basicsr/options/dape.yaml

# general settings
name: DAPE
model_type: DAPEModel
num_gpu: auto  # set num_gpu: 0 for cpu mode
manual_seed: 10
ram_model_path: preset/models/ram_swin_large_14m.pth

# dataset and data loader settings
datasets:
  train:
    name: train_dataset
    type: DAPEDataset
    root: ['your training dataset path']
    ext: ['*.png']
    # data loader
    num_worker_per_gpu: 8
    batch_size_per_gpu: 8
    dataset_enlarge_ratio: 1
    prefetch_mode: ~
    

# network structures
network_g: ram_swin_bert_lora


# path
path:
  pretrain_network_g: ~
  strict_load_g: false
  resume_state: ~

# training settings
train:
  ema_decay: 0
  optim_g:
    type: AdamW
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.999]

  scheduler:
    type: MultiStepLR
    milestones: [20000, 30000]
    gamma: 0.5

  total_iter: 300000
  warmup_iter: 50  # no warm up

  # losses
  cri_feature_opt:
    type: MSELoss
    loss_weight: 1.0
    reduction: mean



# validation settings
val:
  val_freq: !!float 1e8 # !!float 5e3
  save_img: false

# logging settings
logger:
  print_freq: 1
  save_checkpoint_freq: !!float 1e3 # !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

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