BasicSR

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# Modified SRResNet w/o BN from:
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# Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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# ----------- Commands for running
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# ----------- Single GPU with auto_resume
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# PYTHONPATH="./:${PYTHONPATH}"  CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml --auto_resume
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# general settings
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name: 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb
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model_type: SRModel
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scale: 4
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num_gpu: 1  # set num_gpu: 0 for cpu mode
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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_X4_sub
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    meta_info_file: basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
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    # (for lmdb)
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    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
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    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
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    filename_tmpl: '{}'
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    io_backend:
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      type: disk
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      # (for lmdb)
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      # type: lmdb
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    gt_size: 128
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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: 16
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    dataset_enlarge_ratio: 100
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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/LRbicx4
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    io_backend:
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      type: disk
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  val_2:
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    name: Set14
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    type: PairedImageDataset
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    dataroot_gt: datasets/Set14/GTmod12
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    dataroot_lq: datasets/Set14/LRbicx4
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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: MSRResNet
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  num_in_ch: 3
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  num_out_ch: 3
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  num_feat: 64
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  num_block: 16
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  upscale: 4
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# path
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path:
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  pretrain_network_g: ~
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  param_key_g: params
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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: CosineAnnealingRestartLR
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    periods: [250000, 250000, 250000, 250000]
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    restart_weights: [1, 1, 1, 1]
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    eta_min: !!float 1e-7
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  total_iter: 1000000
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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: 4
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      test_y_channel: false
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      better: higher  # the higher, the better. Default: higher
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    niqe:
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      type: calculate_niqe
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      crop_border: 4
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      better: lower  # the lower, the better
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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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