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fix

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pikaliov
2026-07-08 11:31:51 +03:00
parent 4e76326c4f
commit 531a65c2f0
2 changed files with 83 additions and 28 deletions
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45
collect_results.py
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@@ -42,19 +42,33 @@ def load_experiment(exp_dir: Path) -> dict | None:
if not history:
return None
# primary метрика — q2g R@1 (drone→satellite), см. протокол §6.2;
# eval_recall@* дублируют q2g, eval_mAP дублирует q2g_mAP.
best = max(history, key=lambda r: r.get("eval_recall@1", 0))
latest = history[-1]
return {
"dir": exp_dir.name,
"seed": config.get("seed", "?"),
"text_levels": " + ".join(config.get("text_levels", ["?"])),
"epochs_done": latest["epoch"],
"epochs_total": config.get("epochs", "?"),
"best_epoch": best["epoch"],
# q2g (primary); split — только official cross-area (§5.3),
# same-area прогонов нет, поэтому cross−same gap не считается.
"best_R@1": best.get("eval_recall@1", 0),
"best_R@5": best.get("eval_recall@5", 0),
"best_R@10": best.get("eval_recall@10", 0),
"best_AP": best.get("eval_AP", 0),
"best_R@1%": best.get("eval_q2g_recall@1%", 0),
"best_mAP": best.get("eval_mAP", 0), # настоящий AP, не MRR
"best_median_rank": best.get("eval_q2g_median_rank", 0),
"best_mean_rank": best.get("eval_q2g_mean_rank", 0),
# g2q (satellite→drone), та же best-эпоха
"best_g2q_R@1": best.get("eval_g2q_recall@1", 0),
"best_g2q_R@5": best.get("eval_g2q_recall@5", 0),
"best_g2q_R@10": best.get("eval_g2q_recall@10", 0),
"best_g2q_R@1%": best.get("eval_g2q_recall@1%", 0),
"best_g2q_mAP": best.get("eval_g2q_mAP", 0),
"latest_loss": latest.get("train_loss", 0),
"latest_R@1": latest.get("eval_recall@1", 0),
"avg_epoch_time": sum(r.get("elapsed_s", 0) for r in history) / len(history),
@@ -63,14 +77,17 @@ def load_experiment(exp_dir: Path) -> dict | None:
def collect_version(version: str) -> list[dict]:
"""Собрать результаты одной версии (v1 или v2)."""
"""Собрать результаты одной версии (v1 или v2).
Структура: outputs/<version>/<seed>/<exp_dir>/history.json.
"""
version_dir = OUTPUTS_DIR / version
if not version_dir.exists():
print(f"⚠️ Папка не найдена: {version_dir}")
return []
results = []
for exp_dir in sorted(version_dir.iterdir()):
for exp_dir in sorted(version_dir.glob("*/*")):
if exp_dir.is_dir() and (exp_dir / "history.json").exists():
data = load_experiment(exp_dir)
if data:
@@ -91,14 +108,14 @@ def print_table(results: list[dict], version: str) -> None:
results.sort(key=lambda r: -r["best_R@1"])
header = (
f"{'Levels':<24} {'Prog':<8} "
f"{'BestEp':>6} {'R@1':>7} {'R@5':>7} {'R@10':>7} "
f"{'AP':>7} {'Loss':>8} {'Time':>6}"
f"{'Levels':<24} {'Seed':>5} {'Prog':<8} {'BestEp':>6} "
f"{'R@1':>7} {'R@5':>7} {'R@10':>7} {'R@1%':>7} {'mAP':>7} "
f"{'medR':>6} {'g2q@1':>7} {'Loss':>8} {'Time':>6}"
)
sep = "─" * len(header)
print(sep)
print(f" {version.upper()} Results")
print(f" {version.upper()} Results (q2g = primary; g2q@1 = satellite→drone)")
print(sep)
print(header)
print(sep)
@@ -106,9 +123,10 @@ def print_table(results: list[dict], version: str) -> None:
for r in results:
prog = f"{r['epochs_done']}/{r['epochs_total']}"
print(
f"{r['text_levels']:<24} {prog:<8} "
f"{r['best_epoch']:>6} {r['best_R@1']:>7.4f} {r['best_R@5']:>7.4f} "
f"{r['best_R@10']:>7.4f} {r['best_AP']:>7.4f} "
f"{r['text_levels']:<24} {str(r['seed']):>5} {prog:<8} {r['best_epoch']:>6} "
f"{r['best_R@1']:>7.4f} {r['best_R@5']:>7.4f} {r['best_R@10']:>7.4f} "
f"{r['best_R@1%']:>7.4f} {r['best_mAP']:>7.4f} {r['best_median_rank']:>6.0f} "
f"{r['best_g2q_R@1']:>7.4f} "
f"{r['latest_loss']:>8.4f} {r['avg_epoch_time']:>5.0f}s"
)
@@ -123,8 +141,11 @@ def print_table(results: list[dict], version: str) -> None:
def save_csv(results: list[dict], version: str) -> None:
fields = [
"version", "text_levels", "epochs_done", "epochs_total",
"best_epoch", "best_R@1", "best_R@5", "best_R@10", "best_AP",
"version", "seed", "text_levels", "epochs_done", "epochs_total",
"best_epoch",
"best_R@1", "best_R@5", "best_R@10", "best_R@1%", "best_mAP",
"best_median_rank", "best_mean_rank",
"best_g2q_R@1", "best_g2q_R@5", "best_g2q_R@10", "best_g2q_R@1%", "best_g2q_mAP",
"latest_loss", "latest_R@1", "avg_epoch_time", "dir",
]
path = RESULTS_DIR / f"results_{version}.csv"