model = torch.hub.load('pytorch/vision:v0.10.0', 'mobilenet_v2', pretrained=True)
model.eval()
x = torch.randn(1, 3, 224, 224)
torch.onnx.export(model, x, "mobilenet_v2.onnx", export_params=True)

atc --model=./mobilenet_v2.onnx --framework=5 --output=mobilenet --soc_version=Ascend310B4

def main():
    device = 0
    images_path = './data'
    model_path = './mobilenet_v2.om'
    label_path = './imagenet-simple-labels.json'
    idx2label_list = load_imagenet_labels(label_path))
    net = Net(device, model_path, idx2label_list)
    
    images_list = [os.path.join(images_path, img)
                   for img in os.listdir(images_path)
                   if os.path.splitext(img)[1] in IMG_EXT]
    
    for image in images_list:
        print("images:{}".format(image))
        img = preprocess_img(image)
        pred_dict = net.run([img])
        display_image(image, pred_dict)
    
    print("*****run finish******")
    net.release_resource()