update repo

嘉渊

2023-05-12 ebdf631d98bc5eeae086a4cf036dedb0dc6aa58f

 funasr/train/trainer.py

@@ -39,7 +39,7 @@
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.recursive_op import recursive_average
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.train.abs_espnet_model import AbsESPnetModel
from funasr.models.base_model import FunASRModel
from funasr.train.distributed_utils import DistributedOption
from funasr.train.reporter import Reporter
from funasr.train.reporter import SubReporter
@@ -165,7 +165,7 @@
    @classmethod
    def run(
        cls,
        model: AbsESPnetModel,
        model: FunASRModel,
        optimizers: Sequence[torch.optim.Optimizer],
        schedulers: Sequence[Optional[AbsScheduler]],
        train_iter_factory: AbsIterFactory,
@@ -579,9 +579,10 @@
            reporter.measure_iter_time(iterator, "iter_time"), 1
        ):
            assert isinstance(batch, dict), type(batch)
        
            if rank == 0 and hasattr(model.module, "num_updates"):
                num_batch_updates = model.module.get_num_updates()

            if rank == 0:
                if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")):
                    num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else model.module.get_num_updates()
                if (num_batch_updates%batch_interval == 0) and (options.oss_bucket is not None) and options.use_pai:
                    buffer = BytesIO()
                    torch.save(model.state_dict(), buffer)