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<p>Pytorch 是一种开源机器学习框架，可加速从研究原型设计到生产部署的过程，备忘单是 官网
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备忘清单为您提供了 <a href="https://pytorch.org/">Pytorch</a> 基本语法和初步应用参考</p>
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</svg></div><div class="menu-modal"><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#入门">入门</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#介绍">介绍</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#认识-pytorch">认识 Pytorch</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#创建一个全零矩阵">创建一个全零矩阵</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#数据创建张量">数据创建张量</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#pytorch-的基本语法">Pytorch 的基本语法</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作1">加法操作(1)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作2">加法操作(2)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作3">加法操作(3)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作4">加法操作(4)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#张量操作">张量操作</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#张量形状">张量形状</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#取张量元素">取张量元素</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torch-tensor-和-numpy-array互换">Torch Tensor 和 Numpy array互换</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torch-tensor-转换为-numpy-array">Torch Tensor 转换为 Numpy array</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#numpy-array转换为torch-tensor">Numpy array转换为Torch Tensor</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#导入-imports">导入 Imports</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#一般">一般</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#神经网络-api">神经网络 API</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torchscript-和-jit">Torchscript 和 JIT</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#onnx">ONNX</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#vision">Vision</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#分布式训练">分布式训练</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#另见">另见</a></div></div><div class="h1wrap-body"><div class="wrap h2body-exist"><div class="wrap-header h2wrap"><h2 id="入门"><a aria-hidden="true" tabindex="-1" href="#入门"><span class="icon icon-link"></span></a>入门</h2><div class="wrap-body">
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<ul>
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<li><a href="https://pytorch.org/">Pytorch 官网</a> <em>(pytorch.org)</em></li>
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<li><a href="https://pytorch.org/tutorials/beginner/ptcheat.html">Pytorch 官方备忘清单</a> <em>(pytorch.org)</em></li>
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</ul>
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</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="认识-pytorch"><a aria-hidden="true" tabindex="-1" href="#认识-pytorch"><span class="icon icon-link"></span></a>认识 Pytorch</h3><div class="wrap-body">
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<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token keyword">from</span> __future__ <span class="token keyword">import</span> print_function
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</span><span class="code-line"><span class="token keyword">import</span> torch
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</span><span class="code-line">x <span class="token operator">=</span> torch<span class="token punctuation">.</span>empty<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">)</span>
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</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">2.4835e+27</span><span class="token punctuation">,</span> <span class="token number">2.5428e+30</span><span class="token punctuation">,</span> <span class="token number">1.0877e-19</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">1.5163e+23</span><span class="token punctuation">,</span> <span class="token number">2.2012e+12</span><span class="token punctuation">,</span> <span class="token number">3.7899e+22</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">5.2480e+05</span><span class="token punctuation">,</span> <span class="token number">1.0175e+31</span><span class="token punctuation">,</span> <span class="token number">9.7056e+24</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">1.6283e+32</span><span class="token punctuation">,</span> <span class="token number">3.7913e+22</span><span class="token punctuation">,</span> <span class="token number">3.9653e+28</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">1.0876e-19</span><span class="token punctuation">,</span> <span class="token number">6.2027e+26</span><span class="token punctuation">,</span> <span class="token number">2.3685e+21</span><span class="token punctuation">]</span>
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</span><span class="code-line"><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span></code></pre>
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<!--rehype:className=wrap-text-->
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<p>Tensors 张量: 张量的概念类似于Numpy中的ndarray数据结构, 最大的区别在于Tensor可以利用GPU的加速功能.</p>
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</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="创建一个全零矩阵"><a aria-hidden="true" tabindex="-1" href="#创建一个全零矩阵"><span class="icon icon-link"></span></a>创建一个全零矩阵</h3><div class="wrap-body">
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<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">x <span class="token operator">=</span> torch<span class="token punctuation">.</span>zeros<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>torch<span class="token punctuation">.</span><span class="token builtin">long</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">)</span>
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</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
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</span><span class="code-line"> <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span></code></pre>
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<p>创建一个全零矩阵并可指定数据元素的类型为long</p>
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</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="数据创建张量"><a aria-hidden="true" tabindex="-1" href="#数据创建张量"><span class="icon icon-link"></span></a>数据创建张量</h3><div class="wrap-body">
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<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">x <span class="token operator">=</span> torch<span class="token punctuation">.</span>tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2.5</span><span class="token punctuation">,</span> <span class="token number">3.5</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">)</span>
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</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2.5000</span><span class="token punctuation">,</span> <span class="token number">3.3000</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span></code></pre>
|
||
</div></div></div></div></div><div class="wrap h2body-exist"><div class="wrap-header h2wrap"><h2 id="pytorch-的基本语法"><a aria-hidden="true" tabindex="-1" href="#pytorch-的基本语法"><span class="icon icon-link"></span></a>Pytorch 的基本语法</h2><div class="wrap-body">
|
||
</div></div><div class="h2wrap-body"><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="加法操作1"><a aria-hidden="true" tabindex="-1" href="#加法操作1"><span class="icon icon-link"></span></a>加法操作(1)</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">y <span class="token operator">=</span> torch<span class="token punctuation">.</span>rand<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x <span class="token operator">+</span> y<span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1.6978</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1.6979</span><span class="token punctuation">,</span> <span class="token number">0.3093</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.4953</span><span class="token punctuation">,</span> <span class="token number">0.3954</span><span class="token punctuation">,</span> <span class="token number">0.0595</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">0.9540</span><span class="token punctuation">,</span> <span class="token number">0.3353</span><span class="token punctuation">,</span> <span class="token number">0.1251</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.6883</span><span class="token punctuation">,</span> <span class="token number">0.9775</span><span class="token punctuation">,</span> <span class="token number">1.1764</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">2.6784</span><span class="token punctuation">,</span> <span class="token number">0.1209</span><span class="token punctuation">,</span> <span class="token number">1.5542</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="加法操作2"><a aria-hidden="true" tabindex="-1" href="#加法操作2"><span class="icon icon-link"></span></a>加法操作(2)</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>torch<span class="token punctuation">.</span>add<span class="token punctuation">(</span>x<span class="token punctuation">,</span> y<span class="token punctuation">)</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1.6978</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1.6979</span><span class="token punctuation">,</span> <span class="token number">0.3093</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.4953</span><span class="token punctuation">,</span> <span class="token number">0.3954</span><span class="token punctuation">,</span> <span class="token number">0.0595</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">0.9540</span><span class="token punctuation">,</span> <span class="token number">0.3353</span><span class="token punctuation">,</span> <span class="token number">0.1251</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.6883</span><span class="token punctuation">,</span> <span class="token number">0.9775</span><span class="token punctuation">,</span> <span class="token number">1.1764</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">2.6784</span><span class="token punctuation">,</span> <span class="token number">0.1209</span><span class="token punctuation">,</span> <span class="token number">1.5542</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="加法操作3"><a aria-hidden="true" tabindex="-1" href="#加法操作3"><span class="icon icon-link"></span></a>加法操作(3)</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line"><span class="token comment"># 提前设定一个空的张量</span>
|
||
</span><span class="code-line">result <span class="token operator">=</span> torch<span class="token punctuation">.</span>empty<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># 将空的张量作为加法的结果存储张量</span>
|
||
</span><span class="code-line"> torch<span class="token punctuation">.</span>add<span class="token punctuation">(</span>x<span class="token punctuation">,</span> y<span class="token punctuation">,</span> out<span class="token operator">=</span>result<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>result<span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1.6978</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1.6979</span><span class="token punctuation">,</span> <span class="token number">0.3093</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.4953</span><span class="token punctuation">,</span> <span class="token number">0.3954</span><span class="token punctuation">,</span> <span class="token number">0.0595</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">0.9540</span><span class="token punctuation">,</span> <span class="token number">0.3353</span><span class="token punctuation">,</span> <span class="token number">0.1251</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.6883</span><span class="token punctuation">,</span> <span class="token number">0.9775</span><span class="token punctuation">,</span> <span class="token number">1.1764</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">2.6784</span><span class="token punctuation">,</span> <span class="token number">0.1209</span><span class="token punctuation">,</span> <span class="token number">1.5542</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="加法操作4"><a aria-hidden="true" tabindex="-1" href="#加法操作4"><span class="icon icon-link"></span></a>加法操作(4)</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">y<span class="token punctuation">.</span>add_<span class="token punctuation">(</span>x<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>y<span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1.6978</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1.6979</span><span class="token punctuation">,</span> <span class="token number">0.3093</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.4953</span><span class="token punctuation">,</span> <span class="token number">0.3954</span><span class="token punctuation">,</span> <span class="token number">0.0595</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">0.9540</span><span class="token punctuation">,</span> <span class="token number">0.3353</span><span class="token punctuation">,</span> <span class="token number">0.1251</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">0.6883</span><span class="token punctuation">,</span> <span class="token number">0.9775</span><span class="token punctuation">,</span> <span class="token number">1.1764</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
|
||
</span><span class="code-line"> <span class="token punctuation">[</span> <span class="token number">2.6784</span><span class="token punctuation">,</span> <span class="token number">0.1209</span><span class="token punctuation">,</span> <span class="token number">1.5542</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
<p>注意: 所有 <code>in-place</code> 的操作函数都有一个下划线的后缀。
|
||
比如 <code>x.copy_(y)</code>, <code>x.add_(y)</code>, 都会直接改变x的值</p>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="张量操作"><a aria-hidden="true" tabindex="-1" href="#张量操作"><span class="icon icon-link"></span></a>张量操作</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">2.0902</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">0.4489</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">0.1441</span><span class="token punctuation">,</span> <span class="token number">0.8035</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">0.8341</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="张量形状"><a aria-hidden="true" tabindex="-1" href="#张量形状"><span class="icon icon-link"></span></a>张量形状</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line">x <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># tensor.view()操作需要保证数据元素的总数量不变</span>
|
||
</span><span class="code-line">y <span class="token operator">=</span> x<span class="token punctuation">.</span>view<span class="token punctuation">(</span><span class="token number">16</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># -1代表自动匹配个数</span>
|
||
</span><span class="code-line">z <span class="token operator">=</span> x<span class="token punctuation">.</span>view<span class="token punctuation">(</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">.</span>size<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> y<span class="token punctuation">.</span>size<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> z<span class="token punctuation">.</span>size<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">]</span><span class="token punctuation">)</span> torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">16</span><span class="token punctuation">]</span><span class="token punctuation">)</span> torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="取张量元素"><a aria-hidden="true" tabindex="-1" href="#取张量元素"><span class="icon icon-link"></span></a>取张量元素</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">x <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>x<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">0.3531</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">-</span><span class="token number">0.3530771732330322</span>
|
||
</span></code></pre>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="torch-tensor-和-numpy-array互换"><a aria-hidden="true" tabindex="-1" href="#torch-tensor-和-numpy-array互换"><span class="icon icon-link"></span></a>Torch Tensor 和 Numpy array互换</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">a <span class="token operator">=</span> torch<span class="token punctuation">.</span>ones<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">)</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1.</span><span class="token punctuation">,</span> <span class="token number">1.</span><span class="token punctuation">,</span> <span class="token number">1.</span><span class="token punctuation">,</span> <span class="token number">1.</span><span class="token punctuation">,</span> <span class="token number">1.</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
<p>Torch Tensor和Numpy array共享底层的内存空间, 因此改变其中一个的值, 另一个也会随之被改变</p>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="torch-tensor-转换为-numpy-array"><a aria-hidden="true" tabindex="-1" href="#torch-tensor-转换为-numpy-array"><span class="icon icon-link"></span></a>Torch Tensor 转换为 Numpy array</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">b <span class="token operator">=</span> a<span class="token punctuation">.</span>numpy<span class="token punctuation">(</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>b<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token punctuation">[</span><span class="token number">1.</span> <span class="token number">1.</span> <span class="token number">1.</span> <span class="token number">1.</span> <span class="token number">1.</span><span class="token punctuation">]</span>
|
||
</span></code></pre>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="numpy-array转换为torch-tensor"><a aria-hidden="true" tabindex="-1" href="#numpy-array转换为torch-tensor"><span class="icon icon-link"></span></a>Numpy array转换为Torch Tensor</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token keyword">import</span> numpy <span class="token keyword">as</span> np
|
||
</span><span class="code-line">a <span class="token operator">=</span> np<span class="token punctuation">.</span>ones<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line">b <span class="token operator">=</span> torch<span class="token punctuation">.</span>from_numpy<span class="token punctuation">(</span>a<span class="token punctuation">)</span>
|
||
</span><span class="code-line">np<span class="token punctuation">.</span>add<span class="token punctuation">(</span>a<span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> out<span class="token operator">=</span>a<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> <span class="token keyword">print</span><span class="token punctuation">(</span>b<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token punctuation">[</span><span class="token number">2.</span> <span class="token number">2.</span> <span class="token number">2.</span> <span class="token number">2.</span> <span class="token number">2.</span><span class="token punctuation">]</span>
|
||
</span><span class="code-line">tensor<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2.</span><span class="token punctuation">,</span> <span class="token number">2.</span><span class="token punctuation">,</span> <span class="token number">2.</span><span class="token punctuation">,</span> <span class="token number">2.</span><span class="token punctuation">,</span> <span class="token number">2.</span><span class="token punctuation">]</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>torch<span class="token punctuation">.</span>float64<span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
<p>注意: 所有在CPU上的Tensors, 除了CharTensor, 都可以转换为Numpy array并可以反向转换.</p>
|
||
</div></div></div></div></div><div class="wrap h2body-exist"><div class="wrap-header h2wrap"><h2 id="导入-imports"><a aria-hidden="true" tabindex="-1" href="#导入-imports"><span class="icon icon-link"></span></a>导入 Imports</h2><div class="wrap-body">
|
||
</div></div><div class="h2wrap-body"><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="一般"><a aria-hidden="true" tabindex="-1" href="#一般"><span class="icon icon-link"></span></a>一般</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token comment"># 根包</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torch
|
||
</span><span class="code-line"><span class="token comment"># 数据集表示和加载</span>
|
||
</span><span class="code-line"><span class="token keyword">from</span> torch<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>data <span class="token keyword">import</span> Dataset<span class="token punctuation">,</span> DataLoader
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="神经网络-api"><a aria-hidden="true" tabindex="-1" href="#神经网络-api"><span class="icon icon-link"></span></a>神经网络 API</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line"><span class="token comment"># 计算图</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torch<span class="token punctuation">.</span>autograd <span class="token keyword">as</span> autograd
|
||
</span><span class="code-line"><span class="token comment"># 计算图中的张量节点</span>
|
||
</span><span class="code-line"><span class="token keyword">from</span> torch <span class="token keyword">import</span> Tensor
|
||
</span><span class="code-line"><span class="token comment"># 神经网络</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn <span class="token keyword">as</span> nn
|
||
</span><span class="code-line"><span class="token comment"># 层、激活等</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>functional <span class="token keyword">as</span> F
|
||
</span><span class="code-line"><span class="token comment"># 优化器，例如 梯度下降、ADAM等</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torch<span class="token punctuation">.</span>optim <span class="token keyword">as</span> optim
|
||
</span><span class="code-line"><span class="token comment"># 混合前端装饰器和跟踪 jit</span>
|
||
</span><span class="code-line"><span class="token keyword">from</span> torch<span class="token punctuation">.</span>jit <span class="token keyword">import</span> script<span class="token punctuation">,</span> trace
|
||
</span></code></pre>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="torchscript-和-jit"><a aria-hidden="true" tabindex="-1" href="#torchscript-和-jit"><span class="icon icon-link"></span></a>Torchscript 和 JIT</h3><div class="wrap-body">
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line">torch<span class="token punctuation">.</span>jit<span class="token punctuation">.</span>trace<span class="token punctuation">(</span><span class="token punctuation">)</span>
|
||
</span></code></pre>
|
||
<p>使用你的模块或函数和一个例子，数据输入，并追溯计算步骤，数据在模型中前进时遇到的情况</p>
|
||
<pre class="language-python"><code class="language-python code-highlight"><span class="code-line"><span class="token decorator annotation punctuation">@script</span>
|
||
</span></code></pre>
|
||
<p>装饰器用于指示被跟踪代码中的数据相关控制流</p>
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="onnx"><a aria-hidden="true" tabindex="-1" href="#onnx"><span class="icon icon-link"></span></a>ONNX</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line">torch<span class="token punctuation">.</span>onnx<span class="token punctuation">.</span>export<span class="token punctuation">(</span>model<span class="token punctuation">,</span> dummy data<span class="token punctuation">,</span> xxxx<span class="token punctuation">.</span>proto<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># 导出 ONNX 格式</span>
|
||
</span><span class="code-line"><span class="token comment"># 使用经过训练的模型模型，dummy</span>
|
||
</span><span class="code-line"><span class="token comment"># 数据和所需的文件名</span>
|
||
</span><span class="code-line">
|
||
</span><span class="code-line">model <span class="token operator">=</span> onnx<span class="token punctuation">.</span>load<span class="token punctuation">(</span><span class="token string">"alexnet.proto"</span><span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># 加载 ONNX 模型</span>
|
||
</span><span class="code-line">onnx<span class="token punctuation">.</span>checker<span class="token punctuation">.</span>check_model<span class="token punctuation">(</span>model<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># 检查模型，IT 是否结构良好</span>
|
||
</span><span class="code-line">
|
||
</span><span class="code-line">onnx<span class="token punctuation">.</span>helper<span class="token punctuation">.</span>printable_graph<span class="token punctuation">(</span>model<span class="token punctuation">.</span>graph<span class="token punctuation">)</span>
|
||
</span><span class="code-line"><span class="token comment"># 打印一个人类可读的，图的表示</span>
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="vision"><a aria-hidden="true" tabindex="-1" href="#vision"><span class="icon icon-link"></span></a>Vision</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token comment"># 视觉数据集，架构 & 变换</span>
|
||
</span><span class="code-line"><span class="token keyword">from</span> torchvision <span class="token keyword">import</span> datasets<span class="token punctuation">,</span> models<span class="token punctuation">,</span> transforms
|
||
</span><span class="code-line"><span class="token comment"># 组合转换</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torchvision<span class="token punctuation">.</span>transforms <span class="token keyword">as</span> transforms
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="分布式训练"><a aria-hidden="true" tabindex="-1" href="#分布式训练"><span class="icon icon-link"></span></a>分布式训练</h3><div class="wrap-body">
|
||
<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token comment"># 分布式通信</span>
|
||
</span><span class="code-line"><span class="token keyword">import</span> torch<span class="token punctuation">.</span>distributed <span class="token keyword">as</span> dist
|
||
</span><span class="code-line"><span class="token comment"># 内存共享进程</span>
|
||
</span><span class="code-line"><span class="token keyword">from</span> torch<span class="token punctuation">.</span>multiprocessing <span class="token keyword">import</span> Process
|
||
</span></code></pre>
|
||
<!--rehype:className=wrap-text-->
|
||
</div></div></div></div></div><div class="wrap h2body-not-exist"><div class="wrap-header h2wrap"><h2 id="另见"><a aria-hidden="true" tabindex="-1" href="#另见"><span class="icon icon-link"></span></a>另见</h2><div class="wrap-body">
|
||
<ul>
|
||
<li><a href="https://pytorch.org/">Pytorch 官网</a> <em>(pytorch.org)</em></li>
|
||
<li><a href="https://pytorch.org/tutorials/beginner/ptcheat.html">Pytorch 官方备忘清单</a> <em>(pytorch.org)</em></li>
|
||
</ul>
|
||
</div></div><div class="h2wrap-body"></div></div></div><script src="https://giscus.app/client.js" data-repo="jaywcjlove/reference" data-repo-id="R_kgDOID2-Mw" data-category="Q&A" data-category-id="DIC_kwDOID2-M84CS5wo" data-mapping="pathname" data-strict="0" data-reactions-enabled="1" data-emit-metadata="0" data-input-position="bottom" data-theme="dark" data-lang="zh-CN" crossorigin="anonymous" async></script><div class="giscus"></div></div><footer class="footer-wrap"><footer class="max-container">© 2022 Kenny Wang.</footer></footer><script src="../data.js?v=1.5.2" defer></script><script src="../js/fuse.min.js?v=1.5.2" defer></script><script src="../js/main.js?v=1.5.2" defer></script><div id="mysearch"><div class="mysearch-box"><div class="mysearch-input"><div><svg xmlns="http://www.w3.org/2000/svg" height="1em" width="1em" viewBox="0 0 18 18">
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