游雁
2024-02-21 2bc330a59934c436d8637cc3a69f198f672a3d4a
funasr/train_utils/trainer.py
@@ -302,17 +302,14 @@
                )
                pbar.set_description(description)
                if self.writer:
                    self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(),
                                           epoch*len(self.dataloader_train) + batch_idx)
                    self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(), self.batch_total)
                    self.writer.add_scalar(f'rank{self.local_rank}_lr/train', lr, self.batch_total)
                    for key, var in stats.items():
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(),
                                               epoch * len(self.dataloader_train) + batch_idx)
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(), self.batch_total)
                    for key, var in speed_stats.items():
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var),
                                               epoch * len(self.dataloader_train) + batch_idx)
            # if batch_idx == 2:
            #     break
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var), self.batch_total)
        pbar.close()
    def _validate_epoch(self, epoch):
@@ -356,7 +353,10 @@
                
                if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val):
                    pbar.update(self.log_interval)
                    time_now = datetime.now()
                    time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
                    description = (
                        f"{time_now}, "
                        f"rank: {self.local_rank}, "
                        f"validation epoch: {epoch}/{self.max_epoch}, "
                        f"step: {batch_idx+1}/{len(self.dataloader_val)}, "