58建筑网官网,深圳排名seo公司,聚豪云免费虚拟主机,什么叫做门户网站有关 Python 和 Anaconda 及 RestoreFormer 运行环境的安装与设置请参阅#xff1a;
Python开源项目CodeFormer——人脸重建#xff08;Face Restoration#xff09;#xff0c;模糊清晰、划痕修复及黑白上色的实践https://blog.csdn.net/beijinghorn/article/details/134…有关 Python 和 Anaconda 及 RestoreFormer 运行环境的安装与设置请参阅
Python开源项目CodeFormer——人脸重建Face Restoration模糊清晰、划痕修复及黑白上色的实践https://blog.csdn.net/beijinghorn/article/details/134334021
本文介绍两个开源项目 RestoreFormer 及其后续 RestoreFormer。 1 RESTOREFORMER
https://github.com/wzhouxiff/RestoreFormer
1.1 进化史Updating 20230915 Update an online demo Huggingface Gradio20230915 A more user-friendly and comprehensive inference method refer to our RestoreFormer20230116 For convenience, we further upload the test datasets, including CelebA (both HQ and LQ data), LFW-Test, CelebChild-Test, and Webphoto-Test, to OneDrive and BaiduYun.20221003 We provide the link of the test datasets.20220924 We add the code for metrics in scripts/metrics.
1.2 论文RestoreFormer This repo includes the source code of the paper: RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs (CVPR 2022) by Zhouxia Wang, Jiawei Zhang, Runjian Chen, Wenping Wang, and Ping Luo. RestoreFormer tends to explore fully-spatial attentions to model contextual information and surpasses existing works that use local operators. It has several benefits compared to prior arts. First, it incorporates a multi-head coross-attention layer to learn fully-spatial interations between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in RestoreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction.
1.3 运行环境Environment python3.7 pytorch1.7.1 pytorch-lightning1.0.8 omegaconf2.0.0 basicsr1.3.3.4 Warning Different versions of pytorch-lightning and omegaconf may lead to errors or different results.
1.4 数据集与模型Preparations of dataset and models 1.4.1 Dataset:
Training data: Both HQ Dictionary and RestoreFormer in our work are trained with FFHQ which attained from FFHQ repository. The original size of the images in FFHQ are 1024x1024. We resize them to 512x512 with bilinear interpolation in our work. Link this dataset to ./data/FFHQ/image512x512.
https://pan.baidu.com/s/1SjBfinSL1F-bbOpXiD0nlw?pwdnren
1.4.2 测试数据Test data: CelebA-Test-HQ: OneDrive; BaiduYun(code mp9t) https://pan.baidu.com/s/1tMpxz8lIW50U8h00047GIw?pwdmp9t
CelebA-Test-LQ: OneDrive; BaiduYun(code 7s6h) https://pan.baidu.com/s/1y6ZcQPCLyggj9VB5MgoWyg?pwd7s6h
LFW-Test: OneDrive; BaiduYun(code 7fhr). Note that it was align with dlib. https://pan.baidu.com/s/1UkfYLTViL8XVdZ-Ej-2G9g?pwd7fhr
CelebChild: OneDrive; BaiduYun(code rq65) https://pan.baidu.com/s/1pGCD4TkhtDsmp8emZd8smA?pwdrq65
WepPhoto-Test: OneDrive; BaiduYun(code nren) https://pan.baidu.com/s/1SjBfinSL1F-bbOpXiD0nlw?pwdnren
Model: Both pretrained models used for training and the trained model of our RestoreFormer can be attained from OneDrive or BaiduYun(code x6nn). Link these models to ./experiments.
https://pan.baidu.com/s/1EO7_1dYyCuORpPNosQgogg?pwdx6nn
1.5 测试Test sh scripts/test.sh
1.6 自训练Training sh scripts/run.sh
Note.
The first stage is to attain HQ Dictionary by setting conf_name in scripts/run.sh to HQ_Dictionary. The second stage is blind face restoration. You need to add your trained HQ_Dictionary model to ckpt_path in config/RestoreFormer.yaml and set conf_name in scripts/run.sh to RestoreFormer. Our model is trained with 4 V100 GPUs.
1.7 度量 Metrics
sh scripts/metrics/run.sh
Note. You need to add the path of CelebA-Test dataset in the script if you want get IDD, PSRN, SSIM, LIPIS.
1.8 引用 Citation article{wang2022restoreformer, title{RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs}, author{Wang, Zhouxia and Zhang, Jiawei and Chen, Runjian and Wang, Wenping and Luo, Ping}, booktitle{The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year{2022} }
1.9 知识 Acknowledgement
We thank everyone who makes their code and models available, especially Taming Transformer, basicsr, and GFPGAN.
1.10 联系 Contact For any question, feel free to email wzhouxconnect.hku.hk or zhouzi1212gmail.com. 2 RESTOREFORMER
https://github.com/wzhouxiff/RestoreFormerPlusPlus 2.1 进化史ToDo List 20230915 Update an online demo Huggingface Gradio 20230915 Provide a user-friendly method for inference. It is avaliable for background SR with RealESRGAN. basicsr should be upgraded to 1.4.2. 20230914 Upload model 20230914 Realse Code 20221120 Introducing the project.
2.2 论文RestoreFormer This repo is a official implementation of RestoreFormer: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris. https://arxiv.org/pdf/2308.07228.pdf
RestoreFormer is an extension of our RestoreFormer. It proposes to restore a degraded face image with both fidelity and realness by using the powerful fully-spacial attention mechanisms to model the abundant contextual information in the face and its interplay with our reconstruction-oriented high-quality priors. Besides, it introduces an extending degrading model (EDM) that contains more realistic degraded scenarios for training data synthesizing, which helps to enhance its robustness and generalization towards real-world scenarios. Our results compared with the state-of-the-art methods and performance with/without EDM are in following:
RestoreFormer是RestoreFormer的扩展。它提出了利用强大的全空间注意机制来模拟人脸中丰富的上下文信息及其与我们面向重构的高质量先验的相互作用以保真度和真实度恢复退化的人脸图像。此外它还引入了一个扩展的退化模型EDM该模型包含更真实的退化场景用于训练数据合成这有助于增强其鲁棒性和对真实场景的泛化。我们的结果与最先进的方法和性能有/没有EDM的比较如下 2.3 运行环境Environment python3.7 pytorch1.7.1 pytorch-lightning1.0.8 omegaconf2.0.0 basicsr1.3.3.4 basicsr1.4.2 realesrgan0.3.0
Warning Different versions of pytorch-lightning and omegaconf may lead to errors or different results. 警告不同版本的pytorch-lightning和omegaconf可能导致错误或不同的结果。
2.4 数据集与模型Preparations of dataset and models
Dataset:
Training data: Both ROHQD and RestoreFormer in our work are trained with FFHQ which attained from FFHQ repository. The original size of the images in FFHQ are 1024x1024. We resize them to 512x512 with bilinear interpolation in our work. Link this dataset to ./data/FFHQ/image512x512. https://github.com/NVlabs/ffhq-dataset Test data: CelebA-Test, LFW-Test, WebPhoto-Test, and CelebChild-Test https://pan.baidu.com/s/1iUvBBFMkjgPcWrhZlZY2og?pwdtest http://vis-www.cs.umass.edu/lfw/#views https://xinntao.github.io/projects/gfpgan 训练数据在我们的工作中ROHQD和RestoreFormer都是用FFHQ库获得的FFHQ训练的。FFHQ中的图像的原始大小是1024x1024。在我们的工作中我们用双线性插值将它们调整为512x512。将此数据集链接到./data/FFHQ/image512x512。
Model: Both pretrained models used for training and the trained model of our RestoreFormer and RestoreFormer can be attained from Google Driver. Link these models to ./experiments. https://connecthkuhk-my.sharepoint.com/:f:/g/personal/wzhoux_connect_hku_hk/EkZhGsLBtONKsLlWRmf6g7AB_VOA_6XAKmYUXLGKuNBsHQ?eic2LPl 模型用于训练的预训练模型和我们的RestoreFormer和RestoreFormer的训练模型都可以从谷歌盘中获得。将这些模型链接存放到/experiments 文件夹。
2.5 快速指南Quick Inference python inference.py -i data/aligned -o results/RF/aligned -v RestoreFormer -s 2 --aligned --save python inference.py -i data/raw -o results/RF/raw -v RestoreFormer -s 2 --save python inference.py -i data/aligned -o results/RF/aligned -v RestoreFormer -s 2 --aligned --save python inference.py -i data/raw -o results/RF/raw -v RestoreFormer -s 2 --save
Note: Related codes are borrowed from GFPGAN. https://github.com/TencentARC/GFPGAN
2.6 测试Test sh scripts/test.sh scripts/test.sh
exp_nameRestoreFormer exp_nameRestoreFormerPlusPlus
root_pathexperiments out_root_pathresults align_test_pathdata/aligned # unalign_test_pathdata/raw tagtest
outdir$out_root_path/$exp_name_$tag
if [ ! -d $outdir ];then mkdir -m 777 $outdir fi
CUDA_VISIBLE_DEVICES0 python -u scripts/test.py \ --outdir $outdir \ -r $root_path/$exp_name/last.ckpt \ -c configs/$exp_name.yaml \ --test_path $align_test_path \ --aligned
This codebase is available for both RestoreFormer and RestoreFormerPlusPlus. Determinate the specific model with exp_name. 这个代码库可用于RestoreFormer和RestoreFormer。使用exp_name确定特定的模型。 Setting the model path with root_path 使用root_path设置模型路径 Restored results are save in out_root_path 恢复的结果将保存在out_root_path中 Put the degraded face images in test_path 将退化的人脸图像放入test_path中 If the degraded face images are aligned, set --aligned, else remove it from the script. The provided test images in data/aligned are aligned, while images in data/raw are unaligned and contain several faces. 如果退化的人脸图像对齐设置对齐否则将其从脚本中删除。所提供的数据/对齐中的测试图像是对齐的而数据/原始中的图像是未对齐的并且包含多个面。 2.7 自我训练Training sh scripts/run.sh
scripts/run.sh
export BASICSR_JITTrue
# For RestoreFormer # conf_nameHQ_Dictionary # conf_nameRestoreFormer
# For RestoreFormer conf_nameROHQD conf_nameRestoreFormerPlusPlus
# gpus0,1,2,3,4,5,6,7 # node_n1 # ntasks_per_node8
root_pathPATH_TO_CHECKPOINTS
gpus0, node_n1 ntasks_per_node1
gpu_n$(expr $node_n \* $ntasks_per_node)
python -u main.py \ --root-path $root_path \ --base configs/$conf_name.yaml \ -t True \ --postfix $conf_name_gpus$gpu_n \ --gpus $gpus \ --num-nodes $node_n \ --random-seed True \
This codebase is available for both RestoreFormer and RestoreFormerPlusPlus. Determinate the training model with conf_name. HQ_Dictionary and RestoreFormer are for RestoreFormer, while ROHQD and RestoreFormerPlusPlus are for RestoreFormerPlusPlus. While training RestoreFormer or RestoreFormerPlusPlus, ckpt_path in the corresponding configure files in configs/ sholud be updated with the path of the trained model of HQ_Dictionary or ROHQD. 这个代码库可用于RestoreFormer和RestoreFormer。用conf_name确定训练模型。“HQ_Dictionary”和“RestoreFormer”用于RestoreFormer而“ROHQD”和“RestoreFormer”用于RestoreFormer。 在训练“RestoreFormer”或“RestoreFormer”时配置中相应配置文件中的“ckpt_path”将更新训练模型的“HQ_Dictionary”或“ROHQD”的路径。 2.8 指标Metrics sh scripts/metrics/run.sh Note.
You need to add the path of CelebA-Test dataset in the script if you want get IDD, PSRN, SSIM, LIPIS. Related metric models are in ./experiments/pretrained_models/ 如果您想获得IDDPSRNSSIMLIPIS您需要在脚本中添加CelebA-测试数据集的路径。 相关的度量模型在。/experiments/pretrained_models/
2.9 引用Citation article{wang2023restoreformer, title{RestoreFormer: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris}, author{Wang, Zhouxia and Zhang, Jiawei and Chen, Tianshui and Wang, Wenping and Luo, Ping}, booktitle{IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)}, year{2023} }
article{wang2022restoreformer, title{RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs}, author{Wang, Zhouxia and Zhang, Jiawei and Chen, Runjian and Wang, Wenping and Luo, Ping}, booktitle{The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year{2022} }
2.10 联系Contact For any question, feel free to email wzhouxconnect.hku.hk or zhouzi1212gmail.com. 如有任何问题请随时发邮件至wzhouxconnect.hku.hk或zhouzi1212gmail.com。 这两个代码都写的不好效率低效果差有点应付论文的意思。
