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d2885a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import json
import random
from pathlib import Path
from typing import Any
import numpy as np
import torch
import yaml
def read_yaml(path_to_yaml: Path) -> dict:
"""
Read a YAML file and return its data as a dictionary.
"""
if not path_to_yaml.exists():
raise FileNotFoundError(f"YAML file not found: {path_to_yaml}")
with open(path_to_yaml, "r", encoding="utf-8") as yaml_file:
data = yaml.safe_load(yaml_file)
if data is None:
raise ValueError(f"YAML file is empty: {path_to_yaml}")
return data
def save_yaml(path_to_yaml: Path, data: dict) -> None:
"""
Save dictionary data to a YAML file.
"""
path_to_yaml.parent.mkdir(parents=True, exist_ok=True)
with open(path_to_yaml, "w", encoding="utf-8") as yaml_file:
yaml.safe_dump(data, yaml_file, sort_keys=False)
def create_directories(paths: list[Path]) -> None:
"""
Create many directories safely.
"""
for path in paths:
path.mkdir(parents=True, exist_ok=True)
def save_json(path: Path, data: dict[str, Any]) -> None:
"""
Save dictionary data to JSON.
"""
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as json_file:
json.dump(data, json_file, indent=4, ensure_ascii=False)
def load_json(path: Path) -> dict:
"""
Load dictionary data from JSON.
"""
if not path.exists():
raise FileNotFoundError(f"JSON file not found: {path}")
with open(path, "r", encoding="utf-8") as json_file:
return json.load(json_file)
def save_text(path: Path, content: str) -> None:
"""
Save plain text to a file.
"""
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as text_file:
text_file.write(content)
def seed_everything(seed: int) -> None:
"""
Make training and data processing more reproducible.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def resolve_device(device_preference: str = "auto") -> str:
"""
Resolve device safely for CPU/GPU environments.
Supported values:
- auto
- cpu
- cuda
"""
device_preference = device_preference.lower().strip()
if device_preference == "cpu":
return "cpu"
if device_preference == "cuda":
return "cuda" if torch.cuda.is_available() else "cpu"
if device_preference == "auto":
return "cuda" if torch.cuda.is_available() else "cpu"
raise ValueError(f"Unsupported device preference: {device_preference}")
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