Bump Version to 1.0.0rc6

[Z-Fran]

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Motivation

Please describe the motivation of this PR and the goal you want to achieve through this PR.

Modification

Highlights

We are excited to announce the release of MMEditing 1.0.0rc6. This release supports 50+ models, 222+ configs and 209+ checkpoints in MMGeneration and MMEditing. We highlight the following new features

• Support Gradio gui of Inpainting inference.
• Support Colorization, Translationin and GAN models inference.

Backwards Incompatible changes

1. GenValLoop and MultiValLoop has been merged to EditValLoop, GenTestLoop and MultiTestLoop has been merged to EditTestLoop. Use case:

    Case 1: metrics on a single dataset

    >>> # add the following lines in your config
    >>> # 1. use `EditValLoop` instead of `ValLoop` in MMEngine
    >>> val_cfg = dict(type='EditValLoop')
    >>> # 2. specific EditEvaluator instead of Evaluator in MMEngine
    >>> val_evaluator = dict(
    >>>     type='EditEvaluator',
    >>>     metrics=[
    >>>         dict(type='PSNR', crop_border=2, prefix='Set5'),
    >>>         dict(type='SSIM', crop_border=2, prefix='Set5'),
    >>>     ])
    >>> # 3. define dataloader
    >>> val_dataloader = dict(...)

    Case 2: different metrics on different datasets

    >>> # add the following lines in your config
    >>> # 1. use `EditValLoop` instead of `ValLoop` in MMEngine
    >>> val_cfg = dict(type='EditValLoop')
    >>> # 2. specific a list EditEvaluator
    >>> # do not forget to add prefix for each metric group
    >>> div2k_evaluator = dict(
    >>>     type='EditEvaluator',
    >>>     metrics=dict(type='SSIM', crop_border=2, prefix='DIV2K'))
    >>> set5_evaluator = dict(
    >>>     type='EditEvaluator',
    >>>     metrics=[
    >>>         dict(type='PSNR', crop_border=2, prefix='Set5'),
    >>>         dict(type='SSIM', crop_border=2, prefix='Set5'),
    >>>     ])
    >>> # define evaluator config
    >>> val_evaluator = [div2k_evaluator, set5_evaluator]
    >>> # 3. specific a list dataloader for each metric groups
    >>> div2k_dataloader = dict(...)
    >>> set5_dataloader = dict(...)
    >>> # define dataloader config
    >>> val_dataloader = [div2k_dataloader, set5_dataloader]

2. Support stack and split for EditDataSample, Use case:

# Example for `split`
gen_sample = EditDataSample()
gen_sample.fake_img = outputs  # tensor
gen_sample.noise = noise  # tensor
gen_sample.sample_kwargs = deepcopy(sample_kwargs)  # dict
gen_sample.sample_model = sample_model  # string
# set allow_nonseq_value as True to copy non-sequential data (sample_kwargs and sample_model for this example)
batch_sample_list = gen_sample.split(allow_nonseq_value=True)  

# Example for `stack`
data_sample1 = EditDataSample()
data_sample1.set_gt_label(1)
data_sample1.set_tensor_data({'img': torch.randn(3, 4, 5)})
data_sample1.set_data({'mode': 'a'})
data_sample1.set_metainfo({
    'channel_order': 'rgb',
    'color_flag': 'color'
})
data_sample2 = EditDataSample()
data_sample2.set_gt_label(2)
data_sample2.set_tensor_data({'img': torch.randn(3, 4, 5)})
data_sample2.set_data({'mode': 'b'})
data_sample2.set_metainfo({
    'channel_order': 'rgb',
    'color_flag': 'color'
})
data_sample_merged = EditDataSample.stack([data_sample1, data_sample2])

3. GenDataPreprocessor has been merged into EditDataPreprocessor,

   • No changes are required other than changing the type field in config.
   • Users do not need to define input_view and output_view since we will infer the shape of mean automatically.
   • In evaluation stage, all tensors will be converted to BGR (for three-channel images) and [0, 255].

4. PixelData has been removed

5. For BaseGAN/CondGAN models, real images are passed from data_samples.gt_img instead of inputs['img']

New Features & Improvements

• Refactor FileIO. #1572
• Refactor registry. #1621
• Refactor Random degradations. #1583
• Refactor DataSample, DataPreprocessor, Metric and Loop. #1656
• Use mmengine.basemodule instead of nn.module. #1491
• Refactor Main Page. #1609
• Support Gradio gui of Inpainting inference. #1601
• Support Colorization inferencer. #1588
• Support Translation models inferencer. #1650
• Support GAN models inferencer. #1653, #1659
• Print config tool. #1590
• Improve type hints. #1604
• Update Chinese documents of metrics and datasets. #1568, #1638
• Update Chinese documents of BigGAN and Disco-Diffusion. #1620
• Update Evaluation and README of Guided-Diffusion. #1547

Bug Fixes

• Fix the meaning of momentum in EMA. #1581
• Fix output dtype of RandomNoise. #1585
• Fix pytorch2onnx tool. #1629
• Fix API documents. #1641, #1642
• Fix loading RealESRGAN EMA weights. #1647
• Fix arg passing bug of dataset_converters scripts. #1648

Who can help? @ them here!

BC-breaking (Optional)

Does the modification introduce changes that break the backward-compatibility of the downstream repositories?
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

Before PR:

• I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
• Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
• Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
• New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
• The documentation has been modified accordingly, including docstring or example tutorials.

After PR:

• If the modification has potential influence on downstream or other related projects, this PR should be tested with some of those projects.
• CLA has been signed and all committers have signed the CLA in this PR.

[CLAassistant]

Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.
15 out of 17 committers have signed the CLA.

✅ Z-Fran
✅ liangzelong
✅ liuwenran
✅ dienachtderwelt
✅ zengyh1900
✅ xiaomile
✅ triple-Mu
✅ plyfager
✅ Li-Qingyun
✅ LeoXing1996
✅ i-aki-y
✅ Luo-Yihang
✅ ruoningYu
✅ VongolaWu
✅ vansin
❌ matrixgame2018
❌ ydengbi
_{You have signed the CLA already but the status is still pending? Let us recheck it.}

[codecov]

Codecov Report

Patch coverage has no change and project coverage change: +0.24 🎉

Comparison is base (e7c0539) 87.90% compared to head (9f33d9c) 88.15%.

❗ Current head 9f33d9c differs from pull request most recent head 869f576. Consider uploading reports for the commit 869f576 to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##              1.x    #1661      +/-   ##
==========================================
+ Coverage   87.90%   88.15%   +0.24%     
==========================================
  Files         399      399              
  Lines       26015    26372     +357     
  Branches     4044     4114      +70     
==========================================
+ Hits        22868    23247     +379     
- Misses       2240     2241       +1     
+ Partials      907      884      -23     

Flag	Coverage Δ
unittests	88.15% <ø> (+0.24%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
mmedit/evaluation/functional/inception_utils.py	35.54% <0.00%> (-17.79%)	⬇️
mmedit/evaluation/metrics/sad.py	85.71% <0.00%> (-14.29%)	⬇️
mmedit/models/editors/pix2pix/pix2pix.py	83.48% <0.00%> (-13.72%)	⬇️
mmedit/evaluation/metrics/matting_mse.py	79.31% <0.00%> (-11.17%)	⬇️
mmedit/evaluation/metrics/gradient_error.py	88.88% <0.00%> (-11.12%)	⬇️
mmedit/evaluation/metrics/connectivity_error.py	90.00% <0.00%> (-7.62%)	⬇️
...edit/evaluation/metrics/base_sample_wise_metric.py	81.13% <0.00%> (-6.97%)	⬇️
mmedit/models/editors/eg3d/eg3d.py	93.49% <0.00%> (-6.51%)	⬇️
...medit/apis/inferencers/unconditional_inferencer.py	94.11% <0.00%> (-5.89%)	⬇️
mmedit/models/editors/ddpm/ddpm_scheduler.py	75.51% <0.00%> (-5.61%)	⬇️
... and 137 more

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[LeoXing1996]

LGTM