File size: 4,983 Bytes
fde6d70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | 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()
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