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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../recipes/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>
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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
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<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>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">PyTorch ์๊ฐ - YouTube ์๋ฆฌ์ฆ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">PyTorch ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Pytorch Tensor ์๊ฐ</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" role="heading"><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="tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="torchvision_tutorial.html">TorchVision Object Detection Finetuning Tutorial</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 ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li>
</ul>
<p class="caption" role="heading"><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="speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="text_to_speech_with_torchaudio.html">Text-to-speech with Tacotron2</a></li>
<li class="toctree-l1"><a class="reference internal" href="forced_alignment_with_torchaudio_tutorial.html">wav2vec2์ ์ด์ฉํ ๊ฐ์ ์ ๋ ฌ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/bettertransformer_tutorial.html">Fast Transformer Inference with Better Transformer</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="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"><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> ์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/torchtext_custom_dataset_tutorial.html">Preprocess custom text dataset using Torchtext</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ฐฑ์๋</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="reinforcement_ppo.html">Reinforcement Learning (PPO) with TorchRL Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="mario_rl_tutorial.html">๋ง๋ฆฌ์ค ๊ฒ์ RL ์์ด์ ํธ๋ก ํ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/pendulum.html">Pendulum: Writing your environment and transforms with TorchRL</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">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>
<li class="toctree-l1"><a class="reference internal" href="realtime_rpi.html">Raspberry Pi 4 ์์ ์ค์๊ฐ ์ถ๋ก (Inference) (30fps!)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ๋กํ์ผ๋ง</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_intro_tutorial.html">Introduction to Holistic Trace Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_trace_diff_tutorial.html">Trace Diff using Holistic Trace Analysis</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="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="fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="jacobians_hessians.html">Jacobians, Hessians, hvp, vhp, and more: composing function transforms</a></li>
<li class="toctree-l1"><a class="reference internal" href="ensembling.html">๋ชจ๋ธ ์์๋ธ</a></li>
<li class="toctree-l1"><a class="reference internal" href="per_sample_grads.html">Per-sample-gradients</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" role="heading"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
<li class="toctree-l1"><a class="reference internal" href="../advanced/privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_profiler_tutorial.html">ํ
์๋ณด๋๋ฅผ ์ด์ฉํ ํ์ดํ ์น ํ๋กํ์ผ๋ฌ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Ray Tune์ ์ฌ์ฉํ ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="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="dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="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">(๋ฒ ํ) PyTorch์์ Eager Mode๋ฅผ ์ด์ฉํ ์ ์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchserve_with_ipex_2.html">Grokking PyTorch Intel CPU performance from first principles (Part 2)</a></li>
<li class="toctree-l1"><a class="reference internal" href="nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li>
<li class="toctree-l1"><a class="reference internal" href="ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html">(Beta) Scaled Dot Product Attention (SDPA)๋ก ๊ณ ์ฑ๋ฅ ํธ๋์คํฌ๋จธ(Transformers) ๊ตฌํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> ๊ณผ ํจ๊ป SDPA ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#sdpa-atteition-bias">SDPA๋ฅผ <code class="docutils literal notranslate"><span class="pre">atteition.bias</span></code> ํ์ ํด๋์ค์ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#id8">๊ฒฐ๋ก </a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/knowledge_distillation_tutorial.html">Knowledge Distillation Tutorial</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../distributed/home.html">Distributed and Parallel Training Tutorials</a></li>
<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="../beginner/ddp_series_intro.html">Distributed Data Parallel in PyTorch - Video Tutorials</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_parallel_tutorial.html">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="FSDP_tutorial.html">Getting Started with Fully Sharded Data Parallel(FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="FSDP_adavnced_tutorial.html">Advanced Model Training with Fully Sharded Data Parallel (FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="TP_tutorial.html">Large Scale Transformer model training with Tensor Parallel (TP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="process_group_cpp_extension_tutorial.html">Cpp ํ์ฅ์ ์ฌ์ฉํ ํ๋ก์ธ์ค ๊ทธ๋ฃน ๋ฐฑ์๋ ์ฌ์ฉ์ ์ ์</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">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</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" role="heading"><span class="caption-text">Edge with ExecuTorch</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html">Exporting to ExecuTorch Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href=" https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html">Running an ExecuTorch Model in C++ Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html">Using the ExecuTorch SDK to Profile a Model</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-ios.html">Building an ExecuTorch iOS Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-android.html">Building an ExecuTorch Android Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html">Lowering a Model as a Delegate</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">์ถ์ฒ ์์คํ
</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="torchrec_tutorial.html">TorchRec ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/sharding.html">Exploring TorchRec sharding</a></li>
</ul>
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<div class="sphx-glr-example-title section" id="flask-python-pytorch-rest-api">
<span id="sphx-glr-intermediate-flask-rest-api-tutorial-py"></span><h1>Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ<a class="headerlink" href="#flask-python-pytorch-rest-api" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<dl class="simple">
<dt><strong>Author</strong>: <a class="reference external" href="https://avi.im">Avinash Sajjanshetty</a></dt><dd><p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="http://github.com/9bow">๋ฐ์ ํ</a></p>
</dd>
</dl>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ Flask๋ฅผ ์ฌ์ฉํ์ฌ PyTorch ๋ชจ๋ธ์ ๋ฐฐํฌํ๊ณ ๋ชจ๋ธ ์ถ๋ก (inference)์
ํ ์ ์๋ REST API๋ฅผ ๋ง๋ค์ด๋ณด๊ฒ ์ต๋๋ค. ๋ฏธ๋ฆฌ ํ๋ จ๋ DenseNet 121 ๋ชจ๋ธ์ ๋ฐฐํฌํ์ฌ
์ด๋ฏธ์ง๋ฅผ ์ธ์ํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="admonition tip">
<p class="admonition-title">ํ</p>
<p>์ฌ๊ธฐ์ ์ฌ์ฉํ ๋ชจ๋ ์ฝ๋๋ MIT ๋ผ์ด์ ์ค๋ก ๋ฐฐํฌ๋๋ฉฐ,
<a class="reference external" href="https://github.com/avinassh/pytorch-flask-api">GitHub</a> ์์ ํ์ธํ์ค ์ ์์ต๋๋ค.</p>
</div>
<p>์ด๊ฒ์ PyTorch ๋ชจ๋ธ์ ์์ฉ(production)์ผ๋ก ๋ฐฐํฌํ๋ ํํ ๋ฆฌ์ผ ์๋ฆฌ์ฆ์ ์ฒซ๋ฒ์งธ
ํธ์
๋๋ค. Flask๋ฅผ ์ฌ๊ธฐ์ ์๊ฐ๋ ๊ฒ์ฒ๋ผ ์ฌ์ฉํ๋ ๊ฒ์ด PyTorch ๋ชจ๋ธ์ ์ ๊ณตํ๋
๊ฐ์ฅ ์ฌ์ด ๋ฐฉ๋ฒ์ด์ง๋ง, ๊ณ ์ฑ๋ฅ์ ์๊ตฌํ๋ ๋์๋ ์ ํฉํ์ง ์์ต๋๋ค. ๊ทธ์ ๋ํด์๋:</p>
<blockquote>
<div><ul class="simple">
<li><p>TorchScript์ ์ด๋ฏธ ์ต์ํ๋ค๋ฉด, ๋ฐ๋ก <a class="reference external" href="https://tutorials.pytorch.kr/advanced/cpp_export.html">Loading a TorchScript Model in C++</a> ๋ฌธ์๋ถํฐ ์ฝ์ด๋ณด์ธ์.</p></li>
<li><p>TorchScript๊ฐ ๋ฌด์์ธ์ง ์์๋ณด๋ ๊ฒ์ด ํ์ํ๋ค๋ฉด <a class="reference external" href="https://tutorials.pytorch.kr/beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a> ๋ถํฐ ์ฝ์ด๋ณด์๋ ๊ฒ์ ์ถ์ฒํฉ๋๋ค.</p></li>
</ul>
</div></blockquote>
<div class="section" id="api">
<h2>API ์ ์<a class="headerlink" href="#api" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>๋จผ์ API ์๋ํฌ์ธํธ(endpoint)์ ์์ฒญ(request)์ ์๋ต(response)์ ์ ์ํ๋ ๊ฒ๋ถํฐ
์์ํด๋ณด๊ฒ ์ต๋๋ค. ์๋ก ๋ง๋ค API ์๋ํฌ์ธํธ๋ ์ด๋ฏธ์ง๊ฐ ํฌํจ๋ <code class="docutils literal notranslate"><span class="pre">file</span></code> ๋งค๊ฐ๋ณ์๋ฅผ
HTTP POST๋ก <code class="docutils literal notranslate"><span class="pre">/predict</span></code> ์ ์์ฒญํฉ๋๋ค. ์๋ต์ JSON ํํ์ด๋ฉฐ ๋ค์๊ณผ ๊ฐ์ ์์ธก ๊ฒฐ๊ณผ๋ฅผ
ํฌํจํฉ๋๋ค:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">"class_id"</span>:<span class="w"> </span><span class="s2">"n02124075"</span>,<span class="w"> </span><span class="s2">"class_name"</span>:<span class="w"> </span><span class="s2">"Egyptian_cat"</span><span class="o">}</span>
</pre></div>
</div>
</div>
<div class="section" id="dependencies">
<h2>์์กด์ฑ(Dependencies)<a class="headerlink" href="#dependencies" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์๋ ๋ช
๋ น์ด๋ฅผ ์คํํ์ฌ ํ์ํ ํจํค์ง๋ค์ ์ค์นํฉ๋๋ค:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="nv">Flask</span><span class="o">==</span><span class="m">2</span>.0.1<span class="w"> </span><span class="nv">torchvision</span><span class="o">==</span><span class="m">0</span>.10.0
</pre></div>
</div>
</div>
<div class="section" id="id2">
<h2>๊ฐ๋จํ ์น ์๋ฒ<a class="headerlink" href="#id2" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>Flask์ ๋ฌธ์๋ฅผ ์ฐธ๊ณ ํ์ฌ ์๋์ ๊ฐ์ ์ฝ๋๋ก ๊ฐ๋จํ ์น ์๋ฒ๋ฅผ ๊ตฌ์ฑํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</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="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="k">def</span> <span class="nf">hello</span><span class="p">():</span>
<span class="k">return</span> <span class="s1">'Hello World!'</span>
</pre></div>
</div>
<p>๋ํ, ImageNet ๋ถ๋ฅ ID์ ์ด๋ฆ์ ํฌํจํ๋ JSON์ ํ์ ํ๋๋ก ์๋ต ํ์์ ๋ณ๊ฒฝํ๊ฒ ์ต๋๋ค.
์ด์ <code class="docutils literal notranslate"><span class="pre">app.py</span></code> ๋ ์๋์ ๊ฐ์ด ๋ณ๊ฒฝ๋์์ต๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="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="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">return</span> <span class="n">jsonify</span><span class="p">({</span><span class="s1">'class_id'</span><span class="p">:</span> <span class="s1">'IMAGE_NET_XXX'</span><span class="p">,</span> <span class="s1">'class_name'</span><span class="p">:</span> <span class="s1">'Cat'</span><span class="p">})</span>
</pre></div>
</div>
</div>
<div class="section" id="inference">
<h2>์ถ๋ก (Inference)<a class="headerlink" href="#inference" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>๋ค์ ์น์
์์๋ ์ถ๋ก ์ฝ๋ ์์ฑ์ ์ง์คํ๊ฒ ์ต๋๋ค. ๋จผ์ ์ด๋ฏธ์ง๋ฅผ DenseNet์ ๊ณต๊ธ(feed)ํ ์
์๋๋ก ์ค๋นํ๋ ๋ฐฉ๋ฒ์ ์ดํด๋ณธ ๋ค, ๋ชจ๋ธ๋ก๋ถํฐ ์์ธก ๊ฒฐ๊ณผ๋ฅผ ์ป๋ ๋ฐฉ๋ฒ์ ์ดํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="section" id="id3">
<h3>์ด๋ฏธ์ง ์ค๋นํ๊ธฐ<a class="headerlink" href="#id3" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>DenseNet ๋ชจ๋ธ์ 224 x 224์ 3์ฑ๋ RGB ์ด๋ฏธ์ง๋ฅผ ํ์๋ก ํฉ๋๋ค.
๋ํ ์ด๋ฏธ์ง ํ
์๋ฅผ ํ๊ท ๋ฐ ํ์คํธ์ฐจ ๊ฐ์ผ๋ก ์ ๊ทํํฉ๋๋ค. ์์ธํ ๋ด์ฉ์
<a class="reference external" href="https://pytorch.org/vision/stable/models.html">์ฌ๊ธฐ</a> ๋ฅผ ์ฐธ๊ณ ํ์ธ์.</p>
<p><code class="docutils literal notranslate"><span class="pre">torchvision</span></code> ๋ผ์ด๋ธ๋ฌ๋ฆฌ์ <code class="docutils literal notranslate"><span class="pre">transforms</span></code> ๋ฅผ ์ฌ์ฉํ์ฌ ๋ณํ ํ์ดํ๋ผ์ธ
(transform pipeline)์ ๊ตฌ์ถํฉ๋๋ค. Transforms์ ๊ด๋ จํ ๋ ์์ธํ ๋ด์ฉ์
<a class="reference external" href="https://pytorch.org/vision/stable/transforms.html">์ฌ๊ธฐ</a> ์์
์ฝ์ด๋ณผ ์ ์์ต๋๋ค.</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">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="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">image_bytes</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">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="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">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">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">(</span><span class="n">image_bytes</span><span class="p">))</span>
<span class="k">return</span> <span class="n">my_transforms</span><span class="p">(</span><span class="n">image</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>์ ๋ฉ์๋๋ ์ด๋ฏธ์ง๋ฅผ byte ๋จ์๋ก ์ฝ์ ํ, ์ผ๋ จ์ ๋ณํ์ ์ ์ฉํ๊ณ Tensor๋ฅผ
๋ฐํํฉ๋๋ค. ์ ๋ฉ์๋๋ฅผ ํ
์คํธํ๊ธฐ ์ํด์๋ ์ด๋ฏธ์ง๋ฅผ byte ๋ชจ๋๋ก ์ฝ์ ํ
Tensor๋ฅผ ๋ฐํํ๋์ง ํ์ธํ๋ฉด ๋ฉ๋๋ค. (๋จผ์ <cite>../_static/img/sample_file.jpeg</cite> ์
์ปดํจํฐ ์์ ์ค์ ๊ฒฝ๋ก๋ก ๋ฐ๊ฟ์ผ ํฉ๋๋ค.)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../_static/img/sample_file.jpeg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">image_bytes</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">image_bytes</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="prediction">
<h3>์์ธก(Prediction)<a class="headerlink" href="#prediction" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>๋ฏธ๋ฆฌ ํ์ต๋ DenseNet 121 ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ์ด๋ฏธ์ง ๋ถ๋ฅ(class)๋ฅผ ์์ธกํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">torchvision</span></code> ๋ผ์ด๋ธ๋ฌ๋ฆฌ์ ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ๋ชจ๋ธ์ ์ฝ์ด์ค๊ณ ์ถ๋ก ์ ํฉ๋๋ค.
์ด ์์ ์์๋ ๋ฏธ๋ฆฌ ํ์ต๋ ๋ชจ๋ธ์ ์ฌ์ฉํ์ง๋ง, ์ง์ ๋ง๋ ๋ชจ๋ธ์ ๋ํด์๋
์ด์ ๋์ผํ ๋ฐฉ๋ฒ์ ์ฌ์ฉํ ์ ์์ต๋๋ค. ๋ชจ๋ธ์ ์ฝ์ด์ค๋ ๊ฒ์ ์ด
<a class="reference internal" href="../beginner/saving_loading_models.html"><span class="doc">ํํ ๋ฆฌ์ผ</span></a> ์ ์ฐธ๊ณ ํ์ธ์.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">models</span>
<span class="c1"># ์ด๋ฏธ ํ์ต๋ ๊ฐ์ค์น๋ฅผ ์ฌ์ฉํ๊ธฐ ์ํด `weights` ์ `IMAGENET1K_V1` ๊ฐ์ ์ ๋ฌํฉ๋๋ค:</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">weights</span><span class="o">=</span><span class="s1">'IMAGENET1K_V1'</span><span class="p">)</span>
<span class="c1"># ๋ชจ๋ธ์ ์ถ๋ก ์๋ง ์ฌ์ฉํ ๊ฒ์ด๋ฏ๋ก, `eval` ๋ชจ๋๋ก ๋ณ๊ฒฝํฉ๋๋ค:</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="p">):</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">image_bytes</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">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="k">return</span> <span class="n">y_hat</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">y_hat</span></code> Tensor๋ ์์ธก๋ ๋ถ๋ฅ ID์ ์ธ๋ฑ์ค๋ฅผ ํฌํจํฉ๋๋ค. ํ์ง๋ง ์ฌ๋์ด ์ฝ์ ์
์๋ ๋ถ๋ฅ๋ช
์ด ์์ด์ผ ํ๊ธฐ ๋๋ฌธ์, ์ด๋ฅผ ์ํด ์ด๋ฆ๊ณผ ๋ถ๋ฅ ID๋ฅผ ๋งคํํ๋ ๊ฒ์ด ํ์ํฉ๋๋ค.
<a class="reference external" href="https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json">์ด ํ์ผ</a>
์ ๋ค์ด๋ก๋ ๋ฐ์ <code class="docutils literal notranslate"><span class="pre">imagenet_class_index.json</span></code> ์ด๋ผ๋ ์ด๋ฆ์ผ๋ก ์ ์ฅ ํ, ์ ์ฅํ ๊ณณ์
์์น๋ฅผ ๊ธฐ์ตํด๋์ธ์. (๋๋, ์ด ํํ ๋ฆฌ์ผ๊ณผ ๋๊ฐ์ด ์งํํ๋ ๊ฒฝ์ฐ์๋ <cite>tutorials/_static</cite>
์ ์ ์ฅํ์ธ์.) ์ด ํ์ผ์ ImageNet ๋ถ๋ฅ ID์ ImageNet ๋ถ๋ฅ๋ช
์ ์์ ํฌํจํ๊ณ ์์ต๋๋ค.
์ด์ ์ด JSON ํ์ผ์ ๋ถ๋ฌ์ ์์ธก ๊ฒฐ๊ณผ์ ์ธ๋ฑ์ค์ ํด๋นํ๋ ๋ถ๋ฅ๋ช
์ ๊ฐ์ ธ์ค๊ฒ ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">json</span>
<span class="n">imagenet_class_index</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="nb">open</span><span class="p">(</span><span class="s1">'../_static/imagenet_class_index.json'</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="p">):</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">image_bytes</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">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">predicted_idx</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</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">imagenet_class_index</span><span class="p">[</span><span class="n">predicted_idx</span><span class="p">]</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">imagenet_class_index</span></code> ์ฌ์ (dictionary)์ ์ฌ์ฉํ๊ธฐ ์ ์,
<code class="docutils literal notranslate"><span class="pre">imagenet_class_index</span></code> ์ ํค ๊ฐ์ด ๋ฌธ์์ด์ด๋ฏ๋ก Tensor์ ๊ฐ๋
๋ฌธ์์ด๋ก ๋ณํํด์ผ ํฉ๋๋ค.
์ ๋ฉ์๋๋ฅผ ํ
์คํธํด๋ณด๊ฒ ์ต๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../_static/img/sample_file.jpeg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">image_bytes</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">image_bytes</span><span class="p">))</span>
</pre></div>
</div>
<p>๋ค์๊ณผ ๊ฐ์ ์๋ต์ ๋ฐ๊ฒ ๋ ๊ฒ์
๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s1">'n02124075'</span><span class="p">,</span> <span class="s1">'Egyptian_cat'</span><span class="p">]</span>
</pre></div>
</div>
<p>๋ฐฐ์ด์ ์ฒซ๋ฒ์งธ ํญ๋ชฉ์ ImageNet ๋ถ๋ฅ ID์ด๊ณ , ๋๋ฒ์งธ ํญ๋ชฉ์ ์ฌ๋์ด ์ฝ์ ์ ์๋
์ด๋ฆ์
๋๋ค.</p>
<dl>
<dt>๋ชจ๋ธ์ API ์๋ฒ์ ํตํฉํ๊ธฐ</dt><dd><p>๋ง์ง๋ง์ผ๋ก ์์์ ๋ง๋ Flask API ์๋ฒ์ ๋ชจ๋ธ์ ์ถ๊ฐํ๊ฒ ์ต๋๋ค.
API ์๋ฒ๋ ์ด๋ฏธ์ง ํ์ผ์ ๋ฐ๋ ๊ฒ์ ๊ฐ์ ํ๊ณ ์์ผ๋ฏ๋ก, ์์ฒญ์ผ๋ก๋ถํฐ ํ์ผ์ ์ฝ๋๋ก
<code class="docutils literal notranslate"><span class="pre">predict</span></code> ๋ฉ์๋๋ฅผ ์์ ํด์ผ ํฉ๋๋ค:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">request</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="c1"># we will get the file from the request</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="c1"># convert that to bytes</span>
<span class="n">img_bytes</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">read</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">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">img_bytes</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>
</pre></div>
</div>
</dd>
</dl>
<blockquote>
<div><div class="highlight-python 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">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">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">imagenet_class_index</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="nb">open</span><span class="p">(</span><span class="s1">'<PATH/TO/.json/FILE>/imagenet_class_index.json'</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">weights</span><span class="o">=</span><span class="s1">'IMAGENET1K_V1'</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="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">image_bytes</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">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="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">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">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">(</span><span class="n">image_bytes</span><span class="p">))</span>
<span class="k">return</span> <span class="n">my_transforms</span><span class="p">(</span><span class="n">image</span><span class="p">)</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">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="p">):</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">image_bytes</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">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">predicted_idx</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</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">imagenet_class_index</span><span class="p">[</span><span class="n">predicted_idx</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="n">img_bytes</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">read</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">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">img_bytes</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>
</div></blockquote>
<blockquote>
<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nv">FLASK_ENV</span><span class="o">=</span>development<span class="w"> </span><span class="nv">FLASK_APP</span><span class="o">=</span>app.py<span class="w"> </span>flask<span class="w"> </span>run
</pre></div>
</div>
</div></blockquote>
<blockquote>
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">requests</span>
<span class="n">resp</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">post</span><span class="p">(</span><span class="s2">"http://localhost:5000/predict"</span><span class="p">,</span>
<span class="n">files</span><span class="o">=</span><span class="p">{</span><span class="s2">"file"</span><span class="p">:</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'<PATH/TO/.jpg/FILE>/cat.jpg'</span><span class="p">,</span><span class="s1">'rb'</span><span class="p">)})</span>
</pre></div>
</div>
</div></blockquote>
<p><cite>resp.json()</cite> ์ ํธ์ถํ๋ฉด ๋ค์๊ณผ ๊ฐ์ ๊ฒฐ๊ณผ๋ฅผ ์ถ๋ ฅํฉ๋๋ค:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">"class_id"</span>:<span class="w"> </span><span class="s2">"n02124075"</span>,<span class="w"> </span><span class="s2">"class_name"</span>:<span class="w"> </span><span class="s2">"Egyptian_cat"</span><span class="o">}</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id7">
<h2>๋ค์ ๋จ๊ณ<a class="headerlink" href="#id7" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ง๊ธ๊น์ง ๋ง๋ ์๋ฒ๋ ๋งค์ฐ ๊ฐ๋จํ์ฌ ์์ฉ ํ๋ก๊ทธ๋จ(production application)์ผ๋ก์จ
๊ฐ์ถฐ์ผํ ๊ฒ๋ค์ ๋ชจ๋ ๊ฐ์ถ์ง ๋ชปํ์ต๋๋ค. ๋ฐ๋ผ์, ๋ค์๊ณผ ๊ฐ์ด ๊ฐ์ ํด๋ณผ ์ ์์ต๋๋ค:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">/predict</span></code> ์๋ํฌ์ธํธ๋ ์์ฒญ ์์ ๋ฐ๋์ ์ด๋ฏธ์ง ํ์ผ์ด ์ ๋ฌ๋๋ ๊ฒ์ ๊ฐ์ ํ๊ณ
์์ต๋๋ค. ํ์ง๋ง ๋ชจ๋ ์์ฒญ์ด ๊ทธ๋ ์ง๋ ์์ต๋๋ค. ์ฌ์ฉ์๋ ๋ค๋ฅธ ๋งค๊ฐ๋ณ์๋ก ์ด๋ฏธ์ง๋ฅผ
๋ณด๋ด๊ฑฐ๋, ์ด๋ฏธ์ง๋ฅผ ์์ ๋ณด๋ด์ง ์์์๋ ์์ต๋๋ค.</p></li>
<li><p>์ฌ์ฉ์๊ฐ ์ด๋ฏธ์ง๊ฐ ์๋ ์ ํ์ ํ์ผ์ ๋ณด๋ผ์๋ ์์ต๋๋ค. ์ฌ๊ธฐ์๋ ์๋ฌ๋ฅผ ์ฒ๋ฆฌํ์ง
์๊ณ ์์ผ๋ฏ๋ก, ์ด๋ฌํ ๊ฒฝ์ฐ์ ์๋ฒ๋ ๋ค์ด(break)๋ฉ๋๋ค. ์์ธ ์ ๋ฌ(exception throe)์
์ํ ๋ช
์์ ์ธ ์๋ฌ ํธ๋ค๋ง ๊ฒฝ๋ก๋ฅผ ์ถ๊ฐํ๋ฉด ์๋ชป๋ ์
๋ ฅ์ ๋ ์ ์ฒ๋ฆฌํ ์ ์์ต๋๋ค.</p></li>
<li><p>๋ชจ๋ธ์ ๋ง์ ์ข
๋ฅ์ ์ด๋ฏธ์ง ๋ถ๋ฅ๋ฅผ ์ธ์ํ ์ ์์ง๋ง, ๋ชจ๋ ์ด๋ฏธ์ง๋ฅผ ์ธ์ํ ์ ์๋
๊ฒ์ ์๋๋๋ค. ๋ชจ๋ธ์ด ์ด๋ฏธ์ง์์ ์๋ฌด๊ฒ๋ ์ธ์ํ์ง ๋ชปํ๋ ๊ฒฝ์ฐ๋ฅผ ์ฒ๋ฆฌํ๋๋ก
๊ฐ์ ํฉ๋๋ค.</p></li>
<li><p>์์์๋ Flask ์๋ฒ๋ฅผ ๊ฐ๋ฐ ๋ชจ๋์์ ์คํํ์์ง๋ง, ์ด๋ ์์ฉ์ผ๋ก ๋ฐฐํฌํ๊ธฐ์๋
์ ๋นํ์ง ์์ต๋๋ค. Flask ์๋ฒ๋ฅผ ์์ฉ์ผ๋ก ๋ฐฐํฌํ๋ ๊ฒ์
<a class="reference external" href="https://flask.palletsprojects.com/en/1.1.x/tutorial/deploy/">์ด ํํ ๋ฆฌ์ผ</a>
์ ์ฐธ๊ณ ํด๋ณด์ธ์.</p></li>
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