OpenMMLab Image and Video Editing Toolbox

Last update: Jan 04, 2023

Overview

Introduction

MMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.3 to 1.6.

Documentation: https://mmediting.readthedocs.io/en/latest/.

Major features

Modular design

We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules.
Support of multiple tasks in editing

The toolbox directly supports popular and contemporary inpainting, matting, super-resolution and generation tasks.
State of the art

The toolbox provides state-of-the-art methods in inpainting/matting/super-resolution/generation.

License

This project is released under the Apache 2.0 license.

Changelog

v0.6.0 was released in 2021-3-31.

Note that MMSR has been merged into this repo, as a part of MMEditing. With elaborate designs of the new framework and careful implementations, hope MMEditing could provide better experience.

Benchmark and model zoo

Please refer to model_zoo for more details.

Installation

Please refer to install.md for installation.

Get Started

Please see getting_started.md for the basic usage of MMEditing.

Contributing

We appreciate all contributions to improve MMEditing. Please refer to CONTRIBUTING.md in MMDetection for the contributing guideline.

Acknowledgement

MMEditing is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.

Projects in OpenMMLab

MMCV: OpenMMLab foundational library for computer vision.
MMClassification: OpenMMLab image classification toolbox and benchmark.
MMDetection: OpenMMLab detection toolbox and benchmark.
MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
MMTracking: OpenMMLab video perception toolbox and benchmark.
MMPose: OpenMMLab pose estimation toolbox and benchmark.
MMEditing: OpenMMLab image and video editing toolbox.
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.

Comments

[Feature] Add metric module for FID and KID
Related to https://github.com/open-mmlab/mmediting/issues/772

Unlike common metrics like PSNR or SSIM, some metrics are used to calculate a distance of distributions between the predictions and the GTs. To compute this, feature vectors are extracted and stored from each image.

In the implementation, feature vectors are extracted and stored each step in Dataset.evaluate, and then metrics are computed in EvalIterHook using these feature vectors.

Thank you for any advice for the implementation.

Final test results

Since SRFolderDataset only use a pair of gt and lq, I computed and compared FID and KID results by using fixed fake images with the same length as real images of 50k fake images.

||FID|KID| |-|-|-| |StyleGAN|6.6291332|0.0022586| |Ours (StyleGAN weights)|6.6251227|0.0021804| |Ours (pytorch weights)|6.6007636|0.0021755|

TODO

[x] Unit test for functionality

[x] Verify numerical correctness

[x] Documentation (Use Cases)

[x] Final test

status/WIP priority/P1 kind/feature
opened by KKIEEK 30
[Feature] Support various scales in RRDBNet
Motivation

Current RRDBNet only supports 4x upsampling.

The parameter "radius" in config file of real_esrgan is wrong.

Modification

Add scale parameter in rrdb_net

Edit config file of real_esrgan for supporting various scales

Edit config file of real_esrgan for fixing some errors (radius=50 -> kernel_size=51)
opened by anse3832 19
BasicVSR++: reproduce ntire decompression results on track3

Hi, Thanks for your great work. When I use BasicVSR++ to reproduce ntire decompression results on track3 with the trained model you provided and official testset. the settings include: basicvsr_plusplus_c128n25_600k_ntire_decompress_track3.py and basicvsr_plusplus_c128n25_ntire_decompress_track3_20210304_6daf4a40.pth.

After testing, the Eval-PSNR is 30.0519, and the Eval-lq_PSNR is 28.3367. So I just gain 1.71dB improvements on track3. I set the num_input_frame as length of each sequence to use the full video sequence as inputs when I test.

Can you give some advice?

opened by sxd0071 17
Error occured during training BasicVSR

During training, error as follows occured. I run it with official config in "configs/restorers/basicvsr/basicvsr_reds4.py".

RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argument "find_unused_parameters=True" to "torch.nn.parallel.DistributedDataParallel"; (2) making sure all "forward" function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's "forward" function. Please include the loss function and the structure of the return value of "forward" of your module when reporting this issue (e.g. list, dict, iterable).

Though I solved it by adding find_unused_parameters=True in line 128, "apis/train.py" and removing self.generator.find_unused_parameters = False in line 84, "models/restorers/basicvsr.py", I still don't know why that error happened.

In "models/restorers/basicvsr.py", author declares a variable "self.generator.find_unused_parameters = False", which seems that shouldn't have influence on program, for it's never used as a parameter in program. However, it indeed causes the error mentioned above.

opened by NK-CS-ZZL 17
Progress of Chinese Documentation
We are building Chinese Documentation now, PRs of translation from the community are welcomed.

To make the community fully aware of the progress, we list the progress here. Please feel free to leave a message and create a PR if you are willing to translate any one of the documentation.

[x] docs/changelog.md @kai422

[x] docs/config.md @AlexZou14

[x] docs/config_generation.md @ckkelvinchan

[x] docs/config_inpainting.md @ckkelvinchan

[x] docs/config_matting.md @ckkelvinchan

[x] docs/config_restoration.md @AlexZou14

[x] docs/demo.md @ckkelvinchan

[x] docs/faq.md @Yshuo-Li

[x] docs/getting_started.md @nbei

[x] docs/install.md @LiUzHiAn

[x] docs/notes.md @Yshuo-Li

[x] docs/quick_run.md @Yshuo-Li

[x] docs/tools_scripts.md @ckkelvinchan

[x] configs/restorers/*/README.md (configs of 14 restorers) @ckkelvinchan

[x] configs/mattors/*/README.md (configs of 3 mattors) @Yshuo-Li

[x] configs/inpainting/*/README.md (configs of 4 inpainting models) @Yshuo-Li

[x] configs/synthesizers/*/README.md (configs of 2 synthesizers) @Yshuo-Li

[x] tools/data/super-resolution/*/README.md (4 super-resolution datasets) @LiUzHiAn

[x] tools/data/generation/*/README.md (2 generation datasets) @nbei

[x] tools/data/matting/comp1k/README.md @ckkelvinchan

[x] tools/data/inpainting/*/README.md (3 inpainting datasets) @nbei

[x] demo/restorer_basic_tutorial.ipynb @ckkelvinchan

Guidence

All the docs needed to be translated are now put in docs_zh_CN/. Upon finished, simply create a PR and wait to see how your contribution makes the change. Additional info about the contribution process can be found at https://zhuanlan.zhihu.com/p/387116301 (written in Chinese) and https://github.com/open-mmlab/mmediting/issues/432.
help wanted good first issue
opened by ckkelvinchan 16
gca model missing keys

Hello, thank you very much for the framework you provided. When I used the GCA network training in matting, missing keys in source state_dict appeared. The weight of this backbone model is model_best_resnet34_en_nomixup.pth, Could you please give me a look at what might be the cause? Thank you very much！
kind/bug status/need more info priority/P0

opened by Zenobia7 15
Reading data from S3

Hi! I was wondering if it is possible to read data from AWS S3 to train a model such as BasicVSR.

I have been reading the docs and all indicates it could be done by modifying my config file, and setting the backend option to "ceph" instead of the default "disk".

Then I sett the paths to: lq_folder = "S3://my-bucket/my-dataset/lq_folder" gt_folder = "S3://my-bucket/my-dataset/gt_folder"

But I can't manage to make it work. Probably I am missing something. Could you provide me with some extra guidance? :)
kind/enhancement good first issue

opened by jasoromir 14

Doesn't generalize to other data

I tried applying the video super resolution (EDVR) on other data, but I'm getting very weak results. The output barely seems to differ from the input in quality. Examples below (left is output, right is zoomed in input).

I tried both the EDVR_REDS_SR_L and the EDVR_Vimeo90K_SR_L models, with varying input sizes, getting similar results. Is this to be expected? I would guess given the REDS4 dataset was also mostly street scenes, it should at least perform similarly.

Screenshot 2020-03-07 at 22 11 36

The code I'm using (adapted from test_Vid4_REDS4_with_GT.py and moved to the root folder of the repos. Although I tested it on the REDS4 dataset with no issues.

Test Vid4 (SR) and REDS4 (SR-clean, SR-blur, deblur-clean, deblur-compression) datasets
'''

import sys
sys.path.insert(0, 'codes')

import os
import os.path as osp
import glob
import logging
import numpy as np
import cv2
import torch

import utils.util as util
import data.util as data_util
import models.archs.EDVR_arch as EDVR_arch

#################
# configurations
#################
device = torch.device('cuda')
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
data_mode = 'sharp_bicubic'  # Vid4 | sharp_bicubic | blur_bicubic | blur | blur_comp
# Vid4: SR
# REDS4: sharp_bicubic (SR-clean), blur_bicubic (SR-blur);
#        blur (deblur-clean), blur_comp (deblur-compression).
stage = 1  # 1 or 2, use two stage strategy for REDS dataset.
flip_test = False
############################################################################
#### model
model_path = 'experiments/pretrained_models/EDVR_REDS_SR_L.pth'

N_in = 5  # use N_in images to restore one HR image

predeblur, HR_in = False, False
back_RBs = 40
model = EDVR_arch.EDVR(128, N_in, 8, 5, back_RBs, predeblur=predeblur, HR_in=HR_in)

test_dataset_folder = 'datasets/streetscenes'

#### evaluation
crop_border = 0
border_frame = N_in // 2  # border frames when evaluate
# temporal padding mode
if data_mode == 'Vid4' or data_mode == 'sharp_bicubic':
    padding = 'new_info'
else:
    padding = 'replicate'
save_imgs = True

save_folder = 'results/streetscenes'
util.mkdirs(save_folder)
util.setup_logger('base', save_folder, 'test', level=logging.INFO, screen=True, tofile=True)
logger = logging.getLogger('base')

#### log info
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip test: {}'.format(flip_test))

#### set up the models
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.to(device)


img_path_l = sorted(glob.glob(osp.join(test_dataset_folder, '*')))
max_idx = len(img_path_l)
if save_imgs:
    util.mkdirs(save_folder)

#### read LQ and GT images
imgs_LQ = data_util.read_img_seq(test_dataset_folder)

# process each image
for img_idx, img_path in enumerate(img_path_l):
    print(img_idx, img_path)
    img_name = osp.splitext(osp.basename(img_path))[0]
    select_idx = data_util.index_generation(img_idx, max_idx, N_in, padding=padding)
    print('select_idx:', select_idx)
    imgs_in = imgs_LQ.index_select(0, torch.LongTensor(select_idx)).unsqueeze(0).to(device)
    
    output = util.single_forward(model, imgs_in)
    output = util.tensor2img(output.squeeze(0))

    if save_imgs:
        cv2.imwrite(osp.join(save_folder, '{}.png'.format(img_name)), output)

opened by jorenvs 13

Can't get the reported result in paper

Sorry, I've got a problem. I train RCAN , RDN and EDSR according to your setting. Data preprocess is also done by your scripts. But I can't get the reported results in those papers. Empirically there is always 0.09dB difference. I wonder if you have this difference too. If so, Is that a problem about data preprocess? Thank you.

opened by greatlog 13

[Bug] RealBasicVSR training Error : torch.distributed.elastic.multiprocessing.api:failed

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] I have read the FAQ documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version (master) or latest version (1.x).

Task

I'm using the official example scripts/configs for the officially supported tasks/models/datasets.

Branch

master branch https://github.com/open-mmlab/mmediting

Environment

sys.platform: linux Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] CUDA available: True GPU 0: NVIDIA GeForce RTX 2080 Ti CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.2, V11.2.152 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.2 PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 11.3
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
CuDNN 8.2
Magma 2.5.2
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.3 OpenCV: 4.5.4 MMCV: 1.5.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.3 MMEditing: 0.16.0+7b3a8bd

Reproduces the problem - code sample

I just training again.

Reproduces the problem - command or script

./tools/dist_train.sh ./configs/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds.py 1

Reproduces the problem - error message

  File "./tools/train.py", line 169, in <module>
    main()
  File "./tools/train.py", line 165, in main
    meta=meta)
  File "/home/gihwan/mmedit/mmedit/apis/train.py", line 104, in train_model
    meta=meta)
  File "/home/gihwan/mmedit/mmedit/apis/train.py", line 241, in _dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_iters)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run
    iter_runner(iter_loaders[i], **kwargs)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/mmcv/runner/iter_based_runner.py", line 59, in train
    data_batch = next(data_loader)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/mmcv/runner/iter_based_runner.py", line 32, in __next__
    data = next(self.iter_loader)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
    return self._process_data(data)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
    data.reraise()
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/_utils.py", line 434, in reraise
    raise exception
av.codec.codec.UnknownCodecError: Caught UnknownCodecError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/gihwan/mmedit/mmedit/datasets/dataset_wrappers.py", line 31, in __getitem__
    return self.dataset[idx % self._ori_len]
  File "/home/gihwan/mmedit/mmedit/datasets/base_sr_dataset.py", line 52, in __getitem__
    return self.pipeline(results)
  File "/home/gihwan/mmedit/mmedit/datasets/pipelines/compose.py", line 42, in __call__
    data = t(data)
  File "/home/gihwan/mmedit/mmedit/datasets/pipelines/random_degradations.py", line 547, in __call__
    results = degradation(results)
  File "/home/gihwan/mmedit/mmedit/datasets/pipelines/random_degradations.py", line 465, in __call__
    results[key] = self._apply_random_compression(results[key])
  File "/home/gihwan/mmedit/mmedit/datasets/pipelines/random_degradations.py", line 434, in _apply_random_compression
    stream = container.add_stream(codec, rate=1)
  File "av/container/output.pyx", line 64, in av.container.output.OutputContainer.add_stream
  File "av/codec/codec.pyx", line 184, in av.codec.codec.Codec.__cinit__
  File "av/codec/codec.pyx", line 193, in av.codec.codec.Codec._init
av.codec.codec.UnknownCodecError: libx264

ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 10741) of binary: /home/gihwan/anaconda3/envs/openmmlab2/bin/python
Traceback (most recent call last):
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/distributed/run.py", line 713, in run
    )(*cmd_args)
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/gihwan/anaconda3/envs/openmmlab2/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 261, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

Additional information

I'm trying to train a Real BasicVSR to check if it trains in my environment.
I have similar issue like issue. But that issue isn't resolved yet.

kind/bug

opened by gihwan-kim 12

Questions About Distributed Training

Hi, guys. I meet some issues when trying to use multi-gpus in distribution mode. I found it not faster than a single GPU. Further, I found more GPU take longer training time. So, I make an experiment to verify this. Take the config of configs/synthesizers/pix2pix/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps.py for example, the estimated time of the training process is 2h, 6h, 8h for 1GPU, 2GPU, 8GPU respectively. Is this a normal phenomenon (I guess not)? How to use distributed training correctly to speed up my experiment? I list the commands that I used below.

1gpu: python ./tools/train.py configs/synthesizers/pix2pix/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps.py

2gpus: bash ./tools/dist_train.sh configs/synthesizers/pix2pix/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps.py 2

8gpus: bash ./tools/dist_train.sh configs/synthesizers/pix2pix/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps.py 8

1gpu:

2gpus:

8gpus:

So, how to solve this problem?

opened by Endeavour10020 12
[Docs]: Add metrics and dataset_prepare in user_guides
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.

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

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Who can help? @ them here!

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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

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[ ] 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.

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After PR:

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opened by ruoningYu 1
[release] update changelog
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

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

Modification

Please briefly describe what modification is made in this PR.

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.
opened by liuwenran 2
bump version to v1.0.0rc5
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opened by liuwenran 2
[Feature] Support DreamFusion
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Support Dreamfusion. Refers to https://github.com/ashawkey/stable-dreamfusion

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opened by LeoXing1996 0
[Feature] Support IFRNet
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Support IFRNet model

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opened by VongolaWu 1

Releases(v1.0.0rc4)

v1.0.0rc4(Dec 6, 2022)
v1.0.0rc4 (06/12/2022)

Highlights

We are excited to announce the release of MMEditing 1.0.0rc4. This release supports 45+ models, 176+ configs and 175+ checkpoints in MMGeneration and MMEditing. We highlight the following new features

Support High-level APIs.

Support diffusion models.

Support Text2Image Task.

Support 3D-Aware Generation.

New Features & Improvements

Refactor high-level APIs. (#1410)

Support disco-diffusion text-2-image. (#1234, #1504)

Support EG3D. (#1482, #1493, #1494, #1499)

Support NAFNet model. (#1369)

Bug Fixes

fix srgan train config. (#1441)

fix cain config. (#1404)

fix rdn and srcnn train configs. (#1392)

Revise config and pretrain model loading in esrgan. (#1407)

Contributors A total of 14 developers contributed to this release. Thanks @plyfager, @LeoXing1996, @Z-Fran, @zengyh1900, @VongolaWu, @gaoyang07, @ChangjianZhao, @zxczrx123, @jackghosts, @liuwenran, @CCODING04, @RoseZhao929, @shaocongliu, @liangzelong.

New Contributors

@gaoyang07 made their first contribution in https://github.com/open-mmlab/mmediting/pull/1372

@ChangjianZhao made their first contribution in https://github.com/open-mmlab/mmediting/pull/1461

@zxczrx123 made their first contribution in https://github.com/open-mmlab/mmediting/pull/1462

@jackghosts made their first contribution in https://github.com/open-mmlab/mmediting/pull/1463

@liuwenran made their first contribution in https://github.com/open-mmlab/mmediting/pull/1410

@CCODING04 made their first contribution in https://github.com/open-mmlab/mmediting/pull/783

@RoseZhao929 made their first contribution in https://github.com/open-mmlab/mmediting/pull/1474

@shaocongliu made their first contribution in https://github.com/open-mmlab/mmediting/pull/1470

@liangzelong made their first contribution in https://github.com/open-mmlab/mmediting/pull/1488

Source code(tar.gz)
Source code(zip)
v1.0.0rc3(Nov 10, 2022)
Highlights

We are excited to announce the release of MMEditing 1.0.0rc3. This release supports 43+ models, 170+ configs and 169+ checkpoints in MMGeneration and MMEditing. We highlight the following new features

convert mmdet and clip to optional requirements.

New Features & Improvements

Support try_import for mmdet. (#1408)

Support try_import for flip. (#1420)

Complete requirements (#1419)

Update .gitignore. ($1416)

Set real_feat to cpu in inception_utils. (#1415)

Modify README and configs of StyleGAN2 and PEGAN (#1418)

Improve the rendering of Docs-API (#1373)

Bug Fixes

Revise config and pretrain model loading in ESRGAN (#1407)

Revise config of LSGAN (#1409)

Revise config of CAIN (#1404)

Contributors

A total of 5 developers contributed to this release. @Z-Fran, @zengyh1900, @plyfager, @LeoXing1996, @ruoningYu.
Source code(tar.gz)
Source code(zip)
v1.0.0rc2(Nov 3, 2022)
Highlights

We are excited to announce the release of MMEditing 1.0.0rc2. This release supports 43+ models, 170+ configs and 169+ checkpoints in MMGeneration and MMEditing. We highlight the following new features

patch-based and slider-based image and video comparison viewer. (#1303)

image colorization. (#1370)

We want to sincerely thank our community for continuously improving MMEditing. 🥰🥰🥰

New Features & Improvements

Support qualitative comparison tools. (#1303)

Support instance aware colorization. (#1370)

Support multi-metrics with different sample-model. (#1171)

Improve the implementation

refactoring evaluation metrics. (#1161)

Save gt images in PGGAN's forward. (#1328)

Improve type and change default number of preprocess_div2k_dataset.py. (#1380)

Support pixel value clip in visualizer. (#1365)

Support SinGAN Dataset and SinGAN demo. (#1363)

Avoid cast int and float in GenDataPreprocessor. (#1385)

Improve the documentation

Update a menu switcher. (#1162)

Fix TTSR's README. (#1325)

Revise docs (change PackGenInputs and GenDataSample). (#1382)

Bug Fixes

Fix PPL bug. (#1172)

Fix RDN number of channels. (#1332)

Fix types of exceptions in demos. (#1372)

Fix realesrgan ema. (#1341)

Improve the assertion to ensuer GenerateFacialHeatmap as np.float32. (#1310)

Fix sampling behavior of unpaired_dataset.py and urls in cyclegan's README. (#1308)

Fix vsr models in pytorch2onnx. (#1300)

Fix incorrect settings in configs. (#1167,#1200,#1236,#1293,#1302,#1304,#1319,#1331,#1336,#1349,#1352,#1353,#1358,#1364,#1367,#1384,#1386,#1391,#1392,#1393)

New Contributors

@gaoyang07 made their first contribution in https://github.com/open-mmlab/mmediting/pull/1372

Contributors

A total of 7 developers contributed to this release. Thanks @LeoXing1996, @Z-Fran, @zengyh1900, @plyfager, @ryanxingql, @ruoningYu, @gaoyang07.

Full Changelog: https://github.com/open-mmlab/mmediting/compare/v1.0.0rc1...v1.0.0rc2
Source code(tar.gz)
Source code(zip)

v0.16.0(Nov 1, 2022)

Deprecations

VisualizationHook is deprecated. Users should use MMEditVisualizationHook instead. (#1375)

Old Version Current Version

Old Version	Current Version
`visual_config = dict( # config to register visualization hook type='VisualizationHook', output_dir='visual', interval=1000, res_name_list=[ 'gt_img', 'masked_img', 'fake_res', 'fake_img', 'fake_gt_local' ], )`	`visual_config = dict( # config to register visualization hook type='MMEditVisualizationHook', output_dir='visual', interval=1000, res_name_list=[ 'gt_img', 'masked_img', 'fake_res', 'fake_img', 'fake_gt_local' ], )`

visual_config = dict(  # config to register visualization hook
  type='VisualizationHook',
  output_dir='visual',
  interval=1000,
  res_name_list=[
      'gt_img', 'masked_img', 'fake_res', 'fake_img', 'fake_gt_local'
  ],
)

visual_config = dict(  # config to register visualization hook
  type='MMEditVisualizationHook',
  output_dir='visual',
  interval=1000,
  res_name_list=[
      'gt_img', 'masked_img', 'fake_res', 'fake_img', 'fake_gt_local'
  ],
)

New Features & Improvements

Improve arguments type in preprocess_div2k_dataset.py. (#1381)
Update docstring of RDN. (#1326)
Update the introduction in readme. (#1387)

Bug Fixes

Fix FLAVR register in mmedit/models/video_interpolators when importing FLAVR. (#1186)
Fix data path processing in restoration_video_inference.py. (#1262)
Fix the number of channels in RDB. (#1292, #1311)

Contributors

A total of 5 developers contributed to this release. Thanks @LeoXing1996, @Z-Fran, @zengyh1900, @ryanxingql, @ruoningYu.

Full Changelog: https://github.com/open-mmlab/mmediting/compare/v0.15.2...v0.16.0

Source code(tar.gz)
Source code(zip)

v1.0.0rc1(Sep 24, 2022)
v1.0.0rc1(23/9/2022)

MMEditing 1.0.0rc1 has merged MMGeneration 1.x.

Support 42+ algorithms, 169+ configs and 168+ checkpoints.

Support 26+ loss functions, 20+ metrics.

Support tensorboard, wandb.

Support unconditional GANs, conditional GANs, image2image translation and internal learning.

Source code(tar.gz)
Source code(zip)
v0.15.2(Sep 9, 2022)
Improvements

[Docs] Fix typos in docs. by @Yulv-git in https://github.com/open-mmlab/mmediting/pull/1079

[Docs] fix model_zoo and datasets docs link by @Z-Fran in https://github.com/open-mmlab/mmediting/pull/1043

[Docs] fix typos in readme. by @arch-user-france1 in https://github.com/open-mmlab/mmediting/pull/1078

[Improve] FLAVR demo by @Yshuo-Li in https://github.com/open-mmlab/mmediting/pull/954

[Fix] Update MMCV_MAX to 1.7 by @wangruohui in https://github.com/open-mmlab/mmediting/pull/1001

[Improve] Fix niqe_pris_params.npz path when installed as package by @ychfan in https://github.com/open-mmlab/mmediting/pull/995

[CI] update github workflow, circleci and github templates by @zengyh1900 in https://github.com/open-mmlab/mmediting/pull/1087

New Contributors

@ychfan made their first contribution in https://github.com/open-mmlab/mmediting/pull/995

@arch-user-france1 made their first contribution in https://github.com/open-mmlab/mmediting/pull/1078

@Yulv-git made their first contribution in https://github.com/open-mmlab/mmediting/pull/1079

Source code(tar.gz)
Source code(zip)
v1.0.0rc0(Sep 1, 2022)

MMEditing 1.0.0rc0 is the first version of MMEditing 1.x, a part of the OpenMMLab 2.0 projects.

Built upon the new training engine, MMEditing 1.x unifies the interfaces of dataset, models, evaluation, and visualization.

And there are some BC-breaking changes. Please check the migration tutorial for more details.
Source code(tar.gz)
Source code(zip)
v0.15.1(Jul 4, 2022)
v0.15.1 (04/07/2022)

Bug Fixes

[Fix] Update cain_b5_g1b32_vimeo90k_triplet.py (#929)

[Docs] Fix link to OST dataset (#933)

Improvements

[Docs] Update instruction to OST dataset (#937)

[CI] No actual execution in CUDA envs (#921)

[Docs] Add watermark to demo video (#935)

[Tests] Add mim ci (#928)

[Docs] Update README.md of FLAVR (#919)

[Improve] Update md-format in .pre-commit-config.yaml (#917)

[Improve] Add miminstall.txt in setup.py (#916)

[Fix] Fix clutter in dim/README.md (#913)

[Improve] Skip problematic opencv-python versions (#833)

Contributors

@wangruohui @Yshuo-Li
Source code(tar.gz)
Source code(zip)
v0.15.0(Jun 1, 2022)
v0.15.0 (01/06/2022)

Highlights

Support FLAVR

Support AOT-GAN

Support CAIN with ReduceLROnPlateau Scheduler

New Features

Add configs for AOT-GAN (#681)

Support Vimeo90k-triplet dataset (#810)

Add default config for mm-assistant (#827)

Support CPU demo (#848)

Support use_cache and backend in LoadImageFromFileList (#857)

Support VFIVimeo90K7FramesDataset (#858)

Support ColorJitter for VFI (#859)

Support ReduceLrUpdaterHook (#860)

Support after_val_epoch in IterBaseRunner (#861)

Support FLAVR Net (#866, #867, #897)

Support MAE metric (#871)

Use mdformat (#888)

Support CAIN with ReduceLROnPlateau Scheduler (#906)

Bug Fixes

Change - to _ for restoration_demo.py (#834)

Remove recommonmark in requirements/docs.txt (#844)

Move EDVR to VSR category in README.md (#849)

Remove , in multi-line F-string in crop.py (#855)

Modify double lq_path to gt_path in test_pipeline (#862)

Fix unittest of TOF-VFI (#873)

Fix wrong frames in VFI demo (#891)

Fix logo & contrib guideline on README (#898)

Normalizing trimap in indexnet_dimaug_mobv2_1x16_78k_comp1k.py (#901)

Improvements

Add --cfg-options in train/test scripts (#826)

Update MMCV_MAX to 1.6 (#829)

Update TOFlow in README (#835)

Recover beirf installation steps & merge optional requirements (#836)

Use {MMEditing Contributors} in citation (#838)

Add tutorial for customizing losses (#839)

Add installation guide (wiki ver) in README (#845)

Add a 'need help to traslate' note on Chinese documentation (#850)

Add wechat QR code in README_zh-CN.md (#851)

Support non-zero frame index for SRFolderVideoDataset & Fix Typos (#853)

Create README.md for docker (#856)

Optimize IO for flow_warp (#881)

Move wiki/installation to docs (#883)

Add myst_heading_anchors (#887)

Use checkpoint link in inpainting demo (#892)

Contributors

@wangruohui @quincylin1 @nijkah @jayagami @ckkelvinchan @ryanxingql @NK-CS-ZZL @Yshuo-Li
Source code(tar.gz)
Source code(zip)
v0.14.0(Apr 1, 2022)
v0.14.0 (01/04/2022)

Highlights

Support TOFlow in video frame interpolation

New Features

Support AOT-GAN (#677)

Use --diff-seed to set different torch seed on different rank (#781)

Support streaming reading of frames in video interpolation demo (#790)

Support dist_train without slurm (#791)

Put LQ into CPU for restoration_video_demo (#792)

Support gray normalization constant in EDSR (#793)

Support TOFlow in video frame interpolation (#806, #811)

Support seed in DistributedSampler and sync seed across ranks (#815)

Bug Fixes

Update link in README files (#782, #786, #819, #820)

Fix matting tutorial, and fix links to colab (#795)

Invert flip_ratio in RandomAffine pipeline (#799)

Update preprocess_div2k_dataset.py (#801)

Update SR Colab Demo Installation Method and Set5 link (#807)

Fix Y/GRB mistake in EDSR README (#812)

Replace pytorch install command to conda in README(_zh-CN).md (#816)

Improvements

Update CI (#650)

Update requirements.txt (#725, #817)

Add Tutorial of dataset (#758), pipeline (#779), model (#766)

Update index and TOC tree (#767)

Make update_model_index.py compatible on windows (#768)

Update doc build system (#769)

Update keyword and classifier for setuptools (#773)

Renovate installation (#776, #800)

Update BasicVSR++ and RealBasicVSR docs (#778)

Update citation (#785, #787)

Regroup docs (#788)

Use full name of config as 'Name' in metafile (#798)

Update figure and video demo in README (#802)

Add clamp(0, 1) in test of video frame interpolation (#805)

Use hyphen for command line args in demo & tools (#808), and keep underline for required arguments in python files (#822)

Make dataset.pipeline a dedicated section in doc (#813)

Update mmcv-full>=1.3.13 to support DCN on CPU (#823)

Contributors

@wangruohui @ckkelvinchan @Yshuo-Li @nijkah @wdmwhh @freepoet @quincylin1
Source code(tar.gz)
Source code(zip)
v0.13.0(Mar 2, 2022)
v0.13.0 (01/03/2022)

Highlights

Support CAIN

Support EDVR-L

Support running in Windows

New Features

Add test-time ensemble for images and videos and support ensemble in BasicVSR series (#585)

Support AOT-GAN (work in progress) (#674, #675, #676)

Support CAIN (#683, #691, #709, #713)

Add basic interpolater (#687)

Add BaseVFIDataset and VFIVimeo90KDataset (#695, #697)

Add video interpolation demo (#688, #717)

Support various scales in RRDBNet (#699)

Support Ref-SR inference (#716)

Support EDVR-L on REDS (#719)

Support CPU training (#720)

Support running in Windows (#732, #738)

Support DCN on CPU (#735)

Bug Fixes

Fix link address in docs (#703, #704)

Fix ARG MMCV in Dockerfile (#708)

Fix file permission of non-executable files (#718)

Fix some deprecation warning related to numpy (#728)

Delete __init__ in TestVFIDataset (#731)

Fix data type in docstring of several Datasets (#739)

Fix math notation in docstring (#741)

Fix missing folders in copyright commit hook (#754)

Delete duplicate test in loading (#756)

Improvements

Update Pillow from 6.2.2 to 8.4 in CI (#693)

Add argument 'repeat' to SRREDSMultipleGTDataset (#672)

Deprecate the support for "python setup.py test" (#701)

Add setup multi-processing both in train and test (#707)

Add OpenMMLab website and platform links (#710)

Refact README files of all methods (#712)

Replace string version comparison with package.version.parse (#723)

Add docs of Ref-SR demo and video frame interpolation demo (#724)

Add interpolation and refact README.md (#726)

Update isort version in pre-commit hook (#727)

Redesign CI for Linux (#734)

Update install.md (#763)

Reorganizing OpenMMLab projects in readme (#764)

Add deprecation message for deploy tools (#765)

Contributors

@wangruohui @ckkelvinchan @Yshuo-Li @quincylin1 @Juggernaut93 @anse3832 @nijkah
Source code(tar.gz)
Source code(zip)
v0.12.0(Jan 4, 2022)
Highlights

Support RealBasicVSR

Support Real-ESRGAN checkpoint

New Features

Support video input and output in restoration demo (#622)

Support RealBasicVSR (#632, #633, #647, #680)

Support Real-ESRGAN checkpoint (#635)

Support conversion to y-channel when loading images (643)

Support random video compression during training (#646)

Support crop sequence (#648)

Support pixel_unshuffle (#684)

Bug Fixes

Change 'target_size' for RandomResize from list to tuple (#617)

Fix folder creation in preprocess_df2k_ost_dataset.py (#623)

Change TDAN config path in README (#625)

Change 'radius' to 'kernel_size' for UnsharpMasking in Real-ESRNet config (#626)

Fix bug in MATLABLikeResize (#630)

Fix 'flow_warp' comment (#655)

Fix the error of Model Zoo and Datasets in docs (#664)

Fix bug in 'random_degradations' (#673)

Limit opencv-python version (#689)

Improvements

Translate docs to Chinese (#576, #577, #578, #579, #581, #582, #584, #587, #588, #589, #590, #591, #592, #593, #594, #595, #596, #641, #647, #656, #665, #666)

Add UNetDiscriminatorWithSpectralNorm (#605)

Use PyTorch sphinx theme (#607, #608)

Update mmcv (#609), mmflow (#621), mmfewshot (#634) and mmhuman3d (#649) in docs

Convert minimum GCC version to 5.4 (#612)

Add tiff in SRDataset IMG_EXTENSIONS (#614)

Update metafile and update_model_index.py (#615)

Update preprocess_df2k_ost_dataset.py (#624)

Add Abstract to README (#628, #636)

Align NIQE to MATLAB results (#631)

Add official markdown lint hook (#639)

Skip CI when some specific files were changed (#640)

Update docs/conf.py (#644, #651)

Try to create a symbolic link on windows (#645)

Cancel previous runs that are not completed (#650)

Update path of configs in demo.md and getting_started.md (#658, #659)

Use mmcv root model registry (#660)

Update README.md (#654, #663)

Refactor the structure of documentation (#668)

Add script to crop REDS images into sub-images for faster IO (#669)

Capitalize the first letter of the task name in the metafile (#678)

Update FixedCrop for cropping image sequence (#682)

Contributors @wangruohui @nbei @ckkelvinchan @Yshuo-Li @LeoXing1996 @RangiLyu @matrixgame2018 @huoshuai-dot @innerlee @okotaku @Adenialzz @kai422
Source code(tar.gz)
Source code(zip)
v0.11.0(Nov 3, 2021)
Highlights

GLEAN for blind face image restoration #530

Real-ESRGAN model #546

New Features

Exponential Moving Average Hook #542

Support DF2K_OST dataset #566

Improvements

Add MATLAB-like bicubic interpolation #507

Support random degradations during training #504

Support torchserve #568

Contributors @ckkelvinchan @Yshuo-Li @Adenialzz @kai422 @jiaqixuac @plygager @Ha0Tang @innerlee
Source code(tar.gz)
Source code(zip)
v0.10.0(Aug 20, 2021)
Highlights

Support LIIF-RDN (CVPR'2021)

Support BasicVSR++ (NTIRE'2021)

New Features

Support loading annotation from file for video SR datasets (#423)

Support persistent worker (#426)

Support LIIF-RDN (#428, #440)

Support BasicVSR++ (#451, #467)

Support mim (#455)

Bug Fixes

Fix bug in stat.py (#420)

Fix astype error in function tensor2img (#429)

Fix device error caused by torch.new_tensor when pytorch >= 1.7 (#465)

Fix _non_dist_train in .mmedit/apis/train.py (#473)

Fix multi-node distributed test (#478)

Breaking Changes

Refactor LIIF for pytorch2onnx (#425)

Improvements

Update Chinese docs (#415, #416, #418, #421, #424, #431, #442)

Add CI of pytorch 1.9.0 (#444)

Refactor README.md of configs (#452)

Avoid loading pretrained VGG in unittest (#466)

Support specifying scales in preprocessing div2k dataset (#472)

Support all formats in readthedocs (#479)

Use version_info of mmcv (#480)

Remove unnecessary codes in restoration_video_demo.py (#484)

Change priority of DistEvalIterHook to 'LOW' (#489)

Reset resource limit (#491)

Update QQ QR code in README_CN.md (#494)

Add myst_parser (#495)

Add license header (#496)

Fix typo of StyleGAN modules (#427)

Fix typo in docs/demo.md (#453, #454)

Fix typo in tools/data/super-resolution/reds/README.md (#469)

We thank all the contributors of this release:

@610265158, @AlexZou14, @Ha0Tang, @LiUzHiAn, @Yshuo-Li, @ckkelvinchan, @innerlee, @nbei, @orangeccc, @wileechou, @yivan-WYYGDSG

Thank you! ❤️
Source code(tar.gz)
Source code(zip)
v0.9.0(Jul 6, 2021)
Highlights

Support DIC and DIC-GAN (CVPR'2020)

Support GLEAN Cat 8x (CVPR'2021)

Support TTSR-GAN (CVPR'2020)

Add colab tutorial for super-resolution

New Features

Add DIC (#342, #345, #348, #350, #351, #357, #363, #365, #366)

Add SRFolderMultipleGTDataset (#355)

Add GLEAN Cat 8x (#367)

Add SRFolderVideoDataset (#370)

Add colab tutorial for super-resolution (#380)

Add TTSR-GAN (#372, #381, #383, #398)

Add DIC-GAN (#392, #393, #394)

Bug Fixes

Fix bug in restoration_video_inference.py (#379)

Fix Config of LIIF (#368)

Change the path to pre-trained EDVR-M (#396)

Fix normalization in restoration_video_inference (#406)

Fix [brush_stroke_mask] error in unittest (#409)

Breaking Changes

Change mmcv minimum version to v1.3 (#378)

Improvements

Correct Typos in code (#371)

Add Custom_hooks (#362)

Refactor unittest folder structure (#386)

Add documents and download link for Vid4 (#399)

Update model zoo for documents (#400)

Update metafile (407)

Source code(tar.gz)
Source code(zip)
v0.8.0(Jun 2, 2021)
Highlights

Support GLEAN (CVPR'2021)

Support TTSR (CVPR'2020)

Support TDAN (CVPR'2020)

New Features

Add GLEAN (#296, #332)

Support PWD metafile (#298)

Support CropLike in pipeline (#299)

Add TTSR (#301, #304, #307, #311, #311, #312, #313, #314, #321, #326, #327)

Add TDAN (#316, #334, #347)

Add onnx2tensorrt (#317)

Add tensorrt evaluation (#328)

Add SRFacicalLandmarkDataset (#329)

Add key point auxiliary model for DIC (#336, #341)

Add demo for video super-resolution methods (#275)

Add SR Folder Ref Dataset (#292)

Support FLOPs calculation of video SR models (#309)

Bug Fixes

Fix find_unused_parameters in PyTorch 1.8 for BasicVSR (#290)

Fix error in publish_model.py for pt>=1.6 (#291)

Fix PSNR when input is uint8 (#294)

Improvements

Support backend in LoadImageFromFile (#293, #303)

Update metric_average_mode of video SR dataset (#319)

Add error message in restoration_demo.py (324)

Minor correction in getting_started.md (#339)

Update description for Vimeo90K (#349)

Support start_index in GenerateSegmentIndices (#338)

Support different filename templates in GenerateSegmentIndices (#325)

Support resize by scale-factor (#295, #310)

Source code(tar.gz)
Source code(zip)
v0.7.0(May 6, 2021)
TL;DR

Support BasicVSR (CVPR'2021)

Support IconVSR (CVPR'2021)

Support RDN (CVPR'2018)

Add onnx evaluation tool

New Features

Add RDN (#233, #260, #280)

Add MultipleGT datasets (#238)

Add BasicVSR and IconVSR (#245, #252, #253, #254, #264, #274, #258, #252, #264)

Add onnx evaluation tool (#279)

Bug Fixes

Fix onnx conversion of maxunpool2d (#243)

Fix inpainting in demo.md (#248)

Tiny fix of config file of EDSR (#251)

Fix link in README (#256)

Fix restoration_inference key missing bug (#270)

Fix the usage of channel_order in loading.py (#271)

Fix the command of inpainting (#278)

Fix preprocess_vimeo90k_dataset.py args name (#281)

Improvements

Support empty_cache option in test.py (#261)

Update projects in README (#249, #276)

Support Y-channel PSNR and SSIM (#250)

Add zh-CN README (#262)

Update pytorch2onnx doc (#265)

Remove extra quotation in English readme (#268)

Change tags to comment (#269)

List model zoo in README (#284, #285, #286)

Source code(tar.gz)
Source code(zip)
v0.6.0(Apr 8, 2021)
Highlights

Support Local Implicit Image Function (LIIF)

Support exporting DIM and GCA from Pytorch to ONNX

New Features

Add readthedocs config files and fix docstring (#92)

Add github action file (#94)

Support exporting DIM and GCA from Pytorch to ONNX (#105)

Support concatenating datasets (#106)

Support non_dist_train validation (#110)

Add matting colab tutorial (#111)

Support niqe metric (#114)

Support PoolDataLoader for parrots (#134)

Support collect-env (#137, #143)

Support pt1.6 cpu/gpu in CI (#138)

Support fp16 (139, #144)

Support publishing to pypi (#149)

Add modelzoo statistics (#171, #182, #186)

Add doc of datasets (194)

Support extended foreground option. (#195, #199, #200, #210)

Support nn.MaxUnpool2d (#196)

Add some FBA components (#203, #209, #215, #220)

Support random down sampling in pipeline (#222)

Support SR folder GT Dataset (#223)

Support Local Implicit Image Function (LIIF) (#224, #226, #227, #234, #239)

Bug Fixes

Fix _non_dist_train in train api (#104)

Fix setup and CI (#109)

Fix redundant loop bug in Normalize (#121)

Fix get_hash in setup.py (#124)

Fix tool/preprocess_reds_dataset.py (#148)

Fix slurm train tutorial in getting_started.md (#162)

Fix pip install bug (#173)

Fix bug in config file (#185)

Fix broken links of datasets (#236)

Fix broken links of model zoo (#242)

Breaking Changes

Refactor data loader configs (#201)

Improvements

Updata requirements.txt (#95, #100)

Update teaser (#96)

Updata README (#93, #97, #98, #152)

Updata model_zoo (#101)

Fix typos (#102, #188, #191, #197, #208)

Adopt adjust_gamma from skimage and reduce dependencies (#112)

remove .gitlab-ci.yml (#113)

Update import of first party (#115)

Remove citation and contact (#122)

Update version file (#136)

Update download url (#141)

Update setup.py (#150)

Update the highest version of supported mmcv (#153, #154)

modify Crop to handle a sequence of video frames (#164)

Add links to other mm projects (#179, #180)

Add config type (#181)

Refector docs (#184)

Add config link (#187)

Update file structure (#192)

Update config doc (#202)

Update slurm_train.md script (#204)

Improve code style (#206, #207)

Use file_client in CompositeFg (#212)

Replace random with numpy.random (#213)

Refactor loader_cfg (#214)

Source code(tar.gz)
Source code(zip)
v0.5.0(Oct 11, 2020)
New Features

NIQE metric (#114)

Support FP16 training (#139)

Improvements

Update version file (#136)

Update collect env function (#137)

Update download urls (#141)

Update docker file with pt1.6 (#144)

Source code(tar.gz)
Source code(zip)

OpenMMLab Image and Video Editing Toolbox

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Overview

Introduction

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Releases(v1.0.0rc4)

v1.0.0rc4(Dec 6, 2022)

v1.0.0rc4 (06/12/2022)

v1.0.0rc3(Nov 10, 2022)

Highlights

New Features & Improvements

Bug Fixes

Contributors

v1.0.0rc2(Nov 3, 2022)

Highlights

New Features & Improvements

Bug Fixes

New Contributors

Contributors

v0.16.0(Nov 1, 2022)

Deprecations

New Features & Improvements

Bug Fixes

Contributors

v1.0.0rc1(Sep 24, 2022)

v1.0.0rc1(23/9/2022)

v0.15.2(Sep 9, 2022)

Improvements

New Contributors

v1.0.0rc0(Sep 1, 2022)