输入参数为：
    Net：网络结构
    pth：pytorch模型
    onnx：onnx模型

    1. def Test(Net,pth,onnx):
    2.     import onnxruntime
    3.     from onnxruntime.datasets import get_example
    5.     def to_numpy(tensor):
    6.         return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
    8.     dummy_input = torch.randn(1, 1, 40, 40, device='cpu')
    10.     example_model = get_example(onnx)
    11.     sess = onnxruntime.InferenceSession(example_model)
    13.     onnx_out = sess.run(None, {'input': to_numpy(dummy_input)})
    14.     print(onnx_out)
    16.     model = Net()
    17.     model_dict = model.state_dict()
    18.     pretrained_dict = torch.load(pth, map_location=torch.device('cuda'))
    19.     pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if np.shape(model_dict[k[7:]]) ==  np.shape(v)}
    20.     model_dict.update(pretrained_dict)
    21.     model.load_state_dict(model_dict)
    22.     model.eval()
    23.     with torch.no_grad():
    24.         torch_out = model(dummy_input)
    25.         print(torch_out)