code: def main():
      data_transform = transforms.Compose(
          [
              transforms.Resize([224, 224]),
              transforms.ToTensor(),
              transforms.Normalize(
                  [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
              ),
          ]
      )
  
      data_root = (
          "/home/w180662/my_project/my_github"  # get data root path
      )
      image_path = os.path.join(
          data_root, "data_set/flower_data/"
      )  # flower data set path
      check_path_exist(image_path)
  
      batch_size = 1
  
      validate_dataset = datasets.ImageFolder(
          root=image_path + "val", transform=data_transform
      )
      val_num = len(validate_dataset)
      validate_loader = torch.utils.data.DataLoader(
          validate_dataset,
          batch_size=batch_size,
          shuffle=False,
          num_workers=4,
      )
  
      openvino_model_speed(
          validate_loader, val_num, "./resnet34.xml", "./resnet34.bin"
      )
      openvino_model_speed(
          validate_loader, val_num, "./resnet34a.xml", "./resnet34a.bin"
      )
  
file_path: deep-learning-for-image-processing/others_project/openvinotest/openvino_cls_test/float32vsint8.py
imports: import os
  import time
  import torch
  from torchvision import transforms, datasets
  from tqdm import tqdm
  import numpy
  from openvino.inference_engine import IECore
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types