iomgaa
90f17e330e
- prompt 组装：system(角色+题型+约束+few-shot) + user(card+字幕+干扰项) - VLM 响应解析：JSON 直接 + markdown code block 回退，四选一 schema 校验 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
382 lines
15 KiB
Python
382 lines
15 KiB
Python
"""synthesizer 模块单元测试 — AnchorContext + 题型映射常量 + sample_anchor + prompt 构造 + VLM 解析。"""
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from __future__ import annotations
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import dataclasses
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import random
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from pathlib import Path
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import pytest
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from app.question_gen.synthesizer import (
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TASK_TYPE_LEVEL_MAP,
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AnchorContext,
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TaskTypeSpec,
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build_generation_prompt,
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parse_vlm_response,
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sample_anchor,
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)
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from app.tree.index import TreeIndex
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from core.types import GeneratedQuestion
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ALL_12_TYPES = [
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"Object Recognition",
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"Attribute Perception",
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"OCR Problems",
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"Spatial Reasoning",
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"Spatial Perception",
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"Action Recognition",
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"Action Reasoning",
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"Counting Problem",
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"Temporal Perception",
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"Temporal Reasoning",
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"Information Synopsis",
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"Object Reasoning",
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]
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class TestTaskTypeLevelMap:
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"""TASK_TYPE_LEVEL_MAP 覆盖性与结构测试。"""
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def test_covers_all_12_types(self) -> None:
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"""映射表必须覆盖全部 12 种 Video-MME 题型。"""
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assert set(TASK_TYPE_LEVEL_MAP.keys()) == set(ALL_12_TYPES)
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def test_no_extra_types(self) -> None:
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"""映射表不得包含 12 种标准题型之外的条目。"""
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assert len(TASK_TYPE_LEVEL_MAP) == 12
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def test_all_values_are_task_type_spec(self) -> None:
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"""每个映射值必须是 TaskTypeSpec 实例。"""
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for task_type, spec in TASK_TYPE_LEVEL_MAP.items():
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assert isinstance(spec, TaskTypeSpec), f"{task_type} 映射值类型错误: {type(spec)}"
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def test_level_values_valid(self) -> None:
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"""每个 spec 的 level 必须是合法层级标识。"""
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valid_levels = {"L1", "L2", "L3", "L1-L2"}
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for task_type, spec in TASK_TYPE_LEVEL_MAP.items():
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assert spec.level in valid_levels, (
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f"{task_type} 层级 '{spec.level}' 不在 {valid_levels}"
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)
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def test_context_fields_non_empty(self) -> None:
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"""每个 spec 的 context_fields 至少有一个字段。"""
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for task_type, spec in TASK_TYPE_LEVEL_MAP.items():
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assert len(spec.context_fields) >= 1, f"{task_type} 的 context_fields 为空"
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class TestAnchorContext:
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"""AnchorContext 数据类测试。"""
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def test_frozen(self) -> None:
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"""AnchorContext 是不可变的。"""
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ctx = AnchorContext(
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node_id="L3_001",
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card_text="A person walks into a room",
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frame_paths=["/data/frames/001.jpg"],
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subtitle="Hello there",
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distractor_texts=["A car drives by"],
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)
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assert ctx.node_id == "L3_001"
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assert ctx.card_text == "A person walks into a room"
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assert ctx.frame_paths == ["/data/frames/001.jpg"]
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assert ctx.subtitle == "Hello there"
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assert ctx.distractor_texts == ["A car drives by"]
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def test_mutation_raises(self) -> None:
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"""frozen dataclass 拒绝赋值修改。"""
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ctx = AnchorContext(
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node_id="L3_001",
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card_text="test",
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frame_paths=["a.jpg"],
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subtitle="",
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distractor_texts=["other node"],
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)
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try:
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ctx.node_id = "L3_002" # type: ignore[misc]
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raise AssertionError("应抛出 FrozenInstanceError")
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except dataclasses.FrozenInstanceError:
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pass
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def test_empty_subtitle_allowed(self) -> None:
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"""subtitle 可以为空字符串。"""
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ctx = AnchorContext(
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node_id="L2_010",
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card_text="scene card",
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frame_paths=[],
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subtitle="",
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distractor_texts=[],
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)
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assert ctx.subtitle == ""
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def test_multiple_frame_paths(self) -> None:
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"""frame_paths 可包含多个路径。"""
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paths = ["/data/f1.jpg", "/data/f2.jpg", "/data/f3.jpg"]
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ctx = AnchorContext(
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node_id="L2_005",
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card_text="multi-frame event",
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frame_paths=paths,
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subtitle="Dialogue line",
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distractor_texts=["other1", "other2"],
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)
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assert len(ctx.frame_paths) == 3
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class TestTaskTypeSpec:
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"""TaskTypeSpec 数据类测试。"""
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def test_frozen(self) -> None:
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"""TaskTypeSpec 是不可变的。"""
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spec = TaskTypeSpec(
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level="L3",
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needs_frames=True,
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frame_count="1",
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context_fields=("frame_summary",),
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)
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try:
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spec.level = "L2" # type: ignore[misc]
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raise AssertionError("应抛出 FrozenInstanceError")
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except dataclasses.FrozenInstanceError:
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pass
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def test_context_fields_is_tuple(self) -> None:
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"""context_fields 应为 tuple（不可变）。"""
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for task_type, spec in TASK_TYPE_LEVEL_MAP.items():
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assert isinstance(spec.context_fields, tuple), (
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f"{task_type} 的 context_fields 不是 tuple"
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)
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# ---------------------------------------------------------------------------
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# sample_anchor 测试
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# ---------------------------------------------------------------------------
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def _load_test_tree() -> tuple[TreeIndex, str]:
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"""加载真实测试树（store/videos/ 下第一棵）。"""
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videos_dir = Path("store/videos")
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first_vid = sorted(videos_dir.iterdir())[0]
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tree = TreeIndex.load_json(str(first_vid / "tree.json"))
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return tree, first_vid.name
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class TestSampleAnchor:
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"""sample_anchor 锚节点采样测试（基于真实树数据）。"""
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def test_l3_type_returns_single_frame(self) -> None:
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"""L3 题型（Object Recognition）应返回恰好 1 帧。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Object Recognition", set(), random.Random(42))
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assert len(ctx.frame_paths) == 1
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assert ctx.node_id.startswith("L") or "_L3_" in ctx.node_id
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assert len(ctx.distractor_texts) > 0
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def test_l2_type_returns_multiple_frames(self) -> None:
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"""L2 题型（Action Reasoning）应返回 2-3 帧。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Action Reasoning", set(), random.Random(42))
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assert 2 <= len(ctx.frame_paths) <= 3
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def test_temporal_perception_zero_or_one_frame(self) -> None:
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"""Temporal Perception 特殊处理：0-1 帧，且 card_text 包含 time_range。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Temporal Perception", set(), random.Random(42))
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assert len(ctx.frame_paths) <= 1
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assert "time_range" in ctx.card_text.lower() or "time" in ctx.card_text.lower()
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def test_information_synopsis_uses_all_l2(self) -> None:
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"""Information Synopsis 必须使用目标 L1 下所有 L2 子节点。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Information Synopsis", set(), random.Random(42))
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# 找到被选中的 L1，验证 frame_paths 数量 == 该 L1 下全部 L2 数量
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chosen_l1 = next(r for r in tree.roots if r.id == ctx.node_id)
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assert len(ctx.frame_paths) == len(chosen_l1.children)
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def test_l1_type_l2_nodes_in_time_order(self) -> None:
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"""Temporal Reasoning 应返回 >=3 帧且 card_text 有实质内容。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Temporal Reasoning", set(), random.Random(42))
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assert len(ctx.frame_paths) >= 3
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assert len(ctx.card_text) > 20
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def test_used_node_ids_excluded(self) -> None:
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"""used_node_ids 中的节点不应被再次选中。"""
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tree, _vid = _load_test_tree()
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rng = random.Random(42)
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ctx1 = sample_anchor(tree, "Object Recognition", set(), rng)
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ctx2 = sample_anchor(tree, "Object Recognition", {ctx1.node_id}, random.Random(43))
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assert ctx2.node_id != ctx1.node_id
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def test_insufficient_nodes_raises(self) -> None:
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"""所有候选节点均被排除时应抛出 ValueError。"""
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tree, _vid = _load_test_tree()
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all_l3_ids: set[str] = set()
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for root in tree.roots:
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for l2 in root.children:
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for l3 in l2.children:
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all_l3_ids.add(l3.id)
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with pytest.raises(ValueError, match="锚节点不足"):
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sample_anchor(tree, "Object Recognition", all_l3_ids, random.Random(42))
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def test_object_reasoning_l1_l2_type(self) -> None:
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"""Object Reasoning (L1-L2) 应选 2-3 个 L2 并按时间排序。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Object Reasoning", set(), random.Random(42))
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assert 2 <= len(ctx.frame_paths) <= 3
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assert len(ctx.card_text) > 10
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def test_spatial_reasoning_context_fields(self) -> None:
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"""Spatial Reasoning 的 card_text 应包含 spatial_layout 字段。"""
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tree, _vid = _load_test_tree()
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ctx = sample_anchor(tree, "Spatial Reasoning", set(), random.Random(42))
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# context_fields 包含 spatial_layout，card_text 必须出现该字段名
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assert "spatial_layout" in ctx.card_text
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assert len(ctx.card_text) > 20
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# ---------------------------------------------------------------------------
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# build_generation_prompt 测试
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# ---------------------------------------------------------------------------
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class TestBuildGenerationPrompt:
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"""build_generation_prompt 消息结构与内容测试。"""
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def test_messages_structure(self) -> None:
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"""带 exemplars 时，system 包含题型和示例，image_paths 来自 anchor。"""
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anchor = AnchorContext(
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node_id="L3_001",
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card_text="A person typing on a laptop",
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frame_paths=["store/videos/test/frames/L1_000_L2_000_L3_000.jpg"],
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subtitle="Hello world",
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distractor_texts=["Another person walking in park"],
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)
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exemplars = [
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GeneratedQuestion(
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question_id="ex-1",
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video_id="v1",
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task_type="Object Recognition",
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question="What object?",
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options=("A. Cat", "B. Dog", "C. Bird", "D. Fish"),
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answer="A",
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source_nodes=(),
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difficulty="medium",
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),
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]
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messages, image_paths = build_generation_prompt(
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"Object Recognition",
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anchor,
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exemplars,
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)
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assert messages[0]["role"] == "system"
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assert "Object Recognition" in messages[0]["content"]
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assert any("What object?" in str(m) for m in messages)
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assert image_paths == anchor.frame_paths
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def test_distractor_in_user_message(self) -> None:
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"""干扰项文本应出现在 user message 中。"""
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anchor = AnchorContext(
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node_id="L2_003",
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card_text="Event card text",
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frame_paths=["a.jpg", "b.jpg"],
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subtitle="",
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distractor_texts=["Distractor node summary"],
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)
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messages, _ = build_generation_prompt("Action Reasoning", anchor, [])
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user_msg = [m for m in messages if m["role"] == "user"][0]
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assert "Distractor node summary" in user_msg["content"]
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def test_no_exemplars_no_crash(self) -> None:
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"""exemplars 为空时不应报错，system 消息中无示例段落。"""
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anchor = AnchorContext(
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node_id="L3_010",
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card_text="Some card text",
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frame_paths=["frame.jpg"],
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subtitle="",
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distractor_texts=[],
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)
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messages, image_paths = build_generation_prompt("OCR Problems", anchor, [])
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assert len(messages) >= 2
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assert image_paths == ["frame.jpg"]
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def test_subtitle_included_when_non_empty(self) -> None:
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"""非空 subtitle 应出现在 user message 中。"""
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anchor = AnchorContext(
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node_id="L3_002",
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card_text="Card text here",
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frame_paths=["f.jpg"],
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subtitle="This is a subtitle line",
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distractor_texts=[],
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)
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messages, _ = build_generation_prompt("Attribute Perception", anchor, [])
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user_msg = [m for m in messages if m["role"] == "user"][0]
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assert "This is a subtitle line" in user_msg["content"]
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def test_empty_subtitle_not_in_user_message(self) -> None:
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"""空 subtitle 不应在 user message 中产生 subtitle 段落。"""
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anchor = AnchorContext(
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node_id="L3_003",
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card_text="Card",
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frame_paths=["f.jpg"],
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subtitle="",
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distractor_texts=[],
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)
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messages, _ = build_generation_prompt("OCR Problems", anchor, [])
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user_msg = [m for m in messages if m["role"] == "user"][0]
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# 不应出现空的 subtitle 标记
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assert "字幕" not in user_msg["content"]
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# ---------------------------------------------------------------------------
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# parse_vlm_response 测试
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# ---------------------------------------------------------------------------
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class TestParseVlmResponse:
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"""parse_vlm_response 解析与校验测试。"""
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def test_valid_json(self) -> None:
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"""合法 JSON 正常解析，question_id 格式正确。"""
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raw = '{"question": "What?", "options": ["A. X", "B. Y", "C. Z", "D. W"], "answer": "A"}'
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result = parse_vlm_response(raw, "vid1", "Object Recognition", 1)
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assert result["question"] == "What?"
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assert result["answer"] == "A"
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assert len(result["options"]) == 4
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assert result["question_id"] == "gen-vid1-001"
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def test_json_in_code_block(self) -> None:
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"""从 markdown 代码块中提取 JSON。"""
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raw = '```json\n{"question": "Q?", "options": ["A. 1", "B. 2", "C. 3", "D. 4"], "answer": "B"}\n```'
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result = parse_vlm_response(raw, "vid1", "Object Recognition", 2)
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assert result["question"] == "Q?"
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assert result["question_id"] == "gen-vid1-002"
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def test_invalid_json_raises(self) -> None:
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"""非 JSON 文本应抛出 ValueError。"""
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with pytest.raises(ValueError, match="VLM 返回"):
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parse_vlm_response("not json", "vid1", "Object Recognition", 1)
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def test_missing_fields_raises(self) -> None:
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"""缺少必需字段应抛出 ValueError。"""
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raw = '{"question": "What?"}'
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with pytest.raises(ValueError):
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parse_vlm_response(raw, "vid1", "Object Recognition", 1)
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def test_options_must_be_four(self) -> None:
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"""options 非 4 项应抛出 ValueError。"""
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raw = '{"question": "Q?", "options": ["A. X", "B. Y"], "answer": "A"}'
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with pytest.raises(ValueError, match="4"):
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parse_vlm_response(raw, "vid1", "Object Recognition", 1)
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def test_answer_must_be_abcd(self) -> None:
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"""answer 不在 A-D 范围应抛出 ValueError。"""
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raw = '{"question": "Q?", "options": ["A. X", "B. Y", "C. Z", "D. W"], "answer": "E"}'
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with pytest.raises(ValueError, match="A.*D"):
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parse_vlm_response(raw, "vid1", "Object Recognition", 1)
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def test_seq_zero_padded(self) -> None:
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"""seq 应按 3 位零填充格式化到 question_id 中。"""
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raw = '{"question": "Q?", "options": ["A. 1", "B. 2", "C. 3", "D. 4"], "answer": "C"}'
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result = parse_vlm_response(raw, "video_abc", "Action Reasoning", 42)
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assert result["question_id"] == "gen-video_abc-042"
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