models/SRFlow/code/confs/SRFlow_DF2K_4X.yml

#### general settings
name: train
use_tb_logger: true
model: SRFlow
distortion: sr
scale: 4
gpu_ids: [ 0 ]

#### datasets
datasets:
  train:
    name: DF2K_256_tr
    mode: LRHR_PKL
    dataroot_GT: /kaggle/input/srflow0103/SRFlow/datasets/DF2K-tr.pklv4
    dataroot_LQ: /kaggle/input/srflow0103/SRFlow/datasets/DF2K-tr_X4.pklv4
    quant: 32

    use_shuffle: true
    n_workers: 3  # per GPU
    batch_size: 12
    GT_size: 256
    use_flip: true
    color: RGB
  val:
    name: DF2K_256_tr
    mode: LRHR_PKL
    dataroot_GT: .
    dataroot_LQ: 
    quant: 32
    n_max: 20

#### Test Settings
dataroot: /kaggle/input/test-set/test set
model_path: models/SRFlow/35000_G
heat: 0.7 # This is the standard deviation of the latent vectors

#### network structures
network_G:
  which_model_G: SRFlowNet
  in_nc: 3
  out_nc: 3
  nf: 64
  nb: 23
  upscale: 4
  train_RRDB: false
  train_RRDB_delay: 0.5

  flow:
    K: 16
    L: 3
    noInitialInj: true
    coupling: CondAffineSeparatedAndCond
    additionalFlowNoAffine: 2
    split:
      enable: true
    fea_up0: true
    stackRRDB:
      blocks: [ 1, 8, 15, 22 ]
      concat: true

#### path
path:
  pretrain_model_G: 
  strict_load: true
  resume_state: auto

#### training settings: learning rate scheme, loss
train:
  manual_seed: 10
  lr_G: !!float 2.5e-4
  weight_decay_G: 0
  beta1: 0.9
  beta2: 0.99
  lr_scheme: MultiStepLR
  warmup_iter: -1  # no warm up
  lr_steps_rel: [ 0.5, 0.75, 0.9, 0.95 ]
  lr_gamma: 0.5

  niter: 64185
  val_freq: 40000

#### validation settings
val:
  heats: [ 0.0, 0.5, 0.75, 1.0 ]
  n_sample: 3

#### logger
logger:
  print_freq: 100
  save_checkpoint_freq: !!float 5e3