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README.md
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README.md
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@ -16,7 +16,7 @@ afem/
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│ ├── mesh_refinement.py # ★ 核心：网格细化 RL 环境
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│ │ # - GNN 图观测构建（节点 + 边特征）
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│ │ # - continuous_sizing_field (score-based + budget) 细化策略
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│ │ # - spatial 奖励
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│ │ # - spatial 奖励 + step0 penalty 降权
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│ ├── helmholtz.py # Helmholtz FEM 求解器 + 残差误差估计
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│ ├── fem_problem.py # FEM 问题封装 + PDE 循环缓冲区
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│ ├── fem_util.py # 三角形面积、中点、随机采样、尺寸场函数
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@ -46,7 +46,7 @@ afem/
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- **边界条件**: SBC (Sommerfeld) `∂u/∂n = i·k·u`
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- **域**: 可配矩形域，初始网格密度自适应 + domain area 线性缩放：`N_init = N_base × (k/k_ref)^k_exponent × domain_area`。k_ref 和 k_exponent 均可通过 helmholtz config 配置（默认 k_exponent=2.0, k_ref=6.0），保证不同域尺寸下每单位面积单元数一致
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- 可配 exponent：^2 = P1 Helmholtz 理论最优 (污染误差 ∝ k²)。建议 N_base 配合 exponent 调整，使 N_init 约为 COMSOL 目标 (λ/10√ε_r) 的 30-50%，为 RL agent 留出充分细化空间
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- **介质区前渐近区边缘约束**: 介质内 λ_d = 2π/(k√ε_r) 更短，强制迭代细化至 h ≤ λ_d/N（默认 N=1.5，helmholtz.pre_asymptotic_N 可配）。约 1.5 点/波长，刚好跨过渐近区门槛，赋予初始网格基本相位解析能力但不过度消耗物理预算，为 RL agent 留出充分的选择性细化空间
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- **介质区前渐近区边缘约束**: 介质内 λ_d = 2π/(k√ε_r) 更短，强制迭代细化至 h ≤ λ_d/N（默认 N=2.0，helmholtz.pre_asymptotic_N 可配）。约 2 点/波长，赋予初始网格基本相位解析能力但不过度消耗物理预算，为 RL agent 留出充分的选择性细化空间
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- **后验误差**: 残差型 indicator（Ainsworth & Oden 风格），含单元内部残差 + 梯度跳变 + SBC 边界残差
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### 强化学习建模
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@ -54,29 +54,80 @@ afem/
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| 概念 | 对应实体 |
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|------|---------|
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| **智能体** | 每个三角形网格单元 |
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| **状态** | GNN 节点特征（几何 + PDE 残差 + 振幅 + 相位方向 + 物理参数，节点 13 维 + 边 1 维） |
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| **动作** | 1 维连续标量 x_i → score = -x_i 排序，在物理预算内 top-k 选细化单元（x 越小优先级越高） |
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| **奖励** | 局部子单元 η 的 log-ratio 改善（spatial: sum 聚合 / spatial_max: max 聚合）+ α 衰减全局 η log-ratio shaping |
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| **终止** | 达到最大步数或超过最大单元数 |
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| **状态** | GNN 节点特征（几何 + PDE 残差 + 振幅 + 相位方向 + 物理参数，节点 13 维 + 边 1 维） + 13 维全局统计向量 |
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| **动作** | 1 维连续标量 δ_i → `score_i = log(η_i + ε) + c·tanh(δ_i)` 降序 top-k 选择（η baseline + bounded Actor correction） |
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| **奖励** | 纯局部 r_local = log(η_old) − log(l2(η_child))，clip [0, 2.0]，减去动作惩罚（step0 降权）；未细化单元 r=0 |
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| **终止** | 达到最大步数、超过最大单元数、或 sel=0（无单元可选） |
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---
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## 网络架构
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双 GNN 架构（policy / value 各自独立基座）：
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双流 GNN 架构（Actor / Critic 共享基座，各自独立头）：
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```
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图观测 → MessagePassingBase → MLP → 动作分布 / value 标量
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├─ nn.Linear（嵌入）
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├─ MessagePassingStack（2 层消息传递 + GVN 全局广播，inner 残差 + LayerNorm）
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│ ├─ MessagePassingStep × N
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图观测 → MessagePassingBase → Actor MLP → δ_i (连续动作)
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├─ nn.Linear（嵌入） → Critic MLP → V(s) (标量)
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├─ MessagePassingStack（2 层消息传递 + MultiPoolGVN 全局广播）
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│ ├─ MessagePassingStep × 2
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│ │ ├─ EdgeModule: MLP([src | dst | edge_attr])
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│ │ └─ NodeModule: MLP([node | scatter(入边)])
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│ └─ GlobalVirtualNode (GVN): η_K 加权注意力池化 → 注意力门控广播
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│ h_V = Σ(η_v/Ση)·h_v，α_v = σ(W_att[h_v || h_V])，h_v ← h_v + α_v ⊙ W_V·h_V
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└─ 输出: 节点隐向量
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│ │ 内残差 + LayerNorm
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│ └─ MultiPoolGVN: 多策略池化 + 13 维全局统计 → 注意力门控广播
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│ Stage A: g_global = MLP(concat(g_mean, g_eta, global_stats))
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│ Stage B: α_v = σ(W_att[h_v || g_global])
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│ h_v ← h_v + scale · α_v ⊙ W_V · g_global
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└─ 输出: 节点隐向量 h_i
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Actor 输入: concat(h_i, g_global, rel_logeta, rel_area, is_top_eta, budget_stats) [2D+6]
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Critic 输入: concat(h_i, g_global) [2D]
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```
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### MultiPoolGVN — 多池化全局虚拟节点
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替代原始单一 η 加权 GVN，用多种池化策略聚合节点嵌入，拼接全局统计后生成 `g_global`：
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| 池化模式 | 公式 | 说明 |
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|----------|------|------|
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| `mean` | `g_mean = Σ h_v / N` | 均匀平均 |
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| `eta_softmax` | `g_eta = Σ (η_v / Ση) · h_v` | η 加权 softmax，高误差节点主导 |
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| `top_eta` | `g_top = mean(h_v : log η_v > μ + σ)` | top-η 节点均值（log 空间 >1σ） |
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配置项 `gvn_pooling: [mean, eta_softmax]`，可选加 `top_eta`。
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### Global Stats — 13 维图级统计
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每个图观测附带 13 维全局统计向量（`graph.global_stats`），用于 GVN 和 Actor/Critic 的条件输入：
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| 索引 | 名称 | 说明 |
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|------|------|------|
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| 0 | `remaining_ratio` | (N_budget − N_current) / N_budget |
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| 1 | `step_ratio` | current_step / max_steps |
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| 2 | `elem_ratio` | N_current / N_budget |
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| 3 | `logeta_mean` | log(η) 均值 |
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| 4 | `logeta_std` | log(η) 标准差 |
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| 5 | `logeta_max` | log(η) 最大值 |
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| 6 | `logeta_p90` | log(η) P90 |
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| 7 | `logeta_p75` | log(η) P75 |
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| 8 | `top10_eta_energy` | top 10% η² 能量占比 |
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| 9 | `eligible_ratio` | 面积安全阈值以上元素占比 |
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| 10 | `inside_eta_energy` | 散射体内 η² 能量占比 |
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| 11 | `outside_eta_energy` | 散射体外 η² 能量占比 |
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| 12 | `interface_eta_energy` | 界面附近 η² 能量占比 |
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### 全局条件化 Actor/Critic
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当 `use_global_conditioned_correction: true` 时，Actor 和 Critic 的输入额外拼接全局上下文：
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- **Actor**: `concat(h_i, g_global, rel_logeta, rel_area, is_top_eta, budget_stats)` → 维度 `2D + 6`
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- `rel_logeta`: `(log η_i − μ) / σ`，per-graph 标准化
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- `rel_area`: `log(area_i / mean_area)`，per-graph 相对面积
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- `is_top_eta`: `rel_logeta > 1.0` 的 0/1 标记
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- `budget_stats`: `[remaining_ratio, step_ratio, elem_ratio]`
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- **Critic**: `concat(h_i, g_global)` → 维度 `2D`
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### 超参数
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| 超参数 | 值 |
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|--------|-----|
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| latent_dim | 64 |
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| 边 dropout | 0.1 |
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| Actor MLP | 2 层 tanh |
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| Critic MLP | 2 层 tanh |
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| Optimizer | Adam, lr=3e-4, lr_decay=0.995 |
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| **动作分布** | `DiagGaussianDistribution`（连续 Box 动作空间），`log_std` 可学习，clamp 在 [-4.0, -1.0] |
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| **log_std 策略** | 初始化 -2.0（std≈0.135），每步 optimizer.step() 后 clamp 到 [-4.0, -1.0]（std ∈ [0.018, 0.368]），熵系数 0.001 |
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| Optimizer | Adam, lr=3e-4, lr_decay=1.0 |
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| **动作分布** | `DiagGaussianDistribution`（连续 Box 动作空间），`log_std` 可学习，clamp 在 [-2.5, -1.0] |
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| **log_std 策略** | 初始化 -2.0（std≈0.135），每步 optimizer.step() 后 clamp 到 [-2.5, -1.0]（std ∈ [0.082, 0.368]），熵系数 0.01 |
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| **correction_scale** | 0.3 — Actor 修正幅值 c·tanh(δ) ∈ [−0.3, +0.3] |
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| **correction_reg_coef** | 0.03 — correction 正则化系数，L_corr = coef × mean(correction²) |
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| **step0_penalty_scale** | 0.3 — 第一步 element penalty 降权系数 |
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### 动作分布策略说明
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环境定义的是 `_action_space`（下划线前缀），网络初始化时必须用 `environment._action_space` 而非 `environment.action_space`（后者默认为 None，会错误回退到 `CategoricalDistribution(1)`，导致 policy gradient 恒为零）。
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`continuous_sizing_field`（score-based）的动作有效范围约 [-3, 3]：
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- score = -x_i，x 越小 ⇒ 优先级越高（纯排序，不设正负门槛）
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- `initial_log_std=-2.0`（std≈0.135），clamp 在 [-4.0, -1.0]（std ∈ [0.018, 0.368]）
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- 加 `entropy_coefficient=0.001` 提供微弱探索压力，避免 log_std 过早收敛到下限
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`continuous_sizing_field`（score-based）的 scoring 公式：
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- `score_i = log(η_i + ε) + c·tanh(δ_i)`，其中 c=`correction_scale`
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- Actor 输出 δ_i，经 tanh 限幅，只能微调 log(η) 基准排序，不能覆盖物理先验
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- 选 top-k 按 score 降序（越大越优先）
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- `initial_log_std=-2.0`（std≈0.135），clamp 在 [-2.5, -1.0]（std ∈ [0.082, 0.368]）
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- `entropy_coefficient=0.01`
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---
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└─ train_step() # 多 epoch PPO 更新
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├─ policy_loss() # Clipped PPO
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├─ value_loss() # Clipped value loss
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└─ entropy_loss() # 熵正则
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├─ entropy_loss() # 熵正则
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└─ correction_reg() # Correction 正则化 L_corr
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```
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### 环境内部调用
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└─→ initial_mesh (meshpy → 介质内 前渐近区边缘迭代细化)
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MeshRefinement.step(action)
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├─→ score = -x 排序 + 物理预算约束 → top-k 细化单元
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├─→ score = log(η) + c·tanh(δ) 排序 + 物理预算约束 → top-k 细化单元
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├─→ FEMProblemWrapper.refine_mesh() # scikit-fem refine
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├─→ calculate_solution_and_get_error()
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│ ├─→ HelmholtzProblem.calculate_solution() # FEM 求解
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│ └─→ _compute_residual_indicator() # 残差误差
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├─→ _get_reward_by_type() # spatial 奖励
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└─→ last_observation # 构建 Data(x, edge_index, edge_attr)
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├─→ _get_reward_by_type() # spatial 奖励 + step0 penalty 降权
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└─→ last_observation # 构建 Data(x, edge_index, edge_attr, eta, area, global_stats)
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```
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### 训练
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```bash
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@ -182,16 +238,25 @@ CUDA_VISIBLE_DEVICES=7 python src/main.py --mode train --config src/config.yaml
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首次迭代需收集 256 步 rollout（含 FEM 求解），后续打印：
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```
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it | loss ev agents reward x<0 elig sel time
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it | loss ev agents avg_r sum_r corr_m corr_s r_le_sc δ<0 elig sel rem_r corr_reg corr_l2 corr_a p_sc avg_p avg_rl step_id time
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```
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| 字段 | 含义 | 健康范围 |
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|------|------|---------|
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| `x<0` | `mean(x_i < 0)`，负值动作比例（纯诊断） | 越负的单元优先级越高 |
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| `elig` | 通过双过滤器的候选占比 | 排除数值退化 + 低误差的单元 |
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| `mask` | 被 Reverse Dörfler 剔除的噪声尾部占比（累积能量 <1% 总误差的底部单元） | 因场景而异，非固定比例 |
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| `corr_m` | `c·tanh(δ)` 均值 | 接近 0，Actor 修正无系统性偏差 |
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| `corr_s` | `c·tanh(δ)` 标准差 | 应稳定在 0.03–0.08，不应持续涨到 0.15 |
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| `r_le_sc` | Pearson r(log_η, score) | 接近 1.0 → Actor 修正小；<0.9 → Actor 在主动修正 |
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| `δ<0` | Actor 输出负值的比例（纯诊断） | — |
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| `elig` | 通过双过滤器的候选占比 | — |
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| `sel` | 实际选中的细化单元数 | 每步最多 N_current // 4 |
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| `n_budget` | 全局物理预算（每 episode 固定） | k=30 → ~1800 |
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| `rem_r` | remaining / N_budget | — |
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| `corr_reg` | correction 正则化损失 L_corr | 监控 correction drift |
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| `corr_l2` | mean(correction²) | 监控 correction 幅值增长 |
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| `corr_a` | mean(\|correction\|) | 监控 correction 绝对值 |
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| `p_sc` | penalty_scale | step0=0.3，后续=1.0 |
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| `avg_p` | 平均 element penalty | step0 应明显小于后续 |
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| `avg_r_local` | 平均 r_local（penalty 前） | — |
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| `step_id` | 当前步数 | — |
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### 测试
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输出：
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```
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Step 0: reward=--- error=1.0000 elements=174 budget=1885
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Step 1: reward=+12.345 error=0.7160 elements=618 x<0=0.45 sel=87
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Step 0: reward=--- aw_rel=79.28% max_err=2.2133 elements=1078 budget=...
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Step 1: reward=+2.345 aw_rel=30.10% max_err=0.7096 elements=2020 sel=269
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...
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```
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每步打印 `reward error elements x<0 sel`，第 0 步额外显示 `N_budget`。
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每步打印 `reward aw_rel max_err elements sel`，第 0 步额外显示 `N_budget`。
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### 可视化
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### 连续尺寸场策略（score-based + 物理预算约束 + 动作掩码）
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Actor 输出标量 x_i → score = -x_i 直接排序，在预算和上限内选 top-k：
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Actor 输出标量 δ_i → `score_i = log(η_i + ε) + c·tanh(δ_i)`，在预算和上限内选 top-k：
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```
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A_budget_i = ½(λ_local_i / 6)² // 每局部波长方向 ~6 尺度点（仅用于 N_budget 计算）
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λ_local_i = 2π / (k · √ε_r_i)
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N_budget = max(N_phys, ⌈5·N_init⌉) // rho_min=5.0，至少 5× 初始单元数，保证 RL 多步细化空间
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N_phys = ⌈ Σ |K_i| / A_budget_i ⌉ // 全局物理预算（k=30 真空 ~1800）
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ε = max(0.01·median(η), 1e-12) // 动态 eps，防止 log(0)
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corr_i = c · tanh(δ_i) c = correction_scale // Actor 修正幅值 ∈ [−c, +c]
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score_i = log(η_i + ε) + corr_i // 降序 top-k
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A_budget_i = ½(λ_local_i / 6)² // 每局部波长方向 ~6 尺度点（仅用于 N_budget）
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N_budget = max(N_phys, ⌈5·N_init⌉) // rho_min=5.0
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remaining = N_budget − N_current
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V_min_safeguard = 1e-10 × domain_area // 纯数值底线（防止 FEM 求解器退化）
|
||||
eligible: area > V_min_safeguard AND η_K ∈ Reverse Dörfler 保留集 // 数值底线 + 能量尾部淘汰 (ε_noise=0.01, ≥20% floor)
|
||||
V_min_safeguard = 1e-10 × domain_area
|
||||
eligible: area > V_min_safeguard AND η_K ∈ Reverse Dörfler 保留集 (ε_noise=0.01, ≥20% floor)
|
||||
num = min(|eligible|, N_current//4, remaining//3)
|
||||
selected = top-k by score = -x_i → 1-to-4 切分
|
||||
selected = top-k by score descending → 1-to-4 切分
|
||||
```
|
||||
|
||||
- score = -x_i：x 越小 ⇒ 优先级越高（纯排序，不设正负门槛）
|
||||
- 不再使用 `0.25·A_budget` 启发式面积地板：RL 应自主学会"细化到多细"，而非被人类经验 (12 点/波长) 限制。仅保留数值底线 V_min_safeguard = 1e-10 × domain_area 防止浮点精度问题。
|
||||
- per-step cap 从固定 200 改为自适应 `N_current // 4`，随网格规模缩放但增速更缓，避免大网格时单步消耗过多预算。rho_min 从 3.0 提升到 5.0，赋予更多预算余量。
|
||||
- **sel=0 提前终止**：当 agent 选中 0 个单元细化（预算耗尽或 Reverse Dörfler 屏蔽所有候选）时 episode 自动结束，不再浪费 FEM 求解
|
||||
- **k_exponent 可配**：初始网格缩放指数可通过 `helmholtz.k_exponent` 配置（默认 2.0），² 为 P1 Helmholtz 理论最优；对 k=30 的 $N_{init}$ 为 k=6 的 25× 倍
|
||||
- **动作掩码 (Reverse Dörfler)**：按 η_K 升序排列，剔除累积平方误差贡献 < ε_noise·Ση² 的底部单元（数值噪声/已收敛区）。基于能量分布而非密度分位数，在重尾和均匀误差分布下均自适应。保留率不低于 20% 确保 Agent 始终有充分的选择空间
|
||||
- Actor 通过 bounded correction 微调排序，不能覆盖物理先验 log(η)
|
||||
- Reverse Dörfler 动作掩码剔除噪声尾部，≥20% floor 确保 Agent 始终有选择空间
|
||||
- sel=0 提前终止：agent 选中 0 个单元时 episode 自动结束
|
||||
- k_exponent=2.0：P1 Helmholtz 理论最优初始网格缩放
|
||||
|
||||
### 奖励计算
|
||||
|
||||
---
|
||||
|
||||
#### 变量
|
||||
|
||||
| 符号 | 含义 |
|
||||
|------|------|
|
||||
| `η_K = √(r_int² + r_jump² + r_sbc²)` | 逐单元误差指示子，`r_*` 定义见式 (1)–(3) |
|
||||
| `C(i)` | 父单元 i 经 1-to-4 切分产生的子单元集合 |
|
||||
| `M_new[j]` | 子单元 j 对应的父单元索引 |
|
||||
| `n_i = |C(i)|` | 父单元 i 的子单元数（1 表示未切分） |
|
||||
| `E_global = √(Σ η_K²) / \|\|u_h\|\|_{L₂(Ω)}` | 全局无量纲误差 |
|
||||
|
||||
---
|
||||
|
||||
#### 算法
|
||||
|
||||
**Step 0 — 保存旧状态** (`_set_previous_step`)
|
||||
纯局部改善 reward，无调制、无 bonus：
|
||||
|
||||
```
|
||||
η_old ← 旧逐单元 η_K
|
||||
||u_h_old|| ← 旧解 L₂ 范数 (≈ √(Σ |ū_K|² · area_K))
|
||||
r_local_i = log(η_old_i + ε) − log(l2_η_children_i + ε)
|
||||
l2_η_children_i = √(Σ_{j∈C(i)} η_new_j²)
|
||||
|
||||
reward_i = clip(r_local_i, 0, rmax) − penalty_scale · λ · (n_child_i − 1)
|
||||
|
||||
rmax = 2.0
|
||||
λ = 0.02 (element_penalty.value)
|
||||
|
||||
penalty_scale = step0_penalty_scale if current_step == 0 (默认 0.3)
|
||||
= 1.0 otherwise
|
||||
```
|
||||
|
||||
**Step 1 — 网格细化** (`_refine_mesh`)
|
||||
|
||||
```
|
||||
x = action.flatten()
|
||||
score = -x // x 越小 ⇒ 优先级越高
|
||||
|
||||
remaining = N_budget − N_old
|
||||
max_by_budget = max(0, remaining // 3)
|
||||
// 数值底线 + Reverse Dörfler 能量尾部淘汰
|
||||
V_min_safeguard = 1e-10 × domain_area // 纯数值安全底线，防止 FEM 退化
|
||||
η_sq = η_old²; total_energy = Σ η_sq
|
||||
k_dorfler = searchsorted(cumsum(sort_asc(η_sq)), ε_noise·total_energy) // ε_noise=0.01
|
||||
k = min(k_dorfler, N − max(1, N//5)) // ≥20% floor
|
||||
eligible = {i | V_old[i] > V_min_safeguard AND i ∈ sort_asc_idx[k:] }
|
||||
num = min(|eligible|, N_old//3, max_by_budget)
|
||||
elements_to_refine = top-k of eligible by score
|
||||
|
||||
M_new[j] ∈ {0,…,N_old-1} // 子→父映射
|
||||
```
|
||||
|
||||
**Step 2 — FEM 求解 + 误差估计**
|
||||
|
||||
```
|
||||
η_new ← 新逐单元 η_K
|
||||
||u_h_new|| ← 新解 L₂ 范数
|
||||
```
|
||||
|
||||
**Step 3 — 因果奖励**（零和预算审查）
|
||||
|
||||
ε_dynamic = max(0.01 × η_P95, 1e-6)
|
||||
|
||||
// Refined parents: r_local + zero-sum bonus − penalty
|
||||
if i ∈ refined_parents:
|
||||
r_i = log(η_old + ε) − log(√(Σ η_child²) + ε) // r_local ≥ 0 (L₂ 聚合)
|
||||
+ 0.3 × (η_old / μ − 1.0) // zero-sum bonus (Σ = 0)
|
||||
− 0.06 // action penalty
|
||||
|
||||
// Unrefined parents: causal isolation
|
||||
else:
|
||||
r_i = 0
|
||||
|
||||
> **零和奖金**：α·(η/μ−1) 全场求和为零。细化高于均值的单元得正奖金，低于均值的倒扣。
|
||||
> 这是 Dörfler 准则的 RL 对偶：Agent 必须选出误差超过全均水平的单元。
|
||||
> **因果隔离**：未细化单元 r ≡ 0。零和奖金本身足够强（介质内 +0.51）、
|
||||
> 不再需要忽视惩罚的推力，排序机制自动淘汰不划算的单元。
|
||||
> **L₂ 聚合**：√(Σ η_child²) ≤ η_parent 天然成立，r_local ≥ 0 永不惩罚细化。
|
||||
|
||||
**Step 4 — 全局误差（仅诊断）**
|
||||
|
||||
global_bonus = α·[log(E_old) − log(E_new)]，α = 0.5
|
||||
|
||||
不注入 Actor reward。Helmholtz 污染误差可使 E_new > E_old 在正确细化后发生，
|
||||
注入 global_bonus 导致因果断裂。Actor 仅优化 Step 3 的 per-element reward。
|
||||
|
||||
---
|
||||
|
||||
#### 奖励标度校准（旧尺寸场下测量，score-based 后需重新标定）
|
||||
|
||||
在随机策略下实测各分量量级（1321 个 refined-parent 样本）：
|
||||
|
||||
| 分量 | 均值 | 占 r_local 比例 |
|
||||
|------|------|:---:|
|
||||
| `r_local` (仅 refined parents) | +0.364 | — |
|
||||
| `penalty` λ·(n−1), λ=0.02 | +0.045 | 1/8 |
|
||||
| `α·ΔlogE` α=0.2 | +0.069 | 1/5 |
|
||||
| **net** | **+0.387** | |
|
||||
|
||||
满足 `r_local ≫ penalty` 且 `α·ΔlogE ≈ r_local / 5`，局部 credit assignment 不被全局信号淹没。
|
||||
|
||||
---
|
||||
|
||||
#### 设计要点
|
||||
|
||||
| 组件 | 聚合 | 作用 |
|
||||
|------|------|------|
|
||||
| 局部项 `log(η_old / √(Σ η_child²))` | scatter_add，仅 refined parents | L₂ 保证 r_local ≥ 0；int 主导 +0.69 |
|
||||
| 零和奖金 `0.3×(η/μ−1)` | 仅 refined parents | Σ=0；高于 μ 得正奖，低于 μ 倒扣 (Dörfler 准则的 RL 对偶) |
|
||||
| 动作惩罚 `λ=0.06` | per-refined-parent | 轻微抑制网格膨胀（1-to-4 扣 0.06） |
|
||||
| 因果隔离 `r=0` | unrefined parents | 零和奖金足够强，不需额外推力 |
|
||||
| 全局项 `α·ΔlogE` α=0.5 | 仅诊断 | 不注入 Actor，避免污染误差因果断裂 |
|
||||
| 局部项 `log(η_old / √(Σ η_child²))` | scatter_add，仅 refined parents | L₂ 保证 r_local ≥ 0；clip 到 [0, 2.0] |
|
||||
| 动作惩罚 `λ=0.02` | per-refined-parent | 轻微抑制网格膨胀（1-to-4 扣 0.06） |
|
||||
| **step0 降权** | step 0 时 penalty × 0.3 | 防止第一步"真实误差改善但 reward 给负反馈" |
|
||||
| 因果隔离 `r=0` | unrefined parents | 未细化元素干净零基准 |
|
||||
|
||||
**step0 penalty 降权动机**：诊断发现 step 0 经常出现 reward < 0 但真实 aw_rel 改善的情况，说明 element penalty 淹没了真实的物理改善信号。降权后 step 0 的 reward 与 Δaw_rel 符号一致性提高。
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -388,7 +371,185 @@ global_bonus = α·[log(E_old) − log(E_new)]，α = 0.5
|
|||
- **奖励归一化**: rollout 内 reward 做 z-score 归一化（std < 1e-8 则跳过）
|
||||
- **Value clipping**: 默认 clip_range=0.2
|
||||
- **梯度裁剪**: max_grad_norm=0.5
|
||||
- **log_std clamp**: 每步 `optimizer.step()` 后将 `log_std` clamp 到 `[-3.0, -1.0]`，σ ∈ [0.05, 0.37]<br>
|
||||
- **log_std clamp**: 每步 `optimizer.step()` 后将 `log_std` clamp 到 `[-2.5, -1.0]`，σ ∈ [0.082, 0.37]<br>
|
||||
初始化 `-2.0` (σ≈0.135)，放宽下限防止策略过早确定化
|
||||
- **熵正则**: `entropy_coefficient=0.005`，施加有意义的探索压力防止 x<0 崩塌
|
||||
- **epochs_per_iteration**: 3，减少对同一 rollout 的过拟合
|
||||
- **熵正则**: `entropy_coefficient=0.01`
|
||||
- **epochs_per_iteration**: 3
|
||||
|
||||
### Correction 正则化
|
||||
|
||||
为防止 Actor 学会利用"大 correction"刷局部 residual reward（correction drift / reward hacking），在 PPO loss 中加入 correction 正则项：
|
||||
|
||||
```
|
||||
correction_i = correction_scale · tanh(action_i)
|
||||
L_corr = correction_reg_coef · mean(correction²)
|
||||
|
||||
loss = policy_loss + value_coef · value_loss + entropy_coef · entropy_loss + L_corr
|
||||
```
|
||||
|
||||
- `correction_reg_coef` 默认 0.03，设为 0 可禁用
|
||||
- correction 在 PPO 训练时从 stored actions 重新计算（与环境中的公式一致）
|
||||
- 目标：corr_std 不再从 ~0.03 持续涨到 ~0.15；r_le_sc 保持更接近 η baseline；validation aw_rel 不随训练后期变差
|
||||
- 训练 reward 可能因正则化而下降，这是正常现象；成功标准是测试误差更稳定
|
||||
|
||||
### 训练诊断字段
|
||||
|
||||
| 字段 | 来源 | 说明 |
|
||||
|------|------|------|
|
||||
| `corr_reg` | train_step | L_corr = coef × mean(corr²)，监控 correction drift |
|
||||
| `corr_l2` | train_step | mean(correction²)，correction 幅值 |
|
||||
| `corr_abs` | train_step | mean(\|correction\|)，correction 绝对值 |
|
||||
| `penalty_scale` | environment | step0=0.3，后续=1.0 |
|
||||
| `avg_penalty` | environment | 平均 element penalty（refined parents） |
|
||||
| `avg_r_local` | environment | 平均 r_local（penalty 前，refined parents） |
|
||||
| `step_id` | environment | 当前 timestep |
|
||||
|
||||
---
|
||||
|
||||
## 配置参考
|
||||
|
||||
```yaml
|
||||
algorithm:
|
||||
batch_size: 32
|
||||
discount_factor: 1.0
|
||||
ppo:
|
||||
clip_range: 0.2
|
||||
entropy_coefficient: 0.01
|
||||
correction_reg_coef: 0.03 # correction 正则化系数
|
||||
epochs_per_iteration: 3
|
||||
gae_lambda: 0.95
|
||||
initial_log_std: -2.0
|
||||
max_grad_norm: 0.5
|
||||
num_rollout_steps: 256
|
||||
value_function_coefficient: 0.5
|
||||
use_gpu: true
|
||||
|
||||
environment:
|
||||
mesh_refinement:
|
||||
correction_scale: 0.3 # c in score = log(η) + c·tanh(δ)
|
||||
step0_penalty_scale: 0.3 # step 0 element penalty 降权
|
||||
num_timesteps: 4
|
||||
refinement_strategy: continuous_sizing_field
|
||||
reward_type: spatial
|
||||
element_penalty:
|
||||
value: 0.02
|
||||
maximum_elements: 50000
|
||||
element_limit_penalty: 10000
|
||||
# ... (FEM / Helmholtz / 特征配置见 config.yaml)
|
||||
|
||||
network:
|
||||
latent_dimension: 64
|
||||
use_global_conditioned_correction: true # Actor/Critic 拼接 g_global + 局部相对特征
|
||||
use_global_stats: true # 启用 MultiPoolGVN + 13 维全局统计
|
||||
gvn_pooling: [mean, eta_softmax] # 池化策略（可选加 top_eta）
|
||||
correction_centering: true # correction 在 eligible 集内中心化
|
||||
base:
|
||||
edge_dropout: 0.1
|
||||
scatter_reduce: mean
|
||||
stack:
|
||||
num_steps: 2
|
||||
mlp:
|
||||
activation_function: leakyrelu
|
||||
num_layers: 2
|
||||
actor:
|
||||
mlp:
|
||||
activation_function: tanh
|
||||
num_layers: 2
|
||||
critic:
|
||||
mlp:
|
||||
activation_function: tanh
|
||||
num_layers: 2
|
||||
training:
|
||||
learning_rate: 0.0003
|
||||
lr_decay: 1.0
|
||||
```
|
||||
|
||||
### 实验对照建议
|
||||
|
||||
| 实验 | `correction_reg_coef` | `step0_penalty_scale` | 目的 |
|
||||
|------|----------------------|----------------------|------|
|
||||
| A (baseline) | 0.0 | 1.0 | 无正则、无降权 |
|
||||
| B (corr reg only) | 0.03 | 1.0 | 验证 correction 正则效果（优先） |
|
||||
| C (both) | 0.03 | 0.3 | 正则 + step0 降权 |
|
||||
|
||||
成功标准（非训练 reward 高低）：
|
||||
- `corr_std` 不再持续涨到 ~0.15
|
||||
- `r_le_sc` 保持更接近 η baseline
|
||||
- top-k overlap 不随训练后期明显下降
|
||||
- validation `aw_rel / max_err` 更稳定
|
||||
|
||||
---
|
||||
|
||||
## Correction GNN 训练数据
|
||||
|
||||
Correction GNN 用于二分类预测：给定当前网格，哪些单元需要加密（teacher_mark=1）。
|
||||
训练数据由 `outlook/src/gen.py` 生成。
|
||||
|
||||
### 参数采样
|
||||
|
||||
每个样本随机采样物理参数：
|
||||
|
||||
| 参数 | 分布 | 说明 |
|
||||
|------|------|------|
|
||||
| `k` | Uniform(3, 15) | 波数 |
|
||||
| `eps_r` | Uniform(2, 8) | 介质相对介电常数 |
|
||||
| `cx` | Uniform(0.2, 0.8) | 散射体中心 x |
|
||||
| `cy` | Uniform(0.2, 0.8) | 散射体中心 y |
|
||||
| `radius` | Uniform(0.05, 0.25) | 散射体半径 |
|
||||
|
||||
### 初始网格
|
||||
|
||||
采用物理自适应初始网格（`make_initial_mesh`），元素尺寸由局域波长决定：
|
||||
|
||||
- **介质外**: h ≤ λ₀ / q, λ₀ = 2π / k
|
||||
- **介质内/散射体附近**: h ≤ λ_eff / q, λ_eff = 2π / (k √ε_r)
|
||||
- **q = 2**（每波长 2 个单元）
|
||||
|
||||
网格在 [0,1]×[0,1] 域上通过张量积生成，x/y 方向各自根据散射体位置做分级加密：
|
||||
- 远离散射体：粗网格（h = λ₀/q）
|
||||
- 散射体附近（含过渡区）：细网格（h = λ_eff/q）
|
||||
|
||||
### AMR 循环与标签生成
|
||||
|
||||
对每个参数样本，运行残差驱动 AMR 循环，每步保存快照：
|
||||
|
||||
```
|
||||
for step in range(max_steps):
|
||||
1. FEM 求解 → u_scat
|
||||
2. 计算残差指示子 η（teacher 信号）
|
||||
3. 计算 physics_score = h / λ_eff（物理 baseline）
|
||||
4. teacher_mark = top-fraction(η, mark_fraction) # 二值标签
|
||||
physics_mark = top-fraction(physics_score, mark_fraction) # 物理 baseline
|
||||
correction_label = teacher_mark - physics_mark # {-1, 0, +1}
|
||||
5. 提取 16 维节点特征 + 边索引
|
||||
6. 保存 .npz → 残差指示子 top-k 加密 → 下一步
|
||||
```
|
||||
|
||||
- **mark_fraction**: 默认 0.03（每步标记 top 3% 的单元为正样本）
|
||||
- **top-fraction**: 按 score 降序取 top `n × fraction` 个单元，标记为 1
|
||||
- **teacher_mark**: 以 η（残差指示子）为 score，代表"最优加密目标"
|
||||
- **physics_mark**: 以 h/λ_eff 为 score，代表"纯物理 baseline"
|
||||
- **correction_label**: teacher 与 physics 的差集，+1 = teacher 独有（GNN 应补充），-1 = physics 独有（GNN 应抑制）
|
||||
|
||||
### 数据文件格式
|
||||
|
||||
每个样本每步保存为 `sample{id}_step{step}.npz`，包含：
|
||||
|
||||
| 字段 | 形状 | 说明 |
|
||||
|------|------|------|
|
||||
| `features` | (n_elem, 16) | 15 维几何/PDE 特征 + 1 维 physics_score |
|
||||
| `edge_index` | (2, n_edges) | 双向边 + 自环 |
|
||||
| `physics_score` | (n_elem,) | h / λ_eff |
|
||||
| `teacher_eta` | (n_elem,) | 残差指示子 η |
|
||||
| `teacher_mark` | (n_elem,) | 二值标签 (0/1) |
|
||||
| `physics_mark` | (n_elem,) | 物理 baseline 标签 (0/1) |
|
||||
| `correction_label` | (n_elem,) | 差集标签 (-1/0/+1) |
|
||||
| `k, eps_r, cx, cy, radius` | scalar | 物理参数 |
|
||||
| `elements` | scalar | 当前单元数 |
|
||||
| `step` | scalar | AMR 步数 |
|
||||
|
||||
---
|
||||
|
||||
## One-Shot Density Prediction
|
||||
|
||||
One-shot final mesh density prediction experiments are documented in [`outlook/README.md`](outlook/README.md).
|
||||
|
|
|
|||
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|
|
@ -1,3 +1,4 @@
|
|||
import time
|
||||
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import gym
|
||||
|
|
@ -55,7 +56,6 @@ class MeshRefinement(gym.Env):
|
|||
# graph connectivity, feature and action space #
|
||||
################################################
|
||||
self._reward_type = environment_config.get("reward_type")
|
||||
self._global_reward_alpha = float(environment_config.get("global_reward_alpha", 0.2))
|
||||
_rho_w = environment_config.get("rho_weights", {})
|
||||
self._w_rho_int = float(_rho_w.get("w_int", 1.0))
|
||||
self._w_rho_jump = float(_rho_w.get("w_jump", 1.0))
|
||||
|
|
@ -223,6 +223,25 @@ class MeshRefinement(gym.Env):
|
|||
self._diag_selected_count = -1 # 防止跨 episode 残留触发 is_terminal
|
||||
self._diag_dorfler_tail_ratio = 0.0
|
||||
self._diag_dorfler_floor_active = False
|
||||
self._diag_corr_raw_mean = 0.0
|
||||
self._diag_corr_mean = 0.0
|
||||
self._diag_corr_std = 0.0
|
||||
self._diag_corr_abs = 0.0
|
||||
self._diag_neg_ratio = 0.0
|
||||
self._diag_score_eta_corr = 0.0
|
||||
self._diag_max_by_budget = 0
|
||||
self._diag_max_by_growth = 0
|
||||
self._diag_n_budget = 0
|
||||
self._diag_remaining = 0
|
||||
self._diag_n_eligible = 0
|
||||
self._diag_n_next = 0
|
||||
self._diag_corr_rel_eta_corr = 0.0
|
||||
self._diag_corr_inside_mean = 0.0
|
||||
self._diag_corr_outside_mean = 0.0
|
||||
self._diag_corr_top_eta_mean = 0.0
|
||||
self._diag_corr_low_eta_mean = 0.0
|
||||
self._diag_global_top10_eta_energy = 0.0
|
||||
self._diag_remaining_ratio = 0.0
|
||||
|
||||
# reset internal state that tracks statistics over the episode
|
||||
self._previous_error_per_element = self.error_per_element
|
||||
|
|
@ -325,9 +344,11 @@ class MeshRefinement(gym.Env):
|
|||
# solve equation and calculate error per element/element
|
||||
self._previous_error_per_element = self.error_per_element
|
||||
|
||||
t_fem = time.perf_counter()
|
||||
self._error_estimation_dict = (
|
||||
self.fem_problem.calculate_solution_and_get_error()
|
||||
)
|
||||
self._last_fem_solve_ms = (time.perf_counter() - t_fem) * 1e3
|
||||
|
||||
# query returns
|
||||
observation = self.last_observation
|
||||
|
|
@ -346,12 +367,30 @@ class MeshRefinement(gym.Env):
|
|||
"is_truncated": self.is_truncated,
|
||||
"return": self._cumulative_return,
|
||||
"neg_action_ratio": getattr(self, "_diag_neg_ratio", 0.0),
|
||||
"corr_raw_mean": getattr(self, "_diag_corr_raw_mean", 0.0),
|
||||
"corr_mean": getattr(self, "_diag_corr_mean", 0.0),
|
||||
"corr_std": getattr(self, "_diag_corr_std", 0.0),
|
||||
"corr_abs": getattr(self, "_diag_corr_abs", 0.0),
|
||||
"score_eta_corr": getattr(self, "_diag_score_eta_corr", 0.0),
|
||||
"eligible_ratio": getattr(self, "_diag_eligible_ratio", 0.0),
|
||||
"masked_ratio": getattr(self, "_diag_masked_ratio", 0.0),
|
||||
"selected_count": getattr(self, "_diag_selected_count", 0),
|
||||
"dorfler_tail_ratio": getattr(self, "_diag_dorfler_tail_ratio", 0.0),
|
||||
"dorfler_floor_active": float(getattr(self, "_diag_dorfler_floor_active", False)),
|
||||
"n_budget": self._n_budget,
|
||||
"remaining": getattr(self, "_diag_remaining", 0),
|
||||
"max_by_budget": getattr(self, "_diag_max_by_budget", 0),
|
||||
"max_by_growth": getattr(self, "_diag_max_by_growth", 0),
|
||||
"n_eligible": getattr(self, "_diag_n_eligible", 0),
|
||||
"n_next": getattr(self, "_diag_n_next", 0),
|
||||
"fem_solve_ms": self._last_fem_solve_ms,
|
||||
"corr_rel_eta_corr": getattr(self, "_diag_corr_rel_eta_corr", 0.0),
|
||||
"corr_inside_mean": getattr(self, "_diag_corr_inside_mean", 0.0),
|
||||
"corr_outside_mean": getattr(self, "_diag_corr_outside_mean", 0.0),
|
||||
"corr_top_eta_mean": getattr(self, "_diag_corr_top_eta_mean", 0.0),
|
||||
"corr_low_eta_mean": getattr(self, "_diag_corr_low_eta_mean", 0.0),
|
||||
"global_top10_eta_energy": getattr(self, "_diag_global_top10_eta_energy", 0.0),
|
||||
"remaining_ratio": getattr(self, "_diag_remaining_ratio", 0.0),
|
||||
}
|
||||
)
|
||||
return observation, self._reward, done, info
|
||||
|
|
@ -527,31 +566,56 @@ class MeshRefinement(gym.Env):
|
|||
|
||||
if self._refinement_strategy == "continuous_sizing_field":
|
||||
# ================================================================
|
||||
# Score-based 细化选择（由 actor 直接排序，物理预算约束）
|
||||
# Score-based 细化选择：log(η) baseline + bounded Actor correction
|
||||
#
|
||||
# Actor 输出标量 x_i: x_i < 0 → 希望细化; x_i > 0 → 不希望细化
|
||||
# 排序依据 score = -x_i，在预算和上限内选 top-k
|
||||
# score_i = log(η_i + eps) + c · tanh(δ_i)
|
||||
#
|
||||
# 物理预算 N_budget: Σ area_K / A_budget，其中
|
||||
# A_budget = ½(λ_local/6)²，对应每局部波长方向 ~6 个尺度点
|
||||
# η_i = current-step residual indicator (physical prior)
|
||||
# δ_i = Actor output (continuous scalar per element)
|
||||
# c = correction_scale (0.7) — bounds Actor influence
|
||||
# eps = dynamic: 0.01 · median(η) — prevents log(0)
|
||||
#
|
||||
# 动作掩码 (Reverse Dörfler): 按 η_K 升序排列，剔除累积平方误差
|
||||
# 贡献 < ε_noise·Ση² 的底部单元（数值噪声/已收敛区），保留 ≥20%
|
||||
# 的单元确保 Agent 始终有充分的选择空间
|
||||
# Selection: top-k by score descending (higher score → refine).
|
||||
# Actor can boost or suppress priority by at most ±c in the log-η
|
||||
# domain, but cannot override the physical prior.
|
||||
# ================================================================
|
||||
x = action.flatten()
|
||||
delta = action.flatten()
|
||||
|
||||
# ── 训练监控指标（在所有 early return 之前计算）──
|
||||
self._diag_neg_ratio = float(np.mean(x < 0.0))
|
||||
eta = self._eta_indicator
|
||||
eps_score = max(0.01 * float(np.median(eta)), 1e-12)
|
||||
log_eta = np.log(np.maximum(eta, 1e-30) + eps_score)
|
||||
|
||||
c = float(self._environment_config.get("correction_scale", 0.7))
|
||||
corr_raw = c * np.tanh(delta)
|
||||
|
||||
remaining = self._n_budget - self._num_elements
|
||||
max_parents_by_budget = max(0, remaining // 3)
|
||||
max_parents_by_budget = max(0, remaining // 6)
|
||||
|
||||
self._diag_max_by_budget = max_parents_by_budget
|
||||
|
||||
if max_parents_by_budget <= 0:
|
||||
self._diag_eligible_ratio = 0.0
|
||||
self._diag_selected_count = 0
|
||||
self._diag_dorfler_tail_ratio = 0.0
|
||||
self._diag_dorfler_floor_active = False
|
||||
self._diag_max_by_growth = max(1, self._num_elements // 4)
|
||||
self._diag_n_budget = self._n_budget
|
||||
self._diag_remaining = remaining
|
||||
self._diag_n_eligible = 0
|
||||
self._diag_n_next = self._num_elements
|
||||
self._diag_corr_raw_mean = float(np.mean(corr_raw))
|
||||
self._diag_corr_mean = 0.0
|
||||
self._diag_corr_std = 0.0
|
||||
self._diag_corr_abs = 0.0
|
||||
self._diag_neg_ratio = float(np.mean(delta < 0.0))
|
||||
self._diag_score_eta_corr = 0.0
|
||||
self._diag_corr_rel_eta_corr = 0.0
|
||||
self._diag_corr_inside_mean = 0.0
|
||||
self._diag_corr_outside_mean = 0.0
|
||||
self._diag_corr_top_eta_mean = 0.0
|
||||
self._diag_corr_low_eta_mean = 0.0
|
||||
self._diag_global_top10_eta_energy = 0.0
|
||||
self._diag_remaining_ratio = remaining / max(self._n_budget, 1)
|
||||
return np.array([], dtype=np.int64)
|
||||
|
||||
# 动态计算每单元预算面积（仅用于 N_budget 全局资源上限）
|
||||
|
|
@ -560,32 +624,23 @@ class MeshRefinement(gym.Env):
|
|||
lambda_local = 2.0 * np.pi / (k * np.sqrt(np.maximum(eps_r_elem, 1.0)))
|
||||
A_budget = 0.5 * (lambda_local / 6.0) ** 2
|
||||
|
||||
# 纯数值安全底线：仅防止 scikit-fem 因浮点精度导致的退化/奇异。
|
||||
# 不再用 0.25*A_budget —— RL 应自主学会"多细才够"，
|
||||
# 而非被人为启发式 (12 点/波长) 限制。
|
||||
domain_area = float(np.prod(self.fem_problem.plot_boundary[2:] - self.fem_problem.plot_boundary[:2]))
|
||||
V_min_safeguard = 1e-10 * domain_area
|
||||
|
||||
# Filter 1: numerical safeguard only — no physics heuristic
|
||||
# Filter 1: numerical safeguard only
|
||||
area_eligible = np.where(self.element_volumes > V_min_safeguard)[0]
|
||||
|
||||
# Filter 2: Reverse Dörfler — eliminate the noise tail, not select the elite.
|
||||
# Sort η_K ascending; remove the smallest elements whose cumulative η²
|
||||
# contributes < ε_noise of total error energy. These are numerically
|
||||
# converged or noise — not worth the agent's attention.
|
||||
# A 20% floor on the eligible ratio guarantees the agent meaningful
|
||||
# choices even in heavy-tailed distributions where energy is concentrated.
|
||||
# Filter 2: Reverse Dörfler — eliminate noise tail
|
||||
eta_current = self._eta_indicator
|
||||
eta_sq = eta_current ** 2
|
||||
total_energy = np.sum(eta_sq)
|
||||
|
||||
if total_energy > 0:
|
||||
idx_asc = np.argsort(eta_current) # ascending
|
||||
idx_asc = np.argsort(eta_current)
|
||||
cumsum_asc = np.cumsum(eta_sq[idx_asc])
|
||||
eps_noise = 0.01 # bottom 1% of energy = noise tail
|
||||
eps_noise = 0.01
|
||||
k_dorfler = int(np.searchsorted(cumsum_asc, eps_noise * total_energy))
|
||||
self._diag_dorfler_tail_ratio = float(k_dorfler) / max(self._num_elements, 1)
|
||||
# floor: keep at least 20% of elements for RL agent choice
|
||||
min_keep = max(1, self._num_elements // 5)
|
||||
k = min(k_dorfler, self._num_elements - min_keep)
|
||||
self._diag_dorfler_floor_active = k < k_dorfler
|
||||
|
|
@ -597,26 +652,104 @@ class MeshRefinement(gym.Env):
|
|||
|
||||
eligible = np.intersect1d(area_eligible, error_eligible)
|
||||
|
||||
# ── correction centering (eligible only) ──
|
||||
# Global shift is meaningless for top-k ranking; center within
|
||||
# eligible candidates so the Actor only controls relative priority.
|
||||
self._diag_corr_raw_mean = float(np.mean(corr_raw))
|
||||
if len(eligible) > 0:
|
||||
corr = corr_raw - np.mean(corr_raw[eligible])
|
||||
else:
|
||||
corr = corr_raw - np.mean(corr_raw)
|
||||
|
||||
score = log_eta + corr
|
||||
|
||||
# ── diagnostics ──
|
||||
self._diag_neg_ratio = float(np.mean(delta < 0.0))
|
||||
self._diag_corr_mean = float(np.mean(corr))
|
||||
self._diag_corr_std = float(np.std(corr))
|
||||
self._diag_corr_abs = float(np.mean(np.abs(corr)))
|
||||
# Spearman-like: Pearson r between log_eta and score
|
||||
le = log_eta - log_eta.mean()
|
||||
sc = score - score.mean()
|
||||
denom = np.sqrt(np.sum(le**2) * np.sum(sc**2))
|
||||
self._diag_score_eta_corr = float(np.sum(le * sc) / max(denom, 1e-12))
|
||||
|
||||
self._diag_eligible_ratio = float(len(eligible)) / max(self._num_elements, 1)
|
||||
self._diag_masked_ratio = (
|
||||
1.0 - float(len(eligible)) / max(len(area_eligible), 1)
|
||||
if len(area_eligible) > 0 else 0.0
|
||||
)
|
||||
|
||||
# ── GVN global-conditioned correction diagnostics ──
|
||||
# corr-rel_logeta correlation
|
||||
rel_le = log_eta - log_eta.mean()
|
||||
rel_corr = corr - corr.mean()
|
||||
denom_rc = np.sqrt(np.sum(rel_le**2) * np.sum(rel_corr**2))
|
||||
self._diag_corr_rel_eta_corr = float(
|
||||
np.sum(rel_le * rel_corr) / max(denom_rc, 1e-12)
|
||||
)
|
||||
|
||||
# correction by region (inside/outside scatterer)
|
||||
eps_r = self._epsilon_r_elements
|
||||
inside_mask = eps_r > 1.0
|
||||
outside_mask = ~inside_mask
|
||||
self._diag_corr_inside_mean = float(np.mean(corr[inside_mask])) if inside_mask.any() else 0.0
|
||||
self._diag_corr_outside_mean = float(np.mean(corr[outside_mask])) if outside_mask.any() else 0.0
|
||||
|
||||
# correction by eta rank
|
||||
eta = self._eta_indicator
|
||||
k10 = max(1, int(0.1 * self._num_elements))
|
||||
top_idx = np.argsort(eta)[-k10:]
|
||||
low_idx = np.argsort(eta)[:self._num_elements // 2]
|
||||
self._diag_corr_top_eta_mean = float(np.mean(corr[top_idx]))
|
||||
self._diag_corr_low_eta_mean = float(np.mean(corr[low_idx]))
|
||||
|
||||
# global top10 eta energy and remaining ratio
|
||||
eta_sq = eta ** 2
|
||||
total_energy = float(np.sum(eta_sq))
|
||||
self._diag_global_top10_eta_energy = (
|
||||
float(np.sum(eta_sq[top_idx])) / (total_energy + 1e-12)
|
||||
)
|
||||
self._diag_remaining_ratio = remaining / max(self._n_budget, 1)
|
||||
|
||||
max_by_growth = max(1, self._num_elements // 4)
|
||||
self._diag_max_by_growth = max_by_growth
|
||||
self._diag_n_budget = self._n_budget
|
||||
self._diag_remaining = remaining
|
||||
self._diag_n_eligible = len(eligible)
|
||||
|
||||
num = min(
|
||||
len(eligible),
|
||||
max(1, self._num_elements // 4),
|
||||
max_by_growth,
|
||||
max_parents_by_budget,
|
||||
)
|
||||
|
||||
if num <= 0:
|
||||
self._diag_selected_count = 0
|
||||
self._diag_n_next = self._num_elements
|
||||
return np.array([], dtype=np.int64)
|
||||
|
||||
# x 越小 ⇒ 优先级越高（纯排序，不设正负门槛）
|
||||
score = -x
|
||||
selected = eligible[np.argsort(score[eligible])[-num:]]
|
||||
# top-k by score descending with physical tie-breaking
|
||||
# (avoids spatially arbitrary selection when scores are tied)
|
||||
_fp = self.fem_problem.fem_problem
|
||||
_cx = getattr(_fp, "_cx", 0.5)
|
||||
_cy = getattr(_fp, "_cy", 0.5)
|
||||
_radius = getattr(_fp, "_radius", 0.2)
|
||||
_mesh = self.mesh
|
||||
_p = _mesh.p
|
||||
_t = _mesh.t
|
||||
_mx = (_p[0, _t[0]] + _p[0, _t[1]] + _p[0, _t[2]]) / 3.0
|
||||
_my = (_p[1, _t[0]] + _p[1, _t[1]] + _p[1, _t[2]]) / 3.0
|
||||
_dist = np.sqrt((_mx - _cx)**2 + (_my - _cy)**2)
|
||||
_sd = _dist - _radius
|
||||
_inside = (_dist <= _radius).astype(np.float32)
|
||||
_abs_sd = np.abs(_sd[eligible])
|
||||
_inside_elig = _inside[eligible]
|
||||
_tie_key = -_abs_sd + _inside_elig
|
||||
_composite = score[eligible] * 1e6 + _tie_key
|
||||
selected = eligible[np.argsort(_composite)[-num:]]
|
||||
self._diag_selected_count = len(selected)
|
||||
self._diag_n_next = self._num_elements + len(selected) * 3 # estimate
|
||||
elements_to_refine = selected
|
||||
|
||||
elif self._refinement_strategy in ["absolute", "absolute_discrete"]:
|
||||
|
|
@ -700,6 +833,93 @@ class MeshRefinement(gym.Env):
|
|||
error_per_dim = np.sqrt(np.sum(error_per_element**2, axis=0))
|
||||
return float(self.project_to_scalar(error_per_dim))
|
||||
|
||||
@property
|
||||
def num_global_stats(self) -> int:
|
||||
"""Number of global statistics attached to each graph observation."""
|
||||
return 13
|
||||
|
||||
def _compute_global_stats(self) -> np.ndarray:
|
||||
"""Compute graph-level global statistics for GVN conditioning.
|
||||
|
||||
Returns: np.ndarray of shape (num_global_stats,) with keys:
|
||||
[0] remaining_ratio — (N_budget - N_current) / N_budget
|
||||
[1] step_ratio — current_step / max_steps
|
||||
[2] elem_ratio — N_current / N_budget
|
||||
[3] logeta_mean
|
||||
[4] logeta_std
|
||||
[5] logeta_max
|
||||
[6] logeta_p90
|
||||
[7] logeta_p75
|
||||
[8] top10_eta_energy_ratio — top 10% eta^2 energy / total
|
||||
[9] eligible_ratio — elements above area safeguard / total
|
||||
[10] inside_eta_energy — eta energy inside scatterer / total
|
||||
[11] outside_eta_energy — eta energy outside scatterer / total
|
||||
[12] interface_eta_energy — eta energy near interface / total
|
||||
"""
|
||||
eta = self._eta_indicator
|
||||
N = self._num_elements
|
||||
eps = 1e-12
|
||||
|
||||
# log-eta for percentile stats
|
||||
eps_score = max(0.01 * float(np.median(eta)), 1e-12)
|
||||
logeta = np.log(np.maximum(eta, 1e-30) + eps_score)
|
||||
|
||||
# budget stats
|
||||
remaining = max(0, self._n_budget - N)
|
||||
remaining_ratio = remaining / max(self._n_budget, 1)
|
||||
step_ratio = self._timestep / max(self._max_timesteps, 1)
|
||||
elem_ratio = N / max(self._n_budget, 1)
|
||||
|
||||
# logeta stats
|
||||
le_mean = float(np.mean(logeta))
|
||||
le_std = float(np.std(logeta)) + 1e-8
|
||||
le_max = float(np.max(logeta))
|
||||
le_p90 = float(np.percentile(logeta, 90))
|
||||
le_p75 = float(np.percentile(logeta, 75))
|
||||
|
||||
# top 10% eta energy ratio
|
||||
eta_sq = eta ** 2
|
||||
total_energy = float(np.sum(eta_sq))
|
||||
if total_energy > 0:
|
||||
k10 = max(1, int(0.1 * N))
|
||||
top10_idx = np.argsort(eta_sq)[-k10:]
|
||||
top10_ratio = float(np.sum(eta_sq[top10_idx])) / (total_energy + eps)
|
||||
else:
|
||||
top10_ratio = 0.0
|
||||
|
||||
# eligible ratio (area safeguard)
|
||||
domain_area = float(np.prod(
|
||||
self.fem_problem.plot_boundary[2:] - self.fem_problem.plot_boundary[:2]
|
||||
))
|
||||
V_min = 1e-10 * domain_area
|
||||
eligible_ratio = float(np.sum(self.element_volumes > V_min)) / max(N, 1)
|
||||
|
||||
# region eta energy ratios
|
||||
eps_r = self._epsilon_r_elements
|
||||
inside_mask = eps_r > 1.0
|
||||
outside_mask = ~inside_mask
|
||||
|
||||
fp = self.fem_problem.fem_problem
|
||||
cx = getattr(fp, "_cx", 0.5)
|
||||
cy = getattr(fp, "_cy", 0.5)
|
||||
radius = getattr(fp, "_radius", 0.2)
|
||||
midpoints = self._element_midpoints
|
||||
dist_raw = np.sqrt((midpoints[:, 0] - cx)**2 + (midpoints[:, 1] - cy)**2)
|
||||
lam = 2.0 * np.pi / max(self._wave_number, 1e-8)
|
||||
interface_mask = np.abs(dist_raw - radius) < 0.2 * lam
|
||||
|
||||
inside_energy = float(np.sum(eta_sq[inside_mask])) / (total_energy + eps)
|
||||
outside_energy = float(np.sum(eta_sq[outside_mask])) / (total_energy + eps)
|
||||
interface_energy = float(np.sum(eta_sq[interface_mask])) / (total_energy + eps)
|
||||
|
||||
stats = np.array([
|
||||
remaining_ratio, step_ratio, elem_ratio,
|
||||
le_mean, le_std, le_max, le_p90, le_p75,
|
||||
top10_ratio, eligible_ratio,
|
||||
inside_energy, outside_energy, interface_energy,
|
||||
], dtype=np.float32)
|
||||
return stats
|
||||
|
||||
@property
|
||||
def last_observation(self) -> Data:
|
||||
"""
|
||||
|
|
@ -716,6 +936,10 @@ class MeshRefinement(gym.Env):
|
|||
|
||||
observation_graph = Data(**graph_dict)
|
||||
observation_graph.eta = torch.tensor(self._eta_indicator, dtype=torch.float32)
|
||||
observation_graph.area = torch.tensor(self.element_volumes, dtype=torch.float32)
|
||||
observation_graph.global_stats = torch.tensor(
|
||||
self._compute_global_stats(), dtype=torch.float32
|
||||
).unsqueeze(0) # [1, num_global_stats]
|
||||
|
||||
return observation_graph
|
||||
|
||||
|
|
@ -932,25 +1156,16 @@ class MeshRefinement(gym.Env):
|
|||
|
||||
reward_per_agent = self.project_to_scalar(reward_per_agent_and_dim)
|
||||
|
||||
# ── Causal isolation + bounded signals ──
|
||||
# r_local: clipped to [−1, +1] — prevents pollution-error inversions
|
||||
# (±4.6) from hijacking the Critic's value estimate.
|
||||
# r_bonus: 0.5·tanh(η/μ − 1) — linear near μ (preserves Dörfler),
|
||||
# saturates at ±0.5 for extreme η, bounded and safe.
|
||||
# Unrefined parents: r = 0 (causal isolation).
|
||||
# ── Pure local improvement reward (no modulation, no bonus) ──
|
||||
# r_i = clip(log(η_old) − log(l2(η_child)), 0, rmax)
|
||||
# L₂ aggregation guarantees r_local ≥ 0; clip lower bound at 0 as
|
||||
# a safety floor against floating-point noise.
|
||||
rmax = 2.0
|
||||
unique_old, counts = np.unique(self.agent_mapping, return_counts=True)
|
||||
refined_mask = np.zeros(len(reward_per_agent), dtype=bool)
|
||||
refined_mask[unique_old[counts > 1]] = True
|
||||
|
||||
# Clip r_local to prevent outlier-driven value collapse
|
||||
reward_per_agent = np.clip(reward_per_agent, -1.0, 1.0)
|
||||
|
||||
# Bounded state bonus: tanh preserves Dörfler near μ, caps at extreme η
|
||||
eta_raw = self._previous_eta_indicator
|
||||
mu_eta = float(np.mean(eta_raw))
|
||||
reward_per_agent[refined_mask] += 0.5 * np.tanh(
|
||||
eta_raw[refined_mask] / (mu_eta + 1e-8) - 1.0
|
||||
)
|
||||
reward_per_agent = np.clip(reward_per_agent, 0.0, rmax)
|
||||
|
||||
# Unrefined: clean zero (causal isolation)
|
||||
reward_per_agent[~refined_mask] = 0.0
|
||||
|
|
@ -958,31 +1173,30 @@ class MeshRefinement(gym.Env):
|
|||
# apply action/element penalty (refined parents only)
|
||||
element_penalty = np.zeros(len(reward_per_agent), dtype=reward_per_agent.dtype)
|
||||
element_penalty[unique_old] = self._element_penalty_lambda * (counts - 1)
|
||||
|
||||
# Step 0 penalty scaling: reduce element penalty on first refinement step
|
||||
# to prevent "reward < 0 but aw_rel improved" feedback inversion.
|
||||
step0_scale = float(self._environment_config.get("step0_penalty_scale", 1.0))
|
||||
penalty_scale = step0_scale if self._timestep == 1 else 1.0
|
||||
element_penalty = element_penalty * penalty_scale
|
||||
|
||||
element_limit_penalty = (
|
||||
(self._element_limit_penalty / self._previous_num_elements)
|
||||
if self.reached_element_limits
|
||||
else 0
|
||||
)
|
||||
|
||||
# Step 0 diagnostics (r_local = reward before penalty, already clipped)
|
||||
r_local_pre_penalty = reward_per_agent.copy()
|
||||
_step0_penalty_scale = penalty_scale
|
||||
_step0_avg_penalty = float(np.mean(element_penalty[refined_mask])) if refined_mask.any() else 0.0
|
||||
_step0_avg_r_local = float(np.mean(r_local_pre_penalty[refined_mask])) if refined_mask.any() else 0.0
|
||||
_step0_step_id = self._timestep
|
||||
|
||||
reward_per_agent = (
|
||||
reward_per_agent - element_penalty - element_limit_penalty
|
||||
)
|
||||
|
||||
# ── Global error change (diagnostic only, NOT injected into Actor reward) ──
|
||||
# Removing global_bonus from per-element reward eliminates the broken causal
|
||||
# chain: Helmholtz pollution error can make E_new > E_old even when the
|
||||
# selected elements were the right choice, punishing agents for physics
|
||||
# they didn't cause. Actor optimises r_local only; Critic captures global
|
||||
# effects through value estimation.
|
||||
l2_old = self._previous_solution_l2_norm
|
||||
l2_new = self._compute_solution_l2_norm()
|
||||
eta_l2_old = float(np.sqrt(np.sum(old_eta ** 2)))
|
||||
eta_l2_new = float(np.sqrt(np.sum(new_eta ** 2)))
|
||||
eps_l2 = 1e-12
|
||||
E_old = eta_l2_old / max(l2_old, eps_l2)
|
||||
E_new = eta_l2_new / max(l2_new, eps_l2)
|
||||
global_bonus = self._global_reward_alpha * float(np.log(E_old + eps_l2) - np.log(E_new + eps_l2))
|
||||
# global_bonus intentionally NOT added to reward_per_agent — see above.
|
||||
|
||||
self._reward_per_agent = reward_per_agent
|
||||
self._cumulative_reward_per_agent = (
|
||||
self._cumulative_reward_per_agent[self._previous_agent_mapping]
|
||||
|
|
@ -991,11 +1205,15 @@ class MeshRefinement(gym.Env):
|
|||
reward = reward_per_agent
|
||||
|
||||
reward_dict["reward"] = reward
|
||||
reward_dict["potential_bonus"] = global_bonus
|
||||
reward_dict["penalty"] = -reward
|
||||
reward_dict["element_limit_penalty"] = element_limit_penalty
|
||||
reward_dict["element_penalty"] = element_penalty
|
||||
reward_dict["element_penalty_lambda"] = self._element_penalty_lambda
|
||||
# Step 0 penalty scaling diagnostics
|
||||
reward_dict["penalty_scale"] = _step0_penalty_scale
|
||||
reward_dict["avg_penalty"] = _step0_avg_penalty
|
||||
reward_dict["avg_r_local"] = _step0_avg_r_local
|
||||
reward_dict["step_id"] = _step0_step_id
|
||||
return reward, reward_dict
|
||||
|
||||
@property
|
||||
|
|
|
|||
|
|
@ -0,0 +1,469 @@
|
|||
# Outlook: GNN-Guided Adaptive Mesh Refinement for 2D Helmholtz Scattering
|
||||
|
||||
## 1. 问题定义
|
||||
|
||||
求解 2D 介质圆柱的电磁散射（散射场公式）：
|
||||
|
||||
```
|
||||
∇²u + k²·ε_r·u = −k²·(ε_r − 1)·u_inc
|
||||
∂u/∂n − i·k_local·u = 0 (Sommerfeld 辐射 BC)
|
||||
```
|
||||
|
||||
- 入射波：`u_inc = exp(i·k·x)`，参考解：Mie 解析解
|
||||
- 参数空间：k∈[3,15]，eps_r∈[2,8]，cx/cy∈[0.2,0.8]，radius∈[0.05,0.25]
|
||||
- 核心目标：在参数化 Helmholtz 散射问题中，学习一个无在线求解的预算约束网格预测器，用于近似 residual-AMR 的最终加密分布，并在低预算下优于简单物理启发式网格。
|
||||
|
||||
---
|
||||
|
||||
## 2. 算法全流程
|
||||
|
||||
```
|
||||
Step 1. 数据生成 (gen.py)
|
||||
残差驱动 AMR → 每步保存 cell 状态 + 标签
|
||||
│
|
||||
▼
|
||||
Step 2. 训练 (train_correction.py)
|
||||
features(15-dim) + physics_score → GNN → sigmoid → 二分类 teacher_mark
|
||||
│
|
||||
▼
|
||||
Step 3. 评估
|
||||
3a. 离线指标 (test_correction.py) — top-k overlap / AUC
|
||||
3b. Rollout 评估 (eval_correction.py) — 迭代加密 → FEM → aw_rel
|
||||
│
|
||||
▼
|
||||
Step 4. 可视化 (viz_correction.py)
|
||||
amr 模式: GNN 驱动完整 AMR → 网格 + 场 + 误差
|
||||
step 模式: 单步 GNN vs teacher vs physics 标记对比
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. 数据生成 (`gen.py`)
|
||||
|
||||
### 3.1 初始网格
|
||||
|
||||
物理自适应初始网格（`build_physics_safe_initial_mesh`）：
|
||||
|
||||
- 均匀基底网格 + 迭代局部加密
|
||||
- 介质外：h ≤ λ₀/q，介质内：h ≤ λ_eff/q（q=2，每波长 2 个单元）
|
||||
- 加密准则：`score = max_edge / h_target > 1` 的单元被加密
|
||||
- 二分搜索控制每批加密数量，不超预算
|
||||
|
||||
### 3.2 AMR 循环（每步保存）
|
||||
|
||||
```
|
||||
初始 mesh → FEM solve → 残差估计器 η → teacher_mark (η top-k)
|
||||
├── physics_score → physics_mark (top-k)
|
||||
├── correction_label = teacher_mark − physics_mark
|
||||
└── 保存 .npz → 按 η 选单元加密 → 下一步
|
||||
```
|
||||
|
||||
**残差估计器 η**（`environment/helmholtz.py:_compute_residual_indicator`）：
|
||||
- 内部残差：`h_K/k · √V_K · |k²ε_r·u_h + k²(ε_r−1)·u_inc|`
|
||||
- 梯度跳变：`√(½ Σ h_e/k · |[[∇u_h·n]]|²)`
|
||||
- SBC 边界残差：`h_bnd/k · |∂u/∂n − i·k_local·u|`
|
||||
|
||||
**标记策略**：
|
||||
- teacher_mark：按 η 取 top `mark_fraction`（默认 3%）
|
||||
- physics_mark：按 `physics_score` 取 top `mark_fraction`
|
||||
- correction_label = +1（teacher 独有）、0（一致）、−1（physics 独有）
|
||||
|
||||
**安全过滤**：
|
||||
1. 面积过滤：排除面积 ≤ 1e-10 的退化单元
|
||||
2. 反向 Dörfler：排除误差贡献最低 1% 的单元
|
||||
|
||||
### 3.3 输出格式
|
||||
|
||||
```
|
||||
outlook/data_correction/
|
||||
├── params_list.npz # (n_samples, 5) PDE 参数
|
||||
├── sample0000_step000.npz # 逐样本逐步数据
|
||||
└── summary.json
|
||||
```
|
||||
|
||||
每个 step .npz：
|
||||
|
||||
| 字段 | 形状 | 说明 |
|
||||
|------|------|------|
|
||||
| `features` | `(n_elem, 15)` | 几何/物理特征 |
|
||||
| `edge_index` | `(2, n_edges)` | 网格图结构 |
|
||||
| `physics_score` | `(n_elem,)` | h/λ_eff |
|
||||
| `teacher_eta` | `(n_elem,)` | 残差估计器 |
|
||||
| `teacher_mark` | `(n_elem,)` | η top-k 标记 (0/1) |
|
||||
| `physics_mark` | `(n_elem,)` | physics top-k 标记 (0/1) |
|
||||
| `correction_label` | `(n_elem,)` | teacher − physics (−1/0/+1) |
|
||||
|
||||
---
|
||||
|
||||
## 4. 特征工程
|
||||
|
||||
### 4.1 15 维基础特征
|
||||
|
||||
| 维 | 特征 | 说明 |
|
||||
|----|------|------|
|
||||
| 0,1 | x, y | 单元中点坐标 |
|
||||
| 2 | area | 单元面积 |
|
||||
| 3 | dist_to_center | 到圆柱中心距离 |
|
||||
| 4 | signed_dist | dist − radius（负=介质内） |
|
||||
| 5 | inside | 是否在圆柱内 (0/1) |
|
||||
| 6 | k | 波数 |
|
||||
| 7 | eps_r | 介电常数 |
|
||||
| 8 | radius | 半径 |
|
||||
| 9,10 | cx, cy | 圆柱中心 |
|
||||
| 11 | k_h | k × √area |
|
||||
| 12 | k_eps_h | k × √eps_r × √area |
|
||||
| 13,14 | sin(k·x), cos(k·x) | 入射波相位 |
|
||||
|
||||
### 4.2 Physics Score（第 16 维输入）
|
||||
|
||||
```python
|
||||
lambda_eff = 2π / (k · √eps_r) # 介质内
|
||||
或 2π / k # 介质外
|
||||
physics_score = max_edge / lambda_eff # >1 = 分辨率不足
|
||||
```
|
||||
|
||||
### 4.3 归一化
|
||||
|
||||
训练集 z-score 归一化，推理复用同一统计量：
|
||||
```python
|
||||
x_norm = (concat(features, physics_score) - mean) / scale
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. GNN 架构
|
||||
|
||||
### 5.1 CorrectionGNN
|
||||
|
||||
基于 DensityGNN 骨干，替换密度回归头为二分类 logit 头：
|
||||
|
||||
```
|
||||
Input: (n_cells, 16) node features
|
||||
│
|
||||
├── node_embedding: Linear(16 → latent_dim)
|
||||
├── edge_embedding: Linear(16 → latent_dim)
|
||||
│
|
||||
├── N × MessagePassingStep:
|
||||
│ ├── EdgeModule: MLP([src | dst | edge_attr]) → latent_dim
|
||||
│ ├── NodeModule: MLP([node | mean(incoming_edges)]) → latent_dim
|
||||
│ └── LayerNorm + Residual
|
||||
│
|
||||
├── GlobalVirtualNode: mean_pool → attention_gate → broadcast
|
||||
│
|
||||
└── head: Linear(latent → hidden) → ReLU → Linear(hidden → 1 logit)
|
||||
```
|
||||
|
||||
### 5.2 关键设计
|
||||
|
||||
- 边特征：`edge_attr = |x[src] - x[dst]|`（节点特征差绝对值）
|
||||
- 边丢弃：训练 0.1，推理 0.0
|
||||
- 损失：BCEWithLogitsLoss + per-graph `pos_weight = neg/pos`
|
||||
|
||||
### 5.3 训练配置
|
||||
|
||||
| 参数 | 值 |
|
||||
|------|-----|
|
||||
| latent_dim | 64 |
|
||||
| num_mp_steps | 3 |
|
||||
| head_hidden | 64 |
|
||||
| lr | 1e-3 |
|
||||
| optimizer | Adam |
|
||||
| scheduler | ReduceLROnPlateau (patience=10) |
|
||||
|
||||
---
|
||||
|
||||
## 6. 评估体系
|
||||
|
||||
### 6.1 离线指标（test_correction.py）
|
||||
|
||||
- **top-k overlap**：GNN 概率最高的 k 个 cell 与 teacher_mark 的交集 / k
|
||||
- **AUC**：ROC-AUC（GNN vs physics baseline）
|
||||
- **gnn_beats_physics_ratio**：GNN 优于 physics 的样本比例
|
||||
|
||||
### 6.2 Rollout 评估（eval_correction.py）
|
||||
|
||||
从初始 mesh 出发，**无 teacher_eta，无残差标记，无中间 FEM solve**。每步只用分数决定加密单元，最终一次 FEM solve 计算误差。
|
||||
|
||||
三种方法：
|
||||
|
||||
| 方法 | 打分 | 说明 |
|
||||
|------|------|------|
|
||||
| `physics` | `physics_score` | 纯物理先验 |
|
||||
| `neural` | `model(features + physics_score)` | 纯 GNN |
|
||||
| `hybrid` | `α·zscore(physics) + β·zscore(neural)` | 混合（默认 α=β=0.5） |
|
||||
|
||||
### 6.3 误差指标
|
||||
|
||||
**aw_rel**（面积加权相对误差）：
|
||||
```
|
||||
aw_rel = √( Σ err²_tri · area / Σ ref²_tri · area )
|
||||
```
|
||||
|
||||
**max_err**（最大逐点误差）：
|
||||
```
|
||||
max_err = max |Re(u_fem) − Re(u_mie)|
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. 可视化
|
||||
|
||||
### 7.1 amr 模式
|
||||
|
||||
GNN 驱动完整 AMR，每步 FEM solve，展示网格和场演变。
|
||||
|
||||
输出：
|
||||
- `amr_overview.png` — 所有步骤总览
|
||||
- `amr_steps/step{XX}.png` — 每步 3 面板（FEM 场 / Mie 参考 / 误差）
|
||||
- `ground_truth.png` — 高保真参考解（`--compare` 时）
|
||||
- `compare.png` — Physics vs GNN vs Eta 对比（`--compare` 时）
|
||||
|
||||
### 7.2 step 模式
|
||||
|
||||
重建指定 AMR 步的 mesh，对比 GNN / teacher / physics 标记。
|
||||
|
||||
输出：
|
||||
- `marks_*.png` — 2×2 对比图（teacher / GNN / physics / TP/FP/FN/TN）
|
||||
- `field_gnn_*.png` — GNN 加密后 3 面板图
|
||||
- `field_eta_*.png` — 传统 η 加密后 3 面板图
|
||||
|
||||
---
|
||||
|
||||
## 8. 使用方法
|
||||
|
||||
### 8.1 数据生成
|
||||
|
||||
```bash
|
||||
python outlook/src/gen.py \
|
||||
--n-samples 100 \
|
||||
--max-elements 12000 \
|
||||
--mark-fraction 0.03 \
|
||||
--output-dir outlook/data_correction
|
||||
```
|
||||
|
||||
### 8.2 训练
|
||||
|
||||
```bash
|
||||
python outlook/src/train_correction.py \
|
||||
--data-dir outlook/data_correction \
|
||||
--epochs 100 \
|
||||
--batch-size 32 \
|
||||
--lr 1e-3 \
|
||||
--device cuda \
|
||||
--checkpoint-out outlook/ckpt/correction.pt
|
||||
```
|
||||
|
||||
输出：
|
||||
- `correction.pt` — 最终模型
|
||||
- `correction_best.pt` — val_loss 最低 checkpoint
|
||||
- `correction_train_log.json` — 逐 epoch 日志
|
||||
|
||||
### 8.3 离线指标评估
|
||||
|
||||
```bash
|
||||
python outlook/src/test_correction.py \
|
||||
--checkpoint outlook/ckpt/correction.pt \
|
||||
--data-dir outlook/data_correction \
|
||||
--device cuda \
|
||||
--visualize \
|
||||
--output-dir outlook/result/correction/test
|
||||
```
|
||||
|
||||
### 8.4 Rollout 评估
|
||||
|
||||
```bash
|
||||
python outlook/src/eval_correction.py \
|
||||
--checkpoint outlook/ckpt/correction.pt \
|
||||
--data-dir outlook/data_correction \
|
||||
--target-elements 2000,4000,8000,12000 \
|
||||
--mark-fraction 0.03 \
|
||||
--max-steps 40 \
|
||||
--methods physics,neural,hybrid \
|
||||
--alpha 0.5 --beta 0.5 \
|
||||
--device cuda \
|
||||
--output-dir outlook/result/correction/rollout
|
||||
```
|
||||
|
||||
输出：
|
||||
- `eval_results.json` — 详细结果 + 聚合统计 + 改善比例
|
||||
- `summary.csv` — 每行一个 (method, target) 汇总
|
||||
- `aw_rel_vs_elements.png` / `max_err_vs_elements.png`
|
||||
|
||||
### 8.5 可视化
|
||||
|
||||
```bash
|
||||
# 端到端 AMR
|
||||
python outlook/src/viz_correction.py \
|
||||
--checkpoint outlook/ckpt/correction.pt \
|
||||
--data-dir outlook/data_correction \
|
||||
--mode amr --sample-id 0 --max-elements 5000 \
|
||||
--device cuda \
|
||||
--output-dir outlook/result/correction/viz
|
||||
|
||||
# 单步对比
|
||||
python outlook/src/viz_correction.py \
|
||||
--checkpoint outlook/ckpt/correction.pt \
|
||||
--data-dir outlook/data_correction \
|
||||
--mode step --sample-id 0 --step 0 \
|
||||
--device cuda \
|
||||
--output-dir outlook/result/correction/viz
|
||||
|
||||
# OOD 评估（自定义物理参数，需同时指定 k, eps-r, cx, cy, radius）
|
||||
python outlook/src/viz_correction.py \
|
||||
--checkpoint outlook/ckpt/correction.pt \
|
||||
--data-dir outlook/data_correction \
|
||||
--mode amr \
|
||||
--k 30 --eps-r 4 --cx 0.5 --cy 0.5 --radius 0.15 \
|
||||
--max-elements 6000 --compare \
|
||||
--device cuda \
|
||||
--output-dir outlook/result/correction/viz
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 9. 目录结构
|
||||
|
||||
```
|
||||
outlook/
|
||||
├── README.md # 本文档
|
||||
├── train.sh # SLURM 训练脚本（可配置 batch_size，默认 1）
|
||||
├── analyze_budget_teacher.py # Budget teacher 数据集分析
|
||||
├── check_correction_data.py # 数据质量校验
|
||||
│
|
||||
├── src/
|
||||
│ ├── gen.py # 数据生成
|
||||
│ ├── train_correction.py # 训练
|
||||
│ ├── test_correction.py # 离线指标评估
|
||||
│ ├── eval_correction.py # Rollout 评估
|
||||
│ ├── viz_correction.py # 可视化
|
||||
│ ├── rollout.py # 统一 AMR rollout（核心循环 + 共享 refinement 工具）
|
||||
│ ├── gnn.py # DensityGNN 模型
|
||||
│ ├── feat.py # 特征提取
|
||||
│ ├── graph.py # mesh → PyG graph（含 build_edge_index_np）
|
||||
│ ├── mesh.py # score → refined mesh
|
||||
│ ├── metrics.py # aw_rel / max_err
|
||||
│ ├── problem.py # PDE 参数 → HelmholtzProblem
|
||||
│ └── amr.py # 残差 AMR teacher
|
||||
│
|
||||
├── ckpt/ # checkpoint
|
||||
├── data_correction/ # 训练数据
|
||||
└── result/ # 评估结果
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 10. 辅助模块
|
||||
|
||||
| 模块 | 职责 |
|
||||
|------|------|
|
||||
| `rollout.py` | 统一 AMR rollout：`run_rollout_to_budget()` 驱动完整加密循环，支持 physics/neural/hybrid/eta 四种打分方法 |
|
||||
| `feat.py` | 构建 15 维基础特征 + budget_code |
|
||||
| `graph.py` | mesh → PyG Data（边特征 = phase_distance） |
|
||||
| `mesh.py` | score → 迭代 top-k 加密（叶子继承初始单元 score） |
|
||||
| `problem.py` | 参数字典 → HelmholtzProblem 实例 |
|
||||
| `amr.py` | 纯残差驱动 AMR teacher（无网络） |
|
||||
| `metrics.py` | `compute_mie_error`（aw_rel + max_err） |
|
||||
|
||||
---
|
||||
|
||||
## 11. 测试结果
|
||||
|
||||
### 11.1 训练配置
|
||||
|
||||
- 数据集：100 样本，~19 步/样本，1888 个 step 文件
|
||||
- 训练/验证划分：80/20（按 sample_id，seed=42）
|
||||
- 训练图：1513 个，验证图：375 个
|
||||
- 正样本比例：3.0%（teacher_mark top-3%）
|
||||
- 训练 100 epoch，最佳 epoch 57（val_loss=0.5873）
|
||||
|
||||
### 11.2 离线指标（test_correction.py）
|
||||
|
||||
在 20 个验证样本的 375 个 step 图上评估：
|
||||
|
||||
| 指标 | GNN | Physics Baseline |
|
||||
|------|-----|-----------------|
|
||||
| **AUC** | **0.9412** | 0.0000 |
|
||||
| **top-k overlap 均值** | **0.440** | 0.131 |
|
||||
| GNN beats physics | **372/375 (99.2%)** | — |
|
||||
|
||||
> Physics AUC=0.0 是因为 physics_score 在均匀网格上只有 2 个离散值，无法区分排序。GNN 通过学习 η 的空间分布模式，top-k overlap 提升 **3.4 倍**。
|
||||
|
||||
### 11.3 Rollout 评估（eval_correction.py）
|
||||
|
||||
20 个验证样本 × 3 种方法 × 3 个目标预算，每次 rollout 最终做 1 次 FEM solve：
|
||||
|
||||
**aw_rel（面积加权相对误差）**：
|
||||
|
||||
| 方法 | target=2000 | target=4000 | target=8000 |
|
||||
|------|------------|------------|------------|
|
||||
| physics | 19.19% | 14.04% | 13.25% |
|
||||
| neural | 15.96% | 13.72% | 12.98% |
|
||||
| **hybrid** | **15.78%** | **13.57%** | **12.94%** |
|
||||
|
||||
**max_err（最大逐点误差）**：
|
||||
|
||||
| 方法 | target=2000 | target=4000 | target=8000 |
|
||||
|------|------------|------------|------------|
|
||||
| physics | 0.3301 | 0.2514 | 0.2418 |
|
||||
| neural | 0.2741 | 0.2431 | 0.2376 |
|
||||
| **hybrid** | **0.2686** | **0.2430** | **0.2383** |
|
||||
|
||||
**相对 physics-only 的改善**：
|
||||
|
||||
| 方法 | target | aw_rel 改善 | max_err 改善 |
|
||||
|------|--------|-----------|-------------|
|
||||
| hybrid | 2000 | **+9.8%** | **+6.9%** |
|
||||
| neural | 2000 | +9.9% | +6.2% |
|
||||
| hybrid | 4000 | +1.9% | +1.5% |
|
||||
| neural | 4000 | +1.1% | +1.4% |
|
||||
| hybrid | 8000 | +1.9% | +1.3% |
|
||||
| neural | 8000 | +1.8% | +1.5% |
|
||||
|
||||
### 11.4 关键发现
|
||||
|
||||
1. **GNN 显著优于 physics baseline**：离线 top-k overlap 从 0.131 提升到 0.440（3.4×），99.2% 的验证图上 GNN 胜出
|
||||
2. **低预算改善最大**：target=2000 时 aw_rel 改善 ~10%，max_err 改善 ~7%；高预算时改善收窄到 ~2%（因为预算充足时 physics 也够用）
|
||||
3. **hybrid 略优于 neural**：z-score 混合策略在多数场景下比纯 GNN 更稳定
|
||||
4. **GNN 推理效率**：neural 方法比 physics 方法少用 ~12% 的 refinement 步数达到相同预算（8.9 vs 9.4 步 @2000），因为 GNN 的标记更精准
|
||||
|
||||
### 11.5 OOD 评估（k 超出训练范围 [3,15]）
|
||||
|
||||
固定 `eps_r=4, cx=cy=0.5, radius=0.15`，在 k=20/30/50/80 上做三方法对比（target=5000/10000/20000）：
|
||||
|
||||
**aw_rel (%)**：
|
||||
|
||||
| k | 方法 | target=5000 | target=10000 | target=20000 |
|
||||
|---|------|------------|-------------|-------------|
|
||||
| 20 | physics | 32.17 | 24.55 | 10.61 |
|
||||
| 20 | neural | 32.56 | **17.85** | 10.68 |
|
||||
| 20 | hybrid | **31.73** | 19.57 | 14.03 |
|
||||
| 30 | physics | 33.94 | 15.15 | 10.14 |
|
||||
| 30 | neural | **23.52** | 13.40 | 8.38 |
|
||||
| 30 | hybrid | 27.94 | **13.34** | **7.96** |
|
||||
| 50 | physics | 94.00 | 73.56 | 33.00 |
|
||||
| 50 | neural | **88.16** | **52.92** | **27.99** |
|
||||
| 50 | hybrid | 90.97 | 68.47 | 29.27 |
|
||||
| 80 | physics | 122.06 | 135.78 | 101.64 |
|
||||
| 80 | neural | 139.84 | **127.65** | **92.67** |
|
||||
| 80 | hybrid | 139.84 | 127.68 | 105.94 |
|
||||
|
||||
**相对 physics-only 的改善 (%)**：
|
||||
|
||||
| k | 方法 | target=5000 | target=10000 | target=20000 |
|
||||
|---|------|------------|-------------|-------------|
|
||||
| 20 | neural | −1.2 | **+27.3** | −0.7 |
|
||||
| 20 | hybrid | +1.4 | +20.3 | −32.3 |
|
||||
| 30 | neural | **+30.7** | +11.6 | +17.4 |
|
||||
| 30 | hybrid | +17.7 | +12.0 | +21.5 |
|
||||
| 50 | neural | +6.2 | **+28.1** | **+15.2** |
|
||||
| 50 | hybrid | +3.2 | +6.9 | +11.3 |
|
||||
| 80 | neural | −14.6 | +6.0 | **+8.8** |
|
||||
| 80 | hybrid | −14.6 | +6.0 | −4.2 |
|
||||
|
||||
**OOD 关键发现**：
|
||||
|
||||
1. **neural 在远 OOD（k=50）优势最大**：target=10000 时 aw_rel 从 73.56% 降至 52.92%（改善 +28.1%），hybrid 仅改善 +6.9%
|
||||
2. **hybrid 的 physics 先验在 OOD 时成为拖累**：z-score 混合中 physics_score 的 `max_edge/h_target` 在高 k 下不再准确，导致 hybrid 的标记不如纯 neural
|
||||
3. **neural 的泛化来自几何特征**：GNN 学到的是"界面附近 + 介质内 → 高密度"的空间模式，这一模式在 k 超出训练范围时仍然成立；而 physics_score 依赖 `lambda_eff = 2π/(k√ε_r)` 的具体数值
|
||||
4. **极端 OOD（k=80）两者都差**：误差超过 100%，说明 20000 个单元完全不够分辨 k=80 的短波结构（λ_eff≈0.028，需要 ~70000+ 单元）
|
||||
5. **k=20/30 时 hybrid 反而更好**：接近训练分布时 physics 先验有价值，hybrid 通过混合两种信号获得更稳定的标记
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,203 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Analyze budgeted teacher dataset: per-budget stats, error improvement, saturation.
|
||||
|
||||
Usage:
|
||||
python outlook/analyze_budget_teacher.py
|
||||
python outlook/analyze_budget_teacher.py --data-dir outlook/data/budget_density_dataset_check
|
||||
python outlook/analyze_budget_teacher.py --sat-threshold 0.05
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
def load_dataset(data_dir: Path):
|
||||
"""Load all sample .npz files, return list of dicts."""
|
||||
data_dir = Path(data_dir)
|
||||
params_path = data_dir / "params_list.npz"
|
||||
if params_path.exists():
|
||||
n_samples = len(np.load(params_path)["params"])
|
||||
else:
|
||||
# Fall back to scanning for numbered .npz files
|
||||
n_samples = len(sorted(data_dir.glob("[0-9]*.npz")))
|
||||
|
||||
samples = []
|
||||
for sid in range(n_samples):
|
||||
npz_path = data_dir / f"{sid:04d}.npz"
|
||||
if not npz_path.exists():
|
||||
continue
|
||||
d = np.load(npz_path)
|
||||
samples.append({
|
||||
"sid": sid,
|
||||
"budgets": d["budgets"],
|
||||
"actual_elements": d["actual_elements"],
|
||||
"teacher_aw_rel": d["teacher_aw_rel"],
|
||||
"teacher_max_err": d["teacher_max_err"],
|
||||
"valid_budgets": d["valid_budgets"] if "valid_budgets" in d else np.ones(len(d["budgets"]), dtype=bool),
|
||||
"match_ratios": d["match_ratios"] if "match_ratios" in d else d["actual_elements"] / d["budgets"],
|
||||
"k": float(d["k"]),
|
||||
"eps_r": float(d["eps_r"]),
|
||||
})
|
||||
return samples
|
||||
|
||||
|
||||
def per_budget_summary(samples):
|
||||
"""Aggregate stats grouped by budget value."""
|
||||
groups = {} # budget -> list of sample dicts
|
||||
for s in samples:
|
||||
for bi, b in enumerate(s["budgets"]):
|
||||
b = int(b)
|
||||
groups.setdefault(b, {"aw_rel": [], "max_err": [], "actual": [], "valid": []})
|
||||
groups[b]["aw_rel"].append(s["teacher_aw_rel"][bi])
|
||||
groups[b]["max_err"].append(s["teacher_max_err"][bi])
|
||||
groups[b]["actual"].append(s["actual_elements"][bi])
|
||||
groups[b]["valid"].append(bool(s["valid_budgets"][bi]))
|
||||
|
||||
rows = []
|
||||
for b in sorted(groups.keys()):
|
||||
g = groups[b]
|
||||
aw = np.array(g["aw_rel"])
|
||||
me = np.array(g["max_err"])
|
||||
rows.append({
|
||||
"budget": b,
|
||||
"n_samples": len(aw),
|
||||
"n_valid": sum(g["valid"]),
|
||||
"mean_actual": np.mean(g["actual"]),
|
||||
"mean_aw_rel": np.mean(aw),
|
||||
"std_aw_rel": np.std(aw),
|
||||
"mean_max_err": np.mean(me),
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def compute_improvements(rows):
|
||||
"""Compute Δaw_rel and relative improvement between adjacent budgets."""
|
||||
for i in range(len(rows)):
|
||||
rows[i]["delta_aw_rel"] = None
|
||||
rows[i]["rel_improvement"] = None
|
||||
for i in range(1, len(rows)):
|
||||
prev = rows[i - 1]["mean_aw_rel"]
|
||||
curr = rows[i]["mean_aw_rel"]
|
||||
delta = prev - curr # positive = improvement
|
||||
rows[i]["delta_aw_rel"] = delta
|
||||
rows[i]["rel_improvement"] = delta / prev if prev > 0 else 0.0
|
||||
return rows
|
||||
|
||||
|
||||
def estimate_saturation(samples, threshold=0.02):
|
||||
"""For each sample, find the first budget where further improvement < threshold.
|
||||
|
||||
Saturation budget = first budget b such that
|
||||
(aw_rel[b-1] - aw_rel[b]) / aw_rel[b-1] < threshold
|
||||
i.e. relative improvement from previous budget is below threshold.
|
||||
|
||||
Returns list of (sid, saturation_budget, sat_reason).
|
||||
"""
|
||||
results = []
|
||||
for s in samples:
|
||||
budgets = s["budgets"]
|
||||
aw = s["teacher_aw_rel"]
|
||||
sat_budget = None
|
||||
for bi in range(1, len(budgets)):
|
||||
if np.isnan(aw[bi - 1]) or np.isnan(aw[bi]):
|
||||
continue
|
||||
if aw[bi - 1] <= 0:
|
||||
continue
|
||||
rel_imp = (aw[bi - 1] - aw[bi]) / aw[bi - 1]
|
||||
if rel_imp < threshold:
|
||||
sat_budget = int(budgets[bi])
|
||||
break
|
||||
# If never saturated, mark as the largest budget
|
||||
if sat_budget is None:
|
||||
sat_budget = int(budgets[-1])
|
||||
results.append({
|
||||
"sid": s["sid"],
|
||||
"saturation_budget": sat_budget,
|
||||
})
|
||||
return results
|
||||
|
||||
|
||||
def print_table(rows):
|
||||
"""Print the per-budget summary table."""
|
||||
hdr = (f"{'budget':>8s} {'n':>4s} {'valid':>5s} "
|
||||
f"{'mean_act':>9s} {'mean_aw':>9s} {'std_aw':>9s} {'mean_me':>9s} "
|
||||
f"{'Δaw_rel':>9s} {'rel_imp':>9s}")
|
||||
print(hdr)
|
||||
print("-" * len(hdr))
|
||||
for r in rows:
|
||||
delta = f"{r['delta_aw_rel']:.6f}" if r["delta_aw_rel"] is not None else "—"
|
||||
rel = f"{r['rel_improvement']:.4f}" if r["rel_improvement"] is not None else "—"
|
||||
print(f"{r['budget']:8d} {r['n_samples']:4d} {r['n_valid']:5d} "
|
||||
f"{r['mean_actual']:9.1f} {r['mean_aw_rel']:.6f} {r['std_aw_rel']:.6f} "
|
||||
f"{r['mean_max_err']:.6f} {delta:>9s} {rel:>9s}")
|
||||
|
||||
|
||||
def print_saturation(sat_results, threshold):
|
||||
"""Print saturation budget distribution."""
|
||||
budgets = [r["saturation_budget"] for r in sat_results]
|
||||
unique = sorted(set(budgets))
|
||||
print(f"\nSaturation budget distribution (threshold={threshold:.0%} rel. improvement):")
|
||||
print(f" {'sat_budget':>12s} {'count':>6s} {'pct':>6s}")
|
||||
for b in unique:
|
||||
count = budgets.count(b)
|
||||
pct = 100.0 * count / len(budgets)
|
||||
print(f" {b:12d} {count:6d} {pct:5.1f}%")
|
||||
print(f" {'median':>12s} {np.median(budgets):.0f}")
|
||||
print(f" {'mean':>12s} {np.mean(budgets):.1f}")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Analyze budgeted teacher dataset")
|
||||
parser.add_argument("--data-dir", type=str, default="outlook/data",
|
||||
help="Path to budget dataset directory (default: outlook/data)")
|
||||
parser.add_argument("--sat-threshold", type=float, default=0.02,
|
||||
help="Relative improvement threshold for saturation (default: 0.02)")
|
||||
parser.add_argument("--sat-threshold-2", type=float, default=0.05,
|
||||
help="Second saturation threshold to report (default: 0.05)")
|
||||
args = parser.parse_args()
|
||||
|
||||
data_dir = Path(args.data_dir)
|
||||
|
||||
print("=" * 70)
|
||||
print("Budget Teacher Dataset Analysis")
|
||||
print("=" * 70)
|
||||
print(f" Data dir: {data_dir}")
|
||||
|
||||
samples = load_dataset(data_dir)
|
||||
print(f" Samples: {len(samples)}")
|
||||
|
||||
budgets_all = samples[0]["budgets"]
|
||||
print(f" Budgets: {[int(b) for b in budgets_all]}")
|
||||
print()
|
||||
|
||||
# 1. Per-budget summary
|
||||
rows = per_budget_summary(samples)
|
||||
rows = compute_improvements(rows)
|
||||
|
||||
print("Per-budget summary:")
|
||||
print_table(rows)
|
||||
|
||||
# 2. Saturation analysis
|
||||
for thresh, label in [(args.sat_threshold, "primary"),
|
||||
(args.sat_threshold_2, "secondary")]:
|
||||
sat = estimate_saturation(samples, threshold=thresh)
|
||||
print_saturation(sat, thresh)
|
||||
|
||||
# 3. Per-sample detail (compact)
|
||||
print(f"\nPer-sample detail:")
|
||||
print(f" {'sid':>4s} {'k':>6s} {'eps_r':>6s} "
|
||||
+ " ".join(f"b{int(b):>5d}" for b in budgets_all))
|
||||
print(f" {'':>4s} {'':>6s} {'':>6s} "
|
||||
+ " ".join(f"{'aw_rel':>5s}" for _ in budgets_all))
|
||||
print(" " + "-" * (20 + 8 * len(budgets_all)))
|
||||
for s in samples:
|
||||
vals = " ".join(f"{v:.4f}" for v in s["teacher_aw_rel"])
|
||||
print(f" {s['sid']:4d} {s['k']:6.2f} {s['eps_r']:6.2f} {vals}")
|
||||
|
||||
print("\nDone.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,457 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
check_correction_data.py — Validate step-wise correction dataset.
|
||||
|
||||
Checks:
|
||||
1. Global statistics (elements, marks, overlap, IoU, correction ratios)
|
||||
2. Per-step trend analysis (correction signal vs AMR step)
|
||||
3. Spatial visualization of 5 random samples
|
||||
4. Field & shape consistency
|
||||
5. Final verdict: PASS / WARNING / FAIL
|
||||
|
||||
Usage:
|
||||
python outlook/check_correction_data.py \
|
||||
--data-dir outlook/data_correction \
|
||||
--output-dir outlook/data_correction_check
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# Required fields and their expected properties
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
REQUIRED_FIELDS = {
|
||||
"features": {"ndim": 2, "dtype_kind": "f"},
|
||||
"edge_index": {"ndim": 2, "dtype_kind": "i"},
|
||||
"physics_score": {"ndim": 1, "dtype_kind": "f"},
|
||||
"teacher_eta": {"ndim": 1, "dtype_kind": "f"},
|
||||
"teacher_mark": {"ndim": 1, "dtype_kind": "i"},
|
||||
"physics_mark": {"ndim": 1, "dtype_kind": "i"},
|
||||
"correction_label": {"ndim": 1, "dtype_kind": "i"},
|
||||
"elements": {"ndim": 0},
|
||||
"step": {"ndim": 0},
|
||||
"aw_rel_before": {"ndim": 0},
|
||||
"max_err_before": {"ndim": 0},
|
||||
"k": {"ndim": 0},
|
||||
"eps_r": {"ndim": 0},
|
||||
"cx": {"ndim": 0},
|
||||
"cy": {"ndim": 0},
|
||||
"radius": {"ndim": 0},
|
||||
}
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# Helpers
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def iou_binary(a: np.ndarray, b: np.ndarray) -> float:
|
||||
"""IoU between two binary (0/1) arrays."""
|
||||
a = a.astype(bool)
|
||||
b = b.astype(bool)
|
||||
inter = np.sum(a & b)
|
||||
union = np.sum(a | b)
|
||||
return float(inter) / float(union) if union > 0 else 1.0
|
||||
|
||||
|
||||
def load_all_samples(data_dir: Path):
|
||||
"""Load all sampleXXXX_stepYYY.npz files, return list of dicts."""
|
||||
files = sorted(data_dir.glob("sample*_step*.npz"))
|
||||
samples = []
|
||||
for f in files:
|
||||
try:
|
||||
d = dict(np.load(f, allow_pickle=True))
|
||||
d["_path"] = str(f)
|
||||
d["_name"] = f.name
|
||||
samples.append(d)
|
||||
except Exception as e:
|
||||
print(f" [WARN] Cannot load {f.name}: {e}")
|
||||
return samples
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# 1. Field & shape consistency
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def check_fields(samples, issues):
|
||||
"""Check every file has the required fields with correct ndim."""
|
||||
print("\n" + "=" * 70)
|
||||
print(" [1/5] Field & Shape Consistency")
|
||||
print("=" * 70)
|
||||
|
||||
n_files = len(samples)
|
||||
n_issues_before = len(issues)
|
||||
|
||||
for s in samples:
|
||||
name = s["_name"]
|
||||
for field, spec in REQUIRED_FIELDS.items():
|
||||
if field not in s:
|
||||
issues.append(f"MISSING field '{field}' in {name}")
|
||||
continue
|
||||
arr = s[field]
|
||||
if arr.ndim != spec["ndim"]:
|
||||
issues.append(
|
||||
f"BAD ndim for '{field}' in {name}: "
|
||||
f"expected {spec['ndim']}, got {arr.ndim}"
|
||||
)
|
||||
if "dtype_kind" in spec and arr.dtype.kind != spec["dtype_kind"]:
|
||||
issues.append(
|
||||
f"BAD dtype for '{field}' in {name}: "
|
||||
f"expected kind={spec['dtype_kind']}, got {arr.dtype}"
|
||||
)
|
||||
|
||||
# Cross-check: features rows == len(physics_score) == elements
|
||||
if "features" in s and "physics_score" in s:
|
||||
nf = s["features"].shape[0]
|
||||
ns = len(s["physics_score"])
|
||||
ne = int(s["elements"])
|
||||
if not (nf == ns == ne):
|
||||
issues.append(
|
||||
f"SIZE MISMATCH in {name}: features={nf}, "
|
||||
f"physics_score={ns}, elements={ne}"
|
||||
)
|
||||
|
||||
n_new = len(issues) - n_issues_before
|
||||
if n_new == 0:
|
||||
print(f" All {n_files} files have consistent fields & shapes.")
|
||||
else:
|
||||
print(f" Found {n_new} field/shape issues.")
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# 2. Global statistics
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def compute_global_stats(samples):
|
||||
"""Compute and print global statistics across all files."""
|
||||
print("\n" + "=" * 70)
|
||||
print(" [2/5] Global Statistics")
|
||||
print("=" * 70)
|
||||
|
||||
elems = []
|
||||
teacher_ratios = []
|
||||
physics_ratios = []
|
||||
overlaps = []
|
||||
overlap_teacher = []
|
||||
overlap_physics = []
|
||||
ious = []
|
||||
pos_ratios = []
|
||||
neg_ratios = []
|
||||
|
||||
for s in samples:
|
||||
n = int(s["elements"])
|
||||
tm = s["teacher_mark"]
|
||||
pm = s["physics_mark"]
|
||||
cl = s["correction_label"]
|
||||
|
||||
elems.append(n)
|
||||
n_tm = int(np.sum(tm))
|
||||
n_pm = int(np.sum(pm))
|
||||
ov = int(np.sum((tm == 1) & (pm == 1)))
|
||||
|
||||
teacher_ratios.append(n_tm / max(n, 1))
|
||||
physics_ratios.append(n_pm / max(n, 1))
|
||||
overlaps.append(ov)
|
||||
overlap_teacher.append(ov / max(n_tm, 1))
|
||||
overlap_physics.append(ov / max(n_pm, 1))
|
||||
ious.append(iou_binary(tm, pm))
|
||||
pos_ratios.append(int(np.sum(cl == 1)) / max(n, 1))
|
||||
neg_ratios.append(int(np.sum(cl == -1)) / max(n, 1))
|
||||
|
||||
stats = {
|
||||
"total_files": len(samples),
|
||||
"elements": elems,
|
||||
"teacher_mark_ratio": teacher_ratios,
|
||||
"physics_mark_ratio": physics_ratios,
|
||||
"overlap_count": overlaps,
|
||||
"overlap/teacher": overlap_teacher,
|
||||
"overlap/physics": overlap_physics,
|
||||
"IoU": ious,
|
||||
"correction_pos_ratio": pos_ratios,
|
||||
"correction_neg_ratio": neg_ratios,
|
||||
}
|
||||
|
||||
print(f" {'Metric':<30s} {'mean':>10s} {'min':>10s} {'max':>10s}")
|
||||
print(f" {'-'*30} {'-'*10} {'-'*10} {'-'*10}")
|
||||
for key in [
|
||||
"elements", "teacher_mark_ratio", "physics_mark_ratio",
|
||||
"overlap_count", "overlap/teacher", "overlap/physics",
|
||||
"IoU", "correction_pos_ratio", "correction_neg_ratio",
|
||||
]:
|
||||
arr = np.array(stats[key])
|
||||
print(f" {key:<30s} {arr.mean():10.4f} {arr.min():10.4f} {arr.max():10.4f}")
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# 3. Per-step trend analysis
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def check_per_step_trends(samples, issues):
|
||||
"""Group by step, print trend table, flag anomalies."""
|
||||
print("\n" + "=" * 70)
|
||||
print(" [3/5] Per-Step Trend Analysis")
|
||||
print("=" * 70)
|
||||
|
||||
from collections import defaultdict
|
||||
step_data = defaultdict(lambda: {
|
||||
"elems": [], "tm_ratio": [], "pm_ratio": [],
|
||||
"overlap": [], "iou": [], "pos_ratio": [], "neg_ratio": [],
|
||||
})
|
||||
|
||||
for s in samples:
|
||||
step = int(s["step"])
|
||||
n = int(s["elements"])
|
||||
tm = s["teacher_mark"]
|
||||
pm = s["physics_mark"]
|
||||
cl = s["correction_label"]
|
||||
n_tm = int(np.sum(tm))
|
||||
n_pm = int(np.sum(pm))
|
||||
ov = int(np.sum((tm == 1) & (pm == 1)))
|
||||
|
||||
d = step_data[step]
|
||||
d["elems"].append(n)
|
||||
d["tm_ratio"].append(n_tm / max(n, 1))
|
||||
d["pm_ratio"].append(n_pm / max(n, 1))
|
||||
d["overlap"].append(ov)
|
||||
d["iou"].append(iou_binary(tm, pm))
|
||||
d["pos_ratio"].append(int(np.sum(cl == 1)) / max(n, 1))
|
||||
d["neg_ratio"].append(int(np.sum(cl == -1)) / max(n, 1))
|
||||
|
||||
steps = sorted(step_data.keys())
|
||||
header = f" {'step':>4s} {'n_files':>7s} {'mean_elem':>9s} {'tm_ratio':>9s} {'pm_ratio':>9s} {'IoU':>7s} {'pos%':>7s} {'neg%':>7s}"
|
||||
print(header)
|
||||
print(f" {'-'*4} {'-'*7} {'-'*9} {'-'*9} {'-'*9} {'-'*7} {'-'*7} {'-'*7}")
|
||||
|
||||
for st in steps:
|
||||
d = step_data[st]
|
||||
nf = len(d["elems"])
|
||||
me = np.mean(d["elems"])
|
||||
mt = np.mean(d["tm_ratio"])
|
||||
mp = np.mean(d["pm_ratio"])
|
||||
mi = np.mean(d["iou"])
|
||||
mpos = np.mean(d["pos_ratio"])
|
||||
mneg = np.mean(d["neg_ratio"])
|
||||
print(f" {st:4d} {nf:7d} {me:9.1f} {mt:9.4f} {mp:9.4f} {mi:7.4f} {mpos:7.4f} {mneg:7.4f}")
|
||||
|
||||
# Trend checks
|
||||
if len(steps) >= 3:
|
||||
# Check: correction signal should diminish at late steps (elements grow → marks shrink)
|
||||
late = steps[-1]
|
||||
early = steps[0]
|
||||
late_pos = np.mean(step_data[late]["pos_ratio"])
|
||||
early_pos = np.mean(step_data[early]["pos_ratio"])
|
||||
late_neg = np.mean(step_data[late]["neg_ratio"])
|
||||
early_neg = np.mean(step_data[early]["neg_ratio"])
|
||||
late_iou = np.mean(step_data[late]["iou"])
|
||||
early_iou = np.mean(step_data[early]["iou"])
|
||||
|
||||
print(f"\n Trend summary (step {early} → {late}):")
|
||||
print(f" pos_ratio: {early_pos:.4f} → {late_pos:.4f}")
|
||||
print(f" neg_ratio: {early_neg:.4f} → {late_neg:.4f}")
|
||||
print(f" IoU: {early_iou:.4f} → {late_iou:.4f}")
|
||||
|
||||
# Warning: if late-step IoU is much lower than early, marks diverge
|
||||
if late_iou < early_iou * 0.5:
|
||||
issues.append(
|
||||
f"TREND: IoU drops significantly from step {early} ({early_iou:.3f}) "
|
||||
f"to step {late} ({late_iou:.3f}) — marks diverge at late steps."
|
||||
)
|
||||
|
||||
# Warning: if correction signal vanishes (pos+neg → 0) at late steps
|
||||
late_corr = late_pos + late_neg
|
||||
early_corr = early_pos + early_neg
|
||||
if early_corr > 0.01 and late_corr < early_corr * 0.1:
|
||||
issues.append(
|
||||
f"TREND: Correction signal nearly vanishes at step {late} "
|
||||
f"({late_corr:.4f}) vs step {early} ({early_corr:.4f})."
|
||||
)
|
||||
|
||||
# Warning: if IoU is very low overall (teacher and physics barely agree)
|
||||
all_ious = [iou for s in steps for iou in step_data[s]["iou"]]
|
||||
overall_iou = np.mean(all_ious)
|
||||
if overall_iou < 0.1:
|
||||
issues.append(
|
||||
f"LOW IoU: mean IoU across all steps is {overall_iou:.4f} — "
|
||||
f"teacher_mark and physics_mark barely overlap."
|
||||
)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# 4. Spatial visualization
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def plot_spatial_samples(samples, output_dir: Path, n_plot: int = 5):
|
||||
"""Randomly select n_plot files and save 4-panel spatial maps."""
|
||||
print("\n" + "=" * 70)
|
||||
print(" [4/5] Spatial Visualization")
|
||||
print("=" * 70)
|
||||
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
rng = np.random.RandomState(1234)
|
||||
indices = rng.choice(len(samples), size=min(n_plot, len(samples)), replace=False)
|
||||
selected = [samples[i] for i in sorted(indices)]
|
||||
|
||||
for s in selected:
|
||||
name = s["_name"].replace(".npz", "")
|
||||
features = s["features"]
|
||||
x = features[:, 0]
|
||||
y = features[:, 1]
|
||||
|
||||
physics_score = s["physics_score"]
|
||||
teacher_eta = s["teacher_eta"]
|
||||
teacher_mark = s["teacher_mark"]
|
||||
correction_label = s["correction_label"]
|
||||
|
||||
fig, axes = plt.subplots(1, 4, figsize=(20, 5))
|
||||
fig.suptitle(
|
||||
f"{name} (elem={int(s['elements'])}, k={float(s['k']):.2f}, "
|
||||
f"step={int(s['step'])})",
|
||||
fontsize=12,
|
||||
)
|
||||
|
||||
# Panel 1: physics_score
|
||||
sc0 = axes[0].scatter(x, y, c=physics_score, s=8, cmap="viridis")
|
||||
axes[0].set_title("physics_score")
|
||||
axes[0].set_aspect("equal")
|
||||
plt.colorbar(sc0, ax=axes[0], shrink=0.8)
|
||||
|
||||
# Panel 2: teacher_eta
|
||||
sc1 = axes[1].scatter(x, y, c=teacher_eta, s=8, cmap="magma")
|
||||
axes[1].set_title("teacher_eta")
|
||||
axes[1].set_aspect("equal")
|
||||
plt.colorbar(sc1, ax=axes[1], shrink=0.8)
|
||||
|
||||
# Panel 3: teacher_mark
|
||||
sc2 = axes[2].scatter(x, y, c=teacher_mark, s=8, cmap="Reds", vmin=0, vmax=1)
|
||||
axes[2].set_title("teacher_mark")
|
||||
axes[2].set_aspect("equal")
|
||||
plt.colorbar(sc2, ax=axes[2], shrink=0.8)
|
||||
|
||||
# Panel 4: correction_label
|
||||
sc3 = axes[3].scatter(
|
||||
x, y, c=correction_label, s=8, cmap="coolwarm", vmin=-1, vmax=1
|
||||
)
|
||||
axes[3].set_title("correction_label")
|
||||
axes[3].set_aspect("equal")
|
||||
plt.colorbar(sc3, ax=axes[3], shrink=0.8)
|
||||
|
||||
for ax in axes:
|
||||
ax.set_xlabel("x")
|
||||
ax.set_ylabel("y")
|
||||
|
||||
plt.tight_layout()
|
||||
out_path = output_dir / f"{name}.png"
|
||||
fig.savefig(out_path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f" ✓ Saved: {out_path}")
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# 5. Verdict
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def print_verdict(issues):
|
||||
"""Print final PASS / WARNING / FAIL."""
|
||||
print("\n" + "=" * 70)
|
||||
print(" [5/5] Verdict")
|
||||
print("=" * 70)
|
||||
|
||||
if not issues:
|
||||
print("\n ✅ PASS — Data is consistent and ready for training.")
|
||||
return "PASS"
|
||||
|
||||
# Classify issues
|
||||
errors = [i for i in issues if any(k in i.upper() for k in ["MISSING", "BAD ", "MISMATCH"])]
|
||||
warnings = [i for i in issues if i not in errors]
|
||||
|
||||
if errors:
|
||||
print(f"\n ❌ FAIL — {len(errors)} error(s) found:")
|
||||
for e in errors:
|
||||
print(f" • {e}")
|
||||
if warnings:
|
||||
print(f"\n ⚠️ Also {len(warnings)} warning(s):")
|
||||
for w in warnings:
|
||||
print(f" • {w}")
|
||||
return "FAIL"
|
||||
|
||||
print(f"\n ⚠️ WARNING — {len(warnings)} suspicious pattern(s):")
|
||||
for w in warnings:
|
||||
print(f" • {w}")
|
||||
return "WARNING"
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
# Main
|
||||
# ──────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Check step-wise correction dataset quality"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--data-dir", type=str, required=True,
|
||||
help="Directory containing sampleXXXX_stepYYY.npz files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-dir", type=str, default="outlook/data_correction_check",
|
||||
help="Directory to save visualization plots",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
data_dir = Path(args.data_dir)
|
||||
output_dir = Path(args.output_dir)
|
||||
|
||||
print("=" * 70)
|
||||
print(" Correction Data Quality Check")
|
||||
print("=" * 70)
|
||||
print(f" Data dir: {data_dir}")
|
||||
print(f" Output dir: {output_dir}")
|
||||
|
||||
# Load
|
||||
print("\n Loading files...")
|
||||
samples = load_all_samples(data_dir)
|
||||
if not samples:
|
||||
print(" ✗ No sample files found!")
|
||||
sys.exit(1)
|
||||
print(f" Loaded {len(samples)} files.")
|
||||
|
||||
issues = []
|
||||
|
||||
# 1. Field consistency
|
||||
check_fields(samples, issues)
|
||||
|
||||
# 2. Global stats
|
||||
compute_global_stats(samples)
|
||||
|
||||
# 3. Per-step trends
|
||||
check_per_step_trends(samples, issues)
|
||||
|
||||
# 4. Visualization
|
||||
plot_spatial_samples(samples, output_dir)
|
||||
|
||||
# 5. Verdict
|
||||
verdict = print_verdict(issues)
|
||||
|
||||
print()
|
||||
return 0 if verdict == "PASS" else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
|
@ -0,0 +1,47 @@
|
|||
{
|
||||
"config": {
|
||||
"data_dir": "outlook/data/budget_density_dataset_debug",
|
||||
"epochs": 2,
|
||||
"batch_size": 2,
|
||||
"lr": 0.001,
|
||||
"density_weight": 1.0,
|
||||
"mono_weight": 0.0,
|
||||
"seed": 42
|
||||
},
|
||||
"per_budget": {
|
||||
"budget_1000": {
|
||||
"mse": 2.209566116333008,
|
||||
"corr": -0.5653519502499024,
|
||||
"top20_overlap": 0.037037037037037035,
|
||||
"n_graphs": 1
|
||||
},
|
||||
"budget_2000": {
|
||||
"mse": 4.281199932098389,
|
||||
"corr": -0.4546216071701198,
|
||||
"top20_overlap": 0.07407407407407407,
|
||||
"n_graphs": 1
|
||||
},
|
||||
"overall": {
|
||||
"mse": 3.2453832626342773,
|
||||
"corr": -0.4747666722392795
|
||||
}
|
||||
},
|
||||
"training_log": [
|
||||
{
|
||||
"epoch": 0,
|
||||
"train_loss": 10.175954818725586,
|
||||
"val_loss": 3.5997772216796875,
|
||||
"val_corr": -0.5083683560193551,
|
||||
"val_top20_overlap": 0.07407407407407407,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 1,
|
||||
"train_loss": 7.313832759857178,
|
||||
"val_loss": 3.2453830242156982,
|
||||
"val_corr": -0.4747666660841676,
|
||||
"val_top20_overlap": 0.05555555555555555,
|
||||
"lr": 0.001
|
||||
}
|
||||
]
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,902 @@
|
|||
[
|
||||
{
|
||||
"epoch": 0,
|
||||
"train_loss": 1.214073695242405,
|
||||
"val_loss": 1.1141467094421387,
|
||||
"val_auc": 0.7793237914165878,
|
||||
"val_topk_overlap": 0.18174046167047223,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 1,
|
||||
"train_loss": 0.9936319440603256,
|
||||
"val_loss": 0.8714833855628967,
|
||||
"val_auc": 0.868452222365281,
|
||||
"val_topk_overlap": 0.25161985623836425,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 2,
|
||||
"train_loss": 0.8490312211215496,
|
||||
"val_loss": 0.8512038290500641,
|
||||
"val_auc": 0.8743725472266488,
|
||||
"val_topk_overlap": 0.27610961191386213,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 3,
|
||||
"train_loss": 0.789196622868379,
|
||||
"val_loss": 0.8102231820424398,
|
||||
"val_auc": 0.8853419495372643,
|
||||
"val_topk_overlap": 0.27663024403011677,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 4,
|
||||
"train_loss": 0.7635955549776554,
|
||||
"val_loss": 0.7610340118408203,
|
||||
"val_auc": 0.897967248359232,
|
||||
"val_topk_overlap": 0.3101760507386457,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 5,
|
||||
"train_loss": 0.7362100593745708,
|
||||
"val_loss": 0.748906339208285,
|
||||
"val_auc": 0.9006133073599438,
|
||||
"val_topk_overlap": 0.31304398098650305,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 6,
|
||||
"train_loss": 0.7291262559592724,
|
||||
"val_loss": 0.738037109375,
|
||||
"val_auc": 0.9036701915596148,
|
||||
"val_topk_overlap": 0.32063751054106754,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 7,
|
||||
"train_loss": 0.7118012420833111,
|
||||
"val_loss": 0.7175028175115585,
|
||||
"val_auc": 0.9082043418016371,
|
||||
"val_topk_overlap": 0.33353017529134166,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 8,
|
||||
"train_loss": 0.7020367321868738,
|
||||
"val_loss": 0.7400095015764236,
|
||||
"val_auc": 0.9038924270293778,
|
||||
"val_topk_overlap": 0.3313267280645091,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 9,
|
||||
"train_loss": 0.6895044669508934,
|
||||
"val_loss": 0.7122037708759308,
|
||||
"val_auc": 0.9097822096475472,
|
||||
"val_topk_overlap": 0.33196020452732494,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 10,
|
||||
"train_loss": 0.6835353250304858,
|
||||
"val_loss": 0.7104852745930353,
|
||||
"val_auc": 0.9102591502276932,
|
||||
"val_topk_overlap": 0.3377191635459625,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 11,
|
||||
"train_loss": 0.6724687827130159,
|
||||
"val_loss": 0.7091294328371683,
|
||||
"val_auc": 0.9097279573159085,
|
||||
"val_topk_overlap": 0.32852482645677916,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 12,
|
||||
"train_loss": 0.6608287704487642,
|
||||
"val_loss": 0.7088778764009476,
|
||||
"val_auc": 0.9102058348049611,
|
||||
"val_topk_overlap": 0.3395986399411693,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 13,
|
||||
"train_loss": 0.6516422840456167,
|
||||
"val_loss": 0.6851696570714315,
|
||||
"val_auc": 0.9161228078916188,
|
||||
"val_topk_overlap": 0.3576108115171014,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 14,
|
||||
"train_loss": 0.641690748433272,
|
||||
"val_loss": 0.6870623429616293,
|
||||
"val_auc": 0.9166719819324596,
|
||||
"val_topk_overlap": 0.35467355015247576,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 15,
|
||||
"train_loss": 0.6213903104265531,
|
||||
"val_loss": 0.7060682823260626,
|
||||
"val_auc": 0.9133957909689026,
|
||||
"val_topk_overlap": 0.3541738121319473,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 16,
|
||||
"train_loss": 0.6141671799123287,
|
||||
"val_loss": 0.6649165799220403,
|
||||
"val_auc": 0.9223772612302986,
|
||||
"val_topk_overlap": 0.3727640261277016,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 17,
|
||||
"train_loss": 0.6156036208073298,
|
||||
"val_loss": 0.6852359771728516,
|
||||
"val_auc": 0.917546789308805,
|
||||
"val_topk_overlap": 0.3619924910260988,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 18,
|
||||
"train_loss": 0.6078479687372843,
|
||||
"val_loss": 0.6580855945746104,
|
||||
"val_auc": 0.9232592198731486,
|
||||
"val_topk_overlap": 0.36904381643112044,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 19,
|
||||
"train_loss": 0.6046239572266737,
|
||||
"val_loss": 0.6743976672490438,
|
||||
"val_auc": 0.9211166203403452,
|
||||
"val_topk_overlap": 0.3716380846859304,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 20,
|
||||
"train_loss": 0.6046414325634638,
|
||||
"val_loss": 0.6747777163982391,
|
||||
"val_auc": 0.9219827510935892,
|
||||
"val_topk_overlap": 0.36950146318342597,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 21,
|
||||
"train_loss": 0.5860095359385014,
|
||||
"val_loss": 0.6683605263630549,
|
||||
"val_auc": 0.9212946098630218,
|
||||
"val_topk_overlap": 0.36769860427540924,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 22,
|
||||
"train_loss": 0.5924560849865278,
|
||||
"val_loss": 0.6608883539835612,
|
||||
"val_auc": 0.9237303628010585,
|
||||
"val_topk_overlap": 0.38014786877240736,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 23,
|
||||
"train_loss": 0.5699630603194237,
|
||||
"val_loss": 0.6451186140378317,
|
||||
"val_auc": 0.9271866474545916,
|
||||
"val_topk_overlap": 0.3881152462988639,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 24,
|
||||
"train_loss": 0.5860506445169449,
|
||||
"val_loss": 0.6344831412037214,
|
||||
"val_auc": 0.9279389861019536,
|
||||
"val_topk_overlap": 0.37646285728709716,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 25,
|
||||
"train_loss": 0.5696679763495922,
|
||||
"val_loss": 0.651521734893322,
|
||||
"val_auc": 0.926708376460331,
|
||||
"val_topk_overlap": 0.3929882611353039,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 26,
|
||||
"train_loss": 0.5605074142416319,
|
||||
"val_loss": 0.6179426908493042,
|
||||
"val_auc": 0.9313555002762031,
|
||||
"val_topk_overlap": 0.38825309368394906,
|
||||
"physics_topk_overlap": 0.13027826412738905,
|
||||
"lr": 0.001
|
||||
},
|
||||
{
|
||||
"epoch": 27,
|
||||
"train_loss": 0.5452606951196989,
|
||||
"val_loss": 0.6548436383406321,
|
||||
"val_auc": 0.9257514085577712,
|
||||
"val_topk_overlap": 0.3863040639453161,
|
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Reference in New Issue