From 29ff7a2c9beba93a83b24702f3899a2c6c89eb0a Mon Sep 17 00:00:00 2001 From: Shaun Zhou Date: Thu, 8 Oct 2026 14:57:11 +0800 Subject: [PATCH] =?UTF-8?q?docs:=20=E6=B8=85=E6=8E=89=E5=A4=B1=E6=95=88?= =?UTF-8?q?=E7=9A=84=20train.py=20=E5=BC=95=E7=94=A8=E4=B8=8E=20ONNX=20?= =?UTF-8?q?=E5=AF=BC=E5=87=BA=E4=BE=9D=E8=B5=96=E7=BB=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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 --- README.md | 30 +++++------ pyproject.toml | 7 +-- scripts/generate_boxes.py | 2 +- uv.lock | 110 +------------------------------------- 4 files changed, 18 insertions(+), 131 deletions(-) diff --git a/README.md b/README.md index 6b68385..020dd2a 100644 --- a/README.md +++ b/README.md @@ -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 | diff --git a/pyproject.toml b/pyproject.toml index 59f237e..e12fa7d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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： diff --git a/scripts/generate_boxes.py b/scripts/generate_boxes.py index 6224243..3ebf705 100644 --- a/scripts/generate_boxes.py +++ b/scripts/generate_boxes.py @@ -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// diff --git a/uv.lock b/uv.lock index fb2e1f1..f3bd1eb 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 5 requires-python = ">=3.12" resolution-markers = [ "python_full_version >= '4' and sys_platform == 'linux'", @@ -2192,47 +2192,6 @@ wheels = [ { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" }, ] -[[package]] -name = "ml-dtypes" -version = "0.6.0" -source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } -dependencies = [ - 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