Modify eval_mm for MiniCPM-V 2.6

eval_mm/vlmevalkit/vlmeval/dataset/text_mcq.py

@@ -0,0 +1,123 @@
+from .text_base import TextBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+from ..smp import *
+
+
+class TextMCQDataset(TextBaseDataset):
+    TYPE = 'MCQ'
+
+    DATASET_URL = {}
+
+    DATASET_MD5 = {}
+
+    def build_prompt(self, line):
+
+        if isinstance(line, int):
+            line = self.data.iloc[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 = []
+
+        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
+
+        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)
+
+        return acc
+
+
+class CustomTextMCQDataset(TextMCQDataset):
+
+    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)