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romeokienzlerromeokienzler
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Merge branch 'main' into custom_modules
2 parents b4c602d + 23944e3 commit 55acdb9

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‎.github/workflows/pylint.yml

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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.10","3.11"]
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python-version: ["3.10","3.11","3.12"]
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}

‎.github/workflows/test.yaml

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timeout-minutes: 20
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strategy:
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matrix:
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python-version: ["3.10", "3.11"]
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python-version: ["3.10", "3.11", "3.12"]
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steps:
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- name: Clone repo

‎docs/quick_start.md

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@@ -107,9 +107,8 @@ model_args = dict(
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HLSBands.SWIR_1,
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HLSBands.SWIR_2,
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],
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necks=[{"name": "SelectIndices", "indices": -1},
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necks=[{"name": "SelectIndices", "indices": [-1]},
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{"name": "ReshapeTokensToImage"}],
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num_classes=4,
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backbone_pretrained=True,
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backbone_num_frames=1,
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decoder_channels=128,

‎examples/confs/multimae_sen1floods11.yaml

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# lightning.pytorch==2.1.1
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seed_everything: 0
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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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class_path: TensorBoardLogger
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init_args:
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save_dir: output
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name: multimae_sen1floods11
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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: 40
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max_epochs: 2
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check_val_every_n_epoch: 1
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log_every_n_steps: 50
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enable_checkpointing: true
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default_root_dir: output/multimae_sen1floods11/
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data:
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class_path: GenericMultiModalDataModule
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init_args:
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task: 'segmentation'
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batch_size: 4
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num_workers: 0
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modalities:
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- S2L2A
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- S1
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- LULC
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rgb_modality: S2L2A # If not provided, uses first modality
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rgb_indices:
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- 3
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- 2
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- 1
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train_data_root:
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S2L2A: data/sen1floods11/data/data/flood_events/HandLabeled/S2L2AHand
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S1: data/sen1floods11/data/data/flood_events/HandLabeled/S1Hand
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LULC: data/sen1floods11/data/data/flood_events/HandLabeled/LULCHand
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train_label_data_root: data/sen1floods11/data/data/flood_events/HandLabeled/LabelHand
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val_data_root:
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S2L2A: data/sen1floods11/data/data/flood_events/HandLabeled/S2L2AHand
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S1: data/sen1floods11/data/data/flood_events/HandLabeled/S1Hand
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LULC: data/sen1floods11/data/data/flood_events/HandLabeled/LULCHand
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val_label_data_root: data/sen1floods11/data/data/flood_events/HandLabeled/LabelHand
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test_data_root:
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S2L2A: data/sen1floods11/data/data/flood_events/HandLabeled/S2L2AHand
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S1: data/sen1floods11/data/data/flood_events/HandLabeled/S1Hand
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LULC: data/sen1floods11/data/data/flood_events/HandLabeled/LULCHand
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test_label_data_root: data/sen1floods11/data/data/flood_events/HandLabeled/LabelHand
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train_split: data/sen1floods11/splits/splits/flood_handlabeled/dev_train.txt
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val_split: data/sen1floods11/splits/splits/flood_handlabeled/dev_valid.txt
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test_split: data/sen1floods11/splits/splits/flood_handlabeled/dev_test.txt
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allow_substring_file_names: True
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image_grep:
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S2L2A: "*_S2L2AHand.tif"
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S1: "*_S1Hand.tif"
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LULC: "*_LULCHand.npy"
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label_grep: "*_LabelHand.tif"
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no_label_replace: -1
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no_data_replace: 0
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means:
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S2L2A:
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- 1793.243
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- 1924.863
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- 2184.553
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- 2340.936
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- 2671.402
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- 3240.082
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- 3468.412
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- 3563.244
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- 3627.704
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- 3711.071
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- 3416.714
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- 2849.625
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S1:
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- -12.577
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- -20.265
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stds:
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S2L2A:
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- 1160.144
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- 1201.092
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- 1219.943
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- 1397.225
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- 1400.035
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- 1373.136
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- 1429.17
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- 1485.025
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- 1447.836
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- 1652.703
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- 1471.002
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- 1365.30
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S1:
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- 5.179
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- 5.872
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num_classes: 2
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train_transform:
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- class_path: albumentations.RandomCrop
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init_args:
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height: 224
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width: 224
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- class_path: albumentations.D4
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- class_path: ToTensorV2
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model:
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class_path: terratorch.tasks.SemanticSegmentationTask
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init_args:
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model_factory: EncoderDecoderFactory
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model_args:
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backbone_pretrained: false
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backbone: multimae_base
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backbone_input_adapters:
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- S1
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- S2L2A
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- LULC
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decoder: FCNDecoder # UperNetDecoder
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decoder_num_convs: 4 # only for FCNDecoder
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# decoder_scale_modules: True # only for UperNetDecoder
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decoder_channels: 256
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num_classes: 2
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head_dropout: 0.1
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head_channel_list:
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- 256
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loss: ce
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ignore_index: -1
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class_weights:
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- 0.3
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- 0.7
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class_names:
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- Others
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- Flood
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freeze_backbone: false
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freeze_decoder: false
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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: 6.e-5
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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
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‎examples/confs/multimodal_prithvi_sen1floods11.yaml

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# lightning.pytorch==2.1.1
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seed_everything: 0
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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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class_path: TensorBoardLogger
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init_args:
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save_dir: output
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name: multimodal_prithvi_sen1floods11
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version: test_best
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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: 40
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max_epochs: 100
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check_val_every_n_epoch: 1
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log_every_n_steps: 50
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enable_checkpointing: True
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default_root_dir: output/multimodal_prithvi_sen1floods11/
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data:
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class_path: GenericMultiModalDataModule
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init_args:
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task: 'segmentation'
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batch_size: 16
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num_workers: 4
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modalities: # Define names of modalities
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- S2L2A
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- S1
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rgb_modality: S2L2A # If not provided, uses first modality
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rgb_indices:
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- 3
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- 2
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- 1
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# Data roots are defined as dicts with modalities as keys
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train_data_root:
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S2L2A: data/sen1floods11/data/data/flood_events/HandLabeled/S2L2AHand
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S1: data/sen1floods11/data/data/flood_events/HandLabeled/S1Hand
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train_label_data_root: data/sen1floods11/data/data/flood_events/HandLabeled/LabelHand
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val_data_root:
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S2L2A: data/sen1floods11/data/data/flood_events/HandLabeled/S2L2AHand
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S1: data/sen1floods11/data/data/flood_events/HandLabeled/S1Hand
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val_label_data_root: data/sen1floods11/data/data/flood_events/HandLabeled/LabelHand
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test_data_root:
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S2L2A: data/sen1floods11/data/data/flood_events/HandLabeled/S2L2AHand
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S1: data/sen1floods11/data/data/flood_events/HandLabeled/S1Hand
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test_label_data_root: data/sen1floods11/data/data/flood_events/HandLabeled/LabelHand
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train_split: data/sen1floods11/splits/splits/flood_handlabeled/flood_train_data.txt
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val_split: data/sen1floods11/splits/splits/flood_handlabeled/flood_valid_data.txt
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test_split: data/sen1floods11/splits/splits/flood_handlabeled/flood_test_data.txt
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allow_substring_file_names: True
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image_grep:
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S2L2A: "*_S2L2AHand.tif"
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S1: "*_S1Hand.tif"
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label_grep: "*_LabelHand.tif"
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no_label_replace: -1
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no_data_replace: 0
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concat_bands: true # Concatenate modalities along band dim for single-modal models like Prithvi
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# Define standardization values as dicts (no scaling if modality is not included)
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means:
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S2L2A:
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- 1793.243
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- 1924.863
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- 2184.553
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- 2340.936
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- 2671.402
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- 3240.082
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- 3468.412
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- 3563.244
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- 3627.704
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- 3711.071
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- 3416.714
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- 2849.625
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S1:
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- -12.577
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- -20.265
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stds:
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S2L2A:
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- 1160.144
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- 1201.092
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- 1219.943
97+
- 1397.225
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- 1400.035
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- 1373.136
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- 1429.17
101+
- 1485.025
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- 1447.836
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- 1652.703
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- 1471.002
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- 1365.30
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S1:
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- 5.179
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- 5.872
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num_classes: 2
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# Transforms are shared between all image modalities (e.g. same crop area)
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train_transform:
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- class_path: albumentations.RandomCrop
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init_args:
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height: 224
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width: 224
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- class_path: albumentations.D4
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- class_path: ToTensorV2
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121+
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model:
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class_path: terratorch.tasks.SemanticSegmentationTask
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init_args:
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model_factory: EncoderDecoderFactory
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model_args:
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backbone: prithvi_vit_100
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backbone_pretrained: false
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backbone_bands:
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- COASTAL_AEROSOL
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- BLUE
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- GREEN
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- RED
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- RED_EDGE_1
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- RED_EDGE_2
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- RED_EDGE_3
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- NIR_BROAD
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- NIR_NARROW
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- CIRRUS
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- SWIR_1
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- SWIR_2
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- VV
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- VH
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decoder: FCNDecoder # FCNDecoder
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decoder_num_convs: 4 # only for FCNDecoder
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# decoder_scale_modules: True # only for UperNetDecoder
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decoder_channels: 256
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num_classes: 2
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head_dropout: 0.1
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head_channel_list:
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- 256
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loss: dice
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ignore_index: -1
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class_weights:
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- 0.3
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- 0.7
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class_names:
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- Others
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- Flood
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freeze_backbone: false
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freeze_decoder: false
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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: 6.e-5
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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
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