Optimize Factory Vision Models with OpenVINO and ExecuTorch

                      
                     
import os
import cv2
import numpy as np
from openvino.inference_engine import IECore
from executorch import Executor

# Configuration
model_path = os.getenv('MODEL_PATH')  # Path to the OpenVINO model
input_shape = (1, 3, 224, 224)  # Example input shape for the model

# Initialize OpenVINO Inference Engine
ie = IECore()
net = ie.read_network(model=model_path + '.xml', weights=model_path + '.bin')
exec_net = ie.load_network(network=net, device_name='CPU')

# Executor for ExecuTorch
executor = Executor(model_path)

def preprocess_image(image_path: str) -> np.ndarray:
    # Load and preprocess image
    image = cv2.imread(image_path)
    image = cv2.resize(image, (224, 224))
    image = image.transpose((2, 0, 1))  # Change data layout from HWC to CHW
    image = image[np.newaxis, :]  # Add batch dimension
    return image

def infer(image: np.ndarray) -> np.ndarray:
    try:
        # Run inference
        result = exec_net.infer(inputs={'input': image})
        return result['output']  # Assuming 'output' is your output layer name
    except Exception as e:
        print(f'Inference error: {e}')
        return None

if __name__ == '__main__':
    image_path = os.getenv('IMAGE_PATH')  # Path to input image
    image = preprocess_image(image_path)
    output = infer(image)
    if output is not None:
        print('Inference completed successfully.')