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"""
Model loader - Handles VGG16 loading and inference
"""
import torch
import torch.nn as nn
from torchvision import models, transforms
from .config import IMAGE_SIZE, NORMALIZE_MEAN, NORMALIZE_STD
class VGG16ModelLoader:
"""Loads and manages VGG16 model for fish disease detection"""
def __init__(self, model_path, num_classes, device='cpu'):
"""
Initialize model loader
Args:
model_path: Path to trained model weights (.pth file)
num_classes: Number of disease classes
device: 'cpu' or 'cuda'
"""
self.model_path = model_path
self.num_classes = num_classes
self.device = torch.device(device)
self.model = None
self.transform = None
self._load_model()
self._setup_transform()
def _load_model(self):
"""Load VGG16 with custom classifier"""
try:
# Create VGG16 architecture
self.model = models.vgg16(weights="IMAGENET1K_V1")
# Replace final layer for our classes
self.model.classifier[6] = nn.Linear(4096, self.num_classes)
# Load trained weights
state_dict = torch.load(
self.model_path,
map_location=self.device,
weights_only=True # Security: only load weights
)
self.model.load_state_dict(state_dict)
# Move to device and set eval mode
self.model = self.model.to(self.device)
self.model.eval()
print(f"✅ Model loaded: {self.model_path}")
print(f"✅ Device: {self.device}")
except FileNotFoundError:
raise RuntimeError(f"Model file not found: {self.model_path}")
except Exception as e:
raise RuntimeError(f"Failed to load model: {e}")
def _setup_transform(self):
"""Setup image preprocessing pipeline"""
self.transform = transforms.Compose([
transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
transforms.ToTensor(),
transforms.Normalize(NORMALIZE_MEAN, NORMALIZE_STD),
])
def predict(self, image):
"""
Predict disease from PIL Image
Args:
image: PIL Image in RGB format
Returns:
tuple: (predicted_class_idx, confidence_score, all_probabilities)
"""
try:
# Preprocess image
input_tensor = self.transform(image).unsqueeze(0).to(self.device)
# Run inference
with torch.no_grad():
outputs = self.model(input_tensor)
probabilities = torch.nn.functional.softmax(outputs, dim=1)
confidence, predicted_idx = torch.max(probabilities, 1)
return (
predicted_idx.item(),
confidence.item() * 100, # Convert to percentage
probabilities[0]
)
except Exception as e:
raise RuntimeError(f"Prediction failed: {e}")
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