[Refactor] Add transform tutorial for dev-1.x (#1985)

* transform tutorial

* fix

docs/en/advanced_guides/add_transform.md

@@ -0,0 +1,37 @@
+# Adding New Data Transforms
+
+1. Write a new pipeline in any file, e.g., `my_pipeline.py`. It takes a dict as input and return a dict.
+
+   ```python
+   from mmseg.datasets import TRANSFORMS
+   @TRANSFORMS.register_module()
+   class MyTransform:
+       def __call__(self, results):
+           results['dummy'] = True
+           return results
+   ```
+
+2. Import the new class.
+
+   ```python
+   from .my_pipeline import MyTransform
+   ```
+
+3. Use it in config files.
+
+   ```python
+   crop_size = (512, 1024)
+   train_pipeline = [
+       dict(type='LoadImageFromFile'),
+       dict(type='LoadAnnotations'),
+       dict(type='RandomResize',
+            scale=(2048, 1024),
+            ratio_range=(0.5, 2.0),
+            keep_ratio=True),
+       dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+       dict(type='RandomFlip', flip_ratio=0.5),
+       dict(type='PhotoMetricDistortion'),
+       dict(type='MyTransform'),
+       dict(type='PackSegInputs'),
+   ]
+   ```

docs/en/advanced_guides/transforms.md

@@ -87,41 +87,3 @@ Before pipelines, the information we can directly obtain from the datasets are i
 
 - add: inputs, data_sample
 - remove: keys specified by `meta_keys` (merged into the metainfo of data_sample), all other keys
-
-## Extend and use custom pipelines
-
-1. Write a new pipeline in any file, e.g., `my_pipeline.py`. It takes a dict as input and return a dict.
-
-   ```python
-   from mmseg.datasets import PIPELINES
-   @PIPELINES.register_module()
-   class MyTransform:
-       def __call__(self, results):
-           results['dummy'] = True
-           return results
-   ```
-
-2. Import the new class.
-
-   ```python
-   from .my_pipeline import MyTransform
-   ```
-
-3. Use it in config files.
-
-   ```python
-   crop_size = (512, 1024)
-   train_pipeline = [
-       dict(type='LoadImageFromFile'),
-       dict(type='LoadAnnotations'),
-       dict(type='RandomResize',
-            scale=(2048, 1024),
-            ratio_range=(0.5, 2.0),
-            keep_ratio=True),
-       dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-       dict(type='RandomFlip', flip_ratio=0.5),
-       dict(type='PhotoMetricDistortion'),
-       dict(type='MyTransform'),
-       dict(type='PackSegInputs'),
-   ]
-   ```