[Doc] Refine docs for Quick Run and Useful Tools (#686)

* Fix typos

* Update sample configs

* Fix typos

* Refine docs

docs/1_exist_data_model.md

@@ -16,7 +16,7 @@ You can use the following commands to test a dataset.
 
 ```shell
 # single-gpu testing
-python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show] [--show-dir ${SHOW_DIR}]
 
 # multi-gpu testing
 ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
@@ -122,7 +122,7 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
 
 ## Train predefined models on standard datasets
 
-MMDetection implements distributed training and non-distributed training,
+MMDetection3D implements distributed training and non-distributed training,
 which uses `MMDistributedDataParallel` and `MMDataParallel` respectively.
 
 All outputs (log files and checkpoints) will be saved to the working directory,
@@ -153,7 +153,7 @@ If you want to specify the working directory in the command, you can add an argu
 
 Optional arguments are:
 
-- `--no-validate` (**not suggested**): By default, the codebase will perform evaluation at every k (default value is 1, which can be modified like [this](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py#L174)) epochs during the training. To disable this behavior, use `--no-validate`.
+- `--no-validate` (**not suggested**): By default, the codebase will perform evaluation at every k (default value is 1, which can be modified like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py#L75)) epochs during the training. To disable this behavior, use `--no-validate`.
 - `--work-dir ${WORK_DIR}`: Override the working directory specified in the config file.
 - `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
 - `--options 'Key=value'`: Overide some settings in the used config.
@@ -164,7 +164,7 @@ Difference between `resume-from` and `load-from`:
 
 ### Train with multiple machines
 
-If you run MMDetection on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
+If you run MMDetection3D on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
 
 ```shell
 [GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
@@ -173,13 +173,13 @@ If you run MMDetection on a cluster managed with [slurm](https://slurm.schedmd.c
 Here is an example of using 16 GPUs to train Mask R-CNN on the dev partition.
 
 ```shell
-GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x
+GPUS=16 ./tools/slurm_train.sh dev pp_kitti_3class hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py /nfs/xxxx/pp_kitti_3class
 ```
 
 You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) for full arguments and environment variables.
 
 If you have just multiple machines connected with ethernet, you can refer to
-PyTorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility).
+PyTorch [launch utility](https://pytorch.org/docs/stable/distributed.html).
 Usually it is slow if you do not have high speed networking like InfiniBand.
 
 ### Launch multiple jobs on a single machine
@@ -217,7 +217,7 @@ If you use launch training jobs with Slurm, there are two ways to specify the po
    dist_params = dict(backend='nccl', port=29501)
    ```
 
-   Then you can launch two jobs with `config1.py` ang `config2.py`.
+   Then you can launch two jobs with `config1.py` and `config2.py`.
 
    ```shell
    CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}

docs/2_new_data_model.md

@@ -79,7 +79,7 @@ For more details about the intermediate results of preprocessing of Waymo datase
 
 The second step is to prepare configs such that the dataset could be successfully loaded. In addition, adjusting hyperparameters is usually necessary to obtain decent performance in 3D detection.
 
-Suppose we would like to train PointPillars on Waymo to achieve 3D detection for 3 classes, vehilce, cyclist and pedestrian, we need to prepare dataset config like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/waymoD5-3d-3class.py), model config like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_waymo.py) and combine them like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py), compared to KITTI [dataset config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/kitti-3d-3class.py), [model config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_kitti.py) and [overall](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py).
+Suppose we would like to train PointPillars on Waymo to achieve 3D detection for 3 classes, vehicle, cyclist and pedestrian, we need to prepare dataset config like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/waymoD5-3d-3class.py), model config like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_waymo.py) and combine them like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py), compared to KITTI [dataset config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/kitti-3d-3class.py), [model config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_kitti.py) and [overall](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py).
 
 ## Train a new model
 

docs/useful_tools.md

@@ -65,7 +65,7 @@ python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --show --show-dir ${SHOW_DIR}
 
 After running this command, plotted results including input data and the output of networks visualized on the input (e.g. `***_points.obj` and `***_pred.obj` in single-modality 3D detection task) will be saved in `${SHOW_DIR}`.
 
-To see the prediction results during evaluation time, you can run the following command
+To see the prediction results during evaluation, you can run the following command
 
 ```bash
 python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --eval 'mAP' --eval-options 'show=True' 'out_dir=${SHOW_DIR}'
@@ -80,9 +80,9 @@ To visualize the results with `Open3D` backend, you can run the following comman
 python tools/misc/visualize_results.py ${CONFIG_FILE} --result ${RESULTS_PATH} --show-dir ${SHOW_DIR}
 ```
 
-![Open3D_visualization](../resources/open3d_visual.gif)
+![](../resources/open3d_visual.gif)
 
-Or you can use 3D visualization software such as the [MeshLab](http://www.meshlab.net/) to open the these files under `${SHOW_DIR}` to see the 3D detection output. Specifically, open `***_points.obj` to see the input point cloud and open `***_pred.obj` to see the predicted 3D bounding boxes. This allows the inference and results generation be done in remote server and the users can open them on their host with GUI.
+Or you can use 3D visualization software such as the [MeshLab](http://www.meshlab.net/) to open these files under `${SHOW_DIR}` to see the 3D detection output. Specifically, open `***_points.obj` to see the input point cloud and open `***_pred.obj` to see the predicted 3D bounding boxes. This allows the inference and results generation to be done in remote server and the users can open them on their host with GUI.
 
 **Notice**: The visualization API is a little unstable since we plan to refactor these parts together with MMDetection in the future.
 
@@ -102,7 +102,7 @@ If you also want to show 2D images with 3D bounding boxes projected onto them, y
 python tools/misc/browse_dataset.py configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py --task multi_modality-det --output-dir ${OUTPUT_DIR} --online
 ```
 
-![Open3D_visualization](../resources/browse_dataset_multi_modality.png)
+![](../resources/browse_dataset_multi_modality.png)
 
 You can simply browse different datasets using different configs, e.g. visualizing the ScanNet dataset in 3D semantic segmentation task
 
@@ -110,7 +110,7 @@ You can simply browse different datasets using different configs, e.g. visualizi
 python tools/misc/browse_dataset.py configs/_base_/datasets/scannet_seg-3d-20class.py --task seg --output-dir ${OUTPUT_DIR} --online
 ```
 
-![Open3D_visualization](../resources/browse_dataset_seg.png)
+![](../resources/browse_dataset_seg.png)
 
 And browsing the nuScenes dataset in monocular 3D detection task
 
@@ -118,7 +118,7 @@ And browsing the nuScenes dataset in monocular 3D detection task
 python tools/misc/browse_dataset.py configs/_base_/datasets/nus-mono3d.py --task mono-det --output-dir ${OUTPUT_DIR} --online
 ```
 
-![Open3D_visualization](../resources/browse_dataset_mono.png)
+![](../resources/browse_dataset_mono.png)
 
  
 
@@ -198,7 +198,7 @@ The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth
 
 # Dataset Conversion
 
-`tools/data_converter/` contains tools to convert datasets to other formats. Most of them convert datasets to pickle based info files, like kitti, nuscenes and lyft. Waymo converter is used to reorganize waymo raw data like KITTI style. Users could refer to them for our approach to converting data format. It is also convenient to modify them to use as scripts like nuImages converter.
+`tools/data_converter/` contains tools for converting datasets to other formats. Most of them convert datasets to pickle based info files, like kitti, nuscenes and lyft. Waymo converter is used to reorganize waymo raw data like KITTI style. Users could refer to them for our approach to converting data format. It is also convenient to modify them to use as scripts like nuImages converter.
 
 To convert the nuImages dataset into COCO format, please use the command below: