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"""
Model Manager - Handles loading and caching of YOLO and VLM models
"""
import torch
from transformers import (
Qwen2_5_VLForConditionalGeneration,
AutoProcessor,
BitsAndBytesConfig
)
from ultralytics import YOLO
import os
from typing import Tuple
from config import (
YOLO_MODEL_PATH,
VLM_MODEL_ID,
QUANTIZATION_CONFIG,
YOLO_CONFIDENCE_THRESHOLD
)
class ModelManager:
"""Singleton class to manage model loading and inference"""
_instance = None
_initialized = False
def __new__(cls):
if cls._instance is None:
cls._instance = super(ModelManager, cls).__new__(cls)
return cls._instance
def __init__(self):
if not ModelManager._initialized:
self.yolo_model = None
self.vlm_model = None
self.processor = None
ModelManager._initialized = True
def load_models(self):
"""Load both YOLO and VLM models into memory"""
print("🚀 Starting model loading...")
# Load YOLO model
self.yolo_model = self._load_yolo_model()
# Load VLM model
self.vlm_model, self.processor = self._load_vlm_model()
# Warm up models to initialize CUDA context
self._warmup_models()
print("✅ All models loaded successfully!")
def _load_yolo_model(self) -> YOLO:
"""Load trained YOLO model for signature and stamp detection"""
if not os.path.exists(YOLO_MODEL_PATH):
raise FileNotFoundError(
f"YOLO model not found at {YOLO_MODEL_PATH}. "
"Please ensure best.pt is in utils/models/"
)
yolo_model = YOLO(str(YOLO_MODEL_PATH))
print(f"✅ YOLO model loaded from {YOLO_MODEL_PATH}")
return yolo_model
def _load_vlm_model(self) -> Tuple:
"""
Load Qwen2.5-VL model with 4-bit quantization
Downloads from Hugging Face on first run
"""
print(f"📥 Loading VLM model: {VLM_MODEL_ID}")
print(" (This will download ~4GB on first run)")
# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
load_in_4bit=QUANTIZATION_CONFIG["load_in_4bit"],
bnb_4bit_quant_type=QUANTIZATION_CONFIG["bnb_4bit_quant_type"],
bnb_4bit_compute_dtype=getattr(torch, QUANTIZATION_CONFIG["bnb_4bit_compute_dtype"]),
bnb_4bit_use_double_quant=QUANTIZATION_CONFIG["bnb_4bit_use_double_quant"]
)
# Load processor
processor = AutoProcessor.from_pretrained(
VLM_MODEL_ID,
trust_remote_code=True
)
# Load model with quantization
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
VLM_MODEL_ID,
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
model.eval()
print(f"✅ Qwen2.5-VL model loaded successfully")
return model, processor
def _warmup_models(self):
"""Warm up models with a dummy inference to initialize CUDA context"""
print("🔥 Warming up models (initializing CUDA context)...")
import time
from PIL import Image
import numpy as np
warmup_start = time.time()
# Create a small dummy image
dummy_image = Image.fromarray(np.ones((100, 100, 3), dtype=np.uint8) * 255)
try:
# Warm up VLM
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": dummy_image},
{"type": "text", "text": "warm up"}
]
}
]
from qwen_vl_utils import process_vision_info
text = self.processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Run a quick inference
with torch.no_grad():
_ = self.vlm_model.generate(**inputs, max_new_tokens=5)
# Clean up
del inputs
if torch.cuda.is_available():
torch.cuda.empty_cache()
warmup_time = time.time() - warmup_start
print(f"✅ Models warmed up in {warmup_time:.2f}s (CUDA context initialized)")
except Exception as e:
print(f"⚠️ Warmup failed (non-critical): {e}")
def detect_sign_stamp(self, image_path: str):
"""
Detect signature and stamp in the image using YOLO
Returns:
tuple: (signature_info, stamp_info, signature_conf, stamp_conf)
"""
if self.yolo_model is None:
raise RuntimeError("YOLO model not loaded. Call load_models() first.")
results = self.yolo_model(image_path, verbose=False)[0]
signature_info = {"present": False, "bbox": None}
stamp_info = {"present": False, "bbox": None}
signature_conf = 0.0
stamp_conf = 0.0
if results.boxes is not None:
for box in results.boxes:
cls_id = int(box.cls[0])
conf = float(box.conf[0])
if conf > YOLO_CONFIDENCE_THRESHOLD:
bbox = box.xyxy[0].cpu().numpy().tolist()
bbox = [int(coord) for coord in bbox]
# Class 0: signature, Class 1: stamp
if cls_id == 0 and conf > signature_conf:
signature_info = {"present": True, "bbox": bbox}
signature_conf = conf
elif cls_id == 1 and conf > stamp_conf:
stamp_info = {"present": True, "bbox": bbox}
stamp_conf = conf
return signature_info, stamp_info, signature_conf, stamp_conf
def is_loaded(self) -> bool:
"""Check if models are loaded"""
return (self.yolo_model is not None and
self.vlm_model is not None and
self.processor is not None)
# Global model manager instance
model_manager = ModelManager()
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