Upload src/utils\config.py with huggingface_hub

src/utils//config.py

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+"""
+Configuration utilities for architectural style classification experiments.
+"""
+
+import json
+import os
+from typing import Dict, Any, Optional
+import yaml
+
+
+def load_config(config_path: str) -> Dict[str, Any]:
+    """Load configuration from JSON or YAML file."""
+    if not os.path.exists(config_path):
+        raise FileNotFoundError(f"Config file not found: {config_path}")
+    
+    with open(config_path, 'r') as f:
+        if config_path.endswith('.json'):
+            config = json.load(f)
+        elif config_path.endswith('.yaml') or config_path.endswith('.yml'):
+            config = yaml.safe_load(f)
+        else:
+            raise ValueError(f"Unsupported config file format: {config_path}")
+    
+    return config
+
+
+def save_config(config: Dict[str, Any], config_path: str):
+    """Save configuration to JSON or YAML file."""
+    os.makedirs(os.path.dirname(config_path), exist_ok=True)
+    
+    with open(config_path, 'w') as f:
+        if config_path.endswith('.json'):
+            json.dump(config, f, indent=2, default=str)
+        elif config_path.endswith('.yaml') or config_path.endswith('.yml'):
+            yaml.dump(config, f, default_flow_style=False)
+        else:
+            raise ValueError(f"Unsupported config file format: {config_path}")
+
+
+def validate_config(config: Dict[str, Any]) -> bool:
+    """Validate configuration parameters."""
+    required_fields = ['experiment_name', 'model_type', 'num_classes']
+    
+    for field in required_fields:
+        if field not in config:
+            raise ValueError(f"Missing required field: {field}")
+    
+    # Validate model type
+    valid_model_types = ['hierarchical', 'resnet', 'efficientnet', 'vit']
+    if config['model_type'] not in valid_model_types:
+        raise ValueError(f"Invalid model_type: {config['model_type']}. Must be one of {valid_model_types}")
+    
+    # Validate numeric fields
+    numeric_fields = ['num_classes', 'learning_rate', 'max_epochs', 'batch_size']
+    for field in numeric_fields:
+        if field in config and not isinstance(config[field], (int, float)):
+            raise ValueError(f"Field {field} must be numeric")
+    
+    return True
+
+
+def create_default_config() -> Dict[str, Any]:
+    """Create a default configuration."""
+    return {
+        'experiment_name': 'architectural_classification',
+        'model_type': 'hierarchical',
+        'num_classes': 25,
+        'num_broad_classes': 5,
+        'num_fine_classes': 25,
+        'learning_rate': 1e-4,
+        'weight_decay': 1e-5,
+        'max_epochs': 100,
+        'batch_size': 16,
+        'use_hierarchical_loss': True,
+        'use_contrastive_loss': False,
+        'use_style_relationship_loss': True,
+        'use_mixed_precision': False,
+        'gradient_clip_val': 1.0,
+        'accumulate_grad_batches': 1,
+        'use_wandb': False,
+        'curriculum_stages': [
+            {'epochs': 20, 'classes': ['ancient', 'medieval', 'modern']},
+            {'epochs': 80, 'classes': list(range(25))}
+        ]
+    }