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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>
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๋ ์ํผ ๋ณด๊ธฐ</a></li>
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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>
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<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>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
<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>
<p class="caption" role="heading"><span class="caption-text">Multimodality</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/flava_finetuning_tutorial.html">TorchMultimodal ํํ ๋ฆฌ์ผ: FLAVA ๋ฏธ์ธ์กฐ์ </a></li>
</ul>
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<li>๊ณต๊ฐ ๋ณํ ๋คํธ์ํฌ(Spatial Transformer Networks) ํํ ๋ฆฌ์ผ</li>
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<p class="admonition-title">์ฐธ๊ณ </p>
<p>Click <a class="reference internal" href="#sphx-glr-download-intermediate-spatial-transformer-tutorial-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="spatial-transformer-networks">
<span id="sphx-glr-intermediate-spatial-transformer-tutorial-py"></span><h1>๊ณต๊ฐ ๋ณํ ๋คํธ์ํฌ(Spatial Transformer Networks) ํํ ๋ฆฌ์ผ<a class="headerlink" href="#spatial-transformer-networks" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<dl class="simple">
<dt><strong>Author</strong>: <a class="reference external" href="https://github.com/GHamrouni">Ghassen HAMROUNI</a></dt><dd><p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/adonisues">ํฉ์ฑ์</a> , <a class="reference external" href="https://github.com/SSinyu">์ ์ ์ </a></p>
</dd>
</dl>
<div class="figure align-default">
<img alt="../_images/FSeq.png" src="../_images/FSeq.png" />
</div>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ ๊ณต๊ฐ ๋ณํ ๋คํธ์ํฌ(spatial transformer networks, ์ดํ STN)์ด๋ผ
๋ถ๋ฆฌ๋ ๋น์ฃผ์ผ ์ดํ
์
๋ฉ์ปค๋์ฆ์ ์ด์ฉํด ์ ๊ฒฝ๋ง์ ์ฆ๊ฐ(augment)์ํค๋ ๋ฐฉ๋ฒ์ ๋ํด
ํ์ตํฉ๋๋ค. ์ด ๋ฐฉ๋ฒ์ ๋ํ ์์ธํ ๋ด์ฉ์ <a class="reference external" href="https://arxiv.org/abs/1506.02025">DeepMind paper</a> ์์
ํ์ธํ ์ ์์ต๋๋ค.
STN์ ์ด๋ ํ ๊ณต๊ฐ์ ๋ณํ(spatial transformation)์๋ ์ ์ฉํ ์ ์๋ ๋ฏธ๋ถ ๊ฐ๋ฅํ
์ดํ
์
์ ์ผ๋ฐํ์
๋๋ค. ๋ฐ๋ผ์ STN์ ์ ๊ฒฝ๋ง์ ๊ธฐํํ์ ๋ถ๋ณ์ฑ(geometric invariance)์
๊ฐํํ๊ธฐ ์ํด ์
๋ ฅ ์ด๋ฏธ์ง๋ฅผ ๋์์ผ๋ก ์ด๋ ํ ๊ณต๊ฐ์ ๋ณํ์ ์ํํด์ผ ํ๋์ง ํ์ตํ๋๋ก
ํฉ๋๋ค.
์๋ฅผ ๋ค์ด ์ด๋ฏธ์ง์ ๊ด์ฌ ์์ญ์ ์๋ผ๋ด๊ฑฐ๋, ํฌ๊ธฐ๋ฅผ ์กฐ์ ํ๊ฑฐ๋, ๋ฐฉํฅ(orientation)์
์์ ํ ์ ์์ต๋๋ค. CNN์ ์ด๋ฌํ ํ์ , ํฌ๊ธฐ ์กฐ์ ๋ฑ์ ์ผ๋ฐ์ ์ธ ์ํ(affine) ๋ณํ๋
์
๋ ฅ์ ๋ํด ๊ฒฐ๊ณผ์ ๋ณ๋์ด ํฌ๊ธฐ ๋๋ฌธ์ (๋ฏผ๊ฐํ๊ธฐ ๋๋ฌธ์), STN์ ์ด๋ฅผ ๊ทน๋ณตํ๋๋ฐ ๋งค์ฐ
์ ์ฉํ ๋ฉ์ปค๋์ฆ์ด ๋ ์ ์์ต๋๋ค.
STN์ด ๊ฐ์ง ์ฅ์ ์ค ํ๋๋ ์์ฃผ ์์ ์์ ๋ง์ผ๋ก ๊ธฐ์กด์ ์ฌ์ฉํ๋ CNN์ ๊ฐ๋จํ๊ฒ ์ฐ๊ฒฐ ์ํฌ
์ ์๋ค๋ ๊ฒ์
๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ๋ผ์ด์ผ์ค: BSD</span>
<span class="c1"># ์ ์: Ghassen Hamrouni</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</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="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span> <span class="c1"># ๋ํํ ๋ชจ๋</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><contextlib.ExitStack object at 0x75247c4e7eb0>
</pre></div>
</div>
<div class="section" id="id3">
<h2>๋ฐ์ดํฐ ๋ถ๋ฌ์ค๊ธฐ<a class="headerlink" href="#id3" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ MNIST ๋ฐ์ดํฐ์
์ ์ด์ฉํด ์คํํฉ๋๋ค. ์คํ์๋ STN์ผ๋ก
์ฆ๊ฐ๋ ์ผ๋ฐ์ ์ธ CNN์ ์ฌ์ฉํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">six.moves</span> <span class="kn">import</span> <span class="n">urllib</span>
<span class="n">opener</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">build_opener</span><span class="p">()</span>
<span class="n">opener</span><span class="o">.</span><span class="n">addheaders</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'User-agent'</span><span class="p">,</span> <span class="s1">'Mozilla/5.0'</span><span class="p">)]</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">install_opener</span><span class="p">(</span><span class="n">opener</span><span class="p">)</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"</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>
<span class="c1"># ํ์ต์ฉ ๋ฐ์ดํฐ์
</span>
<span class="n">train_loader</span> <span class="o">=</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">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">transform</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">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">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))</span>
<span class="p">])),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</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">4</span><span class="p">)</span>
<span class="c1"># ํ
์คํธ์ฉ ๋ฐ์ดํฐ์
</span>
<span class="n">test_loader</span> <span class="o">=</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">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">transform</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">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">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))</span>
<span class="p">])),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</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">4</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="spatial-transformer-networks-stn">
<h2>Spatial Transformer Networks(STN) ๊ตฌ์ฑํ๊ธฐ<a class="headerlink" href="#spatial-transformer-networks-stn" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>STN์ ๋ค์์ ์ธ ๊ฐ์ง ์ฃผ์ ๊ตฌ์ฑ ์์๋ก ์์ฝ๋ฉ๋๋ค.</p>
<ul class="simple">
<li><p>์์น ๊ฒฐ์ ๋คํธ์ํฌ(localization network)๋ ๊ณต๊ฐ ๋ณํ ํ๋ผ๋ฏธํฐ๋ฅผ ์์ธก(regress)
ํ๋ ์ผ๋ฐ์ ์ธ CNN ์
๋๋ค. ๊ณต๊ฐ ๋ณํ์ ๋ฐ์ดํฐ ์
์ผ๋ก๋ถํฐ ๋ช
์์ ์ผ๋ก ํ์ต๋์ง ์๊ณ ,
์ ๊ฒฝ๋ง์ด ์ ์ฒด ์ ํ๋๋ฅผ ํฅ์ ์ํค๋๋ก ๊ณต๊ฐ ๋ณํ์ ์๋์ผ๋ก ํ์ตํฉ๋๋ค.</p></li>
<li><p>๊ทธ๋ฆฌ๋ ์์ฑ๊ธฐ(grid generator)๋ ์ถ๋ ฅ ์ด๋ฏธ์ง๋ก๋ถํฐ ๊ฐ ํฝ์
์ ๋์ํ๋ ์
๋ ฅ
์ด๋ฏธ์ง ๋ด ์ขํ ๊ทธ๋ฆฌ๋๋ฅผ ์์ฑํฉ๋๋ค.</p></li>
<li><p>์ํ๋ฌ(sampler)๋ ๊ณต๊ฐ ๋ณํ ํ๋ผ๋ฏธํฐ๋ฅผ ์
๋ ฅ ์ด๋ฏธ์ง์ ์ ์ฉํฉ๋๋ค.</p></li>
</ul>
<div class="figure align-default">
<img alt="../_images/stn-arch.png" src="../_images/stn-arch.png" />
</div>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p>affine_grid ๋ฐ grid_sample ๋ชจ๋์ด ํฌํจ๋ ์ต์ ๋ฒ์ ์ PyTorch๊ฐ ํ์ํฉ๋๋ค.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2_drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">320</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="c1"># ๊ณต๊ฐ ๋ณํ์ ์ํ ์์น ๊ฒฐ์ ๋คํธ์ํฌ (localization-network)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">localization</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">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># [3 * 2] ํฌ๊ธฐ์ ์ํ(affine) ํ๋ ฌ์ ๋ํด ์์ธก</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</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">Linear</span><span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</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="mi">32</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># ํญ๋ฑ ๋ณํ(identity transformation)์ผ๋ก ๊ฐ์ค์น/๋ฐ์ด์ด์ค ์ด๊ธฐํ</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</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="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">))</span>
<span class="c1"># STN์ forward ํจ์</span>
<span class="k">def</span> <span class="nf">stn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">xs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">localization</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="n">theta</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">affine_grid</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">grid_sample</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">grid</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># ์
๋ ฅ์ ๋ณํ</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># ์ผ๋ฐ์ ์ธ forward pass๋ฅผ ์ํ</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2_drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">320</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h2>๋ชจ๋ธ ํ์ตํ๊ธฐ<a class="headerlink" href="#id4" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ด์ SGD ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํด ๋ชจ๋ธ์ ํ์ต์์ผ ๋ด
์๋ค. ์์ ๊ตฌ์ฑํ ์ ๊ฒฝ๋ง์
๊ฐ๋
ํ์ต ๋ฐฉ์(supervised way)์ผ๋ก ๋ถ๋ฅ ๋ฌธ์ ๋ฅผ ํ์ตํฉ๋๋ค. ๋ํ ์ด ๋ชจ๋ธ์
end-to-end ๋ฐฉ์์ผ๋ก STN์ ์๋์ผ๋ก ํ์ตํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">optimizer</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</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="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">epoch</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="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span>
<span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</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">target</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">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</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="k">if</span> <span class="n">batch_idx</span> <span class="o">%</span> <span class="mi">500</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Train Epoch: </span><span class="si">{}</span><span class="s1"> [</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)]</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{:.6f}</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">batch_idx</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">),</span>
<span class="mf">100.</span> <span class="o">*</span> <span class="n">batch_idx</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">),</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
<span class="c1">#</span>
<span class="c1"># MNIST ๋ฐ์ดํฐ์
์์ STN์ ์ฑ๋ฅ์ ์ธก์ ํ๊ธฐ ์ํ ๊ฐ๋จํ ํ
์คํธ ์ ์ฐจ</span>
<span class="c1">#</span>
<span class="k">def</span> <span class="nf">test</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="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">test_loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">test_loader</span><span class="p">:</span>
<span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</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">target</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">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="c1"># ๋ฐฐ์น ์์ค ํฉํ๊ธฐ</span>
<span class="n">test_loss</span> <span class="o">+=</span> <span class="n">F</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">size_average</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="c1"># ๋ก๊ทธ-ํ๋ฅ ์ ์ต๋๊ฐ์ ํด๋นํ๋ ์ธ๋ฑ์ค ๊ฐ์ ธ์ค๊ธฐ</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">output</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">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">correct</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">test_loss</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Test set: Average loss: </span><span class="si">{:.4f}</span><span class="s1">, Accuracy: </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)</span><span class="se">\n</span><span class="s1">'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">),</span>
<span class="mf">100.</span> <span class="o">*</span> <span class="n">correct</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)))</span>
</pre></div>
</div>
</div>
<div class="section" id="stn">
<h2>STN ๊ฒฐ๊ณผ ์๊ฐํํ๊ธฐ<a class="headerlink" href="#stn" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ด์ ํ์ตํ ๋น์ฃผ์ผ ์ดํ
์
๋ฉ์ปค๋์ฆ์ ๊ฒฐ๊ณผ๋ฅผ ์ดํด๋ณด๊ฒ ์ต๋๋ค.</p>
<p>ํ์ตํ๋ ๋์ ๋ณํ๋ ๊ฒฐ๊ณผ๋ฅผ ์๊ฐํํ๊ธฐ ์ํด ์์ ๋์(helper) ํจ์๋ฅผ ์ ์ํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">convert_image_np</span><span class="p">(</span><span class="n">inp</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Convert a Tensor to numpy image."""</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">return</span> <span class="n">inp</span>
<span class="c1"># ํ์ต ํ ๊ณต๊ฐ ๋ณํ ๊ณ์ธต์ ์ถ๋ ฅ์ ์๊ฐํํ๊ณ , ์
๋ ฅ ์ด๋ฏธ์ง ๋ฐฐ์น ๋ฐ์ดํฐ ๋ฐ</span>
<span class="c1"># STN์ ์ฌ์ฉํด ๋ณํ๋ ๋ฐฐ์น ๋ฐ์ดํฐ๋ฅผ ์๊ฐํ ํฉ๋๋ค.</span>
<span class="k">def</span> <span class="nf">visualize_stn</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="c1"># ํ์ต ๋ฐ์ดํฐ์ ๋ฐฐ์น ๊ฐ์ ธ์ค๊ธฐ</span>
<span class="n">data</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">test_loader</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</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">input_tensor</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">transformed_input_tensor</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stn</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">in_grid</span> <span class="o">=</span> <span class="n">convert_image_np</span><span class="p">(</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">input_tensor</span><span class="p">))</span>
<span class="n">out_grid</span> <span class="o">=</span> <span class="n">convert_image_np</span><span class="p">(</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">transformed_input_tensor</span><span class="p">))</span>
<span class="c1"># ๊ฒฐ๊ณผ๋ฅผ ๋๋ํ ํ์ํ๊ธฐ</span>
<span class="n">f</span><span class="p">,</span> <span class="n">axarr</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="mi">2</span><span class="p">)</span>
<span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">in_grid</span><span class="p">)</span>
<span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Dataset Images'</span><span class="p">)</span>
<span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">out_grid</span><span class="p">)</span>
<span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Transformed Images'</span><span class="p">)</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="mi">1</span><span class="p">,</span> <span class="mi">20</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">train</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
<span class="n">test</span><span class="p">()</span>
<span class="c1"># ์ผ๋ถ ์
๋ ฅ ๋ฐฐ์น ๋ฐ์ดํฐ์์ STN ๋ณํ ๊ฒฐ๊ณผ๋ฅผ ์๊ฐํ</span>
<span class="n">visualize_stn</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">show</span><span class="p">()</span>
</pre></div>
</div>
<img src="../_images/sphx_glr_spatial_transformer_tutorial_001.png" srcset="../_images/sphx_glr_spatial_transformer_tutorial_001.png" alt="Dataset Images, Transformed Images" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:4404: UserWarning:
Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:4343: UserWarning:
Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
Train Epoch: 1 [0/60000 (0%)] Loss: 2.315648
Train Epoch: 1 [32000/60000 (53%)] Loss: 1.057736
/usr/local/lib/python3.10/dist-packages/torch/nn/_reduction.py:42: UserWarning:
size_average and reduce args will be deprecated, please use reduction='sum' instead.
Test set: Average loss: 0.2478, Accuracy: 9291/10000 (93%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.472433
Train Epoch: 2 [32000/60000 (53%)] Loss: 0.312316
Test set: Average loss: 0.1413, Accuracy: 9588/10000 (96%)
Train Epoch: 3 [0/60000 (0%)] Loss: 0.311842
Train Epoch: 3 [32000/60000 (53%)] Loss: 0.200255
Test set: Average loss: 0.1610, Accuracy: 9510/10000 (95%)
Train Epoch: 4 [0/60000 (0%)] Loss: 0.457400
Train Epoch: 4 [32000/60000 (53%)] Loss: 0.184728
Test set: Average loss: 0.1578, Accuracy: 9481/10000 (95%)
Train Epoch: 5 [0/60000 (0%)] Loss: 0.251555
Train Epoch: 5 [32000/60000 (53%)] Loss: 0.199258
Test set: Average loss: 0.2816, Accuracy: 9109/10000 (91%)
Train Epoch: 6 [0/60000 (0%)] Loss: 0.594764
Train Epoch: 6 [32000/60000 (53%)] Loss: 0.241562
Test set: Average loss: 0.0814, Accuracy: 9760/10000 (98%)
Train Epoch: 7 [0/60000 (0%)] Loss: 0.072951
Train Epoch: 7 [32000/60000 (53%)] Loss: 0.185238
Test set: Average loss: 0.0843, Accuracy: 9749/10000 (97%)
Train Epoch: 8 [0/60000 (0%)] Loss: 0.243580
Train Epoch: 8 [32000/60000 (53%)] Loss: 0.077991
Test set: Average loss: 0.0743, Accuracy: 9771/10000 (98%)
Train Epoch: 9 [0/60000 (0%)] Loss: 0.117219
Train Epoch: 9 [32000/60000 (53%)] Loss: 0.118457
Test set: Average loss: 0.0592, Accuracy: 9829/10000 (98%)
Train Epoch: 10 [0/60000 (0%)] Loss: 0.054736
Train Epoch: 10 [32000/60000 (53%)] Loss: 0.206718
Test set: Average loss: 0.0604, Accuracy: 9817/10000 (98%)
Train Epoch: 11 [0/60000 (0%)] Loss: 0.178927
Train Epoch: 11 [32000/60000 (53%)] Loss: 0.059567
Test set: Average loss: 0.0639, Accuracy: 9815/10000 (98%)
Train Epoch: 12 [0/60000 (0%)] Loss: 0.162848
Train Epoch: 12 [32000/60000 (53%)] Loss: 0.199922
Test set: Average loss: 0.0522, Accuracy: 9831/10000 (98%)
Train Epoch: 13 [0/60000 (0%)] Loss: 0.149074
Train Epoch: 13 [32000/60000 (53%)] Loss: 0.096983
Test set: Average loss: 0.0565, Accuracy: 9828/10000 (98%)
Train Epoch: 14 [0/60000 (0%)] Loss: 0.072109
Train Epoch: 14 [32000/60000 (53%)] Loss: 0.126184
Test set: Average loss: 0.0543, Accuracy: 9831/10000 (98%)
Train Epoch: 15 [0/60000 (0%)] Loss: 0.037677
Train Epoch: 15 [32000/60000 (53%)] Loss: 0.075240
Test set: Average loss: 0.0522, Accuracy: 9854/10000 (99%)
Train Epoch: 16 [0/60000 (0%)] Loss: 0.088973
Train Epoch: 16 [32000/60000 (53%)] Loss: 0.138924
Test set: Average loss: 0.0512, Accuracy: 9848/10000 (98%)
Train Epoch: 17 [0/60000 (0%)] Loss: 0.263063
Train Epoch: 17 [32000/60000 (53%)] Loss: 0.235070
Test set: Average loss: 0.0453, Accuracy: 9862/10000 (99%)
Train Epoch: 18 [0/60000 (0%)] Loss: 0.037475
Train Epoch: 18 [32000/60000 (53%)] Loss: 0.092032
Test set: Average loss: 0.0507, Accuracy: 9846/10000 (98%)
Train Epoch: 19 [0/60000 (0%)] Loss: 0.103860
Train Epoch: 19 [32000/60000 (53%)] Loss: 0.120228
Test set: Average loss: 0.0422, Accuracy: 9876/10000 (99%)
Train Epoch: 20 [0/60000 (0%)] Loss: 0.041891
Train Epoch: 20 [32000/60000 (53%)] Loss: 0.096982
Test set: Average loss: 0.0449, Accuracy: 9866/10000 (99%)
</pre></div>
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