[Doc] Add Chinese doc for `waymo_det.md` (#859)

* Add complete translation

* Refinements

* Fix comments

* Fix a minor typo
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

docs/datasets/waymo_det.md

 # Waymo Dataset
 
-This page provides specific tutorials about the usage of MMDetection3D for waymo dataset.
+This page provides specific tutorials about the usage of MMDetection3D for Waymo dataset.
 
 ## Prepare dataset
 
-Before preparing waymo dataset, if you only installed requirements in `requirements/build.txt` and `requirements/runtime.txt` before, please install the official package for this dataset at first by running
+Before preparing Waymo dataset, if you only installed requirements in `requirements/build.txt` and `requirements/runtime.txt` before, please install the official package for this dataset at first by running
 
 ```
 # tf 2.1.0.
@@ -22,7 +22,7 @@ pip install -r requirements/optional.txt
 ```
 
 Like the general way to prepare dataset, it is recommended to symlink the dataset root to `$MMDETECTION3D/data`.
-Due to the original waymo data format is based on `tfrecord`, we need to preprocess the raw data for convenient usage in the training and evaluation procedure. Our approach is to convert them into KITTI format.
+Due to the original Waymo data format is based on `tfrecord`, we need to preprocess the raw data for convenient usage in the training and evaluation procedure. Our approach is to convert them into KITTI format.
 
 The folder structure should be organized as follows before our processing.
 
@@ -43,13 +43,13 @@ mmdetection3d
 
 ```
 
-You can download Waymo open dataset V1.2 [HERE](https://waymo.com/open/download/) and its data split [HERE](https://drive.google.com/drive/folders/18BVuF_RYJF0NjZpt8SnfzANiakoRMf0o?usp=sharing). Then put tfrecord files into corresponding folders in `data/waymo/waymo_format/` and put the data split txt files into `data/waymo/kitti_format/ImageSets`. Download ground truth bin files for validation set [HERE](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_2_0/validation/ground_truth_objects) and put it into `data/waymo/waymo_format/`. A tip is that you can use `gsutil` to download the large-scale dataset with commands. You can take this [tool](https://github.com/RalphMao/Waymo-Dataset-Tool) as an example for more details. Subsequently, prepare waymo data by running
+You can download Waymo open dataset V1.2 [HERE](https://waymo.com/open/download/) and its data split [HERE](https://drive.google.com/drive/folders/18BVuF_RYJF0NjZpt8SnfzANiakoRMf0o?usp=sharing). Then put `tfrecord` files into corresponding folders in `data/waymo/waymo_format/` and put the data split txt files into `data/waymo/kitti_format/ImageSets`. Download ground truth bin files for validation set [HERE](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_2_0/validation/ground_truth_objects) and put it into `data/waymo/waymo_format/`. A tip is that you can use `gsutil` to download the large-scale dataset with commands. You can take this [tool](https://github.com/RalphMao/Waymo-Dataset-Tool) as an example for more details. Subsequently, prepare Waymo data by running
 
 ```bash
 python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/waymo/ --workers 128 --extra-tag waymo
 ```
 
-Note that if your local disk does not have enough space for saving converted data, you can change the `out-dir` to anywhere else. Just remember to create folders and prepare data there in advance and link them back to `data/waymo/kitti_format` after the data conversion.
+Note that if your local disk does not have enough space for saving converted data, you can change the `--out-dir` to anywhere else. Just remember to create folders and prepare data there in advance and link them back to `data/waymo/kitti_format` after the data conversion.
 
 After the data conversion, the folder structure and info files should be organized as below.
 
@@ -93,7 +93,7 @@ mmdetection3d
 
 ```
 
-Here because there are several cameras, we store the corresponding image and labels that can be projected to that camera respectively and save pose for further usage of consecutive frames point clouds. We use a coding way `{a}{bbb}{ccc}` to name the data for each frame, where `a` is the prefix for different split (`0` for training, `1` for validation and `2` for testing), `bbb` for segment index and `ccc` for frame index. You can easily locate the required frame according to this naming rule. We gather the data for training and validation together as KITTI and store the indices for different set in the ImageSet files.
+Here because there are several cameras, we store the corresponding image and labels that can be projected to that camera respectively and save pose for further usage of consecutive frames point clouds. We use a coding way `{a}{bbb}{ccc}` to name the data for each frame, where `a` is the prefix for different split (`0` for training, `1` for validation and `2` for testing), `bbb` for segment index and `ccc` for frame index. You can easily locate the required frame according to this naming rule. We gather the data for training and validation together as KITTI and store the indices for different set in the `ImageSet` files.
 
 ## Training
 
@@ -101,7 +101,7 @@ Considering there are many similar frames in the original dataset, we can basica
 
 ## Evaluation
 
-For evaluation on waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`.  Basically, you can follow the commands below to install bazel and build the file.
+For evaluation on Waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`.  Basically, you can follow the commands below to install `bazel` and build the file.
 
    ```shell
    git clone https://github.com/waymo-research/waymo-open-dataset.git waymo-od
@@ -120,7 +120,7 @@ For evaluation on waymo, please follow the [instruction](https://github.com/waym
    cp bazel-bin/waymo_open_dataset/metrics/tools/compute_detection_metrics_main ../mmdetection3d/mmdet3d/core/evaluation/waymo_utils/
    ```
 
-Then you can evaluate your models on waymo. An example to evaluate PointPillars on waymo with 8 GPUs with waymo metrics is as follows.
+Then you can evaluate your models on Waymo. An example to evaluate PointPillars on Waymo with 8 GPUs with Waymo metrics is as follows.
 
    ```shell
    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car.py \
@@ -129,19 +129,19 @@ Then you can evaluate your models on waymo. An example to evaluate PointPillars
        'submission_prefix=results/waymo-car/kitti_results'
    ```
 
-`pklfile_prefix` should be given in the `--eval-options` if the bin file is needed to be generated. For metrics, `waymo` is the recommended official evaluation prototype. Currently, evaluating with choice `kitti` is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evalution, the lack of occlusion and truncation in the converted data, different definitions of difficulty and different methods of computing average precision.
+`pklfile_prefix` should be given in the `--eval-options` if the bin file is needed to be generated. For metrics, `waymo` is the recommended official evaluation prototype. Currently, evaluating with choice `kitti` is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evalution, the lack of occlusion and truncation in the converted data, different definitions of difficulty and different methods of computing Average Precision.
 
 **Notice**:
 
-1. Sometimes when using bazel to build `compute_detection_metrics_main`, an error `'round' is not a member of 'std'` may appear. We just need to remove the `std::` before `round` in that file.
+1. Sometimes when using `bazel` to build `compute_detection_metrics_main`, an error `'round' is not a member of 'std'` may appear. We just need to remove the `std::` before `round` in that file.
 
 2. Considering it takes a little long time to evaluate once, we recommend to evaluate only once at the end of model training.
 
-3. To use tensorflow with cuda9, it is recommended to compile it from source. Apart from official tutorials, you can refer to this [link](https://github.com/SmileTM/Tensorflow2.X-GPU-CUDA9.0) for possibly suitable precompiled packages and useful information for compiling it from source.
+3. To use TensorFlow with CUDA 9, it is recommended to compile it from source. Apart from official tutorials, you can refer to this [link](https://github.com/SmileTM/Tensorflow2.X-GPU-CUDA9.0) for possibly suitable precompiled packages and useful information for compiling it from source.
 
 ## Testing and make a submission
 
-An example to test PointPillars on waymo with 8 GPUs, generate the bin files and make a submission to the leaderboard.
+An example to test PointPillars on Waymo with 8 GPUs, generate the bin files and make a submission to the leaderboard.
 
    ```shell
    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car.py \
@@ -167,4 +167,4 @@ After generating the bin file, you can simply build the binary file `create_subm
    gzip results/waymo-car/submission/my_model.tar
    ```
 
-For evaluation on the validation set with the eval server, you can also use the same way to generate a submission. Make sure you change the fields in submission.txtpb before running the command above.
+For evaluation on the validation set with the eval server, you can also use the same way to generate a submission. Make sure you change the fields in `submission.txtpb` before running the command above.

docs_zh-CN/datasets/waymo_det.md

-# Waymo数据集
+# Waymo 数据集
\ No newline at end of file
+
+本文档页包含了关于 MMDetection3D 中 Waymo 数据集用法的教程。
+
+## 数据集准备
+
+在准备 Waymo 数据集之前，如果您之前只安装了 `requirements/build.txt` 和 `requirements/runtime.txt` 中的依赖，请通过运行如下指令额外安装 Waymo 数据集所依赖的官方包：
+
+```
+# tf 2.1.0.
+pip install waymo-open-dataset-tf-2-1-0==1.2.0
+# tf 2.0.0
+# pip install waymo-open-dataset-tf-2-0-0==1.2.0
+# tf 1.15.0
+# pip install waymo-open-dataset-tf-1-15-0==1.2.0
+```
+
+或者
+
+```
+pip install -r requirements/optional.txt
+```
+
+和准备数据集的通用方法一致，我们推荐将数据集根目录软链接至 `$MMDETECTION3D/data`。
+由于原始 Waymo 数据的格式基于 `tfrecord`，我们需要将原始数据进行预处理，以便于训练和测试时使用。我们的方法是将它们转换为 KITTI 格式。
+
+处理之前，文件目录结构组织如下：
+
+```
+mmdetection3d
+├── mmdet3d
+├── tools
+├── configs
+├── data
+│   ├── waymo
+│   │   ├── waymo_format
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── testing
+│   │   │   ├── gt.bin
+│   │   ├── kitti_format
+│   │   │   ├── ImageSets
+
+```
+
+您可以在[这里](https://waymo.com/open/download/)下载 1.2 版本的 Waymo 公开数据集，并在[这里](https://drive.google.com/drive/folders/18BVuF_RYJF0NjZpt8SnfzANiakoRMf0o?usp=sharing)下载其训练/验证/测试集拆分文件。接下来，请将 `tfrecord` 文件放入 `data/waymo/waymo_format/` 下的对应文件夹，并将 txt 格式的数据集拆分文件放入 `data/waymo/kitti_format/ImageSets`。在[这里](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_2_0/validation/ground_truth_objects)下载验证集使用的 bin 格式真实标注 (Ground Truth) 文件并放入 `data/waymo/waymo_format/`。小窍门：您可以使用 `gsutil` 来在命令行下载大规模数据集。您可以将该[工具](https://github.com/RalphMao/Waymo-Dataset-Tool) 作为一个例子来查看更多细节。之后，通过运行如下指令准备 Waymo 数据：
+
+```bash
+python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/waymo/ --workers 128 --extra-tag waymo
+```
+
+请注意，如果您的本地磁盘没有足够空间保存转换后的数据，您可以将 `--out-dir` 改为其他目录；只要在创建文件夹、准备数据并转换格式后，将数据文件链接到 `data/waymo/kitti_format` 即可。
+
+在数据转换后，文件目录结构应组织如下：
+
+```
+mmdetection3d
+├── mmdet3d
+├── tools
+├── configs
+├── data
+│   ├── waymo
+│   │   ├── waymo_format
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── testing
+│   │   │   ├── gt.bin
+│   │   ├── kitti_format
+│   │   │   ├── ImageSets
+│   │   │   ├── training
+│   │   │   │   ├── calib
+│   │   │   │   ├── image_0
+│   │   │   │   ├── image_1
+│   │   │   │   ├── image_2
+│   │   │   │   ├── image_3
+│   │   │   │   ├── image_4
+│   │   │   │   ├── label_0
+│   │   │   │   ├── label_1
+│   │   │   │   ├── label_2
+│   │   │   │   ├── label_3
+│   │   │   │   ├── label_4
+│   │   │   │   ├── label_all
+│   │   │   │   ├── pose
+│   │   │   │   ├── velodyne
+│   │   │   ├── testing
+│   │   │   │   ├── (the same as training)
+│   │   │   ├── waymo_gt_database
+│   │   │   ├── waymo_infos_trainval.pkl
+│   │   │   ├── waymo_infos_train.pkl
+│   │   │   ├── waymo_infos_val.pkl
+│   │   │   ├── waymo_infos_test.pkl
+│   │   │   ├── waymo_dbinfos_train.pkl
+
+```
+
+因为 Waymo 数据的来源包含数个相机，这里我们将每个相机对应的图像和标签文件分别存储，并将相机位姿 (pose) 文件存储下来以供后续处理连续多帧的点云。我们使用 `{a}{bbb}{ccc}` 的名称编码方式为每帧数据命名，其中 `a` 是不同数据拆分的前缀（`0` 指代训练集，`1` 指代验证集，`2` 指代测试集），`bbb` 是分割部分 (segment) 的索引，而 `ccc` 是帧索引。您可以轻而易举地按照如上命名规则定位到所需的帧。我们将训练和验证所需数据按 KITTI 的方式集合在一起，然后将训练集/验证集/测试集的索引存储在 `ImageSet` 下的文件中。
+
+## 训练
+
+考虑到原始数据集中的数据有很多相似的帧，我们基本上可以主要使用一个子集来训练我们的模型。在我们初步的基线中，我们在每五帧图片中加载一帧。得益于我们的超参数设置和数据增强方案，我们得到了比 Waymo [原论文](https://arxiv.org/pdf/1912.04838.pdf)中更好的性能。请移步 `configs/pointpillars/` 下的 README.md 以查看更多配置和性能相关的细节。我们会尽快发布一个更完整的 Waymo 基准榜单 (benchmark)。
+
+## 评估
+
+为了在 Waymo 数据集上进行检测性能评估，请按照[此处指示](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/)构建用于计算评估指标的二进制文件 `compute_detection_metrics_main`，并将它置于 `mmdet3d/core/evaluation/waymo_utils/` 下。您基本上可以按照下方命令安装 `bazel`，然后构建二进制文件：
+
+   ```shell
+   git clone https://github.com/waymo-research/waymo-open-dataset.git waymo-od
+   cd waymo-od
+   git checkout remotes/origin/master
+
+   sudo apt-get install --assume-yes pkg-config zip g++ zlib1g-dev unzip python3 python3-pip
+   wget https://github.com/bazelbuild/bazel/releases/download/0.28.0/bazel-0.28.0-installer-linux-x86_64.sh
+   sudo bash bazel-0.28.0-installer-linux-x86_64.sh
+   sudo apt install build-essential
+
+   ./configure.sh
+   bazel clean
+
+   bazel build waymo_open_dataset/metrics/tools/compute_detection_metrics_main
+   cp bazel-bin/waymo_open_dataset/metrics/tools/compute_detection_metrics_main ../mmdetection3d/mmdet3d/core/evaluation/waymo_utils/
+   ```
+
+接下来，您就可以在 Waymo 上评估您的模型了。如下示例是使用 8 个图形处理器 (GPU) 在 Waymo 上用 Waymo 评价指标评估 PointPillars 模型的情景：
+
+   ```shell
+   ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car.py \
+       checkpoints/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car_latest.pth --out results/waymo-car/results_eval.pkl \
+       --eval waymo --eval-options 'pklfile_prefix=results/waymo-car/kitti_results' \
+       'submission_prefix=results/waymo-car/kitti_results'
+   ```
+
+如果需要生成 bin 文件，应在 `--eval-options` 中给出 `pklfile_prefix`。对于评价指标， `waymo` 是我们推荐的官方评估原型。目前，`kitti` 这一评估选项是从 KITTI 迁移而来的，且每个难度下的评估结果和 KITTI 数据集中定义得到的不尽相同——目前大多数物体被标记为难度 0（日后会修复）。`kitti` 评估选项的不稳定来源于很大的计算量，转换的数据中遮挡 (occlusion) 和截断 (truncation) 的缺失，难度的不同定义方式，以及不同的平均精度 (Average Precision) 计算方式。
+
+**注意**:
+
+1. 有时用 `bazel` 构建 `compute_detection_metrics_main` 的过程中会出现如下错误：`'round' 不是 'std' 的成员` (`'round' is not a member of 'std'`)。我们只需要移除该文件中，`round` 前的 `std::`。
+
+2. 考虑到 Waymo 上评估一次耗时不短，我们建议只在模型训练结束时进行评估。
+
+3. 为了在 CUDA 9 环境使用 TensorFlow，我们建议通过编译 TensorFlow 源码的方式使用。除了官方教程之外，您还可以参考该[链接](https://github.com/SmileTM/Tensorflow2.X-GPU-CUDA9.0)以寻找可能合适的预编译包以及编译源码的实用攻略。
+
+## 测试并提交到官方服务器
+
+如下是一个使用 8 个图形处理器在 Waymo 上测试 PointPillars，生成 bin 文件并提交结果到官方榜单的例子：
+
+   ```shell
+   ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car.py \
+       checkpoints/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car_latest.pth --out results/waymo-car/results_eval.pkl \
+       --format-only --eval-options 'pklfile_prefix=results/waymo-car/kitti_results' \
+       'submission_prefix=results/waymo-car/kitti_results'
+   ```
+
+在生成 bin 文件后，您可以简单地构建二进制文件 `create_submission`，并按照[指示](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) 创建一个提交文件。下面是一些示例：
+
+   ```shell
+   cd ../waymo-od/
+   bazel build waymo_open_dataset/metrics/tools/create_submission
+   cp bazel-bin/waymo_open_dataset/metrics/tools/create_submission ../mmdetection3d/mmdet3d/core/evaluation/waymo_utils/
+   vim waymo_open_dataset/metrics/tools/submission.txtpb  # set the metadata information
+   cp waymo_open_dataset/metrics/tools/submission.txtpb ../mmdetection3d/mmdet3d/core/evaluation/waymo_utils/
+
+   cd ../mmdetection3d
+   # suppose the result bin is in `results/waymo-car/submission`
+   mmdet3d/core/evaluation/waymo_utils/create_submission  --input_filenames='results/waymo-car/kitti_results_test.bin' --output_filename='results/waymo-car/submission/model' --submission_filename='mmdet3d/core/evaluation/waymo_utils/submission.txtpb'
+
+   tar cvf results/waymo-car/submission/my_model.tar results/waymo-car/submission/my_model/
+   gzip results/waymo-car/submission/my_model.tar
+   ```
+
+如果想用官方评估服务器评估您在验证集上的结果，您可以使用同样的方法生成提交文件，只需确保您在运行如上指令前更改 `submission.txtpb` 中的字段值即可。