scripts/result.py

"""Evaluate Prithvi reconstruction and visualize temporal HLS samples."""

import json
from pathlib import Path

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import yaml


ROOT = Path(__file__).resolve().parents[1]


def main():
    config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
    prediction = np.load(ROOT / config["paths"]["inference_dir"] / "predictions.npz")
    pixels, reconstruction = prediction["pixels"], prediction["reconstruction"]
    error = np.abs(reconstruction - pixels)
    per_frame = error.mean(axis=(0, 1, 3, 4))
    embeddings = prediction["embedding"]
    metrics = {
        "samples": int(len(pixels)),
        "masked_patch_mse": float(prediction["masked_patch_mse"]),
        "reconstruction_mae": float(error.mean()),
        "per_frame_reconstruction_mae": [float(value) for value in per_frame],
        "mean_embedding_norm": float(np.linalg.norm(embeddings, axis=1).mean()),
    }
    output = ROOT / config["paths"]["evaluation_dir"]
    output.mkdir(parents=True, exist_ok=True)
    (output / "metrics.json").write_text(json.dumps(metrics, indent=2) + "\n")
    figure, axes = plt.subplots(3, int(config["data"]["frames"]), figsize=(12, 8))
    for frame in range(int(config["data"]["frames"])):
        source = pixels[0, [2, 1, 0], frame].transpose(1, 2, 0)
        rebuilt = reconstruction[0, [2, 1, 0], frame].transpose(1, 2, 0)
        low, high = np.percentile(source, (2, 98))
        source = np.clip((source - low) / max(high - low, 1e-6), 0, 1)
        rebuilt = np.clip((rebuilt - low) / max(high - low, 1e-6), 0, 1)
        axes[0, frame].imshow(source)
        axes[1, frame].imshow(rebuilt)
        axes[2, frame].imshow(error[0, :, frame].mean(axis=0), cmap="magma")
        axes[0, frame].set_title(f"time {frame + 1}")
        for axis in axes[:, frame]:
            axis.axis("off")
    axes[0, 0].set_ylabel("input")
    axes[1, 0].set_ylabel("reconstruction")
    axes[2, 0].set_ylabel("absolute error")
    figure.tight_layout()
    figure.savefig(output / "comparison.png", dpi=150)
    plt.close(figure)
    print(f"metrics={output.relative_to(ROOT) / 'metrics.json'}")


if __name__ == "__main__":
    main()