fix 60 min hardcode NWP resolution in multimodal.py

NWP resolution fixed to pull form config; if config values not available, default 60 min resolution supplied for backward compatibility

pvnet/models/multimodal/multimodal.py

@@ -2,6 +2,7 @@
 
 from collections import OrderedDict
 from typing import Optional
+from omegaconf import DictConfig
 
 import torch
 from ocf_datapipes.batch import BatchKey, NWPBatchKey
@@ -52,15 +53,16 @@ def __init__(
         history_minutes: int = 60,
         sat_history_minutes: Optional[int] = None,
         min_sat_delay_minutes: Optional[int] = 30,
-        nwp_forecast_minutes: Optional[int] = None,
-        nwp_history_minutes: Optional[int] = None,
+        nwp_forecast_minutes: Optional[DictConfig] = None,
+        nwp_history_minutes: Optional[DictConfig] = None,
         pv_history_minutes: Optional[int] = None,
         wind_history_minutes: Optional[int] = None,
         sensor_history_minutes: Optional[int] = None,
         sensor_forecast_minutes: Optional[int] = None,
         optimizer: AbstractOptimizer = pvnet.optimizers.Adam(),
         target_key: str = "gsp",
         interval_minutes: int = 30,
+        nwp_interval_minutes: Optional[DictConfig] = None,
         pv_interval_minutes: int = 5,
         sat_interval_minutes: int = 5,
         sensor_interval_minutes: int = 30,
@@ -80,16 +82,16 @@ def __init__(
             - for example if `m` is a regular function.
 
         Args:
-            output_network: A partially instatiated pytorch Module class used to combine the 1D
+            output_network: A partially instantiated pytorch Module class used to combine the 1D
                 features to produce the forecast.
             output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to
                 None the output is a single value.
-            nwp_encoders_dict: A dictionary of partially instatiated pytorch Module class used to
-                encode the NWP data from 4D into an 1D feature vector from different sources.
-            sat_encoder: A partially instatiated pytorch Module class used to encode the satellite
-                data from 4D into an 1D feature vector.
-            pv_encoder: A partially instatiated pytorch Module class used to encode the site-level
-                PV data from 2D into an 1D feature vector.
+            nwp_encoders_dict: A dictionary of partially instantiated pytorch Module class used to
+                encode the NWP data from 4D into a 1D feature vector from different sources.
+            sat_encoder: A partially instantiated pytorch Module class used to encode the satellite
+                data from 4D into a 1D feature vector.
+            pv_encoder: A partially instantiated pytorch Module class used to encode the site-level
+                PV data from 2D into a 1D feature vector.
             add_image_embedding_channel: Add a channel to the NWP and satellite data with the
                 embedding of the GSP ID.
             include_gsp_yield_history: Include GSP yield data.
@@ -106,14 +108,15 @@ def __init__(
                 `forecast_minutes` if not provided.
             nwp_history_minutes: Period of historical NWP forecast used as input. Defaults to
                 `history_minutes` if not provided.
-            pv_history_minutes: Length of recent site-level PV data data used as
+            pv_history_minutes: Length of recent site-level PV data used as
             input. Defaults to `history_minutes` if not provided.
             optimizer: Optimizer factory function used for network.
             target_key: The key of the target variable in the batch.
             interval_minutes: The interval between each sample of the target data
             wind_interval_minutes: The interval between each sample of the wind data
             wind_encoder: Encoder for wind data
             wind_history_minutes: Length of recent wind data used as input.
+            nwp_interval_minutes: DIctionary of the intervals between each sample of the NWP data for each source
             pv_interval_minutes: The interval between each sample of the PV data
             sat_interval_minutes: The interval between each sample of the satellite data
             sensor_interval_minutes: The interval between each sample of the sensor data
@@ -153,7 +156,7 @@ def __init__(
         )
 
         # Number of features expected by the output_network
-        # Add to this as network pices are constructed
+        # Add to this as network pieces are constructed
         fusion_input_features = 0
 
         if self.include_sat:
@@ -186,14 +189,17 @@ def __init__(
             assert set(nwp_encoders_dict.keys()) == set(nwp_forecast_minutes.keys())
             assert set(nwp_encoders_dict.keys()) == set(nwp_history_minutes.keys())
 
+            if nwp_interval_minutes is None:
+                nwp_interval_minutes = dict.fromkeys(nwp_encoders_dict.keys(), 60)
+
             self.nwp_encoders_dict = torch.nn.ModuleDict()
             if add_image_embedding_channel:
                 self.nwp_embed_dict = torch.nn.ModuleDict()
 
             for nwp_source in nwp_encoders_dict.keys():
                 nwp_sequence_len = (
-                    nwp_history_minutes[nwp_source] // 60
-                    + nwp_forecast_minutes[nwp_source] // 60
+                    nwp_history_minutes[nwp_source] // nwp_interval_minutes[nwp_source]
+                    + nwp_forecast_minutes[nwp_source] // nwp_interval_minutes[nwp_source]
                     + 1
                 )