[Docs] Update Chinese documentation (open-mmlab#1891)

README.md

@@ -60,7 +60,7 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
 - **High efficiency**
 
- It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
+ It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
 
  | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
  | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
@@ -226,15 +226,15 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 | MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
 | SA-SSD | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
 
-**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
+**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 
 ## Installation
 
 Please refer to [getting_started.md](docs/en/getting_started.md) for installation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/1_exist_data_model.md) and [with customized dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [adding new dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/train_test.md) and [with customized dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [adding new dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
 
 Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/notes/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
 

README_zh-CN.md

@@ -24,21 +24,21 @@
 [![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
 
-**新闻**:
+**新闻**：
 
 **v1.1.0rc1** 版本已经在 2022.10.11 发布。
 
-由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在接下来的版本中，我们将更新所有的模型权重文件和基准。您可以在 [变更日志](docs/en/changelog.md) 和 [v1.0.x版本变更日志](docs/en/notes/changelog_v1.0.x.md) 中查看更多详细信息。
+由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在接下来的版本中，我们将更新所有的模型权重文件和基准。您可以在[变更日志](docs/zh_cn/notes/changelog.md)和 [v1.0.x版本变更日志](docs/zh_cn/notes/changelog_v1.0.x.md)中查看更多详细信息。
 
-文档: https://mmdetection3d.readthedocs.io/
+文档：https://mmdetection3d.readthedocs.io/
 
 ## 简介
 
 [English](README.md) | 简体中文
 
 主分支代码目前支持 PyTorch 1.6 以上的版本。
 
-MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代面向3D检测的平台. 它是 OpenMMlab 项目的一部分，这个项目由香港中文大学多媒体实验室和商汤科技联合发起.
+MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代面向 3D 检测的平台。它是 OpenMMlab 项目的一部分，这个项目由香港中文大学多媒体实验室和商汤科技联合发起。
 
 ![demo image](resources/mmdet3d_outdoor_demo.gif)
 
@@ -50,17 +50,17 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 - **支持户内/户外的数据集**
 
- 支持室内/室外的3D检测数据集，包括 ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, KITTI.
+ 支持室内/室外的3D检测数据集，包括 ScanNet，SUNRGB-D，Waymo，nuScenes，Lyft，KITTI。
 
- 对于 nuScenes 数据集, 我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/1.1/configs/nuimages).
+ 对于 nuScenes 数据集，我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/1.1/configs/nuimages)。
 
 - **与 2D 检测器的自然整合**
 
- [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
+ [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的 **300+ 个模型，40+ 的论文算法**，和相关模块都可以在此代码库中训练或使用。
 
 - **性能高**
 
- 训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
+ 训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/notes/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
 
  | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
  | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
@@ -70,7 +70,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
  | SECOND | 40 | 30 | × | × |
  | Part-A2 | 17 | 14 | × | × |
 
-和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样, MMDetection3D 也可以作为一个库去支持各式各样的项目.
+和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样，MMDetection3D 也可以作为一个库去支持各式各样的项目。
 
 ## 开源许可证
 
@@ -80,7 +80,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 我们在 2022.10.11 发布了 **1.1.0rc1** 版本.
 
-更多细节和版本发布历史可以参考[changelog.md](docs/en/notes/changelog.md).
+更多细节和版本发布历史可以参考 [changelog.md](docs/zh_cn/notes/changelog.md)。
 
 ## 基准测试和模型库
 
@@ -226,15 +226,15 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 | MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
 | SA-SSD | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
 
-**注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的基于2D检测的**300+个模型 , 40+的论文算法**在 MMDetection3D 中都可以被训练或使用。
+**注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。
 
 ## 安装
 
 请参考[快速入门文档](docs/zh_cn/getting_started.md)进行安装。
 
 ## 快速入门
 
-请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/zh_cn/user_guides/1_exist_data_model.md)和[新数据集](docs/zh_cn/user_guides/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/user_guides/config.md), [增加数据集支持](docs/zh_cn/advanced_guides/customize_dataset.md), [设计新的数据预处理流程](docs/zh_cn/user_guides/data_pipeline.md), [增加自定义模型](docs/zh_cn/advanced_guides/customize_models.md), [增加自定义的运行时配置](docs/zh_cn/advanced_guides/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/advanced_guides/datasets/waymo.md).
+请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。我们为新手提供了分别针对[已有数据集](docs/zh_cn/user_guides/train_test.md)和[新数据集](docs/zh_cn/user_guides/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/user_guides/config.md)，[增加数据集支持](docs/zh_cn/advanced_guides/customize_dataset.md)，[设计新的数据预处理流程](docs/zh_cn/user_guides/data_pipeline.md)，[增加自定义模型](docs/zh_cn/advanced_guides/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/advanced_guides/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/advanced_guides/datasets/waymo_det.md)。
 
 请参考 [FAQ](docs/zh_cn/notes/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/zh_cn/notes/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
 

docs/en/advanced_guides/customize_dataset.md

@@ -83,7 +83,7 @@ mmdetection3d
 
 #### Vision-Based 3D Detection
 
-The raw data for vision-based 3D object detection are typically organized as follows, where `ImageSets` contains split files indicating which files belong to training/validation set, `images` contains the images from different cameras, for example, images from `camera_x` need to be placed in `images\images_x`. `calibs` contains calibration information files which store the camera intrinsic matrix of each camera, and `labels` includes label files for 3D detection.
+The raw data for vision-based 3D object detection are typically organized as follows, where `ImageSets` contains split files indicating which files belong to training/validation set, `images` contains the images from different cameras, for example, images from `camera_x` need to be placed in `images/images_x`. `calibs` contains calibration information files which store the camera intrinsic matrix of each camera, and `labels` includes label files for 3D detection.
 
 ```
 mmdetection3d
@@ -201,18 +201,13 @@ class MyDataset(Det3DDataset):
  }
 
  def parse_ann_info(self, info):
- """Get annotation info according to the given index.
+ """Process the `instances` in data info to `ann_info`
 
  Args:
- info (dict): Data information of single data sample.
+ info (dict): Info dict.
 
  Returns:
- dict: annotation information consists of the following keys:
-
- - gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`):
- 3D ground truth bboxes.
- - bbox_labels_3d (np.ndarray): Labels of ground truths.
-
+ dict | None: Processed `ann_info`
  """
  ann_info = super().parse_ann_info(info)
  if ann_info is None:
@@ -464,9 +459,8 @@ _base_ = [
 
 #### Visualize your dataset (optional)
 
-To valiate whether your prepared data and config are correct, it's highly recommended to use `tools/browse_dataest.py` script
-to visualize your dataset and annotations before training and validation, more details refer to the [visualization](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/en/user_guides/visualization.md/) doc.
-s
+To valiate whether your prepared data and config are correct, it's highly recommended to use `tools/misc/browse_dataest.py` script
+to visualize your dataset and annotations before training and validation, more details refer to the [visualization](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/en/user_guides/visualization.md) doc.
 
 ## Evaluation
 

docs/en/advanced_guides/customize_models.md

@@ -128,7 +128,7 @@ Create a new file `mmdet3d/models/necks/second_fpn.py`.
 ```python
 from mmdet3d.registry import MODELS
 
-@MODELS.register
+@MODELS.register_module()
 class SECONDFPN(BaseModule):
 
  def __init__(self,
@@ -571,16 +571,16 @@ The decorator `weighted_loss` enable the loss to be weighted for each element.
 import torch
 import torch.nn as nn
 
-from ..builder import LOSSES
-from .utils import weighted_loss
+from mmdet3d.registry import MODELS
+from mmdet.models.losses.utils import weighted_loss
 
 @weighted_loss
 def my_loss(pred, target):
  assert pred.size() == target.size() and target.numel() > 0
  loss = torch.abs(pred - target)
  return loss
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class MyLoss(nn.Module):
 
  def __init__(self, reduction='mean', loss_weight=1.0):

docs/en/advanced_guides/customize_runtime.md

@@ -26,7 +26,7 @@ optim_wrapper = dict(
  clip_grad=dict(max_norm=0.01, norm_type=2))
 ```
 
-### Customize optimizer supported by Pytorch
+### Customize optimizer supported by PyTorch
 
 We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field in `optim_wrapper` field of config files. For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following.
 
@@ -192,7 +192,7 @@ Tricks not implemented by the optimizer should be implemented through optimizer
 
 ## Customize training schedules
 
-By default we use step learning rate with 1x schedule, this calls [MultiStepLR](https://github.com/open-mmlab/mmengine/blob/main/mmengine/optim/scheduler/lr_scheduler.py#L139) in MMEngine.
+By default we use step learning rate with 1x schedule, this calls [`MultiStepLR`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/optim/scheduler/lr_scheduler.py#L139) in MMEngine.
 We support many other learning rate schedule [here](https://github.com/open-mmlab/mmengine/blob/main/mmengine/optim/scheduler/lr_scheduler.py), such as `CosineAnnealingLR` and `PolyLR` schedules. Here are some examples
 
 - Poly schedule:
@@ -219,7 +219,6 @@ We support many other learning rate schedule [here](https://github.com/open-mmla
  begin=0,
  end=8,
  by_epoch=True)]
-
  ```
 
 ## Customize train loop
@@ -257,7 +256,9 @@ MMEngine provides many useful [hooks](https://github.com/open-mmlab/mmengine/blo
 Here we give an example of creating a new hook in mmdet3d and using it in training.
 
 ```python
-from mmengine.hooks import HOOKS, Hook
+from mmengine.hooks import Hook
+
+from mmdet3d.registry import HOOKS
 
 
 @HOOKS.register_module()
@@ -341,7 +342,7 @@ There are some common hooks that are registered through `default_hooks`, they ar
 - `CheckpointHook`: A hook that saves checkpoints periodically.
 - `DistSamplerSeedHook`: A hook that sets the seed for sampler and batch_sampler.
 
-`IterTimerHook`, `ParamSchedulerHook` and `DistSamplerSeedHook` are simple and no need to be modified usually, so here we reveals how what we can do with `LoggerHook`, `CheckpointHook` and `DetVisualizationHook`.
+`IterTimerHook`, `ParamSchedulerHook` and `DistSamplerSeedHook` are simple and no need to be modified usually, so here we reveals how what we can do with `LoggerHook`, `CheckpointHook` and `Det3DVisualizationHook`.
 
 #### CheckpointHook