Upload change_lineage.py

change_lineage.py

@@ -0,0 +1,117 @@
+import json
+import pickle
+import numpy as np
+import os
+
+
+def convert_lineage_to_split_genealogy(
+        lineage_json_path: str,
+        save_pkl_path: str,
+        max_children: int = 8,  # merge后每个粗点平均对应4个细点，留余量
+):
+    """
+    正确的转换方向：
+
+    lineage["L1"][i] = [a, b, c, d]
+      含义：L1第i个粗点 由 L0中的a,b,c,d细点 merge而来
+      反转：L1第i个粗点 → 子节点是 L0中的 a,b,c,d
+
+    genealogy[1]['children_ids'] shape (N_L1粗, max_children)
+      含义：L1第i个粗点，在L0中对应的细节点索引
+
+    使用时：
+      父节点 = L3/L2/L1 的粗粒度高斯点
+      子节点 = 更细粒度的高斯点（向原始方向展开）
+      序列：L3(粗)→L2(细)→L1(更细)→L0(原始)
+    """
+    print(f"[convert] 加载 {lineage_json_path}")
+    with open(lineage_json_path, 'r') as f:
+        lineage = json.load(f)
+
+    genealogy = {}
+
+    # 注意方向：粗→细，所以 key 含义变了
+    # genealogy[1]: L1粗节点 → L0细节点
+    # genealogy[2]: L2粗节点 → L1细节点
+    # genealogy[3]: L3粗节点 → L2细节点
+    level_map = {
+        "L1": 1,
+        "L2": 2,
+        "L3": 3,
+    }
+
+    for level_name, gen_key in level_map.items():
+        if level_name not in lineage:
+            continue
+
+        # merge_list[粗点索引] = [细点索引, ...]
+        merge_list = lineage[level_name]
+        n_coarse = len(merge_list)
+
+        # 统计每个粗点有多少个细点
+        child_counts = [len(parents) for parents in merge_list]
+        max_actual = max(child_counts)
+        avg_actual = np.mean(child_counts)
+        print(f"[convert] {level_name}（粗→细方向）：")
+        print(f"           粗节点数={n_coarse}")
+        print(f"           每个粗节点平均对应细节点数={avg_actual:.2f}")
+        print(f"           最多子节点数={max_actual}")
+
+        # 直接用 merge_list 构建 children_ids
+        # merge_list[i] 就是 L(k-1)_粗节点i 对应的所有 L(k-2)_细节点索引
+        actual_max = min(max_actual, max_children)
+        children_ids = np.full((n_coarse, actual_max), fill_value=-1, dtype=np.int32)
+
+        truncated = 0
+        for coarse_idx, fine_indices in enumerate(merge_list):
+            n = min(len(fine_indices), actual_max)
+            children_ids[coarse_idx, :n] = fine_indices[:n]
+            if len(fine_indices) > actual_max:
+                truncated += 1
+
+        if truncated:
+            print(f"           ⚠️  {truncated} 个粗节点子节点数超过{actual_max}被截断")
+
+        genealogy[gen_key] = {
+            'children_ids': children_ids,  # (N_coarse, max_children)
+        }
+
+    with open(save_pkl_path, 'wb') as f:
+        pickle.dump(genealogy, f, protocol=4)
+
+    size_mb = os.path.getsize(save_pkl_path) / 1024 / 1024
+    print(f"\n[convert] 已保存 → {save_pkl_path}  ({size_mb:.2f} MB)")
+    return genealogy
+
+
+def verify(lineage_json_path, genealogy_pkl_path, n_sample=5):
+    with open(lineage_json_path, 'r') as f:
+        lineage = json.load(f)
+    with open(genealogy_pkl_path, 'rb') as f:
+        genealogy = pickle.load(f)
+
+    print("\n[verify] 抽查：粗节点的子节点应与 lineage 一致")
+    for level_name, gen_key in [("L1", 1), ("L2", 2), ("L3", 3)]:
+        if level_name not in lineage or gen_key not in genealogy:
+            continue
+        merge_list = lineage[level_name]
+        children_ids = genealogy[gen_key]['children_ids']
+        sample_idx = np.random.choice(len(merge_list), min(n_sample, len(merge_list)), replace=False)
+        errors = 0
+        for i in sample_idx:
+            expected = set(merge_list[i])
+            actual = set(int(x) for x in children_ids[i] if x >= 0)
+            if not expected.issubset(actual):
+                print(f"  ❌ {level_name}[{i}]: expected={expected}, actual={actual}")
+                errors += 1
+        if errors == 0:
+            print(f"  ✅ {level_name}：{len(sample_idx)} 个粗节点验证通过")
+
+
+if __name__ == '__main__':
+    genealogy = convert_lineage_to_split_genealogy(
+        lineage_json_path="outputs/lineage.json",
+        save_pkl_path="outputs/genealogy.pkl",
+        max_children=4,
+    )
+    verify("outputs/lineage.json", "outputs/genealogy.pkl", n_sample=10)