Win and linux can run the code

example/platform_utils.py

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+#!/usr/bin/env python3
+"""跨平台工具函数：设备检测和路径处理"""
+
+import os
+import sys
+import platform
+from pathlib import Path
+from typing import Optional, Union
+
+# 尝试导入torch，但不是强制的
+try:
+    import torch
+    TORCH_AVAILABLE = True
+except ImportError:
+    TORCH_AVAILABLE = False
+    torch = None
+
+
+def get_platform_info() -> dict:
+    """获取平台信息"""
+    return {
+        "system": platform.system(),
+        "release": platform.release(),
+        "version": platform.version(),
+        "machine": platform.machine(),
+        "processor": platform.processor(),
+        "python_version": platform.python_version(),
+        "python_executable": sys.executable,
+        "current_dir": os.getcwd(),
+    }
+
+
+def is_windows() -> bool:
+    """检查是否在Windows上运行"""
+    return platform.system().lower() == "windows"
+
+
+def is_linux() -> bool:
+    """检查是否在Linux上运行"""
+    return platform.system().lower() == "linux"
+
+
+def is_macos() -> bool:
+    """检查是否在macOS上运行"""
+    return platform.system().lower() == "darwin"
+
+
+def resolve_device(device: str = "auto", verbose: bool = True) -> str:
+    """
+    解析设备字符串，自动检测最佳设备
+
+    Args:
+        device: "auto", "cpu", "cuda", or "mps" (macOS)
+        verbose: 是否打印信息
+
+    Returns:
+        设备字符串: "cpu", "cuda", or "mps"
+    """
+    if not TORCH_AVAILABLE:
+        if verbose:
+            print("警告: PyTorch未安装，只能使用CPU")
+        return "cpu"
+
+    device = device.lower().strip()
+
+    if device == "cpu":
+        if verbose:
+            print("设备: CPU (用户指定)")
+        return "cpu"
+
+    if device == "cuda":
+        if torch.cuda.is_available():
+            if verbose:
+                gpu_count = torch.cuda.device_count()
+                gpu_name = torch.cuda.get_device_name(0) if gpu_count > 0 else "未知"
+                print(f"设备: CUDA (GPU: {gpu_name}, 数量: {gpu_count})")
+            return "cuda"
+        else:
+            if verbose:
+                print("警告: 指定了CUDA但不可用，自动回退到CPU")
+                print("提示: 检查PyTorch是否安装了GPU版本")
+            return "cpu"
+
+    if device == "mps" and is_macos():
+        # macOS的Metal Performance Shaders
+        if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            if verbose:
+                print("设备: MPS (macOS Metal)")
+            return "mps"
+        else:
+            if verbose:
+                print("警告: MPS不可用，回退到CPU")
+            return "cpu"
+
+    # 自动检测
+    if torch.cuda.is_available():
+        if verbose:
+            gpu_count = torch.cuda.device_count()
+            gpu_name = torch.cuda.get_device_name(0) if gpu_count > 0 else "未知"
+            print(f"设备: CUDA (自动检测到GPU: {gpu_name})")
+        return "cuda"
+
+    if is_macos() and hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        if verbose:
+            print("设备: MPS (macOS Metal, 自动检测)")
+        return "mps"
+
+    if verbose:
+        print("设备: CPU (自动检测，无GPU可用)")
+    return "cpu"
+
+
+def safe_path(path: Union[str, Path]) -> str:
+    """
+    安全地处理路径，确保跨平台兼容性
+
+    Args:
+        path: 路径字符串或Path对象
+
+    Returns:
+        字符串形式的路径
+    """
+    if isinstance(path, Path):
+        return str(path)
+
+    # 如果是字符串，确保使用正确的路径分隔符
+    path_str = str(path)
+
+    # 替换可能存在的错误分隔符
+    if is_windows():
+        # Windows上，确保使用反斜杠
+        path_str = path_str.replace('/', '\\')
+    else:
+        # Linux/macOS上，确保使用正斜杠
+        path_str = path_str.replace('\\', '/')
+
+    return path_str
+
+
+def ensure_dir(path: Union[str, Path]) -> Path:
+    """
+    确保目录存在，如果不存在则创建
+
+    Args:
+        path: 目录路径
+
+    Returns:
+        Path对象
+    """
+    path_obj = Path(path) if isinstance(path, str) else path
+    path_obj.mkdir(parents=True, exist_ok=True)
+    return path_obj
+
+
+def get_relative_path(base: Union[str, Path], target: Union[str, Path]) -> Path:
+    """
+    获取相对于基路径的相对路径
+
+    Args:
+        base: 基路径
+        target: 目标路径
+
+    Returns:
+        相对路径的Path对象
+    """
+    base_path = Path(base) if isinstance(base, str) else base
+    target_path = Path(target) if isinstance(target, str) else target
+
+    # 如果目标路径是绝对路径，直接返回
+    if target_path.is_absolute():
+        return target_path
+
+    # 否则相对于基路径
+    return (base_path / target_path).resolve()
+
+
+def print_platform_summary():
+    """打印平台摘要信息"""
+    info = get_platform_info()
+    print("=" * 50)
+    print("平台信息:")
+    print(f"  系统: {info['system']} {info['release']}")
+    print(f"  处理器: {info['processor']}")
+    print(f"  Python: {info['python_version']}")
+    print(f"  当前目录: {info['current_dir']}")
+
+    if TORCH_AVAILABLE:
+        print(f"  PyTorch: {torch.__version__}")
+        print(f"  CUDA可用: {torch.cuda.is_available()}")
+        if torch.cuda.is_available():
+            print(f"  GPU数量: {torch.cuda.device_count()}")
+            for i in range(torch.cuda.device_count()):
+                print(f"    GPU {i}: {torch.cuda.get_device_name(i)}")
+    else:
+        print("  PyTorch: 未安装")
+
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    # 测试代码
+    print_platform_summary()
+
+    print("\n设备检测测试:")
+    for device in ["auto", "cpu", "cuda", "mps"]:
+        try:
+            result = resolve_device(device, verbose=True)
+            print(f"  输入: '{device}' -> 输出: '{result}'")
+        except Exception as e:
+            print(f"  输入: '{device}' -> 错误: {e}")
+
+    print("\n路径处理测试:")
+    test_path = "some/path/to/file.txt"
+    print(f"  原始路径: {test_path}")
+    print(f"  安全路径: {safe_path(test_path)}")