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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import argparse
- import random
-
- import paddle
- import numpy as np
- import cv2
-
- from paddleseg.cvlibs import Config, SegBuilder
- from paddleseg.utils import get_sys_env, logger, utils
- from paddleseg.core import train
-
-
- def parse_args():
- parser = argparse.ArgumentParser(description='Model training')
-
- # Common params
- parser.add_argument("--config", help="The path of config file.", type=str)
- parser.add_argument(
- '--device',
- help='Set the device place for training model.',
- default='gpu',
- choices=['cpu', 'gpu', 'xpu', 'npu', 'mlu'],
- type=str)
- parser.add_argument(
- '--save_dir',
- help='The directory for saving the model snapshot.',
- type=str,
- default='./output')
- parser.add_argument(
- '--num_workers',
- help='Number of workers for data loader. Bigger num_workers can speed up data processing.',
- type=int,
- default=0)
- parser.add_argument(
- '--do_eval',
- help='Whether to do evaluation in training.',
- action='store_true')
- parser.add_argument(
- '--use_vdl',
- help='Whether to record the data to VisualDL in training.',
- action='store_true')
- parser.add_argument(
- '--use_ema',
- help='Whether to ema the model in training.',
- action='store_true')
-
- # Runntime params
- parser.add_argument(
- '--resume_model',
- help='The path of the model to resume training.',
- type=str)
- parser.add_argument('--iters', help='Iterations in training.', type=int)
- parser.add_argument(
- '--batch_size', help='Mini batch size of one gpu or cpu. ', type=int)
- parser.add_argument('--learning_rate', help='Learning rate.', type=float)
- parser.add_argument(
- '--save_interval',
- help='How many iters to save a model snapshot once during training.',
- type=int,
- default=1000)
- parser.add_argument(
- '--log_iters',
- help='Display logging information at every `log_iters`.',
- default=10,
- type=int)
- parser.add_argument(
- '--keep_checkpoint_max',
- help='Maximum number of checkpoints to save.',
- type=int,
- default=5)
-
- # Other params
- parser.add_argument(
- '--seed',
- help='Set the random seed in training.',
- default=None,
- type=int)
- parser.add_argument(
- "--precision",
- default="fp32",
- type=str,
- choices=["fp32", "fp16"],
- help="Use AMP (Auto mixed precision) if precision='fp16'. If precision='fp32', the training is normal."
- )
- parser.add_argument(
- "--amp_level",
- default="O1",
- type=str,
- choices=["O1", "O2"],
- help="Auto mixed precision level. Accepted values are “O1” and “O2”: O1 represent mixed precision, the input \
- data type of each operator will be casted by white_list and black_list; O2 represent Pure fp16, all operators \
- parameters and input data will be casted to fp16, except operators in black_list, don’t support fp16 kernel \
- and batchnorm. Default is O1(amp).")
- parser.add_argument(
- '--profiler_options',
- type=str,
- help='The option of train profiler. If profiler_options is not None, the train ' \
- 'profiler is enabled. Refer to the paddleseg/utils/train_profiler.py for details.'
- )
- parser.add_argument(
- '--data_format',
- help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".',
- type=str,
- default='NCHW')
- parser.add_argument(
- '--repeats',
- type=int,
- default=1,
- help="Repeat the samples in the dataset for `repeats` times in each epoch."
- )
- parser.add_argument(
- '--opts', help='Update the key-value pairs of all options.', nargs='+')
-
- return parser.parse_args()
-
-
- def main(args):
- assert args.config is not None, \
- 'No configuration file specified, please set --config'
- cfg = Config(
- args.config,
- learning_rate=args.learning_rate,
- iters=args.iters,
- batch_size=args.batch_size,
- opts=args.opts)
- builder = SegBuilder(cfg)
-
- utils.show_env_info()
- utils.show_cfg_info(cfg)
- utils.set_seed(args.seed)
- utils.set_device(args.device)
- utils.set_cv2_num_threads(args.num_workers)
-
- # TODO refactor
- # Only support for the DeepLabv3+ model
- if args.data_format == 'NHWC':
- if cfg.dic['model']['type'] != 'DeepLabV3P':
- raise ValueError(
- 'The "NHWC" data format only support the DeepLabV3P model!')
- cfg.dic['model']['data_format'] = args.data_format
- cfg.dic['model']['backbone']['data_format'] = args.data_format
- loss_len = len(cfg.dic['loss']['types'])
- for i in range(loss_len):
- cfg.dic['loss']['types'][i]['data_format'] = args.data_format
-
- model = utils.convert_sync_batchnorm(builder.model, args.device)
-
- train_dataset = builder.train_dataset
- # TODO refactor
- if args.repeats > 1:
- train_dataset.file_list *= args.repeats
- val_dataset = builder.val_dataset if args.do_eval else None
- optimizer = builder.optimizer
- loss = builder.loss
-
- train(
- model,
- train_dataset,
- val_dataset=val_dataset,
- optimizer=optimizer,
- save_dir=args.save_dir,
- iters=cfg.iters,
- batch_size=cfg.batch_size,
- resume_model=args.resume_model,
- save_interval=args.save_interval,
- log_iters=args.log_iters,
- num_workers=args.num_workers,
- use_vdl=args.use_vdl,
- use_ema=args.use_ema,
- losses=loss,
- keep_checkpoint_max=args.keep_checkpoint_max,
- test_config=cfg.test_config,
- precision=args.precision,
- amp_level=args.amp_level,
- profiler_options=args.profiler_options,
- to_static_training=cfg.to_static_training)
-
-
- if __name__ == '__main__':
- args = parse_args()
- main(args)
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