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% Document Info
\author{Yuxuejie0912}
\pdfinfo{
  /Title (pytorch.pdf)
  /Creator (Cheatography)
  /Author (Yuxuejie0912)
  /Subject (Pytorch入门 Cheat Sheet)
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\noindent
\begin{multicols}{3}
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    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Pytorch入门 Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Yuxuejie0912} via \textcolor{DarkBackground}{\uline{cheatography.com/123584/cs/23342/}}}
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\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Yuxuejie0912 \\
  \uline{cheatography.com/yuxuejie0912} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 18th June, 2020.\\
   Updated 18th June, 2020.\\
   Page {\thepage} of \pageref{LastPage}.
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Pytorch是什么？}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{PyTorch是一个基于 Torch 的 Python \seqsplit{开源机器学习库。具有强大的} GPU \seqsplit{加速的张量计算（运算机制类似于} \seqsplit{NumPy），包含自动求导系统，简洁优雅，大佬支持，新手友好。}} \tn 
% Row Count 5 (+ 5)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{\seqsplit{Pytorch神经网络示例}}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}import torch\{\{nl\}\}import torch.nn as nn\{\{nl\}\}from torch.autograd import Variable\{\{nl\}\}import torch.nn.functional as F} \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{model = \seqsplit{nn.Sequential(nn.Linear(2}, 10), nn.ReLU(), \{\{nl\}\} nn.Linear(10, 1),     nn.Sigmoid() ) \# 搭建网络} \tn 
% Row Count 6 (+ 3)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{if torch.cuda.is\_available():\{\{nl\}\} ~~~~~model = model.cuda() \seqsplit{\#判断GPU的cuda计算是否可用}} \tn 
% Row Count 9 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{optimzer = \seqsplit{torch.optim.SGD(model.parameters()}, lr=0.05)   \# 优化器\{\{nl\}\}loss\_func = nn.MSELoss()  \# 损失函数} \tn 
% Row Count 12 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{out = model(x)\{\{nl\}\}loss = loss\_func(out, y) \{\{nl\}\} optimzer.zero\_grad()  \# 清除梯度 \{\{nl\}\} loss.backward()  \# 反向传播\{\{nl\}\}   optimzer.step() \# 梯度更新} \tn 
% Row Count 16 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Tensor and Variable}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{Tensor运算机制类似于Numpy，支持GPU加速}} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{Variable是对Tensor的封装，支持神经网络图计算，具备三个属性} .data, .grad, .grad\_fn} \tn 
% Row Count 4 (+ 3)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{x\_tensor = torch.randn(5, 5)\{\{nl\}\}x\_var\_regular = Variable(x\_tensor, requires\_grad=True)\{\{nl\}\}x\_var\_volatile = Variable(x\_tensor, volatile=True)} \tn 
% Row Count 7 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{自动求导}}  \{\{nl\}\}自动构造反向求导流图，通过requires\_grad或volatile判断Variable参数是否需要更新，常用于冻结模型或inference阶段，节省内存} \tn 
% Row Count 11 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{torch.nn.functional}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{torch.nn：可看作对nn.functional的类包装，同时继承了nn}.Module相关属性和方法，适合构建模型；\{\{nl\}\}torch.nn.functional：直接使用def function( \seqsplit{)定义，使用灵活，但需要手动传入weight，不利于代码复用。}} \tn 
% Row Count 6 (+ 6)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{conv = nn.Conv2d(3, 64, 3, 2, 1)} \tn 
% Row Count 7 (+ 1)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{output = \seqsplit{nn.functional.conv2d(inputs}, weight, bias, padding=1)} \tn 
% Row Count 9 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{torch.nn}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{专为神经网络设计的模块化接口}}} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{激活函数}}~~~~torch.nn.ReLU(inplace=False)} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{损失函数}}~~~~torch.nn.BCELoss(weight=None, size\_average=True, reduce=None, reduction='mean')} \tn 
% Row Count 6 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{全连接层}}~~~~torch.nn.Linear(in\_features, out\_features, bias=True)} \tn 
% Row Count 8 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{卷积层}}~~~~~~~torch.nn.Conv2d(in\_channels, out\_channels, kernel\_size, stride=1, padding=0, dilation=1, groups=1, bias=True)} \tn 
% Row Count 12 (+ 4)
% Row 5
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{卷积层代码示例：} \tn 
% Row Count 13 (+ 1)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{input = Variable(torch.randn(32, 3, 28, 28))  \{\{nl\}\}conv1 = nn.Conv2d(in\_channels=3, out\_channels=10, kernel\_size=3, stride=1, padding=1)\{\{nl\}\}conv2 = \seqsplit{nn.Conv2d(in\_channels=10}, out\_channels=128, kernel\_size=3, stride=1, padding=1)} \tn 
% Row Count 18 (+ 5)
% Row 7
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{标准化层}}~~~~torch.nn.BatchNorm2d(num\_features, eps=1e-05, momentum=0.1, affine=True)} \tn 
% Row Count 21 (+ 3)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{池化层}}~~~~~~~torch.nn.MaxPool2d(kernel\_size, stride=None, padding=0, dilation=1, return\_indices=False, ceil\_mode=False)} \tn 
% Row Count 25 (+ 4)
% Row 9
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{torch.nn.AvgPool2d(kernel\_size}, stride=None, padding=0, ceil\_mode=False, \seqsplit{count\_include\_pad=True)}} \tn 
% Row Count 27 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{Dropout层}}~~torch.nn.Dropout(p=0.5, inplace=False)} \tn 
% Row Count 29 (+ 2)
% Row 11
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{插值函数}} \seqsplit{~~~~torch.nn.Upsample(size=None}, scale\_factor=None, mode='nearest', align\_corners=None)} \tn 
% Row Count 32 (+ 3)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{torch.nn (cont)}}  \tn
% Row 12
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{bt\}\}{\bf{容器}} \seqsplit{~~~~torch.nn.Module~~~~torch.nn.Sequential(*} args)} \tn 
% Row Count 3 (+ 3)
% Row 13
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{nn.Module}}是所有神经网络的基类，定义任一网络应继承该类。\{\{nl\}\}model.modules \#返回一个包含当前模型所有模块的迭代器\{\{nl\}\}model.state\_dict() \#返回字典，保存module的所有状态\{\{nl\}\}model.forward() \#前向计算，自动调用，所有子类必须重写\{\{nl\}\}model.train() \#模型在train和evaluation模式的切换\{\{nl\}\}model.eval() \seqsplit{\#仅当存在BN层和dropout层时有影响}} \tn 
% Row Count 12 (+ 9)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{nn.Sequential(* args)}} \seqsplit{是一个时序容器，modules} \seqsplit{会以他们传入的顺序被添加到容器中。}} \tn 
% Row Count 15 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{数据预处理}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{class torchvision.transforms  \seqsplit{\#实现数据增广变换}} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\# \seqsplit{使用Compose将变换操作串联起来}} \tn 
% Row Count 3 (+ 1)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{transforms.Compose({[} \seqsplit{transforms.CenterCrop(224)}, \{\{nl\}\}transforms.RandomHorizontalFlip(),\{\{nl\}\}transforms.ToTensor(),\{\{nl\}\} transforms.Normalize(0.5, 0.2) {]})} \tn 
% Row Count 7 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{模型的保存和加载}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{torch.save(\{  'epoch': epoch, \{\{nl\}\}  'model\_state\_dict': model.state\_dict(), \{\{nl\}\} \seqsplit{'optimizer\_state\_dict':} optimizer.state\_dict(),   \{\{nl\}\} 'loss': loss,             ...             \}, PATH)} \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{checkpoint = torch.load(PATH) \{\{nl\}\}model.load\_state\_dict(checkpoint{[}'model\_state\_dict'{]}) \{\{nl\}\}optimizer.load\_state\_dict(checkpoint{[}'optimizer\_state\_dict'{]}) \{\{nl\}\}epoch = checkpoint{[}'epoch'{]} loss = checkpoint{[}'loss'{]}} \tn 
% Row Count 9 (+ 5)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{模型优化 torch.optim}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{torch.optim.SGD(model.parameters()}, lr = 0.01, momentum=0.9)} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{torch.optim.RMSprop(model.parameters()}, lr=0.01, alpha=0.99, eps=1e-08, weight\_decay=0, momentum=0, centered=False)} \tn 
% Row Count 5 (+ 3)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{torch.optim.Adam(model.parameters()}, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight\_decay=0, amsgrad=False)} \tn 
% Row Count 8 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{optimizer.zero\_grad()  \# \seqsplit{pytorch梯度backward累积而不是替换，每批次清零}} \tn 
% Row Count 10 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{optimizer.step()   \# 模型更新} \tn 
% Row Count 11 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{数据集}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\# 使用pytorch内置数据集\{\{nl\}\}import torchvision.datasets as datasets\{\{nl\}\}cifar10 = datasets.CIFAR10()\{\{nl\}\}torch.utils.data.DataLoader(cifar10, \seqsplit{batch\_size=args.batchSize}, shuffle=True, \seqsplit{num\_workers=args.nThreads)}} \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\# 封装自定义数据集必须继承Dataset类\{\{nl\}\}from torch.utils.data.dataset import Dataset} \tn 
% Row Count 7 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{预训练模型}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{import torchvision.models as models \{\{nl\}\}resnet18 = models.resnet18( )\{\{nl\}\} alexnet = models.alexnet(pretrained=True)\{\{nl\}\}vgg19 =  \seqsplit{models.vgg19(pretrained=True)}} \tn 
% Row Count 4 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{可视化}}  \tn
% Row 0
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\mymulticolumn{1}{x{5.377cm}}{{\bf{TensorboardX}}} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{from tensorboardX import SummaryWriter\{\{nl\}\}with \seqsplit{SummaryWriter(comment='LeNet')} as w:     \{\{nl\}\}~~~~~~~w.add\_graph(model, (input, ))\{\{nl\}\}tensorboard -{}-logdir runs} \tn 
% Row Count 5 (+ 4)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{pytorchviz}} github项目} \tn 
% Row Count 6 (+ 1)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{\seqsplit{https://github.com/szagoruyko/pytorchviz}} \tn 
% Row Count 7 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}