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name: HAT-S_SRx2
model_type: HATModel
scale: 2
num_gpu: 1  # set num_gpu: 0 for cpu mode
manual_seed: 0

datasets:
  test_1:  # the 1st test dataset
    name: Set5
    type: PairedImageDataset
    dataroot_gt: ./datasets/Set5/GTmod2
    dataroot_lq: ./datasets/Set5/LRbicx2
    io_backend:
      type: disk

  test_2:  # the 2nd test dataset
    name: Set14
    type: PairedImageDataset
    dataroot_gt: ./datasets/Set14/GTmod2
    dataroot_lq: ./datasets/Set14/LRbicx2
    io_backend:
      type: disk

  test_3:
    name: Urban100
    type: PairedImageDataset
    dataroot_gt: ./datasets/urban100/GTmod2
    dataroot_lq: ./datasets/urban100/LRbicx2
    io_backend:
      type: disk

  test_4:
     name: BSDS100
     type: PairedImageDataset
     dataroot_gt: ./datasets/BSDS100/GTmod2
     dataroot_lq: ./datasets/BSDS100/LRbicx2
     io_backend:
       type: disk

  test_5:
      name: Manga109
      type: PairedImageDataset
      dataroot_gt: ./datasets/manga109/GTmod2
      dataroot_lq: ./datasets/manga109/LRbicx2
      io_backend:
        type: disk

# network structures
network_g:
  type: HAT
  upscale: 2
  in_chans: 3
  img_size: 64
  window_size: 16
  compress_ratio: 24
  squeeze_factor: 24
  conv_scale: 0.01
  overlap_ratio: 0.5
  img_range: 1.
  depths: [6, 6, 6, 6, 6, 6]
  embed_dim: 144
  num_heads: [6, 6, 6, 6, 6, 6]
  mlp_ratio: 2
  upsampler: 'pixelshuffle'
  resi_connection: '1conv'


# path
path:
  pretrain_network_g: ./experiments/pretrained_models/HAT-S_SRx2.pth
  strict_load_g: true
  param_key_g: 'params_ema'

# validation settings
val:
  save_img: true
  suffix: ~  # add suffix to saved images, if None, use exp name

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 2
      test_y_channel: true
    ssim:
      type: calculate_ssim
      crop_border: 2
      test_y_channel: true