return 20*torch.log10(Ps/Pn)
wt_compare_dict = ns.compare_weights(fp32_model.state_dict(), int8_model.state_dict())
for key in wt_compare_dict:
    print(key, compute_error(wt_compare_dict[key]['float'], wt_compare_dict[key]['quantized'].dequantize()))
act_compare_dict = ns.compare_model_outputs(fp32_model, int8_model, input_data)
for key in act_compare_dict:
    print(key, compute_error(act_compare_dict[key]['float'][0], act_compare_dict[key]['quantized'][0].dequantize()))
```
Recommendations for your workflow



  Fig 9. Suggested quantization workflow

 Click for larger image 
Points to note

Large (10M+ parameters) models are more robust to quantization error. [[2]]
