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import os |
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from typing import List |
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from mmengine.dataset import Compose |
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from torch.utils.data import Dataset |
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from opencompass.registry import DATASETS |
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@DATASETS.register_module() |
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class MMEDataset(Dataset): |
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"""Dataset to load MME dataset. |
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Args: |
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data_dir (str): The path of the dataset. |
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pipeline (List[dict]): The data augmentation. |
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""" |
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tasks = [ |
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'artwork', 'celebrity', 'code_reasoning', 'color', |
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'commonsense_reasoning', 'count', 'existence', 'landmark', |
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'numerical_calculation', 'OCR', 'position', 'posters', 'scene', |
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'text_translation' |
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] |
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sub_dir_name = ('images', 'questions_answers_YN') |
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def __init__(self, data_dir: str, pipeline: List[dict]) -> None: |
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self.pipeline = Compose(pipeline) |
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self.load_data(data_dir) |
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def load_data(self, data_dir: str): |
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self.data_list = [] |
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image_dir, question_dir = self.sub_dir_name |
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for task in self.tasks: |
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if os.path.exists(os.path.join(data_dir, task, question_dir)): |
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q_list = os.listdir(os.path.join(data_dir, task, question_dir)) |
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i_list = os.listdir(os.path.join(data_dir, task, image_dir)) |
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q_prefix = os.path.join(data_dir, task, question_dir) |
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i_prefix = os.path.join(data_dir, task, image_dir) |
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else: |
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fn_list = os.listdir(os.path.join(data_dir, task)) |
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q_list = [fn for fn in fn_list if '.txt' in fn] |
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i_list = [fn for fn in fn_list if fn not in q_list] |
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q_prefix = i_prefix = os.path.join(data_dir, task) |
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q_list.sort() |
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i_list.sort() |
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assert len(q_list) == len(i_list) |
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for q_fn, i_fn in zip(q_list, i_list): |
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assert q_fn.split('.')[0] == i_fn.split('.')[0] |
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q_path = os.path.join(q_prefix, q_fn) |
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image_path = os.path.join(i_prefix, i_fn) |
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with open(q_path, 'r') as f: |
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q1, a1 = f.readline().strip().split('\t') |
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q2, a2 = f.readline().strip().split('\t') |
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self.data_list.append({ |
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'img_path': image_path, |
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'question': q1, |
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'answer': a1, |
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'task': task |
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}) |
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self.data_list.append({ |
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'img_path': image_path, |
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'question': q2, |
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'answer': a2, |
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'task': task |
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}) |
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def __len__(self) -> None: |
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return len(self.data_list) |
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def __getitem__(self, idx: int) -> dict: |
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data_sample = self.data_list[idx] |
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data_sample = self.pipeline(data_sample) |
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return data_sample |
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