49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 argparse
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import functools
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import shutil
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import sys
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def main():
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"""
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Usage: when training with flag use_ema, and evaluating EMA model, should clean the saved model at first.
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To generate clean model:
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python ema_clean.py ema_model_dir cleaned_model_dir
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"""
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cleaned_model_dir = sys.argv[1]
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ema_model_dir = sys.argv[2]
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if not os.path.exists(cleaned_model_dir):
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os.makedirs(cleaned_model_dir)
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items = os.listdir(ema_model_dir)
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for item in items:
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if item.find('ema') > -1:
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item_clean = item.replace('_ema_0', '')
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shutil.copyfile(os.path.join(ema_model_dir, item),
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os.path.join(cleaned_model_dir, item_clean))
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elif item.find('mean') > -1 or item.find('variance') > -1:
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shutil.copyfile(os.path.join(ema_model_dir, item),
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os.path.join(cleaned_model_dir, item))
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if __name__ == '__main__':
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main()
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