# general settings
name: 4x_figsr
model_type: SRModel
scale: 4
num_gpu: 1  # set num_gpu: 0 for cpu mode
manual_seed: 1024

# dataset and data loader settings
datasets:
  train:
    name: BHI
    type: PairedImageDataset
    dataroot_gt: datasets/BHI
    dataroot_lq: datasets/BHI_lq

    filename_tmpl: '{}'
    io_backend:
      type: disk

    gt_size: 256
    use_hflip: true
    use_rot: true

    # data loader
    num_worker_per_gpu: 6
    batch_size_per_gpu: 64
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

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

# network structures
network_g:
  type: FIGSR

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

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 5e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [200000,400000,600000,800000]
    gamma: 0.5

  total_iter: 1000000
  warmup_iter: -1  # no warm up

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

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

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 4
      test_y_channel: false

# 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