133 lines
4.6 KiB
Python
133 lines
4.6 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 argparse
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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.abspath(os.path.join(__dir__, '..')))
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import paddle
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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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'-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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paddle.seed(12345)
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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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use_gpu = config.get("use_gpu", True)
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place = paddle.set_device('gpu' if use_gpu else 'cpu')
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trainer_num = paddle.distributed.get_world_size()
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use_data_parallel = trainer_num != 1
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config["use_data_parallel"] = use_data_parallel
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if config["use_data_parallel"]:
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paddle.distributed.init_parallel_env()
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net = program.create_model(config.ARCHITECTURE, config.classes_num)
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optimizer, lr_scheduler = program.create_optimizer(
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config, parameter_list=net.parameters())
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dp_net = net
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if config["use_data_parallel"]:
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find_unused_parameters = config.get("find_unused_parameters", False)
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dp_net = paddle.DataParallel(
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net, find_unused_parameters=find_unused_parameters)
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# load model from checkpoint or pretrained model
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init_model(config, net, optimizer)
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train_dataloader = Reader(config, 'train', places=place)()
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if config.validate:
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valid_dataloader = Reader(config, 'valid', places=place)()
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last_epoch_id = config.get("last_epoch", -1)
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best_top1_acc = 0.0 # best top1 acc record
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best_top1_epoch = last_epoch_id
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vdl_writer_path = config.get("vdl_dir", None)
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vdl_writer = None
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if vdl_writer_path:
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from visualdl import LogWriter
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vdl_writer = LogWriter(vdl_writer_path)
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# Ensure that the vdl log file can be closed normally
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try:
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for epoch_id in range(last_epoch_id + 1, config.epochs):
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net.train()
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# 1. train with train dataset
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program.run(train_dataloader, config, dp_net, optimizer,
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lr_scheduler, epoch_id, 'train', vdl_writer)
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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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net.eval()
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with paddle.no_grad():
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top1_acc = program.run(valid_dataloader, config, net, None,
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None, epoch_id, 'valid', vdl_writer)
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if top1_acc > best_top1_acc:
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best_top1_acc = top1_acc
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best_top1_epoch = epoch_id
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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(net, optimizer, model_path, "best_model")
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message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
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best_top1_acc, best_top1_epoch)
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logger.info(message)
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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(net, optimizer, model_path, epoch_id)
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except Exception as e:
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logger.error(e)
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finally:
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vdl_writer.close() if vdl_writer else None
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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