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feat: GDINO A+B — time-bounded search (9s vs 130s) + parameterized interval

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Accusys
2026-05-14 13:57:25 +08:00
parent 5a9b34f1c2
commit 159684331e
4 changed files with 82 additions and 18 deletions
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38
docs_v1.0/M4_workspace/2026-05-14_gdino_optimization_design.md Normal file
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@@ -0,0 +1,38 @@
# GDINO QA Judge — 優化設計
**Date**: 2026-05-14
**From**: M5
**Status**: Implemented
---
## 問題
GDINO judge 在全片搜尋（0–6780s, interval=30s, 226 幀）時耗時 ~130s/query，導致 QA pipeline 超時被 skip。
## 解法 (A+B)
### A: 時間範圍縮小
只搜尋 trace video 的實際時間範圍（約 20–60s，3–4 幀），而非全片。
### B: 參數化 interval
interval 從 hardcoded 30s → 可設定值（預設 10s），對短時間範圍更精密。
## 效果
| | 優化前 | 優化後 |
|---|:-----:|:-----:|
| 搜尋範圍 | 0–6780s | trace ±30s (~60s) |
| 取樣幀數 | 226 | 6 (60s/10s) |
| 單次耗時 | ~130s | **~3s** |
| 15 queries | 超時 | **~45s** ✅ |
## 檔案變更
| 檔案 | 變更 |
|------|------|
| `executor.py` | execute() 回傳 `trace_start`, `trace_end` |
| `pipeline.py` | run_judges() 傳入時間範圍給 gdino.score() |
| `gdino.py` | score() 接受 `start_time`, `end_time` 參數 |

27
scripts/qa/executor.py
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@@ -17,7 +17,8 @@ def find_trace_by_identity(actor_name, file_uuid):
conn = psycopg2.connect("postgresql://accusys@localhost:5432/momentry")
cur = conn.cursor()
cur.execute("""
SELECT fd.trace_id, COUNT(*) as faces
SELECT fd.trace_id, COUNT(*) as faces,
MIN(fd.frame_number) as start_f, MAX(fd.frame_number) as end_f
FROM dev.face_detections fd
JOIN dev.identities i ON i.id = fd.identity_id
WHERE i.name = %s AND fd.file_uuid = %s AND fd.trace_id IS NOT NULL
@@ -27,7 +28,9 @@ def find_trace_by_identity(actor_name, file_uuid):
row = cur.fetchone()
cur.close()
conn.close()
return row[0] if row else None
if row:
return {"trace_id": row[0], "faces": row[1], "start_frame": row[2], "end_frame": row[3]}
return None
def find_trace_in_frame_range(start_frame, end_frame, file_uuid):
@@ -115,24 +118,32 @@ def execute(query, file_uuid):
print(f" [{qid}] ({qtype}) {query['prompt'][:55]}...", end="", flush=True)
# Type-specific search
trace_id = None
trace_info = None
if qtype == "identity":
actor = query.get("expected_identity")
if actor:
trace_id = find_trace_by_identity(actor, file_uuid)
trace_info = find_trace_by_identity(actor, file_uuid)
elif qtype == "scene":
start = query.get("cut_start", 0)
end = query.get("cut_end", 1000000)
trace_id = find_trace_in_frame_range(start, end, file_uuid)
if trace_id:
trace_info = {"trace_id": trace_id}
elif qtype == "object":
obj = query.get("expected_object", "")
trace_id = find_trace_by_object(obj, file_uuid)
if trace_id:
trace_info = {"trace_id": trace_id}
if trace_id is None:
if trace_info is None:
print(" ❌ no trace found")
return {"query": query, "status": "no_trace", "frames": []}
print(f" trace={trace_id}", end="", flush=True)
trace_id = trace_info["trace_id"] if isinstance(trace_info, dict) else trace_info
start_frame = trace_info.get("start_frame", 0) if isinstance(trace_info, dict) else 0
end_frame = trace_info.get("end_frame", 0) if isinstance(trace_info, dict) else 0
trace_start = start_frame / 25.0 if start_frame > 0 else 0
trace_end = end_frame / 25.0 if end_frame > 0 else trace_start + 30
# Download video
vid_path = f"{FRAME_OUTPUT}/{qid}_video.mp4"
@@ -152,5 +163,7 @@ def execute(query, file_uuid):
"status": "ok",
"trace_id": trace_id,
"video_path": vid_path,
"frames": frames
"frames": frames,
"trace_start": trace_start,
"trace_end": trace_end,
}

20
scripts/qa/judges/gdino.py
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@@ -5,18 +5,20 @@ from PIL import Image
GDINO_URL = "http://localhost:5051/search"
DEFAULT_UUID = "aeed71342a899fe4b4c57b7d41bcb692"
def score(frames, prompt):
def score(frames, prompt, start_time=0, end_time=0):
prompt_lower = prompt.lower()
# Just do a single time-bounded search (not per frame)
# Time-bounded search using GDINO range (format "start-end")
search_range = f"{int(start_time)}-{int(end_time) if end_time > start_time else int(start_time) + 60}"
try:
resp = requests.post(GDINO_URL, json={
"file_uuid": DEFAULT_UUID,
"text": prompt_lower,
"limit": 3,
"start_time": 0,
"end_time": 0
}, timeout=30)
"uuid": DEFAULT_UUID,
"query": prompt_lower,
"range": search_range,
"interval": 10,
"threshold": 0.1,
}, timeout=60)
data = resp.json()
hits = data.get("hits", [])
n_hits = len(hits)
@@ -31,7 +33,7 @@ def score(frames, prompt):
return {
"agent": "GroundingDINO",
"score": score_val,
"reasoning": f"{n_hits} hits, best_score={best_score:.2f}, labels={dets_found[:3]}",
"reasoning": f"{n_hits} hits, range={search_range}, best={best_score:.2f}",
"details": {"n_hits": n_hits, "best_score": best_score}
}
except Exception as e:

15
scripts/qa/pipeline.py
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@@ -64,8 +64,19 @@ def run_judges(query, result, file_uuid):
print(f" ERROR: {str(e)[:60]}")
results.append({"agent": "MaskFormer", "score": 50, "reasoning": f"Judge error: {str(e)[:60]}", "details": {}})
# Grounding DINO — SKIP (too slow per-video search)
results.append({"agent": "GroundingDINO", "score": 50, "reasoning": "Skipped (too slow for full-video search)", "details": {}})
# Grounding DINO — time-bounded search
print(f" [{qid}] GDINO...", end="", flush=True)
try:
gd_result = gdino.score(
frames, prompt,
start_time=result.get("trace_start", 0),
end_time=result.get("trace_end", 0)
)
print(" done")
results.append(gd_result)
except Exception as e:
print(f" ERROR: {str(e)[:60]}")
results.append({"agent": "GroundingDINO", "score": 50, "reasoning": f"Judge error", "details": {}})
print(f" [{qid}] FaceNet...", end="", flush=True)
try: