Co-authored-by: CSH <40987381+csatsurnh@users.noreply.github.com>

## Motivation

Change the dependency `mmcls` to `mmpretrain`

## Modification

- modify `mmcls` to `mmpretrain`
- modify CI requirements

## BC-breaking (Optional)

If users have installed mmcls but not install mmpretrain, it might raise some error.

## Motivation

- Create the `main` branch

## Modification

Modify links from `dev-1.x` to `main`

as title

## Motivation

As the mmdet and mmcls are not very stabel, and mim can install repo
from source code, we remove them from mminstall and they won't be
installed automatically when run `mim install mmsegmentation`

## Modification

1. remove mmdet and mcls from mminstall
2. add explanation in faq

---------

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

as title

## 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

To release 1.0.0rc3  

## Modification

1. Modify mmseg version 
2. Add change log
3. Modify README
4. Modify faq 
5. Revise docker file

* Bump 1.0.0rc2

* typo

* [WIP] Refine documentation

* get started done

* config refine

* train_test

* refine user guides

* add contribution

* add contribution

* refine visualization

* advanced tutorial

* advanced guides

* tricks

* refine zh doc

* refactor changelog

* [Doc] MMSeg 1.x Documentation

* revise index

* [Docs] add faq document

* Update docs/en/faq.md

Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>

* Update docs/zh_cn/faq.md

Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>

* modify readme

* modify index.rst

Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>