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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</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>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/captumyt.html">Model Understanding with Captum</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</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 ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_conv_bn_fuser.html">(๋ฒ ํ) FX์์ ํฉ์ฑ๊ณฑ/๋ฐฐ์น ์ ๊ทํ(Convolution/Batch Norm) ๊ฒฐํฉ๊ธฐ(Fuser) ๋ง๋ค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/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>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(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>
</ul>
<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</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">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</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="../advanced/rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/ddp_pipeline.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ์ ๋ณ๋ ฌ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ฌ์ฉํ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
</ul>
<p class="caption"><span class="caption-text">Mobile</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deeplabv3_on_ios.html">iOS์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deeplabv3_on_android.html">์๋๋ก์ด๋์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
</ul>
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<div class="section" id="flask">
<h1>Flask๋ก ๋ฐฐํฌํ๊ธฐ<a class="headerlink" href="#flask" title="Permalink to this headline">ยถ</a></h1>
<p>์ด ๋ ์ํผ์์๋ ๋ค์๊ณผ ๊ฐ์ ๋ด์ฉ๋ค์ ํ์ตํฉ๋๋ค:</p>
<ul class="simple">
<li>ํ์ตํ PyTorch ๋ชจ๋ธ์ Flask ์ปจํ
์ด๋๋ก ๊ฐ์ธ๊ณ ์น API๋ก ๋
ธ์ถํ๋ ๋ฐฉ๋ฒ</li>
<li>์น ์์ฒญ(request)์ ๋ชจ๋ธ์ ์
๋ ฅํ๊ธฐ ์ํด PyTorch ํ
์(tensor)๋ก ๋ณํํ๋ ๋ฐฉ๋ฒ</li>
<li>๋ชจ๋ธ์ ๊ฒฐ๊ณผ๋ฅผ HTTP ์๋ต(response)๋ก ํจํค์งํ๋ ๋ฐฉ๋ฒ</li>
</ul>
<div class="section" id="id1">
<h2>ํ์์ฌํญ<a class="headerlink" href="#id1" title="Permalink to this headline">ยถ</a></h2>
<p>์๋์ ๊ฐ์ ํจํค์ง๋ค(๊ณผ ์์กด์ฑ์ด ์๋ ํจํค์ง๋ค)์ด ์ค์น๋ Python 3 ํ๊ฒฝ์ด ํ์ํฉ๋๋ค:</p>
<ul class="simple">
<li>PyTorch 1.5</li>
<li>TorchVision 0.6.0</li>
<li>Flask 1.1</li>
</ul>
<p>์ ํ ์ฌํญ์ผ๋ก, ์ผ๋ถ ์์ ํ์ผ๋ค(supporting files)์ ๋ฐ๊ธฐ ์ํด์ git์ด ํ์ํฉ๋๋ค.</p>
<p>PyTorch์ TorchVision ์ค์น๋ฐฉ๋ฒ์ <a class="reference external" href="https://pytorch.org">pytorch.org</a> ์์ ํ์ธํ์ค ์ ์์ต๋๋ค.
Flask ์ค์น ๋ฐฉ๋ฒ์ <a class="reference external" href="https://flask.palletsprojects.com/en/1.1.x/installation/">Flask ์ค์น ๋ฌธ์</a> ์์ ํ์ธํ์ค ์ ์์ต๋๋ค.</p>
</div>
<div class="section" id="id2">
<h2>Flask๋?<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h2>
<p>Flask๋ Python์ผ๋ก ์์ฑ๋ ๊ฐ๋ฒผ์ด ์น ์๋ฒ์
๋๋ค. ํ์ตํ PyTorch ๋ชจ๋ธ์ ์์ธก์
์ํ ์น API๋ฅผ ํธ๋ฆฌํ๊ฒ ์์ฑํ์ฌ, ์ง์ ์ฌ์ฉํ๊ฑฐ๋ ๋ ํฐ ์์คํ
์ ํฌํจ๋ ์น ์๋น์ค๋ฅผ
๋ง๋ค ์ ์์ต๋๋ค.</p>
</div>
<div class="section" id="id3">
<h2>์ค์น ๋ฐ ์์ ํ์ผ<a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h2>
<p>์ด๋ฏธ์ง๋ฅผ ๋ฐ์์ ImageNet ๋ฐ์ดํฐ์
์ 1000๊ฐ ํด๋์ค(class) ์ค ํ๋๋ก ์์ธก(map)ํ๋
์น ์๋น์ค๋ฅผ ๋ง๋ค๋ ค๊ณ ํฉ๋๋ค. ์ด๋ฅผ ์ํด ํ
์คํธํ ์ด๋ฏธ์ง ํ์ผ๋ค์ด ํ์ํฉ๋๋ค.
์ ํ ์ฌํญ์ผ๋ก, ๋ชจ๋ธ์ด ์์ธกํ ํด๋์ค ๋ฒํธ(index)๋ฅผ ์ฌ๋์ด ์ฝ์ ์ ์๋ ํด๋์ค ์ด๋ฆ์ผ๋ก
๋ฐ๊พธ๊ธฐ ์ํ ํ์ผ์ ๋ฐ์ ์๋ ์์ต๋๋ค.</p>
<div class="section" id="id4">
<h3>์ต์
1: ์์ ํ์ผ ๋ชจ๋๋ฅผ ๋น ๋ฅด๊ฒ ๋ฐ๊ธฐ<a class="headerlink" href="#id4" title="Permalink to this headline">ยถ</a></h3>
<p>TorchServe ์ ์ฅ์์ ์ฒดํฌ์์ํ์ฌ ์์ ํ์ผ ๋ชจ๋๋ฅผ ๊ฐ์ ธ์ค๊ณ (pull), ์์
ํด๋๋ก
๋ณต์ฌํ ์ ์์ต๋๋ค. <em>(์๋ด: ์ด ํํ ๋ฆฌ์ผ์ TorchServe์ ์ด๋ ํ ์์กด์ฑ๋ ๊ฐ์ง ์์ต๋๋ค.
- ๋จ์ํ ๋น ๋ฅด๊ฒ ํ์ผ๋ค์ ๊ฐ์ ธ์ค๊ธฐ ์ํ ๋ฐฉ๋ฒ์
๋๋ค)</em> ์๋ ๋ช
๋ น์ ์์์ ์คํํ์ธ์:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">pytorch</span><span class="o">/</span><span class="n">serve</span>
<span class="n">cp</span> <span class="n">serve</span><span class="o">/</span><span class="n">examples</span><span class="o">/</span><span class="n">image_classifier</span><span class="o">/</span><span class="n">kitten</span><span class="o">.</span><span class="n">jpg</span> <span class="o">.</span>
<span class="n">cp</span> <span class="n">serve</span><span class="o">/</span><span class="n">examples</span><span class="o">/</span><span class="n">image_classifier</span><span class="o">/</span><span class="n">index_to_name</span><span class="o">.</span><span class="n">json</span> <span class="o">.</span>
</pre></div>
</div>
<p>์ด์ ํ์ผ๋ค์ ๋ฐ์์ต๋๋ค!</p>
</div>
<div class="section" id="id5">
<h3>์ต์
2: ์ด๋ฏธ์ง ์ง์ ๊ฐ์ ธ์ค๊ธฐ<a class="headerlink" href="#id5" title="Permalink to this headline">ยถ</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">index_to_name.json</span></code> ํ์ผ์ ์๋ Flask ์๋น์ค์์ ์ ํ ์ฌํญ์
๋๋ค.
์ง์ ๊ฐ์ ธ์จ ์ด๋ฏธ์ง(๋ฌผ๋ก 3์ JPEG ์ด๋ฏธ์ง์ฌ์ผ ํฉ๋๋ค)๋ ํ
์คํธ๋ฅผ ํ ์ ์์ต๋๋ค.</p>
</div>
</div>
<div class="section" id="id6">
<h2>Flask ์๋น์ค ๊ตฌ์ถํ๊ธฐ<a class="headerlink" href="#id6" title="Permalink to this headline">ยถ</a></h2>
<p>Flask ์๋น์ค๋ฅผ ๊ตฌ์ฑํ๋ ์ ์ฒด Python ์คํฌ๋ฆฝํธ๋ ์ด ๋ ์ํผ์ ๋ ๋ถ๋ถ์ ์์ต๋๋ค;
<code class="docutils literal notranslate"><span class="pre">app.py</span></code> ํ์ผ์ ๋ณต์ฌํด์ ๋ถ์ฌ๋ฃ์ ์ ์์ต๋๋ค.
์๋์์ ๊ฐ๊ฐ์ ๊ธฐ๋ฅ๋ค์ ๋ช
ํํ ์ดํด๋ณด๊ธฐ ์ํด ๊ฐ ์น์
๋ณ๋ก ์ดํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="section" id="import">
<h3>Import<a class="headerlink" href="#import" title="Permalink to this headline">ยถ</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
</pre></div>
</div>
<p>์์๋๋ก:</p>
<ul class="simple">
<li>๋ฏธ๋ฆฌ ํ์ต๋ DenseNet์ <code class="docutils literal notranslate"><span class="pre">torchvision.models</span></code> ์์ ๊ฐ์ ธ์์ ์ฌ์ฉํ ๊ฒ์
๋๋ค.</li>
<li><code class="docutils literal notranslate"><span class="pre">torchvision.transforms</span></code> ์๋ ์ด๋ฏธ์ง ๋ฐ์ดํฐ๋ฅผ ๋ค๋ฃฐ ๋๊ตฌ๋ค์ด ํฌํจ๋์ด ์์ต๋๋ค.</li>
<li>Pillow (<code class="docutils literal notranslate"><span class="pre">PIL</span></code>) ๋ ์ด๋ฏธ์ง ํ์ผ์ ์ฒ์ ๋ถ๋ฌ์ฌ ๋ ์ฌ์ฉํ ๊ฒ์
๋๋ค.</li>
<li>๋ฌผ๋ก <code class="docutils literal notranslate"><span class="pre">flask</span></code> ์ ํด๋์ค๋ค๋ ํ์ํฉ๋๋ค.</li>
</ul>
</div>
<div class="section" id="pre-processing">
<h3>์ ์ฒ๋ฆฌ(Pre-Processing)<a class="headerlink" href="#pre-processing" title="Permalink to this headline">ยถ</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">infile</span><span class="p">):</span>
<span class="n">input_transforms</span> <span class="o">=</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">255</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="n">my_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span><span class="n">input_transforms</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">infile</span><span class="p">)</span>
<span class="n">timg</span> <span class="o">=</span> <span class="n">my_transforms</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
<span class="n">timg</span><span class="o">.</span><span class="n">unsqueeze_</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">timg</span>
</pre></div>
</div>
<p>์น ์์ฒญ์ผ๋ก ์ด๋ฏธ์ง ํ์ผ์ ๋ฐ์ง๋ง, ๋ชจ๋ธ์ (N, 3, 224, 224) ๋ชจ์(shape)์ PyTorch ํ
์๊ฐ
ํ์ํฉ๋๋ค. ์ฌ๊ธฐ์ <em>N</em> ์ ์
๋ ฅ ๋ฐฐ์น(input batch)์ ํญ๋ชฉ์ ๊ฐ์์
๋๋ค. (๋ฐฐ์น์ ํฌ๊ธฐ๋ 1์
๋๋ค.)
๋จผ์ ์ด๋ฏธ์ง์ ํฌ๊ธฐ๋ฅผ ์กฐ์ (resize)ํ๊ณ ์๋ฅด๊ณ , ํ
์๋ก ๋ณํํ ๋ค, ์ ๊ทํ(normalization)ํ๋
TorchVision transforms(๋ณํ) ์ธํธ๋ฅผ ๊ตฌ์ฑํฉ๋๋ค. (์ ๊ทํ์ ๋ํ ์์ธํ ๋ด์ฉ์
<code class="docutils literal notranslate"><span class="pre">torchvision.models_</span></code> ๋ฌธ์๋ฅผ ์ฐธ๊ณ ํ์ธ์.)</p>
<p>๊ทธ๋ฐ ๋ค์, ํ์ผ์ ์ด๊ณ (์์์ ๊ตฌ์ฑํ) ๋ณํ๋ค์ ์ ์ฉํฉ๋๋ค. ๊ฒฐ๊ณผ๋ก๋
3๊ฐ์ ์ปฌ๋ฌ์ฑ๋์ ๊ฐ๋ 244x244 ํฌ๊ธฐ์ ์ด๋ฏธ์ง๋ฏ๋ก (3, 224, 224) ๋ชจ์์ผ๋ก ๋ฐํ๋ฉ๋๋ค.
์ด ํ๋์ ์ด๋ฏธ์ง๋ฅผ ๋ฐฐ์น๋ก ๋ง๋ค์ด์ผ ํ๋ฏ๋ก, <code class="docutils literal notranslate"><span class="pre">unsqueeze_(0)</span></code> ์ ์ฌ์ฉํ์ฌ
ํ
์์ ์ฒซ๋ฒ์งธ ์ฐจ์์ ์ถ๊ฐํด์ค๋๋ค. ํ
์๋ ๊ฐ์ ๋ฐ์ดํฐ๋ฅผ ๊ฐ์ง๋ง ๋ชจ์(shape)์ด
(1, 3, 224, 224)์ด ๋์์ต๋๋ค.</p>
<p>์ผ๋ฐ์ ์ผ๋ก ์ด๋ฏธ์ง ๋ฐ์ดํฐ์ ์์
์ ํ์ง ์๋๋ผ๋, HTTP ์์ฒญ(request)์ผ๋ก ๋ฐ์
์
๋ ฅ์ PyTorch์์ ์ฌ์ฉํ ์ ์๋๋ก ํ
์๋ก ๋ณํํด์ผ ํฉ๋๋ค.</p>
</div>
<div class="section" id="inference">
<h3>์ถ๋ก (Inference)<a class="headerlink" href="#inference" title="Permalink to this headline">ยถ</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">):</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">y_hat</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">y_hat</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">return</span> <span class="n">prediction</span>
</pre></div>
</div>
<p>์ถ๋ก ์์ฒด๋ ๊ฐ์ฅ ๊ฐ๋จํ ๋ถ๋ถ์
๋๋ค: ์
๋ ฅ ํ
์๋ฅผ ๋ชจ๋ธ์ ์ ๋ฌํ๋ฉด, ๋ชจ๋ธ์ ์ด๋ฏธ์ง๊ฐ
ํน์ ํด๋์ค์ ์ํ ๊ฒ์ผ๋ก ์ถ์ ํ๋ ๊ฐ๋ฅ์ฑ(likelihood)์ ๋ํ๋ด๋ ๊ฐ๋ค์ ํ
์๋ฅผ
๋ฐํํฉ๋๋ค. <code class="docutils literal notranslate"><span class="pre">max()</span></code> ๋ฅผ ํธ์ถํ์ฌ ๊ฐ์ฅ ๊ฐ๋ฅ์ฑ์ด ๋์ ํด๋์ค๋ฅผ ์ฐพ๊ณ , ImageNet ํด๋์ค
๋ฒํธ(index)์ ํด๋นํ๋ ๊ฐ์ ๋ฐํํฉ๋๋ค. ๋ง์ง๋ง์ผ๋ก, <code class="docutils literal notranslate"><span class="pre">item()</span></code> ์ ํธ์ถํ์ฌ ํ
์์
ํฌํจ๋ ํด๋์ค ๋ฒํธ(index)๋ฅผ ์ถ์ถํ์ฌ ๋ฐํํฉ๋๋ค.</p>
</div>
<div class="section" id="post-processing">
<h3>ํ์ฒ๋ฆฌ(Post-Processing)<a class="headerlink" href="#post-processing" title="Permalink to this headline">ยถ</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">render_prediction</span><span class="p">(</span><span class="n">prediction_idx</span><span class="p">):</span>
<span class="n">stridx</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">prediction_idx</span><span class="p">)</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="s1">'Unknown'</span>
<span class="k">if</span> <span class="n">img_class_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">stridx</span> <span class="ow">in</span> <span class="n">img_class_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">img_class_map</span><span class="p">[</span><span class="n">stridx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="n">prediction_idx</span><span class="p">,</span> <span class="n">class_name</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">render_prediction()</span></code> ๋ฉ์๋(method)๋ ์์ธก๋ ํด๋์ค ๋ฒํธ(index)๋ฅผ ์ฌ๋์ด ์ฝ์ ์ ์๋
ํด๋์ค ๋ผ๋ฒจ(label)์ ๋งคํํฉ๋๋ค. ๋ชจ๋ธ์์ ์์ธก ๊ฐ์ ์ป์ ํ์๋ ํ์ฒ๋ฆฌ๋ฅผ ์ํํ์ฌ
์ฌ๋์ด ์๋นํ๊ฑฐ๋ ๋ค๋ฅธ ์ํํธ์จ์ด์์ ์ฌ์ฉํ ์ ์๋๋ก ์ค๋นํ๋ ๊ฒ์ด ์ผ๋ฐ์ ์
๋๋ค.</p>
</div>
</div>
<div class="section" id="flask-app">
<h2>์ ์ฒด Flask App ์คํ<a class="headerlink" href="#flask-app" title="Permalink to this headline">ยถ</a></h2>
<p>์๋ ๋ด์ฉ์ <code class="docutils literal notranslate"><span class="pre">app.py</span></code> ํ์ผ์ ๋ถ์ฌ๋ฃ์ต๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">io</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
<span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</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="c1"># ImageNet์ 1000๊ฐ ํด๋์ค๋ฅผ ํ์ต</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> <span class="c1"># autograd ๋๊ธฐ</span>
<span class="n">img_class_map</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">mapping_file_path</span> <span class="o">=</span> <span class="s1">'index_to_name.json'</span> <span class="c1"># ์ฌ๋์ด ์ฝ์ ์ ์๋ ImageNet ํด๋์ค ์ด๋ฆ</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">mapping_file_path</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span> <span class="p">(</span><span class="n">mapping_file_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">img_class_map</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="c1"># Transform input into the form our model expects</span>
<span class="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">infile</span><span class="p">):</span>
<span class="n">input_transforms</span> <span class="o">=</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">255</span><span class="p">),</span> <span class="c1"># ์ด๋ฏธ์ง ์ค๋น๋ฅผ ์ํด ์ฌ๋ฌ TorchVision transforms ์ฌ์ฉ</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="c1"># ImageNet ๋ชจ๋ธ ์
๋ ฅ์ ๋ํ ํ์ค ์ ๊ทํ</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">my_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span><span class="n">input_transforms</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">infile</span><span class="p">)</span> <span class="c1"># ์ด๋ฏธ์ง ํ์ผ ์ด๊ธฐ</span>
<span class="n">timg</span> <span class="o">=</span> <span class="n">my_transforms</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> <span class="c1"># PIL ์ด๋ฏธ์ง๋ฅผ ์ ์ ํ ๋ชจ์์ PyTorch ํ
์๋ก ๋ณํ</span>
<span class="n">timg</span><span class="o">.</span><span class="n">unsqueeze_</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># PyTorch ๋ชจ๋ธ์ ๋ฐฐ์น ์
๋ ฅ์ ์์ํ๋ฏ๋ก 1์ง๋ฆฌ ๋ฐฐ์น๋ฅผ ๋ง๋ฆ</span>
<span class="k">return</span> <span class="n">timg</span>
<span class="c1"># Get a prediction</span>
<span class="k">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">):</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span> <span class="c1"># ๋ชจ๋ ImageNet ํด๋์ค์ ๋ํ ๊ฐ๋ฅ์ฑ(likelihood) ์ป๊ธฐ</span>
<span class="n">_</span><span class="p">,</span> <span class="n">y_hat</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># ๊ฐ์ฅ ๊ฐ๋ฅ์ฑ ๋์ ํด๋์ค ์ถ์ถ</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">y_hat</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="c1"># PyTorch ํ
์์์ int ๊ฐ ์ถ์ถ</span>
<span class="k">return</span> <span class="n">prediction</span>
<span class="c1"># Make the prediction human-readable</span>
<span class="k">def</span> <span class="nf">render_prediction</span><span class="p">(</span><span class="n">prediction_idx</span><span class="p">):</span>
<span class="n">stridx</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">prediction_idx</span><span class="p">)</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="s1">'Unknown'</span>
<span class="k">if</span> <span class="n">img_class_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">stridx</span> <span class="ow">in</span> <span class="n">img_class_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">img_class_map</span><span class="p">[</span><span class="n">stridx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="n">prediction_idx</span><span class="p">,</span> <span class="n">class_name</span>
<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">'/'</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">'GET'</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">root</span><span class="p">():</span>
<span class="k">return</span> <span class="n">jsonify</span><span class="p">({</span><span class="s1">'msg'</span> <span class="p">:</span> <span class="s1">'Try POSTing to the /predict endpoint with an RGB image attachment'</span><span class="p">})</span>
<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">'/predict'</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">'POST'</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">():</span>
<span class="k">if</span> <span class="n">request</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'POST'</span><span class="p">:</span>
<span class="n">file</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">files</span><span class="p">[</span><span class="s1">'file'</span><span class="p">]</span>
<span class="k">if</span> <span class="n">file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">input_tensor</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
<span class="n">prediction_idx</span> <span class="o">=</span> <span class="n">get_prediction</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span>
<span class="n">class_id</span><span class="p">,</span> <span class="n">class_name</span> <span class="o">=</span> <span class="n">render_prediction</span><span class="p">(</span><span class="n">prediction_idx</span><span class="p">)</span>
<span class="k">return</span> <span class="n">jsonify</span><span class="p">({</span><span class="s1">'class_id'</span><span class="p">:</span> <span class="n">class_id</span><span class="p">,</span> <span class="s1">'class_name'</span><span class="p">:</span> <span class="n">class_name</span><span class="p">})</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</pre></div>
</div>
<p>๋ค์ ๋ช
๋ น์ด๋ฅผ ์คํํ์ฌ ์๋ฒ๋ฅผ ์์ํฉ๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">FLASK_APP</span><span class="o">=</span><span class="n">app</span><span class="o">.</span><span class="n">py</span> <span class="n">flask</span> <span class="n">run</span>
</pre></div>
</div>
<p>๊ธฐ๋ณธ์ ์ผ๋ก 5000๋ฒ ํฌํธ์์ ์์ ๋๊ธฐ(listen)ํฉ๋๋ค. ์๋ฒ๋ฅผ ์คํํ๊ณ ๋๋ฉด,
๋ค๋ฅธ ํฐ๋ฏธ๋ ์ฐฝ์ ์ด์ด์ ์ถ๋ก ์๋ฒ(inference server)๋ฅผ ํ
์คํธํด๋ณด์ธ์:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">curl</span> <span class="o">-</span><span class="n">X</span> <span class="n">POST</span> <span class="o">-</span><span class="n">H</span> <span class="s2">"Content-Type: multipart/form-data"</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">localhost</span><span class="p">:</span><span class="mi">5000</span><span class="o">/</span><span class="n">predict</span> <span class="o">-</span><span class="n">F</span> <span class="s2">"file=@kitten.jpg"</span>
</pre></div>
</div>
<p>๋ชจ๋ ๊ฒ๋ค์ด ์ ๋๋ก ์ค์ ๋์๋ค๋ฉด, ์๋์ ๋น์ทํ ์๋ต(response)์ ๋ฐ์ ๊ฒ์
๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s2">"class_id"</span><span class="p">:</span><span class="mi">285</span><span class="p">,</span><span class="s2">"class_name"</span><span class="p">:</span><span class="s2">"Egyptian_cat"</span><span class="p">}</span>
</pre></div>
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<li>์ค์น ๋ฐฉ๋ฒ๊ณผ ๋ ๋ง์ ๋ฌธ์, ํํ ๋ฆฌ์ผ์ ๋ณด์๋ ค๋ฉด <a class="reference external" href="https://pytorch.org">pytorch.org</a></li>
<li><a class="reference external" href="https://flask.palletsprojects.com/en/1.1.x/">Flask ์ฌ์ดํธ</a> ์๋ ๊ฐ๋จํ Flask ์๋น์ค๋ฅผ ์ค์ ์ ๋ํ ์์ธํ ๋ด์ฉ์ด ํฌํจ๋ <a href="#id10"><span class="problematic" id="id11">`๋น ๋ฅธ ์์ ๊ฐ์ด๋(Quickstart guide)`_</span></a> ๊ฐ ์์ต๋๋ค.</li>
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