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| | | import torch |
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| | | import torch.nn |
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| | | import torch.optim |
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| | | from typeguard import check_argument_types |
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| | | |
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| | | from funasr.iterators.abs_iter_factory import AbsIterFactory |
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| | | from funasr.main_funcs.average_nbest_models import average_nbest_models |
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| | |
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| | | @classmethod |
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| | | def build_options(cls, args: argparse.Namespace) -> TrainerOptions: |
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| | | """Build options consumed by train(), eval()""" |
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| | | assert check_argument_types() |
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| | | return build_dataclass(TrainerOptions, args) |
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| | | |
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| | | @classmethod |
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| | |
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| | | distributed_option: DistributedOption, |
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| | | ) -> None: |
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| | | """Perform training. This method performs the main process of training.""" |
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| | | assert check_argument_types() |
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| | | # NOTE(kamo): Don't check the type more strictly as far trainer_options |
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| | | assert is_dataclass(trainer_options), type(trainer_options) |
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| | | assert len(optimizers) == len(schedulers), (len(optimizers), len(schedulers)) |
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| | |
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| | | options: TrainerOptions, |
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| | | distributed_option: DistributedOption, |
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| | | ) -> Tuple[bool, bool]: |
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| | | assert check_argument_types() |
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| | | |
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| | | grad_noise = options.grad_noise |
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| | | accum_grad = options.accum_grad |
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| | |
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| | | options: TrainerOptions, |
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| | | distributed_option: DistributedOption, |
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| | | ) -> None: |
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| | | assert check_argument_types() |
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| | | ngpu = options.ngpu |
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| | | no_forward_run = options.no_forward_run |
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| | | distributed = distributed_option.distributed |
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