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[ICCV 2023] I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference

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您好你可以提供一下以及训练好的模型,供下载吗,DeiT-T

Hi @zkkli, many thanks for your work, it is a quite nice contribution to the state-of-the-art. After training a model a model by running: `python quant_train.py --model deit_tiny --data --epochs...

我使用的是deit_small模型,精度fine-tune至79.1%, ![image](https://github.com/zkkli/I-ViT/assets/56783525/01dc9185-722f-4196-9313-6d6446871bd1) 但是预测提供的cat.jpg的标签为 ![94e270515ebd33ea0715891c07f575b](https://github.com/zkkli/I-ViT/assets/56783525/beb55c31-7f3a-483e-87d6-627d1216ab37) 我经过比对,发现这些标签都对不上cat这类,请问是为什么呢?

![lsy](https://github.com/zkkli/I-ViT/assets/102045635/2dbb0778-a742-4be2-b5c1-1786a1896ece)

Hi I’m currently working on compiling I-ViT using TVM. On this project, The error appears. > Check failed: value < 1LL

非常棒的工作,我比较好奇,纯int量化的优势在于速度,但是好像没有底层kernel的支持,还是以全精度(TVM)的方式去计算的,这样int量化的实际价值没有发挥出来,看论文中的数据实际latency没有较FasterTransformer提升太多。

Hi, I tried to replicate your speed experiment, I tested the deit_tiny, batch size=1, RTX3090 environment, after a few days of autotune, compared to tensorrt FP16, speed is still slower....

I thought I-ViT works based on PTQ when I read the paper but your code and readme shows that it's based on QAT. If I have the quantized int8 weight...

Hello, I am JIHO LEE. I thoroughly enjoyed reading your paper and was deeply impressed by it. I believe that the ultimate goal of this research is to improve efficiency...