TensorFlowの学習済みモデルをOpenVINOに変換する

from openvino import inference_engine as ie
from openvino.inference_engine import IENetwork, IEPlugin
from PIL import Image
import numpy as np

class OpenVinoModel:
    def __init__(self, model_xml_path, model_bin_path, device):
        self.size_x = 512
        self.size_y = 512
        self.size_c = 3
        self.model_xml_path = model_xml_path
        self.model_bin_path = model_bin_path
        self.device = device
        self.cpu_ext_path = r'C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\inference_engine\bin\intel64\Release\cpu_extension_avx2.dll'
        self.plugin = IEPlugin(self.device)
        if self.device == 'CPU':
            self.plugin.add_cpu_extension(self.cpu_ext_path)
        net = IENetwork.from_ir(model=self.model_xml_path, weights=self.model_bin_path)
        self.input_blob = next(iter(net.inputs))
        self.out_blob = next(iter(net.outputs))
        self.exec_net = self.plugin.load(network=net)

    def predict(self, img_path):
        img = self.pre_process_image(img_path)
        res = self.exec_net.infer(inputs={self.input_blob: img})
        output_node_name = list(res.keys())[0]
        res = res[output_node_name]
        return res

    def pre_process_image(self, imagePath):
        n, c, h, w = [1, self.size_c, self.size_x, self.size_y]
        image = Image.open(imagePath)
        processedImg = image.resize((h, w), resample=Image.BILINEAR)
        processedImg = (np.array(processedImg) - 0) / 255.0
        processedImg = processedImg.transpose((2, 0, 1))
        processingImg = processingImg.reshape((n, c, h, w))
        return processingImg