{
    "task": "baseline"     // real-world image sr. root/task/images-models-options
    , "model": "plain" // "plain" | "plain2" if two inputs
    , "gpu_ids": [0]
    , "dist": false

    , "scale": 1       // broadcast to "datasets"
    , "n_channels": 3  // broadcast to "datasets", 1 for grayscale, 3 for color

    , "path": {
      "root": "masked_denoising"            // "denoising" | "superresolution" | "dejpeg"
      , "pretrained_netG": null      // path of pretrained model
      , "pretrained_netE": null      // path of pretrained model
    }

    , "datasets": {
      "train": {
        "name": "train_dataset"                    // just name
        , "dataset_type": "masked_denoising"       // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg" | "masked_denoising"
        , "dataroot_H": "trainsets/trainH"  // path of H training dataset. DIV2K (800 training images) + Flickr2K (2650 images) + + OST (10324 images)
        , "dataroot_L": null            // path of L training dataset

        , "H_size": 64                   // patch_size 256 | 288 | 320   (256)
        , "lq_patchsize": 64              //  (64)

        , "dataloader_shuffle": true
        , "dataloader_num_workers": 2
        , "dataloader_batch_size": 16      // batch size 1 | 16 | 32 | 48 | 64 | 128. Total batch size =4x8=32 in SwinIR  (32)

        , "noise_level": 15

        , "if_mask": false
        , "mask1": 75
        , "mask2": 75


      }
      , "test": {
        "name": "test_dataset"            // just name
        , "dataset_type": "plain"         // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"
        , "dataroot_H": "testset/McM/HR"  // path of H testing dataset
        , "dataroot_L": "testset/McM/McM_poisson_20"    // path of L testing dataset

      }
    }

    , "netG": {
      "net_type": "swinir"
      , "upscale": 1
      , "in_chans": 3
      , "img_size": 64
      , "window_size": 8    //    8     !!!!!!!!!!!!!!!
      , "img_range": 1.0
      , "depths": [6, 6, 6, 6]
      , "embed_dim": 60
      , "num_heads": [6, 6, 6, 6]
      , "mlp_ratio": 2
      , "upsampler": null        // "pixelshuffle" | "pixelshuffledirect" | "nearest+conv" | null
      , "resi_connection": "3conv"        // "1conv" | "3conv"

      , "init_type": "default"

      , "talking_heads": false
      , "attn_fn": "softmax"       // null | "softmax" | "entmax15" |
      , "head_scale": false
      , "on_attn": false

      , "use_mask": false         // if use attention mask
      , "mask_ratio1": 75        // attention mask ratio,
      , "mask_ratio2": 75        // randomly sampling from [mask_ratio1, mask_ratio2]
      , "mask_is_diff": false

      , "type": "stand"


    }

    , "train": {
        "manual_seed": 1

      , "G_lossfn_type": "l1"               // "l1" preferred | "l2sum" | "l2" | "ssim" | "charbonnier"
      , "G_lossfn_weight": 1.0            // default

      , "E_decay": 0.999                  // Exponential Moving Average for netG: set 0 to disable; default setting 0.999

      , "G_optimizer_type": "adam"        // fixed, adam is enough
      , "G_optimizer_lr": 1e-4    // 2e-4            // learning rate
      , "G_optimizer_wd": 0               // weight decay, default 0
      , "G_optimizer_clipgrad": null      // unused
      , "G_optimizer_reuse": true         //

      , "G_scheduler_type": "MultiStepLR" // "MultiStepLR" is enough
      , "G_scheduler_milestones": []   // [250000, 400000, 450000, 475000, 500000]
      , "G_scheduler_gamma": 0.5

      , "G_regularizer_orthstep": null    // unused
      , "G_regularizer_clipstep": null    // unused

      , "G_param_strict": true
      , "E_param_strict": true

      , "checkpoint_test": 5000           // for testing  (5000)
      , "checkpoint_save": 5000           // for saving model
      , "checkpoint_print": 100           // for print

      , "save_image": ["img_043_x1", "img_021_x1", "img_024_x1", "img_031_x1", "img_041_x1", "img_032_x1"]   // [250000, 400000, 450000, 475000, 500000]

    }
  }