update docs for WOD

HannahJHan · Nov 10, 2020 · 8df9187 · 8df9187
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diff --git a/README.md b/README.md
@@ -109,7 +109,7 @@ All models are trained with 8 GTX 1080Ti GPUs and are available for download.
 | [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, 
+We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) 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` even if you only have limited GPU resources. 
 
 By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    

diff --git a/docs/GETTING_STARTED.md b/docs/GETTING_STARTED.md
@@ -85,7 +85,8 @@ pip3 install --upgrade pip
 pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
 ```
 
-* Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours): 
+* Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, 
+and you could refer to `data/waymo/waymo_processed_data` to see how many records that have been processed): 
 ```python 
 python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
     --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml