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

model:
  name: BasicVSRModel
  fix_iter: 5000
  lr_mult: 0.25
  generator:
    name: BasicVSRPlusPlus
    mid_channels: 64
    num_blocks: 7
    is_low_res_input: True
  pixel_criterion:
    name: CharbonnierLoss
    reduction: mean

dataset:
  train:
    name: RepeatDataset
    times: 1000
    num_workers: 4
    batch_size: 1  #4 gpus
    dataset:
      name: VSRVimeo90KDataset
      # mode: train
      lq_folder: data/vimeo90k/vimeo_septuplet_BD_matlabLRx4/sequences
      gt_folder: data/vimeo90k/vimeo_septuplet/sequences
      ann_file: data/vimeo90k/vimeo_septuplet/sep_trainlist.txt
      preprocess:
        - name: ReadImageSequence
          key: lq
        - name: ReadImageSequence
          key: gt
        - name: Transforms
          input_keys: [lq, gt]
          pipeline:
            - name: SRPairedRandomCrop
              gt_patch_size: 256
              scale: 4
              keys: [image, image]
            - name: PairedRandomHorizontalFlip
              keys: [image, image]
            - name: PairedRandomVerticalFlip
              keys: [image, image]
            - name: PairedRandomTransposeHW
              keys: [image, image]
            - name: TransposeSequence
              keys: [image, image]
            - name: MirrorVideoSequence
            - name: NormalizeSequence
              mean: [0., .0, 0.]
              std: [255., 255., 255.]
              keys: [image, image]

  test:
    name: VSRFolderDataset
    # for UDM10 dataset
    # lq_folder: data/udm10/BDx4
    # gt_folder: data/udm10/GT
    lq_folder: data/Vid4/BDx4
    gt_folder: data/Vid4/GT
    preprocess:
        - name: GetNeighboringFramesIdx
          interval_list: [1]
          # for UDM10 dataset
          # filename_tmpl: '{:04d}.png'
          filename_tmpl: '{:08d}.png'
        - name: ReadImageSequence
          key: lq
        - name: ReadImageSequence
          key: gt
        - name: Transforms
          input_keys: [lq, gt]
          pipeline:
            - name: TransposeSequence
              keys: [image, image]
            - name: NormalizeSequence
              mean: [0., .0, 0.]
              std: [255., 255., 255.]
              keys: [image, image]

lr_scheduler:
  name: CosineAnnealingRestartLR
  learning_rate: !!float 1e-4
  periods: [600000]
  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: true
    ssim:
      name: SSIM
      crop_border: 0
      test_y_channel: true

log_config:
  interval: 10
  visiual_interval: 500

snapshot_config:
  interval: 5000