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# 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[-1], label[-2]
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
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