Merge branch 'main' of github.com:alibaba-damo-academy/FunASR merge

游雁

2024-02-22 543d900522403eccb4e387cbc41c5dce24091d1d

 funasr/train_utils/trainer.py

@@ -108,7 +108,7 @@
        filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
        torch.save(state, filename)
        
        print(f'Checkpoint saved to {filename}')
        print(f'\nCheckpoint saved to {filename}\n')
        latest = Path(os.path.join(self.output_dir, f'model.pt'))
        torch.save(state, latest)

@@ -181,7 +181,7 @@

            time2 = time.perf_counter()
            time_escaped = (time2 - time1)/3600.0
            print(f"time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish: {(self.max_epoch-epoch)*time_escaped:.3f}")
            print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f} hours\n")

        if self.rank == 0:
            average_checkpoints(self.output_dir, self.avg_nbest_model)
@@ -293,7 +293,7 @@
                    f"{time_now}, "
                    f"rank: {self.local_rank}, "
                    f"epoch: {epoch}/{self.max_epoch}, "
                    f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, "
                    f"step: {batch_idx+1}/{len(self.dataloader_train)}, total step: {self.batch_total}, "
                    f"(loss: {loss.detach().cpu().item():.3f}), "
                    f"(lr: {lr:.3e}), "
                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
@@ -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)}, "