File size: 5,190 Bytes
371b59f | 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 | 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
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,
relative_l2,
resolve_device,
resolve_dtype,
)
from data_utils import batched_predict, load_allen_cahn # noqa: E402
from model.fpinn import build_model # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
TASKS = ("forward", "inverse")
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
common = config["common"]
task = str(common["task"]).lower()
if task not in TASKS:
raise ValueError("common.task must be one of: forward, inverse")
task_config = config["tasks"][task]
device = resolve_device(str(common["device"]))
dtype = resolve_dtype(str(common["dtype"]))
weight_dir = project_path(common["weight_dir"], PROJECT_ROOT)
result_dir = project_path(common["result_dir"], PROJECT_ROOT)
checkpoint_path = weight_dir / task_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 FPINN checkpoint: {checkpoint_path}")
state, metadata = checkpoint_state(checkpoint)
checkpoint_task = metadata.get("task", task)
if checkpoint_task != task:
raise ValueError(
f"checkpoint task is {checkpoint_task}, but common.task is {task}"
)
model_config = metadata.get("model_config", config["model"])
pde_config = metadata.get("pde_config", task_config["pde"])
data_config = dict(metadata.get("data_config", config["data"]))
data_path = project_path(data_config["mat_file"], PROJECT_ROOT)
dataset = load_allen_cahn(data_path)
model = build_model(task, model_config, pde_config, dtype).to(
device=device, dtype=dtype
)
model.load_state_dict(state, strict=True)
model.eval()
prediction = batched_predict(
model,
np.asarray(dataset["coordinates"]),
int(data_config["evaluation_batch_size"]),
device,
dtype,
)
exact = np.asarray(dataset["exact"])
error = relative_l2(prediction, exact)
rmse = float(np.sqrt(np.mean((prediction - exact) ** 2)))
max_error = float(np.max(np.abs(prediction - exact)))
result_dir.mkdir(parents=True, exist_ok=True)
predictions_path = result_dir / task_config["output"]["predictions_name"]
figure_path = result_dir / task_config["output"]["figure_name"]
payload = {
"coordinates": dataset["coordinates"],
"exact": exact,
"prediction": prediction,
"relative_l2": error,
"rmse": rmse,
"max_abs_error": max_error,
}
if task == "inverse":
payload["lambda_1"] = model.lambda_1.item()
payload["lambda_2"] = model.lambda_2.item()
np.savez_compressed(predictions_path, **payload)
shape = dataset["grid_shape"]
exact_grid = exact.reshape(shape)
prediction_grid = prediction.reshape(shape)
error_grid = np.abs(prediction_grid - exact_grid)
extent = (
float(np.min(dataset["space"])),
float(np.max(dataset["space"])),
float(np.min(dataset["time"])),
float(np.max(dataset["time"])),
)
figure, axes = plt.subplots(1, 3, figsize=(15, 4))
for axis, title, field in zip(
axes,
("Exact", "FPINN", "Absolute error"),
(exact_grid, prediction_grid, error_grid),
strict=True,
):
image = axis.imshow(
field,
extent=extent,
origin="lower",
aspect="auto",
cmap="viridis" if title != "Absolute error" else "hot",
)
axis.set_title(title)
axis.set_xlabel("x")
axis.set_ylabel("t")
figure.colorbar(image, ax=axis)
title = f"Allen-Cahn {task} | L2={error:.3e}"
if task == "inverse":
title += (
f" | lambda=({model.lambda_1.item():.3e}, "
f"{model.lambda_2.item():.3e})"
)
figure.suptitle(title)
figure.tight_layout()
figure.savefig(figure_path, dpi=150)
plt.close(figure)
print(f"Config: {config_path}")
print(f"Task: {task}")
print(f"Data: {data_path}")
print(f"Checkpoint: {checkpoint_path}")
print(f"Device: {device}")
print(f"Relative L2={error:.6e}, RMSE={rmse:.6e}, MaxAbs={max_error:.6e}")
if task == "inverse":
print(
f"Recovered lambda_1={model.lambda_1.item():.8e}, "
f"lambda_2={model.lambda_2.item():.8e}"
)
print(f"Predictions: {predictions_path}")
print(f"Plot: {figure_path}")
if __name__ == "__main__":
main()
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