total_iters: 600000
output_dir: output_dir
checkpoints_dir: checkpoints
# tensor range for function tensor2img
min_max:
  (0., 1.)

model:
  name: EDVRModel
  tsa_iter: 0
  generator:
    name: EDVRNet
    in_nf: 3
    out_nf: 3
    scale_factor: 4
    nf: 128
    nframes: 5
    groups: 8
    front_RBs: 5
    back_RBs: 40
    center: 2
    predeblur: False
    HR_in: False
    w_TSA: False
  pixel_criterion:
    name: CharbonnierLoss

dataset:
  train:
    name: REDSDataset
    mode: train
    gt_folder: data/REDS/train_sharp/X4
    lq_folder: data/REDS/train_sharp_bicubic/X4
    img_format: png
    crop_size: 256
    interval_list: [1]
    random_reverse: False
    number_frames: 5
    use_flip: True
    use_rot: True
    buf_size: 1024
    scale: 4
    fix_random_seed: 10
    num_workers: 3
    batch_size: 4  # 8GUPs


  test:
    name: REDSDataset
    mode: test
    gt_folder: data/REDS/REDS4_test_sharp/X4
    lq_folder: data/REDS/REDS4_test_sharp_bicubic/X4
    img_format: png
    interval_list: [1]
    random_reverse: False
    number_frames: 5
    batch_size: 1
    use_flip: False
    use_rot: False
    buf_size: 1024
    scale: 4
    fix_random_seed: 10

lr_scheduler:
  name: CosineAnnealingRestartLR
  learning_rate: !!float 4e-4
  periods: [150000, 150000, 150000, 150000]
  restart_weights: [1, 0.5, 0.5, 0.5]
  eta_min: !!float 1e-7

optimizer:
  name: Adam
  # add parameters of net_name to optim
  # name should in self.nets
  net_names:
    - generator
  beta1: 0.9
  beta2: 0.99

validate:
  interval: 5000
  save_img: false

  metrics:
    psnr: # metric name, can be arbitrary
      name: PSNR
      crop_border: 0
      test_y_channel: False
    ssim:
      name: SSIM
      crop_border: 0
      test_y_channel: False

log_config:
  interval: 10
  visiual_interval: 500

snapshot_config:
  interval: 5000