diff --git a/app/harness/config.py b/app/harness/config.py index d0ba2e3..13778a9 100644 --- a/app/harness/config.py +++ b/app/harness/config.py @@ -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}") diff --git a/app/question_gen/synthesizer.py b/app/question_gen/synthesizer.py index fbbb83e..de6c6d7 100644 --- a/app/question_gen/synthesizer.py +++ b/app/question_gen/synthesizer.py @@ -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）。 diff --git a/tests/unit/test_synthesizer.py b/tests/unit/test_synthesizer.py index f564ae4..5040b3b 100644 --- a/tests/unit/test_synthesizer.py +++ b/tests/unit/test_synthesizer.py @@ -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", ) diff --git a/tools/generate_questions.py b/tools/generate_questions.py index a646027..aee5bff 100644 --- a/tools/generate_questions.py +++ b/tools/generate_questions.py @@ -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, )