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b871dba | 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 | #!/usr/bin/env python3
"""Evaluate a PointCFD checkpoint on the fixed test split."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
from typing import Any, Dict
import numpy as np
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
project_root_string = str(PROJECT_ROOT)
if project_root_string in sys.path:
sys.path.remove(project_root_string)
sys.path.insert(0, project_root_string)
from models import PointNetCFD, count_trainable_parameters # noqa: E402
from scripts.common import ( # noqa: E402
AVAILABLE_SAMPLE_COUNT,
PAPER_SAMPLE_COUNT,
PointCFDDataset,
choose_device,
configured_paths,
evaluate_model,
load_checkpoint,
load_config,
load_data_and_splits,
make_loader,
resolve_path,
selected_indices,
set_deterministic_seed,
write_json,
write_npz,
)
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(
"--checkpoint",
type=Path,
default=None,
help="Checkpoint path (default: paths.checkpoint from config)",
)
parser.add_argument("--device", default="auto", help="auto, cpu, cuda, or cuda:N")
parser.add_argument("--batch-size", type=int, default=None)
parser.add_argument("--num-workers", type=int, default=None)
parser.add_argument(
"--output-dir",
type=Path,
default=None,
help="Output directory (default: paths.results_dir from config)",
)
parser.add_argument(
"--max-cases",
type=int,
default=None,
help="Evaluate only the first N fixed test cases (smoke testing only)",
)
return parser.parse_args()
def validate_checkpoint_contract(checkpoint: Dict[str, Any], config: Dict[str, Any]) -> None:
expected_metadata = {
"source_channels": list(config["data"]["source_channels"]),
"input_names": list(config["data"]["input_names"]),
"target_names": list(config["data"]["target_names"]),
"input_indices": list(config["data"]["input_indices"]),
"target_indices": list(config["data"]["target_indices"]),
}
for key, expected in expected_metadata.items():
if list(checkpoint.get(key, [])) != expected:
raise ValueError(f"Checkpoint {key} metadata does not match config: {key}")
target_min = np.asarray(checkpoint.get("target_min"), dtype=np.float32)
target_max = np.asarray(checkpoint.get("target_max"), dtype=np.float32)
if target_min.shape != (3,) or target_max.shape != (3,):
raise ValueError("Checkpoint target normalization must contain three variables")
if np.any(target_max <= target_min):
raise ValueError("Checkpoint target normalization spans must be positive")
def main() -> None:
args = parse_args()
config = load_config(args.config)
seed = int(config["training"]["seed"])
set_deterministic_seed(seed)
device = choose_device(args.device)
paths = configured_paths(config, PROJECT_ROOT)
checkpoint_path = (
resolve_path(PROJECT_ROOT, str(args.checkpoint))
if args.checkpoint is not None
else paths["checkpoint"]
)
output_dir = (
resolve_path(PROJECT_ROOT, str(args.output_dir))
if args.output_dir is not None
else paths["results_dir"]
)
checkpoint = load_checkpoint(checkpoint_path, device)
validate_checkpoint_contract(checkpoint, config)
checkpoint_model_config = checkpoint.get("model_config", config["model"])
model = PointNetCFD(
input_dim=int(checkpoint_model_config["input_dim"]),
output_dim=int(checkpoint_model_config["output_dim"]),
).to(device=device, dtype=torch.float32)
model.load_state_dict(checkpoint["model_state_dict"], strict=True)
print(
f"checkpoint={checkpoint_path} epoch={checkpoint.get('epoch')} device={device} "
f"trainable_parameters={count_trainable_parameters(model)}",
flush=True,
)
data, splits, _ = load_data_and_splits(config, PROJECT_ROOT)
test_indices = selected_indices(splits["test"], args.max_cases)
target_min = np.asarray(checkpoint["target_min"], dtype=np.float32)
target_max = np.asarray(checkpoint["target_max"], dtype=np.float32)
test_dataset = PointCFDDataset(
data,
test_indices,
config["data"]["input_indices"],
config["data"]["target_indices"],
target_min,
target_max,
)
batch_size = int(
args.batch_size if args.batch_size is not None else config["training"]["batch_size"]
)
num_workers = int(
args.num_workers if args.num_workers is not None else config["training"]["num_workers"]
)
test_loader = make_loader(
test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
seed=seed,
pin_memory=device.type == "cuda",
)
target_names = list(config["data"]["target_names"])
metrics, arrays = evaluate_model(
model,
test_loader,
device,
target_min,
target_max,
target_names,
float(config["evaluation"]["relative_l2_epsilon"]),
)
metrics_payload: Dict[str, Any] = {
"checkpoint": str(checkpoint_path),
"checkpoint_epoch": int(checkpoint.get("epoch", -1)),
"device": str(device),
"evaluated_test_cases": len(test_dataset),
"fixed_test_split_cases": int(splits["test"].size),
"available_sample_count": AVAILABLE_SAMPLE_COUNT,
"paper_sample_count": PAPER_SAMPLE_COUNT,
"metrics": metrics,
"paper_reference_mean_relative_l2": config["evaluation"][
"paper_reference_mean_relative_l2"
],
"dataset_limitation": (
"The supplied dataset has 2215 cases rather than the paper's 2595; "
"these metrics are a best-available subset reproduction."
),
}
output_dir.mkdir(parents=True, exist_ok=True)
metrics_path = output_dir / "test_metrics.json"
predictions_path = output_dir / "predictions.npz"
write_json(metrics_path, metrics_payload)
write_npz(
predictions_path,
coordinates=arrays["coordinates"],
predictions=arrays["predictions"],
targets=arrays["targets"],
case_indices=arrays["case_indices"],
target_names=np.asarray(target_names),
target_min=target_min,
target_max=target_max,
)
print(
"normalized_mse={:.9e}".format(metrics["normalized_mse"]), flush=True
)
for name in target_names:
relative = metrics["relative_l2"][name]
print(
f"variable={name} rmse={metrics['rmse'][name]:.9e} "
f"relative_l2_mean={relative['mean']:.9e} "
f"relative_l2_max={relative['max']:.9e} "
f"relative_l2_min={relative['min']:.9e}",
flush=True,
)
print(f"metrics={metrics_path}", flush=True)
print(f"predictions={predictions_path}", flush=True)
if __name__ == "__main__":
main()
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