Method log

funasr/train_utils/trainer.py:656–747

Log. Args: epoch: TODO. batch_idx: TODO. step_in_epoch: TODO. batch_num_epoch: TODO. lr: TODO. loss: TODO. speed_stats: TODO. stats: TODO.

(
        self,
        epoch=0,
        batch_idx=0,
        step_in_epoch=0,
        batch_num_epoch=-1,
        lr=0.0,
        loss=0.0,
        speed_stats=None,
        stats=None,
        writer=None,
        tag="train",
        data_split_i=0,
        data_split_num=1,
        log_step=None,
        **kwargs,
    )

            iterator_stop = torch.tensor(0).to(self.device)

    def log(
        self,
        epoch=0,
        batch_idx=0,
        step_in_epoch=0,
        batch_num_epoch=-1,
        lr=0.0,
        loss=0.0,
        speed_stats=None,
        stats=None,
        writer=None,
        tag="train",
        data_split_i=0,
        data_split_num=1,
        log_step=None,
        **kwargs,
    ):

        """Log.
        
            Args:
                epoch: TODO.
                batch_idx: TODO.
                step_in_epoch: TODO.
                batch_num_epoch: TODO.
                lr: TODO.
                loss: TODO.
                speed_stats: TODO.
                stats: TODO.
                writer: TODO.
                tag: TODO.
                data_split_i: TODO.
                data_split_num: TODO.
                log_step: TODO.
                **kwargs: Additional keyword arguments.
            """
        if (batch_idx + 1) % self.log_interval == 0:
            batch_idx = log_step if log_step is not None else batch_idx
            gpu_info = (
                "GPU, memory: usage: {:.3f} GB, "
                "peak: {:.3f} GB, "
                "cache: {:.3f} GB, "
                "cache_peak: {:.3f} GB".format(
                    torch.cuda.memory_allocated() / 1024 / 1024 / 1024,
                    torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024,
                    torch.cuda.memory_reserved() / 1024 / 1024 / 1024,
                    torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024,
                )
            )

            loss_avg_epoch = getattr(self, f"{tag}_loss_avg")
            acc_avg_epoch = getattr(self, f"{tag}_acc_avg")
            description = (
                f"{tag}, "
                f"rank: {self.rank}, "
                f"epoch: {epoch}/{self.max_epoch}, "
                f"data_slice: {data_split_i}/{data_split_num}, "
                f"step_in_slice: {batch_idx + 1}/{batch_num_epoch}, step_in_epoch: {step_in_epoch}, total step: {self.batch_total}, "