eval.py 2.8 KB
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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

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import os
import sys
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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
from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model
from ppocr.utils.utility import print_dict
import tools.program as program
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def main():
    global_config = config['Global']
    # build dataloader
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    valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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    # build post process
    post_process_class = build_post_process(config['PostProcess'],
                                            global_config)
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    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
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        char_num = len(getattr(post_process_class, 'character'))
        if config['Architecture']["algorithm"] in ["Distillation",
                                                   ]:  # distillation model
            for key in config['Architecture']["Models"]:
                config['Architecture']["Models"][key]["Head"][
                    'out_channels'] = char_num
        else:  # base rec model
            config['Architecture']["Head"]['out_channels'] = char_num

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    model = build_model(config['Architecture'])
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    use_srn = config['Architecture']['algorithm'] == "SRN"
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    model_type = config['Architecture']['model_type']
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    best_model_dict = init_model(config, model)
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    if len(best_model_dict):
        logger.info('metric in ckpt ***************')
        for k, v in best_model_dict.items():
            logger.info('{}:{}'.format(k, v))
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    # build metric
    eval_class = build_metric(config['Metric'])
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    # start eval
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    metric = program.eval(model, valid_dataloader, post_process_class,
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                          eval_class, model_type, use_srn)
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    logger.info('metric eval ***************')
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    for k, v in metric.items():
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        logger.info('{}:{}'.format(k, v))
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if __name__ == '__main__':
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    config, device, logger, vdl_writer = program.preprocess()
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    main()