@hx89 actually it seems like all these have pretty high relative errors -
[ basenet.slice1.7.0.weight , basenet.slice1.10.0.weight , basenet.slice2.14.0.weight , basenet.slice2.17.0.weight , basenet.slice3.20.0.weight ,basenet.slice3.24.0.weight , basenet.slice3.27.0.weight , basenet.slice4.30.0.weight ,basenet.slice4.34.0.weight ]
although that seems like a good idea, keeping a few layers as float while converting the rest to int8.
I am not sure how to pass the partial model to torch.quantization.convert() for quantization and then combining the partially quantized model and unquantized layers together for inference on the image.
Could you provide an example ? Thanks a ton