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fix: Codex 全量审查修正

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Critical:
- C1: assemble_mode 'plain' → 'ids'（合法枚举值）
- C2: question_id 加入 task_type slug 避免跨题型冲突

Important/Minor:
- generate_one 移除未用的 embed_fn/similarity_threshold 参数
- config.py 注释 11→12 同步
- 测试 question_id 断言更新

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
iomgaa
2026-07-09 07:40:27 -04:00
parent fad8147d71
commit f57ee45dc0
4 changed files with 7 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files
app/harness/config.py
+1 -1
View File
@@ -267,7 +267,7 @@ def _validate_minibatch(config: RunConfig) -> None:
关键实现细节:
val_size 必须 >= eval_min_per_class * _VIDEO_MME_TASK_TYPE_COUNT，保证验证池
能为 Video-MME 的全部 11 个任务类型各保底 eval_min_per_class 题。
能为 Video-MME 的全部 12 个任务类型各保底 eval_min_per_class 题。
"""
if config.batch_size <= 0:
raise ValueError(f"batch_size 必须 > 0，实际: {config.batch_size}")
app/question_gen/synthesizer.py
+1 -5
View File
@@ -624,7 +624,7 @@ def parse_vlm_response(
raise ValueError(f"answer 必须是 A/B/C/D 之一，实际 '{answer}': {raw[:200]}")
return {
"question_id": f"gen-{video_id}-{seq:03d}",
"question_id": f"gen-{video_id}-{task_type.lower().replace(' ', '_')}-{seq:03d}",
"question": data["question"],
"options": list(options),
"answer": answer,
@@ -669,7 +669,6 @@ def is_duplicate(
async def generate_one(
vlm: VLMProvider,
embed_fn: Callable[[str | list[str]], np.ndarray],
tree: TreeIndex,
video_id: str,
task_type: str,
@@ -678,7 +677,6 @@ async def generate_one(
exemplars: list[GeneratedQuestion],
used_node_ids: set[str],
max_retries: int,
similarity_threshold: float,
rng: random.Random,
session_id: str,
) -> GeneratedQuestion | None:
@@ -695,7 +693,6 @@ async def generate_one(
参数:
vlm: VLM 调用端口。
embed_fn: 文本嵌入函数（本函数内未使用，由调用方统一去重）。
tree: 三层树索引。
video_id: 所属视频标识。
task_type: 12 种 Video-MME 题型之一。
@@ -703,7 +700,6 @@ async def generate_one(
exemplars: 少样本示例列表。
used_node_ids: 已用节点 ID 集合。
max_retries: 最大重试次数。
similarity_threshold: 余弦相似度阈值（本函数内未使用）。
rng: 可控随机数生成器。
session_id: 会话 ID（传递给 VLM 遥测）。
tests/unit/test_synthesizer.py
+4 -15
View File
@@ -346,14 +346,14 @@ class TestParseVlmResponse:
assert result["question"] == "What?"
assert result["answer"] == "A"
assert len(result["options"]) == 4
assert result["question_id"] == "gen-vid1-001"
assert result["question_id"] == "gen-vid1-object_recognition-001"
def test_json_in_code_block(self) -> None:
"""从 markdown 代码块中提取 JSON。"""
raw = '```json\n{"question": "Q?", "options": ["A. 1", "B. 2", "C. 3", "D. 4"], "answer": "B"}\n```'
result = parse_vlm_response(raw, "vid1", "Object Recognition", 2)
assert result["question"] == "Q?"
assert result["question_id"] == "gen-vid1-002"
assert result["question_id"] == "gen-vid1-object_recognition-002"
def test_invalid_json_raises(self) -> None:
"""非 JSON 文本应抛出 ValueError。"""
@@ -382,7 +382,7 @@ class TestParseVlmResponse:
"""seq 应按 3 位零填充格式化到 question_id 中。"""
raw = '{"question": "Q?", "options": ["A. 1", "B. 2", "C. 3", "D. 4"], "answer": "C"}'
result = parse_vlm_response(raw, "video_abc", "Action Reasoning", 42)
assert result["question_id"] == "gen-video_abc-042"
assert result["question_id"] == "gen-video_abc-action_reasoning-042"
# ---------------------------------------------------------------------------
@@ -448,14 +448,9 @@ class TestGenerateOne:
content='{"question":"Q?","options":["A. 1","B. 2","C. 3","D. 4"],"answer":"A"}',
)
def embed_fn(t: str | list[str]) -> np.ndarray:
shape = (1, 4) if isinstance(t, str) else (len(t), 4)
return np.zeros(shape, dtype=np.float32)
tree, vid = self._load_test_tree()
result = await generate_one(
vlm=vlm,
embed_fn=embed_fn,
tree=tree,
video_id=vid,
task_type="Object Recognition",
@@ -463,12 +458,11 @@ class TestGenerateOne:
exemplars=[],
used_node_ids=set(),
max_retries=3,
similarity_threshold=0.85,
rng=random.Random(42),
session_id="test",
)
assert result is not None
assert result.question_id == f"gen-{vid}-001"
assert result.question_id == f"gen-{vid}-object_recognition-001"
assert result.task_type == "Object Recognition"
assert result.source_nodes # non-empty
assert result.difficulty == "medium"
@@ -479,13 +473,9 @@ class TestGenerateOne:
vlm = AsyncMock()
vlm.chat_with_images.return_value = MagicMock(content="invalid")
def embed_fn(t: str | list[str]) -> np.ndarray:
return np.zeros((1, 4), dtype=np.float32)
tree, vid = self._load_test_tree()
result = await generate_one(
vlm=vlm,
embed_fn=embed_fn,
tree=tree,
video_id=vid,
task_type="Object Recognition",
@@ -493,7 +483,6 @@ class TestGenerateOne:
exemplars=[],
used_node_ids=set(),
max_retries=2,
similarity_threshold=0.85,
rng=random.Random(42),
session_id="test",
)
tools/generate_questions.py
+1 -7
View File
@@ -594,7 +594,7 @@ async def _run_inference_for_questions(
ocr=None,
verify_vision=False,
anchor=False,
assemble_mode="plain",
assemble_mode="ids",
)
result = await run_inference(
group,
@@ -861,7 +861,6 @@ async def _run_generate(args: argparse.Namespace) -> None:
async def _generate_with_sem(
vlm_client,
embed_fn_inner,
tree,
video_id,
task_type,
@@ -870,7 +869,6 @@ async def _run_generate(args: argparse.Namespace) -> None:
exemplars,
used_node_ids,
max_retries_inner,
similarity_threshold_inner,
rng_inner,
session_id,
):
@@ -878,7 +876,6 @@ async def _run_generate(args: argparse.Namespace) -> None:
async with sem:
return await generate_one(
vlm_client,
embed_fn_inner,
tree,
video_id,
task_type,
@@ -886,7 +883,6 @@ async def _run_generate(args: argparse.Namespace) -> None:
exemplars=exemplars,
used_node_ids=used_node_ids,
max_retries=max_retries_inner,
similarity_threshold=similarity_threshold_inner,
rng=rng_inner,
session_id=session_id,
)
@@ -929,7 +925,6 @@ async def _run_generate(args: argparse.Namespace) -> None:
for _attempt in range(max_retries):
candidate = await _generate_with_sem(
vlm,
embed_fn,
tree,
video_id,
task_type,
@@ -937,7 +932,6 @@ async def _run_generate(args: argparse.Namespace) -> None:
exemplars=exemplars,
used_node_ids=used_node_ids,
max_retries_inner=1,
similarity_threshold_inner=similarity_threshold,
rng_inner=rng,
session_id=session_id,
)