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| import warnings | |
| from .image_base import ImageBaseDataset | |
| from .utils import build_judge, DEBUG_MESSAGE | |
| from ..smp import * | |
| 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/VLMEval/MMBench_DEV_EN.tsv', | |
| 'MMBench_TEST_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv', | |
| 'MMBench_DEV_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv', | |
| 'MMBench_TEST_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv', | |
| 'MMBench': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench.tsv', # Internal Only | |
| 'MMBench_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN.tsv', # Internal Only | |
| # MMBench v1.1 | |
| 'MMBench_DEV_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN_V11.tsv', | |
| 'MMBench_TEST_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN_V11.tsv', | |
| 'MMBench_DEV_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN_V11.tsv', | |
| 'MMBench_TEST_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN_V11.tsv', | |
| 'MMBench_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_V11.tsv', # Internal Only | |
| 'MMBench_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN_V11.tsv', # Internal Only | |
| # SEEDBench Series | |
| 'SEEDBench_IMG': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench_IMG.tsv', | |
| 'SEEDBench2': 'https://huggingface.co/datasets/VLMEval/SEEDBench2/resolve/main/SEEDBench2.tsv', | |
| 'SEEDBench2_Plus': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench2_Plus.tsv', | |
| # ScienceQA Series | |
| 'ScienceQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_VAL.tsv', | |
| 'ScienceQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_TEST.tsv', | |
| # MMT-Bench | |
| 'MMT-Bench_ALL_MI': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_ALL_MI.tsv', | |
| 'MMT-Bench_ALL': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_ALL.tsv', | |
| 'MMT-Bench_VAL_MI': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_VAL_MI.tsv', | |
| 'MMT-Bench_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/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', | |
| # 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' | |
| } | |
| 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', | |
| # Other Benchmarks | |
| 'CCBench': 'f5dde47f24dc5a6fb6e595b409b466ac', | |
| 'AI2D_TEST': '0f593e0d1c7df9a3d69bf1f947e71975', | |
| 'AI2D_TEST_NO_MASK': 'fd8f463634d4fe9fbd23b876e8eea5be', | |
| 'MMStar': 'e1ecd2140806c1b1bbf54b43372efb9e', | |
| 'RealWorldQA': '92321028d2bc29040284b6674721e48f', | |
| 'MLLMGuard_DS': '975fc0dd7119386e198c37d71e274b3f', | |
| 'BLINK': '3b6649b6a662184ea046908e5506260e', | |
| 'TaskMeAnything_v1_imageqa_random': '023fef69e2ca21827afb77c5ec3bc889' | |
| } | |
| 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.') | |
| 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': '521afc0f3bf341e6654327792781644d', | |
| 'MMMU_TEST': 'c19875d11a2d348d07e5eb4bdf33166d', | |
| } | |
| 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 ') | |
| if len(text_segs) == 1: | |
| return msgs | |
| segs = [dict(type='text', value=text_segs[0])] | |
| for i, seg in enumerate(text_segs): | |
| if i == 0: | |
| continue | |
| assert istype(seg[0], int) and seg[1] == '>' | |
| 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' | |
| } | |
| 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 <image> tags | |
| text_segs = text.split('<image>') | |
| # 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' | |
| } | |
| DATASET_MD5 = { | |
| 'GMAI-MMBench_VAL': '254bd581627866f1c499d3d6b4422324' | |
| } | |
| 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.") | |
| 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 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) | |