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SR-GAN

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SR-GAN
File size: 1,150 Bytes
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Data:
  train_batch_size: 24
  val_batch_size: 8
  num_workers: 8
  prefetch_factor: 4
  dataset_type: S2_6b
  normalization: normalise_10k
Model:
  in_bands: 6
  continue_training: false
  load_checkpoint: false
Training:
  gpus:
  - 2
  - 3
  max_epochs: 9999
  val_check_interval: 1.0
  limit_val_batches: 250
  pretrain_g_only: true
  g_pretrain_steps: 15000
  adv_loss_ramp_steps: 2500
  label_smoothing: true
  EMA:
    enabled: false
    decay: 0.999
    update_after_step: 0
    use_num_updates: true
  Losses:
    adv_loss_beta: 0.001
    adv_loss_schedule: cosine
    l1_weight: 1.0
    sam_weight: 0.05
    perceptual_weight: 0.1
    perceptual_metric: vgg
    tv_weight: 0.0
    max_val: 1.0
    ssim_win: 11
Generator:
  model_type: rcab
  large_kernel_size: 9
  small_kernel_size: 3
  n_channels: 96
  n_blocks: 32
  scaling_factor: 8
Discriminator:
  model_type: standard
  n_blocks: 8
Optimizers:
  optim_g_lr: 0.0001
  optim_d_lr: 0.0001
Schedulers:
  g_warmup_steps: 2500
  g_warmup_type: cosine
  metric: val_metrics/l1
  patience_g: 50
  patience_d: 50
  factor_g: 0.5
  factor_d: 0.5
  verbose: true
Logging:
  num_val_images: 5