train.py

"""
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation

Dae-Young Song†, Geonsoo Lee, HeeKyung Lee, Gi-Mun Um, and Donghyeon Cho*
Chungnam National University & Electronic and Telecommunications Research Institute (ETRI)
†: Source Code Author, *: Corresponding Author

Copyright 2022. ETRI Allright reserved.


3-Clause BSD License(BSD-3-Clause)

SPDX short identifier: BSD-3-Clause



Note: This license has also been called the "New BSD License" or "Modified BSD License". See also the 2-clause BSD License.



Copyright 2022 ETRI.



Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:



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"""
import os
from engines.trainer import Trainer
from engines.trainerDDP import DistributedTrainer
from options import Options


if __name__ == '__main__':
    opt = Options().parse()
    # os.environ[
    #     "TORCH_DISTRIBUTED_DEBUG"
    # ] = "DETAIL"
    if len(opt.gpu) > 1:  # multi-GPU case
        trainer = DistributedTrainer(opt)  # DDP
        trainer.main()
    else:  # single python process
        trainer = Trainer(opt)
        trainer.main()