Basic training config yaml

name: '4x_template'
use_tb_logger: false
model: 'srragan'
scale: 4
batch_multiplier: 1
gpu_ids: [0]

# Dataset options:
datasets:
  train:
    name: 'Dataset'
    mode: 'LRHROTF'
    dataroot_HR: '../datasets/train/hr'
    dataroot_LR: '../datasets/train/lr'
    subset_file: null
    use_shuffle: true
    n_workers: 4
    batch_size: 8
    HR_size: 128 # patch size. Default: 128

    # Rotations augmentations:
    use_flip: true
    use_rot: true
  val:
    name: 'Validation'
    mode: 'LRHROTF'
    dataroot_HR: '../datasets/val/hr'
    dataroot_LR: '../datasets/val/lr'

path:
  root: 'D:/Code/GitHub/BasicSR'
  pretrain_model_G: '../experiments/pretrained_models/RRDB_PSNR_x4.pth'
  # resume_state: '../experiments/debug_002_RRDB_ESRGAN_x4_DIV2K/training_state/'

# Generator:
network_G:
  which_model_G: 'RRDB_net'
  norm_type: null
  mode: 'CNA'
  nf: 64 
  nb: 23
  in_nc: 3 
  out_nc: 3 
  gc: 32
  group: 1
  convtype: 'Conv2D' 
  net_act: 'leakyrelu'

# Discriminator:
network_D:
  which_model_D: 'discriminator_vgg'
  norm_type: 'batch'
  act_type: 'leakyrelu'
  mode: 'CNA'
  nf: 64
  in_nc: 3

# Training options:
train:
  # Optimizer:
  lr_G: !!float 1e-4 
  lr_D: !!float 1e-4

  # ESRGAN-FS Augmentations
  use_frequency_separation: false

  # Scheduler:
  lr_scheme: 'MultiStepLR'
  lr_steps: [50000, 100000, 200000, 300000]
  lr_gamma: 0.5

  # Losses:
  pixel_criterion: 'l1'
  pixel_weight: !!float 1e-2
  feature_criterion: 'l1'
  feature_weight: 1
  gan_type: 'vanilla'
  gan_weight: !!float 5e-3

# Other training options:
  manual_seed: 0
  niter: !!float 5e5
  val_freq: 1000 # 5e3

logger:
  print_freq: 200
  save_checkpoint_freq: !!float 5e3
  backup_freq: 200