@Zafar  Thank you so much for the reply!!  I successfully converted the model by setting quantized.engine = 'qnnpack'  and get_default_qconfig('qnnpack')
But the quantized model predicts a totally wrong result(my original model works fine)
I carefully studied the official tutorial https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html#model-architecture 5
It seems that nothing special needs to be done when using a quantized model, just the usual way
model.eval()
with torch.no_grad():
    output = model(image)

Any hints?