Initial Experiment for Mozambique LULC #207
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| model: | ||
| class_path: rslearn.train.lightning_module.RslearnLightningModule | ||
| init_args: | ||
| model: | ||
| class_path: rslearn.models.multitask.MultiTaskModel | ||
| init_args: | ||
| encoder: | ||
| - class_path: rslp.helios.model.Helios | ||
| init_args: | ||
| checkpoint_path: /weka/dfive-default/helios/checkpoints/henryh/base_v6.1_add_chm_cdl_worldcereal/step300000 | ||
| selector: ["encoder"] | ||
| forward_kwargs: | ||
| patch_size: 1 | ||
| decoders: | ||
| crop_type_classification: | ||
| - class_path: rslp.crop.kenya_nandi.train.SegmentationPoolingDecoder | ||
| init_args: | ||
| in_channels: 768 | ||
| out_channels: 7 | ||
| - class_path: rslearn.train.tasks.segmentation.SegmentationHead | ||
| lr: 0.0001 | ||
| scheduler: | ||
| class_path: rslearn.train.scheduler.PlateauScheduler | ||
| init_args: | ||
| factor: 0.2 | ||
| patience: 2 | ||
| min_lr: 0 | ||
| cooldown: 10 | ||
| data: | ||
| class_path: rslearn.train.data_module.RslearnDataModule | ||
| init_args: | ||
| path: /weka/dfive-default/rslearn-eai/datasets/crop/mozambique_lulc | ||
| inputs: | ||
| sentinel2_l2a: | ||
| data_type: "raster" | ||
| layers: ["sentinel2"] | ||
| bands: ["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B8A", "B11", "B12", "B01", "B09"] | ||
| passthrough: true | ||
| dtype: FLOAT32 | ||
| load_all_item_groups: true | ||
| load_all_layers: true | ||
| sentinel1: | ||
| data_type: "raster" | ||
| layers: ["sentinel1_descending"] | ||
| bands: ["vv", "vh"] | ||
| passthrough: true | ||
| dtype: FLOAT32 | ||
| load_all_item_groups: true | ||
| load_all_layers: true | ||
| label: | ||
| data_type: "raster" | ||
| layers: ["label_raster"] | ||
| bands: ["label"] | ||
| is_target: true | ||
| dtype: INT32 | ||
| task: | ||
| class_path: rslearn.train.tasks.multi_task.MultiTask | ||
| init_args: | ||
| tasks: | ||
| crop_type_classification: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationTask | ||
| init_args: | ||
| num_classes: 7 | ||
| zero_is_invalid: false | ||
| metric_kwargs: | ||
| average: "micro" | ||
| other_metrics: | ||
| water_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 0 | ||
| water_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 0 | ||
| bareground_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 1 | ||
| bareground_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 1 | ||
| rangeland_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 2 | ||
| rangeland_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 2 | ||
| floodedvegetation_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 3 | ||
| floodedvegetation_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 3 | ||
| trees_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 4 | ||
| trees_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 4 | ||
| cropland_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 5 | ||
| cropland_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 5 | ||
| buildings_precision: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassPrecision | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 6 | ||
| buildings_recall: | ||
| class_path: rslearn.train.tasks.segmentation.SegmentationMetric | ||
| init_args: | ||
| metric: | ||
| class_path: torchmetrics.classification.MulticlassRecall | ||
| init_args: | ||
| num_classes: 7 | ||
| average: null | ||
| class_idx: 6 | ||
| input_mapping: | ||
| crop_type_classification: | ||
| label: "targets" | ||
| batch_size: 32 | ||
| num_workers: 32 | ||
| default_config: | ||
| transforms: | ||
| - class_path: rslp.helios.norm.HeliosNormalize | ||
| init_args: | ||
| config_fname: "/opt/helios/data/norm_configs/computed.json" | ||
| band_names: | ||
| sentinel2_l2a: ["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B8A", "B11", "B12", "B01", "B09"] | ||
| sentinel1: ["vv", "vh"] | ||
| - class_path: rslearn.train.transforms.pad.Pad | ||
| init_args: | ||
| size: 4 | ||
| mode: "center" | ||
| image_selectors: ["sentinel2_l2a", "sentinel1", "target/crop_type_classification/classes", "target/crop_type_classification/valid"] | ||
|
There was a problem hiding this comment. It makes sense that we need to pad (or crop) both the inputs and the targets - the naming convention for the targets here is confusing to me. |
||
| train_config: | ||
| groups: ["gaza"] | ||
| tags: | ||
| split: "train" | ||
| val_config: | ||
| groups: ["gaza"] | ||
| tags: | ||
| split: "test" | ||
| test_config: | ||
| groups: ["gaza"] | ||
| tags: | ||
| split: "test" | ||
| trainer: | ||
| max_epochs: 100 | ||
| callbacks: | ||
| - class_path: lightning.pytorch.callbacks.LearningRateMonitor | ||
| init_args: | ||
| logging_interval: "epoch" | ||
| - class_path: lightning.pytorch.callbacks.ModelCheckpoint | ||
| init_args: | ||
| save_top_k: 1 | ||
| save_last: true | ||
| monitor: val_loss | ||
| mode: min | ||
| - class_path: rslearn.train.callbacks.freeze_unfreeze.FreezeUnfreeze | ||
| init_args: | ||
| module_selector: ["model", "encoder", 0] | ||
| unfreeze_at_epoch: 20 | ||
| unfreeze_lr_factor: 10 | ||
| # - class_path: rslearn.train.prediction_writer.RslearnWriter | ||
| # init_args: | ||
| # path: /weka/dfive-default/rslearn-eai/datasets/crop/mozambique_lulc | ||
| # output_layer: prediction_v0 | ||
| # selector: ["crop_type_classification"] | ||
| # merger: | ||
| # class_path: rslearn.train.prediction_writer.RasterMerger | ||
| # init_args: | ||
| # padding: 2 | ||
| rslp_project: 2025_09_18_mozambique_lulc | ||
| rslp_experiment: mozambique_lulc_helios_base_S1_S2_ts_ws4_ps1_gaza | ||
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@gabrieltseng just a heads-up: I merged the Nandi esrun PR and reorganized the folders, it’s better not to keep all Ag/LULC projects under
cropfolder. You’ll need to update your import here torslp.nandi.train.SegmentationPoolingDecoderinstead ofrslp.crop.kenya_nandi.train.SegmentationPoolingDecoder, and you can move themozambiquefolder directly underesrun_dataandrslpto align with thedata/heliosstructure. Let me know if you hit any issues and I can help fix.