## Motivation

Make MMSeginferencer easier to be used

## Modification

1. Add `_load_weights_to_model` to MMSeginferencer, it is for get
`dataset_meta` from ckpt
2. Modify and remove some parameters of `__call__`, `visualization` and
`postprocess`
3. Add function of save seg mask, remove dump pkl.
4. Refine docstring of MMSeginferencer and SegLocalVisualizer
5. Add the user documentation of MMSeginferencer

## BC-breaking (Optional)

yes, remove some parameters, we need to discuss whether keep them with
deprecated waring or just remove them as the MMSeginferencer just merged
in mmseg a few days.

Co-authored-by: xiexinch <xiexinch@outlook.com>

## Modification
Add Chinese dataflow markdown

---------

Signed-off-by: csatsurnh <cshan1995@126.com>
Co-authored-by: csatsurnh <cshan1995@126.com>
Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>

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>

## 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

update link in dock

## Modification

docs/en/device/npu.md
docs/zh_cn/device/npu.md

## 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 NPU results -- apcnet, bisenetv1, bisenetv2 

Modification
add docs/en/device/npu.md and docs/zh_cn/device/npu.md that accompanies
the submission results.

---------

Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>

as title

## Motivation

Docs for Visualization featusre map using wandb backend.

## Modification

Add a new markdown file and result demo of wandb.

---------

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

Motivation
add NPU results.

Modification
add docs/en/device/npu.md and docs/zh_cn/device/npu.md that accompanies
the submission results.

---------

Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>

## Motivation
Translate Chinese version customized runtime doc into English
https://github.com/open-mmlab/mmsegmentation/pull/2502.

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



---------

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.

Co-authored-by: xiexinch <xiexinch@outlook.com>

Add English version of `datasets.md`, the Chinese version is in
https://github.com/open-mmlab/mmsegmentation/pull/2387

.

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

Old PR: https://github.com/open-mmlab/mmsegmentation/pull/2169

## Motivation

Ref:
https://github.com/open-mmlab/mmsegmentation/pull/2464#discussion_r1072211706



Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>

## Motivation

As title.

## Modification

- docs/en/api.rst
- docs/zh_cn/api.rst
- add `scipy` to readthedocs requirement.

## Motivation
- Add Chinese version of Synapse dataset preparation.
- Modify all `，` and `。` to `,` and `.` in
`docs/zh_cn/user_guides/2_dataset_prepare.md`.

## Motivation

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

Add the random gamma correction transform for biomedical images, which
follows the design of the nnUNet.

## 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

* [Doc] Change some content from customize_runtime to engine ZN doc

* fix comments

* add customize runtime setting zn doc

* move optimizer content into one section

* fix

* fix

* fix

* fix

* draft

* update loss

* update

* add runner

* add steps

* update

* DOC

Update docs/zh_cn/get_started.md

Co-authored-by: 谢昕辰 <xiexinch@outlook.com>

* [Doc] Add ZN datasets.md in dev-1.x

* fix typo

* fix

* fix

* CodeCamp #1562 [Doc] update `overview.md`

* Update overview.md

* Update docs/zh_cn/overview.md

Co-authored-by: 谢昕辰 <xiexinch@outlook.com>

* Update docs/zh_cn/overview.md

Co-authored-by: 谢昕辰 <xiexinch@outlook.com>

doc

modify part of content

changed parts of content

modified

Update docs/zh_cn/user_guides/4_train_test.md

Co-authored-by: 谢昕辰 <xiexinch@outlook.com>

* [Doc]Translate the 1_config.md and modify a wrong statement in 1_config.md

* Translate the 1_config.md and modify a wrong statement in 1_config.md

* Modify some expressions

* Apply suggestions from code review

* Bump 1.0.0rc2

* typo