75 lines
2.4 KiB
Python
75 lines
2.4 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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__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.join(__dir__, '..', '..', '..'))
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sys.path.append(os.path.join(__dir__, '..', '..', '..', 'tools'))
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import program
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import paddle
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from paddle import fluid
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from ppocr.utils.save_load import init_model
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from paddleslim.prune import load_model
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def main():
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# Run code with static graph mode.
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try:
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paddle.enable_static()
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except:
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pass
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startup_prog, eval_program, place, config, _ = program.preprocess()
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feeded_var_names, target_vars, fetches_var_name = program.build_export(
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config, eval_program, startup_prog)
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eval_program = eval_program.clone(for_test=True)
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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if config['Global']['checkpoints'] is not None:
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path = config['Global']['checkpoints']
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else:
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path = config['Global']['pretrain_weights']
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load_model(exe, eval_program, path)
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save_inference_dir = config['Global']['save_inference_dir']
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if not os.path.exists(save_inference_dir):
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os.makedirs(save_inference_dir)
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fluid.io.save_inference_model(
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dirname=save_inference_dir,
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feeded_var_names=feeded_var_names,
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main_program=eval_program,
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target_vars=target_vars,
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executor=exe,
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model_filename='model',
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params_filename='params')
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print("inference model saved in {}/model and {}/params".format(
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save_inference_dir, save_inference_dir))
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print("save success, output_name_list:", fetches_var_name)
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if __name__ == '__main__':
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main()
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