#### general settings
name: train_vix_resgenv2
use_tb_logger: true
model: srgan
distortion: sr
scale: 4
gpu_ids: [0]
amp_opt_level: O1

#### datasets
datasets:
  train:
    name: vixcloseup
    mode: LQGT
    dataroot_GT: E:\4k6k\datasets\vixen\4k_closeup\hr
    dataroot_LQ: E:\4k6k\datasets\vixen\4k_closeup\lr_corrupted
    doCrop: false
    use_shuffle: true
    n_workers: 0  # per GPU
    batch_size: 8
    target_size: 192
    color: RGB
  val:
    name: adrianna_val
    mode: LQGT
    dataroot_GT: E:\4k6k\datasets\adrianna\val\hhq
    dataroot_LQ: E:\4k6k\datasets\adrianna\val\hr

#### network structures
network_G:
  which_model_G: ResGenV2
  nf: 256
  nb_denoiser: 20
  nb_upsampler: 10
  upscale_applications: 0
network_D:
  which_model_D: discriminator_resnet_passthrough
  nf: 64
# LR corruption network.
network_C:
  which_model_G: ResGenV2
  nf: 192
  nb_denoiser: 20
  nb_upsampler: 0
  upscale_applications: 0
  inject_noise: False

#### path
path:
  #pretrain_model_G: ../experiments/pretrained_resnet_G.pth
  #pretrain_model_D: ~
  pretrain_model_C: ../experiments/pretrained_corruptors/resgen_xl_noise_19000.pth
  strict_load: true
  resume_state: ~

#### training settings: learning rate scheme, loss
train:
  lr_G: !!float 1e-4
  weight_decay_G: 0
  beta1_G: 0.9
  beta2_G: 0.99
  lr_D: !!float 1e-4
  weight_decay_D: 0
  beta1_D: 0.9
  beta2_D: 0.99
  lr_scheme: MultiStepLR

  niter: 400000
  warmup_iter: -1  # no warm up
  lr_steps: [20000, 60000, 80000, 100000]
  lr_gamma: 0.5
  mega_batch_factor: 2

  pixel_criterion: l1
  pixel_weight: .01
  feature_criterion: l1
  feature_weight: 1
  feature_weight_decay: 1
  feature_weight_decay_steps: 500
  feature_weight_minimum: 1
  gan_type: gan  # gan | ragan
  gan_weight: .01

  D_update_ratio: 1
  D_init_iters: 0

  manual_seed: 10
  val_freq: !!float 5e2

#### logger
logger:
  print_freq: 50
  save_checkpoint_freq: !!float 5e2