load_pretrained: True
strict_load: True
pretrained_path: "./pretrained/DCC_x4st39_Set14_2n8d32T_4_epoch=119_val_psnr=28.32.pth"

loggers:
  log_images: True

network:
  target: models.modle_sr28_New.DCC_st39  # Super_Resolution.bicubic_plusplus_main.bicubic_plusplus_main.
  params:

     upscale: 4
     growth_rate: 2
     num_blocks: 8
     dim: 32


trainer:
  base_lr_rate: 5e-4
  num_epochs: 1500
  use_Y_channel_in_val: True
  check_val_every_n_epoch: 20
  lr_scheduler:
    target: training.schedulers.KneeLRScheduler    #KneeLRScheduler   CosineAnnealingLR_Restart
    params:
      peak_lr: 5e-4
      warmup_steps: 0
      total_steps: 1000
      min_lr: 5e-6
#    optimizer: torch.optim.Adam([torch.zeros(3, 64, 3, 3)], lr=2e-4, weight_decay=0, betas=(0.9, 0.99))
#    T_period: [155, 155, 155, 155, 155, 155]
#    restarts: [155, 310, 465, 620, 775]
#    weights: [1, 1, 1, 1, 1]
#    eta_min: 1e-5
#    last_epoch: -1

degradation:
  train:
    blur: False
    img_noise: False
    kernel_noise: False
    load_kernels_from_disc: False
    kernel_path: [""]
    ksize: 21
    rate_iso: 1.0
    sig_min: 0.2
    sig_max: 2.6
    img_noise_level: 0.2
  val:
    blur: False
    img_noise: False
    kernel_noise: False
    load_kernels_from_disc: False
    kernel_path: [""]
    ksize: 21
    rate_iso: 1.0
    sig_min: 0.2
    sig_max: 2.6
    img_noise_level: 0.2

data:
  train:
    lr_path: ["/FU2K_LR_bicubic/X4"]
    hr_path: ["/FU2K/HR"]   ##64 ##288
    augment: True
    scale: 4
    patch_cropsize: 288
    pool_lr: True
    pool_hr: True
    is_train: True
  val:
    lr_path: ["\\benchmark\\Set14\\x4"]
    hr_path: ["\\benchmark\\Set14\\GTmod12"]
    augment: False
    scale: 4
    patch_cropsize: False
    pool_lr: True
    pool_hr: True
    is_train: False

loader:
  train:
    batch_size: 64
    shuffle: False
    num_workers: 8
    pin_memory: False
    persistent_workers: True
  val:
    batch_size: 1
    shuffle: True
    num_workers: 8
    pin_memory: False