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pvnet/models/multimodal/site_encoders/encoders.py

@@ -140,7 +140,7 @@ def __init__(
             n_kv_res_blocks: Number of residual blocks to use in the key and value encoders.
             kv_res_block_layers: Number of fully-connected layers used in each residual block within
                 the key and value encoders.
-            use_pv_id_in_value: Whether to use a PV ID embedding in network used to produce the 
+            use_pv_id_in_value: Whether to use a PV ID embedding in network used to produce the
                 value for the attention layer.
 
         """
@@ -232,8 +232,8 @@ def _attention_forward(self, x, average_attn_weights=True):
 
         attn_output, attn_weights = self.multihead_attn(
             query, key, value, average_attn_weights=average_attn_weights
-        )        
-        
+        )
+
         return attn_output, attn_weights
 
     def forward(self, x):

requirements.txt

@@ -24,4 +24,4 @@ omegaconf
 hydra-core
 python-dotenv
 hydra-optuna-sweeper
-rich
+rich

tests/conftest.py

@@ -5,7 +5,7 @@
 import numpy as np
 import xarray as xr
 import torch
-import hydra 
+import hydra
 
 from ocf_datapipes.utils.consts import BatchKey
 from datetime import timedelta
@@ -144,62 +144,57 @@ def encoder_model_kwargs():
     )
     return kwargs
 
+
 @pytest.fixture()
 def site_encoder_model_kwargs():
     # Used to test site encoder model on PV data
     kwargs = dict(
-        sequence_length=180 // 5 +1,
+        sequence_length=180 // 5 + 1,
         num_sites=349,
         out_features=128,
     )
     return kwargs
 
+
 @pytest.fixture()
 def multimodal_model_kwargs(model_minutes_kwargs):
-
     kwargs = dict(
-
         sat_encoder=dict(
-          _target_=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet,
-          _partial_=True,
-          in_channels=11,
-          out_features=128,
-          number_of_conv3d_layers=6,
-          conv3d_channels=32,
-          image_size_pixels=24,
+            _target_=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet,
+            _partial_=True,
+            in_channels=11,
+            out_features=128,
+            number_of_conv3d_layers=6,
+            conv3d_channels=32,
+            image_size_pixels=24,
         ),
-
         nwp_encoder=dict(
-          _target_=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet,
-          _partial_=True,
-          in_channels=2,
-          out_features=128,
-          number_of_conv3d_layers=6,
-          conv3d_channels=32,
-          image_size_pixels=24,
+            _target_=pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet,
+            _partial_=True,
+            in_channels=2,
+            out_features=128,
+            number_of_conv3d_layers=6,
+            conv3d_channels=32,
+            image_size_pixels=24,
         ),
-
         add_image_embedding_channel=True,
-
         pv_encoder=dict(
-          _target_=pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork,
-          _partial_=True,
-          num_sites=349,
-          out_features=40,
-          num_heads=4,
-          kdim=40,
-          pv_id_embed_dim=20,
+            _target_=pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork,
+            _partial_=True,
+            num_sites=349,
+            out_features=40,
+            num_heads=4,
+            kdim=40,
+            pv_id_embed_dim=20,
         ),
-
         output_network=dict(
-          _target_=pvnet.models.multimodal.linear_networks.networks.ResFCNet2,
-          _partial_=True,
-          fc_hidden_features=128,
-          n_res_blocks=6,
-          res_block_layers=2,
-          dropout_frac=0.0,
+            _target_=pvnet.models.multimodal.linear_networks.networks.ResFCNet2,
+            _partial_=True,
+            fc_hidden_features=128,
+            n_res_blocks=6,
+            res_block_layers=2,
+            dropout_frac=0.0,
         ),
-
         embedding_dim=16,
         include_sun=True,
         include_gsp_yield_history=True,
@@ -208,10 +203,9 @@ def multimodal_model_kwargs(model_minutes_kwargs):
         nwp_forecast_minutes=480,
         pv_history_minutes=180,
         min_sat_delay_minutes=30,
-
     )
-    
+
     kwargs = hydra.utils.instantiate(kwargs)
-    
+
     kwargs.update(model_minutes_kwargs)
     return kwargs

tests/models/multimodal/site_encoders/test_encoders.py

@@ -26,7 +26,7 @@ def _test_model_backward(batch, model_class, kwargs):
 # Test model forward on all models
 def test_simplelearnedaggregator_forward(sample_batch, site_encoder_model_kwargs):
     _test_model_forward(sample_batch, SimpleLearnedAggregator, site_encoder_model_kwargs)
-    
+
 
 def test_singleattentionnetwork_forward(sample_batch, site_encoder_model_kwargs):
     _test_model_forward(sample_batch, SingleAttentionNetwork, site_encoder_model_kwargs)
@@ -35,7 +35,7 @@ def test_singleattentionnetwork_forward(sample_batch, site_encoder_model_kwargs)
 # Test model backward on all models
 def test_simplelearnedaggregator_backward(sample_batch, site_encoder_model_kwargs):
     _test_model_backward(sample_batch, SimpleLearnedAggregator, site_encoder_model_kwargs)
-    
-    
+
+
 def test_singleattentionnetwork_backward(sample_batch, site_encoder_model_kwargs):
     _test_model_backward(sample_batch, SingleAttentionNetwork, site_encoder_model_kwargs)

tests/models/multimodal/test_deep_supervision.py

@@ -8,6 +8,7 @@ def deepsupervision_model(multimodal_model_kwargs):
     model = Model(**multimodal_model_kwargs)
     return model
 
+
 @pytest.mark.skip(reason="This model is no longer maintained")
 def test_model_forward(deepsupervision_model, sample_batch):
     y = deepsupervision_model(sample_batch)
@@ -16,6 +17,7 @@ def test_model_forward(deepsupervision_model, sample_batch):
     # batch size=2, forecast_len=15
     assert tuple(y.shape) == (2, 16), y.shape
 
+
 @pytest.mark.skip(reason="This model is no longer maintained")
 def test_model_backwards(deepsupervision_model, sample_batch):
     opt = SGD(deepsupervision_model.parameters(), lr=0.001)

tests/models/multimodal/test_nwp_weighting.py

@@ -12,6 +12,7 @@ def nwp_weighting_model(model_minutes_kwargs):
     )
     return model
 
+
 @pytest.mark.skip(reason="This model is no longer maintained")
 def test_model_forward(nwp_weighting_model, sample_batch):
     y = nwp_weighting_model(sample_batch)
@@ -20,6 +21,7 @@ def test_model_forward(nwp_weighting_model, sample_batch):
     # batch size=2, forecast_len=15
     assert tuple(y.shape) == (2, 16), y.shape
 
+
 @pytest.mark.skip(reason="This model is no longer maintained")
 def test_model_backwards(nwp_weighting_model, sample_batch):
     opt = SGD(nwp_weighting_model.parameters(), lr=0.001)

tests/models/multimodal/test_weather_residual.py

@@ -8,6 +8,7 @@ def weather_residual_model(multimodal_model_kwargs):
     model = Model(**multimodal_model_kwargs)
     return model
 
+
 @pytest.mark.skip(reason="This model is no longer maintained")
 def test_model_forward(weather_residual_model, sample_batch):
     y = weather_residual_model(sample_batch)
@@ -16,6 +17,7 @@ def test_model_forward(weather_residual_model, sample_batch):
     # batch size=2, forecast_len=15
     assert tuple(y.shape) == (2, 16), y.shape
 
+
 @pytest.mark.skip(reason="This model is no longer maintained")
 def test_model_backwards(weather_residual_model, sample_batch):
     opt = SGD(weather_residual_model.parameters(), lr=0.001)