[project]
name = "modeltraineval"
version = "0.1.0"
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）"
readme = "README.md"
authors = [
    { name = "Shaun Zhou", email = "xiangyuzhou823@gmail.com" }
]
requires-python = ">=3.12"
dependencies = [
    "torch>=2.4",
    "torchvision>=0.19",
    "numpy>=1.26",
    "pillow>=10.3",
    "pyyaml>=6.0",
    "swanlab>=0.10.0",
]

[dependency-groups]
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",
]

[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。
package = false

# 训练机（Linux + Nvidia GPU）请用 CUDA 版 torch，例如：
#   uv pip install --index-url https://download.pytorch.org/whl/cu124 torch torchvision
# 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）。

[tool.pytest.ini_options]
testpaths = ["tests"]
pythonpath = ["."]
addopts = "-q"

[tool.ruff]
line-length = 110
target-version = "py312"

[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B"]