linjing7 / vr-baseline Goto Github PK
View Code? Open in Web Editor NEWVideo Restoration Toolbox including FGST (ICML 2022), S2SVR (ICML 2022), etc.
License: Apache License 2.0
Video Restoration Toolbox including FGST (ICML 2022), S2SVR (ICML 2022), etc.
License: Apache License 2.0
Hello,
I follow your instruction and recollect GoPro dataset, for example: ./GoPro/test/blur/GOPR0384_11_00/000000.png.
I run the test code by "python demo/restoration_video_demo.py ./configs/FGST_deblur_gopro.py FGST.pth ./data/GoPro results
However, an error occurs "No such file or directory: './data/GoPro/00000000.png'."
It seems like the loader does not load the name of the video files, what should I do to test successfully?
Hello @linjing7 :
Thank you for your excellent work. How did you make the feature map visualization in Figure 6? it's so perfect!
您好,请教一下,模型可以应用实时视频的去模糊吗,FPS在25之上。如果现模型复杂度过高不行的话,是否可以修改模型参数,来进行尝试,能否给些指导建议?
Great work! It seems that configs/S2SVR_vqe_mfqev2.py is missing.
Where can I find that?
原始的代码里面没有对 visual_config配置,默认为none
下面是本人找到的一个模板,仅供参考
inpainting下:
visual_config = dict(
type='VisualizationHook',
output_dir='visual',
interval=10000,
res_name_list=[
'gt_img', 'masked_img', 'stage1_fake_res', 'stage1_fake_img',
'stage2_fake_res', 'stage2_fake_img', 'fake_gt_local'
],
)
from NJUST
Hello,
Do you release the pre-trained weights of the GoPro and DVD datasets?
I want to reproduce your GoPro and DVD results, but you only release the weights called 'FGST.pth', could I reproduce the GoPro and DVD results on 'FGST.pth'?
非常nice的工作,请问pwc-lite的预训练模型是基于S2SVR论文中中描述的无监督训练方式获得的吗?
您好,请问可以直接提供GOPRO数据集训练后的可视化结果吗,这里搭环境总是会出问题
你好,请教一下,你的视频增强对比的GIF图是怎么生成的
Greetings,
the abstract of your paper claims that "Code and models are publicly available at https://github.com/linjing7/VR-Baseline".
Am I missing something?
Thanks.
I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.
Here are the OpenMMLab 2.0 repos branches:
OpenMMLab 1.0 branch | OpenMMLab 2.0 branch | |
---|---|---|
MMEngine | 0.x | |
MMCV | 1.x | 2.x |
MMDetection | 0.x 、1.x、2.x | 3.x |
MMAction2 | 0.x | 1.x |
MMClassification | 0.x | 1.x |
MMSegmentation | 0.x | 1.x |
MMDetection3D | 0.x | 1.x |
MMEditing | 0.x | 1.x |
MMPose | 0.x | 1.x |
MMDeploy | 0.x | 1.x |
MMTracking | 0.x | 1.x |
MMOCR | 0.x | 1.x |
MMRazor | 0.x | 1.x |
MMSelfSup | 0.x | 1.x |
MMRotate | 1.x | 1.x |
MMYOLO | 0.x |
Attention: please create a new virtual environment for OpenMMLab 2.0.
Hi. I found that the initial dataset downloaded from the given link doesn't match the path and indexes you give in VR_Baseline/configs/FGST_deblur_gopro.py.
It raises an error that: FileNotFoundError: [Errno 2] No such file or directory: 'data/GoPro/train/blur/GOPR0868_11_01/00000044.png'.
I wonder what's the specific steps to prepare the data needed? Looking forward to your early reply. Thanks!
同学你好,很感谢你对你的工作进行开源。
我有一些问题想咨询一下。
我看代码中关于FGSW-MSA这部分,只有当前帧与前后一帧的光流偏移作为输入。因此想问一下你有试过对2r+1,r≥2帧进行实验吗?实验的结果如何。
还有就是在mmedit/model/backbones/sr_backbones/FGST_util.py中125-145行对前后帧进行的retrive key elements请问这个操作的含义是什么?
如果你能回复我一下,将不胜感激。谢谢。
Hi,linjing. Thank you very much for the open source code. Unfortunately, I'm meeting some problems while running your code. I used the pre-trained model to test on my dataset, with the command python demo/restoration_video_demo.py configs/FGST_deblur_dvd_test.py pretrained_models/FGST/FGST_dvd.pth data/test.mp4 data/
, but I got the following Error Message
RuntimeError: CUDA out of memory. Tried to allocate 2.97 GiB (GPU 0; 11.77 GiB total capacity; 8.25 GiB already allocated; 420.50 MiB free; 9.19 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
My GPU is one Nvidia Geforce rtx3080ti 12G. So, can I do this test on an existing GPU?
Thanks for your excellent work, I would like to know whether the S2SVR model is provided, after config the environment with your guidance and run the command bash tools/dist_train.sh configs/S2SVR_sr_reds4.py 8
I met a problem :KeyError: "BasicVSR: 'S2SVR is not in the model registry'"
logs are as follows
Traceback (most recent call last):
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg
return obj_cls(**args)
File "/mnt/sdb/xiaorunyu/xry/video_SR/S2S/VR-Baseline/mmedit/models/restorers/basicvsr.py", line 41, in init
pretrained)
File "/mnt/sdb/xiaorunyu/xry/video_SR/S2S/VR-Baseline/mmedit/models/restorers/basic_restorer.py", line 48, in init
self.generator = build_backbone(generator)
File "/mnt/sdb/xiaorunyu/xry/video_SR/S2S/VR-Baseline/mmedit/models/builder.py", line 31, in build_backbone
return build(cfg, BACKBONES)
File "/mnt/sdb/xiaorunyu/xry/video_SR/S2S/VR-Baseline/mmedit/models/builder.py", line 22, in build
return build_from_cfg(cfg, registry, default_args)
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/site-packages/mmcv/utils/registry.py", line 45, in build_from_cfg
f'{obj_type} is not in the {registry.name} registry')
KeyError: 'S2SVR is not in the model registry'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "tools/train.py", line 169, in
main()
File "tools/train.py", line 133, in main
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
File "/mnt/sdb/xiaorunyu/xry/video_SR/S2S/VR-Baseline/mmedit/models/builder.py", line 60, in build_model
return build(cfg, MODELS, dict(train_cfg=train_cfg, test_cfg=test_cfg))
File "/mnt/sdb/xiaorunyu/xry/video_SR/S2S/VR-Baseline/mmedit/models/builder.py", line 22, in build
return build_from_cfg(cfg, registry, default_args)
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg
raise type(e)(f'{obj_cls.name}: {e}')
KeyError: "BasicVSR: 'S2SVR is not in the model registry'"
Killing subprocess 922430
Traceback (most recent call last):
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in
main()
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main
sigkill_handler(signal.SIGTERM, None) # not coming back
File "/home/zhangyang/envs/anaconda3/envs/video/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/home/zhangyang/envs/anaconda3/envs/video/bin/python', '-u', 'tools/train.py', '--local_rank=0', 'configs/S2SVR_sr_reds4.py', '--seed', '0', '--launcher', 'pytorch']' returned non-zero exit status 1.
If you could give me any instructions? Thank you.
Hello, thanks for your code and models!
Since nearest
sampling is performed in alignment stage, so how can the parameters of optical flow network update?
@linjing7
It's posible like below
bash tools/dist_train.sh configs/FGST_deblur_dvd.py 8
Hello, thank you for your excellent work! When I tried to use the command "python demo/restoration_video_demo.py configs/FGST_deblur_dvd_test.py pretrained_models/FGST_dvd.pth data/test.mp4 res/test.mp4 --max-seq-len=1" to test the pre-trained model, I encountered the following issues. Even after changing the test video, the problem still persists. I would like to know where the problem lies and would appreciate your advice! Thank you very much.
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