106 lines
3.2 KiB
Python
106 lines
3.2 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 argparse
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import os
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import sys
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import paddle
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import paddle.fluid as fluid
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import program
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from ppcls.data import Reader
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import ppcls.utils.environment as env
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from ppcls.utils.config import get_config
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from ppcls.utils.save_load import init_model, save_model
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from ppcls.utils import logger
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from paddle.fluid.incubate.fleet.collective import fleet
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from paddle.fluid.incubate.fleet.base import role_maker
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def parse_args():
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parser = argparse.ArgumentParser("PaddleClas train script")
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parser.add_argument(
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'-c',
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'--config',
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type=str,
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default='configs/ResNet/ResNet18_vd.yaml',
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help='config file path')
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parser.add_argument(
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'-o',
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'--override',
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action='append',
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default=[],
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help='config options to be overridden')
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args = parser.parse_args()
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return args
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def main(args):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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config = get_config(args.config, overrides=args.override, show=True)
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place = env.place()
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startup_prog = fluid.Program()
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train_prog = fluid.Program()
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train_dataloader, train_fetchs = program.build(
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config, train_prog, startup_prog, is_train=True)
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if config.validate:
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valid_prog = fluid.Program()
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valid_dataloader, valid_fetchs = program.build(
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config, valid_prog, startup_prog, is_train=False)
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valid_prog = valid_prog.clone(for_test=True)
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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init_model(config, train_prog, exe)
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train_reader = Reader(config, 'train')()
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train_dataloader.set_sample_list_generator(train_reader, place)
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if config.validate:
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valid_reader = Reader(config, 'valid')()
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valid_dataloader.set_sample_list_generator(valid_reader, place)
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compiled_valid_prog = program.compile(config, valid_prog)
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compiled_train_prog = fleet.main_program
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for epoch_id in range(config.epochs):
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program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
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epoch_id, 'train')
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if config.validate and epoch_id % config.valid_interval == 0:
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program.run(valid_dataloader, exe, compiled_valid_prog,
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valid_fetchs, epoch_id, 'valid')
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if epoch_id % config.save_interval == 0:
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model_path = os.path.join(config.model_save_dir,
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config.architecture)
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save_model(train_prog, model_path, epoch_id)
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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