name: HAT_SRx4_ImageNet-LR
model_type: HATModel
scale: 4
num_gpu: 1  # set num_gpu: 0 for cpu mode
manual_seed: 0

tile:
  tile_size: 512 # max patch size for the tile mode
  tile_pad: 32

datasets:
  test_1:  # the 1st test dataset
    name: custom
    type: SingleImageDataset
    dataroot_lq: ./datasets/Set5/LRbicx4
    io_backend:
      type: disk

# network structures
network_g:
  type: HAT
  upscale: 4
  in_chans: 3
  img_size: 64
  window_size: 16
  compress_ratio: 3
  squeeze_factor: 30
  conv_scale: 0.01
  overlap_ratio: 0.5
  img_range: 1.
  depths: [6, 6, 6, 6, 6, 6]
  embed_dim: 180
  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_SRx4_ImageNet-pretrain.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