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config_RGB-NIR.yaml

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+Data:
+  train_batch_size: 12
+  val_batch_size: 8
+  num_workers: 6
+  prefetch_factor: 2
+  dataset_type: SISR_WW
+  normalization: normalise_10k
+Model:
+  in_bands: 4
+  continue_training: false
+  load_checkpoint: false
+Training:
+  gpus:
+  - 0
+  - 1
+  - 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: true
+    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: SRResNet
+  large_kernel_size: 9
+  small_kernel_size: 3
+  n_channels: 96
+  n_blocks: 16
+  scaling_factor: 4
+Discriminator:
+  model_type: patchgan
+  n_blocks: 3
+Optimizers:
+  optim_g_lr: 0.0001
+  optim_d_lr: 0.0001
+Schedulers:
+  g_warmup_steps: 5000
+  g_warmup_type: cosine
+  metric: val_metrics/l1
+  patience_g: 100
+  patience_d: 100
+  factor_g: 0.5
+  factor_d: 0.5
+  verbose: true
+Logging:
+  num_val_images: 5

config_SWIR.yaml

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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