> > Not true. When there is activation, the range is not always 0 ~ 255. For > > example RELU, > > I believe tflite extends the quantization range so it always includes 0, as > done in the gemmlowp quantization example below. I have dumped my min and max > saturation input values from the six quantized tflite models (two mobilenets > and four inceptions). They are all 0 and 255. > > `https://github.com/google/gemmlowp/blob/master/doc/quantization_example.cc` > > ``` > // Given the min and max values of a float array, return > // reasonable quantization parameters to use for this array. > QuantizationParams ChooseQuantizationParams(float min, float max) { > // We extend the [min, max] interval to ensure that it contains 0. > // Otherwise, we would not meet the requirement that 0 be an exactly > // representable value. > min = std::min(min, 0.f); > max = std::max(max, 0.f); > ```
I think you maybe don't understand fully of my previous comment. One question I want to ask: Do your quantized models have conv + relu / relu6 like our model? If no, obviously is 0 ~ 255, no matter how many models are. Please see: https://github.com/tensorflow/tensorflow/blob/v2.0.0-beta1/tensorflow/lite/kernels/kernel_util.cc#L138 I and @jackwish have emphasized many times of this function code. Please construct one quantized model like us:  I can make sure you will observe another result. -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/2351#issuecomment-502543075
