buggy egnn - fix linter

Author: GiovanniCanali

pina/model/block/message_passing/egnn_block.py

@@ -1,53 +1,65 @@
 import torch
-import torch.nn as nn
 from torch_geometric.nn import MessagePassing
 from torch_geometric.utils import degree
-from ....utils import check_consistency
 
 
 class EnEquivariantGraphBlock(MessagePassing):
-    def __init__(self,
-                 channels_h,
-                 channels_m, 
-                 channels_a,
-                 aggr: str = 'add', 
-                 hidden_channels: int = 64,
-                 **kwargs):
+    def __init__(
+        self,
+        channels_h,
+        channels_m,
+        channels_a,
+        aggr: str = "add",
+        hidden_channels: int = 64,
+        **kwargs,
+    ):
         super().__init__(aggr=aggr, **kwargs)
 
-        self.phi_e = nn.Sequential(
-                nn.Linear(2 * channels_h + 1 + channels_a, hidden_channels),
-                nn.LayerNorm(hidden_channels),
-                nn.SiLU(),
-                nn.Linear(hidden_channels, channels_m),
-                nn.LayerNorm(channels_m),
-                nn.SiLU()
+        self.phi_e = torch.nn.Sequential(
+            torch.nn.Linear(2 * channels_h + 1 + channels_a, hidden_channels),
+            torch.nn.LayerNorm(hidden_channels),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_channels, channels_m),
+            torch.nn.LayerNorm(channels_m),
+            torch.nn.SiLU(),
         )
-        self.phi_x = nn.Sequential(
-                nn.Linear(channels_m, hidden_channels),
-                nn.LayerNorm(hidden_channels),
-                nn.SiLU(),
-                nn.Linear(hidden_channels, 1),
+        self.phi_x = torch.nn.Sequential(
+            torch.nn.Linear(channels_m, hidden_channels),
+            torch.nn.LayerNorm(hidden_channels),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_channels, 1),
+        )
+        self.phi_h = torch.nn.Sequential(
+            torch.nn.Linear(channels_h + channels_m, hidden_channels),
+            torch.nn.LayerNorm(hidden_channels),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_channels, channels_h),
         )
-        self.phi_h = nn.Sequential(
-                nn.Linear(channels_h + channels_m, hidden_channels),
-                nn.LayerNorm(hidden_channels),
-                nn.SiLU(),
-                nn.Linear(hidden_channels, channels_h),
-            )
 
     def forward(self, x, h, edge_attr, edge_index, c=None):
         if c is None:
             c = degree(edge_index[0], x.shape[0]).unsqueeze(-1)
-        return self.propagate(edge_index=edge_index, x=x, h=h, edge_attr=edge_attr, c=c)
+        return self.propagate(
+            edge_index=edge_index, x=x, h=h, edge_attr=edge_attr, c=c
+        )
 
     def message(self, x_i, x_j, h_i, h_j, edge_attr):
-        mh_ij = self.phi_e(torch.cat([h_i, h_j, torch.norm(x_i - x_j, dim=-1, keepdim=True)**2, edge_attr], dim=-1))
+        mh_ij = self.phi_e(
+            torch.cat(
+                [
+                    h_i,
+                    h_j,
+                    torch.norm(x_i - x_j, dim=-1, keepdim=True) ** 2,
+                    edge_attr,
+                ],
+                dim=-1,
+            )
+        )
         mx_ij = (x_i - x_j) * self.phi_x(mh_ij)
         return torch.cat((mx_ij, mh_ij), dim=-1)
 
     def update(self, aggr_out, x, h, edge_attr, c):
-        m_x, m_h = aggr_out[:, :self.m_len], aggr_out[:, self.m_len:]
+        m_x, m_h = aggr_out[:, : self.m_len], aggr_out[:, self.m_len :]
         h_l1 = self.phi_h(torch.cat([h, m_h], dim=-1))
         x_l1 = x + (m_x / c)
         return x_l1, h_l1