131 lines
4.7 KiB
Python
131 lines
4.7 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 paddle.fluid as fluid
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from paddle.fluid.incubate.fleet.base import role_maker
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from paddle.fluid.incubate.fleet.collective import fleet
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from ppcls.data import Reader
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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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import program
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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/ResNet50.yaml',
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help='config file path')
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parser.add_argument(
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'--vdl_dir',
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type=str,
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default="scaler",
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help='VisualDL logging directory for image.')
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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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# assign the place
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gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
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place = fluid.CUDAPlace(gpu_id)
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# startup_prog is used to do some parameter init work,
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# and train prog is used to hold the network
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startup_prog = fluid.Program()
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train_prog = fluid.Program()
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best_top1_acc = 0.0 # best top1 acc record
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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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# clone to prune some content which is irrelevant in valid_prog
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valid_prog = valid_prog.clone(for_test=True)
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# create the "Executor" with the statement of which place
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exe = fluid.Executor(place=place)
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# only run startup_prog once to init
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exe.run(startup_prog)
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# load model from checkpoint or pretrained model
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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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# 1. train with train dataset
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program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
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epoch_id, 'train', args.vdl_dir)
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if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
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# 2. validate with validate dataset
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if config.validate and epoch_id % config.valid_interval == 0:
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top1_acc = program.run(valid_dataloader, exe,
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compiled_valid_prog, valid_fetchs,
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epoch_id, 'valid')
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if top1_acc > best_top1_acc:
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best_top1_acc = top1_acc
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message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
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best_top1_acc, epoch_id)
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logger.info("{:s}".format(logger.coloring(message, "RED")))
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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["name"])
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save_model(train_prog, model_path,
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"best_model_in_epoch_" + str(epoch_id))
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# 3. save the persistable model
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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["name"])
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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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