Real-ESRGAN

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# general settings
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name: finetune_RealESRGANx4plus_400k
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model_type: RealESRGANModel
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scale: 4
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num_gpu: auto
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manual_seed: 0
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# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
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# USM the ground-truth
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l1_gt_usm: True
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percep_gt_usm: True
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gan_gt_usm: False
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# the first degradation process
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resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
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resize_range: [0.15, 1.5]
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gaussian_noise_prob: 0.5
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noise_range: [1, 30]
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poisson_scale_range: [0.05, 3]
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gray_noise_prob: 0.4
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jpeg_range: [30, 95]
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# the second degradation process
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second_blur_prob: 0.8
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resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
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resize_range2: [0.3, 1.2]
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gaussian_noise_prob2: 0.5
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noise_range2: [1, 25]
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poisson_scale_range2: [0.05, 2.5]
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gray_noise_prob2: 0.4
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jpeg_range2: [30, 95]
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gt_size: 256
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queue_size: 180
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# dataset and data loader settings
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datasets:
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  train:
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    name: DF2K+OST
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    type: RealESRGANDataset
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    dataroot_gt: datasets/DF2K
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    meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
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    io_backend:
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      type: disk
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    blur_kernel_size: 21
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    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
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    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
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    sinc_prob: 0.1
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    blur_sigma: [0.2, 3]
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    betag_range: [0.5, 4]
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    betap_range: [1, 2]
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    blur_kernel_size2: 21
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    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
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    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
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    sinc_prob2: 0.1
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    blur_sigma2: [0.2, 1.5]
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    betag_range2: [0.5, 4]
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    betap_range2: [1, 2]
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    final_sinc_prob: 0.8
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    gt_size: 256
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    use_hflip: True
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    use_rot: False
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    # data loader
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    use_shuffle: true
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    num_worker_per_gpu: 5
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    batch_size_per_gpu: 12
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    dataset_enlarge_ratio: 1
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    prefetch_mode: ~
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  # Uncomment these for validation
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  # val:
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  #   name: validation
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  #   type: PairedImageDataset
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  #   dataroot_gt: path_to_gt
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  #   dataroot_lq: path_to_lq
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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: RRDBNet
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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: 23
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  num_grow_ch: 32
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network_d:
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  type: UNetDiscriminatorSN
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  num_in_ch: 3
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  num_feat: 64
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  skip_connection: True
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# path
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path:
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  # use the pre-trained Real-ESRNet model
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  pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
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  param_key_g: params_ema
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  strict_load_g: true
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  pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
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  param_key_d: params
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  strict_load_d: 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 1e-4
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    weight_decay: 0
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    betas: [0.9, 0.99]
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  optim_d:
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    type: Adam
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    lr: !!float 1e-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: [400000]
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    gamma: 0.5
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  total_iter: 400000
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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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  # perceptual loss (content and style losses)
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  perceptual_opt:
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    type: PerceptualLoss
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    layer_weights:
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      # before relu
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      'conv1_2': 0.1
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      'conv2_2': 0.1
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      'conv3_4': 1
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      'conv4_4': 1
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      'conv5_4': 1
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    vgg_type: vgg19
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    use_input_norm: true
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    perceptual_weight: !!float 1.0
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    style_weight: 0
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    range_norm: false
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    criterion: l1
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  # gan loss
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  gan_opt:
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    type: GANLoss
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    gan_type: vanilla
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    real_label_val: 1.0
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    fake_label_val: 0.0
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    loss_weight: !!float 1e-1
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  net_d_iters: 1
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  net_d_init_iters: 0
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# Uncomment these for validation
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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: True
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#   metrics:
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#     psnr: # metric name
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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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# 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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