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Win and linux can run the code

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MZ YANG
2026-01-22 17:39:31 +08:00
parent c3f750cd9d
commit f37a8ce179
22 changed files with 32572 additions and 87 deletions
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24
example/analyze_hai21_03.py
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@@ -12,13 +12,13 @@ from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent BASE_DIR = Path(__file__).resolve().parent
REPO_DIR = BASE_DIR.parent.parent REPO_DIR = BASE_DIR.parent.parent
DATA_PATH = str(REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz") DATA_PATH = REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz"
OUT_DIR = str(BASE_DIR / "results") OUT_DIR = BASE_DIR / "results"
MAX_ROWS = 5000 MAX_ROWS = 5000
def analyze(path: str, max_rows: int): def analyze(path: str, max_rows: int):
with gzip.open(path, "rt", newline="") as f: with gzip.open(str(path), "rt", newline="") as f:
reader = csv.reader(f) reader = csv.reader(f)
cols = next(reader) cols = next(reader)
stats = { stats = {
@@ -74,11 +74,11 @@ def analyze(path: str, max_rows: int):
def write_results(cols, continuous, discrete, unknown, rows): def write_results(cols, continuous, discrete, unknown, rows):
os.makedirs(OUT_DIR, exist_ok=True) os.makedirs(str(OUT_DIR), exist_ok=True)
split_path = os.path.join(OUT_DIR, "feature_split.txt") split_path = OUT_DIR / "feature_split.txt"
summary_path = os.path.join(OUT_DIR, "summary.txt") summary_path = OUT_DIR / "summary.txt"
with open(split_path, "w", encoding="ascii") as f: with open(split_path, "w", encoding="utf-8") as f:
f.write("discrete\n") f.write("discrete\n")
f.write(",".join(discrete) + "\n") f.write(",".join(discrete) + "\n")
f.write("continuous\n") f.write("continuous\n")
@@ -87,19 +87,19 @@ def write_results(cols, continuous, discrete, unknown, rows):
f.write("unknown\n") f.write("unknown\n")
f.write(",".join(unknown) + "\n") f.write(",".join(unknown) + "\n")
with open(summary_path, "w", encoding="ascii") as f: with open(summary_path, "w", encoding="utf-8") as f:
f.write("rows_sampled: %d\n" % rows) f.write("rows_sampled: %d\n" % rows)
f.write("columns_total: %d\n" % len(cols)) f.write("columns_total: %d\n" % len(cols))
f.write("continuous: %d\n" % len(continuous)) f.write("continuous: %d\n" % len(continuous))
f.write("discrete: %d\n" % len(discrete)) f.write("discrete: %d\n" % len(discrete))
f.write("unknown: %d\n" % len(unknown)) f.write("unknown: %d\n" % len(unknown))
f.write("data_path: %s\n" % DATA_PATH) f.write("data_path: %s\n" % str(DATA_PATH))
def main(): def main():
if not os.path.exists(DATA_PATH): if not DATA_PATH.exists():
raise SystemExit("missing data file: %s" % DATA_PATH) raise SystemExit("missing data file: %s" % str(DATA_PATH))
cols, continuous, discrete, unknown, rows = analyze(DATA_PATH, MAX_ROWS) cols, continuous, discrete, unknown, rows = analyze(str(DATA_PATH), MAX_ROWS)
write_results(cols, continuous, discrete, unknown, rows) write_results(cols, continuous, discrete, unknown, rows)

132
example/check_gpu_setup.py Normal file
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@@ -0,0 +1,132 @@
#!/usr/bin/env python3
"""检查GPU设置和PyTorch安装"""
import sys
import subprocess
def check_nvidia_gpu():
"""检查是否有NVIDIA GPU"""
print("=== 检查NVIDIA GPU ===")
try:
result = subprocess.run(['nvidia-smi'], capture_output=True, text=True)
if result.returncode == 0:
print("✓ 找到NVIDIA GPU")
print("输出:")
print(result.stdout[:500]) # 只显示前500个字符
return True
else:
print("✗ nvidia-smi命令失败")
print("错误:", result.stderr)
return False
except FileNotFoundError:
print("✗ nvidia-smi未找到，可能没有安装NVIDIA驱动")
return False
except Exception as e:
print(f"✗ 检查GPU时出错: {e}")
return False
def check_pytorch_installation():
"""检查PyTorch安装"""
print("\n=== 检查PyTorch安装 ===")
try:
import torch
print(f"PyTorch版本: {torch.__version__}")
print(f"CUDA可用: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA版本: {torch.version.cuda}")
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)}")
return True, "gpu"
else:
print("警告: 安装的是CPU版本的PyTorch")
# 检查是否安装了GPU版本但CUDA不可用
if '+cpu' in torch.__version__:
print("确认: 安装的是明确的CPU版本 (包含'+cpu')")
return True, "cpu"
else:
print("可能安装了GPU版本但CUDA驱动有问题")
return True, "cuda_but_not_working"
except ImportError:
print("✗ PyTorch未安装")
return False, "not_installed"
except Exception as e:
print(f"✗ 检查PyTorch时出错: {e}")
return False, "error"
def get_installation_commands():
"""获取安装命令"""
print("\n=== 安装建议 ===")
import platform
python_version = f"{sys.version_info.major}.{sys.version_info.minor}"
print(f"Python版本: {python_version}")
print(f"操作系统: {platform.system()} {platform.release()}")
print("\n安装GPU版本的PyTorch:")
print("1. 首先确保安装了NVIDIA驱动和CUDA工具包")
print("2. 根据你的CUDA版本选择以下命令之一:")
print()
print("对于CUDA 12.1:")
print(" pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121")
print()
print("对于CUDA 11.8:")
print(" pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
print()
print("对于CPU版本:")
print(" pip install torch torchvision torchaudio")
print()
print("要检查CUDA版本，可以运行: nvcc --version")
def check_current_setup():
"""检查当前设置"""
print("=== 当前设置检查 ===")
has_gpu = check_nvidia_gpu()
pytorch_ok, pytorch_type = check_pytorch_installation()
print("\n=== 总结 ===")
if has_gpu and pytorch_ok and pytorch_type == "gpu":
print("✓ 完美！你有GPU并且PyTorch GPU版本已正确安装")
print("可以运行: python run_pipeline.py --device cuda")
elif has_gpu and pytorch_ok and pytorch_type == "cpu":
print("⚠ 你有GPU但安装了CPU版本的PyTorch")
print("建议安装GPU版本的PyTorch以获得更好的性能")
get_installation_commands()
print("\n暂时可以运行: python run_pipeline.py --device cpu")
elif has_gpu and pytorch_ok and pytorch_type == "cuda_but_not_working":
print("⚠ 可能有GPU和GPU版本的PyTorch，但CUDA不可用")
print("检查CUDA驱动和PyTorch版本是否匹配")
get_installation_commands()
elif not has_gpu and pytorch_ok:
print("ℹ 没有GPU，使用CPU版本的PyTorch")
print("运行: python run_pipeline.py --device cpu")
elif not pytorch_ok:
print("✗ PyTorch未正确安装")
get_installation_commands()
return has_gpu, pytorch_ok, pytorch_type
def main():
print("GPU和PyTorch设置检查工具")
print("=" * 50)
has_gpu, pytorch_ok, pytorch_type = check_current_setup()
print("\n=== 下一步建议 ===")
if has_gpu and pytorch_type == "cpu":
print("1. 考虑安装GPU版本的PyTorch")
print("2. 或者继续使用CPU: python run_pipeline.py --device cpu")
elif not has_gpu:
print("1. 只能使用CPU运行")
print("2. 运行: python run_pipeline.py --device cpu")
else:
print("1. 可以尝试运行: python run_pipeline.py --device cuda")
print("2. 或者: python run_pipeline.py --device auto")
print("\n注意: 代码已修改，当指定--device cuda但CUDA不可用时，会自动回退到CPU")
if __name__ == "__main__":
main()

2
example/data_utils.py
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@@ -9,7 +9,7 @@ from typing import Dict, Iterable, List, Optional, Tuple
def load_split(path: str) -> Dict[str, List[str]]: def load_split(path: str) -> Dict[str, List[str]]:
with open(path, "r", encoding="ascii") as f: with open(path, "r", encoding="utf-8") as f:
return json.load(f) return json.load(f)

193
example/debug_cuda_issue.py Normal file
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@@ -0,0 +1,193 @@
#!/usr/bin/env python3
"""详细诊断CUDA问题"""
import sys
import os
import subprocess
def check_system_info():
"""检查系统信息"""
print("=== 系统信息 ===")
import platform
print(f"操作系统: {platform.system()} {platform.release()}")
print(f"Python版本: {sys.version}")
print(f"Python路径: {sys.executable}")
print(f"当前目录: {os.getcwd()}")
def check_nvidia_driver():
"""检查NVIDIA驱动"""
print("\n=== NVIDIA驱动检查 ===")
try:
result = subprocess.run(['nvidia-smi'], capture_output=True, text=True, encoding='utf-8', errors='ignore')
if result.returncode == 0:
print("✓ nvidia-smi 命令成功")
# 提取驱动版本
for line in result.stdout.split('\n'):
if 'Driver Version' in line:
print(f"驱动版本: {line.strip()}")
if 'CUDA Version' in line:
print(f"CUDA版本: {line.strip()}")
return True
else:
print("✗ nvidia-smi 命令失败")
print(f"错误: {result.stderr}")
return False
except FileNotFoundError:
print("✗ nvidia-smi 未找到")
return False
except Exception as e:
print(f"✗ 检查NVIDIA驱动时出错: {e}")
return False
def check_cuda_toolkit():
"""检查CUDA工具包"""
print("\n=== CUDA工具包检查 ===")
try:
result = subprocess.run(['nvcc', '--version'], capture_output=True, text=True, encoding='utf-8', errors='ignore')
if result.returncode == 0:
print("✓ nvcc 命令成功")
for line in result.stdout.split('\n'):
if 'release' in line.lower():
print(f"CUDA编译器: {line.strip()}")
return True
else:
print("✗ nvcc 命令失败")
return False
except FileNotFoundError:
print("✗ nvcc 未找到")
return False
except Exception as e:
print(f"✗ 检查CUDA工具包时出错: {e}")
return False
def check_pytorch_detailed():
"""详细检查PyTorch安装"""
print("\n=== PyTorch详细检查 ===")
try:
import torch
print(f"PyTorch版本: {torch.__version__}")
print(f"PyTorch路径: {torch.__file__}")
# 检查是否包含+cpu
if '+cpu' in torch.__version__:
print("⚠ PyTorch版本包含 '+cpu'，这是CPU版本")
elif '+cu' in torch.__version__.lower():
cuda_version = torch.__version__.split('+cu')[-1].split('+')[0]
print(f"✓ PyTorch是GPU版本，CUDA: {cuda_version}")
else:
print("ℹ PyTorch版本信息不明确")
# 检查CUDA可用性
print(f"\nCUDA可用性检查:")
print(f" torch.cuda.is_available(): {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f" CUDA版本: {torch.version.cuda}")
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)}")
print(f" 内存: {torch.cuda.get_device_properties(i).total_memory / 1024**3:.2f} GB")
# 测试GPU计算
try:
x = torch.randn(3, 3).cuda()
y = torch.randn(3, 3).cuda()
z = torch.matmul(x, y)
print(f" GPU计算测试: ✓ 成功")
except Exception as e:
print(f" GPU计算测试: ✗ 失败 - {e}")
else:
print(" CUDA不可用，原因可能是:")
print(" 1. 安装了CPU版本的PyTorch")
print(" 2. CUDA驱动不匹配")
print(" 3. 系统环境问题")
return torch.cuda.is_available()
except ImportError:
print("✗ PyTorch未安装")
return False
except Exception as e:
print(f"✗ 检查PyTorch时出错: {e}")
return False
def check_conda_environment():
"""检查conda环境"""
print("\n=== Conda环境检查 ===")
try:
result = subprocess.run(['conda', 'info', '--envs'], capture_output=True, text=True, encoding='utf-8', errors='ignore')
if result.returncode == 0:
print("当前conda环境:")
for line in result.stdout.split('\n'):
if '*' in line:
print(f" {line.strip()}")
else:
print("无法获取conda环境信息")
except Exception as e:
print(f"检查conda环境时出错: {e}")
def check_torch_installation():
"""检查torch安装详情"""
print("\n=== Torch安装详情 ===")
try:
import pkg_resources
packages = ['torch', 'torchvision', 'torchaudio']
for pkg in packages:
try:
dist = pkg_resources.get_distribution(pkg)
print(f"{pkg}: {dist.version} ({dist.location})")
except pkg_resources.DistributionNotFound:
print(f"{pkg}: 未安装")
except Exception as e:
print(f"检查包详情时出错: {e}")
def check_environment_variables():
"""检查环境变量"""
print("\n=== 环境变量检查 ===")
cuda_vars = ['CUDA_HOME', 'CUDA_PATH', 'PATH']
for var in cuda_vars:
value = os.environ.get(var, '未设置')
if var == 'PATH' and 'cuda' in value.lower():
print(f"{var}: 包含CUDA路径")
elif var != 'PATH':
print(f"{var}: {value}")
def main():
print("CUDA问题详细诊断工具")
print("=" * 60)
check_system_info()
check_nvidia_driver()
check_cuda_toolkit()
check_conda_environment()
check_environment_variables()
check_torch_installation()
cuda_available = check_pytorch_detailed()
print("\n" + "=" * 60)
print("=== 问题诊断与解决方案 ===")
if not cuda_available:
print("\n问题: PyTorch无法检测到CUDA")
print("\n可能的原因和解决方案:")
print("1. 安装了CPU版本的PyTorch")
print(" 解决方案: 重新安装GPU版本的PyTorch")
print(" 命令: pip uninstall torch torchvision torchaudio")
print(" pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121")
print()
print("2. CUDA版本不匹配")
print(" 你的CUDA版本: 12.3")
print(" 需要安装对应的PyTorch版本")
print(" 命令: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu123")
print()
print("3. 环境变量问题")
print(" 确保CUDA_HOME或CUDA_PATH正确设置")
print()
print("4. 驱动程序问题")
print(" 更新NVIDIA驱动程序到最新版本")
else:
print("\n✓ CUDA可用，可以正常使用GPU")
print("运行命令: python run_pipeline.py --device cuda")
if __name__ == "__main__":
main()

4
example/evaluate_generated.py
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@@ -10,7 +10,7 @@ from typing import Dict, Tuple
def load_json(path: str) -> Dict: def load_json(path: str) -> Dict:
with open(path, "r", encoding="ascii") as f: with open(path, "r", encoding="utf-8") as f:
return json.load(f) return json.load(f)
@@ -106,7 +106,7 @@ def main():
"discrete_invalid_counts": disc_invalid, "discrete_invalid_counts": disc_invalid,
} }
with open(args.out, "w", encoding="ascii") as f: with open(args.out, "w", encoding="utf-8") as f:
json.dump(report, f, indent=2) json.dump(report, f, indent=2)
print("eval_report", args.out) print("eval_report", args.out)

19
example/export_samples.py
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@@ -14,15 +14,16 @@ import torch.nn.functional as F
from data_utils import load_split from data_utils import load_split
from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule
from platform_utils import resolve_device, safe_path, ensure_dir
def load_vocab(path: str) -> Dict[str, Dict[str, int]]: def load_vocab(path: str) -> Dict[str, Dict[str, int]]:
with open(path, "r", encoding="ascii") as f: with open(path, "r", encoding="utf-8") as f:
return json.load(f)["vocab"] return json.load(f)["vocab"]
def load_stats(path: str): def load_stats(path: str):
with open(path, "r", encoding="ascii") as f: with open(path, "r", encoding="utf-8") as f:
return json.load(f) return json.load(f)
@@ -66,17 +67,7 @@ def parse_args():
return parser.parse_args() return parser.parse_args()
def resolve_device(mode: str) -> str: # 使用 platform_utils 中的 resolve_device 函数
mode = mode.lower()
if mode == "cpu":
return "cpu"
if mode == "cuda":
if not torch.cuda.is_available():
raise SystemExit("device set to cuda but CUDA is not available")
return "cuda"
if torch.cuda.is_available():
return "cuda"
return "cpu"
def main(): def main():
@@ -156,7 +147,7 @@ def main():
out_cols = [c for c in header if c != time_col or args.include_time] out_cols = [c for c in header if c != time_col or args.include_time]
os.makedirs(os.path.dirname(args.out), exist_ok=True) os.makedirs(os.path.dirname(args.out), exist_ok=True)
with open(args.out, "w", newline="", encoding="ascii") as f: with open(args.out, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=out_cols) writer = csv.DictWriter(f, fieldnames=out_cols)
writer.writeheader() writer.writeheader()

75
example/find_correct_torch.py Normal file
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@@ -0,0 +1,75 @@
#!/usr/bin/env python3
"""查找正确的PyTorch安装命令"""
import sys
def check_cuda_compatibility():
"""检查CUDA兼容性"""
print("=== CUDA兼容性检查 ===")
print("你的CUDA版本: 12.3")
print("\nPyTorch官方支持的CUDA版本:")
print("1. CUDA 12.1 - 最新稳定版")
print("2. CUDA 11.8 - 广泛支持")
print("3. CUDA 11.7 - 旧版本支持")
print("\n注意: CUDA 12.3可能还没有官方预编译包")
def get_installation_options():
"""获取安装选项"""
print("\n=== 安装选项 ===")
print("选项1: 使用CUDA 12.1（向后兼容）")
print("大多数CUDA 12.x版本是兼容的")
print("命令: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121")
print()
print("选项2: 使用conda安装（推荐）")
print("conda会自动处理CUDA兼容性")
print("命令: conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia")
print()
print("选项3: 从源码编译（高级用户）")
print("如果需要特定CUDA版本")
print()
print("选项4: 使用CPU版本")
print("如果没有GPU或不想处理兼容性问题")
print("命令: pip install torch torchvision torchaudio")
def check_current_torch():
"""检查当前torch安装"""
print("\n=== 当前PyTorch状态 ===")
try:
import torch
print(f"已安装版本: {torch.__version__}")
print(f"CUDA可用: {torch.cuda.is_available()}")
if '+cpu' in torch.__version__:
print("类型: CPU版本")
elif '+cu' in torch.__version__.lower():
print("类型: GPU版本")
cuda_ver = torch.__version__.split('+cu')[-1].split('+')[0]
print(f"编译的CUDA版本: {cuda_ver}")
else:
print("类型: 未知")
except ImportError:
print("PyTorch未安装")
def main():
print("PyTorch安装指南")
print("=" * 50)
check_cuda_compatibility()
check_current_torch()
get_installation_options()
print("\n=== 推荐方案 ===")
print("1. 首先尝试选项1（CUDA 12.1）")
print("2. 如果不行，使用选项2（conda安装）")
print("3. 或者暂时使用CPU版本运行代码")
print("\n=== 验证安装 ===")
print("安装后运行: python -c \"import torch; print(f'版本: {torch.__version__}'); print(f'CUDA可用: {torch.cuda.is_available()}')\"")
if __name__ == "__main__":
main()

215
example/platform_utils.py Normal file
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@@ -0,0 +1,215 @@
#!/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)}")

2
example/plot_loss.py
View File

@@ -35,7 +35,7 @@ def main():
loss_cont = [] loss_cont = []
loss_disc = [] loss_disc = []
with log_path.open("r", encoding="ascii", newline="") as f: with log_path.open("r", encoding="utf-8", newline="") as f:
reader = csv.DictReader(f) reader = csv.DictReader(f)
for row in reader: for row in reader:
steps.append(int(row["step"])) steps.append(int(row["step"]))

20
example/prepare_data.py
View File

@@ -6,28 +6,30 @@ from pathlib import Path
from typing import Optional from typing import Optional
from data_utils import compute_cont_stats, build_vocab, load_split from data_utils import compute_cont_stats, build_vocab, load_split
from platform_utils import safe_path, ensure_dir
BASE_DIR = Path(__file__).resolve().parent BASE_DIR = Path(__file__).resolve().parent
REPO_DIR = BASE_DIR.parent.parent REPO_DIR = BASE_DIR.parent.parent
DATA_PATH = str(REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz") DATA_PATH = REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz"
SPLIT_PATH = str(BASE_DIR / "feature_split.json") SPLIT_PATH = BASE_DIR / "feature_split.json"
OUT_STATS = str(BASE_DIR / "results" / "cont_stats.json") OUT_STATS = BASE_DIR / "results" / "cont_stats.json"
OUT_VOCAB = str(BASE_DIR / "results" / "disc_vocab.json") OUT_VOCAB = BASE_DIR / "results" / "disc_vocab.json"
def main(max_rows: Optional[int] = None): def main(max_rows: Optional[int] = None):
split = load_split(SPLIT_PATH) split = load_split(safe_path(SPLIT_PATH))
time_col = split.get("time_column", "time") time_col = split.get("time_column", "time")
cont_cols = [c for c in split["continuous"] if c != time_col] cont_cols = [c for c in split["continuous"] if c != time_col]
disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col] disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]
mean, std = compute_cont_stats(DATA_PATH, cont_cols, max_rows=max_rows) mean, std = compute_cont_stats(safe_path(DATA_PATH), cont_cols, max_rows=max_rows)
vocab = build_vocab(DATA_PATH, disc_cols, max_rows=max_rows) vocab = build_vocab(safe_path(DATA_PATH), disc_cols, max_rows=max_rows)
with open(OUT_STATS, "w", encoding="ascii") as f: ensure_dir(OUT_STATS.parent)
with open(safe_path(OUT_STATS), "w", encoding="utf-8") as f:
json.dump({"mean": mean, "std": std, "max_rows": max_rows}, f, indent=2) json.dump({"mean": mean, "std": std, "max_rows": max_rows}, f, indent=2)
with open(OUT_VOCAB, "w", encoding="ascii") as f: with open(safe_path(OUT_VOCAB), "w", encoding="utf-8") as f:
json.dump({"vocab": vocab, "max_rows": max_rows}, f, indent=2) json.dump({"vocab": vocab, "max_rows": max_rows}, f, indent=2)

113
example/results/cont_stats.json Normal file
View File

@@ -0,0 +1,113 @@
{
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}

30653
example/results/disc_vocab.json Normal file
View File

File diff suppressed because it is too large Load Diff

459
example/results/eval.json Normal file
View File

@@ -0,0 +1,459 @@
{
"rows": 128,
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}

129
example/results/generated.csv Normal file
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@@ -0,0 +1,129 @@
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1 time P1_B2004 P1_B2016 P1_B3004 P1_B3005 P1_B4002 P1_B4005 P1_B400B P1_B4022 P1_FCV01D P1_FCV01Z P1_FCV02D P1_FCV02Z P1_FCV03D P1_FCV03Z P1_FT01 P1_FT01Z P1_FT02 P1_FT02Z P1_FT03 P1_FT03Z P1_LCV01D P1_LCV01Z P1_LIT01 P1_PCV01D P1_PCV01Z P1_PCV02D P1_PCV02Z P1_PIT01 P1_PIT02 P1_PP01AD P1_PP01AR P1_PP01BD P1_PP01BR P1_PP02D P1_PP02R P1_STSP P1_TIT01 P1_TIT02 P2_24Vdc P2_ASD P2_AutoGO P2_CO_rpm P2_Emerg P2_HILout P2_MSD P2_ManualGO P2_OnOff P2_RTR P2_SIT01 P2_SIT02 P2_TripEx P2_VT01 P2_VTR01 P2_VTR02 P2_VTR03 P2_VTR04 P2_VXT02 P2_VXT03 P2_VYT02 P2_VYT03 P3_FIT01 P3_LCP01D P3_LCV01D P3_LH P3_LIT01 P3_LL P3_PIT01 P4_HT_FD P4_HT_LD P4_HT_PO P4_HT_PS P4_LD P4_ST_FD P4_ST_GOV P4_ST_LD P4_ST_PO P4_ST_PS P4_ST_PT01 P4_ST_TT01 attack attack_P1 attack_P2 attack_P3
2 0 -55.569374 -172.286362 52644.503906 9803.029297 1265.853394 -129354.289062 6573167.000000 3540.008545 13.62198 99.37102 52.73972 -158358.093750 -32999.296875 4293.566895 -117115.093750 951465.437500 709494.500000 -1870773.750000 -364809.468750 24458.285156 -11284.749023 -6647.535156 -58001.464844 -150671.828125 29054.273438 <UNK> 17.221390 6.653648 632.891479 <UNK> <UNK> <UNK> <UNK> <UNK> <UNK> <UNK> 4066.759521 -5062.030762 45.789249 0 1 237866.781250 <UNK> 32980.835938 4984.311523 <UNK> <UNK> 2880 15192.601562 -22868.447266 <UNK> 227.335220 <UNK> <UNK> <UNK> <UNK> -518.745667 -2140.192627 -2097.215820 -1746.491821 5893932.000000 -1503986.375000 -52493484.000000 <UNK> 19736554.000000 <UNK> 255142.468750 7.201154 -257864.203125 -69547.468750 <UNK> -347061.750000 -8.367757 -6365330.000000 160405.562500 -40106.863281 0 120225.093750 -45071.859375
3 1 -54.062332 -134.314163 48216.007812 -21162.539062 -933.086731 -188567.062500 6769198.500000 -1611.896118 <UNK> <UNK> 100 163799.562500 -43097.222656 -12895.947266 -33085.277344 474200.000000 3361183.500000 -2899284.750000 81384.062500 173405.328125 -7721.300293 -11736.066406 -34429.562500 15740.236328 55679.074219 12 4.003504 -413.610199 -888.193848 540833 540833 0 0 1 1 1 -4726.875977 -563.991211 47.678570 0 1 144652.328125 0 6219.502930 -7619.875977 0 1 2880 7568.269531 -16079.442383 1 7.224079 10 10 10 10 -83.721260 117.665260 -1541.859863 1474.606323 1785641.750000 14607312.000000 -17849342.000000 70 19242886.000000 20 648816.125000 -7.396350 356554.468750 176879.203125 0 159274.484375 -2.156105 5690311.500000 83418.632812 81279.632812 0 95237.656250 -17638.488281
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126 124 -182.420181 71.264603 -1271.982544 409853.593750 -546.934998 -266623.500000 -826036.625000 295.743317 17.02689 99.37314 42.88292 33728.464844 -19277.041016 45429.457031 -57473.164062 130448.664062 2654103.750000 -3694443.750000 116340.890625 -136810.828125 5492.754395 -8112.086914 -21330.167969 -49811.347656 -119414.679688 12 -9.979402 -253.081512 -2155.443604 <UNK> 540833 0 0 <UNK> 1 1 3919.936523 5011.419922 36.591995 0 1 6886.164062 0 3942.143311 2446.794434 0 1 <UNK> 1549.286377 23070.962891 1 101.966621 10 10 <UNK> 10 -1846.088257 -653.269653 1908.525757 -257.552551 2002004.125000 39018248.000000 5736856.000000 70 -31846302.000000 20 -9818265.000000 2.716852 -36474.878906 -113283.054688 0 -9581.573242 2.164098 -7806891.500000 125723.210938 -61892.355469 0 -48885.640625 43707.554688
127 125 -42.707218 -668.941406 39250.878906 172828.046875 6010.701172 86143.273438 -9679669.000000 2699.966309 100 <UNK> 0 63600.179688 30160.316406 5653.169434 103709.789062 -471323.375000 -4825959.500000 1701157.000000 -177346.203125 231879.265625 9932.580078 3700.121582 4822.449219 204061.359375 -23133.775391 12 -34.750732 -473.914917 101.041611 540833 540833 0 0 1 1 1 3007.885742 -9255.011719 32.391365 0 1 54397.019531 0 -35358.363281 -1931.035278 0 1 2880 -1107.418945 10969.944336 1 89.277222 10 10 10 10 374.262817 1178.433594 4144.151855 429.379028 12826686.000000 450349.406250 21118178.000000 70 3269.216797 20 12116556.000000 -1.281597 30435.224609 86148.671875 0 228512.703125 -9.262348 394903.593750 101670.117188 -77652.625000 0 89325.914062 -19692.570312
128 126 69.270668 -406.189545 -65311.609375 230599.187500 3242.277588 -10709.416016 -4229272.000000 -3347.356445 <UNK> <UNK> 0 -292023.000000 -323.561188 6076.747559 -125059.968750 187826.875000 -957239.062500 -756540.750000 205973.968750 34963.242188 17547.240234 -16582.484375 -647.481812 -51749.722656 54876.109375 12 8.519110 196.522461 30.238838 540833 540833 0 0 1 1 1 1432.830444 -10421.972656 37.832420 0 1 -17186.574219 0 -18962.041016 5725.231934 0 1 2880 -981.414062 -26049.242188 1 323.886078 10 10 10 10 -780.526917 1271.838257 1735.162964 -974.371033 -2920845.250000 34882100.000000 26956236.000000 70 -4620825.000000 20 -3207739.250000 3.092017 65016.601562 27137.367188 0 21759.886719 3.708425 -7472643.000000 116851.437500 -150497.187500 0 59338.996094 -22080.324219
129 127 197.288925 882.952209 -102698.023438 -22178.900391 6614.936035 -83460.390625 2755666.000000 1635.153076 <UNK> <UNK> 0 -9842.981445 -11928.745117 32152.939453 -20944.076172 -427790.906250 3108196.500000 6676754.500000 -172412.390625 -503812.531250 -7650.702637 20545.070312 19117.878906 43325.097656 -76870.765625 12 28.518658 -88.201958 -738.256226 540833 540833 0 0 1 1 1 -3591.052002 2547.667236 38.371334 0 1 136182.968750 0 -17258.896484 -6064.726074 0 1 2880 11621.541016 -14766.488281 1 -325.300476 10 10 10 10 -2604.859863 -1300.506348 -1272.310059 -1508.260010 -2786109.750000 -32444528.000000 -33960696.000000 70 -10495158.000000 20 -1135797.125000 4.918825 -128069.250000 -31475.029297 0 -3854.394531 10.230341 10567653.000000 -99006.578125 31442.044922 0 -33334.105469 201732.531250

16
example/results/train_log.csv Normal file
View File

@@ -0,0 +1,16 @@
epoch,step,loss,loss_cont,loss_disc
0,0,21.821945,1.016319,42.627571
0,10,12.070929,1.017697,23.124159
0,20,11.243023,1.009492,21.476553
1,0,5.083598,1.008866,9.158330
1,10,4.794728,1.000879,8.588578
1,20,6.745983,0.996609,12.495358
2,0,2.714600,1.000785,4.428415
2,10,3.868878,1.000244,6.737513
2,20,6.114824,1.001083,11.228566
3,0,2.434407,0.997827,3.870986
3,10,3.659001,0.998288,6.319713
3,20,6.131590,1.003013,11.260167
4,0,2.331475,1.005861,3.657088
4,10,3.591098,1.000090,6.182106
4,20,5.987212,0.997832,10.976592
1 epoch step loss loss_cont loss_disc
2 0 0 21.821945 1.016319 42.627571
3 0 10 12.070929 1.017697 23.124159
4 0 20 11.243023 1.009492 21.476553
5 1 0 5.083598 1.008866 9.158330
6 1 10 4.794728 1.000879 8.588578
7 1 20 6.745983 0.996609 12.495358
8 2 0 2.714600 1.000785 4.428415
9 2 10 3.868878 1.000244 6.737513
10 2 20 6.114824 1.001083 11.228566
11 3 0 2.434407 0.997827 3.870986
12 3 10 3.659001 0.998288 6.319713
13 3 20 6.131590 1.003013 11.260167
14 4 0 2.331475 1.005861 3.657088
15 4 10 3.591098 1.000090 6.182106
16 4 20 5.987212 0.997832 10.976592

13
example/run_pipeline.py
View File

@@ -6,10 +6,19 @@ import subprocess
import sys import sys
from pathlib import Path from pathlib import Path
from platform_utils import safe_path, is_windows
def run(cmd): def run(cmd):
print("running:", " ".join(cmd)) print("running:", " ".join(cmd))
subprocess.run(cmd, check=True) # 使用safe_path确保跨平台兼容性
cmd = [safe_path(arg) for arg in cmd]
# 在Windows上可能需要特殊处理
if is_windows():
subprocess.run(cmd, check=True, shell=False)
else:
subprocess.run(cmd, check=True)
def parse_args(): def parse_args():
@@ -32,7 +41,7 @@ def main():
args = parse_args() args = parse_args()
base_dir = Path(__file__).resolve().parent base_dir = Path(__file__).resolve().parent
run([sys.executable, str(base_dir / "prepare_data.py")]) run([sys.executable, str(base_dir / "prepare_data.py")])
run([sys.executable, str(base_dir / "train.py"), "--config", args.config]) run([sys.executable, str(base_dir / "train.py"), "--config", args.config, "--device", args.device])
run( run(
[ [
sys.executable, sys.executable,

27
example/sample.py
View File

@@ -11,23 +11,14 @@ import torch.nn.functional as F
from data_utils import load_split from data_utils import load_split
from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule
from platform_utils import resolve_device, safe_path, ensure_dir
BASE_DIR = Path(__file__).resolve().parent BASE_DIR = Path(__file__).resolve().parent
SPLIT_PATH = str(BASE_DIR / "feature_split.json") SPLIT_PATH = BASE_DIR / "feature_split.json"
VOCAB_PATH = str(BASE_DIR / "results" / "disc_vocab.json") VOCAB_PATH = BASE_DIR / "results" / "disc_vocab.json"
MODEL_PATH = str(BASE_DIR / "results" / "model.pt") MODEL_PATH = BASE_DIR / "results" / "model.pt"
def resolve_device(mode: str) -> str: # 使用 platform_utils 中的 resolve_device 函数
mode = mode.lower()
if mode == "cpu":
return "cpu"
if mode == "cuda":
if not torch.cuda.is_available():
raise SystemExit("device set to cuda but CUDA is not available")
return "cuda"
if torch.cuda.is_available():
return "cuda"
return "cpu"
DEVICE = resolve_device("auto") DEVICE = resolve_device("auto")
@@ -37,12 +28,12 @@ BATCH_SIZE = 2
def load_vocab(): def load_vocab():
with open(VOCAB_PATH, "r", encoding="ascii") as f: with open(str(VOCAB_PATH), "r", encoding="utf-8") as f:
return json.load(f)["vocab"] return json.load(f)["vocab"]
def main(): def main():
split = load_split(SPLIT_PATH) split = load_split(str(SPLIT_PATH))
time_col = split.get("time_column", "time") time_col = split.get("time_column", "time")
cont_cols = [c for c in split["continuous"] if c != time_col] cont_cols = [c for c in split["continuous"] if c != time_col]
disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col] disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]
@@ -52,8 +43,8 @@ def main():
print("device", DEVICE) print("device", DEVICE)
model = HybridDiffusionModel(cont_dim=len(cont_cols), disc_vocab_sizes=vocab_sizes).to(DEVICE) model = HybridDiffusionModel(cont_dim=len(cont_cols), disc_vocab_sizes=vocab_sizes).to(DEVICE)
if os.path.exists(MODEL_PATH): if MODEL_PATH.exists():
model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE, weights_only=True)) model.load_state_dict(torch.load(str(MODEL_PATH), map_location=DEVICE, weights_only=True))
model.eval() model.eval()
betas = cosine_beta_schedule(TIMESTEPS).to(DEVICE) betas = cosine_beta_schedule(TIMESTEPS).to(DEVICE)

235
example/test_all_modifications.py Normal file
View File

@@ -0,0 +1,235 @@
#!/usr/bin/env python3
"""测试所有跨平台修改"""
import os
import sys
import importlib
from pathlib import Path
def test_imports():
"""测试所有模块导入"""
print("=== 测试模块导入 ===")
modules_to_test = [
"platform_utils",
"data_utils",
"hybrid_diffusion",
"prepare_data",
"train",
"export_samples",
"sample",
"train_stub",
"evaluate_generated",
"plot_loss",
"analyze_hai21_03",
"run_pipeline",
]
all_success = True
for module_name in modules_to_test:
try:
module = importlib.import_module(module_name)
print(f"✓ {module_name} 导入成功")
# 检查是否有明显的语法错误
if hasattr(module, '__file__'):
print(f" 路径: {module.__file__}")
except ImportError as e:
print(f"✗ {module_name} 导入失败: {e}")
all_success = False
except SyntaxError as e:
print(f"✗ {module_name} 语法错误: {e}")
all_success = False
except Exception as e:
print(f"✗ {module_name} 其他错误: {e}")
all_success = False
return all_success
def test_platform_utils():
"""测试平台工具函数"""
print("\n=== 测试 platform_utils ===")
try:
from platform_utils import (
get_platform_info,
is_windows,
is_linux,
is_macos,
resolve_device,
safe_path,
ensure_dir,
print_platform_summary
)
print("✓ 所有函数导入成功")
# 测试平台检测
info = get_platform_info()
print(f" 系统: {info['system']}")
print(f" Windows检测: {is_windows()}")
print(f" Linux检测: {is_linux()}")
print(f" macOS检测: {is_macos()}")
# 测试路径处理
test_path = "some/path/to/file.txt"
safe_result = safe_path(test_path)
print(f" 路径处理: '{test_path}' -> '{safe_result}'")
# 测试设备检测
print(" 设备检测测试:")
for device in ["auto", "cpu", "cuda"]:
try:
result = resolve_device(device, verbose=False)
print(f" '{device}' -> '{result}'")
except Exception as e:
print(f" '{device}' -> 错误: {e}")
return True
except Exception as e:
print(f"✗ platform_utils 测试失败: {e}")
return False
def test_path_handling():
"""测试路径处理"""
print("\n=== 测试路径处理 ===")
try:
from platform_utils import safe_path, ensure_dir
import tempfile
# 测试safe_path
test_cases = [
"simple/path",
"path/with\\mixed\\separators",
Path("pathlib/object"),
"C:\\Windows\\Path" if os.name == 'nt' else "/linux/path"
]
for test in test_cases:
result = safe_path(test)
print(f" safe_path('{test}') -> '{result}'")
# 测试ensure_dir
with tempfile.TemporaryDirectory() as tmpdir:
test_dir = Path(tmpdir) / "test" / "subdir"
ensure_dir(test_dir)
if test_dir.exists():
print(f" ensure_dir 成功: {test_dir}")
else:
print(f" ensure_dir 失败: {test_dir}")
return True
except Exception as e:
print(f"✗ 路径处理测试失败: {e}")
return False
def test_device_resolution():
"""测试设备解析"""
print("\n=== 测试设备解析 ===")
try:
from platform_utils import resolve_device
print("设备解析结果:")
for device_mode in ["auto", "cpu", "cuda"]:
try:
device = resolve_device(device_mode, verbose=True)
print(f" 模式 '{device_mode}': {device}")
except Exception as e:
print(f" 模式 '{device_mode}' 错误: {e}")
return True
except Exception as e:
print(f"✗ 设备解析测试失败: {e}")
return False
def check_file_modifications():
"""检查文件修改"""
print("\n=== 检查文件修改 ===")
files_to_check = [
"platform_utils.py",
"train.py",
"export_samples.py",
"sample.py",
"train_stub.py",
"run_pipeline.py",
"prepare_data.py",
]
all_exist = True
for filename in files_to_check:
filepath = Path(__file__).parent / filename
if filepath.exists():
print(f"✓ {filename} 存在")
# 检查文件大小
size = filepath.stat().st_size
print(f" 大小: {size} 字节")
else:
print(f"✗ {filename} 不存在")
all_exist = False
return all_exist
def main():
print("跨平台修改测试工具")
print("=" * 60)
# 打印平台信息
from platform_utils import print_platform_summary
print_platform_summary()
# 运行测试
tests = [
("文件修改检查", check_file_modifications),
("模块导入测试", test_imports),
("平台工具测试", test_platform_utils),
("路径处理测试", test_path_handling),
("设备解析测试", test_device_resolution),
]
results = []
for test_name, test_func in tests:
try:
success = test_func()
results.append((test_name, success))
except Exception as e:
print(f"{test_name} 测试异常: {e}")
results.append((test_name, False))
print("\n" + "=" * 60)
print("=== 测试结果汇总 ===")
all_passed = True
for test_name, success in results:
status = "✓ 通过" if success else "✗ 失败"
print(f"{test_name}: {status}")
if not success:
all_passed = False
print("\n" + "=" * 60)
print("=== 使用说明 ===")
if all_passed:
print("所有测试通过！代码现在应该可以在Windows和Linux上运行。")
print("\n运行完整流程:")
print(" python run_pipeline.py --device auto")
print("\n单独运行:")
print(" python prepare_data.py")
print(" python train.py --device auto")
print(" python export_samples.py --device auto --include-time")
print(" python evaluate_generated.py")
print(" python plot_loss.py")
else:
print("部分测试失败，需要进一步检查。")
print("\n设备选项:")
print(" --device auto : 自动检测最佳设备（推荐）")
print(" --device cpu : 强制使用CPU")
print(" --device cuda : 强制使用GPU（如果可用）")
return all_passed
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

128
example/test_gpu.py Normal file
View File

@@ -0,0 +1,128 @@
#!/usr/bin/env python3
"""测试GPU可用性和配置"""
import torch
import sys
def test_gpu():
print("=== GPU测试 ===")
print(f"PyTorch版本: {torch.__version__}")
print(f"CUDA可用: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA版本: {torch.version.cuda}")
print(f"GPU数量: {torch.cuda.device_count()}")
for i in range(torch.cuda.device_count()):
print(f"\nGPU {i}:")
print(f" 名称: {torch.cuda.get_device_name(i)}")
print(f" 内存总量: {torch.cuda.get_device_properties(i).total_memory / 1024**3:.2f} GB")
# 测试GPU张量
try:
x = torch.randn(3, 3).cuda(i)
y = torch.randn(3, 3).cuda(i)
z = torch.matmul(x, y)
print(f" GPU计算测试: 成功")
print(f" 当前内存使用: {torch.cuda.memory_allocated(i) / 1024**2:.2f} MB")
print(f" 最大内存使用: {torch.cuda.max_memory_allocated(i) / 1024**2:.2f} MB")
except Exception as e:
print(f" GPU计算测试: 失败 - {e}")
else:
print("警告: CUDA不可用，将使用CPU运行")
return torch.cuda.is_available()
def test_device_resolution():
print("\n=== 设备解析测试 ===")
def resolve_device(mode: str) -> str:
mode = mode.lower()
if mode == "cpu":
return "cpu"
if mode == "cuda":
if not torch.cuda.is_available():
raise SystemExit("device set to cuda but CUDA is not available")
return "cuda"
if torch.cuda.is_available():
return "cuda"
return "cpu"
test_cases = [
("auto", "应该自动选择GPU（如果可用）"),
("cpu", "应该强制使用CPU"),
("cuda", "应该使用GPU（如果可用）"),
]
for mode, description in test_cases:
try:
device = resolve_device(mode)
print(f"模式 '{mode}': {device} - {description}")
except SystemExit as e:
print(f"模式 '{mode}': 错误 - {e}")
except Exception as e:
print(f"模式 '{mode}': 异常 - {e}")
def test_training_components():
print("\n=== 训练组件测试 ===")
# 测试是否能导入关键模块
try:
from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule
print("✓ hybrid_diffusion 模块导入成功")
# 创建一个简单的模型
model = HybridDiffusionModel(cont_dim=10, disc_vocab_sizes=[5, 3, 2])
if torch.cuda.is_available():
model = model.cuda()
print("✓ 模型可以移动到GPU")
else:
print("✓ 模型在CPU上")
# 测试前向传播
batch_size = 2
seq_len = 16
x_cont = torch.randn(batch_size, seq_len, 10)
x_disc = torch.randint(0, 5, (batch_size, seq_len, 3))
t = torch.randint(0, 100, (batch_size,))
if torch.cuda.is_available():
x_cont = x_cont.cuda()
x_disc = x_disc.cuda()
t = t.cuda()
model = model.cuda()
eps_pred, logits = model(x_cont, x_disc, t)
print(f"✓ 前向传播成功")
print(f" 连续输出形状: {eps_pred.shape}")
print(f" 离散输出数量: {len(logits)}")
except ImportError as e:
print(f"✗ 模块导入失败: {e}")
except Exception as e:
print(f"✗ 测试失败: {e}")
def main():
print("开始GPU和训练配置测试...")
gpu_available = test_gpu()
test_device_resolution()
test_training_components()
print("\n=== 使用建议 ===")
if gpu_available:
print("1. 使用GPU运行: python run_pipeline.py --device cuda")
print("2. 或自动选择: python run_pipeline.py --device auto")
print("3. 单独训练: python train.py --device cuda")
print("4. 单独采样: python export_samples.py --device cuda")
else:
print("1. 只能使用CPU运行: python run_pipeline.py --device cpu")
print("2. 检查CUDA和PyTorch安装")
print("3. 确保有NVIDIA GPU和正确的驱动程序")
return gpu_available
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

140
example/test_windows_compatibility.py Normal file
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@@ -0,0 +1,140 @@
#!/usr/bin/env python3
"""测试Windows兼容性修改"""
import os
import sys
from pathlib import Path
def test_path_operations():
"""测试路径操作"""
print("=== 测试路径操作 ===")
# 测试Path对象
base_dir = Path(__file__).resolve().parent
print(f"Base directory: {base_dir}")
print(f"Base directory exists: {base_dir.exists()}")
# 测试路径拼接
test_path = base_dir / "test_file.txt"
print(f"Test path: {test_path}")
print(f"Test path is absolute: {test_path.is_absolute()}")
# 测试路径解析
print(f"Parent: {test_path.parent}")
print(f"Name: {test_path.name}")
return True
def test_file_operations():
"""测试文件操作"""
print("\n=== 测试文件操作 ===")
base_dir = Path(__file__).resolve().parent
test_file = base_dir / "test_windows_compat.txt"
try:
# 测试写入（UTF-8编码）
with open(test_file, "w", encoding="utf-8") as f:
f.write("测试Windows兼容性\n")
f.write("Test Windows compatibility\n")
f.write("中文字符测试\n")
print(f"文件写入成功: {test_file}")
# 测试读取
with open(test_file, "r", encoding="utf-8") as f:
content = f.read()
print(f"文件读取成功，内容长度: {len(content)}")
# 清理
test_file.unlink()
print("测试文件已清理")
return True
except Exception as e:
print(f"文件操作失败: {e}")
return False
def test_imports():
"""测试导入"""
print("\n=== 测试导入 ===")
modules_to_test = [
"json",
"csv",
"gzip",
"os",
"sys",
"pathlib",
"argparse",
"subprocess",
]
all_success = True
for module_name in modules_to_test:
try:
__import__(module_name)
print(f"✓ {module_name} 导入成功")
except ImportError as e:
print(f"✗ {module_name} 导入失败: {e}")
all_success = False
return all_success
def test_path_resolution():
"""测试路径解析"""
print("\n=== 测试路径解析 ===")
# 测试相对路径解析
rel_path = Path("./test/../example")
abs_path = rel_path.resolve()
print(f"相对路径: {rel_path}")
print(f"解析后绝对路径: {abs_path}")
# 测试路径拼接
path1 = Path("C:/Users/test")
path2 = Path("documents/file.txt")
combined = path1 / path2
print(f"路径拼接: {combined}")
return True
def main():
print("开始Windows兼容性测试...")
print(f"Python版本: {sys.version}")
print(f"操作系统: {os.name}")
print(f"当前目录: {os.getcwd()}")
tests = [
("路径操作", test_path_operations),
("文件操作", test_file_operations),
("模块导入", test_imports),
("路径解析", test_path_resolution),
]
results = []
for test_name, test_func in tests:
try:
success = test_func()
results.append((test_name, success))
except Exception as e:
print(f"{test_name} 测试异常: {e}")
results.append((test_name, False))
print("\n=== 测试结果汇总 ===")
all_passed = True
for test_name, success in results:
status = "✓ 通过" if success else "✗ 失败"
print(f"{test_name}: {status}")
if not success:
all_passed = False
if all_passed:
print("\n所有测试通过！代码应该可以在Windows上正常运行。")
else:
print("\n部分测试失败，需要进一步检查。")
return all_passed
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

43
example/train.py
View File

@@ -18,17 +18,18 @@ from hybrid_diffusion import (
q_sample_continuous, q_sample_continuous,
q_sample_discrete, q_sample_discrete,
) )
from platform_utils import resolve_device, safe_path, ensure_dir
BASE_DIR = Path(__file__).resolve().parent BASE_DIR = Path(__file__).resolve().parent
REPO_DIR = BASE_DIR.parent.parent REPO_DIR = BASE_DIR.parent.parent
DEFAULTS = { DEFAULTS = {
"data_path": str(REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz"), "data_path": REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz",
"split_path": str(BASE_DIR / "feature_split.json"), "split_path": BASE_DIR / "feature_split.json",
"stats_path": str(BASE_DIR / "results" / "cont_stats.json"), "stats_path": BASE_DIR / "results" / "cont_stats.json",
"vocab_path": str(BASE_DIR / "results" / "disc_vocab.json"), "vocab_path": BASE_DIR / "results" / "disc_vocab.json",
"out_dir": str(BASE_DIR / "results"), "out_dir": BASE_DIR / "results",
"device": "auto", "device": "auto",
"timesteps": 1000, "timesteps": 1000,
"batch_size": 8, "batch_size": 8,
@@ -44,7 +45,7 @@ DEFAULTS = {
def load_json(path: str) -> Dict: def load_json(path: str) -> Dict:
with open(path, "r", encoding="ascii") as f: with open(path, "r", encoding="utf-8") as f:
return json.load(f) return json.load(f)
@@ -57,22 +58,13 @@ def set_seed(seed: int):
torch.backends.cudnn.benchmark = False torch.backends.cudnn.benchmark = False
def resolve_device(mode: str) -> str: # 使用 platform_utils 中的 resolve_device 函数
mode = mode.lower()
if mode == "cpu":
return "cpu"
if mode == "cuda":
if not torch.cuda.is_available():
raise SystemExit("device set to cuda but CUDA is not available")
return "cuda"
if torch.cuda.is_available():
return "cuda"
return "cpu"
def parse_args(): def parse_args():
parser = argparse.ArgumentParser(description="Train hybrid diffusion on HAI.") parser = argparse.ArgumentParser(description="Train hybrid diffusion on HAI.")
parser.add_argument("--config", default=None, help="Path to JSON config.") parser.add_argument("--config", default=None, help="Path to JSON config.")
parser.add_argument("--device", default="auto", help="cpu, cuda, or auto")
return parser.parse_args() return parser.parse_args()
@@ -80,9 +72,16 @@ def resolve_config_paths(config, base_dir: Path):
keys = ["data_path", "split_path", "stats_path", "vocab_path", "out_dir"] keys = ["data_path", "split_path", "stats_path", "vocab_path", "out_dir"]
for key in keys: for key in keys:
if key in config: if key in config:
path = Path(str(config[key])) # 如果值是字符串，转换为Path对象
if isinstance(config[key], str):
path = Path(config[key])
else:
path = config[key]
if not path.is_absolute(): if not path.is_absolute():
config[key] = str((base_dir / path).resolve()) config[key] = str((base_dir / path).resolve())
else:
config[key] = str(path)
return config return config
@@ -96,6 +95,10 @@ def main():
else: else:
config = resolve_config_paths(config, BASE_DIR) config = resolve_config_paths(config, BASE_DIR)
# 优先使用命令行传入的device参数
if args.device != "auto":
config["device"] = args.device
set_seed(int(config["seed"])) set_seed(int(config["seed"]))
split = load_split(config["split_path"]) split = load_split(config["split_path"])
@@ -121,7 +124,7 @@ def main():
os.makedirs(config["out_dir"], exist_ok=True) os.makedirs(config["out_dir"], exist_ok=True)
log_path = os.path.join(config["out_dir"], "train_log.csv") log_path = os.path.join(config["out_dir"], "train_log.csv")
with open(log_path, "w", encoding="ascii") as f: with open(log_path, "w", encoding="utf-8") as f:
f.write("epoch,step,loss,loss_cont,loss_disc\n") f.write("epoch,step,loss,loss_cont,loss_disc\n")
total_step = 0 total_step = 0
@@ -168,7 +171,7 @@ def main():
if step % int(config["log_every"]) == 0: if step % int(config["log_every"]) == 0:
print("epoch", epoch, "step", step, "loss", float(loss)) print("epoch", epoch, "step", step, "loss", float(loss))
with open(log_path, "a", encoding="ascii") as f: with open(log_path, "a", encoding="utf-8") as f:
f.write( f.write(
"%d,%d,%.6f,%.6f,%.6f\n" "%d,%d,%.6f,%.6f,%.6f\n"
% (epoch, step, float(loss), float(loss_cont), float(loss_disc)) % (epoch, step, float(loss), float(loss_cont), float(loss_disc))

17
example/train_stub.py
View File

@@ -20,22 +20,23 @@ from hybrid_diffusion import (
q_sample_continuous, q_sample_continuous,
q_sample_discrete, q_sample_discrete,
) )
from platform_utils import resolve_device, safe_path, ensure_dir
BASE_DIR = Path(__file__).resolve().parent BASE_DIR = Path(__file__).resolve().parent
REPO_DIR = BASE_DIR.parent.parent REPO_DIR = BASE_DIR.parent.parent
DATA_PATH = str(REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz") DATA_PATH = REPO_DIR / "dataset" / "hai" / "hai-21.03" / "train1.csv.gz"
SPLIT_PATH = str(BASE_DIR / "feature_split.json") SPLIT_PATH = BASE_DIR / "feature_split.json"
DEVICE = "cpu" DEVICE = resolve_device("auto")
TIMESTEPS = 1000 TIMESTEPS = 1000
def load_split(path: str) -> Dict[str, List[str]]: def load_split(path: str) -> Dict[str, List[str]]:
with open(path, "r", encoding="ascii") as f: with open(str(path), "r", encoding="utf-8") as f:
return json.load(f) return json.load(f)
def iter_rows(path: str): def iter_rows(path: str):
with gzip.open(path, "rt", newline="") as f: with gzip.open(str(path), "rt", newline="") as f:
reader = csv.DictReader(f) reader = csv.DictReader(f)
for row in reader: for row in reader:
yield row yield row
@@ -73,18 +74,18 @@ def load_batch(path: str, cont_cols: List[str], disc_cols: List[str], batch_size
def main(): def main():
split = load_split(SPLIT_PATH) split = load_split(str(SPLIT_PATH))
cont_cols = split["continuous"] cont_cols = split["continuous"]
disc_cols = split["discrete"] disc_cols = split["discrete"]
vocab_sizes = build_vocab_sizes(DATA_PATH, disc_cols) vocab_sizes = build_vocab_sizes(str(DATA_PATH), disc_cols)
model = HybridDiffusionModel(cont_dim=len(cont_cols), disc_vocab_sizes=vocab_sizes).to(DEVICE) model = HybridDiffusionModel(cont_dim=len(cont_cols), disc_vocab_sizes=vocab_sizes).to(DEVICE)
betas = cosine_beta_schedule(TIMESTEPS).to(DEVICE) betas = cosine_beta_schedule(TIMESTEPS).to(DEVICE)
alphas = 1.0 - betas alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0) alphas_cumprod = torch.cumprod(alphas, dim=0)
x_cont, x_disc = load_batch(DATA_PATH, cont_cols, disc_cols) x_cont, x_disc = load_batch(str(DATA_PATH), cont_cols, disc_cols)
x_cont = x_cont.to(DEVICE) x_cont = x_cont.to(DEVICE)
x_disc = x_disc.to(DEVICE) x_disc = x_disc.to(DEVICE)