Feature/allow multiple static inputs

fme/downscaling/_deterministic_models.py

@@ -12,7 +12,7 @@
 from fme.core.optimization import NullOptimization, Optimization
 from fme.core.packer import Packer
 from fme.core.typing_ import TensorDict
-from fme.downscaling.data import BatchData, PairedBatchData, Topography
+from fme.downscaling.data import BatchData, PairedBatchData, StaticInputs
 from fme.downscaling.metrics_and_maths import filter_tensor_mapping, interpolate
 from fme.downscaling.models import ModelOutputs, PairedNormalizationConfig
 from fme.downscaling.modules.registry import ModuleRegistrySelector
@@ -121,20 +121,20 @@ def modules(self) -> torch.nn.ModuleList:
     def train_on_batch(
         self,
         batch: PairedBatchData,
-        topography: Topography | None,
+        static_inputs: StaticInputs | None,
         optimization: Optimization | NullOptimization,
     ) -> ModelOutputs:
-        return self._run_on_batch(batch, topography, optimization)
+        return self._run_on_batch(batch, static_inputs, optimization)
 
     def generate_on_batch(
         self,
         batch: PairedBatchData,
-        topography: Topography | None,
+        static_inputs: StaticInputs | None,
         n_samples: int = 1,
     ) -> ModelOutputs:
         if n_samples != 1:
             raise ValueError("n_samples must be 1 for deterministic models")
-        result = self._run_on_batch(batch, topography, self.null_optimization)
+        result = self._run_on_batch(batch, static_inputs, self.null_optimization)
         for k, v in result.prediction.items():
             result.prediction[k] = v.unsqueeze(1)  # insert sample dimension
         for k, v in result.target.items():
@@ -144,7 +144,7 @@ def generate_on_batch(
     def generate_on_batch_no_target(
         self,
         batch: BatchData,
-        topography: Topography | None,
+        static_inputs: StaticInputs | None,
         n_samples: int = 1,
     ) -> TensorDict:
         raise NotImplementedError(
@@ -154,7 +154,7 @@ def generate_on_batch_no_target(
     def _run_on_batch(
         self,
         batch: PairedBatchData,
-        topography: Topography | None,
+        static_inputs: StaticInputs | None,
         optimizer: Optimization | NullOptimization,
     ) -> ModelOutputs:
         coarse, fine = batch.coarse.data, batch.fine.data
@@ -166,14 +166,14 @@ def _run_on_batch(
         interpolated = interpolate(coarse_norm, self.downscale_factor)
 
         if self.config.use_fine_topography:
-            if topography is None:
+            if static_inputs is None:
                 raise ValueError(
                     "Topography must be provided for each batch when use of fine "
                     "topography is enabled."
                 )
             else:
                 # Join the normalized topography to the input (see dataset for details)
-                topo = topography.data.unsqueeze(self._channel_axis)
+                topo = static_inputs.fields[0].data.unsqueeze(self._channel_axis)
                 coarse_norm = torch.concat(
                     [interpolated, topo], axis=self._channel_axis
                 )

fme/downscaling/configs/test_train_config.yaml

@@ -1,4 +1,6 @@
 experiment_dir: /will/be/overwritten
+static_inputs:
+  HGTsfc: /will/be/overwritten
 max_epochs: 2
 validate_using_ema: true
 logging:

fme/downscaling/data/__init__.py

@@ -12,7 +12,7 @@
     PairedBatchItem,
     PairedGriddedData,
 )
-from .topography import StaticInputs, Topography, get_normalized_topography
+from .topography import StaticInput, StaticInputs, get_normalized_topography
 from .utils import (
     BatchedLatLonCoordinates,
     ClosedInterval,

fme/downscaling/data/config.py

@@ -23,7 +23,6 @@
 )
 from fme.downscaling.data.topography import (
     StaticInputs,
-    Topography,
     get_normalized_topography,
     get_topography_downscale_factor,
 )
@@ -177,41 +176,39 @@ def get_xarray_dataset(
             strict=self.strict_ensemble,
         )
 
-    def build_topography(
+    def build_static_inputs(
         self,
         coarse_coords: LatLonCoordinates,
         requires_topography: bool,
-        static_inputs_from_checkpoint: StaticInputs | None = None,
-    ) -> Topography | None:
+        static_inputs: StaticInputs | None = None,
+    ) -> StaticInputs | None:
         if requires_topography is False:
             return None
-        if static_inputs_from_checkpoint is not None:
+        if static_inputs is not None:
             # TODO: change to use full static inputs list
-            topography = static_inputs_from_checkpoint[0]
+            full_static_inputs = static_inputs
         else:
-            if self.topography is None:
-                raise ValueError(
-                    "Topography is required for this model, but no topography "
-                    "dataset was specified in the configuration nor provided "
-                    "in model checkpoint."
-                )
-            topography = get_normalized_topography(self.topography)
+            raise ValueError(
+                "Static inputs required for this model, but no static inputs "
+                "datasets were specified in the trainer configuration or provided "
+                "in model checkpoint."
+            )
 
         # Fine grid boundaries are adjusted to exactly match the coarse grid
         fine_lat_interval = adjust_fine_coord_range(
             self.lat_extent,
             full_coarse_coord=coarse_coords.lat,
-            full_fine_coord=topography.coords.lat,
+            full_fine_coord=full_static_inputs.coords.lat,
         )
         fine_lon_interval = adjust_fine_coord_range(
             self.lon_extent,
             full_coarse_coord=coarse_coords.lon,
-            full_fine_coord=topography.coords.lon,
+            full_fine_coord=full_static_inputs.coords.lon,
         )
-        subset_topography = topography.subset_latlon(
+        subset_static_inputs = full_static_inputs.subset_latlon(
             lat_interval=fine_lat_interval, lon_interval=fine_lon_interval
         )
-        return subset_topography.to_device()
+        return subset_static_inputs.to_device()
 
     def build_batchitem_dataset(
         self,
@@ -244,7 +241,7 @@ def build(
         self,
         requirements: DataRequirements,
         dist: Distributed | None = None,
-        static_inputs_from_checkpoint: StaticInputs | None = None,
+        static_inputs: StaticInputs | None = None,
     ) -> GriddedData:
         # TODO: static_inputs_from_checkpoint is currently passed from the model
         # to allow loading fine topography when no fine data is available.
@@ -286,14 +283,14 @@ def build(
             persistent_workers=True if self.num_data_workers > 0 else False,
         )
         example = dataset[0]
-        subset_topography = self.build_topography(
+        subset_static_inputs = self.build_static_inputs(
             coarse_coords=latlon_coords,
             requires_topography=requirements.use_fine_topography,
-            static_inputs_from_checkpoint=static_inputs_from_checkpoint,
+            static_inputs=static_inputs,
         )
         return GriddedData(
             _loader=dataloader,
-            topography=subset_topography,
+            static_inputs=subset_static_inputs,
             shape=example.horizontal_shape,
             dims=example.latlon_coordinates.dims,
             variable_metadata=dataset.variable_metadata,
@@ -397,7 +394,7 @@ def build(
         train: bool,
         requirements: DataRequirements,
         dist: Distributed | None = None,
-        static_inputs_from_checkpoint: StaticInputs | None = None,
+        static_inputs: StaticInputs | None = None,
     ) -> PairedGriddedData:
         # TODO: static_inputs_from_checkpoint is currently passed from the model
         # to allow loading fine topography when no fine data is available.
@@ -458,9 +455,8 @@ def build(
         )
 
         if requirements.use_fine_topography:
-            if static_inputs_from_checkpoint is not None:
-                # TODO: change to use full static inputs list
-                fine_topography = static_inputs_from_checkpoint[0]
+            if static_inputs is not None:
+                fine_topography = static_inputs
             elif self.topography is None:
                 data_path = self.fine[0].data_path
                 file_pattern = self.fine[0].file_pattern
@@ -469,14 +465,18 @@ def build(
                     raise ValueError(
                         f"No files found matching '{data_path}/{file_pattern}'."
                     )
-                fine_topography = get_normalized_topography(raw_paths[0])
+                fine_topography = StaticInputs(
+                    fields=[get_normalized_topography(raw_paths[0])]
+                )
             else:
-                fine_topography = get_normalized_topography(self.topography)
+                fine_topography = StaticInputs(
+                    fields=[get_normalized_topography(self.topography)]
+                )
 
             fine_topography = fine_topography.to_device()
             if (
                 get_topography_downscale_factor(
-                    fine_topography.data.shape,
+                    fine_topography.shape,
                     properties_fine.horizontal_coordinates.shape,
                 )
                 != 1
@@ -558,7 +558,7 @@ def build(
 
         return PairedGriddedData(
             _loader=dataloader,
-            topography=fine_topography,
+            static_inputs=fine_topography,
             coarse_shape=example.coarse.horizontal_shape,
             downscale_factor=example.downscale_factor,
             dims=example.fine.latlon_coordinates.dims,