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"""
Model utilities for fire detection classification
Handles ConvNeXt model loading and adaptation for transfer learning
"""
import torch
import torch.nn as nn
import timm
import os
from typing import Dict, Any, Optional, Tuple
class FireDetectionClassifier(nn.Module):
"""
ConvNeXt-based fire detection classifier
Uses transfer learning from ImageNet pretrained model
"""
def __init__(self, num_classes: int = 2, pretrained: bool = True):
super(FireDetectionClassifier, self).__init__()
# Load ConvNeXt Large model
self.backbone = timm.create_model(
'convnext_large.fb_in22k_ft_in1k',
pretrained=pretrained,
num_classes=0 # Remove classification head
)
# Get feature dimensions
self.feature_dim = self.backbone.num_features
# Custom classification head for fire detection
self.classifier = nn.Sequential(
nn.LayerNorm(self.feature_dim),
nn.Linear(self.feature_dim, 512),
nn.ReLU(inplace=True),
nn.Dropout(0.3),
nn.Linear(512, 128),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Linear(128, num_classes)
)
# Initialize classifier weights
self._init_classifier_weights()
def _init_classifier_weights(self):
"""Initialize classifier weights using Xavier initialization"""
for module in self.classifier.modules():
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
nn.init.constant_(module.bias, 0)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass through the model"""
# Extract features using ConvNeXt backbone
features = self.backbone(x)
# Classify using custom head
output = self.classifier(features)
return output
def freeze_backbone(self):
"""Freeze backbone parameters for transfer learning"""
for param in self.backbone.parameters():
param.requires_grad = False
print("🔒 Backbone frozen for transfer learning")
def unfreeze_backbone(self):
"""Unfreeze backbone parameters for fine-tuning"""
for param in self.backbone.parameters():
param.requires_grad = True
print("🔓 Backbone unfrozen for fine-tuning")
def get_parameter_count(self) -> Dict[str, int]:
"""Get parameter counts for different parts of the model"""
backbone_params = sum(p.numel() for p in self.backbone.parameters())
classifier_params = sum(p.numel() for p in self.classifier.parameters())
total_params = backbone_params + classifier_params
trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
return {
'backbone': backbone_params,
'classifier': classifier_params,
'total': total_params,
'trainable': trainable_params
}
def create_fire_detection_model(
num_classes: int = 2,
freeze_backbone: bool = True
) -> FireDetectionClassifier:
"""
Create fire detection classifier model with transfer learning
Args:
num_classes: Number of output classes (2 for fire/no_fire)
freeze_backbone: Whether to freeze backbone for transfer learning
Returns:
FireDetectionClassifier model ready for training
"""
print("🔥 Creating fire detection classifier...")
# Create the model
model = FireDetectionClassifier(num_classes=num_classes, pretrained=True)
# Freeze backbone if requested
if freeze_backbone:
model.freeze_backbone()
# Print model information
param_counts = model.get_parameter_count()
print(f"📊 Model Statistics:")
print(f" Backbone parameters: {param_counts['backbone']:,}")
print(f" Classifier parameters: {param_counts['classifier']:,}")
print(f" Total parameters: {param_counts['total']:,}")
print(f" Trainable parameters: {param_counts['trainable']:,}")
print(f" Model size: ~{param_counts['total'] * 4 / 1024**2:.1f} MB")
return model
def save_model(
model: FireDetectionClassifier,
save_path: str,
epoch: int,
best_acc: float,
optimizer_state: Optional[Dict] = None,
additional_info: Optional[Dict] = None
) -> None:
"""
Save model checkpoint with training information
Args:
model: The model to save
save_path: Path to save the model
epoch: Current epoch number
best_acc: Best accuracy achieved
optimizer_state: Optimizer state dict
additional_info: Additional information to save
"""
# Create directory if it doesn't exist
os.makedirs(os.path.dirname(save_path), exist_ok=True)
# Prepare checkpoint
checkpoint = {
'model_state_dict': model.state_dict(),
'epoch': epoch,
'best_acc': best_acc,
'model_info': {
'num_classes': 2,
'class_names': ['fire', 'no_fire'],
'parameter_count': model.get_parameter_count()
}
}
# Add optional information
if optimizer_state:
checkpoint['optimizer_state_dict'] = optimizer_state
if additional_info:
checkpoint.update(additional_info)
# Save checkpoint
torch.save(checkpoint, save_path)
print(f"💾 Model saved to: {save_path}")
print(f"📈 Best accuracy: {best_acc:.4f}")
def load_model(
model_path: str,
num_classes: int = 2,
device: str = 'cpu'
) -> Tuple[FireDetectionClassifier, Dict[str, Any]]:
"""
Load a trained fire detection model
Args:
model_path: Path to the saved model
num_classes: Number of classes (should be 2)
device: Device to load model on
Returns:
Tuple of (model, model_info)
"""
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model not found at: {model_path}")
# Load checkpoint
checkpoint = torch.load(model_path, map_location=device)
# Create model
model = FireDetectionClassifier(num_classes=num_classes, pretrained=False)
# Load state dict
model.load_state_dict(checkpoint['model_state_dict'])
# Move to device
model = model.to(device)
# Extract model info
model_info = checkpoint.get('model_info', {})
model_info['epoch'] = checkpoint.get('epoch', 'Unknown')
model_info['best_acc'] = checkpoint.get('best_acc', 'Unknown')
print(f"✅ Model loaded from: {model_path}")
print(f"📊 Model accuracy: {model_info.get('best_acc', 'Unknown')}")
return model, model_info
def get_model_summary(model: FireDetectionClassifier) -> str:
"""
Get a summary of the model architecture
Args:
model: The model to summarize
Returns:
String summary of the model
"""
param_counts = model.get_parameter_count()
summary = f"""
🔥 Fire Detection Model Summary
{'='*50}
Architecture: ConvNeXt Large + Custom Classifier
Classes: fire, no_fire
Parameters:
Backbone: {param_counts['backbone']:,}
Classifier: {param_counts['classifier']:,}
Total: {param_counts['total']:,}
Trainable: {param_counts['trainable']:,}
Model Size: ~{param_counts['total'] * 4 / 1024**2:.1f} MB
{'='*50}
"""
return summary |