Delete configs/config_full_tile.yaml

configs/config_full_tile.yaml

@@ -1,176 +0,0 @@
-# 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