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ef2ae28 | 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 | """Infer next-day center-pixel wildfire probabilities."""
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.firecubenet import FireCubeNet
from train import WildfireDataset, 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 = WildfireDataset(ROOT / config["data"]["root"] / "test.npz", config)
loader = DataLoader(dataset, batch_size=int(config["train"]["batch_size"]), shuffle=False)
model = FireCubeNet(**checkpoint["model_config"]).to(device)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
mean = torch.from_numpy(checkpoint["channel_mean"]).to(device).view(1, 1, -1, 1, 1)
std = torch.from_numpy(checkpoint["channel_std"]).to(device).view(1, 1, -1, 1, 1)
probabilities = []
with torch.no_grad():
for inputs, _ in loader:
probabilities.append(torch.sigmoid(model((inputs.to(device) - mean) / std)).cpu().numpy())
probabilities = np.concatenate(probabilities).astype(np.float32)
if probabilities.shape != dataset.data["labels"].shape or not np.isfinite(probabilities).all():
raise FloatingPointError("invalid inference probabilities")
output = ROOT / config["paths"]["inference_dir"] / "predictions.npz"
output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(
output, probabilities=probabilities, labels=dataset.data["labels"],
timestamps=dataset.data["timestamps_unix_s"], coords=dataset.data["coords"],
format_version=np.asarray(config["data"]["format_version"]),
)
print(f"predictions={output.relative_to(ROOT)} shape={probabilities.shape}")
if __name__ == "__main__":
main()
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