invdn_denoising.yaml

total_iters: 150000
output_dir: output_dir

model:
  name: InvDNModel
  generator:
    name: InvDN
    channel_in: 3
    channel_out: 3
    block_num: [8, 8]
    scale: 4
    down_num: 2

dataset:
  train:
    name: InvDNDataset
    num_workers: 10
    batch_size: 14 # 4 GPUs
    opt:
      phase: train
      scale: 4
      crop_size: 144
      train_dir: data/SIDD_Medium_Srgb_Patches_512/train/
  test:
    name: InvDNDataset
    num_workers: 1
    batch_size: 1
    opt:
      phase: test
      scale: 4
      val_dir: data/SIDD_Valid_Srgb_Patches_256/valid/

export_model:
  - {name: 'generator', inputs_num: 1}

lr_scheduler:
  name: MultiStepDecay
  learning_rate: 8e-4 # num_gpu * 2e-4
  milestones: [25000, 50000, 75000, 100000, 125000, 135000, 145000]
  gamma: 0.5

validate:
  interval: 500
  save_img: True

  metrics:
    psnr: # metric name, can be arbitrary
      name: PSNR
      crop_border: 4
      test_y_channel: True
    ssim:
      name: SSIM
      crop_border: 4
      test_y_channel: True

optimizer:
  name: Adam
  # add parameters of net_name to optim
  # name should in self.nets
  net_names:
    - generator
  beta1: 0.9
  beta2: 0.99
  epsilon: 1e-8
  clip_grad_norm: 10

log_config:
  interval: 100
  visiual_interval: 5000

snapshot_config:
  interval: 500