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"""Evaluate a trained CNO on the paper's ID and OOD Navier--Stokes sets."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
from typing import Any
import numpy as np
import torch
from torch.utils.data import DataLoader
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from models.FNO import build_model
from scripts.common import (
MinMaxNormalizer,
NavierStokesH5Dataset,
atomic_json_dump,
atomic_npz_save,
data_file,
load_config,
numeric_sample_ids,
project_path,
relative_l1_per_sample,
select_device,
set_reproducibility,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--config", default=str(PROJECT_ROOT / "config" / "config.yaml")
)
parser.add_argument("--checkpoint", default=None)
parser.add_argument("--device", default=None)
parser.add_argument("--batch-size", type=int, default=None)
parser.add_argument("--output-dir", default=None)
parser.add_argument(
"--splits",
nargs="+",
choices=("id", "ood"),
default=("id", "ood"),
)
return parser.parse_args()
def load_trained_model(
config: dict[str, Any], checkpoint_path: Path, device: torch.device
) -> tuple[torch.nn.Module, MinMaxNormalizer, dict[str, Any]]:
if not checkpoint_path.is_file():
raise FileNotFoundError(f"checkpoint not found: {checkpoint_path}")
checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
required = {
"model_state_dict",
"normalization",
"epoch",
"best_val_relative_l1",
}
missing = sorted(required.difference(checkpoint))
if missing:
raise KeyError(f"checkpoint is missing required keys: {missing}")
checkpoint_config = checkpoint.get("config", {})
if checkpoint_config and checkpoint_config.get("model") != config["model"]:
raise ValueError("checkpoint model configuration differs from config.yaml")
model = build_model(config["model"]).to(device)
model.load_state_dict(checkpoint["model_state_dict"], strict=True)
model.eval()
normalizer = MinMaxNormalizer.from_state(checkpoint["normalization"])
return model, normalizer, checkpoint
@torch.inference_mode()
def evaluate_split(
split_name: str,
model: torch.nn.Module,
loader: DataLoader,
normalizer: MinMaxNormalizer,
device: torch.device,
epsilon: float,
) -> tuple[dict[str, np.ndarray], dict[str, Any]]:
all_ids: list[np.ndarray] = []
all_inputs: list[np.ndarray] = []
all_targets: list[np.ndarray] = []
all_predictions: list[np.ndarray] = []
all_ratios: list[np.ndarray] = []
completed = 0
total = len(loader.dataset)
for batch_index, (inputs, targets, sample_ids) in enumerate(loader, start=1):
inputs = inputs.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
predictions = model(inputs)
physical_inputs = normalizer.denormalize_input(inputs)
physical_targets = normalizer.denormalize_output(targets)
physical_predictions = normalizer.denormalize_output(predictions)
ratios = relative_l1_per_sample(
physical_predictions, physical_targets, epsilon
)
all_ids.append(np.asarray(sample_ids, dtype=np.int64))
all_inputs.append(physical_inputs.cpu().numpy().astype(np.float32))
all_targets.append(physical_targets.cpu().numpy().astype(np.float32))
all_predictions.append(physical_predictions.cpu().numpy().astype(np.float32))
all_ratios.append(ratios.cpu().numpy().astype(np.float64))
completed += inputs.shape[0]
running = np.concatenate(all_ratios) * 100.0
print(
f"inference split={split_name} batch={batch_index}/{len(loader)} "
f"samples={completed}/{total} running_rel_l1_median={np.median(running):.6f}%",
flush=True,
)
arrays = {
"sample_ids": np.concatenate(all_ids),
"inputs": np.concatenate(all_inputs),
"targets": np.concatenate(all_targets),
"predictions": np.concatenate(all_predictions),
"relative_l1": np.concatenate(all_ratios),
}
percentages = arrays["relative_l1"] * 100.0
metrics = {
"sample_count": int(percentages.size),
"resolution": [int(arrays["inputs"].shape[-2]), int(arrays["inputs"].shape[-1])],
"relative_l1_median_percent": float(np.median(percentages)),
"relative_l1_mean_percent": float(np.mean(percentages)),
"relative_l1_std_percent": float(np.std(percentages)),
"relative_l1_min_percent": float(np.min(percentages)),
"relative_l1_max_percent": float(np.max(percentages)),
}
return arrays, metrics
def main() -> None:
args = parse_args()
config = load_config(args.config)
inference_config = config["inference"]
device = select_device(args.device or str(inference_config["device"]))
batch_size = int(
args.batch_size if args.batch_size is not None else inference_config["batch_size"]
)
if batch_size < 1:
raise ValueError("batch_size must be positive")
seed = int(config["experiment"]["seed"])
set_reproducibility(seed, bool(config["experiment"].get("deterministic", True)))
checkpoint_path = (
Path(args.checkpoint).expanduser().resolve()
if args.checkpoint
else project_path(config["paths"]["checkpoint"])
)
output_dir = (
Path(args.output_dir).expanduser().resolve()
if args.output_dir
else project_path(config["paths"]["results_dir"])
)
output_dir.mkdir(parents=True, exist_ok=True)
model, normalizer, checkpoint = load_trained_model(config, checkpoint_path, device)
split_specs = {
"id": ("id_test_file", "test_id"),
"ood": ("ood_test_file", "test_ood"),
}
all_metrics: dict[str, Any] = {
"schema_version": "cno-navier-stokes-metrics-v1",
"checkpoint": str(checkpoint_path),
"checkpoint_epoch": int(checkpoint["epoch"]),
"best_validation_relative_l1_percent": float(
checkpoint["best_val_relative_l1"]
),
"normalization": normalizer.state_dict(),
"paper_reference": config.get("paper_reference", {}),
"splits": {},
}
print(
f"inference device={device} checkpoint_epoch={checkpoint['epoch']} "
f"best_val={float(checkpoint['best_val_relative_l1']):.6f}%",
flush=True,
)
for split_name in args.splits:
filename_key, split_key = split_specs[split_name]
dataset = NavierStokesH5Dataset(
data_file(config, filename_key),
numeric_sample_ids(config["data"][split_key]),
normalizer,
str(config["data"]["input_key"]),
str(config["data"]["output_key"]),
)
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
num_workers=int(inference_config["num_workers"]),
pin_memory=device.type == "cuda",
persistent_workers=int(inference_config["num_workers"]) > 0,
)
arrays, metrics = evaluate_split(
split_name,
model,
loader,
normalizer,
device,
float(inference_config["metric_epsilon"]),
)
artifact_path = output_dir / f"{split_name}_predictions.npz"
atomic_npz_save(artifact_path, **arrays)
metrics["predictions_file"] = str(artifact_path)
all_metrics["splits"][split_name] = metrics
print(
f"evaluation split={split_name} n={metrics['sample_count']} "
f"rel_l1_median={metrics['relative_l1_median_percent']:.6f}% "
f"mean={metrics['relative_l1_mean_percent']:.6f}% "
f"std={metrics['relative_l1_std_percent']:.6f}% "
f"saved={artifact_path}",
flush=True,
)
metrics_path = output_dir / "metrics.json"
atomic_json_dump(all_metrics, metrics_path)
print(f"metrics saved path={metrics_path}", flush=True)
if __name__ == "__main__":
main()
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