Hi @crook52 , with Eager mode quantization the user needs to place quants and dequants at every place in the model where tensors need to convert from fp32 to int8, and vice versa.  If it’s not working for you if you wrap the model with quant/dequant, that likely means that there are places inside your model where the same thing needs to be done.  This is often a pretty tedious process.
For MobileNetV2, we have a quantizeable model with all the quants/dequants/fusions done here (vision/mobilenetv2.py at master · pytorch/vision · GitHub 20), so you are welcome to use that if that works for your use case.