train_msrresnet_psnr.json

{
  "task": "msrresnet_psnr"     //  root/task/images-models-options, pay attention to the difference between "msrresnet0" and "msrresnet1"
  , "model": "plain" // "plain" | "plain2" if two inputs
  , "gpu_ids": [0]
  , "dist": true

  , "scale": 4       // broadcast to "netG" if SISR
  , "n_channels": 3  // broadcast to "datasets", 1 for grayscale, 3 for color
  , "sigma": 0       // 15, 25, 50 for DnCNN | [0, 75] for FDnCNN and FFDNet
  , "sigma_test": 0  // 15, 25, 50 for DnCNN, FDnCNN and FFDNet, 0 for SR

  , "merge_bn": false              // if no BN exists, set false
  , "merge_bn_startpoint": 400000  // merge BN after N iterations

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

  , "datasets": {
    "train": {
      "name": "train_dataset"           // just name
      , "dataset_type": "sr"            // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch"
      , "dataroot_H": "trainsets/trainH"// path of H training dataset
      , "dataroot_L": null              // path of L training dataset
      , "H_size": 96                    // patch size 40 | 64 | 96 | 128 | 192
      , "dataloader_shuffle": true
      , "dataloader_num_workers": 8
      , "dataloader_batch_size": 32     // batch size 1 | 16 | 32 | 48 | 64 | 128
    }
    , "test": {
      "name": "test_dataset"            // just name
      , "dataset_type": "sr"            // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch"
      , "dataroot_H": "testsets/set5"  // path of H testing dataset
      , "dataroot_L": null              // path of L testing dataset
    }
  }

  , "netG": {
    "net_type": "msrresnet0"  // "dncnn" | "fdncnn" | "ffdnet" | "srmd" | "dpsr" | "msrresnet0" |  "msrresnet1" | "rrdb" 
    , "in_nc": 3        // input channel number
    , "out_nc": 3       // ouput channel number
    , "nc": 64          // 96 for DPSR, 128 for SRMD, 64 for "dncnn"
    , "nb": 16          // 12 for "srmd", 15 for "ffdnet", 20 for "dncnn", 16 for "srresnet" and "dpsr"
    , "gc": 32          // unused
    , "ng": 2           // unused
    , "reduction" : 16  // unused
    , "act_mode": "R"   // "BR" for BN+ReLU | "R" for ReLU
    , "upsample_mode": "upconv"         // "pixelshuffle" | "convtranspose" | "upconv"
    , "downsample_mode": "strideconv"   // "strideconv" | "avgpool" | "maxpool"

    , "init_type": "orthogonal"         // "orthogonal" | "normal" | "uniform" | "xavier_normal" | "xavier_uniform" | "kaiming_normal" | "kaiming_uniform"
    , "init_bn_type": "uniform"         // "uniform" | "constant"
    , "init_gain": 0.2
  }

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

    , "G_optimizer_type": "adam"        // fixed, adam is enough
    , "G_optimizer_lr": 1e-4            // learning rate
    , "G_optimizer_clipgrad": null      // unused
    , "G_optimizer_reuse": true

    , "G_scheduler_type": "MultiStepLR" // "MultiStepLR" is enough
    , "G_scheduler_milestones": [200000, 400000, 600000, 800000, 1000000, 2000000]
    , "G_scheduler_gamma": 0.5

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

    , "checkpoint_test": 5000           // for testing
    , "checkpoint_save": 5000           // for saving model
    , "checkpoint_print": 200           // for print
  }
}