[Doc] Add tutorials/data_pipeline Chinese version (#827)

* [Doc] Add tutorials/data_pipeline Chinese version

* refine doc

* Use the absolute link

* Use the absolute link
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

docs/tutorials/data_pipeline.md

@@ -5,7 +5,7 @@
 Following typical conventions, we use `Dataset` and `DataLoader` for data loading
 with multiple workers. `Dataset` returns a dict of data items corresponding
 the arguments of models' forward method.
-Since the data in object detection may not be the same size (image size, gt bbox size, etc.),
+Since the data in object detection may not be the same size (point number, gt bbox size, etc.),
 we introduce a new `DataContainer` type in MMCV to help collect and distribute
 data of different size.
 See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details.
@@ -15,7 +15,7 @@ defines how to process the annotations and a data pipeline defines all the steps
 A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.
 
 We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
-![](../../resources/data_pipeline.png)
+![](https://github.com/open-mmlab/mmdetection3d/blob/master/resources/data_pipeline.png)
 
 The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.
 

docs_zh-CN/tutorials/data_pipeline.md

-# 教程 3: 自定义数据预处理流程
\ No newline at end of file
+# 教程 3: 自定义数据预处理流程
+
+## 数据预处理流程的设计
+
+遵循一般惯例，我们使用 `Dataset` 和 `DataLoader` 来调用多个进程进行数据的加载。`Dataset` 将会返回与模型前向传播的参数所对应的数据项构成的字典。因为目标检测中的数据的尺寸可能无法保持一致（如点云中点的数量、真实标注框的尺寸等），我们在 MMCV 中引入一个 `DataContainer` 类型，来帮助收集和分发不同尺寸的数据。请参考[此处](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py)获取更多细节。
+
+数据预处理流程和数据集之间是互相分离的两个部分，通常数据集定义了如何处理标注信息，而数据预处理流程定义了准备数据项字典的所有步骤。数据集预处理流程包含一系列的操作，每个操作将一个字典作为输入，并输出应用于下一个转换的一个新的字典。
+
+我们将在下图中展示一个最经典的数据集预处理流程，其中蓝色框表示预处理流程中的各项操作。随着预处理的进行，每一个操作都会添加新的键值（图中标记为绿色）到输出字典中，或者更新当前存在的键值（图中标记为橙色）。
+![](https://github.com/open-mmlab/mmdetection3d/blob/master/resources/data_pipeline.png)
+
+预处理流程中的各项操作主要分为数据加载、预处理、格式化、测试时的数据增强。
+
+接下来将展示一个用于 PointPillars 模型的数据集预处理流程的例子。
+
+```python
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectNameFilter', classes=class_names),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1.0,
+        flip=False,
+        pcd_horizontal_flip=False,
+        pcd_vertical_flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+```
+
+对于每项操作，我们将列出相关的被添加/更新/移除的字典项。
+
+### 数据加载
+
+`LoadPointsFromFile`
+- 添加：points
+
+`LoadPointsFromMultiSweeps`
+- 更新：points
+
+`LoadAnnotations3D`
+- 添加：gt_bboxes_3d, gt_labels_3d, gt_bboxes, gt_labels, pts_instance_mask, pts_semantic_mask, bbox3d_fields, pts_mask_fields, pts_seg_fields
+
+### 预处理
+
+`GlobalRotScaleTrans`
+- 添加：pcd_trans, pcd_rotation, pcd_scale_factor
+- 更新：points, *bbox3d_fields
+
+`RandomFlip3D`
+- 添加：flip, pcd_horizontal_flip, pcd_vertical_flip
+- 更新：points, *bbox3d_fields
+
+`PointsRangeFilter`
+- 更新：points
+
+`ObjectRangeFilter`
+- 更新：gt_bboxes_3d, gt_labels_3d
+
+`ObjectNameFilter`
+- 更新：gt_bboxes_3d, gt_labels_3d
+
+`PointShuffle`
+- 更新：points
+
+`PointsRangeFilter`
+- 更新：points
+
+### 格式化
+
+`DefaultFormatBundle3D`
+- 更新：points, gt_bboxes_3d, gt_labels_3d, gt_bboxes, gt_labels
+
+`Collect3D`
+- 添加：img_meta （由 `meta_keys` 指定的键值构成的 img_meta）
+- 移除：所有除 `keys` 指定的键值以外的其他键值
+
+### 测试时的数据增强
+
+`MultiScaleFlipAug`
+- 更新: scale, pcd_scale_factor, flip, flip_direction, pcd_horizontal_flip, pcd_vertical_flip （与这些指定的参数对应的增强后的数据列表）
+
+## 扩展并使用自定义数据集预处理方法
+
+1. 在任意文件中写入新的数据集预处理方法，如 `my_pipeline.py`，该预处理方法的输入和输出均为字典
+
+    ```python
+    from mmdet.datasets import PIPELINES
+
+    @PIPELINES.register_module()
+    class MyTransform:
+
+        def __call__(self, results):
+            results['dummy'] = True
+            return results
+    ```
+
+2. 导入新的预处理方法类
+
+    ```python
+    from .my_pipeline import MyTransform
+    ```
+
+3. 在配置文件中使用该数据集预处理方法
+
+    ```python
+    train_pipeline = [
+        dict(
+            type='LoadPointsFromFile',
+            load_dim=5,
+            use_dim=5,
+            file_client_args=file_client_args),
+        dict(
+            type='LoadPointsFromMultiSweeps',
+            sweeps_num=10,
+            file_client_args=file_client_args),
+        dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+        dict(
+            type='GlobalRotScaleTrans',
+            rot_range=[-0.3925, 0.3925],
+            scale_ratio_range=[0.95, 1.05],
+            translation_std=[0, 0, 0]),
+        dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+        dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+        dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+        dict(type='ObjectNameFilter', classes=class_names),
+        dict(type='MyTransform'),
+        dict(type='PointShuffle'),
+        dict(type='DefaultFormatBundle3D', class_names=class_names),
+        dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+    ]
+    ```