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

## Motivation
Supplementary PR #2444 
Fix tiny bug and add loss_by_feat() to compute loss to train.
The inference process have verified to be accurate.
## Modification
- modify `sep_aspp_contrast_head.py` , add `loss_by_feat()` function to
train(training still has bug, will fix in future)
- fix testing commands path error `bash tools/dist_test.sh
projects/HieraSeg_project/` to `bash tools/dist_test.sh
projects/HieraSeg/` at README.md

## 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

Fix the bug in running ```collect_evn.py```. #2547 

## Modification

Rename typing.py to typing_utils.py

## 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

This is essentially #2535 that I had intended to submit to the `dev-1.x`
branch but accidentally submitted it directly to the `1.x` branch
(apologies!). This also got approved possibly because the core devs also
didn't realize this.

The problem is that now `1.x` and `dev-1.x` are out of sync -- the
changes introduced by #2535 will never be reflected in `dev-1.x`.

## Modification

I'm proposing this "backward-merge" so that `1.x` and `dev-1.x` can be
in sync again. If you look at "files changed", they are exactly the
changes introduced by #2535.

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

as title

## Motivation

This is motivated by a previously unfinished PR (#2332). In that PR, the
label -1 was changed to 255 in `BaseSegDataset`, which is correct.
However, it was changed at only one location. There is another location
in `mmseg/datasets/basesegdataset.py` where -1 was still being used that
was not converted to 255. I have now converted it to 255.

This is exactly same as a similar fix to the `master` branch via #2515 .

## Modification

I've simply converted the snipped

```python
if new_id != -1:
    new_palette.append(palette[old_id])
```
to 
```python
if new_id != 255:
    new_palette.append(palette[old_id])
```

## Checklist

- [x] Pre-commit or other linting tools are used to fix the potential
lint issues.
  - _I've fixed all linting/pre-commit errors._
- [x] The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
  - _No unit tests need to be added or were affected.
- [x] If the modification has potential influence on downstream
projects, this PR should be tested with downstream projects, like MMDet
or MMDet3D.
  - _I don't think this change affects MMDet or MMDet3D._
- [x] The documentation has been modified accordingly, like docstring or
example tutorials.
- _This change fixes an existing bug and doesn't require modifying any
documentation/docstring._

This is an almost exact duplicate of #2500 (that was made to the
`master` branch) now applied to the `1.x` branch.

---

## Motivation

I want to fix a bug through this PR. The bug occurs when two options --
`reduce_zero_label=True`, and custom classes are used.
`reduce_zero_label` remaps the GT seg labels by remapping the zero-class
to 255 which is ignored. Conceptually, this should occur *before* the
`label_map` is applied, which maps *already reduced labels*. However,
currently, the `label_map` is applied before the zero label is reduced.

## Modification

The modification is simple:
- I've just interchanged the order of the two operations by moving a few
lines from bottom to top.
- I've added a test that passes when the fix is introduced, and fails on
the original `master` branch.

## BC-breaking (Optional)

I do not anticipate this change braking any backward-compatibility.

## Checklist

- [x] Pre-commit or other linting tools are used to fix the potential
lint issues.
  - _I've fixed all linting/pre-commit errors._
- [x] The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
  - _I've added a unit test._ 
- [x] If the modification has potential influence on downstream
projects, this PR should be tested with downstream projects, like MMDet
or MMDet3D.
  - _I don't think this change affects MMDet or MMDet3D._
- [x] The documentation has been modified accordingly, like docstring or
example tutorials.
- _This change fixes an existing bug and doesn't require modifying any
documentation/docstring._

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

## Motivation

https://github.com/open-mmlab/mmeval/pull/85



---------

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

as title

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

## Motivation

Support for biomedical 3d images augmentation.

## Modification

Add BioMedical3DRandomFlip in mmseg/datasets/transforms/transforms.py.

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

## Support `Mapillary Vistas Dataset`

## Motivation

Support  **`Mapillary Vistas Dataset`**
Dataset Paper link : https://ieeexplore.ieee.org/document/9878466/
Download and more information view
https://www.mapillary.com/dataset/vistas


```
@InProceedings{Neuhold_2017_ICCV,
author = {Neuhold, Gerhard and Ollmann, Tobias and Rota Bulo, Samuel and Kontschieder, Peter},
title = {The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
```

## Modification

Add `Mapillary_dataset` in `mmsegmentation/projects`
Add `configs/_base_/mapillary_v1_2.py` and
`configs/_base_/mapillary_v2_0.py`
Add `configs/deeplabv3plus_r18-d8_4xb2-80k_mapillay-512x1024.py` to test
training and testing on Mapillary datasets
Add `docs/en/user_guides/2_dataset_prepare.md` , add Mapillary Vistas
Dataset Preparing and Structure.
Add `tools/dataset_converters/mapillary.py` to convert RGB labels to
Mask labels.

Co-authored-by: 谢昕辰 <xiexinch@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

Makes docstring to be consistent with actual argument name.

## Modification

Minor fix

## BC-breaking (Optional)

No

## Motivation

As title.

## Modification

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

## Support `HieraSeg` interface on `cityscapes`

## Motivation

Support `HieraSeg` interface on cityscapes dataset  
Paper link : https://ieeexplore.ieee.org/document/9878466/

```
@article{li2022deep,
  title={Deep Hierarchical Semantic Segmentation},
  author={Li, Liulei and Zhou, Tianfei and Wang, Wenguan and Li, Jianwu and Yang, Yi},
  journal={CVPR},
  year={2022}
}
```

## Modification

Add `HieraSeg_Projects` on `projects/`
Add `sep_aspp_contrast_head` decoder head.
Add `HieraSeg` config.
Add `hiera_loss`, `hiera_triplet_loss_cityscape`, `tree_triplet_loss`

## Motivation

The motivation of this PR is to add `gt_edge_map` field to support
boundary loss.

## Modification

- GenerateEdge
Modify `gt_edge` field to `gt_edge_map`.

- PackSegInputs
Add `gt_edge_map` to data_sample.

- stack_batch
Pad `gt_edge_map` to max_shape.

## BC-breaking (Optional)

No

## Use cases (Optional)

Reference `GenerateEdge`.

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

## Motivation

Fix incorrect `img_shape` value assignment.

## Modification

- mmseg/datasets/transforms/transforms.py

## Motivation

as title

## Modification

1. .circleci/test.yml
2. .github/workflows/merge_stage_test.yml

## Motivation

As title. 

## Modification

- projects/isnet/README.md

## Motivation

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

## Motivation

Support ISNet.
paper link: [ISNet: Integrate Image-Level and Semantic-Level Context for
Semantic
Segmentation](https://openaccess.thecvf.com/content/ICCV2021/papers/Jin_ISNet_Integrate_Image-Level_and_Semantic-Level_Context_for_Semantic_Segmentation_ICCV_2021_paper.pdf)

## Modification

Add ISNet decoder head.
Add ISNet config.

## Motivation
Add the 3d pad transform for biomedical images, which follows the design
of the nnUNet.

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

## Motivation

implement nnUNet-style Gaussian Noise and Blur

## 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

## Motivation

The conflict is caused by:
    The user requested torch==1.12.1+cpu
    torchvision 0.13.0+cpu depends on torch==1.12.0

## Modification

modify the torch==1.12.0+cpu

## Motivation

Calling `mmseg.utils.register_all_modules` will import `MaskFormerHead`
and `Mask2FormerHead`, it will crash if mmdet is not installed as `None`
cannot be initialized.

## Modification

- Modify `MMDET_MaskFormerHead=BaseModule` and `MMDET_Mask2FormerHead =
BaseModule` when cannot import from mmdet

* 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