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import os
import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
# Parameters
IMG_HEIGHT = 224
IMG_WIDTH = 224
# Define classes (must match training - sorted alphabetically)
CLASSES = sorted([
"Healthy",
"Arcing_Contact_Misalignment",
"Arcing_Contact_Wear",
"Main Contact Misalignment",
"main_contact_wear"
])
class ViTClassifier:
_instance = None
_model = None
_device = None
_transform = None
@classmethod
def get_instance(cls, model_path=None):
if model_path is None:
model_path = os.path.join(os.path.dirname(__file__), "vit_model.pth")
if cls._instance is None:
cls._instance = cls()
cls._instance._load_model(model_path)
return cls._instance
def _load_model(self, model_path):
self._transform = transforms.Compose([
transforms.Resize((IMG_HEIGHT, IMG_WIDTH)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
self._device = torch.device("cpu")
print(f"Using device: {self._device}")
print(f"Loading model from {model_path}...")
try:
weights = models.ViT_B_16_Weights.DEFAULT
self._model = models.vit_b_16(weights=weights)
num_features = self._model.heads.head.in_features
self._model.heads.head = nn.Linear(num_features, len(CLASSES))
if os.path.exists(model_path):
self._model.load_state_dict(torch.load(model_path, map_location=self._device))
self._model.to(self._device)
self._model.eval()
print("Model loaded successfully.")
else:
print(f"Error: Model file not found at {model_path}")
self._model = None
except Exception as e:
print(f"Error loading model: {e}")
self._model = None
def predict(self, image_path_or_file):
"""
Returns:
predicted_class (str)
confidence (float)
probabilities (dict) → class: probability
"""
if self._model is None:
return None, 0.0, {}
try:
image = Image.open(image_path_or_file).convert('RGB')
image_tensor = self._transform(image).unsqueeze(0).to(self._device)
with torch.no_grad():
outputs = self._model(image_tensor)
probs = torch.nn.functional.softmax(outputs, dim=1).cpu().numpy()[0]
# Highest confidence prediction
predicted_idx = probs.argmax()
predicted_class = CLASSES[predicted_idx]
confidence = float(probs[predicted_idx])
# All class probabilities
probability_dict = {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))}
return predicted_class, confidence, probability_dict
except Exception as e:
print(f"Error processing image: {e}")
return None, 0.0, {} |