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4a96652 | 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 | from __future__ import annotations
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
import yaml
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
from model.vpinn import VPINN, infer_layers, unpack_checkpoint # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
CASES = ("1d", "2d")
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 load_model(
checkpoint_path: Path, device: torch.device, dtype: torch.dtype
) -> tuple[VPINN, dict]:
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 VPINN checkpoint: {checkpoint_path}")
state, metadata = unpack_checkpoint(checkpoint)
layers = metadata.get("layers") or infer_layers(state)
model = VPINN(layers, dtype=dtype).to(device=device, dtype=dtype)
model.load_state_dict(state, strict=True)
model.eval()
return model, metadata
def exact_poisson1d(x: np.ndarray) -> np.ndarray:
return 0.1 * np.sin(8.0 * np.pi * x) + np.tanh(80.0 * x)
def infer_poisson1d(
weight_dir: Path,
result_dir: Path,
device: torch.device,
dtype: torch.dtype,
) -> float:
checkpoint_path = weight_dir / "hpvpinn_poisson1d.pt"
model, _ = load_model(checkpoint_path, device, dtype)
points = torch.linspace(-1.0, 1.0, 2001, dtype=dtype, device=device).unsqueeze(-1)
exact = exact_poisson1d(points.cpu().numpy().reshape(-1))
with torch.no_grad():
prediction = model(points).cpu().numpy().reshape(-1)
absolute_error = np.abs(prediction - exact)
relative_l2 = float(np.linalg.norm(prediction - exact) / np.linalg.norm(exact))
figure, axes = plt.subplots(1, 2, figsize=(12, 4))
coordinates = points.cpu().numpy().reshape(-1)
axes[0].plot(coordinates, exact, "k-", linewidth=1.5, label="Exact")
axes[0].plot(coordinates, prediction, "r--", linewidth=1.0, label="hp-VPINN")
axes[0].set_xlabel("x")
axes[0].set_ylabel("u")
axes[0].legend()
axes[1].semilogy(coordinates, np.maximum(absolute_error, 1.0e-16), "r-")
axes[1].set_xlabel("x")
axes[1].set_ylabel("Absolute error")
figure.tight_layout()
output_path = result_dir / "hpvpinn_poisson1d.png"
figure.savefig(output_path, dpi=150)
plt.close(figure)
print(f"[1D] relative L2={relative_l2:.6e}, plot={output_path}")
return relative_l2
def exact_poisson2d(x: np.ndarray, y: np.ndarray) -> np.ndarray:
return (0.1 * np.sin(2.0 * np.pi * x) + np.tanh(10.0 * x)) * np.sin(
2.0 * np.pi * y
)
def infer_poisson2d(
weight_dir: Path,
result_dir: Path,
device: torch.device,
dtype: torch.dtype,
) -> float:
checkpoint_path = weight_dir / "hpvpinn_poisson2d.pt"
model, _ = load_model(checkpoint_path, device, dtype)
axis = np.linspace(-1.0, 1.0, 100)
mesh_x, mesh_y = np.meshgrid(axis, axis, indexing="ij")
points = torch.as_tensor(
np.column_stack((mesh_x.ravel(), mesh_y.ravel())), dtype=dtype, device=device
)
with torch.no_grad():
prediction = model(points).cpu().numpy().reshape(mesh_x.shape)
exact = exact_poisson2d(mesh_x, mesh_y)
absolute_error = np.abs(prediction - exact)
relative_l2 = float(
np.linalg.norm(prediction.ravel() - exact.ravel()) / np.linalg.norm(exact.ravel())
)
figure, axes = plt.subplots(1, 3, figsize=(15, 4))
for plot_axis, title, field in zip(
axes,
("Exact", "hp-VPINN", "Absolute error"),
(exact, prediction, absolute_error),
strict=True,
):
image = plot_axis.imshow(
field.T,
extent=(-1, 1, -1, 1),
origin="lower",
aspect="auto",
cmap="jet",
)
plot_axis.set_title(title)
plot_axis.set_xlabel("x")
plot_axis.set_ylabel("y")
figure.colorbar(image, ax=plot_axis)
figure.tight_layout()
output_path = result_dir / "hpvpinn_poisson2d.png"
figure.savefig(output_path, dpi=150)
plt.close(figure)
print(f"[2D] relative L2={relative_l2:.6e}, plot={output_path}")
return relative_l2
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"]))
weight_dir = project_path(common["weight_dir"])
result_dir = project_path(common["result_dir"])
result_dir.mkdir(parents=True, exist_ok=True)
print(f"Config: {config_path}")
print(f"Device: {device}")
selected_case = str(common["case"]).lower()
if selected_case not in (*CASES, "all"):
raise ValueError("common.case must be one of: 1d, 2d, all")
selected_cases = CASES if selected_case == "all" else (selected_case,)
if "1d" in selected_cases:
infer_poisson1d(weight_dir, result_dir, device, dtype)
if "2d" in selected_cases:
infer_poisson2d(weight_dir, result_dir, device, dtype)
if __name__ == "__main__":
main()
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