# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle 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): pred_relations = preds['pred_relations'] if isinstance(preds['pred_relations'], paddle.Tensor): pred_relations = pred_relations.numpy() pred_relations = self.decode_pred(pred_relations) if label is not None: return self._metric(pred_relations, label) else: return self._infer(pred_relations, *args, **kwargs) def _metric(self, pred_relations, label): return pred_relations, label[6], label[5] def _infer(self, pred_relations, *args, **kwargs): ser_results = kwargs['ser_results'] entity_idx_dict_batch = kwargs['entity_idx_dict_batch'] # 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 def decode_pred(self, pred_relations): pred_relations_new = [] for pred_relation in pred_relations: pred_relation_new = [] pred_relation = pred_relation[1:pred_relation[0, 0, 0] + 1] for relation in pred_relation: relation_new = dict() relation_new['head_id'] = relation[0, 0] relation_new['head'] = tuple(relation[1]) relation_new['head_type'] = relation[2, 0] relation_new['tail_id'] = relation[3, 0] relation_new['tail'] = tuple(relation[4]) relation_new['tail_type'] = relation[5, 0] relation_new['type'] = relation[6, 0] pred_relation_new.append(relation_new) pred_relations_new.append(pred_relation_new) return pred_relations_new class DistillationRePostProcess(VQAReTokenLayoutLMPostProcess): """ DistillationRePostProcess """ def __init__(self, model_name=["Student"], key=None, **kwargs): super().__init__(**kwargs) if not isinstance(model_name, list): model_name = [model_name] self.model_name = model_name self.key = key def __call__(self, preds, *args, **kwargs): output = dict() for name in self.model_name: pred = preds[name] if self.key is not None: pred = pred[self.key] output[name] = super().__call__(pred, *args, **kwargs) return output