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tensorRT_quantization

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该代码与B站上的视频 https://www.bilibili.com/video/BV18L41197Uz/?spm_id_from=333.788&vd_source=eefa4b6e337f16d87d87c2c357db8ca7 相关联。

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# TensorRT Quantization Tutorial
***if you feel the tutorial is good! why not give us a star!⭐⭐⭐***

This repository relates two main sections: **Fundamentals** and **Practical Application**, aiming to provide a comprehensive guide on model quantization in TensorRT.

## Fundamentals

Both the video and code for this section are completely open-source.

- **Video Tutorial**: [Bilibili Link](https://www.bilibili.com/video/BV18L41197Uz/?spm_id_from=333.788&vd_source=eefa4b6e337f16d87d87c2c357db8ca7)
- **Code Repository**: [GitHub Link](https://github.com/shouxieai/tensorRT_quantization)

### Table of Contents

1. **Principles of Model Quantization**
   - 1.1 Definition and Significance of Quantization
     - 1.1.1 Model Weight Analysis
     - 1.1.2 Importance of Quantization
   - 1.2 Symmetric vs Asymmetric Quantization
     - 1.2.1 Definition of Symmetric Quantization
     - 1.2.2 Handwritten Code for Symmetric Quantization
     - 1.2.3 Definition of Asymmetric Quantization
     - 1.2.4 Handwritten Code for Asymmetric Quantization
   - 1.3 Common Methods for Dynamic Range Calculation
     - 1.3.1 Max
     - 1.3.2 Histogram
     - 1.3.3 Entropy
   - 1.4 Introduction to PTQ and QAT
   - 1.5 Handwriting a Quantized Program with Ops
2. **TensorRT Quantization Library**
   - 2.1 Understanding Quantizer
   - 2.2 Understanding InputQuant/MixQuant
   - 2.3 Automatic Insertion of QDQ Nodes
   - 2.4 Manual Insertion of QDQ Nodes
   - 2.5 How to Quantize a Custom Layer
   - 2.6 Sensitivity Layer Analysis
   - 2.7 Pitfalls and Lessons Learned

## Practical Application

The practical application section is paid content. Please visit the link below to purchase:

[Buy Now](#)

# TensorRT 量化教程

***如果你觉得这个教程很赞，欢迎给星星哟!⭐⭐⭐***

本仓库分为涉及两个部分：**基础知识**和**实战应用**，旨在全面讲解 TensorRT 下的模型量化。

## 基础知识

该部分的视频和代码完全开源。

- **视频教程**：[B站链接](https://www.bilibili.com/video/BV18L41197Uz/?spm_id_from=333.788&vd_source=eefa4b6e337f16d87d87c2c357db8ca7)
- **代码仓库**：[GitHub链接](https://github.com/shouxieai/tensorRT_quantization)

### 目录

1. **模型量化原理**
   - 1.1 量化的定义及意义
     - 1.1.1 模型权重分析
     - 1.1.2 量化的意义
   - 1.2 对称量化与非对称量化
     - 1.2.1 对称量化的定义
     - 1.2.2 对称量化代码手写
     - 1.2.3 非对称量化的定义
     - 1.2.4 非对称量化代码手写
   - 1.3 动态范围的常用计算方法
     - 1.3.1 Max
     - 1.3.2 Histgram
     - 1.3.3 Entropy
   - 1.4 PTQ 与 QAT 介绍
   - 1.5 手写一个带 op 的量化程序
2. **TensorRT Quantization Library**
   - 2.1 Quantizer 的理解
   - 2.2 InputQuant/MixQuant 的理解
   - 2.3 自动插入 QDQ 节点
   - 2.4 手动插入 QDQ 节点
   - 2.5 如何量化一个自定义层
   - 2.6 敏感层分析
   - 2.7 踩坑实录

## 实战应用

实战部分内容需要付费购买，请访问以下链接：

[购买链接](#)

tensorRT_quantization

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