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<title>(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning) — PyTorch Tutorials 1.9.0+cu102 documentation</title>
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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="tensorboard_tutorial.html">TensorBoard로 모델, 데이터, 학습 시각화하기</a></li>
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<p class="caption"><span class="caption-text">이미지/비디오</span></p>
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<li class="toctree-l1"><a class="reference internal" href="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 Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">Optimizing Vision Transformer Model for Deployment</a></li>
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<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="speech_command_recognition_with_torchaudio_tutorial.html">Speech Command Recognition with torchaudio</a></li>
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<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="char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_generation_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 생성하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="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="reinforcement_q_learning.html">강화 학습 (DQN) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="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="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="../advanced/cpp_export.html">C++에서 TorchScript 모델 로딩하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/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="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">Profiling your PyTorch Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
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<li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/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>
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<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="model_parallel_tutorial.html">단일 머신을 사용한 모델 병렬화 모범 사례</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_tutorial.html">Getting Started with Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/rpc_ddp_tutorial.html">Combining Distributed DataParallel with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="pipeline_tutorial.html">Training Transformer models using Pipeline Parallelism</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
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<div class="section" id="quantized-transfer-learning">
<h1>(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)<a class="headerlink" href="#quantized-transfer-learning" title="Permalink to this headline">¶</a></h1>
<div class="admonition tip">
<p class="first admonition-title">Tip</p>
<p class="last">이 튜토리얼을 최대한 활용하시려면, 다음의 링크를 이용하시길 추천합니다.
<a class="reference external" href="https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/quantized_transfer_learning_tutorial.ipynb">Colab 버전</a>.
이를 통해 아래에 제시된 정보로 실험을 해 볼 수 있습니다.</p>
</div>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/z-a-f">Zafar Takhirov</a>
<strong>Reviewed by</strong>: <a class="reference external" href="https://github.com/raghuramank100">Raghuraman Krishnamoorthi</a>
<strong>Edited by</strong>: <a class="reference external" href="https://github.com/jlin27">Jessica Lin</a>
<strong>번역</strong>: <a class="reference external" href="https://github.com/jjeamin">정재민</a></p>
<p>이 튜토리얼은 <a class="reference external" href="https://chsasank.github.io/">Sasank Chilamkurthy</a> 가 작성한
<a class="reference internal" href="../beginner/transfer_learning_tutorial.html"><span class="doc">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</span></a> 을 기반으로 합니다.</p>
<p>전이학습(Transfer learning)은 다른 데이터셋에 적용하기 위해서 미리 학습된 모델을 사용하는 기술을 말합니다.
전이학습을 사용하는 2가지 주요 방법이 있습니다.</p>
<ol class="arabic">
<li><p class="first"><strong>고정 된 특징 추출기로써 ConvNet</strong>: 여기서는 마지막 몇개의 계층(일명 “헤드(the head)”, 일반적으로 완전히 연결된 계층)
을 제외하고 네트워크의 모든 매개 변수 가중치를 <a class="reference external" href="https://arxiv.org/abs/1706.04983">“고정(freeze)”</a> 합니다.
마지막 계층은 임의의 가중치로 초기화된 새로운 계층으로 대체되며 오직 이 계층만 학습됩니다.</p>
</li>
<li><p class="first"><strong>ConvNet 미세조정(Finetuning)</strong>: 랜덤 초기화 대신, 미리 학습된 네트워크를 이용하여 모델을 초기화합니다.
이후 평소처럼 학습을 진행하지만 다른 데이터셋을 사용합니다.
평소처럼 학습이 진행되지만 다른 데이터셋을 사용합니다.</p>
<p>출력의 수가 다를 수 있기 때문에, 일반적으로 신경망에서 헤드(또는 그 일부)는 교체됩니다.
이 방법에서는 학습률을 더 작은 수로 설정하는 것이 일반적입니다.
이는 네트워크가 이미 학습되었기 때문이며 새로운 데이터셋으로 “미세조정(finetuning)”하려면 약간의 변경만이 필요합니다.</p>
</li>
</ol>
<p>또한 위의 두 방법을 결합할 수도 있습니다.
먼저 특징 추출기를 고정(freeze)하고 헤드(the head)를 학습시킵니다.
그런 다음 특징 추출기(또는 그 일부)를 고정해제(unfreeze)하고 학습률을
더 작은 수로 설정한 다음 학습을 계속할 수 있습니다.</p>
<p>이번 파트에서는 첫번째 방법을 사용해 양자화된 모델로 특징을 추출해봅시다.</p>
<div class="section" id="id2">
<h2>파트 0. 요구사항<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
<p>전이 학습(transfer learning)을 시작하기 전에,
데이터 불러오기 / 시각화와 같은 “요구사항(prerequisites)”을 검토하겠습니다.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Imports</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>
</pre></div>
</div>
<div class="section" id="nightly-build">
<h3>Nightly Build 설치하기<a class="headerlink" href="#nightly-build" title="Permalink to this headline">¶</a></h3>
<p>PyTorch의 베타(beta)를 사용할 것이므로 최신 버전의 <code class="docutils literal notranslate"><span class="pre">torch</span></code> 와 <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> 을 설치하는 것을 권장합니다.
로컬(local) 설치에 대한 최신 지침은 <a class="reference external" href="https://pytorch.org/get-started/locally/">여기</a> 에서 찾을 수 있습니다.
예를 들어 GPU 지원 없이 설치하려면 :</p>
<div class="code shell highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">numpy</span>
<span class="n">pip</span> <span class="n">install</span> <span class="o">--</span><span class="n">pre</span> <span class="n">torch</span> <span class="n">torchvision</span> <span class="o">-</span><span class="n">f</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">download</span><span class="o">.</span><span class="n">pytorch</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">whl</span><span class="o">/</span><span class="n">nightly</span><span class="o">/</span><span class="n">cpu</span><span class="o">/</span><span class="n">torch_nightly</span><span class="o">.</span><span class="n">html</span>
<span class="c1"># CUDA 지원은 https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html를 사용하세요.</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h3>데이터 불러오기<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">이번 섹션은 원본 전이학습(Transfer Learning) 튜토리얼과 동일합니다.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">torchvision</span></code> 과 <code class="docutils literal notranslate"><span class="pre">torch.utils.data</span></code> 패키지를 사용하여 데이터를 불러옵니다.</p>
<p>여기서 풀고자 하는 문제는 이미지로부터 <strong>개미</strong> 와 <strong>벌</strong> 을 분류하는 것입니다.
이 데이터셋은 개미와 벌에 대해 각각 120장의 학습용 이미지, 75개의 검증용 이미지를 포함합니다.
이는 일반화하기에는 아주 작은 데이터셋입니다.
하지만 우리는 전이학습(Transfer Learning)을 사용하기 때문에, 일반화를 꽤 잘 할 수 있을 것입니다.</p>
<p>이 데이터셋은 imagenet의 아주 작은 일부입니다.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last"><a class="reference external" href="https://download.pytorch.org/tutorial/hymenoptera_data.zip">여기</a> 에서 데이터를 다운로드 받아 <code class="docutils literal notranslate"><span class="pre">data</span></code> 디렉토리에 압축을 푸세요.</p>
</div>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">datasets</span>
<span class="c1"># 학습을 위한 데이터 보강(Data augmentation)과 정규화</span>
<span class="c1"># 검증을 위한 정규화</span>
<span class="n">data_transforms</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'train'</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="p">]),</span>
<span class="s1">'val'</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="p">]),</span>
<span class="p">}</span>
<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">'data/hymenoptera_data'</span>
<span class="n">image_datasets</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span>
<span class="n">data_transforms</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'train'</span><span class="p">,</span> <span class="s1">'val'</span><span class="p">]}</span>
<span class="n">dataloaders</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'train'</span><span class="p">,</span> <span class="s1">'val'</span><span class="p">]}</span>
<span class="n">dataset_sizes</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'train'</span><span class="p">,</span> <span class="s1">'val'</span><span class="p">]}</span>
<span class="n">class_names</span> <span class="o">=</span> <span class="n">image_datasets</span><span class="p">[</span><span class="s1">'train'</span><span class="p">]</span><span class="o">.</span><span class="n">classes</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda:0"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id6">
<h3>일부 이미지 시각화하기<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
<p>데이터 보강을 이해하기 위해 일부 학습용 이미지를 시각화 해보겠습니다.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
<span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)):</span>
<span class="sd">"""Imshow for Tensor."""</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">inp</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">inp</span> <span class="o">+</span> <span class="n">mean</span>
<span class="n">inp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
<span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="c1"># 학습 데이터의 배치를 얻습니다.</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">dataloaders</span><span class="p">[</span><span class="s1">'train'</span><span class="p">]))</span>
<span class="c1"># 배치로부터 격자 형태의 이미지를 만듭니다.</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">nrow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">imshow</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="p">[</span><span class="n">class_names</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id7">
<h3>모델 학습을 위한 지원 함수<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h3>
<p>다음은 모델을 학습하기 위한 일반 함수 입니다.</p>
<ul class="simple">
<li>학습률(learning rate)을 관리합니다(schedules).</li>
<li>최적의 모델을 저장합니다.</li>
</ul>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">train_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epochs</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cpu'</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Support function for model training.</span>
<span class="sd"> 모델 학습을 위한 지원 함수</span>
<span class="sd"> 매개변수:</span>
<span class="sd"> model: 학습할 모델</span>
<span class="sd"> criterion: 최적화 기준(손실)</span>
<span class="sd"> optimizer: 학습에 사용할 옵티마이저</span>
<span class="sd"> scheduler: ``torch.optim.lr_scheduler`` 의 인스턴스</span>
<span class="sd"> num_epochs: 에폭의 수</span>
<span class="sd"> device: 학습을 동작시킬 장치. 'cpu' 또는 'cuda'여야 합니다.</span>
<span class="sd"> """</span>
<span class="n">since</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">best_model_wts</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
<span class="n">best_acc</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Epoch </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">num_epochs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="c1"># 각 에폭에는 학습 및 검증 단계가 있습니다.</span>
<span class="k">for</span> <span class="n">phase</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'train'</span><span class="p">,</span> <span class="s1">'val'</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">phase</span> <span class="o">==</span> <span class="s1">'train'</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span> <span class="c1"># 모델을 학습 모드로 설정하기</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> <span class="c1"># 모델을 평가 모드로 설정하기</span>
<span class="n">running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">running_corrects</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># 데이터 반복하기</span>
<span class="k">for</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">dataloaders</span><span class="p">[</span><span class="n">phase</span><span class="p">]:</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="c1"># 매개 변수 기울기를 0으로 설정하기</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="c1"># 순전파</span>
<span class="c1"># 학습 동안만 연산 기록을 추적하기</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="n">phase</span> <span class="o">==</span> <span class="s1">'train'</span><span class="p">):</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="c1"># 역전파 + 학습 단계에서만 최적화</span>
<span class="k">if</span> <span class="n">phase</span> <span class="o">==</span> <span class="s1">'train'</span><span class="p">:</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="c1"># 통계 보기</span>
<span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">*</span> <span class="n">inputs</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">running_corrects</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">preds</span> <span class="o">==</span> <span class="n">labels</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">if</span> <span class="n">phase</span> <span class="o">==</span> <span class="s1">'train'</span><span class="p">:</span>
<span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">epoch_loss</span> <span class="o">=</span> <span class="n">running_loss</span> <span class="o">/</span> <span class="n">dataset_sizes</span><span class="p">[</span><span class="n">phase</span><span class="p">]</span>
<span class="n">epoch_acc</span> <span class="o">=</span> <span class="n">running_corrects</span><span class="o">.</span><span class="n">double</span><span class="p">()</span> <span class="o">/</span> <span class="n">dataset_sizes</span><span class="p">[</span><span class="n">phase</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1"> Loss: </span><span class="si">{:.4f}</span><span class="s1"> Acc: </span><span class="si">{:.4f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">phase</span><span class="p">,</span> <span class="n">epoch_loss</span><span class="p">,</span> <span class="n">epoch_acc</span><span class="p">))</span>
<span class="c1"># 모델 복사하기</span>
<span class="k">if</span> <span class="n">phase</span> <span class="o">==</span> <span class="s1">'val'</span> <span class="ow">and</span> <span class="n">epoch_acc</span> <span class="o">></span> <span class="n">best_acc</span><span class="p">:</span>
<span class="n">best_acc</span> <span class="o">=</span> <span class="n">epoch_acc</span>
<span class="n">best_model_wts</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
<span class="nb">print</span><span class="p">()</span>
<span class="n">time_elapsed</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">since</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Training complete in </span><span class="si">{:.0f}</span><span class="s1">m </span><span class="si">{:.0f}</span><span class="s1">s'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">time_elapsed</span> <span class="o">//</span> <span class="mi">60</span><span class="p">,</span> <span class="n">time_elapsed</span> <span class="o">%</span> <span class="mi">60</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Best val Acc: </span><span class="si">{:4f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_acc</span><span class="p">))</span>
<span class="c1"># 최적의 모델 가중치 불러오기</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">best_model_wts</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
</pre></div>
</div>
</div>
<div class="section" id="id8">
<h3>모델 예측을 시각화하기 위한 지원 함수<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
<p>일부 이미지에 대한 예측을 출력하는 일반 함수</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">visualize_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">rows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">cols</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="n">was_training</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">training</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">current_row</span> <span class="o">=</span> <span class="n">current_col</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="n">cols</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">rows</span><span class="o">*</span><span class="mi">2</span><span class="p">))</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">lbls</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloaders</span><span class="p">[</span><span class="s1">'val'</span><span class="p">]):</span>
<span class="n">imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">lbls</span> <span class="o">=</span> <span class="n">lbls</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">imgs</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">jdx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">imshow</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">jdx</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'predicted: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">class_names</span><span class="p">[</span><span class="n">preds</span><span class="p">[</span><span class="n">jdx</span><span class="p">]]))</span>
<span class="n">current_col</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">current_col</span> <span class="o">>=</span> <span class="n">cols</span><span class="p">:</span>
<span class="n">current_row</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">current_col</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">if</span> <span class="n">current_row</span> <span class="o">>=</span> <span class="n">rows</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">was_training</span><span class="p">)</span>
<span class="k">return</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">was_training</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="quantized-feature-extractor">
<h2>파트 1. 양자화된 특징 추출기(Quantized Feature Extractor)를 기반으로 사용자 지정 분류기 훈련하기<a class="headerlink" href="#quantized-feature-extractor" title="Permalink to this headline">¶</a></h2>
<p>이번 섹션에서는 “고정된(frozen)” 양자화 특징 추출기를 사용하고 그 위에 사용자 지정 분류기 헤드를
학습합니다. 부동 소수점 모델과 다르게 양자화된 모델에는 학습 가능한 매개 변수가 없으므로
requires_grad = False를 설정할 필요가 없습니다. 자세한 내용은 <a class="reference external" href="https://pytorch.org/docs/stable/quantization.html">설명서</a> 를 참조하세요.</p>
<p>미리 학습된 모델을 불러옵니다: 이번 예제에서는 <a class="reference external" href="https://pytorch.org/hub/pytorch_vision_resnet/">ResNet-18</a> 을 사용할 것입니다.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models.quantization</span> <span class="k">as</span> <span class="nn">models</span>
<span class="c1"># 나중에 사용할 수 있게 `fc` 에 필터의 수가 필요합니다.</span>
<span class="c1"># 여기서 각 출력 샘플의 크기는 2로 설정합니다.</span>
<span class="c1"># 또한, nn.Linear(num_ftrs, len(class_names))로 일반화 할 수 있습니다.</span>
<span class="n">model_fe</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">num_ftrs</span> <span class="o">=</span> <span class="n">model_fe</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">in_features</span>
</pre></div>
</div>
<p>이 시점에서 미리 학습된 모델을 수정해야 합니다. 모델의 시작과 끝에는 양자화/역양자화 블록이 있습니다.
그러나 특징 추출기만 사용하기 때문에 역양자화 계층은 선형 계층(헤드) 바로 전으로 이동시켜야 합니다.
가장 쉬운 방법은 모델을 <code class="docutils literal notranslate"><span class="pre">nn.Sequential</span></code> 모듈로 감싸는 것입니다.</p>
<p>첫번째 단계는 ResNet 모델에서 특징 추출기를 분리하는 것입니다.
이 예제에서는 <code class="docutils literal notranslate"><span class="pre">fc</span></code> 를 제외한 모든 계층을 특징 추출기로 사용해야 하지만, 실제로는 필요한 만큼 많은 부분을 사용할 수 있습니다.
이것은 합성곱 계층 중 일부를 교체하려는 경우에도 유용합니다.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">양자화 모델에서 특징 추출기를 분리할 때 양자화를 유지하려는 부분의 시작과 끝에 수동으로 양자화/역양자화를 배치해야 합니다.</p>
</div>
<p>아래 함수는 사용자 지정 헤드로 모델을 생성하는 함수입니다.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="k">def</span> <span class="nf">create_combined_model</span><span class="p">(</span><span class="n">model_fe</span><span class="p">):</span>
<span class="c1"># 1 단계. 특징 추출기를 분리합니다.</span>
<span class="n">model_fe_features</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">quant</span><span class="p">,</span> <span class="c1"># Quantize the input</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">conv1</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">bn1</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">relu</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">maxpool</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">layer1</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">layer2</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">layer3</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">layer4</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">avgpool</span><span class="p">,</span>
<span class="n">model_fe</span><span class="o">.</span><span class="n">dequant</span><span class="p">,</span> <span class="c1"># 출력을 역양자화하기</span>
<span class="p">)</span>
<span class="c1"># 2 단계. 새로운 "헤드(head)"를 만듭니다.</span>
<span class="n">new_head</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">num_ftrs</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># 3 단계. 결합하고 양자 스텁(stubs)을 잊으면 안됩니다.</span>
<span class="n">new_model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">model_fe_features</span><span class="p">,</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
<span class="n">new_head</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">new_model</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">현재 양자화된 모델은 CPU에서만 실행할 수 있습니다.
그러나 모델의 양자화 되지 않은 부분은 GPU로 보낼 수 있습니다.</p>
</div>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="n">new_model</span> <span class="o">=</span> <span class="n">create_combined_model</span><span class="p">(</span><span class="n">model_fe</span><span class="p">)</span>
<span class="n">new_model</span> <span class="o">=</span> <span class="n">new_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="c1"># 헤드(the head)만 훈련 한다는 점을 유의하세요</span>
<span class="n">optimizer_ft</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">new_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
<span class="c1"># 7 에폭마다 0.1배씩 학습률이 감소</span>
<span class="n">exp_lr_scheduler</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">StepLR</span><span class="p">(</span><span class="n">optimizer_ft</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="id10">
<h3>학습과 평가<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h3>
<p>이 단계는 CPU에서 약 15 ~ 25분 걸립니다. 양자화된 모델은 CPU에서만 실행되기 때문에
GPU에서는 훈련을 실행할 수 없습니다.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">new_model</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">(</span><span class="n">new_model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer_ft</span><span class="p">,</span> <span class="n">exp_lr_scheduler</span><span class="p">,</span>
<span class="n">num_epochs</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="n">visualize_model</span><span class="p">(</span><span class="n">new_model</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="finetuning">
<h2>파트 2. 양자화 가능한 모델 미세조정(Finetuning)<a class="headerlink" href="#finetuning" title="Permalink to this headline">¶</a></h2>
<p>이번 파트에서는 전이학습(Transfer Learning)을 사용하여 특징 추출기(Feature Extractor)를
미세조정(Finetuning) 합니다. 파트 1과 2 모두에서 특징 추출기는 양자화됩니다. 차이점은 파트 1에서
미리 학습 된 양자화 모델을 사용합니다. 이번 파트에서, 우리는 관심있는 데이터셋으로 미세조정(Finetuning)한 후
양자화된 특징 추출기를 생성하므로, 양자화의 장점을 가지면서 전이 학습(Transfer Learning)으로 더 나은 정확도를
얻을 수 있습니다. 특정한 예제에서는 학습용 셋은 매우 작기 때문에(120개의 이미지) 전체 모델을
미세조정(Finetuning)하는 장점이 불분명 합니다. 그러나 여기에 표시된 절차는 더욱 더 큰 데이터셋을 사용한 전이 학습(Transfer Learning)의
정확도를 향상시킵니다.</p>
<p>미리 학습된 특징 추출기는 양자화가 가능해야 합니다.
양자화가 가능한지 확인하기 위해서 다음 단계를 수행하세요:</p>
<blockquote>
<div><ol class="arabic simple">
<li><code class="docutils literal notranslate"><span class="pre">torch.quantization.fuse_modules</span></code> 를 사용하여 <code class="docutils literal notranslate"><span class="pre">(Conv,</span> <span class="pre">BN,</span> <span class="pre">ReLU)</span></code> ,
<code class="docutils literal notranslate"><span class="pre">(Conv,</span> <span class="pre">BN)</span></code>, 그리고 <code class="docutils literal notranslate"><span class="pre">(Conv,</span> <span class="pre">ReLU)</span></code> 를 융합합니다.</li>
<li>특징 추출기를 사용자 지정 헤드와 연결합니다. 이를 위해서 특징 추출기의 출력을 역으로 양자화 해야합니다.</li>
<li>특징 추출기의 적합한 위치에 가짜 양자화 모듈을 삽입하여 학습하는 동안에 양자화를 모방합니다.</li>
</ol>
</div></blockquote>
<p>(1) 단계의 경우 멤버 메서드(member method) <code class="docutils literal notranslate"><span class="pre">fuse_model</span></code> 이 있는
<code class="docutils literal notranslate"><span class="pre">torchvision/models/quantization</span></code> 의 모델을 사용합니다.
이 함수는 모든 <code class="docutils literal notranslate"><span class="pre">conv</span></code> , <code class="docutils literal notranslate"><span class="pre">bn</span></code> , 그리고 <code class="docutils literal notranslate"><span class="pre">relu</span></code> 모듈을 통합합니다.
사용자 지정 모델의 경우, 수동으로 통합할 모듈의 목록과 함께 <code class="docutils literal notranslate"><span class="pre">torch.quantization.fuse_modules</span></code> API를 호출해야합니다.</p>
<ol class="arabic simple" start="2">
<li>단계는 이전 섹션에서 사용한 <code class="docutils literal notranslate"><span class="pre">create_combined_model</span></code> 함수에 의해서 수행됩니다.</li>
<li>단계는 가짜 양자화 모듈을 삽입하는 <code class="docutils literal notranslate"><span class="pre">torch.quantization.prepare_qat</span></code> 를 사용하여 수행됩니다.</li>
<li>단계로 모델을 “미세조정(Finetuning)”한 후, 완전하게 양자화된 버전으로 변환(5단계) 할 수 있습니다.</li>
</ol>
<p>미세조정(Finetuning) 모델을 양자화된 모델로 변환하려면 <code class="docutils literal notranslate"><span class="pre">torch.quantization.convert</span></code> 함수를
호출 할 수 있습니다. (이 경우 특징 추출기만 양자화 됩니다.)</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">랜덤 초기화 때문에 여러분의 결과가 튜토리얼에 표시된 결과와 다를 수 있습니다.</p>
</div>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># `quantize=False` 를 주목하세요</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">num_ftrs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">in_features</span>
<span class="c1"># 1 단계</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fuse_model</span><span class="p">()</span>
<span class="c1"># 2 단계</span>
<span class="n">model_ft</span> <span class="o">=</span> <span class="n">create_combined_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">model_ft</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">default_qat_qconfig</span> <span class="c1"># Use default QAT configuration</span>
<span class="c1"># 3 단계</span>
<span class="n">model_ft</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare_qat</span><span class="p">(</span><span class="n">model_ft</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="id11">
<h3>모델 미세조정<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h3>
<p>현재 튜토리얼에서는 전체 모델이 미세조정 되었습니다.
일반적으로 이것은 더 높은 정확도로 이어질 것입니다.
그러나 여기서는 크기가 작은 학습용 데이터셋을 사용했기 때문에 결국 과적합하게 됩니다.</p>
<p>4 단계. 모델 미세조정하기</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model_ft</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">model_ft</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="c1"># GPU에서 미세조정(Finetuning) 할 수 있습니다.</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="c1"># 이미 모든 것이 학습된 상태이므로 학습률이 낮습니다.</span>
<span class="c1"># 더 작은 Learning rate에 주목하세요</span>
<span class="n">optimizer_ft</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model_ft</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="c1"># 학습률을 몇 에폭마다 0.3배 감소시키기</span>
<span class="n">exp_lr_scheduler</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">StepLR</span><span class="p">(</span><span class="n">optimizer_ft</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">model_ft_tuned</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">(</span><span class="n">model_ft</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer_ft</span><span class="p">,</span> <span class="n">exp_lr_scheduler</span><span class="p">,</span>
<span class="n">num_epochs</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
</pre></div>
</div>
<p>5 단계. 양자화된 모델로 변환하기</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="kn">import</span> <span class="n">convert</span>
<span class="n">model_ft_tuned</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">model_quantized_and_trained</span> <span class="o">=</span> <span class="n">convert</span><span class="p">(</span><span class="n">model_ft_tuned</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>양자화된 모델이 일부 이미지에서 어떻게 동작하는지 살펴보겠습니다.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">visualize_model</span><span class="p">(</span><span class="n">model_quantized_and_trained</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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