Clean artifacts and update example pipeline

example/plot_loss.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""Plot training loss curves from train_log.csv."""
+
+import argparse
+import csv
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Plot loss curves from train_log.csv")
+    base_dir = Path(__file__).resolve().parent
+    parser.add_argument(
+        "--log",
+        default=str(base_dir / "results" / "train_log.csv"),
+        help="Path to train_log.csv",
+    )
+    parser.add_argument(
+        "--out",
+        default=str(base_dir / "results" / "train_loss.png"),
+        help="Output PNG path",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    log_path = Path(args.log)
+    if not log_path.exists():
+        raise SystemExit("missing log file: %s" % log_path)
+
+    steps = []
+    loss = []
+    loss_cont = []
+    loss_disc = []
+
+    with log_path.open("r", encoding="ascii", newline="") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            steps.append(int(row["step"]))
+            loss.append(float(row["loss"]))
+            loss_cont.append(float(row["loss_cont"]))
+            loss_disc.append(float(row["loss_disc"]))
+
+    if not steps:
+        raise SystemExit("no rows in log file: %s" % log_path)
+
+    plt.figure(figsize=(8, 5))
+    plt.plot(steps, loss, label="total")
+    plt.plot(steps, loss_cont, label="continuous")
+    plt.plot(steps, loss_disc, label="discrete")
+    plt.xlabel("step")
+    plt.ylabel("loss")
+    plt.title("Training Loss")
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(args.out, dpi=150)
+    print("saved", args.out)
+
+
+if __name__ == "__main__":
+    main()