Clean up

pvnet/models/multimodal/site_encoders/encoders.py

@@ -206,8 +206,8 @@ def __init__(
         )
 
     def _encode_inputs(self, x):
-        # Shape: [batch size, sequence length, number of sites] -> [8, 197, 1]
-        # Shape: [batch size,  station_id, sequence length,  channels] -> [8, 197, 26, 23]
+        # Shape: [batch size, sequence length, number of sites]
+        # Shape: [batch size,  station_id, sequence length,  channels]
         input_data = x[f"{self.input_key_to_use}"]
         if len(input_data.shape) == 2:  # one site per sample
             input_data = input_data.unsqueeze(-1)  # add dimension of 1 to end to make 3D

pvnet/models/utils.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import torch
-from ocf_datapipes.batch import BatchKey
 
 logger = logging.getLogger(__name__)
 
@@ -84,7 +83,7 @@ class BatchAccumulator(DictListAccumulator):
     """A class for accumulating batches when using grad accumulation and the batch size is small.
 
     Attributes:
-        _batches (Dict[BatchKey, list[torch.Tensor]]): Dictionary containing lists of metrics.
+        _batches (Dict[str, list[torch.Tensor]]): Dictionary containing lists of metrics.
     """
 
     def __init__(self, key_to_keep: str = "gsp"):
@@ -105,14 +104,14 @@ def _filter_batch_dict(self, d):
         ]
         return {k: v for k, v in d.items() if k in keep_keys}
 
-    def append(self, batch: dict[BatchKey, list[torch.Tensor]]):
+    def append(self, batch: dict[str, list[torch.Tensor]]):
         """Append batch to self"""
         if not self:
             self._batches = self._dict_init_list(self._filter_batch_dict(batch))
         else:
             self._dict_list_append(self._batches, self._filter_batch_dict(batch))
 
-    def flush(self) -> dict[BatchKey, list[torch.Tensor]]:
+    def flush(self) -> dict[str, list[torch.Tensor]]:
         """Concatenate all accumulated batches, return, and clear self"""
         batch = {}
         for k, v in self._batches.items():

pvnet/utils.py

@@ -258,7 +258,7 @@ def plot_batch_forecasts(
     def _get_numpy(key):
         return batch[key].cpu().numpy().squeeze()
 
-    y_key = f"{key_to_plot}"
+    y_key = key_to_plot
     y_id_key = f"{key_to_plot}_id"
     time_utc_key = f"{key_to_plot}_time_utc"
     y = batch[y_key].cpu().numpy()  # Select the one it is trained on

tests/conftest.py

@@ -9,7 +9,6 @@
 import torch
 import hydra
 
-from ocf_datapipes.batch import BatchKey
 from datetime import timedelta
 
 import pvnet
@@ -164,7 +163,7 @@ def sample_pv_batch():
 def sample_site_batch():
     dm = SiteDataModule(
         configuration=None,
-        batch_size=8,
+        batch_size=2,
         num_workers=0,
         prefetch_factor=None,
         train_period=[None, None],

tests/models/multimodal/site_encoders/test_encoders.py

@@ -1,5 +1,4 @@
 import torch
-from ocf_datapipes.batch import BatchKey
 from torch import nn
 
 from pvnet.models.multimodal.site_encoders.encoders import (
@@ -38,11 +37,11 @@ def test_singleattentionnetwork_forward(sample_site_batch, site_encoder_model_kw
         sample_site_batch,
         SingleAttentionNetwork,
         site_encoder_model_kwargs_dsampler,
-        batch_size=8,
+        batch_size=2,
     )
 
 
-# TODO once we have updated the sample batches for sites include this test
+# TODO once we have test data which inludes sensor data with sites include this test
 # def test_singleattentionnetwork_forward_4d(sample_wind_batch, site_encoder_sensor_model_kwargs):
 #     _test_model_forward(
 #         sample_wind_batch,

tests/models/multimodal/test_multimodal.py

@@ -1,7 +1,5 @@
 from torch.optim import SGD
 import pytest
-from ocf_datapipes.batch.batches import BatchKey, NWPBatchKey
-
 
 def test_model_forward(multimodal_model, sample_batch):
     y = multimodal_model(sample_batch)

tests/test_data/presaved_samples_site/data_configuration.yaml

@@ -12,28 +12,6 @@ input_data:
     dropout_timedeltas_minutes: null
     dropout_fraction: 0 # Fraction of samples with dropout
 
-  # nwp:
-  #   mo_global:
-  #     provider: mo_global
-  #     zarr_path: /mnt/storage_b/nwp/ceda/global/20*.zarr
-  #     interval_start_minutes: -60
-  #     interval_end_minutes: 1860
-  #     time_resolution_minutes: 60
-  #     channels:
-  #       - temperature_sl
-  #       - wind_u_component_10m
-  #       - wind_v_component_10m
-  #     image_size_pixels_height: 4
-  #     image_size_pixels_width: 4
-  #     # A random value from the list below will be chosen as the delay when dropout is used
-  #     # If set to null no dropout is applied. Values must be negative.
-  #     dropout_timedeltas_minutes: [-180]
-  #     # Dropout applied with this probability
-  #     dropout_fraction: 1.0
-  #     # How long after the NWP init-time are we still willing to use this forecast
-  #     # If null we use each init-time for all steps it covers
-  #     max_staleness_minutes: null
-
   nwp:
     ecmwf:
       provider: ecmwf
@@ -56,22 +34,4 @@ input_data:
       image_size_pixels_width: 24
       dropout_timedeltas_minutes: [-360]
       dropout_fraction: 1.0
-      max_staleness_minutes: null
-
-    # gfs:
-    #   provider: gfs
-    #   dropout_fraction: 1.0
-    #   dropout_timedeltas_minutes: [-300]
-    #   interval_start_minutes: 0
-    #   interval_end_minutes: 2160
-    #   channels:
-    #   - t
-    #   - prate
-    #   - u10
-    #   - v10
-    #   - u100
-    #   - v100
-    #   image_size_pixels_height: 2
-    #   image_size_pixels_width: 2
-    #   zarr_path: /mnt/storage_b/nwp/gfs/nw_india/gfs_nw_india_*.zarr
-    #   time_resolution_minutes: 180
+      max_staleness_minutes: null