Brevitas is a Pytorch library for quantization-aware training. Brevitas is currently under active development and on a rolling release. It should be considered in beta stage. Minor API changes are still planned.
Nnieqat is a quantize aware training package for Neural Network Inference Engine(NNIE) on pytorch, it uses hisilicon quantization library to quantize module's weight and activation as fake fp32 format. Table of Contents Installation Supported Platforms: Linux ...
return x+self.conv(x) [1] Benoit Jacob, Skirmantas Kligys. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference [2]https://github.com/pytorch/vision/blob/master/torchvision/models/quantization/mobilenet.py...
Quantization aware training(QAT)-MQBench https://github.com/791136190/awesome-qat/blob/main/docs/MQBench/Introduction.mdMQBench (https://github.com/ModelTC/MQBench)基本简介来自商汤的ModelTC(模型工具链团队?) 依赖与Pyto… 王小二发表于aweso... 量化投资学习笔记97——kaggle量化投资比赛记录6-深度学...
TensorFlow量化源码:https://github.com/tensorflow/model-optimization/tree/master/tensorflow_model_optimization/python/core/quantization TFLite支持Quantization aware training (QAT)以及Post-training quantization。同样支持以以上方法为基础的Collaborative optimization方法,如Cluster preserving quantization (CQAT), Sparsit...
PyTorch Hub中提供的模型也支持Colab。进入每个模型的介绍页面后,你不仅可以看到GitHub代码页的入口,甚至可以一键进入Colab运行模型Demo。 为了调用各种经典机器学习模型,今后你不必重复造轮子了。 刚刚,Facebook宣布推出PyTorch Hub,一个包含计算机视觉、自然语言处理领域的诸多经典模型的聚合中心,让你调用起来更方便。
Armed with this understanding, we design a method that quantizes the layer parameters jointly, enabling significant accuracy improvement over current post-training quantization methods. Reference implementation is available at https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq ....
目前针对轻量级网络直接量化效果差的解决办法是quantization-aware training,就是在FP32模型训练收敛之后,再加入量化的操作,继续进行finetune,这个过程还是比较耗时,且在一些情况下还需要一些调参技巧,如BN操作中的moving_mean和moving_variance要重新校正还是直接冻结等,且在一些深度学习框架上提供模型压缩与量化工具也是更倾...
Armed with this understanding, we design a method that quantizes the layer parameters jointly, enabling significant accuracy improvement over current post-training quantization methods. Reference implementation is available at https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq....
Thanks for your reply. I also inserted QuantStub and DeQuantStub in to my model, well I don't know if they are effective. I also noticed that in the Pytorch documents, quant config is set before the training loop, which means I need to insert this part of code in the runner of mm...