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38f9214 | 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 | """Compute compact MetNet-3 fake-data metrics and visualizations."""
import json
import sys
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import torch
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.metnet3_heads import decode_bins
if __name__ == "__main__":
result_dir = ROOT / "result"
prediction = torch.load(result_dir / "prediction.pt", map_location="cpu", weights_only=True)
target = torch.load(result_dir / "target.pt", map_location="cpu", weights_only=True)
precipitation = decode_bins(prediction["precipitation_logits"])
precipitation_target = target["precipitation"].float() / (prediction["precipitation_logits"].shape[2] - 1)
ground = decode_bins(prediction["ground_logits"])
ground_target = target["ground"].float() / (prediction["ground_logits"].shape[2] - 1)
metrics = {
"precipitation_mae_normalized": float((precipitation - precipitation_target).abs().mean()),
"ground_mae_normalized": float((ground - ground_target).abs().mean()),
"hrrr_rmse_normalized": float(torch.sqrt((prediction["hrrr_regression"] - target["hrrr"]).square().mean())),
"probability_sum_error": float((prediction["precipitation_logits"].softmax(2).sum(2) - 1).abs().max()),
}
(result_dir / "metrics.json").write_text(json.dumps(metrics, indent=2) + "\n")
fig, axes = plt.subplots(1, 3, figsize=(10, 3))
for ax, data, title in zip(
axes,
(precipitation[0, 0], precipitation_target[0, 0], (precipitation - precipitation_target)[0, 0]),
("Prediction", "Target", "Error"),
):
image = ax.imshow(data, cmap="RdBu_r")
ax.set_title(title)
ax.axis("off")
plt.colorbar(image, ax=ax, shrink=0.75)
plt.tight_layout()
plt.savefig(result_dir / "comparison.png", dpi=150)
plt.close()
print(json.dumps(metrics, indent=2))
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