Upload config.yaml

config.yaml

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+# lightning.pytorch==2.1.1
+seed_everything: 0
+trainer:
+  accelerator: cpu
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  logger: True # will use tensorboardlogger
+
+  callbacks:
+    - class_path: RichProgressBar
+    - class_path: LearningRateMonitor
+      init_args:
+        logging_interval: epoch
+    - class_path: EarlyStopping 
+      init_args:
+        monitor: val/loss
+        patience: 30
+
+  max_epochs: 200
+  check_val_every_n_epoch: 1
+  log_every_n_steps: 1
+  enable_checkpointing: true
+  default_root_dir: ./../data/fine_tuning/granite_geospatial_uki_flood_detection_v1
+data:
+  class_path: GenericNonGeoSegmentationDataModule
+  init_args:
+    batch_size: 16
+    num_workers: 1
+    constant_scale: 0.0001
+    dataset_bands: # what bands are in your data 
+      - VV
+      - VH
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+      - CLOUD
+    output_bands: # which bands do you want to fine-tune 
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+      - VV
+      - VH
+      - CLOUD
+    rgb_indices:
+      - 4
+      - 3
+      - 2
+    train_data_root: ./../data/regions/uki/images/
+    train_label_data_root: ./../data/regions/uki/labels_without_cloud/
+    val_data_root: ./../data/regions/uki/images/
+    val_label_data_root: ./../data/regions/uki/labels_without_cloud/
+    test_data_root: ./../data/regions/uki/images/
+    test_label_data_root: ./../data/regions/uki/labels_without_cloud/
+    train_split: ./../data/regions/uki/splits/flood_train_data.txt
+    test_split: ./../data/regions/uki/splits/flood_test_data.txt
+    val_split: ./../data/regions/uki/splits/flood_val_data.txt
+    img_grep: "*_image.tif"
+    label_grep: "*_label.tif"
+    no_label_replace: -1
+    no_data_replace: 0
+    means:
+      - 0.08867253281911215    # BLUE
+      - 0.09101736325581869    # GREEN
+      - 0.08757093732833862    # RED
+      - 0.1670982579167684    # NIR_NARROW
+      - 0.09420119639078776    # SWIR_1
+      - 0.07141083437601725    # SWIR_2
+      - -0.0017641318140774339 # VV
+      - -0.002356150351719506  # VH
+      - 0.00002777560551961263 # CLOUD
+
+    stds:
+      - 0.13656951175974685
+      - 0.13202436625655786
+      - 0.1307223895526036
+      - 0.18946390520629108
+      - 0.11561659013865118
+      - 0.09351007561544347
+      - 0.001035692652952644
+      - 0.000864295592912648
+      - 0.00004478924301636066
+      
+    num_classes: 2
+
+model:
+  class_path: terratorch.tasks.SemanticSegmentationTask 
+  init_args:
+    model_args:
+      decoder: FCNDecoder
+      backbone_pretrained: false
+      backbone: granite_geospatial_uki      
+      backbone_pretrain_img_size: 512
+      decoder_channels: 256
+      backbone_bands:
+        - BLUE
+        - GREEN
+        - RED
+        - NIR_NARROW
+        - SWIR_1
+        - SWIR_2
+        - VV
+        - VH
+        - CLOUD 
+      num_classes: 2
+      head_dropout: 0.1
+      decoder_num_convs: 4
+      head_channel_list:
+        - 256
+      necks:
+        - name: SelectIndices
+          indices:
+            - -1
+        - name: ReshapeTokensToImage
+    loss: ce
+    aux_heads:
+      - name: aux_head
+        decoder: FCNDecoder
+        decoder_args:
+          decoder_channels: 256
+          decoder_in_index: -1
+          decoder_num_convs: 2
+          head_dropout: 0.1
+    aux_loss:
+      aux_head: 1.0
+    ignore_index: -1
+    class_weights:
+      - 0.3
+      - 0.7
+    freeze_backbone: false
+    freeze_decoder: false
+    model_factory: EncoderDecoderFactory
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 6.e-5
+    weight_decay: 0.05
+lr_scheduler:
+  class_path: ReduceLROnPlateau
+  init_args:
+    monitor: val/loss