Shaun ZhouandClaude Opus 5
5d395b7c25
原始数据从 data/<数据集> 移到了 data/raw/<数据集>。
- 聚类配置的 dataset.root：film / pearlescent 均改为 data/raw/ 下
- 检测配置的 img_dir 与 label_dir 注释同步
- README、data/README、算法文档、流程图（含重新渲染的 PNG）与各脚本
docstring 里的路径示例一并更新
- data/README 补充目录约定：原始数据只读放 data/raw/，
export_clusters.py 的派生数据集放在 data/ 下与 raw/ 平级
- 顺手把 unlabeled 数据集加进 datasets/__init__.py：此前它没在包初始化时注册，
build_dataset({"name": "unlabeled"}) 只有在别处先 import 过该模块时才可用
迁移后重跑全量聚类，结果与迁移前逐项一致（轮廓 0.3255、稳定性 0.9641、
簇大小 [4507, 3795, 4849, 1428]）。
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
74 lines
3.2 KiB
Plaintext
74 lines
3.2 KiB
Plaintext
flowchart TB
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classDef input fill:#D1FAE5,stroke:#10B981,stroke-width:2px,color:#064E3B
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classDef prep fill:#DBEAFE,stroke:#2563EB,stroke-width:2px,color:#1E3A8A
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classDef feat fill:#DBEAFE,stroke:#2563EB,stroke-width:2px,color:#1E3A8A
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classDef reduce fill:#EDE9FE,stroke:#7C3AED,stroke-width:2px,color:#4C1D95
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classDef cluster fill:#EDE9FE,stroke:#7C3AED,stroke-width:2px,color:#4C1D95
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classDef out fill:#FFEDD5,stroke:#EA580C,stroke-width:2px,color:#7C2D12
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classDef metric fill:#FEF3C7,stroke:#D97706,stroke-width:2px,color:#78350F
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scan["data/raw/film 递归扫描<br/>N = 14,579 张 PNG<br/>尺寸各异 84×83 ~ 4464×1139"]:::input
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subgraph PRE["① 预处理 — 抹掉采集因子"]
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direction LR
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resize["双线性拉伸<br/>H×W → 128×128<br/>不保长宽比"]:::prep
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tensor["转张量 + RGBA/L → RGB<br/>x ∈ ℝ³ˣ¹²⁸ˣ¹²⁸"]:::prep
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zscore["逐图标准化<br/>x̃ = (x − μₓ) / σₓ<br/>抹掉亮度 μ 与对比度 σ"]:::prep
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resize --> tensor --> zscore
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end
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subgraph FEAT["② 特征提取 — 纹理优先"]
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direction LR
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stem["resnet18 stem<br/>conv1 → bn → relu → maxpool"]:::feat
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l1["layer1<br/>64ch, stride 4"]:::feat
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l2["layer2<br/>128ch, stride 8"]:::feat
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l3["layer3<br/>256ch, stride 16"]:::feat
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gap2["GAP → ℓ₂ 归一化<br/>u⁽²⁾ ∈ S¹²⁷"]:::feat
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gap3["GAP → ℓ₂ 归一化<br/>u⁽³⁾ ∈ S²⁵⁵"]:::feat
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concat["拼接<br/>z = [u⁽²⁾; u⁽³⁾] ∈ ℝ³⁸⁴"]:::feat
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stem --> l1 --> l2 --> l3
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l2 --> gap2 --> concat
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l3 --> gap3 --> concat
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end
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subgraph RED["③ 降维"]
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direction LR
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norm["ℓ₂ 归一化<br/>ẑ = z / ‖z‖₂"]:::reduce
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pca["PCA 投影 384 → 32<br/>y = Uᵀ(ẑ − z̄)<br/>不白化：保留 λ 谱"]:::reduce
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norm --> pca
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end
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subgraph CLU["④ 聚类 K-Means"]
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direction LR
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init["k-means++ 初始化<br/>p(xᵢ) ∝ D(xᵢ)²"]:::cluster
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lloyd["Lloyd 迭代<br/>指派 ↔ 更新质心"]:::cluster
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best["n_init = 10<br/>取 inertia 最小的一次"]:::cluster
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assign["全量指派<br/>cᵢ = argminⱼ ‖yᵢ − μⱼ‖₂"]:::cluster
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init --> lloyd --> best --> assign
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end
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subgraph EVAL["⑤ 无标签评估"]
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direction LR
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sil["轮廓系数 s̄<br/>簇是否紧致"]:::metric
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stab["换种子稳定性<br/>NMI(C_seed0, C_seed7)"]:::metric
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end
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subgraph OUT["⑥ 产出"]
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direction LR
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csv["assignments.csv<br/>路径 / 簇号 / 距质心"]:::out
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links["clusters/cluster_XX/<br/>软链目录，按距质心排序"]:::out
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prev["cluster_XX_preview.png<br/>代表样本拼图"]:::out
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summ["summary.json<br/>簇大小 / 轮廓 / 稳定性"]:::out
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end
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simclr["可选：SimCLR 自监督<br/>method = simclr<br/>cuda / mps 均可"]:::feat
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scan --> PRE
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PRE -->|"x̃ᵢ"| FEAT
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scan -.->|"双视图增强"| simclr
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simclr -.->|"域内骨架权重"| FEAT
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FEAT -->|"z ∈ ℝᴺˣ³⁸⁴"| RED
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RED -->|"y ∈ ℝᴺˣ³²"| CLU
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CLU -->|"标签 c"| EVAL
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CLU -->|"标签 c + 距离 d"| OUT
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