| 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 | |