implement ML correction

ThermalSolver/model/finite_difference.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 from torch_geometric.nn import MessagePassing
+from torch.nn.utils import spectral_norm
 
 
 class FiniteDifferenceStep(MessagePassing):
@@ -8,14 +9,8 @@ class FiniteDifferenceStep(MessagePassing):
     TODO: add docstring.
     """
 
-    def __init__(self, aggr: str = "add", root_weight: float = 1.0):
-        super().__init__(aggr=aggr)
-        assert (
-            aggr == "add"
-        ), "Per somme pesate, l'aggregazione deve essere 'add'."
-        # self.root_weight = float(root_weight)
-        self.p = torch.nn.Parameter(torch.tensor(1.0))
-        self.a = root_weight
+    def __init__(self):
+        super().__init__(aggr="add")
 
     def forward(self, x, edge_index, edge_attr, deg):
         """
@@ -28,8 +23,14 @@ class FiniteDifferenceStep(MessagePassing):
         """
         TODO: add docstring.
         """
-        p = torch.clamp(self.p, 0.0, 1.0)
-        return p * edge_attr.view(-1, 1) * x_j
+        # return self.message_net(x_j * edge_attr)
+        return x_j * edge_attr
+
+    def update(self, aggr_out, _):
+        """
+        TODO: add docstring.
+        """
+        return aggr_out
 
     def aggregate(self, inputs, index, deg):
         """
@@ -38,82 +39,3 @@ class FiniteDifferenceStep(MessagePassing):
         out = super().aggregate(inputs, index)
         deg = deg + 1e-7
         return out / deg.view(-1, 1)
-
-    def update(self, aggr_out, x):
-        """
-        TODO: add docstring.
-        """
-        return aggr_out
-
-
-class GraphFiniteDifference(nn.Module):
-    """
-    TODO: add docstring.
-    """
-
-    def __init__(self, max_iters: int = 5000, threshold: float = 1e-4):
-        """
-        TODO: add docstring.
-        """
-        super().__init__()
-        self.max_iters = max_iters
-        self.threshold = threshold
-        self.fd_step = FiniteDifferenceStep(aggr="add", root_weight=1.0)
-
-    @staticmethod
-    def _compute_deg(edge_index, edge_attr, num_nodes):
-        """
-        TODO: add docstring.
-        """
-        deg = torch.zeros(num_nodes, device=edge_index.device)
-        deg = deg.scatter_add(0, edge_index[1], edge_attr)
-        return deg + 1e-7
-
-    @staticmethod
-    def _compute_c_ij(c, edge_index):
-        """
-        TODO: add docstring.
-        """
-        return (0.5 * (c[edge_index[0]] + c[edge_index[1]])).squeeze()
-
-    def forward(
-        self,
-        x,
-        edge_index,
-        edge_attr,
-        c,
-        boundary_mask,
-        boundary_values,
-        **kwargs,
-    ):
-        """
-        TODO: add docstring.
-        """
-        edge_attr = 1 / edge_attr[:, -1]
-        c_ij = self._compute_c_ij(c, edge_index)
-        edge_attr = edge_attr * c_ij
-        deg = self._compute_deg(edge_index, edge_attr, x.size(0))
-
-        # Calcola la soglia staccando x dal grafo
-        conv_thres = self.threshold * torch.norm(x.detach())
-
-        for _i in range(self.max_iters):
-            out = self.fd_step(x, edge_index, edge_attr, deg)
-            out[boundary_mask] = boundary_values.unsqueeze(-1)
-
-            # Controllo convergenza senza tracciamento gradienti
-            with torch.no_grad():
-                residual_norm = torch.norm(out - x)
-
-            if residual_norm < conv_thres:
-                break
-
-            # --- OTTIMIZZAZIONE CHIAVE ---
-            # Stacca 'out' dal grafo prima della prossima iterazione
-            # per evitare BPTT e risparmiare memoria.
-            x = out.detach()
-
-        # Il 'out' finale restituito mantiene i gradienti
-        # dell'ULTIMA chiamata a fd_step, permettendo al modello
-        # di apprendere correttamente.
-        return out, _i + 1