about the lut

[mrgreen3325]

Hi. Thanks for your work. May I know that your training model is in float32, but how the lut is in int8 format? Thanks.

[ddlee-cn]

The network outputs are clamped and quantized to [-127, 127] in the step 2, i.e., transferring network to LUT (Line here).

To replicate the performance of float-type network with int-type LUTs, we introduce LUT re-indexing and LUT-aware finetune, as described in our paper.

Thanks for your interest and feel free to comment below if you have further questions.

[mrgreen3325]

Hello Thanks for reply. I notice that your input in step 2 are int, is that the reason why you can clamped and quant the output to [-127,127]? I am looking into the https://github.com/zhjy2016/SPLUT/blob/main/training_testing_code/Transfer_SPLUT_M.py It seems that the transfer is very different to yours, do you think this method can also output int format?

[ddlee-cn]

The inputs of SRNet (4D int combinations or pixels) are converted to [0, 1] by dividing 255 (line here). The outputs of SRNet are scaled to [-127, 127] by muliplying 127. This way, the inputs and outputs of both SRNet and MuLUT are integers, while the tensors and gradients inside SRNet are float-point numbers.

As for SPLUT, they split 8bit integers into higher bits and lower bits, and then process them with a parallel structure.

Hope it helps. :)