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import os |
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import os.path as osp |
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import json |
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from mmengine.dist import master_only |
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from .base_eval_dataset import BaseEvalDataset |
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from xtuner.registry import BUILDER |
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from mmengine.logging import print_log |
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from .gqa_eval_utils import eval_gqa |
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from .utils import custom_data_process |
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class GQADataset(BaseEvalDataset): |
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METAINFO: dict = dict(name='gqa') |
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def __init__( |
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self, |
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data_file, |
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ann_file, |
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image_folder, |
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image_processor, |
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pad_image_to_square=True, |
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metainfo=None, |
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): |
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super().__init__(metainfo) |
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self.data_file = data_file |
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self.ann_file = ann_file |
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self.answer_file = 'answer_gqa_results.jsonl' |
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self.prediction_file = 'pred_gqa_results.jsonl' |
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self.image_folder = image_folder |
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self.image_processor = BUILDER.build(image_processor) |
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self.pad_image_to_square = pad_image_to_square |
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self.data = self.load_data_list() |
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def load_data_list(self): |
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question_data = [json.loads(q) for q in open(os.path.expanduser(self.data_file), "r")] |
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data_list = [] |
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for idx in range(len(question_data)): |
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sample = question_data[idx] |
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index = sample['question_id'] |
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image_path = sample['image'] |
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question = sample['text'] |
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category = sample['category'] |
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data = { |
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'img_id': idx, |
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'index': index, |
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'image_path': image_path, |
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'question': question, |
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'category': category, |
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} |
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data_list.append(data) |
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return data_list |
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def __len__(self): |
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return len(self.data) |
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def __getitem__(self, idx): |
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data = self.data[idx] |
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data_dict = custom_data_process(self, data) |
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return data_dict |
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@master_only |
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def evaluate(self, results, work_dir): |
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answers_file = osp.join(work_dir, self.answer_file) |
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ans_file = open(answers_file, "w") |
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for pred_dict in results: |
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idx = pred_dict["img_id"] |
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gt_data = self.data[idx] |
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ans_file.write( |
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json.dumps( |
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{ |
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"question_id": gt_data['index'], |
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"prompt": gt_data['question'], |
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"text": pred_dict['prediction'], |
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"metadata": {}, |
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} |
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) |
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+ "\n" |
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) |
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ans_file.close() |
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all_preds = [] |
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for line_idx, line in enumerate(open(answers_file)): |
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res = json.loads(line) |
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question_id = res['question_id'] |
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text = res['text'].rstrip('.').lower() |
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all_preds.append({"questionId": question_id, "prediction": text}) |
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prediction_file = osp.join(work_dir, self.prediction_file) |
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with open(prediction_file, 'w') as f: |
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json.dump(all_preds, f) |
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evaluator = eval_gqa(questions=self.ann_file, predictions=prediction_file) |
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print_log('============================================', 'current') |
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scores = evaluator.forward() |
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print_log('============================================', 'current') |
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print_log(f'GQA successfully finished evaluating', 'current') |
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return scores |
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