code: def main():
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
  
      data_transform = transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
              ),
          ]
      )
  
      # load image
      img_path = "../tulip.jpg"
      assert os.path.exists(
          img_path
      ), "file: '{}' dose not exist.".format(img_path)
      img = Image.open(img_path)
      plt.imshow(img)
      # [N, C, H, W]
      img = data_transform(img)
      # expand batch dimension
      img = torch.unsqueeze(img, dim=0)
  
      # read class_indict
      json_path = "./class_indices.json"
      assert os.path.exists(
          json_path
      ), "file: '{}' dose not exist.".format(json_path)
  
      with open(json_path, "r") as f:
          class_indict = json.load(f)
  
      # create model
      model = densenet121(num_classes=5).to(device)
      # load model weights
      model_weight_path = "./weights/model-3.pth"
      model.load_state_dict(
          torch.load(model_weight_path, map_location=device)
      )
      model.eval()
      with torch.no_grad():
          # predict class
          output = torch.squeeze(model(img.to(device))).cpu()
          predict = torch.softmax(output, dim=0)
          predict_cla = torch.argmax(predict).numpy()
  
      print_res = "class: {}   prob: {:.3}".format(
          class_indict[str(predict_cla)], predict[predict_cla].numpy()
      )
      plt.title(print_res)
      for i in range(len(predict)):
          print(
              "class: {:10}   prob: {:.3}".format(
                  class_indict[str(i)], predict[i].numpy()
              )
          )
      plt.show()
  
file_path: deep-learning-for-image-processing/pytorch_classification/Test8_densenet/predict.py
imports: import os
  import json
  import torch
  from PIL import Image
  from torchvision import transforms
  import matplotlib.pyplot
  from model import densenet121
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types