74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
# 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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if label is not None:
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return self._metric(preds, label)
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else:
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return self._infer(preds, *args, **kwargs)
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def _metric(self, preds, label):
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return preds['pred_relations'], label[6], label[5]
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def _infer(self, preds, *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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pred_relations = preds['pred_relations']
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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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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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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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