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import os |
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from mmengine.dist import master_only |
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from vlm.datasets.evaluation.base_eval_dataset import BaseEvalDataset |
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class LLaVAPretrainDataset(BaseEvalDataset): |
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METAINFO: dict = dict(name='llava_pretrain') |
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def __init__( |
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self, |
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image_folder, |
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split=8, |
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rank=0, |
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): |
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super().__init__() |
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self.image_files = os.listdir(image_folder) |
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self.image_paths = self._get_image_paths(image_folder) |
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assert rank < split |
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size = len(self.image_paths) // split + 1 |
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i_s, i_e = size * rank, min(size * (rank + 1), len(self.image_paths)) |
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self.image_paths = self.image_paths[i_s: i_e] |
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def _get_image_paths(self, folder_path): |
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ret = [] |
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sub_files = os.listdir(folder_path) |
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for file_name in sub_files: |
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if '.png' not in file_name and '.jpg' not in file_name: |
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sub_folder_files = self._get_image_paths(os.path.join(folder_path, file_name)) |
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ret.extend(sub_folder_files) |
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else: |
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ret.append(os.path.join(folder_path, file_name)) |
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return ret |
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def __len__(self): |
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return len(self.image_paths) |
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def __getitem__(self, idx): |
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data_dict = {'image_path': self.image_paths[idx]} |
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data_dict.update({'text_prompts': self.image_paths[idx], 'pixel_values': None, 'img_id': str(idx)}) |
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return data_dict |
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@master_only |
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def evaluate(self, **kwargs): |
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return {'Acc': 0} |
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