forked from PulseFocusPlatform/PulseFocusPlatform
109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
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# 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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# add python path of PadleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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if parent_path not in sys.path:
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sys.path.append(parent_path)
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# ignore warning log
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import warnings
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warnings.filterwarnings('ignore')
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import paddle
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from ppdet.core.workspace import load_config, merge_config
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from ppdet.utils.check import check_gpu, check_version, check_config
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from ppdet.utils.cli import ArgsParser
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from ppdet.engine import Trainer
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from ppdet.slim import build_slim_model
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('export_model')
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def parse_args():
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parser = ArgsParser()
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parser.add_argument(
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"--output_dir",
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type=str,
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default="output_inference",
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help="Directory for storing the output model files.")
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parser.add_argument(
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"--export_serving_model",
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type=bool,
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default=False,
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help="Whether to export serving model or not.")
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parser.add_argument(
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"--slim_config",
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default=None,
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type=str,
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help="Configuration file of slim method.")
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args = parser.parse_args()
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return args
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def run(FLAGS, cfg):
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# build detector
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trainer = Trainer(cfg, mode='test')
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# load weights
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trainer.load_weights(cfg.weights)
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# export model
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trainer.export(FLAGS.output_dir)
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if FLAGS.export_serving_model:
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from paddle_serving_client.io import inference_model_to_serving
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model_name = os.path.splitext(os.path.split(cfg.filename)[-1])[0]
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inference_model_to_serving(
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dirname="{}/{}".format(FLAGS.output_dir, model_name),
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serving_server="{}/{}/serving_server".format(FLAGS.output_dir,
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model_name),
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serving_client="{}/{}/serving_client".format(FLAGS.output_dir,
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model_name),
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model_filename="model.pdmodel",
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params_filename="model.pdiparams")
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def main():
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paddle.set_device("cpu")
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FLAGS = parse_args()
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cfg = load_config(FLAGS.config)
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# TODO: to be refined in the future
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if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn':
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FLAGS.opt['norm_type'] = 'bn'
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merge_config(FLAGS.opt)
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if FLAGS.slim_config:
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cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
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check_config(cfg)
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check_gpu(cfg.use_gpu)
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check_version()
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run(FLAGS, cfg)
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if __name__ == '__main__':
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main()
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