add model and solver that maybe works

ThermalSolver/model/learnable_finite_difference.py

@@ -1,53 +1,124 @@
+# import torch
+# import torch.nn as nn
+# from torch_geometric.nn import MessagePassing
+# from torch.nn.utils import spectral_norm
+
+# class GCNConvLayer(MessagePassing):
+#     def __init__(self, in_channels, out_channels):
+#         super().__init__(aggr="add")
+#         self.lin_l = spectral_norm(nn.Linear(in_channels, out_channels, bias=False))
+#         self.lin_r = spectral_norm(nn.Linear(in_channels, out_channels, bias=False))
+
+#     def forward(self, x, edge_index, edge_attr, deg):
+#         out = self.propagate(edge_index, x=x, edge_attr=edge_attr, deg=deg)
+#         out = self.lin_l(out)
+#         return out
+
+#     def message(self, x_j, edge_attr):
+#         return x_j * edge_attr
+
+#     def aggregate(self, inputs, index, deg):
+#         """
+#         TODO: add docstring.
+#         """
+#         out = super().aggregate(inputs, index)
+#         deg = deg + 1e-7
+#         return out / deg.view(-1, 1)
+
+
+# class CorrectionNet(nn.Module):
+#     def __init__(self, hidden_dim=8, n_layers=1):
+#         super().__init__()
+#         # self.enc = GCNConvLayer(1, hidden_dim)
+#         self.enc = nn.Sequential(
+#             spectral_norm(nn.Linear(1, hidden_dim//2)),
+#             nn.GELU(),
+#             spectral_norm(nn.Linear(hidden_dim//2, hidden_dim)),
+#         )
+#         self.layers = torch.nn.ModuleList([GCNConvLayer(hidden_dim, hidden_dim) for _ in range(n_layers)])
+#         self.relu = nn.GELU()
+
+#         self.dec = nn.Sequential(
+#             spectral_norm(nn.Linear(hidden_dim, hidden_dim//2)),
+#             nn.GELU(),
+#             spectral_norm(nn.Linear(hidden_dim//2, 1)),
+#         )
+
+#     def forward(self, x, edge_index, edge_attr, deg,):
+#         # h = self.enc(x, edge_index, edge_attr, deg)
+#         # h = self.relu(self.enc(x))
+#         h = self.enc(x)
+#         for layer in self.layers:
+#             h = layer(h, edge_index, edge_attr, deg)
+#             # h = self.norm(h)
+#             h = self.relu(h)
+#         # out = self.dec(h, edge_index, edge_attr, deg)
+#         out = self.dec(h)
+#         return out
+
+
 import torch
 import torch.nn as nn
 from torch_geometric.nn import MessagePassing
-
-# from torch.nn.utils import spectral_norm
+from torch.nn.utils import spectral_norm
 
 
-class GCNConvLayer(MessagePassing):
-    def __init__(self, in_channels, out_channels):
-        super().__init__("add")
-        self.lin = nn.Sequential(
-            nn.Linear(in_channels, out_channels),
-            nn.ReLU(),
-            nn.Linear(out_channels, out_channels),
-            nn.ReLU(),
+class CorrectionNet(MessagePassing):
+    """
+    TODO: add docstring.
+    """
+
+    def __init__(self, hidden_dim=16):
+        super().__init__(aggr="add")
+        self.in_net = nn.Sequential(
+            spectral_norm(nn.Linear(1, hidden_dim // 2)),
+            nn.GELU(),
+            spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
         )
 
-    def _compute_edge_weight(self, edge_index, edge_w, deg):
-        """ """
-        return edge_w.squeeze() / (
-            1 + torch.sqrt(deg[edge_index[0]] * deg[edge_index[1]])
+        self.out_net = nn.Sequential(
+            spectral_norm(nn.Linear(hidden_dim, hidden_dim // 2)),
+            nn.GELU(),
+            spectral_norm(nn.Linear(hidden_dim // 2, 1)),
         )
 
+        self.lin_msg = spectral_norm(
+            nn.Linear(hidden_dim, hidden_dim, bias=False)
+        )
+        self.lin_update = spectral_norm(
+            nn.Linear(hidden_dim, hidden_dim, bias=False)
+        )
+        self.alpha = nn.Parameter(torch.tensor(0.0))
+        self.beta = nn.Parameter(torch.tensor(0.0))
+
     def forward(self, x, edge_index, edge_attr, deg):
-        edge_w = self._compute_edge_weight(edge_index, edge_attr, deg)
-        return self.propagate(edge_index, x=x, edge_weight=edge_w, deg=deg)
+        """
+        TODO: add docstring.
+        """
+        x = self.in_net(x)
+        out = self.propagate(edge_index, x=x, edge_attr=edge_attr, deg=deg)
+        return self.out_net(out)
 
-    def message(self, x_j, edge_weight):
-        return edge_weight.view(-1, 1) * x_j
+    def message(self, x_j, edge_attr):
+        """
+        TODO: add docstring.
+        """
+        alpha = torch.sigmoid(self.alpha)
+        msg = x_j * edge_attr
+        msg = (1 - alpha) * msg + alpha * self.lin_msg(msg)
+        return msg
 
+    def update(self, aggr_out, x):
+        """
+        TODO: add docstring.
+        """
+        beta = torch.sigmoid(self.beta)
+        return aggr_out * (1 - beta) + self.lin_msg(x) * beta
 
-class CorrectionNet(nn.Module):
-    def __init__(self, hidden_dim=8):
-        super().__init__()
-        self.enc = nn.Sequential(
-            nn.Linear(1, hidden_dim // 2),
-            nn.ReLU(),
-            nn.Linear(hidden_dim // 2, hidden_dim),
-            nn.ReLU(),
-        )
-        self.model = GCNConvLayer(hidden_dim, hidden_dim)
-        self.dec = nn.Sequential(
-            nn.Linear(hidden_dim, hidden_dim // 2),
-            nn.ReLU(),
-            nn.Linear(hidden_dim // 2, 1),
-            nn.ReLU(),
-        )
-
-    def forward(self, x, edge_index, edge_attr, deg):
-        h = self.enc(x)
-        h = self.model(h, edge_index, edge_attr, deg)
-        out = self.dec(h)
-        return out
+    def aggregate(self, inputs, index, deg):
+        """
+        TODO: add docstring.
+        """
+        out = super().aggregate(inputs, index)
+        deg = deg + 1e-7
+        return out / deg.view(-1, 1)