Commit beb249e5 authored by Shaoshuai Shi's avatar Shaoshuai Shi
Browse files

update README.md

parent f56e4f17
......@@ -24,7 +24,7 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
[2022-09-02] **NEW:** Update `OpenPCDet` to v0.6.0:
* Official code release of [MPPNet](https://arxiv.org/abs/2205.05979) for temporal 3D object detection, which supports long-term multi-frame 3D object detection and ranks 1st place on 3D detection learderboard of Waymo Open Dataset (see the [guideline](docs/guidelines_of_approaches/mppnet.md) on how to train/test with MPPNet).
* Support multi-frame training/testing on Waymo Open Dataset (see the [change log](docs/changelog.md) for more details on how to process data).
* Support to save changing training details (e.g., loss, iter, epoch) to file (previous tqdm progress bar is still supported by using `--use_tqdm_to_record`).
* Support to save changing training details (e.g., loss, iter, epoch) to file (previous tqdm progress bar is still supported by using `--use_tqdm_to_record`). Please use `pip install gpustat` if you also want to log the GPU related information.
* Support to save latest model every 5 mintues, so you can restore the model training from latest status instead of previous epoch.
[2022-08-22] Added support for [custom dataset tutorial and template](docs/CUSTOM_DATASET_TUTORIAL.md)
......
......@@ -94,15 +94,14 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
f'time_cost(all): {tbar.format_interval(trained_time_past_all)}/{tbar.format_interval(remaining_second_all)}, '
f'{disp_str}')
if show_gpu_stat and accumulated_iter % (3 * logger_iter_interval) == 0:
try:
os.system('gpustat')
except:
print('To show the GPU utilization, please install gpustat through "pip install gpustat"')
# To show the GPU utilization, please install gpustat through "pip install gpustat"
gpu_info = os.popen('gpustat').read()
logger.info(gpu_info)
else:
pbar.update()
pbar.set_postfix(dict(total_it=accumulated_iter))
tbar.set_postfix(disp_dict)
tbar.refresh()
# tbar.refresh()
if tb_log is not None:
tb_log.add_scalar('train/loss', loss, accumulated_iter)
......
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