code: def onnx_model_speed(data_loader, val_num):
      # check onnx model
      onnx_path = "./resnet34.onnx"
      check_path_exist(onnx_path)
      onnx_model = onnx.load(onnx_path)
      onnx.checker.check_model(onnx_model)
  
      ort_session = onnxruntime.InferenceSession(onnx_path)
      input_name = ort_session.get_inputs()[0].name
  
      forward_time = 0
      acc = 0.0  # accumulate accurate number / epoch
      for val_data in tqdm(data_loader, desc="Running onnx model..."):
          val_images, val_labels = val_data
          input_dict = {input_name: to_numpy(val_images)}
          t1 = time.time()
          outputs = ort_session.run(None, input_dict)
          t2 = time.time()
          forward_time += t2 - t1
          outputs = outputs[0]
          predict_y = np.argmax(outputs, axis=1)
          acc += (predict_y == to_numpy(val_labels)).sum()
      val_accurate = acc / val_num
      fps = round(val_num / forward_time, 1)
      print(
          "onnx info:\nfps: {}/s  accuracy: {}\n".format(
              fps, val_accurate
          )
      )
      return fps, val_accurate, "ONNX"
  
file_path: deep-learning-for-image-processing/others_project/openvinotest/openvino_cls_test/speed_test.py
imports: import os
  import time
  import torch
  from torchvision import transforms, datasets
  from tqdm import tqdm
  import onnx
  import onnxruntime
  import numpy
  from openvino.inference_engine import IECore
  from model import resnet34
label_desc: Use image node_id 4 for calling a global function with example usage: onnx_model_speed(data_loader, val_num) and returns: fps,  val_accurate,  str