基于pytorch的ESRGAN（论文阅读笔记+复现）

版权声明： https://blog.csdn.net/gwplovekimi/article/details/84840998

代码的框架——《https://github.com/xinntao/BasicSR》

ESRGAN论文《ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks》的链接——https://arxiv.org/pdf/1809.00219.pdf

代码在目录/home/guanwp/BasicSR-master/codes/下，运行以下命令实现train和test

python train.py -opt options/train/train_esrgan.json

python test.py -opt options/test/test_esrgan.json

理论

代码

给出setting

{
  "name": "ESRGAN_x4_DIV2K" //  please remove "debug_" during training
  , "use_tb_logger": true
  , "model":"srgan"
  , "scale": 4
  , "gpu_ids": [3,4,5]

  , "datasets": {
    "train": {
      "name": "DIV2K"
      , "mode": "LRHR"
      , "dataroot_HR": "/home/guanwp/BasicSR_datasets/DIV2K800_sub"
      , "dataroot_LR": "/home/guanwp/BasicSR_datasets/DIV2K800_sub_bicLRx4"
      , "subset_file": null
      , "use_shuffle": true
      , "n_workers": 8
      , "batch_size": 16
      , "HR_size": 128
      , "use_flip": true
      , "use_rot": true
    }
    , "val": {
      "name": "val_set5"
      , "mode": "LRHR"
      , "dataroot_HR": "/home/guanwp/BasicSR_datasets/val_set5/Set5"
      , "dataroot_LR": "/home/guanwp/BasicSR_datasets/val_set5/Set5_sub_bicLRx4"
    }
  }

  , "path": {
    "root": "/home/guanwp/BasicSR-master",
    "pretrain_model_G": null
     ,"experiments_root": "/home/guanwp/BasicSR-master/experiments/",
    "models": "/home/guanwp/BasicSR-master/experiments/ESRGAN_x4_DIV2K/models",
    "log": "/home/guanwp/BasicSR-master/experiments/ESRGAN_x4_DIV2K",
    "val_images": "/home/guanwp/BasicSR-master/experiments/ESRGAN_x4_DIV2K/val_images"
  }

  , "network_G": {
    "which_model_G": "RRDB_net" // RRDB_net | sr_resnet
    , "norm_type": null
    , "mode": "CNA"
    , "nf": 64
    , "nb": 23// number of residual block
    , "in_nc": 3
    , "out_nc": 3
    , "gc": 32
    , "group": 1
  }
  , "network_D": {
    "which_model_D": "discriminator_vgg_128"
    , "norm_type": "batch"
    , "act_type": "leakyrelu"
    , "mode": "CNA"
    , "nf": 64
    , "in_nc": 3
  }

  , "train": {
    "lr_G": 1e-4
    , "weight_decay_G": 0
    , "beta1_G": 0.9
    , "lr_D": 1e-4
    , "weight_decay_D": 0
    , "beta1_D": 0.9
    , "lr_scheme": "MultiStepLR"
    , "lr_steps": [50000, 100000, 200000, 300000]
    , "lr_gamma": 0.5

    , "pixel_criterion": "l1"
    , "pixel_weight": 0//1e-2//just for the NIQE, you should set to 0
    , "feature_criterion": "l1"
    , "feature_weight": 1
    , "gan_type": "vanilla"
    , "gan_weight": 5e-3

    //for wgan-gp
     , "D_update_ratio": 1//for the D network
     , "D_init_iters": 0
    // , "gp_weigth": 10

    , "manual_seed": 0
    , "niter": 5e5//6e5//5e5
    , "val_freq": 2000//5e3
  }

  , "logger": {
    "print_freq": 200
    , "save_checkpoint_freq": 5e3
  }
}

运行代码

结果