> > 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. > > The quantized mobilenet v1 inference model is from the tflite model > repository. The training model includes relu6 and batch normalization > operations, but these are fused into convolution operations in the inference > model, as the Netron diagram shows. > > The link you reference shows floating point activation values that would be > applied during training. They do represent the range bound that would be > expected of the upscaled values in the accumulator in the inference model. > However the min and max saturation values passed into the inference quantized > convolution are applied _after downscale_ ... I previously provided the code > and the link. They are int32 values, not float values. They are applied after > both downscale and offset are applied. They are 0..255 even though the scaled > up range expected is 0..6 from the fused-in relu6 operation. > > If the convolution and relu operations were separate, you would still see 0 > and 255 for those min and max values because they are applied after downscale > and after offset are applied to the convolution accumulator. The min and max > values only function to saturate the downscaled result to the quantized uint8 > bit range, avoiding wrap-around overflow/underflow of the 8 bit value if the > downscaled accumulator were simply masked to 8 bits.
I have emphasized the model diagram is one `quantized` model. Let me show more detail of the property:  This is to say, not all relu / relu6 can be fused into convolution in TFLite's `quantized` model. Then what the min / max, that is previous code https://github.com/tensorflow/tensorflow/blob/v2.0.0-beta1/tensorflow/lite/kernels/kernel_util.cc#L138 does. `NOT` just simple 0 ~ 255. -- 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-502986425
