File size: 7,383 Bytes
b887635 | 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 | #!/usr/bin/env python3
"""Evaluate the best FAST-DeepONet checkpoint on all ten test fields."""
from __future__ import annotations
import argparse
import csv
import json
import math
import os
import sys
from pathlib import Path
from typing import Any
import numpy as np
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from model.FASTDeepONet import FASTDeepONet
from scripts.common import (
atomic_write_json,
load_config,
load_split,
make_coordinate_features,
relative_l2,
select_device,
)
def load_checkpoint(path: Path, device: torch.device) -> dict[str, Any]:
try:
checkpoint = torch.load(path, map_location=device, weights_only=False)
except TypeError:
checkpoint = torch.load(path, map_location=device)
if not isinstance(checkpoint, dict) or "model_state_dict" not in checkpoint:
raise ValueError(f"Invalid checkpoint payload: {path}")
return checkpoint
def build_from_checkpoint(checkpoint: dict[str, Any], device: torch.device) -> FASTDeepONet:
arguments = dict(checkpoint["model_arguments"])
recorded_effective_rank = int(arguments.pop("effective_factor_rank"))
basis = checkpoint["spectral_basis"]
model = FASTDeepONet(spectral_basis=basis, **arguments).to(device)
if model.effective_factor_rank != recorded_effective_rank:
raise ValueError("Checkpoint effective rank is inconsistent with its spectral basis")
model.load_state_dict(checkpoint["model_state_dict"], strict=True)
model.eval()
return model
def atomic_save_predictions(path: Path, **arrays: np.ndarray) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_suffix(path.suffix + ".tmp")
with temporary.open("wb") as handle:
np.savez_compressed(handle, **arrays)
os.replace(temporary, path)
def write_metrics_csv(path: Path, filenames: list[str], metrics: list[float], mean_metric: float) -> None:
temporary = path.with_suffix(path.suffix + ".tmp")
with temporary.open("w", newline="", encoding="utf-8") as handle:
writer = csv.writer(handle)
writer.writerow(["sample_index", "filename", "relative_l2"])
for index, (filename, metric) in enumerate(zip(filenames, metrics)):
writer.writerow([index, filename, f"{metric:.12e}"])
writer.writerow(["mean", "", f"{mean_metric:.12e}"])
os.replace(temporary, path)
def run_inference(config: dict[str, Any], force_cpu: bool) -> None:
paths = config["paths"]
data_config = config["data"]
model_config = config["model"]
training = config["training"]
device = torch.device("cpu") if force_cpu else select_device(require_accelerator=False)
checkpoint_path = Path(paths["checkpoint"])
if not checkpoint_path.is_file():
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
checkpoint = load_checkpoint(checkpoint_path, device)
model = build_from_checkpoint(checkpoint, device)
filenames, inputs_np, targets_np, masks_np = load_split(
paths["data_root"],
"test",
data_config["split_counts"]["test"],
data_config["points_per_field"],
)
coordinates_np = make_coordinate_features(
data_config["grid_size"], model_config["fourier_order"]
)
coordinates = torch.from_numpy(coordinates_np).to(device)
target_mean = np.asarray(checkpoint["target_mean"].cpu(), dtype=np.float32)
target_std = float(checkpoint["target_std"])
predictions: list[np.ndarray] = []
metrics: list[float] = []
batch_size = int(training["batch_size"])
print(
f"inference_start checkpoint={checkpoint_path} best_epoch={checkpoint['best_epoch']} "
f"best_tune_loss={float(checkpoint['best_tune_mse']):.8e} device={device}",
flush=True,
)
with torch.inference_mode():
for sample_index, (filename, input_field, target_field) in enumerate(
zip(filenames, inputs_np, targets_np)
):
branch = torch.from_numpy(input_field[None, :]).to(device)
branch_latent = model.encode_branch(branch)
normalized_chunks: list[np.ndarray] = []
for start in range(0, coordinates.shape[0], batch_size):
coordinate_batch = coordinates[start : start + batch_size]
expanded_branch = branch_latent.expand(coordinate_batch.shape[0], -1)
normalized = model.predict_from_branch_encoding(expanded_branch, coordinate_batch)
normalized_chunks.append(normalized.cpu().numpy().astype(np.float32, copy=False))
normalized_prediction = np.concatenate(normalized_chunks)
prediction = normalized_prediction * target_std + target_mean
metric = relative_l2(target_field, prediction)
if not math.isfinite(metric):
raise FloatingPointError(f"Non-finite relative L2 for {filename}")
predictions.append(prediction.astype(np.float32, copy=False))
metrics.append(metric)
print(
f"test_sample={sample_index:02d} file={filename} relative_l2={metric:.10e}",
flush=True,
)
mean_metric = float(np.mean(metrics))
print(f"test_mean_relative_l2={mean_metric:.10e} samples={len(metrics)}", flush=True)
results_dir = Path(paths["results_dir"])
results_dir.mkdir(parents=True, exist_ok=True)
metrics_payload = {
"metric": "relative_l2_per_field",
"equation": "||y-y_hat||_2 / (||y||_2 + 1e-12)",
"checkpoint": str(checkpoint_path),
"best_epoch": int(checkpoint["best_epoch"]),
"best_tune_mse": float(checkpoint["best_tune_mse"]),
"sample_count": len(metrics),
"samples": [
{"sample_index": index, "filename": filename, "relative_l2": metric}
for index, (filename, metric) in enumerate(zip(filenames, metrics))
],
"mean_relative_l2": mean_metric,
"paper_comparison_note": (
"This fixed run uses 10 test fields and one model seed; the paper's Table 2 "
"uses 100 independent test fields and summarizes five fits."
),
}
atomic_write_json(results_dir / "test_metrics.json", metrics_payload)
write_metrics_csv(results_dir / "test_metrics.csv", filenames, metrics, mean_metric)
atomic_save_predictions(
results_dir / "test_predictions.npz",
filenames=np.asarray(filenames),
inputs=inputs_np.astype(np.float32, copy=False),
masks=masks_np.astype(np.float32, copy=False),
targets=targets_np.astype(np.float32, copy=False),
predictions=np.stack(predictions).astype(np.float32, copy=False),
relative_l2=np.asarray(metrics, dtype=np.float64),
)
print(f"inference_complete results_dir={results_dir}", flush=True)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", type=Path, default=PROJECT_ROOT / "config" / "config.yaml")
parser.add_argument("--cpu", action="store_true", help="Force CPU inference")
return parser.parse_args()
def main() -> None:
args = parse_args()
run_inference(load_config(args.config), force_cpu=args.cpu)
if __name__ == "__main__":
main()
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