dump(acc_grouped, score_file_grouped) return acc class MMERealWorld(ImageMCQDataset): TYPE = 'MMERealWorld' DATASET_MD5 = { 'MME-RealWorld': '271c33ec814c39533c467ec6fb8a6f36', 'MME-RealWorld-Lite': '4c17057d7d3b6c4a0d4397c3dae0881c', 'MME-RealWorld-CN': 'daaa763d52a760a38606d5dedb3fe444', } SYS = { 'MME-RealWorld': ( 'Select the best answer to the above multiple-choice question based on the image. ' 'Respond with only the letter (A, B, C, D, or E) of the correct option. \n' 'The best answer is:' ), 'MME-RealWorld-Lite': ( 'Select the best answer to the above multiple-choice question based on the image. ' 'Respond with only the letter (A, B, C, D, or E) of the correct option. \n' 'The best answer is:' ), 'MME-RealWorld-CN': ( '根据图像选择上述多项选择题的最佳答案。只需回答正确选项的字母（A, B, C, D 或 E）。\n' '最佳答案为：' ), } @classmethod def supported_datasets(cls): return ['MME-RealWorld', 'MME-RealWorld-CN', 'MME-RealWorld-Lite',] def load_data( self, dataset="MME-RealWorld", repo_id="yifanzhang114/MME-RealWorld-Base64" ): def check_integrity(pth): data_file = osp.join(pth, f"{dataset}.tsv") if not os.path.exists(data_file): return False if md5(data_file) != self.DATASET_MD5[dataset]: return False return True def generate_tsv(pth): tsv_file = os.path.join(pth, f"{dataset}.tsv") if os.path.exists(tsv_file): print(f"{tsv_file} already exists.") return json_dir = os.path.join(pth, dataset) json_files = [f for f in os.listdir(json_dir) if f.endswith(".json")] data_list = [] for json_file in json_files: with open(os.path.join(json_dir, json_file), "r") as f: data = json.load(f) for item in tqdm(data): choice_prompt = ( "The choices are listed below:\n" if dataset in ["MME-RealWorld", "MME-RealWorld-Lite"] else "选项如下所示:\n" ) data_list.append( { "index": item["index"], "image": item["image"], "question": item["question"], "multi-choice options": choice_prompt + "\n".join(item["multi-choice options"]), "A": item["multi-choice options"][0][4:], "B": item["multi-choice options"][1][4:], "C": item["multi-choice options"][2][4:], "D": item["multi-choice options"][3][4:], "E": item["multi-choice options"][4][4:], "answer": item["answer"], "category": item["category"], "l2-category": item["l2-category"], } ) df = pd.DataFrame(data_list) df.to_csv(tsv_file, sep="\t", index=False) print(f"TSV file saved to {tsv_file}") # Check if dataset is cached and has integrity if dataset == "MME-RealWorld-Lite": url = 'https://huggingface.co/datasets/yifanzhang114/MME-RealWorld-Base64/resolve/main/mme_realworld_lite.tsv' # noqa: E501 file_md5 = ( self.DATASET_MD5[dataset] if dataset in self.DATASET_MD5 else None ) datas = self.prepare_tsv(url, file_md5) choice_prompt = "The choices are listed below:\n" for index, item in datas.iterrows(): options = eval(item["multi-choice options"]) datas.loc[index, "multi-choice options"] = choice_prompt + "\n".join( options ) datas.loc[index, "A"] = options[0][4:] datas.loc[index, "B"] = options[1][4:] datas.loc[index, "C"] = options[2][4:] datas.loc[index, "D"] = options[3][4:] datas.loc[index, "E"] = options[4][4:] return datas update_flag = False cache_path = get_cache_path(repo_id) if cache_path is not None and check_integrity(cache_path): dataset_path = cache_path print(f"Using cached dataset from {cache_path}") else: from huggingface_hub import snapshot_download # Download or find the dataset path dataset_path = snapshot_download(repo_id=repo_id, repo_type="dataset") generate_tsv(dataset_path) update_flag = True data_path = os.path.join(dataset_path, f"{dataset}.tsv") if file_size(data_path, "GB") > 1: local_path = data_path.replace(".tsv", "_local.tsv") if ( not osp.exists(local_path) or os.environ.get("FORCE_LOCAL", None) or update_flag ): from vlmeval.tools import LOCALIZE LOCALIZE(data_path, local_path) data_path = local_path return load(data_path) def post_build(self, dataset): self.TYPE = 'MMERealWorld' # 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'] choice_prompt = line['multi-choice options'] + '\n' question += ' ' + choice_prompt + self.SYS[self.dataset_name] 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)] msgs.append(dict(type='text', value=question)) return msgs # It returns a dictionary @classmethod def evaluate(self, eval_file, **judge_kwargs): from .utils.multiple_choice import extract_characters_regex, get_dimension_rating assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file' FAIL_MSG = 'Failed to obtain answer via API.' tmp_file = eval_file.replace('.xlsx', '_tmp.pkl') tgt_file = eval_file.replace('.xlsx', '_rating.json') score_file = eval_file.replace('.xlsx', '_score.xlsx') if not osp.exists(score_file): res = {} if not osp.exists(tmp_file) else load(tmp_file) res = {k: v for k, v in res.items() if FAIL_MSG not in v} data = load(eval_file) cnt_rejected = 0 data_un = data[~pd.isna(data['prediction'])] for idx in data['index']: ans = data.loc[data['index'] == idx, 'answer'].values[0] pred = data.loc[data['index'] == idx, 'prediction'].values[0] extract_pred = extract_characters_regex(pred) if extract_pred == '': cnt_rejected += 1 data.loc[data['index'] == idx, 'score'] = 0 else: data.loc[data['index'] == idx, 'score'] = int(extract_pred == ans) print( f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, ' f'failed to obtain the score for another {cnt_rejected} questions. ' f'Those questions will be counted as 0 score in ALL rating.' ) dump(data, score_file) rating = get_dimension_rating(score_file) dump(rating, tgt_file) return rating class HRBenchDataset(ImageMCQDataset): DATASET_URL = { 'HRBench4K': 'https://huggingface.co/datasets/DreamMr/HR-Bench/resolve/main/hr_bench_4k.tsv', 'HRBench8K': 'https://huggingface.co/datasets/DreamMr/HR-Bench/resolve/main/hr_bench_8k.tsv', } DATASET_MD5 = { 'HRBench4K': 'f6b041b03d49543494b8a56d2e35be65', 'HRBench8K': '274c9c7f89329b804a4723178a00219c', } def evaluate(self, eval_file, **judge_kwargs): assert os.path.exists(eval_file), '{} does not exist!'.format(eval_file) from .utils.multiple_choice import mcq_vanilla_eval from .utils.hrbench import report_acc_hrbench nproc = judge_kwargs.pop('nproc', 4) suffix = eval_file.split('.')[-1] model = judge_kwargs.get('model', 'extract_matching') assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} name_str = name_str_map[model] if model in name_str_map else model if model == 'exact_matching': model = None elif gpt_key_set(): model = build_judge(**judge_kwargs) if not model.working(): warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') warnings.warn(DEBUG_MESSAGE) model = None else: warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') model = None result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') data = load(eval_file) data = data.sort_values(by='index') data['prediction'] = [str(x) for x in data['prediction']] # If not choice label, then use lower case for k in data.keys(): data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) meta = self.data meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} data_map = {x: y for x, y in zip(data['index'], data['question'])} for k in data_map: assert k in meta_q_map, ( f'eval_file should be the same as or a subset of dataset {self.dataset_name}' ) score_file = eval_file.replace(f'.{suffix}', '_acc.csv') if osp.exists(score_file): acc = load(score_file) return acc data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) acc = report_acc_hrbench(data) score_file = eval_file.replace(f'.{suffix}', '_acc.csv') dump(acc, score_file) return acc class CustomMCQDataset(ImageMCQDataset): def load_data(self, dataset): data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv') if file_size(data_path, 'GB') > 1: local_path = data_path.replace('.tsv', '_local.tsv') if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None): from ..tools import LOCALIZE LOCALIZE(data_path, local_path) data_path = local_path return load(data_path) class NaturalBenchDataset(ImageMCQDataset): DATASET_URL = { 'NaturalBenchDataset': ( 'https://huggingface.co/datasets/BaiqiL/' 'NaturalBench/resolve/main/NaturalBenchDataset.tsv' ), } DATASET_MD5 = { 'NaturalBenchDataset':'dbe25b044bc35696426381e9ba4fe930', } def build_prompt(self, line): SUFFIX_FOR_VQA = { "yes_no": "Please answer Yes or No.", "multiple_choice": "Please output the letter corresponding to the correct option." } 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'] prompt = f'{question} {SUFFIX_FOR_VQA[line["type"]]}' 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)] msgs.append(dict(type='text', value=prompt)) return msgs def evaluate(self, eval_file, **judge_kwargs): from .utils.naturalbench import extract_answer, get_scores data = load(eval_file) data = data.sort_values(by='index') predictions = [str(x) for x in data['prediction']] answers = [str(x) for x in data['answer']] indexs = [str(x) for x in data['index']] meta = self.data types = [str(x) for x in meta['type']] results = {} assert len(predictions) == len(answers) == len(indexs) == len(types) == (1900 * 4) number_answered_samples = len(predictions) // 4 for i in range(number_answered_samples): results[i] = { "q0_i0": extract_answer(predictions[i * 4], types[i * 4]), "q0_i1": extract_answer(predictions[i * 4 + 1], types[i * 4 + 1]), "q1_i0": extract_answer(predictions[i * 4 + 2], types[i * 4 + 2]), "q1_i1": extract_answer(predictions[i * 4 + 3], types[i * 4 + 3]) } scores = get_scores(results) print(scores) score_file = 'NaturalBench_acc.csv' df = pd.DataFrame(list(scores.items()), columns=['Metric', 'Score']) dump(df, score_file) return scores