Shaun ZhouandClaude Opus 5
be6173c746
- SimCLRTrainer 记录 simclr/loss、loss_avg、lr（按 step）与 epoch_loss、 epoch_seconds、gpu_mem_peak_mb（按 epoch），超参为完整 yaml 配置； 正常结束与 KeyboardInterrupt 两条路径都会 finish() - 配置新增 simclr.swanlab（字段同 train.swanlab），默认 mode: local， 不需要登录，日志写到 <work_dir>/swanlog - 依赖改为 swanlab[dashboard]：此前 `swanlab watch` 在干净环境里会报 "Please install the swanboard package"，README 里检测训练那节的看板命令 同样受影响，现已随 uv sync 装好 - README 补齐本地看板/云端/离线补传三种用法与端口、局域网参数 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
49 lines
1.2 KiB
TOML
49 lines
1.2 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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]
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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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[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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