train_MSRResNet_x4.yml

# Modified SRResNet w/o BN from:
# Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

# ----------- Commands for running
# ----------- Single GPU with auto_resume
# PYTHONPATH="./:${PYTHONPATH}"  CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml --auto_resume

# general settings
name: 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb
model_type: SRModel
scale: 4
num_gpu: 1  # set num_gpu: 0 for cpu mode
manual_seed: 0

# dataset and data loader settings
datasets:
  train:
    name: DIV2K
    type: PairedImageDataset
    dataroot_gt: datasets/DF2K/DIV2K_train_HR_sub
    dataroot_lq: datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub
    meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
    # (for lmdb)
    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
    filename_tmpl: '{}'
    io_backend:
      type: disk
      # (for lmdb)
      # type: lmdb

    gt_size: 128
    use_hflip: true
    use_rot: true

    # data loader
    num_worker_per_gpu: 6
    batch_size_per_gpu: 16
    dataset_enlarge_ratio: 100
    prefetch_mode: ~

  val:
    name: Set5
    type: PairedImageDataset
    dataroot_gt: datasets/Set5/GTmod12
    dataroot_lq: datasets/Set5/LRbicx4
    io_backend:
      type: disk

  val_2:
    name: Set14
    type: PairedImageDataset
    dataroot_gt: datasets/Set14/GTmod12
    dataroot_lq: datasets/Set14/LRbicx4
    io_backend:
      type: disk

# network structures
network_g:
  type: MSRResNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 64
  num_block: 16
  upscale: 4

# path
path:
  pretrain_network_g: ~
  param_key_g: params
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: CosineAnnealingRestartLR
    periods: [250000, 250000, 250000, 250000]
    restart_weights: [1, 1, 1, 1]
    eta_min: !!float 1e-7

  total_iter: 1000000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# validation settings
val:
  val_freq: !!float 5e3
  save_img: false

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 4
      test_y_channel: false
      better: higher  # the higher, the better. Default: higher
    niqe:
      type: calculate_niqe
      crop_border: 4
      better: lower  # the lower, the better

# logging settings
logger:
  print_freq: 100
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500