update the README

TRAILab · Nov 9, 2020 · 9ca83d4 · 9ca83d4
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diff --git a/README.md b/README.md
@@ -18,9 +18,12 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
 
 
 ## Changelog
-[2020-08-10] **NEW:** Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
+[2020-11-10] **NEW:** The Waymo Open Dataset has been supported with SoTA results. By now we provid the 
+configs of `SECOND`, `PartA2` and `PV-RCNN` on the Waymo Open Dataset, and more models could be easily supported by modifying their dataset configs. 
 
-[2020-07-30] **NEW:** `OpenPCDet` v0.3.0 is released with the following features:
+[2020-08-10] Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
+
+[2020-07-30] `OpenPCDet` v0.3.0 is released with the following features:
    * The Point-based and Anchor-Free models ([`PointRCNN`](#KITTI-3D-Object-Detection-Baselines), [`PartA2-Free`](#KITTI-3D-Object-Detection-Baselines)) are supported now.
    * The NuScenes dataset is supported with strong baseline results ([`SECOND-MultiHead (CBGS)`](#NuScenes-3D-Object-Detection-Baselines) and [`PointPillar-MultiHead`](#NuScenes-3D-Object-Detection-Baselines)).
    * High efficiency than last version, support `PyTorch 1.1~1.5` and `spconv 1.0~1.2` simultaneously.
@@ -87,7 +90,7 @@ Selected supported methods are shown in the below table. The results are the 3D
 * All models are trained with 8 GTX 1080Ti GPUs and are available for download. 
 * The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
 
-|                                             | training time | Car | Pedestrian | Cyclist  | download | 
+|                                             | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11  | download | 
 |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|
 | [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~1.2 hours| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) | 
 | [SECOND](tools/cfgs/kitti_models/second.yaml)       |  ~1.7 hours  | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
@@ -105,6 +108,20 @@ All models are trained with 8 GTX 1080Ti GPUs and are available for download.
 | [PointPillar-MultiHead](tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml) | 33.87	| 26.00 | 32.07	| 28.74 | 20.15 | 44.63 | 58.23	 | [model-23M](https://drive.google.com/file/d/1p-501mTWsq0G9RzroTWSXreIMyTUUpBM/view?usp=sharing) | 
 | [SECOND-MultiHead (CBGS)](tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml) | 31.15 |	25.51 |	26.64 | 26.26 | 20.46 | 50.59 | 62.29 | [model-35M](https://drive.google.com/file/d/1bNzcOnE3u9iooBFMk2xK7HqhdeQ_nwTq/view?usp=sharing) |
 
+### Waymo Open Dataset Baselines
+We provide the setting of `DATA_CONFIG.SAMPLED_INTERVAL` on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation, 
+so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` if you only have limited GPU resources. 
+
+By default, all models are trained with **20% data (~32k frames)** of all the training samples, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set.    
+
+|                                             | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| [SECOND](tools/cfgs/waymo_models/second.yaml) | 68.03/67.44	| 59.57/59.04 | 61.14/50.33	| 53.00/43.56 | 54.66/53.31 | 52.67/51.37 | 
+| [PartA2](tools/cfgs/waymo_models/PartA2.yaml) | 71.82/71.29 | 64.33/63.82 | 63.15/54.96 | 54.24/47.11 | 65.23/63.92 | 62.61/61.35 |
+| [PV-RCNN](tools/cfgs/waymo_models/pv_rcnn.yaml) | 74.06/73.38 | 64.99/64.38 |	62.66/52.68 | 53.80/45.14 |	63.32/61.71	| 60.72/59.18 | 
+
+
+
 
 ### Other datasets
 More datasets are on the way. 
@@ -140,31 +157,15 @@ If you find this project useful in your research, please consider cite:
 
 
 ```
-@inproceedings{shi2020pv,
-  title={Pv-rcnn: Point-voxel feature set abstraction for 3d object detection},
-  author={Shi, Shaoshuai and Guo, Chaoxu and Jiang, Li and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={10529--10538},
-  year={2020}
+@misc{openpcdet2020,
+	title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Cloud},
+	author={OpenPCDet Development Team},
+	howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}}
+	year={2020}
 }
+```
 
+## Contribution
+Welcome to be a member of the OpenPCDet development team by contributing to this repo, and feel free to contact us for any potential contributions. 
 
-@article{shi2020points,
-  title={From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network},
-  author={Shi, Shaoshuai and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
-  year={2020},
-  publisher={IEEE}
-}
-
-@inproceedings{shi2019pointrcnn,
-  title={PointRCNN: 3d Object Progposal Generation and Detection from Point Cloud},
-  author={Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={770--779},
-  year={2019}
-}
-```
 
-## Contact
-This project is currently maintained by Shaoshuai Shi ([@sshaoshuai](http://github.com/sshaoshuai)) and Chaoxu Guo ([@Gus-Guo](https://github.com/Gus-Guo)).
diff --git a/docs/GETTING_STARTED.md b/docs/GETTING_STARTED.md
@@ -57,6 +57,47 @@ python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos
     --version v1.0-trainval
 ```
 
+### Waymo Open Dataset
+* Please download the official [Waymo Open Dataset](https://waymo.com/open/download/), 
+including the training data `training_0000.tar~training_0031.tar` and the validation 
+data `validation_0000.tar~validation_0007.tar`.
+* Unzip all the above `xxxx.tar` files to the directory of `data/waymo/raw_data_v1_2` as follows (You could get 798 *train* tfrecord and 202 *val* tfrecord ):  
+```
+OpenPCDet
+├── data
+│   ├── waymo
+│   │   │── ImageSets
+│   │   │── raw_data_v1_2
+│   │   │   │── segment-xxxxxxxx.tfrecord
+|   |   |   |── ...
+|   |   |   |── segment-xxxxxxxx.tfrecord
+|   |   |── waymo_processed_data
+│   │   │   │── segment-xxxxxxxx/
+|   |   |   |── ...
+|   |   |   |── segment-xxxxxxxx/
+│   │   │── pcdet_gt_database_train_sampled_xx/
+│   │   │── pcdet_waymo_dbinfos_train_sampled_xx.pkl   
+├── pcdet
+├── tools
+```
+* Install the official `waymo-open-dataset` by running the following command: 
+```shell script
+pip3 install --upgrade pip
+# tf 2.1.0.
+pip3 install waymo-open-dataset-tf-2-1-0==1.2.0 --user
+# tf 2.0.0
+# pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
+# tf 1.15.0
+# pip3 install waymo-open-dataset-tf-1-15-0==1.2.0 --user
+```
+
+* Generate the data infos by running the following command (it takes several hours): 
+```python 
+python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml
+```
+
+Note that you do not need to install `waymo-open-dataset` if you have already processed the data before and do not need to evaluate with official Waymo Metrics. 
+
 ## Training & Testing
 
 

diff --git a/pcdet/datasets/waymo/waymo_dataset.py b/pcdet/datasets/waymo/waymo_dataset.py
@@ -347,12 +347,18 @@ def create_waymo_infos(dataset_cfg, class_names, data_path, save_path,
 
 
 if __name__ == '__main__':
-    import sys
-    if sys.argv.__len__() > 1 and sys.argv[1] == 'create_waymo_infos':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset')
+    parser.add_argument('--func', type=str, default='create_waymo_infos', help='')
+    args = parser.parse_args()
+
+    if args.func == 'create_waymo_infos':
         import yaml
         from pathlib import Path
         from easydict import EasyDict
-        dataset_cfg = EasyDict(yaml.load(open(sys.argv[2])))
+        dataset_cfg = EasyDict(yaml.load(open(args.cfg_file)))
         ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
         create_waymo_infos(
             dataset_cfg=dataset_cfg,