180 lines
5.5 KiB
Python
180 lines
5.5 KiB
Python
import torch
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from lightning import LightningModule
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from torch_geometric.data import Batch
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import importlib
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from matplotlib import pyplot as plt
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from matplotlib.tri import Triangulation
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def import_class(class_path: str):
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module_path, class_name = class_path.rsplit(".", 1) # split last dot
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module = importlib.import_module(module_path) # import the module
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cls = getattr(module, class_name) # get the class
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return cls
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def _plot_mesh(pos, y, y_pred, batch):
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idx = batch == 0
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y = y[idx].detach().cpu()
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y_pred = y_pred[idx].detach().cpu()
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pos = pos[idx].detach().cpu()
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pos = pos.detach().cpu()
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tria = Triangulation(pos[:, 0], pos[:, 1])
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plt.figure(figsize=(18, 5))
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plt.subplot(1, 3, 1)
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plt.tricontourf(tria, y.squeeze().numpy(), levels=14)
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plt.colorbar()
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plt.title("True temperature")
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plt.subplot(1, 3, 2)
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plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=14)
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plt.colorbar()
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plt.title("Predicted temperature")
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plt.subplot(1, 3, 3)
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plt.tricontourf(tria, torch.abs(y_pred - y).squeeze().numpy(), levels=14)
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plt.colorbar()
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plt.title("Error")
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plt.suptitle("GNO", fontsize=16)
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plt.savefig("gno.png", dpi=300)
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class GraphSolver(LightningModule):
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def __init__(
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self,
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model_class_path: str,
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model_init_args: dict,
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loss: torch.nn.Module = None,
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unrolling_steps: int = 48,
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):
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super().__init__()
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self.model = import_class(model_class_path)(**model_init_args)
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self.loss = loss if loss is not None else torch.nn.MSELoss()
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self.unrolling_steps = unrolling_steps
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def forward(
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self,
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x: torch.Tensor,
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c: torch.Tensor,
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edge_index: torch.Tensor,
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edge_attr: torch.Tensor,
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unrolling_steps: int = None,
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boundary_mask: torch.Tensor = None,
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boundary_values: torch.Tensor = None,
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):
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return self.model(
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x=x,
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c=c,
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edge_index=edge_index,
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edge_attr=edge_attr,
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unrolling_steps=unrolling_steps,
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boundary_mask=boundary_mask,
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boundary_values=boundary_values,
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)
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def _compute_loss(self, x, y):
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return self.loss(x, y)
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def _preprocess_batch(self, batch: Batch):
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return batch.x, batch.y, batch.c, batch.edge_index, batch.edge_attr
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def _log_loss(self, loss, batch, stage: str):
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self.log(
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f"{stage}/loss",
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loss,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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batch_size=int(batch.num_graphs),
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)
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return loss
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def training_step(self, batch: Batch, _):
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x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
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y_pred, it = self(
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x,
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c,
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edge_index=edge_index,
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edge_attr=edge_attr,
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unrolling_steps=self.unrolling_steps,
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boundary_mask=batch.boundary_mask,
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boundary_values=batch.boundary_values,
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)
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loss = self.loss(y_pred, y)
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boundary_loss = self.loss(
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y_pred[batch.boundary_mask], y[batch.boundary_mask]
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)
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self._log_loss(loss, batch, "train")
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# self._log_loss(boundary_loss, batch, "train_boundary")
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self.log(
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"train/iterations",
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it,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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batch_size=int(batch.num_graphs),
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)
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self.log(
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"train/param_p",
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self.model.fd_step.p,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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batch_size=int(batch.num_graphs),
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)
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# self.log("train/param_a", self.model.fd_step.a, on_step=False, on_epoch=True, prog_bar=True, batch_size=int(batch.num_graphs))
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return loss
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def validation_step(self, batch: Batch, _):
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x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
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y_pred, it = self(
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x,
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c,
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edge_index=edge_index,
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edge_attr=edge_attr,
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unrolling_steps=self.unrolling_steps,
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boundary_mask=batch.boundary_mask,
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boundary_values=batch.boundary_values,
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)
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loss = self.loss(y_pred, y)
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boundary_loss = self.loss(
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y_pred[batch.boundary_mask], y[batch.boundary_mask]
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)
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self._log_loss(loss, batch, "val")
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self.log(
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"val/iterations",
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it,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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batch_size=int(batch.num_graphs),
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)
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return loss
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def test_step(self, batch: Batch, _):
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x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
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y_pred, _ = self.model(
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x=x,
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c=c,
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edge_index=edge_index,
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edge_attr=edge_attr,
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unrolling_steps=self.unrolling_steps,
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batch=batch.batch,
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pos=batch.pos,
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boundary_mask=batch.boundary_mask,
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boundary_values=batch.boundary_values,
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plot_results=False,
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)
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loss = self._compute_loss(y_pred, y)
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_plot_mesh(batch.pos, y, y_pred, batch.batch)
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self._log_loss(loss, batch, "test")
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return loss
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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def _impose_bc(self, x: torch.Tensor, data: Batch):
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x[data.boundary_mask] = data.boundary_values
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return x
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