> From my experience, we needn't q_relu. But we need q_add / q_concate and so > on. I suggest we use MobilenetV2 quant model for example,
Yes, I believe the MobilenetV2 relu_6 is effectively fused in by the downscale saturation. You might need it if you want to support their way of training, though. Yes Mobilenet has the q_add, but I suggest the Inceptionv3 for q_concatenate, since it also has concat nodes feeding into concat nodes, and tflite also has to rescale inside the concat operations. Also, I believe the q_add required rescale... but in both q_concat and q_add you can recalculate the prior op downscale multipliers so you can eliminate the extra rescales. Also, depending on your allocation capabilities, you can get rid of all concats. -- 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-496816409
