[Enhancement] Modify interface of MMSeginferencer and add docs

docs/en/user_guides/3_inference.md

@@ -4,13 +4,130 @@ MMSegmentation provides pre-trained models for semantic segmentation in [Model Z
 This note will show how to use existing models to inference on given images.
 As for how to test existing models on standard datasets, please see this [guide](./4_train_test.md)
 
-## Inference API
-
 MMSegmentation provides several interfaces for users to easily use pre-trained models for inference.
 
-- [mmseg.apis.init_model](#mmsegapisinit_model)
-- [mmseg.apis.inference_model](#mmsegapisinference_model)
-- [mmseg.apis.show_result_pyplot](#mmsegapisshow_result_pyplot)
+- [Tutorial 3: Inference with existing models](#tutorial-3-inference-with-existing-models)
+ - [Inferencer](#inferencer)
+ - [Basic Usage](#basic-usage)
+ - [Initialization](#initialization)
+ - [Visualize prediction](#visualize-prediction)
+ - [List model](#list-model)
+ - [Inference API](#inference-api)
+ - [mmseg.apis.init_model](#mmsegapisinit_model)
+ - [mmseg.apis.inference_model](#mmsegapisinference_model)
+ - [mmseg.apis.show_result_pyplot](#mmsegapisshow_result_pyplot)
+
+## Inferencer
+
+We provides the most **convenient** way to use the model in MMSegmentation `MMSegInferencer`. You can get segmentation mask for an image with only 3 lines of code.
+
+### Basic Usage
+
+The following example shows how to use `MMSegInferencer` to perform inference on a single image.
+
+```
+>>> from mmseg.apis import MMSegInferencer
+>>> # Load models into memory
+>>> inferencer = MMSegInferencer(model='deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024')
+>>> # Inference
+>>> inferencer('demo/demo.png', show=True)
+```
+
+The visualization result should look like:
+
+<div align="center">
+https://user-images.githubusercontent.com/76149310/221507927-ae01e3a7-016f-4425-b966-7b19cbbe494e.png
+</div>
+
+Moreover, you can use `MMSegInferencer` to process a list of images:
+
+```
+# Input a list of images
+>>> images = [image1, image2, ...] # image1 can be a file path or a np.ndarray
+>>> inferencer(images, show=True, wait_time=0.5) # wait_time is delay time, and 0 means forever.
+
+# Or input image directory
+>>> images = $IMAGESDIR
+>>> inferencer(images, show=True, wait_time=0.5)
+
+# Save visualized rendering color maps and predicted results
+# out_dir is the directory to save the output results, img_out_dir and pred_out_dir are subdirectories out_dir
+# to save visualized rendering color maps and predicted results
+>>> inferencer(images, out_dir='outputs', img_out_dir='vis', pred_out_dir='pred')
+```
+
+There are 2 kinds of inferencer outputs, and one is `dict` type the other is [`SegDataSample`](../advanced_guides/structures.md) type
+
+```
+result = inferencer('demo/demo.png')
+# result is a `dict` including 2 keys 'visualization' and 'predictions'.
+# color segmentation map
+print(result['visualization'].shape)
+# (512, 683, 3)
+
+# segmentation mask with label indice
+print(result['predictions'].shape)
+# (512, 683)
+
+result = inferencer('demo/demo.png', return_datasamples=True)
+print(type(result))
+# <class 'mmseg.structures.seg_data_sample.SegDataSample'>
+
+# Input a list of images
+results = inferencer(images)
+# The output is list
+print(type(results['visualization']), results['visualization'][0].shape)
+# <class 'list'> (512, 683, 3)
+print(type(results['predictions']), results['predictions'][0].shape)
+# <class 'list'> (512, 683)
+
+results = inferencer(images, return_datasamples=True)
+# <class 'list'>
+print(type(results[0]))
+# <class 'mmseg.structures.seg_data_sample.SegDataSample'>
+```
+
+### Initialization
+
+`MMSegInferencer` must be initialized from a `model`, which can be a model name or a `Config` even a path of config file.
+The model names can be found in models' metafile, like one model name of maskformer is `maskformer_r50-d32_8xb2-160k_ade20k-512x512`, and if input model name and the weights of the model will be download automatically. Below are other input parameters:
+
+- weights (str, optional) - Path to the checkpoint. If it is not specified and model is a model name of metafile, the weights will be loaded
+ from metafile. Defaults to None.
+- classes (list, optional) - Input classes for result rendering, as the prediction of segmentation
+ model is a segment map with label indices, `classes` is a list which includes
+ items responding to the label indices. If classes is not defined, visualizer will take `cityscapes` classes by default. Defaults to None.
+- palette (list, optional) - Input palette for result rendering, which is a list of color palette
+ responding to the classes. Defaults to None.
+- dataset_name (str, optional)[Dataset name or alias](https:/open-mmlab/mmsegmentation/blob/a35e1c4232a03503a2d549206ca703d3850d5244/mmseg/utils/class_names.py#L302-L317)
+ visulizer will use the meta information of the dataset i.e. classes and palette,
+ but the `classes` and `palette` have higher priority. Defaults to None.
+- device (str, optional) - Device to run inference. If None, the available device will be automatically used. Defaults to None.
+- scope (str, optional) - The scope of the model. Defaults to 'mmseg'.
+
+### Visualize prediction
+
+`MMSegInferencer` supports 4 parameters for visualize prediction, you can use them when call initialized inferencer:
+
+- show (bool) - Whether to display the image in a popup window. Defaults to False.
+- wait_time (float) - The interval of show (s). Defaults to 0.
+- img_out_dir (str) - Subdirectory of `out_dir`, used to save rendering color segmentation mask, so `out_dir` must be defined
+ if you would like to save predicted mask. Defaults to 'vis'.
+- opacity (int, float) - The transparency of segmentation mask. Defaults to 0.8.
+
+The examples of these parameters is in [Basic Usage](#basic-usage)
+
+### List model
+
+There is a very easy to list all model names in MMSegmentation
+
+```
+>>> from mmseg.apis import MMSegInferencer
+# models is a list of model names, and them will print automatically
+>>> models = MMSegInferencer.list_models('mmseg')
+```
+
+## Inference API
 
 ### mmseg.apis.init_model