kimishpatel:

“Could not run ‘quantized::conv2d.new’ with arguments from the ‘CPU’ backend.” it means that your quantized::conv2d op is getting float tensor as input.


yeah, I saw that the same comment at multiple places, but the code runs using uint8, I’ve verified in python.



 kimishpatel:

Regarding runtime on android: are you comparing fp32 model runtime on android vs. quantized model runtime on android?


Yes, a lite version of both



 kimishpatel:

Regarding memory footprint, are you talking about peak memory or you are talking about average memory utilization? And what is the difference compared to fp32 model?


I run a loop of 100 iterations to get better measurement of time consumption, Hence, peak and average are almost same(I guess, garbage collector can’t work fast enough to execute between iterations). I’m seeing around 100MB more memory usage for quantized model.