[Doc] Add Chinese documentation of coord_sys_tutorial and fix some li…

…nks (#1384)

* fix link

* add cn_coord_sys_tutorial

* Update coord_sys_tutorial.md

* update links

* Update coord_sys_tutorial.md

docs/en/1_exist_data_model.md

@@ -105,7 +105,7 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
 
    **Notice**: To generate submissions on Lyft, `csv_savepath` must be given in the `--eval-options`. After generating the csv file, you can make a submission with kaggle commands given on the [website](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/submit).
 
-   Note that in the [config of Lyft dataset](../configs/_base_/datasets/lyft-3d.py), the value of `ann_file` keyword in `test` is `data_root + 'lyft_infos_test.pkl'`, which is the official test set of Lyft without annotation. To test on the validation set, please change this to `data_root + 'lyft_infos_val.pkl'`.
+   Note that in the [config of Lyft dataset](../../configs/_base_/datasets/lyft-3d.py), the value of `ann_file` keyword in `test` is `data_root + 'lyft_infos_test.pkl'`, which is the official test set of Lyft without annotation. To test on the validation set, please change this to `data_root + 'lyft_infos_val.pkl'`.
 
 8. Test PointPillars on waymo with 8 GPUs, and evaluate the mAP with waymo metrics.
 

docs/en/tutorials/coord_sys_tutorial.md

@@ -47,7 +47,7 @@ The definition of coordinate systems in this tutorial is actually **more than ju
 
 The illustration of the three coordinate systems is shown below:
 
-![](https://raw.githubusercontent.com/open-mmlab/mmdetection3d/v1.0.0.dev0/resources/coord_sys_all.png)
+![](https://raw.githubusercontent.com/open-mmlab/mmdetection3d/master/resources/coord_sys_all.png)
 
 The three figures above are the 3D coordinate systems while the three figures below are the bird's eye view.
 
@@ -132,7 +132,7 @@ __|____|____|____|_________\ x right
 
 ### KITTI
 
-The raw annotation of KITTI is under camera coordinate system, see [get_label_anno](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/tools/data_converter/kitti_data_utils.py). In MMDetection3D, to train LiDAR-based models on KITTI, the data is first converted from camera coordinate system to LiDAR coordinate system, see [get_ann_info](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/datasets/kitti_dataset.py). For training vision-based models, the data is kept in the camera coordinate system.
+The raw annotation of KITTI is under camera coordinate system, see [get_label_anno](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/kitti_data_utils.py). In MMDetection3D, to train LiDAR-based models on KITTI, the data is first converted from camera coordinate system to LiDAR coordinate system, see [get_ann_info](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/kitti_dataset.py). For training vision-based models, the data is kept in the camera coordinate system.
 
 In SECOND, the LiDAR coordinate system for a box is defined as follows (a bird's eye view):
 
@@ -153,7 +153,7 @@ We use the KITTI-format data of Waymo dataset. Therefore, KITTI and Waymo also s
 
 ### NuScenes
 
-NuScenes provides a toolkit for evaluation, in which each box is wrapped into a `Box` instance. The coordinate system of `Box` is different from our LiDAR coordinate system in that the first two elements of the box dimension correspond to ``$$`(dy, dx)`$$``, or ``$$`(w, l)`$$``, respectively, instead of the reverse. For more details, please refer to the NuScenes [tutorial](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/docs/datasets/nuscenes_det.md#notes).
+NuScenes provides a toolkit for evaluation, in which each box is wrapped into a `Box` instance. The coordinate system of `Box` is different from our LiDAR coordinate system in that the first two elements of the box dimension correspond to ``$$`(dy, dx)`$$``, or ``$$`(w, l)`$$``, respectively, instead of the reverse. For more details, please refer to the NuScenes [tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/nuscenes_det.md#notes).
 
 Readers may refer to the [NuScenes development kit](https://github.com/nutonomy/nuscenes-devkit/tree/master/python-sdk/nuscenes/eval/detection) for the definition of a [NuScenes box](https://github.com/nutonomy/nuscenes-devkit/blob/2c6a752319f23910d5f55cc995abc547a9e54142/python-sdk/nuscenes/utils/data_classes.py#L457) and implementation of [NuScenes evaluation](https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/nuscenes/eval/detection/evaluate.py).
 
@@ -171,7 +171,7 @@ The raw data of ScanNet is not point cloud but mesh. The sampled point cloud dat
 
 The raw data of SUN RGB-D is not point cloud but RGB-D image. By back projection, we obtain the corresponding point cloud for each image, which is under our Depth coordinate system. However, the annotation is not under our system and thus needs conversion.
 
-For the conversion from raw annotation to annotation under our Depth coordinate system, please refer to [sunrgbd_data_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/tools/data_converter/sunrgbd_data_utils.py).
+For the conversion from raw annotation to annotation under our Depth coordinate system, please refer to [sunrgbd_data_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/sunrgbd_data_utils.py).
 
 ### S3DIS
 
@@ -199,25 +199,25 @@ Finally, the yaw angle should also be converted:
 
 - ``$$`r_{LiDAR}=-\frac{\pi}{2}-r_{camera}`$$``
 
-See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/box_3d_mode.py) for more details.
+See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/box_3d_mode.py) for more details.
 
 ### Bird's Eye View
 
 The BEV of a camera coordinate system box is ``$$`(x, z, dx, dz, -r)`$$`` if the 3D box is ``$$`(x, y, z, dx, dy, dz, r)`$$``. The inversion of the sign of the yaw angle is because the positive direction of the gravity axis of the Camera coordinate system points to the ground.
 
-See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/cam_box3d.py) for more details.
+See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/cam_box3d.py) for more details.
 
 ### Rotation of boxes
 
 We set the rotation of all kinds of boxes to be counter-clockwise about the gravity axis. Therefore, to rotate a 3D box we first calculate the new box center, and then we add the rotation angle to the yaw angle.
 
-See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/cam_box3d.py) for more details.
+See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/cam_box3d.py) for more details.
 
 ## Common FAQ
 
 #### Q1: Are the box related ops universal to all coordinate system types?
 
-No. For example, the ops under [this folder](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/ops/roiaware_pool3d) are applicable to boxes under Depth or LiDAR coordinate system only. The evaluation functions for KITTI dataset [here](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/evaluation/kitti_utils) are only applicable to boxes under Camera coordinate system since the rotation is clockwise if viewed from above.
+No. For example, [RoI-Aware Pooling ops](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/roiaware_pool3d.py) is applicable to boxes under Depth or LiDAR coordinate system only. The evaluation functions for KITTI dataset [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/kitti_utils) are only applicable to boxes under Camera coordinate system since the rotation is clockwise if viewed from above.
 
 For each box related op, we have marked the type of boxes to which we can apply the op.
 

docs/zh_cn/1_exist_data_model.md

@@ -105,7 +105,7 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [-
 
    **注意**：为了生成 Lyft 数据集的提交结果，`--eval-options` 必须指定 `csv_savepath`。生成 csv 文件后，你可以使用[网站](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/submit)上给出的 kaggle 命令提交结果。
 
-   注意在 [Lyft 数据集的配置文件](../configs/_base_/datasets/lyft-3d.py)，`test` 中的 `ann_file` 值为 `data_root + 'lyft_infos_test.pkl'`，是没有标注的 Lyft 官方测试集。要在验证数据集上测试，请把它改为 `data_root + 'lyft_infos_val.pkl'`。
+   注意在 [Lyft 数据集的配置文件](../../configs/_base_/datasets/lyft-3d.py)，`test` 中的 `ann_file` 值为 `data_root + 'lyft_infos_test.pkl'`，是没有标注的 Lyft 官方测试集。要在验证数据集上测试，请把它改为 `data_root + 'lyft_infos_val.pkl'`。
 
 8. 使用8块显卡在 waymo 数据集上测试 PointPillars，使用 waymo 度量方法计算 mAP
 

docs/zh_cn/2_new_data_model.md

@@ -77,7 +77,7 @@ KITTI 官方提供的目标检测开发[工具包](https://s3.eu-central-1.amazo
 
 第二步是准备配置文件来帮助数据集的读取和使用，另外，为了在 3D 检测中获得不错的性能，调整超参数通常是必要的。
 
-假设我们想要使用 PointPillars 模型在 Waymo 数据集上实现三类的 3D 目标检测：vehicle、cyclist、pedestrian，参照 KITTI 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/kitti-3d-3class.py)、模型[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_kitti.py)和[整体配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py)，我们需要准备[数据集配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/waymoD5-3d-3class.py)、[模型配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_waymo.py))，并将这两种文件进行结合得到[整体配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py)。
+假设我们想要使用 PointPillars 模型在 Waymo 数据集上实现三类的 3D 目标检测：vehicle、cyclist、pedestrian，参照 KITTI 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/kitti-3d-3class.py)、模型[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_kitti.py)和[整体配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py)，我们需要准备[数据集配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/waymoD5-3d-3class.py)、[模型配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/models/hv_pointpillars_secfpn_waymo.py)，并将这两种文件进行结合得到[整体配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py)。
 
 ## 训练一个新的模型
 

docs/zh_cn/tutorials/backends_support.md

@@ -1,4 +1,4 @@
-# Tutorial 7: 后端支持
+# 教程 7: 后端支持
 我们支持不同的文件客户端后端：磁盘、Ceph 和 LMDB 等。下面是修改配置使之从 Ceph 加载和保存数据的示例。
 
 ## 从 Ceph 读取数据和标注文件