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Joao-L-S-AlmeidaJoao-L-S-Almeida
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Merge pull request #14 from IBM/feature/add_more_examples
Feature/add more examples
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‎docs/examples.md

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@@ -6,12 +6,10 @@ For some examples of training using the existing tasks, check out the following
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Under `examples/confs`
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* Flood Segmentation with ViT: `segmentation_config_vit.yaml`
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* Flood Segmentation with ViT: `sen1floods11_vit.yaml`
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* Multitemporal Crop Segmentation: `multitemporal_crop.yaml`
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* Scene Classification: `eurosat.yaml`
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* Burn Scar Segmentation: `burn_scars.yaml`
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* Usage of an SMP backbone `geobench/segmentation/m_chesapeake_landcover_smp_resnet_unet.yaml`
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* Usage of a timm backbone `geobench/classification/m_bigearthnet_timm_resnet.yaml`
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* Scene Classification: `eurosat.yaml`

‎examples/confs/burn_scars.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: <path>
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name: fire_scars
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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: 200
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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: <path>
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# dataset available: https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars
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data:
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class_path: GenericNonGeoSegmentationDataModule
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init_args:
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batch_size: 4
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num_workers: 8
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dataset_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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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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rgb_indices:
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- 0
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- 1
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- 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.HorizontalFlip
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init_args:
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p: 0.5
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- class_path: ToTensorV2
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no_data_replace: 0
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no_label_replace: -1
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train_data_root: <data_path>/training
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train_label_data_root: <data_path>/training
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val_data_root: <data_path>/validation
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val_label_data_root: <data_path>/validation
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test_data_root: <data_path>/validation
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test_label_data_root: <data_path>/validation
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img_grep: "*_merged.tif"
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label_grep: "*.mask.tif"
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means:
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- 0.033349706741586264
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- 0.05701185520536176
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- 0.05889748132001316
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- 0.2323245113436119
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- 0.1972854853760658
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- 0.11944914225186566
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stds:
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- 0.02269135568823774
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- 0.026807560223070237
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- 0.04004109844362779
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- 0.07791732423672691
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- 0.08708738838140137
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- 0.07241979477437814
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num_classes: 2
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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_args:
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decoder: FCNDecoder
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pretrained: true
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backbone: prithvi_vit_100
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decoder_channels: 256
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in_channels: 6
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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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num_frames: 1
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num_classes: 2
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head_dropout: 0.1
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decoder_num_convs: 4
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head_channel_list:
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- 256
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loss: dice
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plot_on_val: 10
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ignore_index: -1
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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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tiled_inference_parameters:
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h_crop: 512
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h_stride: 496
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w_crop: 512
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w_stride: 496
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average_patches: true
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optimizer:
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class_path: torch.optim.Adam
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init_args:
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lr: 1.5e-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

‎examples/confs/multi_temporal_crop.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: <path>
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name: replicate
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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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max_epochs: 200
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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: <path>
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# data available at: https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification
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data:
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class_path: GenericNonGeoSegmentationDataModule
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init_args:
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batch_size: 8
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num_workers: 12
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train_transform:
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- class_path: FlattenTemporalIntoChannels
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- class_path: albumentations.Flip
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- class_path: ToTensorV2
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- class_path: UnflattenTemporalFromChannels
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init_args:
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n_timesteps: 3
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dataset_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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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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rgb_indices:
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- 2
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- 1
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- 0
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reduce_zero_label: True
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expand_temporal_dimension: True
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train_data_root: <data_path>/training_chips
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train_label_data_root: <data_path>/training_chips
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val_data_root: <data_path>/validation_chips
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val_label_data_root: <data_path>/validation_chips
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test_data_root: <data_path>/validation_chips
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test_label_data_root: <data_path>/validation_chips
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train_split: <data_path>/training_chips/training_data.txt
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test_split: <data_path>/validation_chips/validation_data.txt
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val_split: <data_path>/validation_chips/validation_data.txt
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img_grep: "*_merged.tif"
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label_grep: "*.mask.tif"
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means:
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- 494.905781
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- 815.239594
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- 924.335066
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- 2968.881459
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- 2634.621962
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- 1739.579917
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stds:
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- 284.925432
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- 357.84876
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- 575.566823
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- 896.601013
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- 951.900334
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- 921.407808
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num_classes: 13
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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_args:
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decoder: FCNDecoder
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pretrained: true
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backbone: prithvi_vit_100
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in_channels: 6
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rescale: False
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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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num_frames: 3
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num_classes: 13
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head_dropout: 0.1
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decoder_channels: 512
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head_channel_list:
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- 128
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- 64
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loss: ce
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class_names:
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- Natural Vegetation
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- Forest
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- Corn
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- Soybeans
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- Wetlands
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- Developed/Barren
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- Open Water
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- Winter Wheat
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- Alfalfa
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- Fallow/Idle Cropland
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- Cotton
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- Sorghum
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- Other
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# aux_heads:
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# - name: aux_head
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# decoder: FCNDecoder
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# decoder_args:
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# decoder_channels: 256
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# decoder_in_index: 2
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# decoder_num_convs: 2
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# head_channel_list:
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# - 64
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# aux_loss:
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# aux_head: 1.0
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class_weights:
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- 0.386375
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- 0.661126
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- 0.548184
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- 0.640482
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- 0.876862
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- 0.925186
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- 3.249462
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- 1.542289
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- 2.175141
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- 2.272419
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- 3.062762
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- 3.626097
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- 1.198702
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ignore_index: -1
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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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tiled_inference_parameters:
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h_crop: 224
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h_stride: 196
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w_crop: 224
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w_stride: 196
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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: 1.5e-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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