Add pointnet

ThermalSolver/point_module.py

@@ -0,0 +1,92 @@
+import torch
+from lightning import LightningModule
+import importlib
+from matplotlib import pyplot as plt
+from matplotlib.tri import Triangulation
+
+
+def _plot_mesh(x, y, y_pred):
+    x = x[0, ...].detach().cpu()
+    pos = x[0, ...].detach().cpu()
+    pos = x[x[:, 0] != -1]
+    y = y[0, ...].detach().cpu()
+    y = y[x[:, 0] != -1]
+    y_pred = y_pred[0, ...].detach().cpu()
+    y_pred = y_pred[x[:, 0] != -1]
+
+    tria = Triangulation(pos[:, 2], pos[:, 3])
+    plt.figure(figsize=(12, 5))
+    plt.subplot(1, 2, 1)
+    plt.tricontourf(tria, y.squeeze().numpy(), levels=14)
+    plt.colorbar()
+    plt.title("True temperature")
+    plt.subplot(1, 2, 2)
+    plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=14)
+    plt.colorbar()
+    plt.title("Predicted temperature")
+    plt.savefig("point_net.png", dpi=300)
+
+
+def import_class(class_path: str):
+    module_path, class_name = class_path.rsplit(".", 1)  # split last dot
+    module = importlib.import_module(module_path)  # import the module
+    cls = getattr(module, class_name)  # get the class
+    return cls
+
+
+class PointSolver(LightningModule):
+    def __init__(
+        self,
+        model_class_path: str,
+        model_init_args: dict,
+        loss: torch.nn.Module = None,
+    ):
+        super().__init__()
+        self.model = import_class(model_class_path)(**model_init_args)
+        self.loss = loss if loss is not None else torch.nn.MSELoss()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ):
+        return self.model(x)
+
+    def _compute_loss(self, x, y):
+        return self.loss(x, y)
+
+    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=len(batch),
+        )
+        return loss
+
+    def training_step(self, batch, _):
+        x, y = batch
+        y_pred = self(x)
+        loss = self.loss(y_pred, y)
+        self._log_loss(loss, batch, "train")
+        return loss
+
+    def validation_step(self, batch, _):
+        x, y = batch
+        y_pred = self(x)
+        loss = self.loss(y_pred, y)
+        self._log_loss(loss, batch, "val")
+        return loss
+
+    def test_step(self, batch, _):
+        x, y = batch
+        y_pred = self.model(x)
+        loss = self._compute_loss(y_pred, y)
+        self._log_loss(loss, batch, "test")
+        _plot_mesh(x, y, y_pred)
+        return loss
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
+        return optimizer