<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[vivavivaa的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/vivavivaa</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; vivavivaa]]></copyright><item><title><![CDATA[Linux source文件后提示 export：command not found]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125231665</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125231665</guid><author>vivavivaa</author><pubDate>Sat, 11 Jun 2022 10:00:50 +0800</pubDate><description><![CDATA[export : command not found 可查看是否shell 问题，并更换为bash]]></description><category></category></item><item><title><![CDATA[关于Verilog中readmemb系统函数]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125211254</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125211254</guid><author>vivavivaa</author><pubDate>Thu, 09 Jun 2022 21:23:22 +0800</pubDate><description><![CDATA[readmemb读取txt文本数据** Warning:(vsim-PLI-3409) Illegal binary digit ']]></description><category></category></item><item><title><![CDATA[Pytorch 量化后权重参数导出]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125174696</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125174696</guid><author>vivavivaa</author><pubDate>Tue, 07 Jun 2022 22:26:38 +0800</pubDate><description><![CDATA[网络量化权重参数导出]]></description><category></category></item><item><title><![CDATA[电脑装机路]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125094109</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125094109</guid><author>vivavivaa</author><pubDate>Thu, 02 Jun 2022 08:46:31 +0800</pubDate><description><![CDATA[电脑装机的一些流程设置等]]></description><category></category></item><item><title><![CDATA[优快云 HelloWorld]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073402</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073402</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 21:03:00 +0800</pubDate><description><![CDATA[`]]></description><category></category></item><item><title><![CDATA[Pytorch 提取权重等参数 写入Excel]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073242</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073242</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:50:54 +0800</pubDate><description><![CDATA[Pytorch网络模型参数提取]]></description><category></category></item><item><title><![CDATA[Pytorch Tutoriais (PROTOTYPE) FX GRAPH MODE POST TRAINING STATIC QUANTIZATION]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073163</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073163</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:46:21 +0800</pubDate><description><![CDATA[Pytorch量化（六）]]></description><category></category></item><item><title><![CDATA[Pytorch Tutoriais (PROTOTYPE) FX GRAPH MODE POST TRAINING DYNAMIC QUANTIZATION]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073152</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073152</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:45:36 +0800</pubDate><description><![CDATA[Pytorch量化（五）]]></description><category></category></item><item><title><![CDATA[Pytorch Tutoriais (PROTOTYPE) FX GRAPH MODE QUANTIZATION USER GUIDE]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073137</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073137</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:44:52 +0800</pubDate><description><![CDATA[Pytorch量化（四）]]></description><category></category></item><item><title><![CDATA[Pytorch Tutoriais (BETA) STATIC QUANTIZATION WITH EAGER MODE IN PYTORCH]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073118</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073118</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:43:58 +0800</pubDate><description><![CDATA[Pytorch量化（三）]]></description><category></category></item><item><title><![CDATA[Pytorch Tutoriais Dynamic Quantization 动态量化]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073094</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073094</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:42:55 +0800</pubDate><description><![CDATA[Pytorch量化（二）]]></description><category></category></item><item><title><![CDATA[Pytorch Tutoriais Quantization Recipe 量化食谱]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125073071</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125073071</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:41:05 +0800</pubDate><description><![CDATA[Pytorch量化(一)]]></description><category></category></item><item><title><![CDATA[Pytorch Quantization]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125072887</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125072887</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:34:39 +0800</pubDate><description><![CDATA[Pytorch官方量化学习]]></description><category></category></item><item><title><![CDATA[Pytorch Quantization Tutoriais]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/125072908</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/125072908</guid><author>vivavivaa</author><pubDate>Tue, 31 May 2022 20:29:24 +0800</pubDate><description><![CDATA[Pytorch学习之路]]></description><category></category></item><item><title><![CDATA[Pycharm踩坑路]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/124862500</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/124862500</guid><author>vivavivaa</author><pubDate>Thu, 19 May 2022 14:38:07 +0800</pubDate><description><![CDATA[Pycharm踩坑路：关于在 Pycharm 中使用Terminal导入pytorch遇到问题的一个解决思路

操作系统  ： win11
Pycharm 版本 ： 社区版 2022.1.1


所遇问题

​	之前使用Pycharm打开终端Terminal是会自动进入所选择的Anaconda环境。但是最近新装了电脑，重新配置Pytorch环境时，使用Anaconda命令行import torch是没问题的，可以正常使用pytorch，但是在Pycharm中提示找不到库，检查Python解释器路径等确认无误]]></description><category></category></item><item><title><![CDATA[关于深度可分离卷积 Depthwise Pointwise Convolution]]></title><link>https://blog.youkuaiyun.com/vivavivaa/article/details/124415526</link><guid>https://blog.youkuaiyun.com/vivavivaa/article/details/124415526</guid><author>vivavivaa</author><pubDate>Mon, 25 Apr 2022 22:00:37 +0800</pubDate><description><![CDATA[关于Pointwise Convolution
​	最近在学习3维点云处理，这边有看到关于pointwise。
（图源 知乎：轻量化网络——MobileNet - 知乎 (zhihu.com)）
​	关于深度可分离卷积，以下我的理解。

​		其中输入通道为M，输出通道为N。
​		考虑kernel size 为(Dk, Dk)的卷积核。
​		这里考虑单个卷积核对应输入feature区域的操作。即kernel size (D, D)  *  feature (D, D)
​			 
​	将 传统卷积 **]]></description><category></category></item></channel></rss>