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