nvidia-tao
时间: 2023-09-27 12:05:45 浏览: 54
nvidia-tao是一个工具包,用于训练和部署深度学习模型。要使用nvidia-tao,你可以按照以下步骤进行操作:
***<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [NVIDIA TAO 工具包 (TAO Toolkit) 的部署和应用【LDR、LPR】](https://blog.csdn.net/qq_44824148/article/details/122595101)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]
相关问题
% eta数据 % 输入:24个激光干涉数据 % 输出:6列eta数据 function [eta,etatstart,etaNpoints]=etadata(t0,sci,scib,tao,epsilon) global Fs M m1 m2 m3 m4 m5 m6; Npoints=length(sci(:,1)); prePoints=10*Fs+2*M; endPoints=2*M; etaNpoints=Npoints-prePoints-endPoints; etatstart=t0+prePoints/Fs; eta=zeros(etaNpoints,12); epsilon1=epsilon(:,1);epsilon2=epsilon(:,2);epsilon3=epsilon(:,3); epsilon4=epsilon(:,4);epsilon5=epsilon(:,5);epsilon6=epsilon(:,6); tao1=tao(:,1);tao2=tao(:,2);tao3=tao(:,3); tao4=tao(:,4);tao5=tao(:,5);tao6=tao(:,6); for ii=prePoints+1:prePoints+etaNpoints ti=t0+(ii-1)/Fs; Ltime=lasertravelTime(ti,0); delay0=Ltime(1:6)*Fs; delay0M=ceil(delay0); delay0e=delay0M-delay0; doppler0=1-Ltime(7:12); d3epsilon5tao5=epsilon5(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1)-tao5(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1); d1epsilon6tao6=epsilon6(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1)-tao6(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1); d2epsilon4tao4=epsilon4(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1)-tao4(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1); d5epsilon3tao3=epsilon3(ii-delay0M(5)+M:-1:ii-delay0M(5)-M+1)-tao3(ii-delay0M(5)+M:-1:ii-delay0M(5)-M+1); d6epsilon1tao1=epsilon1(ii-delay0M(6)+M:-1:ii-delay0M(6)-M+1)-tao1(ii-delay0M(6)+M:-1:ii-delay0M(6)-M+1); d4epsilon2tao2=epsilon2(ii-delay0M(4)+M:-1:ii-delay0M(4)-M+1)-tao2(ii-delay0M(4)+M:-1:ii-delay0M(4)-M+1); d3tao2tao5=tao2(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1)-tao5(ii-delay0M(3)+M:-1:ii-delay0M(3)-M+1); d1tao3tao6=tao3(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1)-tao6(ii-delay0M(1)+M:-1:ii-delay0M(1)-M+1); d2tao1tao4=tao1(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1)-tao4(ii-delay0M(2)+M:-1:ii-delay0M(2)-M+1); eta(ii-prePoints,1)=sci(ii,1)-(epsilon(ii,1)-tao(ii,1))/2-doppler0(3)*fracdelay(delay0e(3))*(d3epsilon5tao5+ d3tao2tao5)/2; eta(ii-prePoints,2)=sci(ii,2)-(epsilon(ii,2)-tao(ii,2))/2-doppler0(1)*fracdelay(delay0e(1))*(d1epsilon6tao6+ d1tao3tao6)/2; eta(ii-prePoints,3)=sci(ii,3)-(epsilon(ii,3)-tao(ii,3))/2-doppler0(2)*fracdelay(delay0e(2))*(d2epsilon4tao4+ d2tao1tao4)/2; eta(ii-prePoints,4)=sci(ii,4)-(epsilon(ii,4)-tao(ii,4))/2-doppler0(5)*fracdelay(delay0e(5))*d5epsilon3tao3/2+(tao(ii,1)-tao(ii,4))/2; eta(ii-prePoints,5)=sci(ii,5)-(epsilon(ii,5)-tao(ii,5))/2-doppler0(6)*fracdelay(delay0e(6))*d6epsilon1tao1/2+(tao(ii,2)-tao(ii,5))/2; eta(ii-prePoints,6)=sci(ii,6)-(epsilon(ii,6)-tao(ii,6))/2-doppler0(4)*fracdelay(delay0e(4))*d4epsilon2tao2/2+(tao(ii,3)-tao(ii,6))/2; eta(ii-prePoints,7)=(scib(ii,1)-sci(ii,1))/m5; eta(ii-prePoints,8)=(scib(ii,2)-sci(ii,2))/m6; eta(ii-prePoints,9)=(scib(ii,3)-sci(ii,3))/m4; eta(ii-prePoints,10)=(scib(ii,4)-sci(ii,4))/m3; eta(ii-prePoints,11)=(scib(ii,5)-sci(ii,5))/m1; eta(ii-prePoints,12)=(scib(ii,6)-sci(ii,6))/m2; end end
这段代码的作用是将24个激光干涉数据转换为6列eta数据。其中,输入参数包括初始时间 t0、激光干涉数据 sci 和 scib、时间延迟 tao 和介电常数 epsilon,以及全局变量 Fs、M、m1、m2、m3、m4 和 m5。输出参数包括 eta 数据、eta 数据的起始时间 etatstart 和 eta 数据的点数 etaNpoints。
在函数中,首先计算出预先点数 prePoints 和结束点数 endPoints,以及 eta 数据的点数 etaNpoints 和起始时间 etatstart。然后,将输入数据中的 epsilon 和 tao 按列分别赋值给对应的变量 epsilon1~6 和 tao1~6。
接下来,通过循环遍历每个需要计算的 eta 数据点,计算出该点对应的激光传输时间 Ltime 和延迟 delay0,以及对应的多普勒效应 doppler0。然后,分别计算出六个通道的 eta 数据,其中包括激光干涉数据和多普勒效应的修正,以及各个通道之间的时间延迟修正。
最后将计算得到的 eta 数据保存到 eta 矩阵中,并返回 eta 数据的起始时间和点数。
ngc registry model download-version nvidia/tao/pretrained_classification:resnet18 --dest $USER_EXPERIMENT_DIR/pretrained_resnet18是什么意思
这个命令的意思是从Nvidia的TAO预训练分类模型中下载resnet18模型,并将其保存到用户实验目录下的pretrained_resnet18文件夹中。具体来说,这个命令使用ngc registry模块从Nvidia GPU Cloud Registry中下载预训练模型,并使用版本nvidia/tao/pretrained_classification:resnet18。最后,将下载的模型保存到环境变量$USER_EXPERIMENT_DIR/pretrained_resnet18所指定的目录中。