60 lines
1.7 KiB
TOML
60 lines
1.7 KiB
TOML
[project]
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name = "modeltraineval"
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version = "0.1.0"
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description = "薄膜缺陷分类定位模型的训练与评估（纯卷积，面向实时设备部署）"
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readme = "README.md"
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authors = [
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{ name = "Shaun Zhou", email = "xiangyuzhou823@gmail.com" }
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]
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requires-python = ">=3.12"
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dependencies = [
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"torch>=2.4",
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"torchvision>=0.19",
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"numpy>=1.26",
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"pillow>=10.3",
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"pyyaml>=6.0",
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"swanlab[dashboard]>=0.10.0",
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"matplotlib>=3.8",
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# 检测训练（detect.py）。注意 ultralytics 是 AGPL-3.0：如果模型要随产品分发，
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# 需要开源整套代码或另外向其购买商业授权。仅内部使用则不受影响。
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"ultralytics>=8.4",
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# SHAP 定位（scripts/generate_boxes.py --method shap）用
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"shap>=0.46",
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"label-studio>=1.23.0",
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]
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[dependency-groups]
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dev = [
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"pytest>=8.0",
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"ruff>=0.6",
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]
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# ONNX 导出（部署用），按需安装：uv sync --group export
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export = [
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"onnx>=1.16",
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"onnxscript>=0.2",
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]
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# SHAP 归因定位（scripts/shap_boxes.py），按需安装：uv sync --group explain
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explain = [
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"shap>=0.48",
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]
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[tool.uv]
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# 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。
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package = false
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# 训练机（Linux + Nvidia GPU）请用 CUDA 版 torch，例如：
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# uv pip install --index-url https://download.pytorch.org/whl/cu124 torch torchvision
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# 默认 PyPI 源在 Linux 上装的是 CUDA 构建，在 macOS 上只有 CPU 构建（仅可用于评估/导出）。
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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pythonpath = ["."]
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addopts = "-q"
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[tool.ruff]
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line-length = 110
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target-version = "py312"
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[tool.ruff.lint]
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select = ["E", "F", "I", "UP", "B"]
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