File size: 4,911 Bytes
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import numpy as np
import pandas as pd
from ultralytics import YOLO
ROOT = Path("/media/rtx5090/Scripts/runs/detect/training_stats/sota_study/SynthRender_Robotics/")
DATA = "/media/z4/DATASETS/SIM2REAL_ROBOTICS/yolo/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=512,
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()
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