docs: 清掉失效的 train.py 引用与 ONNX 导出依赖组

仓库里从来没有 train.py / eval.py，入口是 classify.py 与 detect.py。 - README 的计算后端与 SwanLab 示例改用 classify.py，看板路径跟着 work_dir 改为 runs/classify/swanlog。 - 删掉"train.py 的 train.restart_schedule 同理"：restart_schedule 只在 engine/simclr.py 里实现，分类训练没有这个选项。 - 目录表里 configs/ 的子目录（原写 model/ dataset/ train/ eval/）与 scripts/ 的描述改成实际内容；入口那行补上 detect.py。 - 导出走 OpenVINO，没有脚本用到 onnx/onnxscript，删掉 export 依赖组。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-10-08 14:57:11 +08:00
parent c2bd5301b4
commit 29ff7a2c9b
4 changed files with 18 additions and 131 deletions
@@ -15,36 +15,37 @@

 | 目录 | 作用 |
 | --- | --- |
-| `configs/` | yaml 配置（`model/` 模型、`dataset/` 数据、`train/` 训练、`eval/` 评估） |
+| `configs/` | yaml 配置（`cluster/` 聚类与自监督、`classify/` 分类、`detect/` 检测） |
 | `models/` | 模型骨架：`backbones/` + `necks/` + `heads/`，由 `defect_net.py` 组装 |
 | `datasets/` | 抛出数据样本的 Dataset 类、数据增强、监督信号编码 |
 | `data/` | 数据集本体或软链接（不入库，见 [data/README.md](data/README.md)） |
-| `scripts/` | 格式转换、数据集切分、ONNX 导出等批处理脚本 |
+| `scripts/` | 聚类、框生成、数据集装配与切片、OpenVINO 导出等批处理脚本 |
 | `engine/` | 训练 / 评估循环、损失、指标、优化器、检查点、SwanLab 可视化 |
 | `utils/` | 配置、注册表、日志、随机种子、设备解析（cuda/mps/cpu） |
 | `tests/` | 单元测试 |
 | `docs/` | 算法与数学说明文档 |
 | `figures/` | 文档用的 mermaid 图（`.mmd` 源 + `.md` 预览 + `.png`） |

-入口：[classify.py](classify.py)（分类训练与评估）、[scripts/](scripts/) 下的聚类、导出与框生成脚本。
+入口：[classify.py](classify.py)（分类训练与评估）、[detect.py](detect.py)（YOLO 检测训练与评估），
+以及 [scripts/](scripts/) 下的聚类、框生成、导出脚本。

 ## 环境

 ```bash
 uv sync                      # 安装依赖（含 swanlab）
-uv sync --group export       # 需要导出 ONNX 时再装
 uv run python scripts/check_env.py   # 自检（Python / PyTorch / 可用后端）
 ```

 ### 计算后端

 支持 **CUDA / MPS（Apple Silicon）/ CPU**。配置里的 `device: auto` 按 `cuda > mps > cpu`
-自动挑最快的可用后端，也可以显式写 `cuda` / `mps` / `cpu`，或用命令行 `--device` 覆盖：
+自动挑最快的可用后端（`classify.py` / `detect.py` / `cluster_unlabeled.py` 都一样），也可以显式写
+`cuda` / `mps` / `cpu`，或用命令行 `--device` 覆盖：

 ```bash
-uv run python train.py --config configs/train/baseline.yaml               # auto
-uv run python train.py --config configs/train/baseline.yaml --device mps  # 强制 Mac GPU
-uv run python train.py --config configs/train/baseline.yaml --device cpu
+uv run python classify.py --config configs/classify/film.yaml               # auto
+uv run python classify.py --config configs/classify/film.yaml --device mps  # 强制 Mac GPU
+uv run python classify.py --config configs/classify/film.yaml --device cpu
 ```

 **显式指定不可用的后端会直接报错退出**，不会悄悄退回 CPU——训练慢几十倍却不吭声是最坏的情况。
@@ -61,7 +62,8 @@ uv run python train.py --config configs/train/baseline.yaml --device cpu
 ## 训练可视化（SwanLab）

 训练过程通过 [SwanLab](https://swanlab.cn) 记录：按 step 记录各项损失与学习率，按 epoch 记录
-验证指标与显存峰值，实验的超参就是完整的 yaml 配置。在 `train.swanlab` 里配置：
+验证指标与显存峰值，实验的超参就是完整的 yaml 配置。分类（`configs/classify/`）、检测
+（`configs/detect/`）与自监督（`simclr.swanlab`）都在各自配置的 `swanlab` 段里开关：

 ```yaml
 train:
@@ -75,15 +77,15 @@ train:

 ```bash
 # 本地看板：不需要账号，训练时用 local 模式，另开终端起看板
-uv run python train.py --config configs/train/baseline.yaml --set train.swanlab.mode=local
-uv run swanlab watch runs/baseline/swanlog          # 默认 http://127.0.0.1:5092
+uv run python classify.py --config configs/classify/film.yaml --set train.swanlab.mode=local
+uv run swanlab watch runs/classify/swanlog          # 默认 http://127.0.0.1:5092

 # 云端
 uv run swanlab login                               # 首次使用需登录
-uv run python train.py --config configs/train/baseline.yaml   # mode 默认 online
+uv run python classify.py --config configs/classify/film.yaml   # mode 默认 online

 # 离线跑完后再补传云端
-uv run swanlab sync runs/baseline/swanlog
+uv run swanlab sync runs/classify/swanlog
 ```

 看板可以在训练**进行中**打开，指标边训边刷。端口冲突用 `-p 5100`，要局域网访问加 `-h 0.0.0.0`。
@@ -282,8 +284,6 @@ uv run python scripts/cluster_unlabeled.py --config configs/cluster/film.yaml \
 --set simclr.epochs=5000
 ```

-`train.py` 的 `train.restart_schedule` 同理。
-
 续训时各项配置的行为：

 | 配置项 | 续训时 |
@@ -28,18 +28,13 @@ dev = [
    "pytest>=8.0",
    "ruff>=0.6",
 ]
-# ONNX 导出（部署用），按需安装：uv sync --group export
-export = [
-    "onnx>=1.16",
-    "onnxscript>=0.2",
-]
 # SHAP 归因定位（scripts/shap_boxes.py），按需安装：uv sync --group explain
 explain = [
    "shap>=0.48",
 ]

 [tool.uv]
-# 本仓库是应用而非库：不打包安装，直接从仓库根目录运行 train.py / eval.py。
+# 本仓库是应用而非库：不打包安装，直接从仓库根目录运行 classify.py / detect.py。
 package = false

 # 训练机（Linux + Nvidia GPU）请用 CUDA 版 torch，例如：
@@ -24,7 +24,7 @@ cv 不判类别，只定位，所以它只能配 `--target label`（默认），
 分类器还没训练时就能先跑一版框出来看。它对 blackdot / whitedot / mosquito 这类局部目标
 效果好；ripple / wrinkle 这种铺满整图的纹理类，本来就没有局部目标，它给不出有意义的框。

-产出是一个**自成一体的 YOLO 数据集**，可直接喂给 train.py：
+产出是一个**自成一体的 YOLO 数据集**，可直接喂给 detect.py：

 ```
 runs/<name>/
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 5
 requires-python = ">=3.12"
 resolution-markers = [
    "python_full_version >= '4' and sys_platform == 'linux'",
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