Shaun ZhouandClaude Opus 5
4b1147ed8f
- convert_annotations: labelme / VOC -> YOLO txt，顺带生成 data.yaml
- split_dataset: 切分成 images/{train,val} 与 labels/{train,val}，默认建软链不复制数据
- export_onnx: 导出部署用 ONNX，CPU 可跑，缺依赖时提示 `uv sync --group export`
- profile_inference: torch.profiler 测单帧延迟/FPS、CUDA 显存峰值与算子级明细
- check_env: Python / PyTorch / Nvidia GPU 自检
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
42 lines
1002 B
Python
Executable File
42 lines
1002 B
Python
Executable File
#!/usr/bin/env python
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"""环境自检：确认依赖与 Nvidia GPU 是否就绪。
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python scripts/check_env.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from utils.device import NoNvidiaGpuError, cuda_devices, require_cuda # noqa: E402
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def main() -> int:
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print(f"Python: {sys.version.split()[0]}")
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try:
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import torch
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print(f"PyTorch: {torch.__version__} (CUDA build: {torch.version.cuda})")
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except ImportError:
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print("PyTorch: 未安装 —— 先执行 `uv sync`")
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return 1
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devices = cuda_devices()
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print(f"Nvidia GPU: {devices if devices else '无'}")
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try:
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require_cuda()
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except NoNvidiaGpuError as exc:
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print(f"训练不可用: {exc}")
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print("（评估/导出仍可在 CPU 上进行）")
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return 1
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print("训练环境就绪。")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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