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options/DefocusDeblur_Single_8bit_HINT.yml

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+# general settings
+name: DefocusDeblur_Single_8bit_HINT_n
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./Defocus_Deblurring/Datasets/train/DPDD/target_crops
+    dataroot_lq: ./Defocus_Deblurring/Datasets/train/DPDD/inputC_crops
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+    ### -------------Progressive training--------------------------
+    mini_batch_sizes: [6,3,2]              # Batch size per gpu   
+    iters: [100000,100000,100000]
+    gt_size: 384   # Max patch size for progressive training
+    gt_sizes: [128,256,320]  # Patch sizes for progressive training.
+    ### ------------------------------------------------------------
+
+    ### ------- Training on single fixed-patch size 128x128---------
+    # mini_batch_sizes: [8]   
+    # iters: [300000]
+    # gt_size: 128   
+    # gt_sizes: [128]
+    ### ------------------------------------------------------------
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./Defocus_Deblurring/Datasets/val/DPDD/target_crops
+    dataroot_lq: ./Defocus_Deblurring/Datasets/val/DPDD/inputC_crops
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: false
+
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 300000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [92000, 208000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: false
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: AdamW
+    lr: !!float 3e-4
+    weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+
+# validation settings
+val:
+  window_size: 8
+  val_freq: !!float 4e3
+  save_img: false
+  rgb2bgr: true
+  use_image: false
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 1000
+  save_checkpoint_freq: !!float 4e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/Deraining_HINT_syn_rain100L.yml

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+# general settings
+name: Deraining_HINT_rain100L
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 4  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./dataset/Rain100L/train/clean
+    dataroot_lq: ./dataset/Rain100L/train/rainy
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+    ### -------------Progressive training--------------------------
+    mini_batch_sizes: [6,4,3,1]             # Batch size per gpu   
+    iters: [92000,64000,48000,96000]
+    gt_size: 384   # Max patch size for progressive training
+    gt_sizes: [128,160,192,256]  # Patch sizes for progressive training.
+    ### ------------------------------------------------------------
+
+    ### ------- Training on single fixed-patch size 128x128---------
+    # mini_batch_sizes: [8]   
+    # iters: [300000]
+    # gt_size: 128   
+    # gt_sizes: [128]
+    ### ------------------------------------------------------------
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./dataset/Rain100L/test/clean
+    dataroot_lq: ./dataset/Rain100L/test/rainy
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: false
+
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 300000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [92000, 208000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: false
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: AdamW
+    lr: !!float 3e-4
+    weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+
+
+# validation settings
+val:
+  window_size: 8
+  val_freq: !!float 4e3
+  save_img: false
+  rgb2bgr: true
+  use_image: true
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: true
+
+# logging settings
+logger:
+  print_freq: 1000
+  save_checkpoint_freq: !!float 4e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/Desnow_snow100k_HINT.yml

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+# general settings
+name: Desnow_HINT
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./dataset/Snow100K/train2500/Gt
+    dataroot_lq: ./dataset/Snow100K/train2500/Snow
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+    ### ------- Training on single fixed-patch size 128x128---------
+    mini_batch_sizes: [6,5,2,1,1]    
+    iters: [50000,40000,30000,20000,10000]
+    gt_size: 128   
+    gt_sizes: [128,192,256,320,384]
+    ### ------------------------------------------------------------
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./dataset/Snow100K/test2000/Gt
+    dataroot_lq: ./dataset/Snow100K/test2000/Snow
+    gt_size: 256 
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: False
+
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 300000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [92000, 208000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: true
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: AdamW
+    lr: !!float 3e-4
+    weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+
+# validation settings
+val:
+  window_size: 8
+  val_freq: !!float 4e3
+  save_img: false
+  rgb2bgr: true
+  use_image: false
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 1000
+  save_checkpoint_freq: !!float 4e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/GaussianColorDenoising_HINT.yml

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+# general settings
+name: GaussianColorDenoising_HINT
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_GaussianDenoising
+    sigma_type: random
+    sigma_range: [0,50]
+    in_ch: 3    ## RGB image
+    dataroot_gt: ./Denoising/Datasets/train/WB
+    dataroot_lq: none
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+    # -------------Progressive training--------------------------
+    mini_batch_sizes: [6,4,3,1,1,1]             # Batch size per gpu   
+    iters: [92000,64000,48000,36000,36000,24000]
+    gt_size: 256   # Max patch size for progressive training
+    gt_sizes: [128,160,192,256,320,384]  # Patch sizes for progressive training.
+    ### ------------------------------------------------------------
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_GaussianDenoising
+    sigma_test: 25
+    in_ch: 3  ## RGB image
+    dataroot_gt: ./Denoising/Datasets/test/CBSD68
+    dataroot_lq: none
+    gt_size: 256
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: False
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 300000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [92000, 208000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: true
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: AdamW
+    lr: !!float 3e-4
+    weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+# validation settings
+val:
+  window_size: 8
+  val_freq: !!float 4e3
+  save_img: false
+  rgb2bgr: true
+  use_image: false
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 1000
+  save_checkpoint_freq: !!float 4e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/HINT_LOL_v2_real.yml

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+# general settings
+name: Enhancement_HINT
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./dataset/LOLv2/Real_captured/Train/Normal
+    dataroot_lq: ./dataset/LOLv2/Real_captured/Train/Low
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+    ## -------------Progressive training--------------------------
+    mini_batch_sizes: [6]                # Batch size per gpu   
+    iters: [150000]
+    gt_size: 384   # Max patch size for progressive training
+    gt_sizes: [128]  # Patch sizes for progressive training.
+    ### ------------------------------------------------------------
+
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_PairedImage
+    dataroot_gt: /mnt/sda/zsh/dataset/LOLv2/Real_captured/Test/Normal
+    dataroot_lq: /mnt/sda/zsh/dataset/LOLv2/Real_captured/Test/Low
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: False
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 150000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [46000, 104000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: true
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: Adam
+    lr: !!float 2e-4
+    # weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+
+
+# validation settings
+val:
+  window_size: 4
+  val_freq: !!float 1e3
+  save_img: false
+  rgb2bgr: true
+  use_image: false
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 500
+  save_checkpoint_freq: !!float 1e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/HINT_LOL_v2_synthetic.yml

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+# general settings
+name: Enhancement_HINT
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_PairedImage
+    dataroot_gt: ./dataset/LOLv2/Synthetic/Train/Normal
+    dataroot_lq: ./dataset/LOLv2/Synthetic/Train/Low
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+    ### ------- Training on single fixed-patch size 128x128---------
+    mini_batch_sizes: [6,5,2,1,1]    
+    iters: [50000,40000,30000,20000,10000]
+    gt_size: 128   
+    gt_sizes: [128,192,256,320,384]
+    ### ------------------------------------------------------------
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_PairedImage
+    dataroot_gt: /mnt/sda/zsh/dataset/LOLv2/Synthetic/Test/Normal
+    dataroot_lq: /mnt/sda/zsh/dataset/LOLv2/Synthetic/Test/Low
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: False
+
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 150000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [46000, 104000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: true
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: Adam
+    lr: !!float 2e-4
+    # weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+
+# validation settings
+val:
+  window_size: 4
+  val_freq: !!float 1e3
+  save_img: false
+  rgb2bgr: true
+  use_image: false
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 500
+  save_checkpoint_freq: !!float 1e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/RealDehazing_HINT.yml

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+# general settings
+name: Dehazing_HINT
+model_type: ImageCleanModel
+scale: 1
+num_gpu: 8  # set num_gpu: 0 for cpu mode
+manual_seed: 100
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: TrainSet
+    type: Dataset_PairedImage_dehazeSOT
+    dataroot_gt: ./dataset/haze
+    dataroot_lq: ./dataset/haze
+    geometric_augs: true
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 8
+    batch_size_per_gpu: 8
+
+         ## ------- Training on single fixed-patch size 128x128---------
+    mini_batch_sizes: [6,1] 
+    iters: [200000,100000]
+    gt_size: 256   
+    gt_sizes: [128,256]
+    ## ------------------------------------------------------------
+
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: ValSet
+    type: Dataset_PairedImage_dehazeSOT
+    dataroot_gt: ./dataset/haze
+    dataroot_lq: ./dataset/haze
+    gt_size: 256 
+    io_backend:
+      type: disk
+
+# network structures
+
+# network structures
+network_g:
+  type: HINT
+  inp_channels: 3
+  out_channels: 3
+  dim: 48
+  num_blocks: [4,6,6,8]
+  num_refinement_blocks: 4
+  heads: [8,8,8,8]
+  ffn_expansion_factor: 2.66
+  bias: False
+  LayerNorm_type: WithBias
+  dual_pixel_task: False
+
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  total_iter: 300000
+  warmup_iter: -1 # no warm up
+  use_grad_clip: true
+
+  # Split 300k iterations into two cycles. 
+  # 1st cycle: fixed 3e-4 LR for 92k iters. 
+  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
+  scheduler:
+    type: CosineAnnealingRestartCyclicLR
+    periods: [92000, 208000]       
+    restart_weights: [1,1]
+    eta_mins: [0.0003,0.000001]   
+  
+  mixing_augs:
+    mixup: true
+    mixup_beta: 1.2
+    use_identity: true
+
+  optim_g:
+    type: AdamW
+    lr: !!float 3e-4
+    weight_decay: !!float 1e-4
+    betas: [0.9, 0.999]
+  
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1
+    reduction: mean
+  fft_loss_opt:
+    type: FFTLoss
+    loss_weight: 0.1
+    reduction: mean
+
+# validation settings
+val:
+  window_size: 8
+  val_freq: !!float 4e3
+  save_img: false
+  rgb2bgr: true
+  use_image: false
+  max_minibatch: 8
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 1000
+  save_checkpoint_freq: !!float 4e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500