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2e913c2 | 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 | """Run the four ClimateBench emulators on the held-out scenario."""
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.climatebench import ClimateBench
from train import ClimateDataset, device_from_config
def main() -> None:
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")
model = ClimateBench(**checkpoint["model_config"]).to(device)
model.load_state_dict(checkpoint["model"])
model.eval()
dataset = ClimateDataset(ROOT / config["data"]["root"] / "test.npz", config)
loader = DataLoader(dataset, batch_size=1, shuffle=False)
predictions = []
with torch.no_grad():
for inputs, _ in loader:
predictions.append(model(inputs.to(device)).cpu().numpy())
prediction = np.concatenate(predictions).astype(np.float32)
if not np.isfinite(prediction).all():
raise FloatingPointError("inference produced NaN or Inf")
source = dataset.data
output = ROOT / config["paths"]["inference"]
output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(output, predictions=prediction, targets=source["targets"], years=source["years"],
latitude=source["latitude"], longitude=source["longitude"],
target_names=source["target_names"], scenario=source["scenario"],
target_aggregation=source["target_aggregation"], storage_layout=np.asarray("NCHW"),
format_version=source["format_version"],
evaluation_start_year=source["evaluation_start_year"],
evaluation_end_year=source["evaluation_end_year"])
print(f"predictions={output.relative_to(ROOT)} shape={prediction.shape} test_batches={len(predictions)}")
if __name__ == "__main__":
main()
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