Clean up formatting

src/NanoParticleTools/machine_learning/models/gnn_model/model.py

@@ -1,27 +1,17 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch_geometric import nn as pyg_nn
-import pytorch_lightning as pl
-
 from NanoParticleTools.machine_learning.core import SpectrumModelBase
 from NanoParticleTools.machine_learning.models.mlp_model.model import MLPSpectrumModel
-from typing import Optional, Callable
-from torch_scatter.scatter import scatter
-from NanoParticleTools.machine_learning.core import SpectrumModelBase
-from NanoParticleTools.machine_learning.modules.layer_interaction import InteractionBlock, InteractionConv
+from NanoParticleTools.machine_learning.modules.layer_interaction import (
+    InteractionBlock, InteractionConv)
 from NanoParticleTools.machine_learning.modules.film import FiLMLayer
 from NanoParticleTools.machine_learning.modules import NonLinearMLP
-from torch_geometric.data.batch import Batch
-from torch_geometric.data import HeteroData
+
+from torch_scatter.scatter import scatter
 from torch.nn import functional as F
 from torch import nn
 import torch
-import torch_geometric.nn as gnn
-import warnings
-from typing import Dict, List
-from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
-from torch.nn.functional import pairwise_distance
+from torch_geometric import nn as pyg_nn
+
+from typing import Dict, List, Optional, Callable
 
 
 class GraphRepresentationModel(SpectrumModelBase):
@@ -73,16 +63,16 @@ def __init__(self,
         self.convs = nn.ModuleList()
         for _ in range(self.n_message_passing):
             self.convs.append(
-                gnn.GATv2Conv(embed_dim,
-                              embed_dim,
-                              edge_dim=nsigma,
-                              concat=False,
-                              add_self_loops=False))
+                pyg_nn.GATv2Conv(embed_dim,
+                                 embed_dim,
+                                 edge_dim=nsigma,
+                                 concat=False,
+                                 add_self_loops=False))
 
         if aggregation == 'sum':
-            self.aggregation = gnn.aggr.SumAggregation()
+            self.aggregation = pyg_nn.aggr.SumAggregation()
         elif aggregation == 'mean':
-            self.aggregation = gnn.aggr.MeanAggregation()
+            self.aggregation = pyg_nn.aggr.MeanAggregation()
         elif aggregation == 'set2set':
             # Use the Set2Set aggregation method to pool the graph
             # into a single global feature vector
@@ -211,14 +201,16 @@ def __init__(self,
         self.convs = nn.ModuleList()
         for i in range(self.n_message_passing):
             if i == 0:
-                self.convs.append(InteractionConv(embed_dim, embed_dim, nsigma))
+                self.convs.append(InteractionConv(embed_dim, embed_dim,
+                                                  nsigma))
             else:
-                self.convs.append(InteractionConv(2*embed_dim, embed_dim, nsigma))
+                self.convs.append(
+                    InteractionConv(2 * embed_dim, embed_dim, nsigma))
 
         if aggregation == 'sum':
-            self.aggregation = gnn.aggr.SumAggregation()
+            self.aggregation = pyg_nn.aggr.SumAggregation()
         elif aggregation == 'mean':
-            self.aggregation = gnn.aggr.MeanAggregation()
+            self.aggregation = pyg_nn.aggr.MeanAggregation()
         elif aggregation == 'set2set':
             # Use the Set2Set aggregation method to pool the graph
             # into a single global feature vector