add solar farm config

data/satlas/solar_farm/config.yaml

@@ -0,0 +1,207 @@
+model:
+  class_path: rslearn.train.lightning_module.RslearnLightningModule
+  init_args:
+    model:
+      class_path: rslearn.models.multitask.MultiTaskModel
+      init_args:
+        encoder:
+          - class_path: rslearn.models.simple_time_series.SimpleTimeSeries
+            init_args:
+              encoder:
+                class_path: rslearn.models.swin.Swin
+                init_args:
+                  pretrained: true
+                  input_channels: 9
+                  output_layers: [1, 3, 5, 7]
+              image_channels: 9
+        decoders:
+          segment:
+            - class_path: rslearn.models.unet.UNetDecoder
+              init_args:
+                in_channels: [[4, 128], [8, 256], [16, 512], [32, 1024]]
+                out_channels: 2
+                conv_layers_per_resolution: 2
+            - class_path: rslearn.train.tasks.segmentation.SegmentationHead
+    lr: 0.00002
+    plateau: true
+    plateau_factor: 0.2
+    plateau_patience: 2
+    plateau_min_lr: 0
+    plateau_cooldown: 10
+    restore_config:
+      restore_path: https://ai2-public-datasets.s3.amazonaws.com/satlas/satlas-model-v1-lowres-band-multi.pth
+      remap_prefixes:
+        - ["backbone.backbone.backbone.", "encoder.0.encoder.model."]
+data:
+  class_path: rslearn.train.data_module.RslearnDataModule
+  init_args:
+    path: gs://rslearn-eai/datasets/solar_farm/dataset_v1/20250108/
+    inputs:
+      image1:
+        data_type: "raster"
+        layers: ["sentinel2"]
+        bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
+        passthrough: true
+        dtype: FLOAT32
+      image2:
+        data_type: "raster"
+        layers: ["sentinel2.1"]
+        bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
+        passthrough: true
+        dtype: FLOAT32
+      image3:
+        data_type: "raster"
+        layers: ["sentinel2.2"]
+        bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
+        passthrough: true
+        dtype: FLOAT32
+      image4:
+        data_type: "raster"
+        layers: ["sentinel2.3"]
+        bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
+        passthrough: true
+        dtype: FLOAT32
+      mask:
+        data_type: "raster"
+        layers: ["mask"]
+        bands: ["mask"]
+        passthrough: true
+        dtype: INT32
+        is_target: true
+      targets:
+        data_type: "raster"
+        layers: ["label_raster"]
+        bands: ["label"]
+        dtype: INT32
+        is_target: true
+    task:
+      class_path: rslearn.train.tasks.multi_task.MultiTask
+      init_args:
+        tasks:
+          segment:
+            class_path: rslearn.train.tasks.segmentation.SegmentationTask
+            init_args:
+              num_classes: 2
+              metric_kwargs:
+                average: "micro"
+              remap_values: [[0, 1], [0, 255]]
+        input_mapping:
+          segment:
+            targets: "targets"
+    batch_size: 8
+    num_workers: 32
+    default_config:
+      transforms:
+        - class_path: rslearn.train.transforms.normalize.Normalize
+          init_args:
+            mean: 0
+            std: 3000
+            valid_range: [0, 1]
+            bands: [0, 1, 2]
+            selectors: ["image1", "image2", "image3", "image4"]
+        - class_path: rslearn.train.transforms.normalize.Normalize
+          init_args:
+            mean: 0
+            std: 8160
+            valid_range: [0, 1]
+            bands: [3, 4, 5, 6, 7, 8]
+            selectors: ["image1", "image2", "image3", "image4"]
+        - class_path: rslearn.train.transforms.concatenate.Concatenate
+          init_args:
+            selections:
+              image1: []
+              image2: []
+              image3: []
+              image4: []
+            output_selector: image
+        - class_path: rslp.transforms.mask.Mask
+    train_config:
+      patch_size: 256
+      transforms:
+        - class_path: rslearn.train.transforms.normalize.Normalize
+          init_args:
+            mean: 0
+            std: 3000
+            valid_range: [0, 1]
+            bands: [0, 1, 2]
+            selectors: ["image1", "image2", "image3", "image4"]
+        - class_path: rslearn.train.transforms.normalize.Normalize
+          init_args:
+            mean: 0
+            std: 8160
+            valid_range: [0, 1]
+            bands: [3, 4, 5, 6, 7, 8]
+            selectors: ["image1", "image2", "image3", "image4"]
+        - class_path: rslearn.train.transforms.concatenate.Concatenate
+          init_args:
+            selections:
+              image1: []
+              image2: []
+              image3: []
+              image4: []
+            output_selector: image
+        - class_path: rslp.transforms.mask.Mask
+        - class_path: rslearn.train.transforms.flip.Flip
+          init_args:
+            image_selectors: ["image", "target/segment/classes", "target/segment/valid"]
+      tags:
+        split: train
+    val_config:
+      patch_size: 256
+      tags:
+        split: val
+    test_config:
+      patch_size: 256
+      tags:
+        split: val
+    predict_config:
+      groups: ["predict"]
+      load_all_patches: true
+      skip_targets: true
+      patch_size: 512
+      transforms:
+        - class_path: rslearn.train.transforms.normalize.Normalize
+          init_args:
+            mean: 0
+            std: 3000
+            valid_range: [0, 1]
+            bands: [0, 1, 2]
+            selectors: ["image1", "image2", "image3", "image4"]
+        - class_path: rslearn.train.transforms.normalize.Normalize
+          init_args:
+            mean: 0
+            std: 8160
+            valid_range: [0, 1]
+            bands: [3, 4, 5, 6, 7, 8]
+            selectors: ["image1", "image2", "image3", "image4"]
+        - class_path: rslearn.train.transforms.concatenate.Concatenate
+          init_args:
+            selections:
+              image1: []
+              image2: []
+              image3: []
+              image4: []
+            output_selector: image
+trainer:
+  max_epochs: 500
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: "epoch"
+    - class_path: rslearn.train.prediction_writer.RslearnWriter
+      init_args:
+        path: gs://rslearn-eai/datasets/solar_farm/dataset_v1/20250108/
+        output_layer: output
+        selector: ["detect"]
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        save_top_k: 1
+        save_last: true
+        monitor: val_segment/accuracy
+        mode: max
+    - class_path: rslearn.train.callbacks.freeze_unfreeze.FreezeUnfreeze
+      init_args:
+        module_selector: ["model", "encoder", 0, "encoder", "model"]
+        unfreeze_at_epoch: 2
+rslp_project: satlas_solar_farm
+rslp_experiment: data_20250108_satlaspretrain_patch256_00