Scripts/test_all_iris.py

from pathlib import Path
import numpy as np
import pandas as pd
from ultralytics import YOLO

ROOT = Path("/media/rtx5090/Scripts/runs/detect/training_stats/train_size_study/SUBSETS_4K_Physics_Intrinsics_RGB_Exp/")
DATA = "/media/rtx5090/IRIS/Real_Test_Set/dataset.yaml"
#DATA = "/media/rtx5090/IRIS_Test_Remapped/dataset.yaml"

PROJECT = ROOT / "evaluation"
CSV_PATH = PROJECT / "evaluation_results.csv"

METRICS = [
    "mAP50",
    "mAP50_95",
    "precision",
    "recall",
    "f1",
]

# Bootstrap settings
N_BOOT = 10000
CI_LEVEL = 0.95
BOOT_SEED = 42


def summarize_group(group, metrics=METRICS, n_boot=N_BOOT, ci=CI_LEVEL, seed=BOOT_SEED):
    rng = np.random.default_rng(seed)
    out = {}
    for metric in metrics:
        data = group[metric].to_numpy(dtype=float)
        n = data.size

        out[(metric, "mean")] = data.mean()
        out[(metric, "std")] = data.std(ddof=1) if n > 1 else np.nan
        out[(metric, "min")] = data.min()
        out[(metric, "max")] = data.max()
        out[(metric, "n")] = n

        if n >= 2:
            idx = rng.integers(0, n, size=(n_boot, n))
            boot_means = data[idx].mean(axis=1)
            lo, hi = np.percentile(
                boot_means, [(1 - ci) / 2 * 100, (1 - (1 - ci) / 2) * 100]
            )
        else:
            lo, hi = np.nan, np.nan

        out[(metric, "ci_lo")] = lo
        out[(metric, "ci_hi")] = hi

    return pd.Series(out)


def main():

    PROJECT.mkdir(exist_ok=True)

    results = []

    # Structure:
    # ROOT/
    # ├── experiment/
    # │   ├── computer/
    # │   │   ├── run_1/
    # │   │   ├── run_2/

    for experiment_dir in sorted(ROOT.iterdir()):

        if not experiment_dir.is_dir():
            continue

        if experiment_dir.name == "evaluation":
            continue

        experiment = experiment_dir.name

        # Computer/workstation level
        for computer_dir in sorted(experiment_dir.iterdir()):

            if not computer_dir.is_dir():
                continue

            computer = computer_dir.name

            # Run level
            for run in sorted(computer_dir.glob("run_*")):

                if not run.is_dir():
                    continue

                weights = run / "weights" / "best.pt"

                if not weights.exists():
                    print(
                        f"Skipping {experiment}/{computer}/{run.name}: "
                        "best.pt not found"
                    )
                    continue

                print(
                    f"\nEvaluating "
                    f"{experiment} / {computer} / {run.name}"
                )

                try:
                    model = YOLO(weights)

                    metrics = model.val(
                        data=DATA,
                        split="test",
                        imgsz=1024,
                        batch=28,
                        device=0,
                        workers=8,
                        project=str(PROJECT),
                        name=f"{experiment}_{computer}_{run.name}",
                        exist_ok=True,
                        save_json=True,
                        plots=True,
                        verbose=True,
                    )

                except Exception as e:
                    print(
                        f"Failed evaluating "
                        f"{experiment}/{computer}/{run.name}"
                    )
                    print(e)
                    continue

                box = metrics.box

                results.append(
                    {
                        "experiment": experiment,
                        "computer": computer,
                        "run": run.name,
                        "mAP50": float(box.map50),
                        "mAP50_95": float(box.map),
                        "precision": float(box.mp),
                        "recall": float(box.mr),
                        "f1": float(box.f1.mean()),
                    }
                )

    # Save per-run results
    df = pd.DataFrame(results)

    df.to_csv(CSV_PATH, index=False)

    print(f"\nSaved: {CSV_PATH}")

    if df.empty:
        print("No evaluation results found.")
        return

    # Statistics per experiment (mean, std, min, max, n, bootstrap CI)
    summary = df.groupby("experiment").apply(summarize_group)

    summary_path = PROJECT / "evaluation_summary.csv"

    summary.to_csv(summary_path)

    print(f"Saved: {summary_path}")

    print("\nSummary:")
    print(summary)

    # Statistics per experiment and computer (mean, std, min, max, n, bootstrap CI)
    computer_summary = (
        df.groupby(["experiment", "computer"])
        .apply(summarize_group)
    )

    computer_summary_path = PROJECT / "evaluation_summary_by_computer.csv"

    computer_summary.to_csv(computer_summary_path)

    print(f"\nSaved: {computer_summary_path}")


if __name__ == "__main__":
    main()