BasicSR

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# general settings
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name: train_SwinIR_SRx2_scratch_P48W8_DIV2K_500k_B4G8
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model_type: SwinIRModel
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scale: 2
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num_gpu: auto
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manual_seed: 0
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# dataset and data loader settings
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datasets:
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  train:
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    name: DIV2K
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    type: PairedImageDataset
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    dataroot_gt: datasets/DF2K/DIV2K_train_HR_sub
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    dataroot_lq: datasets/DF2K/DIV2K_train_LR_bicubic_X2_sub
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    meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
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    filename_tmpl: '{}'
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    io_backend:
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      type: disk
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    gt_size: 96
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    use_hflip: true
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    use_rot: true
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    # data loader
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    num_worker_per_gpu: 6
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    batch_size_per_gpu: 4
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    dataset_enlarge_ratio: 1
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    prefetch_mode: ~
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  val:
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    name: Set5
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    type: PairedImageDataset
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    dataroot_gt: datasets/Set5/GTmod12
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    dataroot_lq: datasets/Set5/LRbicx2
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    io_backend:
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      type: disk
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# network structures
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network_g:
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  type: SwinIR
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  upscale: 2
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  in_chans: 3
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  img_size: 48
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  window_size: 8
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  img_range: 1.
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  depths: [6, 6, 6, 6, 6, 6]
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  embed_dim: 180
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  num_heads: [6, 6, 6, 6, 6, 6]
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  mlp_ratio: 2
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  upsampler: 'pixelshuffle'
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  resi_connection: '1conv'
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# path
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path:
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  pretrain_network_g: ~
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  strict_load_g: true
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  resume_state: ~
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# training settings
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train:
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  ema_decay: 0.999
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  optim_g:
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    type: Adam
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    lr: !!float 2e-4
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    weight_decay: 0
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    betas: [0.9, 0.99]
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  scheduler:
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    type: MultiStepLR
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    milestones: [250000, 400000, 450000, 475000]
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    gamma: 0.5
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  total_iter: 500000
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  warmup_iter: -1  # no warm up
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  # losses
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  pixel_opt:
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    type: L1Loss
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    loss_weight: 1.0
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    reduction: mean
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# validation settings
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val:
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  val_freq: !!float 5e3
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  save_img: false
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  metrics:
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    psnr: # metric name, can be arbitrary
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      type: calculate_psnr
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      crop_border: 2
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      test_y_channel: false
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# logging settings
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logger:
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  print_freq: 100
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  save_checkpoint_freq: !!float 5e3
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  use_tb_logger: true
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  wandb:
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    project: ~
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    resume_id: ~
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# dist training settings
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dist_params:
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  backend: nccl
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  port: 29500
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