File size: 8,912 Bytes
1f85b04 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | from __future__ import annotations
import argparse
import os
import sys
from collections.abc import Mapping
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(Path(__file__).resolve().parent))
from common import ( # noqa: E402
checkpoint_state,
load_config,
project_path,
resolve_device,
resolve_dtype,
)
from model.sa_pinn import build_model # noqa: E402
from problems import CASES, generate_data, point_counts, relative_l2, to_tensors # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run SA-PINNs inference and visualization")
parser.add_argument(
"--config",
type=Path,
default=Path(os.environ.get("SAPINN_CONFIG", DEFAULT_CONFIG)),
help="YAML configuration file",
)
parser.add_argument("--case", choices=CASES, default="laplace")
parser.add_argument("--device", help="Override common.device")
parser.add_argument("--checkpoint", type=Path, help="Checkpoint path")
parser.add_argument("--weight-dir", type=Path, help="Override weight directory")
parser.add_argument("--result-dir", type=Path, help="Override result directory")
parser.add_argument("--test-res", type=int, help="Override test resolution per axis")
return parser.parse_args()
def save_figure(
case: str,
data: Mapping,
prediction: np.ndarray,
attention: np.ndarray | None,
output_path: Path,
error: float | None,
) -> None:
if case == "laplace":
exact = data["u_exact"].reshape(-1)
predicted = prediction.reshape(-1)
x_axis = data["x_test"].reshape(-1)
figure, axes = plt.subplots(1, 3, figsize=(15, 4))
axes[0].plot(x_axis, exact, "k-", label="Exact")
axes[0].plot(x_axis, predicted, "r--", label="SA-PINN")
axes[0].set_xlabel("x")
axes[0].set_ylabel("u")
axes[0].legend()
axes[1].semilogy(
x_axis, np.maximum(np.abs(predicted - exact), 1.0e-16), "r-"
)
axes[1].set_xlabel("x")
axes[1].set_ylabel("Absolute error")
if attention is not None:
order = np.argsort(data["x_pde"].reshape(-1))
axes[2].plot(
data["x_pde"].reshape(-1)[order],
attention.reshape(-1)[order],
"b-",
)
axes[2].set_xlabel("x")
axes[2].set_ylabel("Attention weight")
else:
axes[2].text(0.5, 0.5, "Attention disabled", ha="center", va="center")
axes[2].set_axis_off()
elif case == "helmholtz":
shape = data["test_shape"]
exact = data["u_exact"].reshape(shape)
predicted = prediction.reshape(shape)
figure, axes = plt.subplots(1, 3, figsize=(15, 4))
for axis, title, field in zip(
axes,
("Exact", "SA-PINN", "Absolute error"),
(exact, predicted, np.abs(predicted - exact)),
strict=True,
):
image = axis.imshow(field, extent=(-1, 1, -1, 1), origin="lower", cmap="jet")
axis.set_title(title)
axis.set_xlabel("x")
axis.set_ylabel("y")
figure.colorbar(image, ax=axis)
else:
shape = data["test_shape"]
predicted = prediction.reshape(shape)
figure, axes = plt.subplots(1, 2, figsize=(12, 4))
image = axes[0].imshow(
predicted,
extent=(-1, 1, 0, 1),
origin="lower",
aspect="auto",
cmap="jet",
)
axes[0].set_title("SA-PINN Burgers prediction")
axes[0].set_xlabel("x")
axes[0].set_ylabel("t")
figure.colorbar(image, ax=axes[0])
if attention is not None:
scatter = axes[1].scatter(
data["x_pde"][:, 0],
data["x_pde"][:, 1],
c=attention.reshape(-1),
s=8,
cmap="viridis",
)
axes[1].set_title("PDE attention weights")
axes[1].set_xlabel("x")
axes[1].set_ylabel("t")
figure.colorbar(scatter, ax=axes[1])
else:
axes[1].text(0.5, 0.5, "Attention disabled", ha="center", va="center")
axes[1].set_axis_off()
figure.suptitle(f"{case} | L2={'N/A' if error is None else f'{error:.3e}'}")
figure.tight_layout()
figure.savefig(output_path, dpi=150)
plt.close(figure)
def main() -> None:
args = parse_args()
config_path = args.config.expanduser().resolve()
config = load_config(config_path)
common = config["common"]
case_config = config["cases"][args.case]
device = resolve_device(args.device or str(common["device"]))
dtype = resolve_dtype(str(common["dtype"]))
weight_dir = project_path(args.weight_dir or common["weight_dir"], PROJECT_ROOT)
result_dir = project_path(args.result_dir or common["result_dir"], PROJECT_ROOT)
checkpoint_path = (
project_path(args.checkpoint, PROJECT_ROOT)
if args.checkpoint
else weight_dir / case_config["output"]["checkpoint_name"]
)
if not checkpoint_path.is_file():
raise FileNotFoundError(
f"checkpoint not found: {checkpoint_path}. Run scripts/train.py first."
)
checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
if not isinstance(checkpoint, Mapping):
raise ValueError(f"invalid SA-PINN checkpoint: {checkpoint_path}")
state, metadata = checkpoint_state(checkpoint)
checkpoint_case = metadata.get("case", args.case)
if checkpoint_case != args.case:
raise ValueError(
f"checkpoint case is {checkpoint_case}, but --case requested {args.case}"
)
model_config = metadata.get("model_config", case_config["model"])
data_config = dict(metadata.get("data_config", case_config["data"]))
if args.test_res is not None:
data_config["test_res"] = args.test_res
seed = int(metadata.get("seed", common["seed"]))
stored_counts = metadata.get("point_counts")
if stored_counts is None:
stored_counts = {
"pde": int(state["att_pde.alpha"].shape[0])
if "att_pde.alpha" in state
else int(data_config["n_pde"]),
"data": int(state["att_data.alpha"].shape[0])
if "att_data.alpha" in state
else int(data_config["n_sol"]),
"boundary": int(state["att_boundary.alpha"].shape[0])
if "att_boundary.alpha" in state
else int(data_config["n_bnd"]),
}
data_config.update(
{
"n_pde": stored_counts["pde"],
"n_sol": stored_counts["data"],
"n_bnd": stored_counts["boundary"],
}
)
data = generate_data(args.case, data_config, seed)
counts = stored_counts or point_counts(data)
attention_enabled = bool(
metadata.get(
"attention_enabled", any(key.startswith("att_") for key in state)
)
)
model = build_model(model_config, counts, attention_enabled, dtype).to(
device=device, dtype=dtype
)
model.load_state_dict(state, strict=True)
model.eval()
tensors = to_tensors(data, device, dtype)
with torch.no_grad():
prediction = model(tensors["x_test"]).cpu().numpy()
error = relative_l2(prediction, data["u_exact"])
attention = (
model.att_pde().detach().cpu().numpy() if model.att_pde is not None else None
)
result_dir.mkdir(parents=True, exist_ok=True)
predictions_path = result_dir / case_config["output"]["predictions_name"]
figure_path = result_dir / case_config["output"]["figure_name"]
np.savez_compressed(
predictions_path,
coordinates=data["x_test"],
prediction=prediction,
exact=np.array([]) if data["u_exact"] is None else data["u_exact"],
relative_l2=np.nan if error is None else error,
pde_attention=np.array([]) if attention is None else attention,
)
save_figure(args.case, data, prediction, attention, figure_path, error)
print(f"Config: {config_path}")
print(f"Case: {args.case}")
print(f"Checkpoint: {checkpoint_path}")
print(f"Device: {device}")
print(f"Relative L2={'N/A' if error is None else f'{error:.6e}'}")
if attention is not None:
print(
f"PDE attention: mean={attention.mean():.6f}, "
f"std={attention.std():.6f}, min={attention.min():.6f}, "
f"max={attention.max():.6f}"
)
print(f"Predictions: {predictions_path}")
print(f"Plot: {figure_path}")
if __name__ == "__main__":
main()
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