[Docs] add advanced_guides/transforms.md (#463)

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* add transforms.md

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docs/en/advanced_guides/transforms.md

@@ -1 +1,85 @@
 # Transforms
+
+- [Transforms](<>)
+  - [Overview of transforms](#overview-of-transforms)
+  - [Introduction of `MultiView`](#introduction-of-multiview)
+  - [Introduction of `PackSelfSupInputs`](#introduction-of-packselfsupinputs)
+
+## Overview of transforms
+
+We have introduced how to build a `Pipeline` in [add_transforms](./add_transforms.md). A `Pipeline` contains a series of
+`transforms`. There are three main categories of `transforms` in MMSelfSup:
+
+1. Transforms about processing the data. The unique transforms in MMSelfSup are defined in [processing.py](https://github.com/open-mmlab/mmselfsup/blob/1.x/mmselfsup/datasets/transforms/processing.py), e.g. `RandomCrop`, `RandomResizedCrop` and `RandomGaussianBlur`.
+   We may also use some transforms from other repositories, e.g. `LoadImageFromFile` from MMCV.
+
+2. The transform wrapper for multiple views of an image. It is defined in [wrappers.py](https://github.com/open-mmlab/mmselfsup/blob/1.x/mmselfsup/datasets/transforms/wrappers.py).
+
+3. The transform to pack data into a format compatible with the inputs of the algorithm. It is defined in [formatting.py](https://github.com/open-mmlab/mmselfsup/blob/1.x/mmselfsup/datasets/transforms/formatting.py).
+
+In summary, we implement these `transforms` below. The last two transforms will be introduced in detail.
+
+|                                                      class                                                      |                                      function                                      |
+| :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: |
+|                           [`BEiTMaskGenerator`](mmselfsup.datasets.BEiTMaskGenerator)                           |                      Generate mask for image refers to `BEiT`                      |
+|                         [`SimMIMMaskGenerator`](mmselfsup.datasets.SimMIMMaskGenerator)                         |            Generate random block mask for each Image refers to `SimMIM`            |
+|                                 [`ColorJitter`](mmselfsup.datasets.ColorJitter)                                 |      Randomly change the brightness, contrast, saturation and hue of an image      |
+|                                  [`RandomCrop`](mmselfsup.datasets.RandomCrop)                                  |                     Crop the given Image at a random location                      |
+|                          [`RandomGaussianBlur`](mmselfsup.datasets.RandomGaussianBlur)                          |                    GaussianBlur augmentation refers to `SimCLR`                    |
+|                           [`RandomResizedCrop`](mmselfsup.datasets.RandomResizedCrop)                           |                Crop the given image to random size and aspectratio                 |
+| [`RandomResizedCropAndInterpolationWithTwoPic`](mmselfsup.datasets.RandomResizedCropAndInterpolationWithTwoPic) | Crop the given PIL Image to random size and aspect ratio with random interpolation |
+|                              [`RandomSolarize`](mmselfsup.datasets.RandomSolarize)                              |                     Solarization augmentation refers to `BYOL`                     |
+|                          [`RotationWithLabels`](mmselfsup.datasets.RotationWithLabels)                          |                                Rotation prediction                                 |
+|                       [`RandomPatchWithLabels`](mmselfsup.datasets.RandomPatchWithLabels)                       |                 Apply random patch augmentation to the given image                 |
+|                              [`RandomRotation`](mmselfsup.datasets.RandomRotation)                              |                             Rotate the image by angle                              |
+|                             [`MultiView`](mmselfsup.datasets.transforms.MultiView)                              |               A wrapper for algorithms with multi-view image inputs                |
+|                           [`PackSelfSupInputs`](mmselfsup.datasets.PackSelfSupInputs)                           |         Pack data into a format compatible with the inputs of an algorithm         |
+
+## Introduction of `MultiView`
+
+We build a wrapper named [`MultiView`](mmselfsup.datasets.transforms.MultiView) for some algorithms e.g. MOCO, SimCLR and SwAV with multi-view image inputs. In the config file, we can
+define it as:
+
+```python
+pipeline = [
+     dict(type='MultiView',
+          num_views=2,
+          transforms=[
+            [dict(type='Resize', scale=224),]
+          ])
+]
+```
+
+, which means that there are two views in the pipeline.
+
+We can also define pipeline with different views like:
+
+```python
+pipeline = [
+     dict(type='MultiView',
+          num_views=[2, 6],
+          transforms=[
+            [
+              dict(type='Resize', scale=224)],
+            [
+              dict(type='Resize', scale=224),
+              dict(type='RandomSolarize')],
+          ])
+]
+```
+
+This means that there are two pipelines, which contain 2 views and 6 views, respectively.
+More examples can be found in [imagenet_mocov1.py](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/_base_/datasets/imagenet_mocov1.py), [imagenet_mocov2.py](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/_base_/datasets/imagenet_mocov2.py) and [imagenet_swav_mcrop-2-6.py](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/_base_/datasets/imagenet_swav_mcrop-2-6.py) etc.
+
+## Introduction of `PackSelfSupInputs`
+
+We build a class named [`PackSelfSupInputs`](mmselfsup.datasets.transforms.PackSelfSupInputs) to pack data into a format compatible with the inputs of an algorithm. This transform
+is usually put at the end of the pipeline like:
+
+```python
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiView', num_views=2, transforms=[view_pipeline]),
+    dict(type='PackSelfSupInputs', meta_keys=['img_path'])
+]
+```

mmselfsup/datasets/transforms/wrappers.py

@@ -37,7 +37,8 @@ class MultiView(BaseTransform):
         >>>                dict(type='Resize', scale=224))],
         >>>         ])
         >>> ]
-        >>> # Example 2: MultiViews 2 pipeline with 6 views
+        >>> # Example 2: MultiViews 2 pipelines, the first with 2 views,
+        >>> # the second with 6 views
         >>> pipeline = [
         >>>     dict(type='MultiView',
         >>>         num_views=[2, 6],