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granite-geospatial-biomass
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# lightning.pytorch==2.1.1
seed_everything: 42

### Trainer configuration
trainer:
  accelerator: auto
  strategy: auto
  devices: auto
  num_nodes: 1
  # precision: 16-mixed
  logger:
    class_path: TensorBoardLogger
    init_args:
      save_dir: ./experiments
      name: finetune_region
  callbacks:
    - class_path: RichProgressBar
    - class_path: LearningRateMonitor
      init_args:
        logging_interval: epoch
    - class_path: EarlyStopping
      init_args:
        monitor: val/loss
        patience: 100
  max_epochs: 300
  check_val_every_n_epoch: 1
  log_every_n_steps: 20
  enable_checkpointing: true
  default_root_dir: ./experiments

### Data configuration
data:
  class_path: GenericNonGeoPixelwiseRegressionDataModule
  init_args:
    batch_size: 64
    num_workers: 8
    train_transform:
      - class_path: albumentations.HorizontalFlip
        init_args:
          p: 0.5      
      - class_path: albumentations.Rotate
        init_args:
          limit: 30
          border_mode: 0 # cv2.BORDER_CONSTANT
          value: 0
          # mask_value: 1
          p: 0.5
      - class_path: ToTensorV2
    # Specify all bands which are in the input data. 
    # -1 are placeholders for bands that are in the data but that we will discard
    dataset_bands: 
      - -1
      - BLUE
      - GREEN
      - RED
      - NIR_NARROW
      - SWIR_1
      - SWIR_2
      - -1
      - -1
      - -1
      - -1
    output_bands: #Specify the bands which are used from the input data.
      - BLUE
      - GREEN
      - RED
      - NIR_NARROW
      - SWIR_1
      - SWIR_2
    rgb_indices:
      - 2
      - 1
      - 0
    # Directory roots to training, validation and test datasplits:
    train_data_root: train_images
    train_label_data_root: train_labels
    val_data_root: val_images
    val_label_data_root: val_labels
    test_data_root: test_images
    test_label_data_root: test_labels
    means: # Mean value of the training dataset per band
    - 547.36707
    - 898.5121
    - 1020.9082
    - 2665.5352
    - 2340.584
    - 1610.1407
    stds: # Standard deviation of the training dataset per band
    - 411.4701
    - 558.54065
    - 815.94025
    - 812.4403
    - 1113.7145
    - 1067.641
    # Nodata value in label data 
    no_label_replace: -1 
    # Nodata value in the input data
    no_data_replace: 0

### Model configuration
model:
  class_path: terratorch.tasks.PixelwiseRegressionTask 
  init_args:
    model_args:
      decoder: UperNetDecoder
      pretrained: false
      backbone: prithvi_swin_B
      backbone_drop_path_rate: 0.3
      decoder_channels: 32
      in_channels: 6
      bands:
        - BLUE
        - GREEN
        - RED
        - NIR_NARROW
        - SWIR_1
        - SWIR_2
      num_frames: 1
      head_dropout: 0.16194593880230534
      head_final_act: torch.nn.ReLU
      head_learned_upscale_layers: 2
    loss: rmse
    ignore_index: -1
    freeze_backbone: false
    freeze_decoder: false
    model_factory: PrithviModelFactory
    # uncomment this block for tiled inference
    # tiled_inference_parameters:
    #   h_crop: 224
    #   h_stride: 192
    #   w_crop: 224
    #   w_stride: 192
    #   average_patches: true
optimizer:
  class_path: torch.optim.AdamW
  init_args:
    lr: 0.00031406904191973693
    weight_decay: 0.03283253068408954
lr_scheduler:
  class_path: ReduceLROnPlateau
  init_args:
    monitor: val/loss