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| # Copyright (c) 2020 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. | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| import sys | |
| import pickle | |
| __dir__ = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(__dir__) | |
| sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) | |
| from ppocr.data import build_dataloader, set_signal_handlers | |
| from ppocr.modeling.architectures import build_model | |
| from ppocr.postprocess import build_post_process | |
| from ppocr.utils.save_load import load_model | |
| from ppocr.utils.utility import print_dict | |
| import tools.program as program | |
| def main(): | |
| global_config = config['Global'] | |
| # build dataloader | |
| config['Eval']['dataset']['name'] = config['Train']['dataset']['name'] | |
| config['Eval']['dataset']['data_dir'] = config['Train']['dataset'][ | |
| 'data_dir'] | |
| config['Eval']['dataset']['label_file_list'] = config['Train']['dataset'][ | |
| 'label_file_list'] | |
| set_signal_handlers() | |
| eval_dataloader = build_dataloader(config, 'Eval', device, logger) | |
| # build post process | |
| post_process_class = build_post_process(config['PostProcess'], | |
| global_config) | |
| # build model | |
| # for rec algorithm | |
| if hasattr(post_process_class, 'character'): | |
| char_num = len(getattr(post_process_class, 'character')) | |
| config['Architecture']["Head"]['out_channels'] = char_num | |
| #set return_features = True | |
| config['Architecture']["Head"]["return_feats"] = True | |
| model = build_model(config['Architecture']) | |
| best_model_dict = load_model(config, model) | |
| if len(best_model_dict): | |
| logger.info('metric in ckpt ***************') | |
| for k, v in best_model_dict.items(): | |
| logger.info('{}:{}'.format(k, v)) | |
| # get features from train data | |
| char_center = program.get_center(model, eval_dataloader, post_process_class) | |
| #serialize to disk | |
| with open("train_center.pkl", 'wb') as f: | |
| pickle.dump(char_center, f) | |
| return | |
| if __name__ == '__main__': | |
| config, device, logger, vdl_writer = program.preprocess() | |
| main() | |