[pre-commit.ci] auto fixes from pre-commit.com hooks

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pvnet_summation/models/flat_model.py

@@ -7,7 +7,6 @@
 import torch
 import torch.nn.functional as F
 from pvnet.models.multimodal.linear_networks.basic_blocks import AbstractLinearNetwork
-from pvnet.models.multimodal.linear_networks.networks import DefaultFCNet
 from pvnet.optimizers import AbstractOptimizer
 from torch import nn
 
@@ -16,7 +15,7 @@
 
 class FlatModel(BaseModel):
     """Neural network which combines GSP predictions from PVNet naively
-    
+
     This model flattens all the features into a 1D vector before feeding them into the sub network
     """
 
@@ -53,12 +52,12 @@ def __init__(
 
         self.relative_scale_pvnet_outputs = relative_scale_pvnet_outputs
         self.predict_difference_from_sum = predict_difference_from_sum
-        
+
         self.model = output_network(
             in_features=np.prod(self.pvnet_output_shape),
             out_features=self.num_output_features,
         )
-        
+
         # Add linear layer if predicting difference from sum
         # This allows difference to be positive or negative
         if predict_difference_from_sum:
@@ -102,4 +101,4 @@ def forward(self, x):
 
             out = F.leaky_relu(gsp_sum + out)
 
-        return out
+        return out

tests/conftest.py

@@ -135,13 +135,10 @@ def sample_batch(sample_datamodule):
 
 @pytest.fixture()
 def flat_model_kwargs():
-
     kwargs = dict(
-
         # These kwargs define the pvnet model which the summation model uses
         model_name="openclimatefix/pvnet_v2",
         model_version="4203e12e719efd93da641c43d2e38527648f4915",
-
         # These kwargs define the structure of the summation model
         output_network=dict(
             _target_="pvnet.models.multimodal.linear_networks.networks.ResFCNet2",
@@ -151,7 +148,6 @@ def flat_model_kwargs():
             res_block_layers=2,
             dropout_frac=0.0,
         ),
-
     )
     return hydra.utils.instantiate(kwargs)