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78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
# Copyright (c) 2020 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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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import pickle
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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from ppocr.data import build_dataloader
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from ppocr.modeling.architectures import build_model
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from ppocr.postprocess import build_post_process
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from ppocr.utils.save_load import init_model, load_dygraph_params
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from ppocr.utils.utility import print_dict
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import tools.program as program
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def main():
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global_config = config['Global']
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# build dataloader
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config['Eval']['dataset']['name'] = config['Train']['dataset']['name']
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config['Eval']['dataset']['data_dir'] = config['Train']['dataset'][
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'data_dir']
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config['Eval']['dataset']['label_file_list'] = config['Train']['dataset'][
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'label_file_list']
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eval_dataloader = build_dataloader(config, 'Eval', device, logger)
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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global_config)
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# build model
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# for rec algorithm
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if hasattr(post_process_class, 'character'):
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char_num = len(getattr(post_process_class, 'character'))
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config['Architecture']["Head"]['out_channels'] = char_num
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#set return_features = True
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config['Architecture']["Head"]["return_feats"] = True
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model = build_model(config['Architecture'])
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best_model_dict = load_dygraph_params(config, model, logger, None)
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if len(best_model_dict):
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logger.info('metric in ckpt ***************')
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for k, v in best_model_dict.items():
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logger.info('{}:{}'.format(k, v))
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# get features from train data
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char_center = program.get_center(model, eval_dataloader, post_process_class)
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#serialize to disk
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with open("train_center.pkl", 'wb') as f:
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pickle.dump(char_center, f)
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return
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if __name__ == '__main__':
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config, device, logger, vdl_writer = program.preprocess()
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main()
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