| | |
| | | ) |
| | | 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): |
| | |
| | | |
| | | 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)}, " |