43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
# Copyright (c) 2022 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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from ppdet.core.workspace import create
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('ppdet.engine')
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from . import Trainer
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__all__ = ['TrainerCot']
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class TrainerCot(Trainer):
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"""
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Trainer for label-cotuning
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calculate the relationship between base_classes and novel_classes
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"""
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def __init__(self, cfg, mode='train'):
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super(TrainerCot, self).__init__(cfg, mode)
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self.cotuning_init()
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def cotuning_init(self):
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num_classes_novel = self.cfg['num_classes']
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self.load_weights(self.cfg.pretrain_weights)
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self.model.eval()
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relationship = self.model.relationship_learning(self.loader, num_classes_novel)
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self.model.init_cot_head(relationship)
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self.optimizer = create('OptimizerBuilder')(self.lr, self.model)
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