add learnable finite difference and fix __init__.py

ThermalSolver/model/learnable_finite_difference.py

@@ -0,0 +1,125 @@
+import torch
+import torch.nn as nn
+from torch_geometric.nn import MessagePassing
+from tqdm import tqdm
+
+
+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.correction_net = nn.Sequential(
+            nn.Linear(2, 16),
+            nn.GELU(),
+            nn.Linear(16, 1),
+            nn.Softplus(),
+        )
+
+    def forward(self, x, edge_index, edge_attr, deg):
+        """
+        TODO: add docstring.
+        """
+        out = self.propagate(edge_index, x=x, edge_attr=edge_attr, deg=deg)
+        return out
+
+    def message(self, x_j, edge_attr):
+        """
+        TODO: add docstring.
+        """
+        x_in = torch.cat([x_j, edge_attr.view(-1, 1)], dim=-1)
+        correction = self.correction_net(x_in)
+        return edge_attr.view(-1, 1) * x_j + correction
+
+    def aggregate(self, inputs, index, deg):
+        """
+        TODO: add docstring.
+        """
+        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 self.root_weight * aggr_out + (1 - self.root_weight) * x
+
+
+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