SwinIR combat: record the training process of SwinIR in detail

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SwinIR实战：详细记录SwinIR的训练过程.

论文地址：https://arxiv.org/pdf/2108.10257.pdf

预训练模型下载：https://github.com/JingyunLiang/SwinIR/releases

训练代码下载：https://github.com/cszn/KAIR

测试代码：https://github.com/JingyunLiang/SwinIR

论文翻译：https://blog.csdn.net/hhhhhhhhhhwwwwwwwwww/article/details/124434886

测试：https://wanghao.blog.csdn.net/article/details/124517210

在写这边文章之前,我已经翻译了论文,讲解了如何使用SWinIR进行测试？

接下来,我们讲讲如何SwinIR完成训练,有于作者训练了很多任务,我只复现其中的一种任务.

下载训练代码

地址：https://github.com/cszn/KAIR

这是个超分的库,里面包含多个超分的模型,比如SCUNet、VRT、SwinIR、BSRGGAN、USRNet等模型.

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-B5Md9i7H-1651410061139)(https://gitee.com/wanghao1090220084/cloud-image/raw/master/img/face_09_comparison.png)]

下载后解压,训练SwinIR的REANDME.md,路径：./docs/README_SwinIR.md

数据集

训练和测试集可以下载如下. 请将它们分别放在 trainsets 和 testsets 中.

任务	训练集	测试集
classical/lightweight image SR	DIV2K (800 training images) or DIV2K +Flickr2K (2650 images)	set5 + Set14 + BSD100 + Urban100 + Manga109 download all
real-world image SR	SwinIR-M (middle size): DIV2K (800 training images) +Flickr2K (2650 images) + OST (10324 images,sky,water,grass,mountain,building,plant,animal) SwinIR-L (large size): DIV2K + Flickr2K + OST + WED(4744 images) + FFHQ (first 2000 images, face) + Manga109 (manga) + SCUT-CTW1500 (first 100 training images, texts)	RealSRSet+5images
color/grayscale image denoising	DIV2K (800 training images) + Flickr2K (2650 images) + BSD500 (400 training&testing images) + WED(4744 images)	grayscale: Set12 + BSD68 + Urban100 color: CBSD68 + Kodak24 + McMaster + Urban100 download all
JPEG compression artifact reduction	DIV2K (800 training images) + Flickr2K (2650 images) + BSD500 (400 training&testing images) + WED(4744 images)	grayscale: Classic5 +LIVE1 download all

我下载了DIV2K数据集和 Flickr2K数据集,DIV2K大小有7G+,Flickr2K约20G.如果网速不好建议只下载DIV2K.

注：在选用classical任务,做训练时,只能使用DIV2K或者Flickr2K,不能把两种数据集放在一起训练,否则就出现维度对不上的情况,如下图：

暂时没有找到原因.

构建测试集,测试集的路径如下图：

由于表格中的测试集放在google,我不能下载,但是SwinIR的测试代码中有测试集,代码链接：https://github.com/JingyunLiang/SwinIR,下载下来直接复制到testsets文件夹下面.

构建训练集,将下载下来的DIV2K解压.将DIV2K_train_HR复制到trainsets文件夹下面,将其改为trainH.

将DIV2K_train_LR_bicubic文件夹的X2文件夹复制到trainsets文件夹下面,然后将其改名为trainL.

到这里,数据集部分就完成了,接下来开始训练.

训练

首先,打开options/swinir/train_swinir_sr_classical.json文件,查看里面的内容.

"task": "swinir_sr_classical_patch48_x2" 

训练任务的名字.

"gpu_ids": [0,1]

选择GPU的ID,如果只有一快GPU,改为 [0].如果有更多的GPU,直接往后面添加即可.

"scale": 2  //2,3,48

放大的倍数,可以设置为2、3、4、8.

"datasets": {  "train": {    "name": "train_dataset"           // just name    , "dataset_type": "sr"         // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"    , "dataroot_H": "trainsets/trainH"// path of H training dataset. DIV2K (800 training images)    , "dataroot_L": "trainsets/trainL"              // path of L training dataset    , "H_size": 96                   // 96/144|192/384 | 128/192/256/512. LR patch size is set to 48 or 64 when compared with RCAN or RRDB.    , "dataloader_shuffle": true    , "dataloader_num_workers": 4    , "dataloader_batch_size": 1      // batch size 1 | 16 | 32 | 48 | 64 | 128. Total batch size =4x8=32 in SwinIR  }  , "test": {    "name": "test_dataset"            // just name    , "dataset_type": "sr"         // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"    , "dataroot_H": "testsets/Set5/HR"  // path of H testing dataset    , "dataroot_L": "testsets/Set5/LR_bicubic/X2"              // path of L testing dataset  }}

上面的参数是对数据集的设置.
“H_size”: 96 ,HR图像的大小,和下面的img_size有对应关系,大小设置为img_size×scale.

“dataloader_num_workers”: 4,CPU的核数设置.

“dataloader_batch_size”: 32 ,设置训练的batch_size.

dataset_type：sr,指的是数据集类型SwinIR.

"netG": {    "net_type": "swinir"     , "upscale": 2                      // 2 | 3  | 4 | 8    , "in_chans": 3     , "img_size": 48                    // For fair comparison, LR patch size is set to 48 or 64 when compared with RCAN or RRDB.    , "window_size": 8      , "img_range": 1.0     , "depths": [6, 6, 6, 6, 6, 6]     , "embed_dim": 180     , "num_heads": [6, 6, 6, 6, 6, 6]    , "mlp_ratio": 2     , "upsampler": "pixelshuffle"        // "pixelshuffle" | "pixelshuffledirect" | "nearest+conv" | null    , "resi_connection": "1conv"        // "1conv" | "3conv"    , "init_type": "default"  }

upscale：2,放大的倍数,和上面的scale参数对应.

img_size：48,这里可以设置两个数值,48和64.和测试的training_patch_size参数对应.

官方提供的指令是基于DDP方式,比较复杂一下,好处是速度快.如下：

# 001 Classical Image SR (middle size)python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_sr_classical.json  --dist True# 002 Lightweight Image SR (small size)python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_sr_lightweight.json  --dist True# 003 Real-World Image SR (middle size)python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_sr_realworld_psnr.json  --dist True# before training gan, put the PSNR-oriented model into superresolution/swinir_sr_realworld_x4_gan/models/python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_sr_realworld_gan.json  --dist True# 004 Grayscale Image Deoising (middle size)python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_denoising_gray.json  --dist True# 005 Color Image Deoising (middle size)python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_denoising_color.json  --dist True# 006 JPEG Compression Artifact Reduction (middle size)python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/swinir/train_swinir_car_jpeg.json  --dist True

我没有使用上面的方式,而是选择用DP的方式,虽然慢一点,但是简单,更稳定.

在Terminal里面输入：

python main_train_psnr.py --opt options/swinir/train_swinir_sr_classical.json

即可开始训练.

运行结果如下：

等待训练完成后,我们使用测试代码测试.将模型复制到./model_zoo/swinir文件夹下面

输入命令：

python main_test_swinir.py --task classical_sr --scale 2 --training_patch_size 48 --model_path model_zoo/swinir/45000_G.pth --folder_lq testsets/Set5/LR_bicubic/X2 

然后在result下面可以看到测试结果.

完整的代码：

https://download.csdn.net/download/hhhhhhhhhhwwwwwwwwww/85258387