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config_full_tile.yaml

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+# lightning.pytorch==2.1.1
+seed_everything: 0
+
+### Trainer configuration
+trainer:
+  accelerator: auto
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  # precision: 16-mixed
+  logger:
+    # You can swtich to TensorBoard for logging by uncommenting the below line and commenting out the procedding line
+    #class_path: TensorBoardLogger
+    class_path: lightning.pytorch.loggers.csv_logs.CSVLogger
+    init_args:
+      save_dir: ./experiments
+      name: fine_tune_suhi
+  callbacks:
+    - class_path: RichProgressBar
+    - class_path: LearningRateMonitor
+      init_args:
+        logging_interval: epoch
+    - class_path: EarlyStopping
+      init_args:
+        monitor: val/loss
+        patience: 600
+  max_epochs: 600
+  check_val_every_n_epoch: 1
+  log_every_n_steps: 10
+  enable_checkpointing: true
+  default_root_dir: ./experiments
+out_dtype: float32
+
+### Data configuration
+data:
+  class_path: GenericNonGeoPixelwiseRegressionDataModule
+  init_args:
+    batch_size: 1
+    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. 
+    dataset_bands:
+    # 6 HLS bands
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+    # ERA5-Land t2m_spatial_avg
+      - 7
+    # ERA5-Land t2m_sunrise_avg
+      - 8
+    # ERA5-Land t2m_midnight_avg
+      - 9
+    # ERA5-Land t2m_delta_avg
+      - 10
+    # cos_tod
+      - 11
+    # sin_tod
+      - 12
+    # cos_doy
+      - 13
+    # sin_doy
+      - 14
+    # Specify the bands which are used from the input data.
+    # Bands 8 - 14 were discarded in the final model
+    output_bands:
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+      - 7
+    rgb_indices:
+      - 2
+      - 1
+      - 0
+    # Directory roots to training, validation and test datasplits:
+    train_data_root: train/inputs
+    train_label_data_root: train/targets
+    val_data_root: val/inputs
+    val_label_data_root: val/targets
+    test_data_root: test/inputs
+    test_label_data_root: test/targets
+    img_grep: "*.inputs.tif"
+    label_grep: "*.lst.tif"
+    # Nodata value in the input data
+    no_data_replace: 0
+    # Nodata value in label (target) data 
+    no_label_replace: -9999
+    # Mean value of the training dataset per band  
+    means:
+    - 702.4754028320312
+    - 1023.23291015625
+    - 1118.8924560546875 
+    - 2440.750732421875 
+    - 2052.705810546875 
+    - 1514.15087890625 
+    - 21.031919479370117 
+    # Standard deviation of the training dataset per band
+    stds:
+    - 554.8255615234375 
+    - 613.5565185546875 
+    - 745.929443359375
+    - 715.0111083984375 
+    - 761.47607421875 
+    - 734.991943359375 
+    - 8.66781997680664 
+
+### Model configuration
+model:
+  class_path: terratorch.tasks.PixelwiseRegressionTask
+  init_args:
+    model_args:
+      decoder: UperNetDecoder
+      pretrained: false
+      backbone: prithvi_swin_L
+      img_size: 224
+      backbone_drop_path_rate: 0.3
+      decoder_channels: 256
+      in_channels: 7
+      bands:
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+      - 7
+      num_frames: 1
+    loss: rmse
+    aux_heads:
+      - name: aux_head
+        decoder: IdentityDecoder
+        decoder_args:
+          head_dropout: 0.5
+          head_channel_list:
+          - 1
+          head_final_act: torch.nn.LazyLinear
+    aux_loss:
+      aux_head: 0.4
+    ignore_index: -9999
+    freeze_backbone: false
+    freeze_decoder: false
+    model_factory: PrithviModelFactory
+    # This block is commented out when inferencing on full tiles.
+    # It is possible to inference on full tiles with this paramter on, the benefit is that the compute requirement is smaller.
+    # However, using this to inference on a full tile will introduce artefacting/"patchy" predictions.
+    # tiled_inference_parameters:
+    #    h_crop: 224
+    #    h_stride: 224
+    #    w_crop: 224
+    #    w_stride: 224
+    #    average_patches: true
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 0.0001
+    weight_decay: 0.05
+lr_scheduler:
+  class_path: ReduceLROnPlateau
+  init_args:
+    monitor: val/loss