Upload src/utils/config.py with huggingface_hub

src/utils/config.py

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+"""
+Configuration management for the Thermal Pattern Analysis project.
+
+Loads YAML configs, provides attribute-style access, and handles
+device selection + reproducibility seeding.
+"""
+
+import os
+import yaml
+import torch
+import random
+import numpy as np
+from pathlib import Path
+
+
+class Config:
+    """Hierarchical configuration with attribute-style access."""
+
+    def __init__(self, config_dict: dict):
+        for key, value in config_dict.items():
+            if isinstance(value, dict):
+                setattr(self, key, Config(value))
+            elif isinstance(value, list):
+                setattr(self, key, [
+                    Config(v) if isinstance(v, dict) else v for v in value
+                ])
+            else:
+                setattr(self, key, value)
+
+    def to_dict(self) -> dict:
+        """Convert back to a plain dictionary."""
+        result = {}
+        for key, value in self.__dict__.items():
+            if isinstance(value, Config):
+                result[key] = value.to_dict()
+            elif isinstance(value, list):
+                result[key] = [
+                    v.to_dict() if isinstance(v, Config) else v for v in value
+                ]
+            else:
+                result[key] = value
+        return result
+
+    def __repr__(self):
+        return f"Config({self.to_dict()})"
+
+    def get(self, key, default=None):
+        """Safe attribute access with a default value."""
+        return getattr(self, key, default)
+
+
+def load_config(config_path: str = "configs/config.yaml") -> Config:
+    """
+    Load configuration from a YAML file.
+
+    Args:
+        config_path: Path to the YAML configuration file.
+
+    Returns:
+        Config object with attribute-style access.
+    """
+    config_path = Path(config_path)
+    if not config_path.exists():
+        raise FileNotFoundError(f"Config file not found: {config_path}")
+
+    with open(config_path, "r") as f:
+        config_dict = yaml.safe_load(f)
+
+    return Config(config_dict)
+
+
+def setup_device(config: Config) -> torch.device:
+    """
+    Determine the compute device based on config and availability.
+
+    Auto mode picks CUDA if available, otherwise CPU.
+    """
+    device_str = config.get("device", "auto")
+    if device_str == "auto":
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    else:
+        device = torch.device(device_str)
+    return device
+
+
+def set_seed(seed: int = 42):
+    """Set random seeds for reproducibility across all libraries."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+def ensure_dirs(config: Config):
+    """Create all output directories specified in config.paths."""
+    paths = config.get("paths", None)
+    if paths is None:
+        return
+    for attr in ["checkpoints", "logs", "results", "visualizations"]:
+        dir_path = paths.get(attr, None)
+        if dir_path:
+            os.makedirs(dir_path, exist_ok=True)