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<p class="caption"><span class="caption-text">파이토치(PyTorch) 레시피</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">모든 레시피 보기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">모든 프로토타입 레시피 보기</a></li>
</ul>
<p class="caption"><span class="caption-text">파이토치(PyTorch) 시작하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">파이토치(PyTorch) 기본 익히기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">빠른 시작(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">텐서(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset과 Dataloader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">변형(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">신경망 모델 구성하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>를 사용한 자동 미분</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">모델 매개변수 최적화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">모델 저장하고 불러오기</a></li>
</ul>
<p class="caption"><span class="caption-text">파이토치(PyTorch) 배우기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch로 딥러닝하기: 60분만에 끝장내기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">예제로 배우는 파이토치(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> 이 <em>실제로</em> 무엇인가요?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard로 모델, 데이터, 학습 시각화하기</a></li>
</ul>
<p class="caption"><span class="caption-text">이미지/비디오</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision 객체 검출 미세조정(Finetuning) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">적대적 예제 생성(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">배포를 위한 비전 트랜스포머(Vision Transformer) 모델 최적화하기</a></li>
</ul>
<p class="caption"><span class="caption-text">오디오</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html">Audio manipulation with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#audio-i-o">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#resampling">Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#data-augmentation">Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#feature-extractions">Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#feature-augmentation">Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#datasets">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_recognition_with_torchaudio_tutorial.html">Speech Command Recognition with torchaudio</a></li>
</ul>
<p class="caption"><span class="caption-text">텍스트</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer 와 TorchText 로 시퀀스-투-시퀀스(Sequence-to-Sequence) 모델링하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 생성하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext 라이브러리로 텍스트 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html">nn.Transformer와 torchtext로 언어 번역하기</a></li>
</ul>
<p class="caption"><span class="caption-text">강화학습</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">강화 학습 (DQN) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch 모델을 프로덕션 환경에 배포하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask를 사용하여 Python에서 PyTorch를 REST API로 배포하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript 소개</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_export.html">C++에서 TorchScript 모델 로딩하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="super_resolution_with_onnxruntime.html">(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch 모듈 프로파일링 하기</a></li>
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<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
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<p class="caption"><span class="caption-text">병렬 및 분산 학습</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">단일 머신을 사용한 모델 병렬화 모범 사례</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">분산 데이터 병렬 처리 시작하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_ddp_tutorial.html">Combining Distributed DataParallel with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
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<div class="section" id="c-torchscript">
<h1>커스텀 C++ 클래스로 TorchScript 확장하기<a class="headerlink" href="#c-torchscript" title="Permalink to this headline">¶</a></h1>
<p>이 튜토리얼은 <a class="reference internal" href="torch_script_custom_ops.html"><span class="doc">커스텀 오퍼레이터</span></a> 튜토리얼의 후속이며
C++ 클래스를 TorchScript와 Python에 동시에 바인딩하기 위해 구축한 API를 소개합니다.
API는 <a class="reference external" href="https://github.com/pybind/pybind11">pybind11</a> 과
매우 유사하며 해당 시스템에 익숙하다면 대부분의 개념이 이전됩니다.</p>
<div class="section" id="c">
<h2>C++에서 클래스 구현 및 바인딩<a class="headerlink" href="#c" title="Permalink to this headline">¶</a></h2>
<p>이 튜토리얼에서는 멤버 변수에서 지속 상태를 유지하는 간단한 C++ 클래스를 정의할 것입니다.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// This header is all you need to do the C++ portions of this</span>
<span class="c1">// tutorial</span>
<span class="cp">#include</span> <span class="cpf"><torch/script.h></span><span class="cp"></span>
<span class="c1">// This header is what defines the custom class registration</span>
<span class="c1">// behavior specifically. script.h already includes this, but</span>
<span class="c1">// we include it here so you know it exists in case you want</span>
<span class="c1">// to look at the API or implementation.</span>
<span class="cp">#include</span> <span class="cpf"><torch/custom_class.h></span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><string></span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><vector></span><span class="cp"></span>
<span class="k">template</span> <span class="o"><</span><span class="k">class</span> <span class="nc">T</span><span class="o">></span>
<span class="k">struct</span> <span class="nc">MyStackClass</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">CustomClassHolder</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">T</span><span class="o">></span> <span class="n">stack_</span><span class="p">;</span>
<span class="n">MyStackClass</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">T</span><span class="o">></span> <span class="n">init</span><span class="p">)</span> <span class="o">:</span> <span class="n">stack_</span><span class="p">(</span><span class="n">init</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span> <span class="n">init</span><span class="p">.</span><span class="n">end</span><span class="p">())</span> <span class="p">{}</span>
<span class="kt">void</span> <span class="n">push</span><span class="p">(</span><span class="n">T</span> <span class="n">x</span><span class="p">)</span> <span class="p">{</span>
<span class="n">stack_</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">x</span><span class="p">);</span>
<span class="p">}</span>
<span class="n">T</span> <span class="n">pop</span><span class="p">()</span> <span class="p">{</span>
<span class="k">auto</span> <span class="n">val</span> <span class="o">=</span> <span class="n">stack_</span><span class="p">.</span><span class="n">back</span><span class="p">();</span>
<span class="n">stack_</span><span class="p">.</span><span class="n">pop_back</span><span class="p">();</span>
<span class="k">return</span> <span class="n">val</span><span class="p">;</span>
<span class="p">}</span>
<span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o">></span> <span class="n">clone</span><span class="p">()</span> <span class="k">const</span> <span class="p">{</span>
<span class="k">return</span> <span class="n">c10</span><span class="o">::</span><span class="n">make_intrusive</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o">></span><span class="p">(</span><span class="n">stack_</span><span class="p">);</span>
<span class="p">}</span>
<span class="kt">void</span> <span class="n">merge</span><span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o">>&</span> <span class="n">c</span><span class="p">)</span> <span class="p">{</span>
<span class="k">for</span> <span class="p">(</span><span class="k">auto</span><span class="o">&</span> <span class="nl">elem</span> <span class="p">:</span> <span class="n">c</span><span class="o">-></span><span class="n">stack_</span><span class="p">)</span> <span class="p">{</span>
<span class="n">push</span><span class="p">(</span><span class="n">elem</span><span class="p">);</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p>몇 가지 주의할 사항이 있습니다:</p>
<ul class="simple">
<li><code class="docutils literal notranslate"><span class="pre">torch/custom_class.h</span></code> 는 커스텀 클래스로 TorchScript를 확장하기 위해 포함해야하는 헤더입니다.</li>
<li>커스텀 클래스의 인스턴스로 작업할 때마다 <code class="docutils literal notranslate"><span class="pre">c10::intrusive_ptr<></span></code> 의 인스턴스를 통해 작업을 수행합니다.
<code class="docutils literal notranslate"><span class="pre">intrusive_ptr</span></code> 를 <code class="docutils literal notranslate"><span class="pre">std::shared_ptr</span></code> 과 같은 스마트 포인터로 생각하세요. 그러나 참조 계수는
<code class="docutils literal notranslate"><span class="pre">std::shared_ptr</span></code> 같이 별도의 메타데이터 블록과 달리 객체에 직접 저장됩니다.
<code class="docutils literal notranslate"><span class="pre">torch::Tensor</span></code> 는 내부적으로 동일한 포인터 유형을 사용합니다.
커스텀 클래스도 <code class="docutils literal notranslate"><span class="pre">torch::Tensor</span></code> 포인터 유형을 사용해야 다양한 객체 유형을 일관되게 관리할 수 있습니다.</li>
<li>두 번째로 주목해야 할 점은 커스텀 클래스가 <code class="docutils literal notranslate"><span class="pre">torch::CustomClassHolder</span></code> 에서 상속되어야 한다는 것입니다.
이렇게 하면 커스텀 클래스에 참조 계수를 저장할 공간이 있습니다.</li>
</ul>
<p>이제 이 클래스를 어떻게 TorchScript에서 사용가능하게 하는지 살펴보겠습니다.
이런 과정은 클래스를 <em>바인딩</em> 한다고 합니다:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Notice a few things:</span>
<span class="c1">// - We pass the class to be registered as a template parameter to</span>
<span class="c1">// `torch::class_`. In this instance, we've passed the</span>
<span class="c1">// specialization of the MyStackClass class ``MyStackClass<std::string>``.</span>
<span class="c1">// In general, you cannot register a non-specialized template</span>
<span class="c1">// class. For non-templated classes, you can just pass the</span>
<span class="c1">// class name directly as the template parameter.</span>
<span class="c1">// - The arguments passed to the constructor make up the "qualified name"</span>
<span class="c1">// of the class. In this case, the registered class will appear in</span>
<span class="c1">// Python and C++ as `torch.classes.my_classes.MyStackClass`. We call</span>
<span class="c1">// the first argument the "namespace" and the second argument the</span>
<span class="c1">// actual class name.</span>
<span class="n">TORCH_LIBRARY</span><span class="p">(</span><span class="n">my_classes</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span> <span class="p">{</span>
<span class="n">m</span><span class="p">.</span><span class="n">class_</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>></span><span class="p">(</span><span class="s">"MyStackClass"</span><span class="p">)</span>
<span class="c1">// The following line registers the contructor of our MyStackClass</span>
<span class="c1">// class that takes a single `std::vector<std::string>` argument,</span>
<span class="c1">// i.e. it exposes the C++ method `MyStackClass(std::vector<T> init)`.</span>
<span class="c1">// Currently, we do not support registering overloaded</span>
<span class="c1">// constructors, so for now you can only `def()` one instance of</span>
<span class="c1">// `torch::init`.</span>
<span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">init</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>></span><span class="p">())</span>
<span class="c1">// The next line registers a stateless (i.e. no captures) C++ lambda</span>
<span class="c1">// function as a method. Note that a lambda function must take a</span>
<span class="c1">// `c10::intrusive_ptr<YourClass>` (or some const/ref version of that)</span>
<span class="c1">// as the first argument. Other arguments can be whatever you want.</span>
<span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"top"</span><span class="p">,</span> <span class="p">[](</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>>&</span> <span class="n">self</span><span class="p">)</span> <span class="p">{</span>
<span class="k">return</span> <span class="n">self</span><span class="o">-></span><span class="n">stack_</span><span class="p">.</span><span class="n">back</span><span class="p">();</span>
<span class="p">})</span>
<span class="c1">// The following four lines expose methods of the MyStackClass<std::string></span>
<span class="c1">// class as-is. `torch::class_` will automatically examine the</span>
<span class="c1">// argument and return types of the passed-in method pointers and</span>
<span class="c1">// expose these to Python and TorchScript accordingly. Finally, notice</span>
<span class="c1">// that we must take the *address* of the fully-qualified method name,</span>
<span class="c1">// i.e. use the unary `&` operator, due to C++ typing rules.</span>
<span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"push"</span><span class="p">,</span> <span class="o">&</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>::</span><span class="n">push</span><span class="p">)</span>
<span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"pop"</span><span class="p">,</span> <span class="o">&</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>::</span><span class="n">pop</span><span class="p">)</span>
<span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"clone"</span><span class="p">,</span> <span class="o">&</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>::</span><span class="n">clone</span><span class="p">)</span>
<span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"merge"</span><span class="p">,</span> <span class="o">&</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>::</span><span class="n">merge</span><span class="p">)</span>
<span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="cmake-c">
<h2>CMake를 사용하여 C++ 프로젝트로 예제 빌드<a class="headerlink" href="#cmake-c" title="Permalink to this headline">¶</a></h2>
<p>이제 <a class="reference external" href="https://cmake.org">CMake</a> 빌드 시스템을 사용하여 위의 C++ 코드를 빌드합니다.
먼저, 지금까지 다룬 모든 C++ code를 <code class="docutils literal notranslate"><span class="pre">class.cpp</span></code> 라는 파일에 넣습니다.
그런 다음 간단한 <code class="docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> 파일을 작성하여 동일한 디렉토리에 배치합니다.
<code class="docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> 는 다음과 같아야 합니다:</p>
<div class="highlight-cmake notranslate"><div class="highlight"><pre><span></span><span class="nb">cmake_minimum_required</span><span class="p">(</span><span class="s">VERSION</span> <span class="s">3.1</span> <span class="s">FATAL_ERROR</span><span class="p">)</span>
<span class="nb">project</span><span class="p">(</span><span class="s">custom_class</span><span class="p">)</span>
<span class="nb">find_package</span><span class="p">(</span><span class="s">Torch</span> <span class="s">REQUIRED</span><span class="p">)</span>
<span class="c"># Define our library target</span>
<span class="nb">add_library</span><span class="p">(</span><span class="s">custom_class</span> <span class="s">SHARED</span> <span class="s">class.cpp</span><span class="p">)</span>
<span class="nb">set</span><span class="p">(</span><span class="s">CMAKE_CXX_STANDARD</span> <span class="s">14</span><span class="p">)</span>
<span class="c"># Link against LibTorch</span>
<span class="nb">target_link_libraries</span><span class="p">(</span><span class="s">custom_class</span> <span class="s2">"${TORCH_LIBRARIES}"</span><span class="p">)</span>
</pre></div>
</div>
<p>또한 <code class="docutils literal notranslate"><span class="pre">build</span></code> 디렉토리를 만듭니다. 파일 트리는 다음과 같아야 합니다:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">custom_class_project</span><span class="o">/</span>
<span class="n">class</span><span class="o">.</span><span class="n">cpp</span>
<span class="n">CMakeLists</span><span class="o">.</span><span class="n">txt</span>
<span class="n">build</span><span class="o">/</span>
</pre></div>
</div>
<p><a class="reference internal" href="torch_script_custom_ops.html"><span class="doc">이전 튜토리얼</span></a> 에서 설명한 것과 동일한 방식으로 환경을 설정했다고 가정합니다.
계속해서 cmake를 호출한 다음 make를 호출하여 프로젝트를 빌드합니다:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> build
$ cmake -DCMAKE_PREFIX_PATH<span class="o">=</span><span class="s2">"</span><span class="k">$(</span>python -c <span class="s1">'import torch.utils; print(torch.utils.cmake_prefix_path)'</span><span class="k">)</span><span class="s2">"</span> ..
-- The C compiler identification is GNU <span class="m">7</span>.3.1
-- The CXX compiler identification is GNU <span class="m">7</span>.3.1
-- Check <span class="k">for</span> working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc
-- Check <span class="k">for</span> working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - <span class="k">done</span>
-- Detecting C compile features
-- Detecting C compile features - <span class="k">done</span>
-- Check <span class="k">for</span> working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++
-- Check <span class="k">for</span> working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - <span class="k">done</span>
-- Detecting CXX compile features
-- Detecting CXX compile features - <span class="k">done</span>
-- Looking <span class="k">for</span> pthread.h
-- Looking <span class="k">for</span> pthread.h - found
-- Looking <span class="k">for</span> pthread_create
-- Looking <span class="k">for</span> pthread_create - not found
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthreads
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthreads - not found
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthread
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthread - found
-- Found Threads: TRUE
-- Found torch: /torchbind_tutorial/libtorch/lib/libtorch.so
-- Configuring <span class="k">done</span>
-- Generating <span class="k">done</span>
-- Build files have been written to: /torchbind_tutorial/build
$ make -j
Scanning dependencies of target custom_class
<span class="o">[</span> <span class="m">50</span>%<span class="o">]</span> Building CXX object CMakeFiles/custom_class.dir/class.cpp.o
<span class="o">[</span><span class="m">100</span>%<span class="o">]</span> Linking CXX shared library libcustom_class.so
<span class="o">[</span><span class="m">100</span>%<span class="o">]</span> Built target custom_class
</pre></div>
</div>
<p>이제 무엇보다도 빌드 디렉토리에 동적 라이브러리 파일이 있다는 것을 알게 될 것입니다.
리눅스에서는 아마도 <code class="docutils literal notranslate"><span class="pre">libcustom_class.so</span></code> 로 이름이 지정될 것입니다.
따라서 파일 트리는 다음과 같아야 합니다:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">custom_class_project</span><span class="o">/</span>
<span class="n">class</span><span class="o">.</span><span class="n">cpp</span>
<span class="n">CMakeLists</span><span class="o">.</span><span class="n">txt</span>
<span class="n">build</span><span class="o">/</span>
<span class="n">libcustom_class</span><span class="o">.</span><span class="n">so</span>
</pre></div>
</div>
</div>
<div class="section" id="python-torchscript-c">
<h2>Python 및 TorchScript의 C++ 클래스 사용<a class="headerlink" href="#python-torchscript-c" title="Permalink to this headline">¶</a></h2>
<p>이제 클래스와 등록이 <code class="docutils literal notranslate"><span class="pre">.so</span></code> 파일로 컴파일되었으므로 해당 <cite>.so</cite> 를 Python에 읽어들이고 사용해 볼 수 있습니다.
다음은 이를 보여주는 스크립트입니다:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># `torch.classes.load_library()` allows you to pass the path to your .so file</span>
<span class="c1"># to load it in and make the custom C++ classes available to both Python and</span>
<span class="c1"># TorchScript</span>
<span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">"build/libcustom_class.so"</span><span class="p">)</span>
<span class="c1"># You can query the loaded libraries like this:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">loaded_libraries</span><span class="p">)</span>
<span class="c1"># prints {'/custom_class_project/build/libcustom_class.so'}</span>
<span class="c1"># We can find and instantiate our custom C++ class in python by using the</span>
<span class="c1"># `torch.classes` namespace:</span>
<span class="c1">#</span>
<span class="c1"># This instantiation will invoke the MyStackClass(std::vector<T> init)</span>
<span class="c1"># constructor we registered earlier</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">"foo"</span><span class="p">,</span> <span class="s2">"bar"</span><span class="p">])</span>
<span class="c1"># We can call methods in Python</span>
<span class="n">s</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="s2">"pushed"</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">s</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="o">==</span> <span class="s2">"pushed"</span>
<span class="c1"># Returning and passing instances of custom classes works as you'd expect</span>
<span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="n">s</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span>
<span class="k">for</span> <span class="n">expected</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"bar"</span><span class="p">,</span> <span class="s2">"foo"</span><span class="p">,</span> <span class="s2">"bar"</span><span class="p">,</span> <span class="s2">"foo"</span><span class="p">]:</span>
<span class="k">assert</span> <span class="n">s</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="o">==</span> <span class="n">expected</span>
<span class="c1"># We can also use the class in TorchScript</span>
<span class="c1"># For now, we need to assign the class's type to a local in order to</span>
<span class="c1"># annotate the type on the TorchScript function. This may change</span>
<span class="c1"># in the future.</span>
<span class="n">MyStackClass</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">do_stacks</span><span class="p">(</span><span class="n">s</span><span class="p">:</span> <span class="n">MyStackClass</span><span class="p">):</span> <span class="c1"># We can pass a custom class instance</span>
<span class="c1"># We can instantiate the class</span>
<span class="n">s2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">"hi"</span><span class="p">,</span> <span class="s2">"mom"</span><span class="p">])</span>
<span class="n">s2</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="c1"># We can call a method on the class</span>
<span class="c1"># We can also return instances of the class</span>
<span class="c1"># from TorchScript function/methods</span>
<span class="k">return</span> <span class="n">s2</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="n">s2</span><span class="o">.</span><span class="n">top</span><span class="p">()</span>
<span class="n">stack</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="n">do_stacks</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">"wow"</span><span class="p">]))</span>
<span class="k">assert</span> <span class="n">top</span> <span class="o">==</span> <span class="s2">"wow"</span>
<span class="k">for</span> <span class="n">expected</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"wow"</span><span class="p">,</span> <span class="s2">"mom"</span><span class="p">,</span> <span class="s2">"hi"</span><span class="p">]:</span>
<span class="k">assert</span> <span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="o">==</span> <span class="n">expected</span>
</pre></div>
</div>
</div>
<div class="section" id="torchscript">
<h2>커스텀 클래스를 사용하여 TorchScript 코드 저장, 읽기 및 실행<a class="headerlink" href="#torchscript" title="Permalink to this headline">¶</a></h2>
<p>libtorch를 사용하여 C++ 프로세스에서 커스텀 등록 C++ 클래스를 사용할 수도 있습니다.
예를 들어 MyStackClass 클래스에서 메소드를 인스턴스화하고 호출하는 간단한 <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> 을 정의해 보겠습니다:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s1">'build/libcustom_class.so'</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Foo</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="n">stack</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">"hi"</span><span class="p">,</span> <span class="s2">"mom"</span><span class="p">])</span>
<span class="k">return</span> <span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="o">+</span> <span class="n">s</span>
<span class="n">scripted_foo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">Foo</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scripted_foo</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
<span class="n">scripted_foo</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'foo.pt'</span><span class="p">)</span>
</pre></div>
</div>
<p>파일 시스템의 foo.pt는 방금 정의한 직렬화된 TorchScript 프로그램을 포함합니다.</p>
<p>이제 이 모델과 필요한 .so 파일을 읽어들이는 방법을 보여주기 위해 새 CMake 프로젝트를 정의하겠습니다.
이 작업을 수행하는 방법에 대한 자세한 내용은 <a class="reference external" href="https://tutorials.pytorch.kr/advanced/cpp_export.html">C++에서 TorchScript 모델 로딩하기</a> 를
참조하세요.</p>
<p>이전과 유사하게 다음을 포함하는 파일 구조를 생성해 보겠습니다:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cpp_inference_example</span><span class="o">/</span>
<span class="n">infer</span><span class="o">.</span><span class="n">cpp</span>
<span class="n">CMakeLists</span><span class="o">.</span><span class="n">txt</span>
<span class="n">foo</span><span class="o">.</span><span class="n">pt</span>
<span class="n">build</span><span class="o">/</span>
<span class="n">custom_class_project</span><span class="o">/</span>
<span class="n">class</span><span class="o">.</span><span class="n">cpp</span>
<span class="n">CMakeLists</span><span class="o">.</span><span class="n">txt</span>
<span class="n">build</span><span class="o">/</span>
</pre></div>
</div>
<p>직렬화된 foo.pt 파일과 위의 <code class="docutils literal notranslate"><span class="pre">custom_class_project</span></code> 소스 트리를 복사했음을 주목하세요.
커스텀 클래스를 바이너리로 빌드할 수 있도록 <code class="docutils literal notranslate"><span class="pre">custom_class_project</span></code> 를 이 C++ 프로젝트에 의존성으로 추가할 것입니다.</p>
<p><code class="docutils literal notranslate"><span class="pre">infer.cpp</span></code> 를 다음으로 채우겠습니다:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf"><torch/script.h></span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><iostream></span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><memory></span><span class="cp"></span>
<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
<span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
<span class="k">try</span> <span class="p">{</span>
<span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
<span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">"foo.pt"</span><span class="p">);</span>
<span class="p">}</span>
<span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o"><<</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
<span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
<span class="p">}</span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">c10</span><span class="o">::</span><span class="n">IValue</span><span class="o">></span> <span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="s">"foobarbaz"</span><span class="p">};</span>
<span class="k">auto</span> <span class="n">output</span> <span class="o">=</span> <span class="k">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toString</span><span class="p">();</span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o"><<</span> <span class="n">output</span><span class="o">-></span><span class="n">string</span><span class="p">()</span> <span class="o"><<</span> <span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p>마찬가지로 CMakeLists.txt 파일을 정의해 보겠습니다:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">cmake_minimum_required</span><span class="p">(</span><span class="n">VERSION</span> <span class="mf">3.1</span> <span class="n">FATAL_ERROR</span><span class="p">)</span>
<span class="n">project</span><span class="p">(</span><span class="n">infer</span><span class="p">)</span>
<span class="n">find_package</span><span class="p">(</span><span class="n">Torch</span> <span class="n">REQUIRED</span><span class="p">)</span>
<span class="n">add_subdirectory</span><span class="p">(</span><span class="n">custom_class_project</span><span class="p">)</span>
<span class="cp"># Define our library target</span>
<span class="n">add_executable</span><span class="p">(</span><span class="n">infer</span> <span class="n">infer</span><span class="p">.</span><span class="n">cpp</span><span class="p">)</span>
<span class="n">set</span><span class="p">(</span><span class="n">CMAKE_CXX_STANDARD</span> <span class="mi">14</span><span class="p">)</span>
<span class="cp"># Link against LibTorch</span>
<span class="n">target_link_libraries</span><span class="p">(</span><span class="n">infer</span> <span class="s">"${TORCH_LIBRARIES}"</span><span class="p">)</span>
<span class="cp"># This is where we link in our libcustom_class code, making our</span>
<span class="cp"># custom class available in our binary.</span>
<span class="n">target_link_libraries</span><span class="p">(</span><span class="n">infer</span> <span class="o">-</span><span class="n">Wl</span><span class="p">,</span><span class="o">--</span><span class="n">no</span><span class="o">-</span><span class="n">as</span><span class="o">-</span><span class="n">needed</span> <span class="n">custom_class</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">cd</span> <span class="pre">build</span></code>, <code class="docutils literal notranslate"><span class="pre">cmake</span></code>, 및 <code class="docutils literal notranslate"><span class="pre">make</span></code> 에 대한 사용 방법을 알고 있습니다:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> build
$ cmake -DCMAKE_PREFIX_PATH<span class="o">=</span><span class="s2">"</span><span class="k">$(</span>python -c <span class="s1">'import torch.utils; print(torch.utils.cmake_prefix_path)'</span><span class="k">)</span><span class="s2">"</span> ..
-- The C compiler identification is GNU <span class="m">7</span>.3.1
-- The CXX compiler identification is GNU <span class="m">7</span>.3.1
-- Check <span class="k">for</span> working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc
-- Check <span class="k">for</span> working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - <span class="k">done</span>
-- Detecting C compile features
-- Detecting C compile features - <span class="k">done</span>
-- Check <span class="k">for</span> working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++
-- Check <span class="k">for</span> working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - <span class="k">done</span>
-- Detecting CXX compile features
-- Detecting CXX compile features - <span class="k">done</span>
-- Looking <span class="k">for</span> pthread.h
-- Looking <span class="k">for</span> pthread.h - found
-- Looking <span class="k">for</span> pthread_create
-- Looking <span class="k">for</span> pthread_create - not found
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthreads
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthreads - not found
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthread
-- Looking <span class="k">for</span> pthread_create <span class="k">in</span> pthread - found
-- Found Threads: TRUE
-- Found torch: /local/miniconda3/lib/python3.7/site-packages/torch/lib/libtorch.so
-- Configuring <span class="k">done</span>
-- Generating <span class="k">done</span>
-- Build files have been written to: /cpp_inference_example/build
$ make -j
Scanning dependencies of target custom_class
<span class="o">[</span> <span class="m">25</span>%<span class="o">]</span> Building CXX object custom_class_project/CMakeFiles/custom_class.dir/class.cpp.o
<span class="o">[</span> <span class="m">50</span>%<span class="o">]</span> Linking CXX shared library libcustom_class.so
<span class="o">[</span> <span class="m">50</span>%<span class="o">]</span> Built target custom_class
Scanning dependencies of target infer
<span class="o">[</span> <span class="m">75</span>%<span class="o">]</span> Building CXX object CMakeFiles/infer.dir/infer.cpp.o
<span class="o">[</span><span class="m">100</span>%<span class="o">]</span> Linking CXX executable infer
<span class="o">[</span><span class="m">100</span>%<span class="o">]</span> Built target infer
</pre></div>
</div>
<p>이제 흥미로운 C++ 바이너리를 실행할 수 있습니다:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ ./infer
momfoobarbaz
</pre></div>
</div>
<p>대단합니다!</p>
</div>
<div class="section" id="ivalues">
<h2>커스텀 클래스를 IValues로/에서 이동<a class="headerlink" href="#ivalues" title="Permalink to this headline">¶</a></h2>
<p>TorchScript 메소드에서 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 를 가져오거나 반환하기, 또는 C++에서 커스텀 클래스 속성을 인스턴스화하려는
경우와 같이 커스텀 클래스를 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 안팎으로 이동해야 할 수도 있습니다.
커스텀 C++ 클래스 인스턴스에서 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 를 생성하려면:</p>
<ul class="simple">
<li><code class="docutils literal notranslate"><span class="pre">torch::make_custom_class<T>()</span></code> 는 제공하는 인수 집합을 사용하고 해당 인수 집합과 일치하는
T의 생성자를 호출하며 해당 인스턴스를 래핑하고 반환하는 c10::intrusive_ptr<T>와 유사한 API를 제공합니다.
그러나 커스텀 클래스 객체에 대한 포인터만 반환하는 대신 객체를 래핑하는 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 를 반환합니다.
그런 다음 이 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 를 TorchScript에 직접 전달할 수 있습니다.</li>
<li>이미 클래스를 가리키는 <code class="docutils literal notranslate"><span class="pre">intrusive_ptr</span></code> 이 있는 경우 생성자 <code class="docutils literal notranslate"><span class="pre">IValue(intrusive_ptr<T>)</span></code> 를 사용하여
해당 클래스에서 IValue를 직접 생성할 수 있습니다.</li>
</ul>
<p><code class="docutils literal notranslate"><span class="pre">IValue</span></code> 를 커스텀 클래스로 다시 변환하려면:</p>
<ul class="simple">
<li><code class="docutils literal notranslate"><span class="pre">IValue::toCustomClass<T>()</span></code> 는 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 에 포함된 커스텀 클래스를 가리키는 <code class="docutils literal notranslate"><span class="pre">intrusive_ptr<T></span></code> 를
반환합니다. 내부적으로 이 함수는 <code class="docutils literal notranslate"><span class="pre">T</span></code> 가 커스텀 클래스로 등록되어 있고 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 에 실제로 커스텀 클래스가
포함되어 있는지 확인합니다. <code class="docutils literal notranslate"><span class="pre">isCustomClass()</span></code> 를 호출하여 <code class="docutils literal notranslate"><span class="pre">IValue</span></code> 에 커스텀 클래스가 포함되어 있는지
수동으로 확인할 수 있습니다.</li>
</ul>
</div>
<div class="section" id="id2">
<h2>커스텀 C++ 클래스에 대한 직렬화/역직렬화 방법 정의<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
<p>커스텀 바인딩 된 C++ 클래스를 속성으로 사용하여 <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code> 을 저장하려고 하면
다음 오류가 발생합니다:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># export_attr.py</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s1">'build/libcustom_class.so'</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Foo</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stack</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">"just"</span><span class="p">,</span> <span class="s2">"testing"</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="o">+</span> <span class="n">s</span>
<span class="n">scripted_foo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">Foo</span><span class="p">())</span>
<span class="n">scripted_foo</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'foo.pt'</span><span class="p">)</span>
<span class="n">loaded</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'foo.pt'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">loaded</span><span class="o">.</span><span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">())</span>
</pre></div>
</div>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ python export_attr.py
RuntimeError: Cannot serialize custom bound C++ class __torch__.torch.classes.my_classes.MyStackClass. Please define serialization methods via def_pickle <span class="k">for</span> this class. <span class="o">(</span>pushIValueImpl at ../torch/csrc/jit/pickler.cpp:128<span class="o">)</span>
</pre></div>
</div>
<p>TorchScript가 C++ 클래스에서 저장한 정보를 자동으로 파악할 수 없기 때문입니다.
수동으로 지정해야 합니다. 그렇게 하는 방법은 <code class="docutils literal notranslate"><span class="pre">class_</span></code> 에서 특별한 <code class="docutils literal notranslate"><span class="pre">def_pickle</span></code> 메소드를
사용하여 클래스에서 <code class="docutils literal notranslate"><span class="pre">__getstate__</span></code> 및 <code class="docutils literal notranslate"><span class="pre">__setstate__</span></code> 메소드를 정의하는 것입니다.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">TorchScript에서 <code class="docutils literal notranslate"><span class="pre">__getstate__</span></code> 및 <code class="docutils literal notranslate"><span class="pre">__setstate__</span></code> 의 의미는 Python pickle 모듈의 의미와 동일합니다.
이러한 방법을 어떻게 사용하는지에 대하여 <a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/docs/serialization.md#getstate-and-setstate">자세한 내용</a> 을
참조하세요.</p>
</div>
<p>다음은 직렬화 메소드를 포함하기 위해 <code class="docutils literal notranslate"><span class="pre">MyStackClass</span></code> 등록에 추가할 수 있는 <code class="docutils literal notranslate"><span class="pre">def_pickle</span></code> 호출의 예시입니다:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span> <span class="c1">// class_<>::def_pickle allows you to define the serialization</span>
<span class="c1">// and deserialization methods for your C++ class.</span>
<span class="c1">// Currently, we only support passing stateless lambda functions</span>
<span class="c1">// as arguments to def_pickle</span>
<span class="p">.</span><span class="n">def_pickle</span><span class="p">(</span>
<span class="c1">// __getstate__</span>
<span class="c1">// This function defines what data structure should be produced</span>
<span class="c1">// when we serialize an instance of this class. The function</span>
<span class="c1">// must take a single `self` argument, which is an intrusive_ptr</span>
<span class="c1">// to the instance of the object. The function can return</span>
<span class="c1">// any type that is supported as a return value of the TorchScript</span>
<span class="c1">// custom operator API. In this instance, we've chosen to return</span>
<span class="c1">// a std::vector<std::string> as the salient data to preserve</span>
<span class="c1">// from the class.</span>
<span class="p">[](</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>>&</span> <span class="n">self</span><span class="p">)</span>
<span class="o">-></span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">></span> <span class="p">{</span>
<span class="k">return</span> <span class="n">self</span><span class="o">-></span><span class="n">stack_</span><span class="p">;</span>
<span class="p">},</span>
<span class="c1">// __setstate__</span>
<span class="c1">// This function defines how to create a new instance of the C++</span>
<span class="c1">// class when we are deserializing. The function must take a</span>
<span class="c1">// single argument of the same type as the return value of</span>
<span class="c1">// `__getstate__`. The function must return an intrusive_ptr</span>
<span class="c1">// to a new instance of the C++ class, initialized however</span>
<span class="c1">// you would like given the serialized state.</span>
<span class="p">[](</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">></span> <span class="n">state</span><span class="p">)</span>
<span class="o">-></span> <span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>></span> <span class="p">{</span>
<span class="c1">// A convenient way to instantiate an object and get an</span>
<span class="c1">// intrusive_ptr to it is via `make_intrusive`. We use</span>
<span class="c1">// that here to allocate an instance of MyStackClass<std::string></span>
<span class="c1">// and call the single-argument std::vector<std::string></span>
<span class="c1">// constructor with the serialized state.</span>
<span class="k">return</span> <span class="n">c10</span><span class="o">::</span><span class="n">make_intrusive</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>></span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">state</span><span class="p">));</span>
<span class="p">});</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">pickle API에서 pybind11과 다른 접근 방식을 취합니다. pybind11이 <code class="docutils literal notranslate"><span class="pre">class_::def()</span></code> 로
전달되는 특수 함수 <code class="docutils literal notranslate"><span class="pre">pybind11::pickle()</span></code> 인 반면, 이 목적을 위해 별도의 메소드
<code class="docutils literal notranslate"><span class="pre">def_pickle</span></code> 를 가지고 있습니다. 이미 <code class="docutils literal notranslate"><span class="pre">torch::jit::pickle</span></code> 라는 이름이 사용되었고
혼동을 일으키고 싶지 않았기 때문입니다.</p>
</div>
<p>이러한 방식으로 (역)직렬화 동작을 정의하면 이제 스크립트를 성공적으로 실행할 수 있습니다:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ python ../export_attr.py
testing
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h2>바인딩된 C++ 클래스를 사용하거나 반환하는 커스텀 연산자 정의<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
<p>커스텀 C++ 클래스를 정의한 후에는 해당 클래스를 인수로 사용하거나 커스텀 연산자(예를 들어 free 함수)에서
반환할 수도 있습니다. 다음과 같은 free 함수가 있다고 가정합니다:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>></span> <span class="n">manipulate_instance</span><span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">intrusive_ptr</span><span class="o"><</span><span class="n">MyStackClass</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">>>&</span> <span class="n">instance</span><span class="p">)</span> <span class="p">{</span>
<span class="n">instance</span><span class="o">-></span><span class="n">pop</span><span class="p">();</span>
<span class="k">return</span> <span class="n">instance</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">TORCH_LIBRARY</span></code> 블록 내에서 다음 코드를 실행하여 등록할 수 있습니다:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span> <span class="n">m</span><span class="p">.</span><span class="n">def</span><span class="p">(</span>
<span class="s">"foo::manipulate_instance(__torch__.torch.classes.my_classes.MyStackClass x) -> __torch__.torch.classes.my_classes.MyStackClass Y"</span><span class="p">,</span>
<span class="n">manipulate_instance</span>
<span class="p">);</span>
</pre></div>
</div>
<p>등록 API에 대한 자세한 내용은 <a class="reference external" href="https://tutorials.pytorch.kr/advanced/torch_script_custom_ops.html">커스텀 C++ 연산자로 TorchScript 확장</a> 을
참조하세요.</p>
<p>이 작업이 완료되면 다음 예제와 같이 연산자를 사용할 수 있습니다:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">TryCustomOp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">TryCustomOp</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">"foo"</span><span class="p">,</span> <span class="s2">"bar"</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">foo</span><span class="o">.</span><span class="n">manipulate_instance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">C++ 클래스를 인수로 사용하는 연산자를 등록하려면 커스텀 클래스가 이미 등록되어
있어야 합니다. 커스텀 클래스 등록과 free 함수 정의가 동일한 <code class="docutils literal notranslate"><span class="pre">TORCH_LIBRARY</span></code>
블록에 있고 커스텀 클래스 등록이 먼저 오게 하여 이를 시행할 수 있습니다.
향후 어떤 순서로든 등록할 수 있도록 이 요구사항을 완화할 수 있습니다.</p>
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<div class="section" id="id6">
<h2>결론<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
<p>이 튜토리얼에서는 독립된 C++ 프로세스에서 C++ 클래스를 TorchScript 및
확장 Python에 나타내는 방법, 해당 메소드를 등록하는 방법, Python 및 TorchScript에서
해당 클래스를 사용하는 방법, 클래스를 사용하여 코드를 저장 및 읽어들이고 해당 코드를 실행하는 방법을 안내했습니다.
이제 타사 C++ 라이브러리와 인터페이스가 있는 C++ 클래스로 TorchScript 모델을 확장하거나,
Python, TorchScript 및 C++ 간의 라인이 원활하게 혼합되어야 하는 다른 사용 사례를 구현할 준비가 되었습니다.</p>
<p>언제나 처럼 문제를 마주치거나 질문이 있으면 저희 <a class="reference external" href="https://discuss.pytorch.org/">forum</a> 또는
<a class="reference external" href="https://github.com/pytorch/pytorch/issues">GitHub issues</a> 에 올려주시면 되겠습니다.
또한 <a class="reference external" href="https://pytorch.org/cppdocs/notes/faq.html">자주 묻는 질문(FAQ) 페이지</a> 에
유용한 정보가 있을 수 있습니다.</p>
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<ul>
<li><a class="reference internal" href="#">커스텀 C++ 클래스로 TorchScript 확장하기</a><ul>
<li><a class="reference internal" href="#c">C++에서 클래스 구현 및 바인딩</a></li>
<li><a class="reference internal" href="#cmake-c">CMake를 사용하여 C++ 프로젝트로 예제 빌드</a></li>
<li><a class="reference internal" href="#python-torchscript-c">Python 및 TorchScript의 C++ 클래스 사용</a></li>
<li><a class="reference internal" href="#torchscript">커스텀 클래스를 사용하여 TorchScript 코드 저장, 읽기 및 실행</a></li>
<li><a class="reference internal" href="#ivalues">커스텀 클래스를 IValues로/에서 이동</a></li>
<li><a class="reference internal" href="#id2">커스텀 C++ 클래스에 대한 직렬화/역직렬화 방법 정의</a></li>
<li><a class="reference internal" href="#id4">바인딩된 C++ 클래스를 사용하거나 반환하는 커스텀 연산자 정의</a></li>
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