train_IQA.yml

name: LPIPS-Alex_TonPIPAL
use_tb_logger: true
gpu_ids: [2]

#### datasets
datasets:
  train:
    # Select PIPAL or BAPPS as Training Data
    choice: PIPAL

    # Settings for PIPAL
    # Displace 'mos_root' 'ref_root' 'dist_root' with your own path
    train_PIPAL:
      phase: train
      name: PIPAL
      mos_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Full_EloMOSV2
      ref_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Remake_Train_Ref
      dist_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Remake_Train_Dis
      n_workers: 8  # per GPU
      batch_size: 16
      crop_flag: true
      crop_size: 248
      norm_flag : true

    # Settings for BAPPS
    # Displace 'train_root' 'valid_root'
    # Choose 'both' or 'train' or 'valid' for 'train_valid'
    train_BAPPS:
      phase: train
      name: BAPPS
      train_valid: both # both or train or valid | Decide which part of BAPPS dataset you want to use.
      train_root : /home/hmcai/home/Dataset/BAPPS/train
      valid_root : /home/hmcai/home/Dataset/BAPPS/val
      n_workers: 8  # per GPU
      batch_size: 16
      crop_flag: true
      crop_size: 248
      norm_flag : true

  val:
    # Displace Validation & Change data_util.py based on your need.
    test_PIPAL_Full:
      name: PIPAL
      mos_root: /home/hmcai/home/Dataset/PIPAL/PIPAL_Partition/FullDataset/Full_EloMOSV2
      ref_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Remake_Full_Ref
      dist_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Remake_Full_Dis

    test_PIPAL_Valid:
      name: PIPAL
      mos_root: /home/hmcai/home/Dataset/PIPAL/PIPAL_Partition/MOS/Valid_EloMOSV2
      ref_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Remake_Valid_Ref
      dist_root: /home/hmcai/home/PIPAL_SWD_Remake/Remake_Dataset/PIPAL/Remake_Valid_Dis



#### network structures
network_G:
  FENet: Alex  # Alex or VGG16
  tune_flag: false  # Fixed FE or Tunable FE
  bias_flag: true  # Active bias of conv2d in regression layer
  lpips_flag: false # Use LPIPS's regression layer

#### path
path:
  pretrain_model_G:
  pretrain_model_R:
  strict_load: True
  resume_state:

#### training settings: learning rate scheme, loss
train:
  lr_IQA: !!float 1e-4
  weight_decay_IQA: 0
  niter: 400000
  warmup_iter: -1  # no warm up
  loss_weight: !!float 1.0
  manual_seed: 10
  val_freq: !!float 2e3

#### logger
logger:
  print_freq: 50
  save_checkpoint_freq: !!float 2e3