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ibm-granite
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granite-geospatial-land-surface-temperature

Image Feature Extraction
TerraTorch
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Create config.yaml

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  1. config.yaml +174 -0
config.yaml ADDED
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+ # lightning.pytorch==2.1.1
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+ seed_everything: 0
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+
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+ ### Trainer configuration
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+ trainer:
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+ accelerator: auto
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+ strategy: auto
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+ devices: auto
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+ num_nodes: 1
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+ # precision: 16-mixed
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+ logger:
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+ # You can swtich to TensorBoard for logging by uncommenting the below line and commenting out the procedding line
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+ #class_path: TensorBoardLogger
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+ class_path: lightning.pytorch.loggers.csv_logs.CSVLogger
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+ init_args:
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+ save_dir: ./experiments
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+ name: fine_tune_suhi
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+ callbacks:
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+ - class_path: RichProgressBar
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+ - class_path: LearningRateMonitor
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+ init_args:
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+ logging_interval: epoch
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+ - class_path: EarlyStopping
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+ init_args:
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+ monitor: val/loss
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+ patience: 600
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+ max_epochs: 600
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+ check_val_every_n_epoch: 1
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+ log_every_n_steps: 10
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+ enable_checkpointing: true
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+ default_root_dir: ./experiments
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+ out_dtype: float32
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+
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+ ### Data configuration
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+ data:
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+ class_path: GenericNonGeoPixelwiseRegressionDataModule
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+ init_args:
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+ batch_size: 64
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+ num_workers: 8
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+ train_transform:
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+ - class_path: albumentations.HorizontalFlip
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+ init_args:
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+ p: 0.5
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+ - class_path: albumentations.Rotate
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+ init_args:
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+ limit: 30
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+ border_mode: 0 # cv2.BORDER_CONSTANT
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+ value: 0
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+ mask_value: 1
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+ p: 0.5
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+ - class_path: ToTensorV2
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+ # Specify all bands which are in the input data.
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+ dataset_bands:
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+ # 6 HLS bands
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+ - BLUE
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+ - GREEN
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+ - RED
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+ - NIR_NARROW
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+ - SWIR_1
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+ - SWIR_2
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+ # ERA5-Land t2m_spatial_avg
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+ - 7
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+ # ERA5-Land t2m_sunrise_avg
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+ - 8
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+ # ERA5-Land t2m_midnight_avg
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+ - 9
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+ # ERA5-Land t2m_delta_avg
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+ - 10
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+ # cos_tod
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+ - 11
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+ # sin_tod
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+ - 12
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+ # cos_doy
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+ - 13
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+ # sin_doy
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+ - 14
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+ # Specify the bands which are used from the input data.
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+ # Bands 8 - 14 were discarded in the final model
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+ output_bands:
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+ - BLUE
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+ - GREEN
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+ - RED
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+ - NIR_NARROW
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+ - SWIR_1
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+ - SWIR_2
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+ - 7
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+ rgb_indices:
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+ - 2
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+ - 1
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+ - 0
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+ # Directory roots to training, validation and test datasplits:
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+ train_data_root: train/inputs
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+ train_label_data_root: train/targets
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+ val_data_root: val/inputs
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+ val_label_data_root: val/targets
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+ test_data_root: test/inputs
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+ test_label_data_root: test/targets
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+ img_grep: "*.inputs.tif"
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+ label_grep: "*.lst.tif"
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+ # Nodata value in the input data
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+ no_data_replace: 0
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+ # Nodata value in label (target) data
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+ no_label_replace: -9999
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+ # Mean value of the training dataset per band
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+ means:
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+ - 702.4754028320312
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+ - 1023.23291015625
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+ - 1118.8924560546875
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+ - 2440.750732421875
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+ - 2052.705810546875
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+ - 1514.15087890625
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+ - 21.031919479370117
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+ # Standard deviation of the training dataset per band
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+ stds:
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+ - 554.8255615234375
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+ - 613.5565185546875
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+ - 745.929443359375
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+ - 715.0111083984375
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+ - 761.47607421875
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+ - 734.991943359375
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+ - 8.66781997680664
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+
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+ ### Model configuration
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+ model:
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+ class_path: terratorch.tasks.PixelwiseRegressionTask
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+ init_args:
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+ model_args:
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+ decoder: UperNetDecoder
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+ pretrained: false
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+ backbone: prithvi_swin_L
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+ img_size: 224
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+ backbone_drop_path_rate: 0.3
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+ decoder_channels: 256
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+ in_channels: 7
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+ bands:
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+ - BLUE
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+ - GREEN
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+ - RED
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+ - NIR_NARROW
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+ - SWIR_1
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+ - SWIR_2
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+ - 7
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+ num_frames: 1
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+ loss: rmse
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+ aux_heads:
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+ - name: aux_head
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+ decoder: IdentityDecoder
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+ decoder_args:
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+ head_dropout: 0.5
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+ head_channel_list:
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+ - 1
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+ head_final_act: torch.nn.LazyLinear
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+ aux_loss:
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+ aux_head: 0.4
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+ ignore_index: -9999
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+ freeze_backbone: false
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+ freeze_decoder: false
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+ model_factory: PrithviModelFactory
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+ # uncomment this block for tiled inference
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+ tiled_inference_parameters:
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+ h_crop: 224
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+ h_stride: 224
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+ w_crop: 224
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+ w_stride: 224
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+ average_patches: true
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+ optimizer:
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+ class_path: torch.optim.AdamW
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+ init_args:
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+ lr: 0.0001
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+ weight_decay: 0.05
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+ lr_scheduler:
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+ class_path: ReduceLROnPlateau
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+ init_args:
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+ monitor: val/loss