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a00d152 | 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 | """Run all six storm-surge regressors on station-day test records."""
import sys
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.utils.data import DataLoader
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.globalsurgeml import GlobalSurgeML
from train import CONFIGURATIONS, SurgeDataset, device_from_config
def main():
config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
device = device_from_config(config)
checkpoint = torch.load(ROOT / config["paths"]["checkpoint"], map_location=device, weights_only=False)
if checkpoint["format_version"] != config["data"]["format_version"]:
raise ValueError("checkpoint and data format versions differ")
dataset = SurgeDataset(ROOT / config["data"]["root"] / "test.npz", config)
loader = DataLoader(dataset, batch_size=int(config["train"]["batch_size"]), shuffle=False)
predictions = {name: [] for name in CONFIGURATIONS}
models = {}
for index, (name, (feature_key, method)) in enumerate(CONFIGURATIONS.items()):
model = GlobalSurgeML(dataset.data[feature_key].shape[1], method, checkpoint["model_config"], int(config["seed"]) + index)
model.load_state_dict(checkpoint["model"][name])
models[name] = model.to(device).eval()
with torch.no_grad():
for batch in loader:
for name, (feature_key, _) in CONFIGURATIONS.items():
mean = torch.from_numpy(checkpoint["feature_means"][name]).to(device)
scale = torch.from_numpy(checkpoint["feature_scales"][name]).to(device)
predictions[name].append(models[name]((batch[feature_key].to(device) - mean) / scale).cpu().numpy())
payload = {f"predictions_{name}": np.concatenate(values) for name, values in predictions.items()}
for value in payload.values():
if not np.isfinite(value).all():
raise FloatingPointError("inference produced NaN or Inf")
source = dataset.data
output = ROOT / config["paths"]["inference_dir"] / "predictions.npz"
output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(output, **payload, targets_m=source["targets_m"], gtsr_m=source["gtsr_m"],
latitude_degrees=source["latitude_degrees"], longitude_degrees=source["longitude_degrees"],
timestamps_unix_s=source["timestamps_unix_s"], target_unit=np.asarray("m"),
format_version=np.asarray(config["data"]["format_version"]))
print(f"predictions={output.relative_to(ROOT)} shape={source['targets_m'].shape}")
if __name__ == "__main__":
main()
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