[Fix] Add out_channels  in `CascadeEncoderDecoder` and update OCRNet and MobileNet v2 results (#2656)

## Motivation

As title.

## Modification

1. update results in readme
2. fix attr error in cascade encoder decoder

## Motivation

The registry now supports auto-import modules from the given location.

register_all_modules before running is no longer needed. The modules
will be lazy-imported during building.

- [x] This PR can be merged after
https://github.com/open-mmlab/mmengine/pull/643. The MMEngine version
should be updated.

Ref: https://github.com/open-mmlab/mmdetection/pull/9143

## Motivation

In MMEngine >= 0.2.0, it might directly determine what the backend is by
using the `data_root` path.

## Modification

Set all default `backend_args` values are `None`.

## Motivation 
Add REFUGE datasets
Old PR: https://github.com/open-mmlab/mmsegmentation/pull/2420



---------

Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>

## Motivation

The DETR-related modules have been refactored in
open-mmlab/mmdetection#8763, which causes breakings of MaskFormer and
Mask2Former in both MMDetection (has been fixed in
open-mmlab/mmdetection#9515) and MMSegmentation. This pr fix the bugs in
MMSegmentation.

### TO-DO List

- [x] update configs
- [x] check and modify data flow
- [x] fix unit test
- [x] aligning inference
- [x] write a ckpt converter
- [x] write ckpt update script
- [x] update model zoo
- [x] update model link in readme
- [x] update
[faq.md](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/docs/en/notes/faq.md#installation

)

## Tips of Fixing other implementations based on MaskXFormer of mmseg

1. The Transformer modules should be built directly. The original
building with register manner has been refactored.
2. The config requires to be modified. Delete `type` and modify several
keys, according to the modifications in this pr.
3. The `batch_first` is set `True` uniformly in the new implementations.
Hence the data flow requires to be transposed and config of
`batch_first` needs to be modified.
4. The checkpoint trained on the old implementation should be converted
to be used in the new one.

### Convert script

```Python
import argparse
from copy import deepcopy
from collections import OrderedDict

import torch

from mmengine.config import Config
from mmseg.models import build_segmentor
from mmseg.utils import register_all_modules
register_all_modules(init_default_scope=True)


def parse_args():
    parser = argparse.ArgumentParser(
        description='MMSeg convert MaskXFormer model, by Li-Qingyun')
    parser.add_argument('Mask_what_former', type=int,
                        help='Mask what former, can be a `1` or `2`',
                        choices=[1, 2])
    parser.add_argument('CFG_FILE', help='config file path')
    parser.add_argument('OLD_CKPT_FILEPATH', help='old ckpt file path')
    parser.add_argument('NEW_CKPT_FILEPATH', help='new ckpt file path')
    args = parser.parse_args()
    return args


args = parse_args()

def get_new_name(old_name: str):
    new_name = old_name

    if 'encoder.layers' in new_name:
        new_name = new_name.replace('attentions.0', 'self_attn')

    new_name = new_name.replace('ffns.0', 'ffn')

    if 'decoder.layers' in new_name:

        if args.Mask_what_former == 2:
            # for Mask2Former
            new_name = new_name.replace('attentions.0', 'cross_attn')
            new_name = new_name.replace('attentions.1', 'self_attn')
        else:
            # for Mask2Former
            new_name = new_name.replace('attentions.0', 'self_attn')
            new_name = new_name.replace('attentions.1', 'cross_attn')

    return new_name
    
def cvt_sd(old_sd: OrderedDict):
    new_sd = OrderedDict()
    for name, param in old_sd.items():
        new_name = get_new_name(name)
        assert new_name not in new_sd
        new_sd[new_name] = param
    assert len(new_sd) == len(old_sd)
    return new_sd
    
if __name__ == '__main__':
    cfg = Config.fromfile(args.CFG_FILE)
    model_cfg = cfg.model

    segmentor = build_segmentor(model_cfg)

    refer_sd = segmentor.state_dict()
    old_ckpt = torch.load(args.OLD_CKPT_FILEPATH)
    old_sd = old_ckpt['state_dict']

    new_sd = cvt_sd(old_sd)
    print(segmentor.load_state_dict(new_sd))

    new_ckpt = deepcopy(old_ckpt)
    new_ckpt['state_dict'] = new_sd
    torch.save(new_ckpt, args.NEW_CKPT_FILEPATH)
    print(f'{args.NEW_CKPT_FILEPATH} has been saved!')
```

Usage:
```bash
# for example
python ckpt4pr2532.py 1 configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py original_ckpts/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-cbd39cc1.pth cvt_outputs/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724.pth
python ckpt4pr2532.py 2 configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py original_ckpts/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-4c62652d.pth cvt_outputs/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055.pth
```

---------

Co-authored-by: MeowZheng <meowzheng@outlook.com>

## Motivation

Use the new fileio from mmengine
https://github.com/open-mmlab/mmengine/pull/533

## Modification

1. Use `mmengine.fileio` to repalce FileClient  in mmseg/datasets
2. Use `mmengine.fileio` to repalce FileClient in
mmseg/datasets/transforms
3. Use `mmengine.fileio` to repalce FileClient in mmseg/visualization

## BC-breaking (Optional)

we modify all the dataset configurations, so please use the latest config file.

## Motivation

Add Synapse dataset in MMSegmentation.
Old PR: https://github.com/open-mmlab/mmsegmentation/pull/2372.

* tta init

* use mmcv transform

* test city

* add multiscale

* fix merge

* add softmax to post process

* add ut

* add tta pipeline to other datasets

* remove softmax

* add encoder_decoder_tta ut

* add encoder_decoder_tta ut

* rename

* rename file

* rename config

* rm aug_test

* move flip to post process

* fix channel

* init commits

* fix crop size

* add seg_data2instance_data method

* add ut and update requirement

* update configs and readme

* add model-indel

* update optional requirements

* fix results

* fix lint error

* update results

* update results

* remove mmdet from requirements/optional.txt

* use try import and update README

* add docstring to overwrtied method

* minor change

Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>

* [Feature] Support MaskFormer(NeurIPS'2021) in MMSeg 1.x

* add mmdet try except logic

* refactor config files

* add readme

* fix config

* update models & logs

* add MMDET installation and fix info

* fix comments

* fix

* fix config norm optimizer setting

* update models & logs & unittest

* add docstring of MaskFormerHead

* wait for mmdet 3.0.0rc4

* replace seg_mask with seg_logits & add docstring for batch_input_shape

* use mmdet3.0.0rc4

* fix readme and modify config comments

* add mmdet installation in pr_stage_test.yml

* update mmcv version in pr_stage_test.yml

* add mmdet in build_cpu of pr_stage_test.yml

* modify mmdet& mmcv installation in merge_stage_test.yml

* fix typo

* update test.yml

* update test.yml

* [Feature] 2.0 PoolFormer

* fix mmcls version

* fix ut error

* fix ut

* fix ut

* [Fix] Fix several config file errors in 2.0

* change _base_ config file name in configs

* rename ann configs

* update ann yml

* update

* update

* update

* update

* update

* update ann readme

* update

* update deeplabv3

* update readme

* fix yml

* fix beit

* [Fix] Fix SegLocalVisualizer gt_sem_seg cuda tensor error

* fix ut error and add config visualizer dict

* fix ut error