add module and first model

ThermalSolver/module.py

@@ -0,0 +1,74 @@
+import torch
+from lightning import LightningModule
+from torch_geometric.data import Batch
+
+
+class GraphSolver(LightningModule):
+    def __init__(self, model: torch.nn.Module, loss: torch.nn.Module = None, unrolling_steps: int = 10):
+        super().__init__()
+        self.model = model
+        self.loss = loss if loss is not None else torch.nn.MSELoss()
+        self.unrolling_steps = unrolling_steps
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.Tensor,
+        edge_index: torch.Tensor,
+        edge_attr: torch.Tensor,
+    ):
+        return self.model(x, c, edge_index, edge_attr)
+    
+    def _compute_loss_train(self, x, x_prev, y):
+        return self.loss(x, y) + self.loss(x, x_prev)
+
+    def _compute_loss(self, x, y):
+        return self.loss(x, y)
+
+    def _preprocess_batch(self, batch: Batch):
+        return batch.x, batch.y, batch.c, batch.edge_index, batch.edge_attr
+    
+    def _log_loss(self, loss, batch, stage: str):
+        self.log(
+            f"{stage}_loss",
+            loss,
+            on_step=False,
+            on_epoch=True,
+            prog_bar=True,
+            batch_size=int(batch.num_graphs),
+        )
+        return loss
+
+    def training_step(self, batch: Batch, _):
+        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
+        for _ in range(self.unrolling_steps):
+            x_prev = x.detach()
+            x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
+        loss = self.loss(x, y)
+        self._log_loss(loss, batch, "train")
+        return loss
+    
+    def validation_step(self, batch: Batch, _):
+        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
+        for _ in range(self.unrolling_steps):
+            x_prev = x.detach()
+            x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
+            loss = self.loss(x, x_prev)
+            if loss < 1e-5:
+                break
+        loss = self._compute_loss(x, y)
+        self._log_loss(loss, batch, "val")
+        return loss
+
+    def test_step(self, batch: Batch, _):
+        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
+        for _ in range(self.unrolling_steps):
+            x_prev = x.detach()
+            x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
+        loss = self._compute_loss(x, y)
+        self._log_loss(loss, batch, "test")
+        return loss
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=5e-3)
+        return optimizer