#### general settings
resume_training:
  resume_training: True
  resume: must
  id: waye95l7


#### datasets
datasets:
  name: LOL
  train:
    train_path: /mnt/valab-datasets/LOL/train

    n_workers: 4  # per GPU
    batch_size_train: 4
    cropsize: 256   # size you want to crop out as input sample.
    flips: True
    verbose: True
    crop_type: Random
  val:
    test_path: /mnt/valab-datasets/LOL/test
    cropsize: 256
    batch_size_test: 1

#### network structures
network:
  name: Network_v3
  img_channels: 3
  width: 32
  middle_blk_num: 3
  enc_blk_nums: [1, 2, 3]
  dec_blk_nums: [3, 1, 1]
  enc_blk_nums_map: None
  middle_blk_num_map: None
  residual_layers: 1
  dilations: [1, 4]
  spatial: None
  extra_depth_wise: False


#### training settings: learning rate scheme, loss
train:
  lr_initial: !!float 5e-4
  lr_scheme: CosineAnnealing
  betas: [0.9, 0.9]
  epochs: 500
  lr_gamma: 0.5
  weight_decay: !!float 1e-3
  eta_min: !!float 1e-6

  pixel_criterion: l1
  pixel_weight: 1.0

  perceptual: True
  perceptual_criterion: l1
  perceptual_weight: 0.01
  perceptual_reduction: mean

  edge: True 
  edge_criterion: l2
  edge_weight: 50.0
  edge_reduction: mean

  frequency: True
  frequency_criterion: l2
  frequency_weight: 0.01
  frequency_reduction: mean

#### save model
save:
  path: ./models/Network_v3_interpolate.pt
  best: ./models/bests/
#### wandb:
wandb:
  init: True
  project: LOLBlur
  entity: cidautai
  name: Network_v3_interpolate_extraDW_LOLBlur
  save_code: True