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new plotting strategy

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This commit is contained in:
FilippoOlivo
2025-12-19 15:50:47 +01:00
parent 68a7def5e6
commit 92104a6b06
3 changed files with 45 additions and 49 deletions
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80
ThermalSolver/autoregressive_module.py
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@@ -15,7 +15,7 @@ def import_class(class_path: str):
return cls return cls
def _plot_mesh(pos_, y_, y_pred_, y_true_, batch, i, batch_idx): def _plot_mesh(pos_, y_, y_pred_, y_true_, batch, cells, i, batch_idx):
# print(pos_.shape, y_.shape, y_pred_.shape, y_true_.shape) # print(pos_.shape, y_.shape, y_pred_.shape, y_true_.shape)
for j in [0]: for j in [0]:
idx = (batch == j).nonzero(as_tuple=True)[0] idx = (batch == j).nonzero(as_tuple=True)[0]
@@ -28,7 +28,8 @@ def _plot_mesh(pos_, y_, y_pred_, y_true_, batch, i, batch_idx):
folder = f"{batch_idx:02d}_images" folder = f"{batch_idx:02d}_images"
if os.path.exists(folder) is False: if os.path.exists(folder) is False:
os.makedirs(folder) os.makedirs(folder)
tria = Triangulation(pos[:, 0], pos[:, 1]) triangles = torch.vstack([cells[:, [0, 1, 2]], cells[:, [0, 2, 3]]])
tria = Triangulation(pos[:, 0], pos[:, 1], triangles=triangles)
plt.figure(figsize=(24, 6)) plt.figure(figsize=(24, 6))
# plt.subplot(1, 4, 1) # plt.subplot(1, 4, 1)
# plt.tricontourf(tria, y.squeeze().numpy(), levels=100) # plt.tricontourf(tria, y.squeeze().numpy(), levels=100)
@@ -38,51 +39,36 @@ def _plot_mesh(pos_, y_, y_pred_, y_true_, batch, i, batch_idx):
# plt.savefig("test_scatter_step_before.png", dpi=72) # plt.savefig("test_scatter_step_before.png", dpi=72)
# x = z # x = z
plt.subplot(1, 4, 1) plt.subplot(1, 4, 1)
# plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=100) plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=100)
plt.scatter( # plt.scatter(pos[:, 0], pos[:, 1], c=y_pred.squeeze().numpy(), s=20, cmap="viridis",)
pos[:, 0],
pos[:, 1],
c=y_pred.squeeze().numpy(),
s=20,
cmap="viridis",
)
plt.colorbar() plt.colorbar()
plt.title(f"Prediction at timestep {i:03d}") plt.title(f"Prediction at timestep {i:03d}")
plt.subplot(1, 4, 2) plt.subplot(1, 4, 2)
# plt.tricontourf(tria, y_true.squeeze().numpy(), levels=100) plt.tricontourf(tria, y_true.squeeze().numpy(), levels=100)
plt.scatter( # plt.scatter(pos[:, 0], pos[:, 1], c=y_true.squeeze().numpy(), s=20, cmap="viridis")
pos[:, 0],
pos[:, 1],
c=y_true.squeeze().numpy(),
s=20,
cmap="viridis",
)
plt.colorbar() plt.colorbar()
plt.title("Ground Truth Steady State") plt.title("Ground Truth Steady State")
plt.subplot(1, 4, 3) plt.subplot(1, 4, 3)
per_element_relative_error = torch.abs(y_pred - y_true) / (y_true + 1e-6) per_element_relative_error = torch.abs(y_pred - y_true) / (
# plt.tricontourf(tria, per_element_relative_error.squeeze(), levels=100) y_true + 1e-6
plt.scatter( )
pos[:, 0], per_element_relative_error = torch.clamp(
pos[:, 1], per_element_relative_error, max=1.0, min=0.0
c=per_element_relative_error.squeeze().numpy(), )
s=20, plt.tricontourf(
cmap="viridis", tria,
per_element_relative_error.squeeze(),
levels=100,
vmin=0, vmin=0,
vmax=1.0, vmax=1.0,
) )
# plt.scatter(pos[:, 0], pos[:, 1], c=per_element_relative_error.squeeze().numpy(), s=20, cmap="viridis", vmin=0, vmax=1.0)
plt.colorbar() plt.colorbar()
plt.title("Relative Error") plt.title("Relative Error")
plt.subplot(1, 4, 4) plt.subplot(1, 4, 4)
absolute_error = torch.abs(y_pred - y_true) absolute_error = torch.abs(y_pred - y_true)
# plt.tricontourf(tria, absolute_error.squeeze(), levels=100) plt.tricontourf(tria, absolute_error.squeeze(), levels=100)
plt.scatter( # plt.scatter(pos[:, 0], pos[:, 1], c=absolute_error.squeeze().numpy(), s=20, cmap="viridis")
pos[:, 0],
pos[:, 1],
c=absolute_error.squeeze().numpy(),
s=20,
cmap="viridis",
)
plt.colorbar() plt.colorbar()
plt.title("Absolute Error") plt.title("Absolute Error")
plt.suptitle("GNO", fontsize=16) plt.suptitle("GNO", fontsize=16)
@@ -292,15 +278,9 @@ class GraphSolver(LightningModule):
sequence_length = y.size(1) sequence_length = y.size(1)
y = y[:, -1, :].unsqueeze(1) y = y[:, -1, :].unsqueeze(1)
_plot_mesh( _plot_mesh(
batch.pos, batch.pos, x, x, y[:, -1, :], batch.batch, batch.cells, 0, batch_idx
x,
x,
y[:, -1, :],
batch.batch,
0,
batch_idx
) )
for i in range(100): for i in range(200):
out = self._compute_model_steps( out = self._compute_model_steps(
# torch.cat([x,pos], dim=-1), # torch.cat([x,pos], dim=-1),
x, x,
@@ -322,12 +302,15 @@ class GraphSolver(LightningModule):
out, out,
y[:, -1, :], y[:, -1, :],
batch.batch, batch.batch,
i+1, batch.cells,
batch_idx i + 1,
batch_idx,
) )
x = out x = out
loss = self.loss(out, y[:, -1, :]) loss = self.loss(out, y[:, -1, :])
relative_error = torch.abs(out - y[:, -1, :]) / (torch.abs(y[:, -1, :]) + 1e-6) relative_error = torch.abs(out - y[:, -1, :]) / (
torch.abs(y[:, -1, :]) + 1e-6
)
mean_relative_error = relative_error.mean() mean_relative_error = relative_error.mean()
all_losses.append(mean_relative_error.item()) all_losses.append(mean_relative_error.item())
losses.append(loss) losses.append(loss)
@@ -345,7 +328,12 @@ class GraphSolver(LightningModule):
def on_test_end(self): def on_test_end(self):
if len(self.test_losses) > 0: if len(self.test_losses) > 0:
_plot_losses(self.test_relative_errors, self.test_losses, self.test_relative_updates, batch_idx=0) _plot_losses(
self.test_relative_errors,
self.test_losses,
self.test_relative_updates,
batch_idx=0,
)
def configure_optimizers(self): def configure_optimizers(self):
optimizer = torch.optim.AdamW(self.parameters(), lr=1e-3) optimizer = torch.optim.AdamW(self.parameters(), lr=1e-3)

4
ThermalSolver/graph_datamodule.py
View File

@@ -82,7 +82,9 @@ class GraphDataModule(LightningDataModule):
conductivity = torch.tensor( conductivity = torch.tensor(
snapshot["conductivity"], dtype=torch.float32 snapshot["conductivity"], dtype=torch.float32
) )
temperature = torch.tensor(snapshot["temperature"], dtype=torch.float32)[:50] temperature = torch.tensor(
snapshot["temperature"], dtype=torch.float32
)[:50]
pos = torch.tensor(geometry["points"], dtype=torch.float32)[:, :2] pos = torch.tensor(geometry["points"], dtype=torch.float32)[:, :2]

8
ThermalSolver/graph_datamodule_unsteady.py
View File

@@ -117,7 +117,9 @@ class GraphDataModule(LightningDataModule):
if not test: if not test:
for t in range(1, temperatures.size(0)): for t in range(1, temperatures.size(0)):
diff = temperatures[t, :] - temperatures[t - 1, :] diff = temperatures[t, :] - temperatures[t - 1, :]
norm_diff = torch.norm(diff, p=2) / torch.norm(temperatures[t - 1], p=2) norm_diff = torch.norm(diff, p=2) / torch.norm(
temperatures[t - 1], p=2
)
if norm_diff < self.min_normalized_diff: if norm_diff < self.min_normalized_diff:
temperatures = temperatures[: t + 1, :] temperatures = temperatures[: t + 1, :]
break break
@@ -148,6 +150,9 @@ class GraphDataModule(LightningDataModule):
data = [] data = []
if test: if test:
cells = geometry.get("cells", None)
if cells is not None:
cells = torch.tensor(cells, dtype=torch.int64)
data.append( data.append(
MeshData( MeshData(
x=temperatures[0, :].unsqueeze(-1), x=temperatures[0, :].unsqueeze(-1),
@@ -158,6 +163,7 @@ class GraphDataModule(LightningDataModule):
edge_attr=edge_attr, edge_attr=edge_attr,
boundary_mask=boundary_mask, boundary_mask=boundary_mask,
boundary_values=boundary_values, boundary_values=boundary_values,
cells=cells,
) )
) )
return data return data