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import random
import sys
import time
from pathlib import Path
import numpy as np
import torch
import yaml
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(Path(__file__).resolve().parent))
from data_utils import ( # noqa: E402
build_evaluation_points,
build_training_batch,
exact_subdomain_values,
load_mat_data,
)
from model.xpinn import XPINNPoisson2D # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
def load_config(path: Path) -> dict:
with path.open("r", encoding="utf-8") as stream:
config = yaml.safe_load(stream)
if not isinstance(config, dict) or "root" not in config:
raise ValueError(f"config must contain a 'root' mapping: {path}")
return config["root"]
def project_path(value: str | Path) -> Path:
path = Path(value).expanduser()
return path if path.is_absolute() else PROJECT_ROOT / path
def resolve_device(requested: str) -> torch.device:
if requested == "auto":
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device(requested)
if device.type == "cuda" and not torch.cuda.is_available():
raise RuntimeError("CUDA/DCU was requested but torch.cuda.is_available() is false")
return device
def resolve_dtype(name: str) -> torch.dtype:
try:
return {"float32": torch.float32, "float64": torch.float64}[name]
except KeyError as error:
raise ValueError(f"unsupported dtype: {name}") from error
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def compute_loss(
outputs: dict[str, torch.Tensor],
batch: dict[str, torch.Tensor],
weights: dict,
) -> tuple[torch.Tensor, dict[str, float]]:
boundary_loss = torch.mean(
(batch["ub"] - outputs["boundary_prediction"]).square()
)
pde_loss = sum(
torch.mean(outputs[f"residual{domain}"].square()) for domain in (1, 2, 3)
)
interface_residual_loss = torch.mean(
outputs["interface1_residual"].square()
) + torch.mean(outputs["interface2_residual"].square())
interface_value_loss = (
torch.mean(
(outputs["interface1_domain1"] - outputs["interface1_average"]).square()
)
+ torch.mean(
(outputs["interface1_domain2"] - outputs["interface1_average"]).square()
)
+ torch.mean(
(outputs["interface2_domain1"] - outputs["interface2_average"]).square()
)
+ torch.mean(
(outputs["interface2_domain3"] - outputs["interface2_average"]).square()
)
)
total = (
float(weights["boundary"]) * boundary_loss
+ float(weights["pde"]) * pde_loss
+ float(weights["interface_residual"]) * interface_residual_loss
+ float(weights["interface_value"]) * interface_value_loss
)
return total, {
"boundary": boundary_loss.item(),
"pde": pde_loss.item(),
"interface_residual": interface_residual_loss.item(),
"interface_value": interface_value_loss.item(),
}
def clear_coordinate_grads(batch: dict[str, torch.Tensor]) -> None:
for value in batch.values():
if value.requires_grad and value.grad is not None:
value.grad = None
def evaluate(
model: XPINNPoisson2D,
points: dict[str, torch.Tensor],
references: tuple[torch.Tensor, torch.Tensor, torch.Tensor],
) -> tuple[float, float, float, float]:
with torch.no_grad():
predictions = model.predict(points["xy1"], points["xy2"], points["xy3"])
errors = tuple(
(
torch.linalg.vector_norm(prediction - reference)
/ torch.linalg.vector_norm(reference)
).item()
for prediction, reference in zip(predictions, references, strict=True)
)
combined_prediction = torch.cat(predictions)
combined_reference = torch.cat(references)
combined_error = (
torch.linalg.vector_norm(combined_prediction - combined_reference)
/ torch.linalg.vector_norm(combined_reference)
).item()
return errors[0], errors[1], errors[2], combined_error
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
common = config["common"]
device = resolve_device(str(common["device"]))
dtype = resolve_dtype(str(common["dtype"]))
seed = int(common["seed"])
data_path = project_path(config["data"]["mat_file"])
weight_dir = project_path(common["weight_dir"])
checkpoint_path = weight_dir / config["training"]["checkpoint_name"]
steps = int(config["training"]["steps"])
if steps <= 0:
raise ValueError("training steps must be positive")
counts = {key: int(value) for key, value in config["data"]["samples"].items()}
seed_everything(seed)
print(f"Config: {config_path}")
print(f"Data: {data_path}")
print(f"Device: {device}")
print(f"Samples: {counts}")
data = load_mat_data(data_path)
batch = build_training_batch(data, counts, seed, device, dtype)
evaluation_points = build_evaluation_points(data, device, dtype)
references = exact_subdomain_values(data, device, dtype)
model = XPINNPoisson2D(config["model"], dtype=dtype).to(
device=device, dtype=dtype
)
optimizer = torch.optim.Adam(
model.parameters(), lr=float(config["training"]["lr"])
)
log_interval = int(config["training"]["log_interval"])
started = time.time()
for step in range(1, steps + 1):
clear_coordinate_grads(batch)
outputs = model.training_outputs(batch)
loss, parts = compute_loss(outputs, batch, config["loss"])
if not torch.isfinite(loss):
raise FloatingPointError(f"XPINN loss became non-finite at step {step}")
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
if step == 1 or step % log_interval == 0 or step == steps:
error1, error2, error3, combined_error = evaluate(
model, evaluation_points, references
)
print(
f"step={step:4d} loss={loss.item():.3e} "
f"boundary={parts['boundary']:.3e} pde={parts['pde']:.3e} "
f"interface_r={parts['interface_residual']:.3e} "
f"interface_u={parts['interface_value']:.3e} "
f"l2=({error1:.3e}, {error2:.3e}, {error3:.3e}) "
f"combined={combined_error:.3e}"
)
weight_dir.mkdir(parents=True, exist_ok=True)
checkpoint = {
"case": "poisson2d",
"architecture": "xpinn_poisson2d",
"model_state": model.state_dict(),
"model_config": config["model"],
"sample_counts": counts,
"step": steps,
}
torch.save(checkpoint, checkpoint_path)
print(f"Training finished in {time.time() - started:.1f}s")
print(f"Saved checkpoint: {checkpoint_path}")
if __name__ == "__main__":
main()
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