basicvsr_reds.yaml

total_iters: 300000
output_dir: output_dir
find_unused_parameters: True
checkpoints_dir: checkpoints
use_dataset: True
# tensor range for function tensor2img
min_max:
  (0., 1.)

model:
  name: BasicVSRModel
  fix_iter: 5000
  lr_mult: 0.125
  generator:
    name: BasicVSRNet
    mid_channels: 64
    num_blocks: 30
  pixel_criterion:
    name: CharbonnierLoss
    reduction: mean

dataset:
  train:
    name: RepeatDataset
    times: 1000
    num_workers: 4  # 6
    batch_size: 2  # 4*2
    dataset:
      name: SRREDSMultipleGTDataset
      mode: train
      lq_folder: data/REDS/train_sharp_bicubic/X4
      gt_folder: data/REDS/train_sharp/X4
      crop_size: 256
      interval_list: [1]
      random_reverse: False
      number_frames: 15
      use_flip: True
      use_rot: True
      scale: 4
      val_partition: REDS4
      num_clips: 270

  test:
    name: SRREDSMultipleGTDataset
    mode: test
    lq_folder: data/REDS/REDS4_test_sharp_bicubic/X4
    gt_folder: data/REDS/REDS4_test_sharp/X4
    interval_list: [1]
    random_reverse: False
    number_frames: 100
    use_flip: False
    use_rot: False
    scale: 4
    val_partition: REDS4
    num_workers: 0
    batch_size: 1

lr_scheduler:
  name: CosineAnnealingRestartLR
  learning_rate: !!float 2e-4
  periods: [300000]
  restart_weights: [1]
  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: 100
  visiual_interval: 500

snapshot_config:
  interval: 5000

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