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| class VQAReTokenLayoutLMPostProcess(object): | |
| """Convert between text-label and text-index""" | |
| def __init__(self, **kwargs): | |
| super(VQAReTokenLayoutLMPostProcess, self).__init__() | |
| def __call__(self, preds, label=None, *args, **kwargs): | |
| if label is not None: | |
| return self._metric(preds, label) | |
| else: | |
| return self._infer(preds, *args, **kwargs) | |
| def _metric(self, preds, label): | |
| return preds["pred_relations"], label[6], label[5] | |
| def _infer(self, preds, *args, **kwargs): | |
| ser_results = kwargs["ser_results"] | |
| entity_idx_dict_batch = kwargs["entity_idx_dict_batch"] | |
| pred_relations = preds["pred_relations"] | |
| # merge relations and ocr info | |
| results = [] | |
| for pred_relation, ser_result, entity_idx_dict in zip( | |
| pred_relations, ser_results, entity_idx_dict_batch | |
| ): | |
| result = [] | |
| used_tail_id = [] | |
| for relation in pred_relation: | |
| if relation["tail_id"] in used_tail_id: | |
| continue | |
| used_tail_id.append(relation["tail_id"]) | |
| ocr_info_head = ser_result[entity_idx_dict[relation["head_id"]]] | |
| ocr_info_tail = ser_result[entity_idx_dict[relation["tail_id"]]] | |
| result.append((ocr_info_head, ocr_info_tail)) | |
| results.append(result) | |
| return results | |