as title

## Motivation

Added ascending device support in mmseg.

## Modification

The main modification points are as follows:
We added an NPU device in the DDP scenario and DP scenario when using
the NPU.

## BC-breaking (Optional)

None

## Use cases (Optional)

We tested
[fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py)
.

## Motivation

open-mmlab#2779

…pen-mmlab#2813)

…open-mmlab#2730)

Note that this PR is a modified version of the withdrawn PR
open-mmlab#1748

## Motivation

In the last years, panoptic segmentation has become more into the focus
in reseach. Weber et al.
[[Link]](http://www.cvlibs.net/publications/Weber2021NEURIPSDATA.pdf)
have published a quite nice dataset, which is in the same style like
Cityscapes, but for KITTI sequences. Since Cityscapes and KITTI-STEP
share the same classes and also a comparable domain (dashcam view),
interesting investigations, e.g. about relations in the domain e.t.c.
can be done.

Note that KITTI-STEP provices panoptic segmentation annotations which
are out of scope for mmsegmentation.

## Modification

Mostly, I added the new dataset and dataset preparation file. To
simplify the first usage of the new dataset, I also added configs for
the dataset, segformer and deeplabv3plus.

## BC-breaking (Optional)

No BC-breaking

## Use cases (Optional)

Researchers want to test their new methods, e.g. for interpretable AI in
the context of semantic segmentation. They want to show, that their
method is reproducible on comparable datasets. Thus, they can compare
Cityscapes and KITTI-STEP.

---------

Co-authored-by: CSH <[email protected]>
Co-authored-by: csatsurnh <[email protected]>
Co-authored-by: 谢昕辰 <[email protected]>

Thanks for your contribution and we appreciate it a lot. The following
instructions would make your pull request more healthy and more easily
get feedback. If you do not understand some items, don't worry, just
make the pull request and seek help from maintainers.

## Motivation

The focal Tversky loss was proposed in https://arxiv.org/abs/1810.07842.
It has nearly 600 citations and has been shown to be extremely useful
for highly imbalanced (medical) datasets. To add support for the focal
Tversky loss, only few lines of changes are needed for the Tversky loss.

## Modification

Add `gamma` as (optional) argument in the constructor of `TverskyLoss`.
This parameter is then passed to `tversky_loss` to compute the focal
Tversky loss.

## BC-breaking (Optional)

Does the modification introduce changes that break the
backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the
downstream projects should modify their code to keep compatibility with
this PR.

## Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases
here, and update the documentation.

## Checklist

1. Pre-commit or other linting tools are used to fix the potential lint
issues.
2. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
4. The documentation has been modified accordingly, like docstring or
example tutorials.

Reopening of previous
[PR](open-mmlab#2783).