Shaun ZhouandClaude Opus 5
f433e5cf59
把 data/film 下所有图片拉平做聚类，不使用任何目录名，簇数由 num_clusters 决定。 预处理针对该批数据的实际情况（14.5k 张已裁好的缺陷小图，中位 152x113， 宽高比 0.24~39.9）：尺寸与亮度都是 ROI 裁剪和光照的产物、不是缺陷属性， 因此拉伸到 128x128（不用 letterbox，其填充比例等价于编码了宽高比）并逐图 z-score。 - cluster: torch 实现的 kmeans++/Lloyd（含空簇处理与 n_init）、PCA、轮廓系数、 NMI 稳定性、多 stage 特征提取；不引 scikit-learn - 白化默认关闭：CNN 特征谱极陡（λ1/λ32≈165x），白化会把噪声方向抬到与信号同权， 实测最大簇一度吃掉 96% 的样本 - simclr: NT-Xent 自监督预训练，用项目自己的卷积骨架，权重可直接初始化 DefectNet； 属于训练，走 require_cuda() 守卫 - 产出 assignments.csv、summary.json、按簇的软链目录与代表样本拼图，供人工核对 全量 14,579 张：轮廓系数 0.326、换种子稳定性 0.964，CPU 约 100 秒。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
297 lines
12 KiB
Python
297 lines
12 KiB
Python
#!/usr/bin/env python
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"""把未分类的样本做无监督聚类，产出可人工核对的分组结果。
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# 默认 4 类，ImageNet 预训练特征（不训练，CPU 可跑）
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uv run python scripts/cluster_unlabeled.py --config configs/cluster/film.yaml
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# 改簇数 / 换成在本地数据上自监督训练的特征（需要 Nvidia GPU）
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uv run python scripts/cluster_unlabeled.py --config configs/cluster/film.yaml \
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--set num_clusters=6 method=simclr
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产出（work_dir 下）：
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assignments.csv 每张图的簇号、到质心距离、原目录
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summary.json 簇大小、轮廓系数、簇 × 原目录交叉表
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clusters/cluster_00/... 按簇建的软链目录，直接用看图软件翻
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cluster_00_preview.png 每簇代表样本（离质心最近）的拼图
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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import shutil
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import sys
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from collections import defaultdict
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import torch # noqa: E402
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from datasets.build import build_dataset # noqa: E402
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from datasets.unlabeled import unlabeled_collate # noqa: E402
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from engine.cluster import ( # noqa: E402
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StageFeatureExtractor,
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cluster_features,
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cluster_stability,
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extract_features,
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random_fit_index,
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)
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from models.registry import BACKBONES # noqa: E402
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from utils import load_config, seed_everything, setup_logging # noqa: E402
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from utils.device import NoNvidiaGpuError, require_cuda, resolve_device # noqa: E402
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logger = None
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def build_encoder(cfg, device, work_dir):
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"""按 `method` 拿一个特征提取器。
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pretrained: ImageNet 预训练卷积骨架，纯推理，CPU 也能跑。用顶层的 `backbone`。
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simclr: 在本目录数据上自监督训练一个项目自己的卷积骨架——属于训练，先过 GPU 守卫。
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用 `simclr.backbone`，与 pretrained 的骨架配置各自独立：两者的字段完全不同
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（arch/pretrained vs widths/depths），共用一个块会在 `_base_` 合并时串味。
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"""
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method = str(cfg.get("method", "pretrained"))
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if method == "pretrained":
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backbone = BACKBONES.build(dict(cfg.get("backbone", {"name": "torchvision"})))
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return StageFeatureExtractor(backbone, cfg.get("feature_stages")).to(device).eval()
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if method != "simclr":
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raise ValueError(f"未知 method: {method}，可选 pretrained / simclr")
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require_cuda() # 自监督预训练是训练，禁止在无 Nvidia GPU 的设备上跑
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from torch.utils.data import DataLoader
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from engine.simclr import SimCLRTrainer
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simclr_cfg = dict(cfg.get("simclr", {}))
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backbone_cfg = dict(simclr_cfg.pop("backbone", {"name": "tiny_conv"}))
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# 自研骨架不是 nn.Sequential，默认取最终输出而不是中间 stage
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stages = simclr_cfg.pop("feature_stages", None)
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backbone = BACKBONES.build(backbone_cfg).to(device)
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checkpoint = simclr_cfg.pop("checkpoint", None)
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if checkpoint and Path(checkpoint).exists():
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state = torch.load(checkpoint, map_location=device, weights_only=False)
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backbone.load_state_dict(state["model"])
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logger.info("复用已有的自监督骨架: %s", checkpoint)
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return StageFeatureExtractor(backbone, stages).to(device).eval()
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view_dataset = build_dataset(
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{**dict(cfg["dataset"]), "views": 2, "view_transforms": cfg.get("view_transforms", [])}
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)
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loader = DataLoader(
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view_dataset,
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batch_size=int(simclr_cfg.pop("batch_size", 128)),
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shuffle=True,
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drop_last=True,
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num_workers=int(cfg.get("loader", {}).get("num_workers", 4)),
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collate_fn=unlabeled_collate,
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)
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SimCLRTrainer(backbone, loader, simclr_cfg, device, work_dir).fit()
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return StageFeatureExtractor(backbone, stages).to(device).eval()
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def write_outputs(cfg, work_dir, metas, result, num_clusters, stability):
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"""CSV + JSON + 软链目录 + 预览拼图。人工只需要翻软链目录就能确认分组是否合理。"""
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labels = result.labels.tolist()
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distances = result.distances.tolist()
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with (work_dir / "assignments.csv").open("w", newline="", encoding="utf-8") as handle:
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writer = csv.writer(handle)
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writer.writerow(["path", "cluster", "distance_to_centroid", "source_dir", "width", "height"])
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for meta, label, distance in zip(metas, labels, distances, strict=True):
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width, height = meta["size"]
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writer.writerow([meta["path"], label, f"{distance:.6f}", meta["source_dir"], width, height])
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summary = {
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"num_clusters": num_clusters,
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"num_images": len(metas),
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"method": cfg.get("method", "pretrained"),
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"image_size": cfg.get("image_size"),
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"cluster_sizes": result.sizes,
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"silhouette": result.silhouette,
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"stability": stability,
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"inertia": result.inertia,
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"mean_distance_per_cluster": [
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float(result.distances[result.labels == index].mean()) if size else None
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for index, size in enumerate(result.sizes)
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],
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}
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(work_dir / "summary.json").write_text(
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json.dumps(summary, indent=2, ensure_ascii=False), encoding="utf-8"
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)
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link_limit = int(cfg.get("link_limit", 300))
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preview_count = int(cfg.get("preview_count", 25))
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clusters_dir = work_dir / "clusters"
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if clusters_dir.exists():
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shutil.rmtree(clusters_dir)
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order = sorted(range(len(metas)), key=lambda i: distances[i]) # 离质心近的排前面
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per_cluster: dict[int, list[int]] = defaultdict(list)
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for index in order:
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per_cluster[labels[index]].append(index)
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for cluster, indices in sorted(per_cluster.items()):
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target = clusters_dir / f"cluster_{cluster:02d}"
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target.mkdir(parents=True, exist_ok=True)
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# 近的取大头，末尾补几张最远的——离群样本最能暴露这个簇是不是混了东西
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chosen = indices[: max(link_limit - 20, 1)] + indices[-20:] if len(indices) > link_limit else indices
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for rank, index in enumerate(chosen):
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source = Path(metas[index]["path"]).resolve()
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link = target / f"{rank:04d}_{source.stem}{source.suffix}"
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if not link.exists():
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link.symlink_to(source)
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save_preview(work_dir / f"cluster_{cluster:02d}_preview.png", metas, indices[:preview_count])
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return summary
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def save_preview(path: Path, metas: list[dict], indices: list[int], cell: int = 96, columns: int = 5):
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"""把代表样本拼成一张图，方便一眼看出这个簇是什么缺陷。"""
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if not indices:
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return
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from PIL import Image
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rows = (len(indices) + columns - 1) // columns
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canvas = Image.new("RGB", (columns * cell, rows * cell), (32, 32, 32))
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for position, index in enumerate(indices):
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with Image.open(metas[index]["path"]) as raw:
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image = raw.convert("RGB")
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scale = min(cell / image.width, cell / image.height)
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image = image.resize((max(1, round(image.width * scale)), max(1, round(image.height * scale))))
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x = (position % columns) * cell + (cell - image.width) // 2
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y = (position // columns) * cell + (cell - image.height) // 2
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canvas.paste(image, (x, y))
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canvas.save(path)
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def sweep_clusters(embeddings, spec: str, cluster_kwargs: dict) -> None:
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"""扫一遍簇数，同时看轮廓系数和换种子的稳定性。
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无标签时没有"正确答案"，只能看：簇是否紧致（轮廓），以及换初始化后划分是否还一样
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（稳定性）。稳定性低说明那个 k 是在硬切一团连续的数据。
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"""
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start, end = (int(v) for v in spec.split("-"))
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logger.info("%3s %9s %10s %12s 簇大小", "k", "轮廓", "稳定性", "最大簇占比")
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for k in range(start, end + 1):
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result = cluster_features(embeddings, k, seed=42, **cluster_kwargs)
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stability = cluster_stability(embeddings, k, seeds=(0, 7), **cluster_kwargs)
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logger.info(
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"%3d %9.3f %10.3f %12.2f %s",
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k,
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result.silhouette,
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stability,
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max(result.sizes) / sum(result.sizes),
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result.sizes,
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)
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def main() -> int:
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global logger
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
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)
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parser.add_argument("--config", default="configs/cluster/film.yaml")
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parser.add_argument("--root", default=None, help="覆盖配置里的图片根目录")
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parser.add_argument("--num-clusters", type=int, default=None, help="覆盖配置里的簇数")
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parser.add_argument("--work-dir", default=None)
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parser.add_argument("--set", nargs="*", default=[], metavar="KEY=VALUE")
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parser.add_argument("--save-features", action="store_true", help="额外保存 embedding 供复用")
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parser.add_argument(
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"--sweep-k", default=None, metavar="A-B", help="只扫簇数（如 2-8）打印指标表，不产出分组"
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)
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args = parser.parse_args()
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cfg = load_config(args.config, args.set)
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if args.root:
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cfg["dataset"]["root"] = args.root
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if args.num_clusters:
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cfg["num_clusters"] = args.num_clusters
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work_dir = Path(args.work_dir or cfg.get("work_dir", "runs/cluster"))
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work_dir.mkdir(parents=True, exist_ok=True)
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logger = setup_logging(work_dir / "cluster.log")
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seed_everything(int(cfg.get("seed", 42)))
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num_clusters = int(cfg.get("num_clusters", 4))
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try:
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device = resolve_device(str(cfg.get("device", "cuda")))
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except NoNvidiaGpuError as exc:
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logger.error("%s", exc)
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return 1
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dataset = build_dataset(dict(cfg["dataset"]))
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logger.info(
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"扫描到 %d 张图，来自 %d 个目录；簇数 %d，method=%s，device=%s",
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len(dataset),
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len({dataset.source_dir(path) for path in dataset.images}),
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num_clusters,
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cfg.get("method", "pretrained"),
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device,
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)
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try:
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encoder = build_encoder(cfg, device, work_dir)
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except NoNvidiaGpuError as exc:
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logger.error("%s", exc)
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return 1
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from torch.utils.data import DataLoader
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loader = DataLoader(
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dataset,
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batch_size=int(cfg.get("loader", {}).get("batch_size", 64)),
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shuffle=False,
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num_workers=int(cfg.get("loader", {}).get("num_workers", 4)),
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collate_fn=unlabeled_collate,
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)
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embeddings, metas = extract_features(
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encoder, loader, device, use_shape_features=bool(cfg.get("use_shape_features", False))
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)
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logger.info("embedding: %s", tuple(embeddings.shape))
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cluster_kwargs = {
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"pca_dim": int(cfg.get("pca_dim", 32)),
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"whiten": bool(cfg.get("whiten", False)),
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"n_init": int(cfg.get("n_init", 10)),
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}
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fit_index = random_fit_index(
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len(metas), int(cfg.get("fit_max_samples", 0)), seed=int(cfg.get("seed", 42))
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)
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if fit_index is not None:
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logger.info("随机抽 %d 个样本拟合质心（拟合后仍分配全部样本）", len(fit_index))
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if args.sweep_k:
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sweep_clusters(embeddings, args.sweep_k, cluster_kwargs)
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return 0
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result = cluster_features(
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embeddings, num_clusters, seed=int(cfg.get("seed", 42)), fit_index=fit_index, **cluster_kwargs
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)
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stability = cluster_stability(embeddings, num_clusters, seeds=(0, 7), **cluster_kwargs)
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logger.info(
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"簇大小: %s | 轮廓系数: %.4f | 换种子稳定性: %.4f",
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result.sizes,
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result.silhouette,
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stability,
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)
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if args.save_features:
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torch.save({"embeddings": embeddings, "metas": metas}, work_dir / "features.pt")
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write_outputs(cfg, work_dir, metas, result, num_clusters, stability)
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logger.info("结果已写入 %s（clusters/ 下是按簇建的软链目录，直接翻图即可核对）", work_dir)
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for index, size in enumerate(result.sizes):
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mean_distance = float(result.distances[result.labels == index].mean()) if size else float("nan")
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logger.info(" cluster_%02d %6d 张 平均距质心 %.3f", index, size, mean_distance)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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