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try a new model

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This commit is contained in:
Filippo Olivo
2025-11-12 15:20:43 +01:00
parent a2dd348423
commit dc59114f4a
3 changed files with 31 additions and 106 deletions
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9
ThermalSolver/graph_datamodule.py
View File

@@ -122,10 +122,11 @@ class GraphDataModule(LightningDataModule):
edge_index_mask = ~torch.isin(edge_index[1], boundary_idx)
edge_index = edge_index[:, edge_index_mask]
edge_attr = pos[edge_index[0]] - pos[edge_index[1]]
edge_attr = torch.cat(
[edge_attr, torch.norm(edge_attr, dim=1).unsqueeze(-1)], dim=1
)
# edge_attr = pos[edge_index[0]] - pos[edge_index[1]]
# edge_attr = torch.cat(
# [edge_attr, torch.norm(edge_attr, dim=1).unsqueeze(-1)], dim=1
# )
edge_attr = torch.norm(pos[edge_index[0]] - pos[edge_index[1]], dim=1)
x = torch.zeros_like(temperature, dtype=torch.float32).unsqueeze(-1)
if self.remove_boundary_edges:

55
ThermalSolver/graph_module.py
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@@ -75,9 +75,6 @@ class GraphSolver(LightningModule):
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",
@@ -115,19 +112,25 @@ class GraphSolver(LightningModule):
self.manual_backward(loss / max_acc_iters)
return loss_.item()
def _preprocess_batch(self, batch: Batch):
x, y, c, edge_index, edge_attr = (
batch.x,
batch.y,
batch.c,
batch.edge_index,
batch.edge_attr,
)
edge_attr = 1 / edge_attr
c_ij = self._compute_c_ij(c, edge_index)
edge_attr = edge_attr * c_ij
return x, y, edge_index, edge_attr
def training_step(self, batch: Batch, batch_idx: int):
optim = self.optimizers()
optim.zero_grad()
x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
x, y, edge_index, edge_attr = self._preprocess_batch(batch)
edge_w = 1 / edge_attr[:, -1]
c_ij = self._compute_c_ij(c, edge_index)
edge_w = edge_w * c_ij
deg = self._compute_deg(edge_index, edge_w, x.size(0))
edge_attr = torch.cat(
[edge_attr, edge_w.unsqueeze(-1), c_ij.unsqueeze(-1)], dim=1
)
deg = self._compute_deg(edge_index, edge_attr, x.size(0))
losses = []
acc_loss, acc_it = 0, 0
max_acc_iters = (
@@ -139,7 +142,7 @@ class GraphSolver(LightningModule):
out = self._compute_model_steps(
x,
edge_index,
edge_attr,
edge_attr.unsqueeze(-1),
deg,
batch.boundary_mask,
batch.boundary_values,
@@ -199,21 +202,15 @@ class GraphSolver(LightningModule):
return super().on_train_epoch_end()
def validation_step(self, batch: Batch, _):
x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
x, y, edge_index, edge_attr = self._preprocess_batch(batch)
edge_w = 1 / edge_attr[:, -1]
c_ij = self._compute_c_ij(c, edge_index)
edge_w = edge_w * c_ij
deg = self._compute_deg(edge_index, edge_w, x.size(0))
deg = self._compute_deg(edge_index, edge_attr, x.size(0))
edge_attr = torch.cat(
[edge_attr, edge_w.unsqueeze(-1), c_ij.unsqueeze(-1)], dim=1
)
for i in range(self.current_iters):
out = self._compute_model_steps(
x,
edge_index,
edge_attr,
edge_attr.unsqueeze(-1),
deg,
batch.boundary_mask,
batch.boundary_values,
@@ -252,20 +249,15 @@ class GraphSolver(LightningModule):
# loss = self._compute_loss(y_pred, y)
# # _plot_mesh(batch.pos, y, y_pred, batch.batch)
# self._log_loss(loss, batch, "test")
x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
edge_w = 1 / edge_attr[:, -1]
c_ij = self._compute_c_ij(c, edge_index)
edge_w = edge_w * c_ij
deg = self._compute_deg(edge_index, edge_w, x.size(0))
x, y, edge_index, edge_attr = self._preprocess_batch(batch)
deg = self._compute_deg(edge_index, edge_attr, x.size(0))
edge_attr = torch.cat(
[edge_attr, edge_w.unsqueeze(-1), c_ij.unsqueeze(-1)], dim=1
)
for i in range(self.max_iters):
out = self._compute_model_steps(
x,
edge_index,
edge_attr,
edge_attr.unsqueeze(-1),
deg,
batch.boundary_mask,
batch.boundary_values,
@@ -278,7 +270,6 @@ class GraphSolver(LightningModule):
loss = self.loss(out, y)
self._log_loss(loss, batch, "test")
x = u
self.log(
"test/iterations",
i + 1,

73
ThermalSolver/model/learnable_finite_difference.py
View File

@@ -2,40 +2,6 @@ import torch
import torch.nn as nn
from torch_geometric.nn import MessagePassing
from torch.nn.utils import spectral_norm
from matplotlib.tri import Triangulation
from matplotlib import pyplot as plt
def _plot_mesh(y_pred, batch, iteration=None):
idx = batch.batch == 0
y = batch.y[idx].detach().cpu()
y_pred = y_pred[idx].detach().cpu()
pos = batch.pos[idx].detach().cpu()
pos = pos.detach().cpu()
tria = Triangulation(pos[:, 0], pos[:, 1])
plt.figure(figsize=(18, 5))
plt.subplot(1, 3, 1)
plt.tricontourf(tria, y.squeeze().numpy(), levels=14)
plt.colorbar()
plt.title("True temperature")
plt.subplot(1, 3, 2)
plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=14)
plt.colorbar()
plt.title("Predicted temperature")
plt.subplot(1, 3, 3)
plt.tricontourf(tria, torch.abs(y_pred - y).squeeze().numpy(), levels=14)
plt.colorbar()
plt.title("Error")
plt.suptitle("GNO", fontsize=16)
name = (
f"images/gno_iter_{iteration:04d}.png"
if iteration is not None
else "gno.png"
)
plt.savefig(name, dpi=72)
plt.close()
class FiniteDifferenceStep(MessagePassing):
@@ -51,28 +17,12 @@ class FiniteDifferenceStep(MessagePassing):
spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
)
self.edge_embedding = nn.Sequential(
spectral_norm(nn.Linear(edge_ch, hidden_dim // 2)),
nn.GELU(),
spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
)
self.update_net = nn.Sequential(
spectral_norm(nn.Linear(2 * hidden_dim, hidden_dim)),
nn.GELU(),
spectral_norm(nn.Linear(hidden_dim, hidden_dim)),
nn.GELU(),
# spectral_norm(nn.Linear(hidden_dim // 2, 1)),
)
# self.message_net = nn.Sequential(
# spectral_norm(nn.Linear(2 * hidden_dim, hidden_dim)),
# nn.GELU(),
# spectral_norm(nn.Linear(hidden_dim, hidden_dim // 2)),
# nn.GELU(),
# spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
# )
self.out_net = nn.Sequential(
spectral_norm(nn.Linear(hidden_dim, hidden_dim // 2)),
nn.GELU(),
@@ -84,17 +34,14 @@ class FiniteDifferenceStep(MessagePassing):
TODO: add docstring.
"""
x_ = self.x_embedding(x)
edge_attr_ = self.edge_embedding(edge_attr)
out = self.propagate(edge_index, x=x_, edge_attr=edge_attr_, deg=deg)
out = self.propagate(edge_index, x=x_, edge_attr=edge_attr, deg=deg)
return self.out_net(x_ + out)
def message(self, x_j, edge_attr):
def message(self, x_i, x_j, edge_attr):
"""
TODO: add docstring.
"""
# msg_input = torch.cat([x_j, edge_attr], dim=-1)
# return self.message_net(msg_input) * edge_attr[:, 3].view(-1, 1)
return x_j * edge_attr
return (x_j - x_i) * edge_attr.view(-1, 1)
def update(self, aggr_out, x):
"""
@@ -102,10 +49,6 @@ class FiniteDifferenceStep(MessagePassing):
"""
update_input = torch.cat([x, aggr_out], dim=-1)
return self.update_net(update_input)
# return self.update_net(aggr_out)
# return aggr_out
# h = self.update_net(aggr_out, x)
# return h
def aggregate(self, inputs, index, deg):
"""
@@ -114,13 +57,3 @@ class FiniteDifferenceStep(MessagePassing):
out = super().aggregate(inputs, index)
deg = deg + 1e-7
return out / deg.view(-1, 1)
# # Da fare:
# # - Finire calcolo della loss su ogni step e poi media
# # - Test con vari modelli
# # - Se non dovesse funzionare, provare ad adeguare il criterio di uscita
# # PINN batching:
# # - Provare singola condizione
# # - Ottimizzatore del secondo ordine (LBFGS)