Comments (4)
Hi,
Thanks for your interest in this work.
The sensitivity measure, as well as the genetic algorithm, will be released in the future, we are cleaning this code.
For your question of 2-bit permutation, let's consider an example of a block with 3 layers. You should first check 3+3 per-layer permutations for 4-bit and 8-bit. Then, for 2-bit quantization, you need to evaluate 3 possibilities for one layer quantized to 2-bit, and 3 possibilities for two layers quantized to 2-bit, and finally 1 possibilities for all of 3 layers quantized to 2-bit.
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Hi,
Thanks for your interest in this work.
The sensitivity measure, as well as the genetic algorithm, will be released in the future, we are cleaning this code.
For your question of 2-bit permutation, let's consider an example of a block with 3 layers. You should first check 3+3 per-layer permutations for 4-bit and 8-bit. Then, for 2-bit quantization, you need to evaluate 3 possibilities for one layer quantized to 2-bit, and 3 possibilities for two layers quantized to 2-bit, and finally 1 possibilities for all of 3 layers quantized to 2-bit.
Thank you for your reply.
I have another question. Do I have to used the pretrained model from your link to test this git? If it is possible, I want to use some pretrained models that pytorch provides like ResNet and MobileNet.
p.s. I found that you added some extra relu layers for basic block and bottleneck. If you do not mind, would you like to tell me the reason for it?
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Hi. First of all thanks for your work, very interesting paper.
Could you please update if you plan to publish code for mixed-precision with genetic algorithm and hessian off-diagonal loss?
Thanks.
from brecq.
Hi,
Thanks for your interest in this work.
The sensitivity measure, as well as the genetic algorithm, will be released in the future, we are cleaning this code.
For your question of 2-bit permutation, let's consider an example of a block with 3 layers. You should first check 3+3 per-layer permutations for 4-bit and 8-bit. Then, for 2-bit quantization, you need to evaluate 3 possibilities for one layer quantized to 2-bit, and 3 possibilities for two layers quantized to 2-bit, and finally 1 possibilities for all of 3 layers quantized to 2-bit.
This have not been released yet?
from brecq.
Related Issues (20)
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