| """ |
| Aggregate every band-combination run into one comparison table. |
| |
| Reads, for each all_data/checkpoint*/ : |
| training_history.csv -> best val_dice_coef / val_iou_m (+ epoch) |
| run_config.json -> band list, n_bands (written by train.py) |
| and the matching all_data/result*/metrics.json -> test metrics (written by evaluate.py). |
| |
| python results_table.py |
| |
| Prints a table sorted by test_dice and writes all_data/runs_comparison.csv. |
| """ |
|
|
| import os |
| import glob |
| import json |
| import pandas as pd |
|
|
| ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "all_data") |
|
|
|
|
| def _best_val(hist_csv): |
| df = pd.read_csv(hist_csv) |
| if "val_dice_coef" not in df.columns or len(df) == 0: |
| return None |
| r = df.loc[df["val_dice_coef"].idxmax()] |
| return (round(float(r["val_dice_coef"]), 4), |
| round(float(r["val_iou_m"]), 4) if "val_iou_m" in df.columns else None, |
| int(r["epoch"])) |
|
|
|
|
| def main(): |
| rows = [] |
| for cdir in sorted(glob.glob(os.path.join(ROOT, "checkpoint*"))): |
| base = os.path.basename(cdir) |
| name = "all" if base == "checkpoint" else base[len("checkpoint_"):] |
| hist = os.path.join(cdir, "training_history.csv") |
| if not os.path.exists(hist): |
| continue |
| bv = _best_val(hist) |
|
|
| rc = os.path.join(cdir, "run_config.json") |
| cfg = json.load(open(rc)) if os.path.exists(rc) else {} |
| bands = cfg.get("bands") |
| n_bands = cfg.get("n_bands", len(bands) if bands else "?") |
|
|
| rdir = os.path.join(ROOT, "result" if name == "all" else f"result_{name}") |
| mp = os.path.join(rdir, "metrics.json") |
| m = json.load(open(mp)) if os.path.exists(mp) else {} |
|
|
| rows.append({ |
| "run_name": name, |
| "n_bands": n_bands, |
| "val_dice": bv[0] if bv else None, |
| "val_iou": bv[1] if bv else None, |
| "best_ep": bv[2] if bv else None, |
| "test_dice": m.get("test_dice"), |
| "test_iou": m.get("test_iou"), |
| "precision": m.get("test_precision"), |
| "recall": m.get("test_recall"), |
| "img_mIoU": m.get("per_image_mean_iou"), |
| "bands": ", ".join(bands) if bands else "(all 35)", |
| }) |
|
|
| if not rows: |
| print("No runs found under all_data/checkpoint*/ (train something first).") |
| return |
|
|
| df = pd.DataFrame(rows).sort_values( |
| "test_dice", ascending=False, na_position="last").reset_index(drop=True) |
|
|
| pd.set_option("display.width", 240) |
| pd.set_option("display.max_columns", 30) |
| print(df.drop(columns=["bands"]).to_string(index=False)) |
|
|
| out = os.path.join(ROOT, "runs_comparison.csv") |
| df.to_csv(out, index=False) |
| print(f"\nFull table (with band lists) -> {out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
