iomgaa
d3be9b1322
- L3Card/L2Card 新增 subtitle: str 字段（L1Card 不加） - L3Node 移除 subtitle 字段（数据迁入 Card） - assign_subtitles_voronoi 改写 Card.subtitle + L2 聚合 - _collect_card_strings 增加 skip_fields 排除 subtitle - _node_full_text/_node_anchored_text 保持 字幕:/[sN] 语义 - get_subtitle 读 Card.subtitle（L2/L3） - verify.py/synthesizer.py: l3.subtitle → l3.card.subtitle - 迁移脚本 tools/migrate_subtitle_to_card.py（幂等，300 棵树已迁移） - 9→6 个测试文件适配（3 个无需改动） Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
228 lines
6.6 KiB
Python
228 lines
6.6 KiB
Python
"""TreeIndex 数据结构单元测试。"""
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from __future__ import annotations
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import json
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import numpy as np
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import pytest
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from app.tree.index import (
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IndexMeta,
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L1Card,
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L1Node,
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L2Card,
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L2Node,
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L3Card,
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L3Node,
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TreeIndex,
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)
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def _make_l3(idx: int = 0) -> L3Node:
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return L3Node(
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id=f"l1_0_l2_0_l3_{idx}",
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card=L3Card(
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frame_summary=f"帧{idx}描述",
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visible_entities=["实体A"],
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ongoing_actions=["动作A"],
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visible_text=["文字A"],
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spatial_layout="居中构图",
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visual_attributes={"lighting": "明亮"},
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),
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timestamp=idx * 2.0,
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frame_path=f"frames/l1_0_l2_0_l3_{idx}.jpg",
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)
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def _make_l2(n_l3: int = 2) -> L2Node:
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return L2Node(
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id="l1_0_l2_0",
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card=L2Card(
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event_description="事件描述",
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entities=["实体B"],
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actions=["动作B"],
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action_subjects=["主体B"],
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visible_text=["文字B"],
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spatial_relations="左右排列",
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state_changes=None,
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),
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time_range=(0.0, 60.0),
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children=[_make_l3(i) for i in range(n_l3)],
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)
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def _make_l1(n_l2: int = 1, n_l3: int = 2) -> L1Node:
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return L1Node(
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id="l1_0",
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card=L1Card(
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scene_summary="场景摘要",
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main_setting="室内",
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key_entities=["实体C"],
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main_actions=["动作C"],
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topic_keywords=["关键词"],
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visible_text=["文字C"],
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temporal_flow="从左到右",
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),
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time_range=(0.0, 600.0),
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children=[_make_l2(n_l3) for _ in range(n_l2)],
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)
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def _make_index(n_l1: int = 1) -> TreeIndex:
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meta = IndexMeta(source_path="/test/video.mp4", modality="video")
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return TreeIndex(metadata=meta, roots=[_make_l1() for _ in range(n_l1)])
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class TestCards:
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def test_l3_card_frozen(self):
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card = L3Card(
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frame_summary="desc",
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visible_entities=[],
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ongoing_actions=[],
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visible_text=[],
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spatial_layout="",
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visual_attributes={},
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)
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with pytest.raises(AttributeError):
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card.frame_summary = "changed"
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def test_l2_card_fields(self):
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card = L2Card(
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event_description="evt",
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entities=[],
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actions=[],
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action_subjects=[],
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visible_text=[],
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spatial_relations="",
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state_changes=None,
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)
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assert card.event_description == "evt"
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assert card.state_changes is None
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def test_l1_card_fields(self):
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card = L1Card(
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scene_summary="scene",
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main_setting="outdoor",
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key_entities=["e"],
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main_actions=["a"],
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topic_keywords=["k"],
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visible_text=["t"],
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temporal_flow="flow",
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)
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assert card.scene_summary == "scene"
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class TestNodes:
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def test_l3_description_property(self):
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node = _make_l3()
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assert node.description == node.card.frame_summary
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def test_l2_description_property(self):
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node = _make_l2()
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assert node.description == node.card.event_description
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def test_l1_summary_property(self):
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node = _make_l1()
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assert node.summary == node.card.scene_summary
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def test_l3_default_embedding_none(self):
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node = _make_l3()
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assert node.embedding is None
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def test_l3_card_subtitle_default_empty(self):
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node = _make_l3()
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assert node.card.subtitle == ""
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class TestTreeIndex:
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def test_is_embedded_false_by_default(self):
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index = _make_index()
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assert not index.is_embedded
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def test_embed_all(self):
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index = _make_index()
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def fake_embed(texts):
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if isinstance(texts, str):
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texts = [texts]
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return np.random.randn(len(texts), 4).astype(np.float32)
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index.embed_all(fake_embed, "test-model", 4)
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assert index.is_embedded
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assert index.metadata.embed_model == "test-model"
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assert index.metadata.embed_dim == 4
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def test_l1_embeddings_shape(self):
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index = _make_index(n_l1=2)
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def fake_embed(texts):
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if isinstance(texts, str):
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texts = [texts]
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return np.random.randn(len(texts), 4).astype(np.float32)
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index.embed_all(fake_embed, "test-model", 4)
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m = index.l1_embeddings()
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assert m.shape == (2, 4)
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def test_get_node(self):
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index = _make_index()
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node = index.get_node(0, 0, 1)
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assert node.id == "l1_0_l2_0_l3_1"
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def test_get_node_out_of_bounds(self):
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index = _make_index()
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with pytest.raises(IndexError):
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index.get_node(99, 0, 0)
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class TestSerialization:
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def test_json_roundtrip(self, tmp_path):
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index = _make_index()
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path = tmp_path / "tree.json"
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index.save_json(str(path))
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loaded = TreeIndex.load_json(str(path))
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assert len(loaded.roots) == 1
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assert loaded.roots[0].id == "l1_0"
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assert loaded.roots[0].card.scene_summary == "场景摘要"
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assert loaded.roots[0].children[0].children[0].card.frame_summary == "帧0描述"
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def test_json_roundtrip_with_embedding(self, tmp_path):
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index = _make_index()
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def fake_embed(texts):
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if isinstance(texts, str):
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texts = [texts]
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return np.random.randn(len(texts), 4).astype(np.float32)
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index.embed_all(fake_embed, "test-model", 4)
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path = tmp_path / "tree_emb.json"
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index.save_json(str(path), include_embedding=True)
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loaded = TreeIndex.load_json(str(path))
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assert loaded.is_embedded
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np.testing.assert_array_almost_equal(
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loaded.roots[0].embedding, index.roots[0].embedding, decimal=5
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)
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def test_l1_json_roundtrip(self, tmp_path):
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from app.tree.index import load_l1_json, save_l1_json
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l1 = _make_l1()
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path = tmp_path / "l1_0.json"
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save_l1_json(str(path), l1)
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loaded = load_l1_json(str(path))
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assert loaded.id == "l1_0"
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assert len(loaded.children) == 1
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assert len(loaded.children[0].children) == 2
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def test_id_uniqueness_validation(self, tmp_path):
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"""重复 ID 在反序列化时应报错。"""
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index = _make_index()
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d = index.to_dict()
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d["roots"].append(d["roots"][0])
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path = tmp_path / "dup.json"
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with open(path, "w") as f:
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json.dump(d, f)
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with pytest.raises(ValueError, match="重复"):
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TreeIndex.load_json(str(path))
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